[ {"objectID":"37be570e806bda0652c12e236d519ffadb6b031669295dc61644f010539b9f32","title":"NeuroLink Documentation Audit Report","url":"/docs/DOCUMENTATION-AUDIT-REPORT","content":"NeuroLink Documentation Audit Report\n\n⚠️ HISTORICAL DOCUMENT (August 2025)\nThis audit was conducted when NeuroLink shipped 9 providers. At audit time, v9.62.0 (May 2026) shipped 24 providers including DeepSeek, NVIDIA NIM, LM Studio, llama.cpp, plus voice (TTS/STT/realtime). References to \"9 providers\" or \"8/9 working\" in this file reflect the state at time of analysis.\nFor current capabilities see README on GitHub and Provider Capabilities Audit.\n\nSnapshot Date: 2026-03-17 | Branch: fix/documentation | Status: Historical audit record — most items have been resolved. This document is retained for reference.\n\nGoal\n\nPerform a comprehensive audit of the NeuroLink documentation served at docs.neurolink.ink to identify every gap, broken element, missing content, incorrect code, navigation issue, and quality problem. This report is the output of 16 parallel investigation agents that examined the documentation from every angle. This initial audit phase used 16 agents. The full fix cycle across all phases used 77+ agents total. The findings here should be used by a verification agent to confirm each issue and by implementation agents to systematically fix them.\n\nWhat Was Investigated\nDocumentation directory structure — Complete inventory of all 481 files across 28 directories\nDocumentation build system — Docusaurus 3.9.2 config, CI/CD, plugins, search, analytics\nSDK API reference — Every public method on the NeuroLink class vs what's documented\nCLI documentation — Every CLI command, flag, and option vs what's documented\nProvider documentation — All 30+ providers vs their dedicated setup guides\nFeature documentation — All 15 major features vs their feature guides\nGit commit patterns — How documentation is typically written, what got missed\nBroken links and references — Every internal link, anchor, and image reference\nCode example correctness — Every code block compared against actual SDK and CLI APIs\nPublic exports vs documentation — Every index.ts export vs its API reference page\nDocumentation quality and consistency — Formatting, frontmatter, heading structure, terminology\nSidebar navigation completeness — Every sidebar entry verified, orphaned pages identified\nTypeScript type documentation — All 42 type files vs TypeDoc pages and narrative docs\nREADME accuracy — Every claim in README.md vs actual codebase capabilities\nRecently added features — Features from recent commits vs documentation coverage\nGuides, examples, and tutorials — Getting started path, cookbook, runnable examples quality\n\nVerified False Positives (5 corrections applied)\n\nThe following claims from the original 16-agent audit were verified as false positives and have been struck through in-place throughout this document:\n\n| Original Claim | Correction |\n| ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| Claim 1.7b — registerTool using parameters is wrong, should be inputSchema | FALSE POSITIVE. Runtime compatibility shim handles parameters including Zod schemas. The documented code works correctly. |\n| Claim 6.2 — #examples--recipes, #use-cases--examples, #retry--backoff-strategies are broken anchors | FALSE POSITIVE. These are valid anchors where & in headings correctly becomes -- in generated slugs. 3 of 8 broken anchor claims removed. |\n| Claim 7.1 — 351 MkDocs tabbed syntax instances | OVERSTATED ~7x. Actual count outside code blocks: ~49. The grep matched === inside fenced code blocks (Python comparisons, test assertions). |\n| Claim 12.2 — WebSocket Handler not mentioned in README | FALSE POSITIVE. WebSocket IS mentioned at 2 locations in README. |\n| Claim 2.6 — ToolRouter, ToolCache, RequestBatcher undocumented | FALSE POSITIVE / CLAUDE.md INACCURACY. These source files do not exist in the codebase. The CLAUDE.md claim is stale or aspirational. Not a doc gap — the code doesn't exist. CLAUDE.md itself needs correction. |\n\nVe","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"","lvl3":""}}, {"objectID":"fe60ec42216c9030f3b967c5dc62bad01734db640078f0e5063d4266e6303d6c","title":"NeuroLink Documentation Audit Report","url":"/docs/DOCUMENTATION-AUDIT-REPORT#neurolink-documentation-audit-report","content":"⚠️ HISTORICAL DOCUMENT (August 2025)\nThis audit was conducted when NeuroLink shipped 9 providers. At audit time, v9.62.0 (May 2026) shipped 24 providers including DeepSeek, NVIDIA NIM, LM Studio, llama.cpp, plus voice (TTS/STT/realtime). References to \"9 providers\" or \"8/9 working\" in this file reflect the state at time of analysis.\nFor current capabilities see README on GitHub and Provider Capabilities Audit.\n\nSnapshot Date: 2026-03-17 | Branch: fix/documentation | Status: Historical audit record — most items have been resolved. This document is retained for reference.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"NeuroLink Documentation Audit Report","lvl3":""}}, {"objectID":"a681159aaf59ef908976f429ccd1964bcfeaf8e7bc2ddf0dbc45c04c0cfaf69d","title":"Goal","url":"/docs/DOCUMENTATION-AUDIT-REPORT#goal","content":"Perform a comprehensive audit of the NeuroLink documentation served at docs.neurolink.ink to identify every gap, broken element, missing content, incorrect code, navigation issue, and quality problem. This report is the output of 16 parallel investigation agents that examined the documentation from every angle. This initial audit phase used 16 agents. The full fix cycle across all phases used 77+ agents total. The findings here should be used by a verification agent to confirm each issue and by implementation agents to systematically fix them.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"Goal","lvl3":""}}, {"objectID":"846d8eec94be10c71e8464fe6815f187c18e17d5857024dafc89c5360c93a5ee","title":"What Was Investigated","url":"/docs/DOCUMENTATION-AUDIT-REPORT#what-was-investigated","content":"Documentation directory structure — Complete inventory of all 481 files across 28 directories\nDocumentation build system — Docusaurus 3.9.2 config, CI/CD, plugins, search, analytics\nSDK API reference — Every public method on the NeuroLink class vs what's documented\nCLI documentation — Every CLI command, flag, and option vs what's documented\nProvider documentation — All 30+ providers vs their dedicated setup guides\nFeature documentation — All 15 major features vs their feature guides\nGit commit patterns — How documentation is typically written, what got missed\nBroken links and references — Every internal link, anchor, and image reference\nCode example correctness — Every code block compared against actual SDK and CLI APIs\nPublic exports vs documentation — Every index.ts export vs its API reference page\nDocumentation quality and consistency — Formatting, frontmatter, heading structure, terminology\nSidebar navigation completeness — Every sidebar entry verified, orphaned pages identified\nTypeScript type documentation — All 42 type files vs TypeDoc pages and narrative docs\nREADME accuracy — Every claim in README.md vs actual codebase capabilities\nRecently added features — Features from recent commits vs documentation coverage\nGuides, examples, and tutorials — Getting started path, cookbook, runnable examples quality","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"What Was Investigated","lvl3":""}}, {"objectID":"891d8b0a031eb121a5e219169b289baeac6d20c089613798847e29092c6c8da6","title":"Verified False Positives (5 corrections applied)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#verified-false-positives-5-corrections-applied","content":"The following claims from the original 16-agent audit were verified as false positives and have been struck through in-place throughout this document:\n\n| Original Claim | Correction |\n| ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| Claim 1.7b — registerTool using parameters is wrong, should be inputSchema | FALSE POSITIVE. Runtime compatibility shim handles parameters including Zod schemas. The documented code works correctly. |\n| Claim 6.2 — #examples--recipes, #use-cases--examples, #retry--backoff-strategies are broken anchors | FALSE POSITIVE. These are valid anchors where & in headings correctly becomes -- in generated slugs. 3 of 8 broken anchor claims removed. |\n| Claim 7.1 — 351 MkDocs tabbed syntax instances | OVERSTATED ~7x. Actual count outside code blocks: ~49. The grep matched === inside fenced code blocks (Python comparisons, test assertions). |\n| Claim 12.2 — WebSocket Handler not mentioned in README | FALSE POSITIVE. WebSocket IS mentioned at 2 locations in README.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"Verified False Positives (5 corrections applied)","lvl3":""}}, {"objectID":"c3021e129b46bdaed7ba2755dde8e6b3670fa2d45a7023b674cd151a16e196f0","title":"Verification Criteria","url":"/docs/DOCUMENTATION-AUDIT-REPORT#verification-criteria","content":"For each issue category, a verification agent should:\nConfirm the issue exists by checking the referenced file path and line number\nAssess current severity (it may have been partially fixed since audit)\nFlag additional false positives if further analysis reveals incorrect claims\nNote dependencies between issues (e.g., fixing MkDocs syntax may fix some broken rendering)","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"Verification Criteria","lvl3":""}}, {"objectID":"9ae478c6bf245dc316926d140de29cfe76a1462f08566d6d26bdcf0b4ca0bda2","title":"Documentation Infrastructure Summary","url":"/docs/DOCUMENTATION-AUDIT-REPORT#documentation-infrastructure-summary","content":"| Property | Value |\n| ------------------ | ----------------------------------------------------------------------------------------------- |\n| Framework | Docusaurus 3.9.2 (React-based static site generator) |\n| URL | https://docs.neurolink.ink |\n| Hosting | GitHub Pages with custom domain (CNAME) |\n| Source docs | /docs/ directory (synced to /docs-site/docs/ at build time) |\n| Site config | /docs-site/docusaurus.config.ts |\n| Sidebar config | /docs-site/sidebars.ts |\n| Sync script | /docs-site/scripts/sync-docs.ts (MkDocs → Docusaurus transformation) |\n| Search | Algolia (primary) + MiniSearch (local fallback) |\n| Analytics | PostHog (GDPR-compliant) + Google Analytics |\n| Total files | 481 (410 markdown + 71 media/assets) |\n| API docs | 137 auto-generated TypeDoc pages in /docs/api/ |\n| CI/CD | .github/workflows/docs-deploy.yml, docs-pr-validation.yml, docs-version.yml |\n| Custom plugins | docusaurus-plugin-new-docs (badge detection), docusaurus-plugin-search-index (local search) |\n| Redirects | 70+ static redirects in /docs-site/config/redirects.ts |\n| LLM docs | llms.txt (~50KB summary) and llms-full.txt (~3.8MB full) generated at build |","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"Documentation Infrastructure Summary","lvl3":""}}, {"objectID":"dbf4e15bfb69053f7d24aa7d065cf6853d6df59af9a416a0e10be0eb984ff2a8","title":"1.1 README Hero Example (CRITICAL)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#11-readme-hero-example-critical","content":"File: /README.md, lines 9-17\n\nCurrent broken code:\n\nWhy it's broken (3 distinct errors):\nNeurolinkConstructorConfig has no defaultProvider field. Valid fields are: conversationMemory, enableOrchestration, hitl, toolRegistry, observability, modelAliasConfig.\nstream() returns Promise, not an async iterable. Must use result.stream to get the iterable.\nStream chunks are objects ({ content: string } or StreamChunk), not raw strings. process.stdout.write(token) would output [object Object].\n\nCorrect code should be:\n\nVerification: Read src/lib/types/configTypes.ts for constructor options, src/lib/types/streamTypes.ts for StreamResult and StreamChunk types.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"1.1 README Hero Example (CRITICAL)","lvl3":""}}, {"objectID":"8e3b37bf10a99072b3fac8f1a8bc4dd32dac0eaad6ba815b2e32c41933c1b1db","title":"1.2 prompt vs input.text (20+ instances)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#12-prompt-vs-inputtext-20-instances","content":"The problem: GenerateOptions and StreamOptions do NOT have a prompt field. The correct field is input: { text: \"...\" }, or you can pass a bare string to generate(\"Hello\").\n\nAffected files (verified instances):\n\n| File | Lines | Pattern |\n| ------------------------------------- | ----------------------------------------------- | ---------------------------------------------------- |\n| README.md | 586 | generate({ prompt: \"...\", rag: {...} }) |\n| docs/features/observability.md | 72, 204, 216, 228, 248, 281, 305, 337, 583, 587 | generate({ prompt: \"Hello\" }) |\n| docs/features/rag.md | 921-940 | Both generate({ prompt }) and stream({ prompt }) |\n| docs/advanced/builtin-middleware.md | 165, 217, 241, 662, 686, 746 | generate({ prompt }) and stream({ prompt }) |\n| docs/telemetry-guide.md | 82 | generate({ prompt: \"Hello\" }) |\n| docs/sdk/api-reference.md | 523 | generate({ prompt: \"...\", rag: {...} }) |\n\nVerification: Read src/lib/types/generateTypes.ts — search for prompt field on GenerateOptions. It does not exist. The generate() method at src/lib/neurolink.ts:2857 accepts GenerateOptions | string.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"1.2 prompt vs input.text (20+ instances)","lvl3":""}}, {"objectID":"805c6aea39e0de21c200cb6325564f67659e57d2c1f83ae017d00bfd1929cc55","title":"1.3 Streaming Iteration Pattern (10+ instances)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#13-streaming-iteration-pattern-10-instances","content":"The problem: stream() returns Promise where StreamResult.stream is the async iterable. Code must use result.stream, not iterate the result directly.\n\nAffected files:\n\n| File | Lines | Broken Pattern |\n| -------------------------------------- | -------------------------- | ----------------------------------- |\n| docs/advanced/streaming.md | 33-36, 51-53, 68-74, 92-94 | for await (const chunk of stream) |\n| docs/features/rag.md | 133-150 | for await (const chunk of stream) |\n| docs/reference/faq.md | 264 | for await (const chunk of stream) |\n| docs/mem0-integration.md | 221 | for await (const chunk of stream) |\n| docs/reference/provider-selection.md | 141 | for await (const chunk of stream) |\n| docs/playground/index.md | 60 | for await (const chunk of stream) |\n| docs/cookbook/error-recovery.md | 301 | process.stdout.write(chunk) |\n| docs/features/file-processors.md | 192 | process.stdout.write(chunk) |\n\nVerification: Read src/lib/types/streamTypes.ts — StreamResult has a .stream property that yields StreamChunk objects.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"1.3 Streaming Iteration Pattern (10+ instances)","lvl3":""}}, {"objectID":"a730ebce34d1930d6a66adad61ee373e37495cbabbde622fc20b833d651808c2","title":"1.4 Invalid Constructor Options (5 instances)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#14-invalid-constructor-options-5-instances","content":"Affected files:\n\n| File | Lines | Invalid Options |\n| ------------------------------ | ------- | ------------------------------------------------------------ |\n| README.md | 12 | defaultProvider: \"anthropic\" |\n| README.md | 345-368 | store: \"redis\", ttl: 86400 |\n| docs/sdk/index.md | 113-117 | defaultProvider, timeout, enableAnalytics |\n| docs/sdk/index.md | 167-172 | memory: { type: \"redis\" } (should be conversationMemory) |\n| docs/examples/basic-usage.md | 673-693 | defaultProvider, timeout, retryAttempts, analytics |\n\nVerification: Read src/lib/types/configTypes.ts for NeurolinkConstructorConfig.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"1.4 Invalid Constructor Options (5 instances)","lvl3":""}}, {"objectID":"c0c4f2515a3a4c2bcb1552d37e575ffe3c444d68e5440e079f9bf4d05cb8931c","title":"1.5 Non-Existent Methods Referenced","url":"/docs/DOCUMENTATION-AUDIT-REPORT#15-non-existent-methods-referenced","content":"| File | Line | Method | Reality |\n| ----------- | ---- | ---------------------- | --------------------------------- |\n| README.md | 76 | generateImage() | Does not exist on NeuroLink class |\n| README.md | 371 | exportConversation() | Does not exist on NeuroLink class |\n\nVerification: grep -n \"generateImage\\|exportConversation\" src/lib/neurolink.ts — returns no results.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"1.5 Non-Existent Methods Referenced","lvl3":""}}, {"objectID":"1538d0e1960d1aca4a6a01e822daac9e29e0df8485ef34dd55c9a9894d987108","title":"1.6 Tools Passed as Array Instead of Record","url":"/docs/DOCUMENTATION-AUDIT-REPORT#16-tools-passed-as-array-instead-of-record","content":"File: docs/features/rag.md, lines 124, 139, 245, 320, 349\n\nBroken: tools: [ragTool]\nCorrect: tools: { [ragTool.name]: ragTool }\n\nVerification: GenerateOptions.tools type in src/lib/types/generateTypes.ts is Record, not Tool[].","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"1.6 Tools Passed as Array Instead of Record","lvl3":""}}, {"objectID":"90b21cca38305c6ae83d7a78dafb8b281b0623f76bd1dc792fc28319e7011bfd","title":"1.7 Other Broken Examples","url":"/docs/DOCUMENTATION-AUDIT-REPORT#17-other-broken-examples","content":"| File | Line | Issue |\n| ---------------------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| README.md | 610 | thinkingLevel used as top-level GenerateOptions field (should be inside thinkingConfig) |\n| docs/sdk/index.md | 148-155 | createProviderWithFallback() used synchronously (missing await) |\n| docs/sdk/index.md | 176-183 | sessionId used in GenerateOptions (doesn't exist on this type) |\n| docs/sdk/index.md | 224-271 | ~~registerTool uses parameters: z.object(...)~~ FALSE POSITIVE — Runtime compatibility shim handles parameters including Zod schemas; this code works correctly |\n| docs/features/rag.md | 691 | GOOGLE_API_KEY env var (should be GOOGLE_AI_API_KEY) |\n| docs/advanced/streaming.md | 71 | chunk.type === \"tool_use\" (valid types are \"text\" and \"audio\" only) |\n| docs/cli/examples.md | 365, 369 | --criteria speed and --use-case cheapest flags don't exist |\n| docs/cli/advanced.md | 628 | neurolink mcp list --server \"$name\" — --server flag doesn't exist","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"1.7 Other Broken Examples","lvl3":""}}, {"objectID":"c76d8807c4de837fafc4aea77c8173624bb33a2806a7cb943ff33282d24a0684","title":"2.1 Workflow System (CRITICAL — 25 files, ~20K lines, zero user guide)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#21-workflow-system-critical-25-files-20k-lines-zero-user-guide","content":"Source code: src/lib/workflow/ — 25 files including:\ncore/workflowRunner.ts — Main execution engine\ncore/ensembleExecutor.ts — Multi-model ensemble execution\ncore/judgeScorer.ts — Judge-based scoring\nworkflows/consensusWorkflow.ts, fallbackWorkflow.ts, adaptiveWorkflow.ts, multiJudgeWorkflow.ts\nutils/workflowValidation.ts, workflowMetrics.ts\n\nWhat exists in docs:\ndocs/WORKFLOW-ENGINE-HLD.md (847 lines) — Internal design doc, NOT in sidebar\ndocs/WORKFLOW-ENGINE-LLD.md (2,024 lines) — Internal design doc, NOT in sidebar\ndocs/advanced-orchestration.md (448 lines) — Maps to sidebar but is generic orchestration, not workflow-engine-specific\n\nWhat's completely missing:\nUser-facing feature guide explaining how to use the workflow engine\nDocumentation for runWorkflow() function\nDocumentation for 9 pre-built workflow constants: CONSENSUS_3_WORKFLOW, CONSENSUS_3_FAST_WORKFLOW, BALANCED_ADAPTIVE_WORKFLOW, QUALITY_MAX_WORKFLOW, SPEED_FIRST_WORKFLOW, AGGRESSIVE_FALLBACK_WORKFLOW, FAST_FALLBACK_WORKFLOW, MULTI_JUDGE_3_WORKFLOW, MULTI_JUDGE_5_WORKFLOW\nDocumentation for WorkflowConfig, WorkflowType, WorkflowResult types\nCLI workflow command documentation (workflow list, workflow info , workflow execute )\nFluent API documentation\nCheckpointing documentation\nHITL integration with workflows\n\nVerification: ls src/lib/workflow/ and grep -r \"workflow\" docs-site/sidebars.ts — the sidebar references workflows/orchestration but NOT the workflow engine docs.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"2.1 Workflow System (CRITICAL — 25 files, ~20K lines, zero user guide)","lvl3":""}}, {"objectID":"249b7a94884880bf1ed66bee559c9a917bcd8ec0ac1d38cfb62d11a5e7295b53","title":"2.2 Observability — 8 of 9 Exporters Undocumented (CRITICAL — ~6,800 lines)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#22-observability-8-of-9-exporters-undocumented-critical-6800-lines","content":"Source code: src/lib/observability/ — includes exporters for:\n\n| Exporter | Source File | Documented? |\n| ------------- | --------------------------------- | -------------------------------------- |\n| Langfuse | exporters/langfuseExporter.ts | YES — docs/features/observability.md |\n| LangSmith | exporters/langsmithExporter.ts | NO |\n| Datadog | exporters/datadogExporter.ts | NO |\n| Sentry | exporters/sentryExporter.ts | NO |\n| Braintrust | exporters/braintrustExporter.ts | NO |\n| Arize | exporters/arizeExporter.ts | NO |\n| PostHog | exporters/posthogExporter.ts | NO |\n| Laminar | exporters/laminarExporter.ts | NO |\n| OpenTelemetry | exporters/otelExporter.ts | NO |\n\nAlso undocumented:\n9 samplers: AlwaysSampler, NeverSampler, RatioSampler, TraceIdRatioSampler, AttributeBasedSampler, PrioritySampler, ErrorOnlySampler, CompositeSampler, CustomSampler\n7 span processors: PassThrough, AttributeEnrichment, Filter, Redaction, Truncation, Composite, Batch\nExporterRegistry, MetricsAggregator, TokenTracker\n5 retry policies: Exponential, Linear, Fixed, NoRetry, CircuitBreakerAware\n\nThe internal status file src/lib/observability/FEATURE-STATUS.md documents all of this but is NOT synced to the docs site.\n\nVerification: ls src/lib/observability/exporters/ and grep -i \"langsmith\\|datadog\\|sentry\\|braintrust\\|arize\\|posthog\\|laminar\" docs/features/observability.md — returns 0 matches.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"2.2 Observability — 8 of 9 Exporters Undocumented (CRITICAL — ~6,800 lines)","lvl3":""}}, {"objectID":"b6df9c944a2522e16c136901be2185b7d248aab4aba9b325acfcf0fa4aec025b","title":"2.3 Dynamic Arguments (CRITICAL — zero docs, 269 tests)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#23-dynamic-arguments-critical-zero-docs-269-tests","content":"CLAUDE.md states: \"Dynamic Arguments: Complete — CLI context flags, runtime resolution, 269 tests\"\n\nWhat's missing: No documentation file exists. Zero mentions of \"dynamic arguments\" in any doc.\n\nVerification: grep -ri \"dynamic.arg\" docs/ — returns 0 results. docs/advanced/dynamic-models.md is a different feature (dynamic model configuration).","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"2.3 Dynamic Arguments (CRITICAL — zero docs, 269 tests)","lvl3":""}}, {"objectID":"546eb604eda18f3b8743dfda7c4d1852e8c2ee9f42aa7323bb0d5105edcf3e60","title":"2.4 Embeddings (HIGH — no dedicated page)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#24-embeddings-high-no-dedicated-page","content":"Source code:\nsrc/lib/core/baseProvider.ts — embed() and embedMany() stubs\nProvider implementations in OpenAI, Google AI Studio, Vertex, Bedrock\nServer routes: POST /api/agent/embed, POST /api/agent/embed-many\n\nWhat exists: Brief mention in docs/sdk/api-reference.md (lines 474-513) — 2 code snippets, provider table.\n\nWhat's missing:\nNo docs/features/embeddings.md page\nNot in sidebar navigation\nNo documentation on which providers do NOT support embeddings (and what error they throw)\nNo batch size limits or chunking behavior for embedMany\nNo usage examples with InMemoryVectorStore\nServer route documentation not in API reference (only in server adapters guide)","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"2.4 Embeddings (HIGH — no dedicated page)","lvl3":""}}, {"objectID":"ec3c5d1bad8eec2093493fd5c349955435034af0c9de5c48a13123d5cdd1cb3d","title":"2.5 Bash Tool (HIGH — zero docs)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#25-bash-tool-high-zero-docs","content":"Source: Commit 9a915e6f added the bash tool as a built-in tool option.\n\nWhat's missing: Zero documentation anywhere. The README's \"6 Core Tools\" table does not list it.\n\nVerification: grep -ri \"bash.tool\\|bashTool\\|enableBashTool\" docs/ — check results.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"2.5 Bash Tool (HIGH — zero docs)","lvl3":""}}, {"objectID":"0803324c1f79aede30301993a26c09c86247d9cf99872a1441d300b06ee79e65","title":"~~2.6 MCP Enhancements — ToolRouter, ToolCache, RequestBatcher~~","url":"/docs/DOCUMENTATION-AUDIT-REPORT#26-mcp-enhancements-toolrouter-toolcache-requestbatcher","content":"FALSE POSITIVE / CLAUDE.md INACCURACY: CLAUDE.md states \"ToolRouter, ToolCache, RequestBatcher (1,702 new lines)\" but verification confirmed these source files do not exist in the codebase. The CLAUDE.md claim is stale or aspirational. This is not a documentation gap — the code itself doesn't exist. However, this means CLAUDE.md itself needs to be corrected to remove this false claim.\nThe MCP circuit breaker (mcpCircuitBreaker.ts) does exist and remains undocumented — that is a real gap.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"~~2.6 MCP Enhancements — ToolRouter, ToolCache, RequestBatcher~~","lvl3":""}}, {"objectID":"ed09f75fa07716a0d970406aee91e6c10d8a2e781017a87753037d2e11df4702","title":"2.7 Streaming Architecture — 24 Event Types (MEDIUM)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#27-streaming-architecture-24-event-types-medium","content":"CLAUDE.md claims: \"All 4 streaming patterns, 24 event types, backpressure\"\n\nWhat's missing:\nNo enumeration of the 24 event types in any doc\nNo description of the 4 streaming patterns\nBackpressure gets a single bullet mention with no guidance\nStreamChunk discriminated union (text vs audio variants) undocumented","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"2.7 Streaming Architecture — 24 Event Types (MEDIUM)","lvl3":""}}, {"objectID":"c62d7f2ec372372563ee93df577f488fb5022a29fa3252b3e169774b7a1e7e44","title":"3.1 Providers With Zero Documentation","url":"/docs/DOCUMENTATION-AUDIT-REPORT#31-providers-with-zero-documentation","content":"| Provider | Source File | Default Model | Key Env Vars |\n| ------------- | -------------------------------------- | -------------- | ----------------------------------------------------------------------------------------------------- |\n| OpenAI | src/lib/providers/openAI.ts | gpt-4o | OPENAI_API_KEY, OPENAI_MODEL, OPENAI_EMBEDDING_MODEL |\n| Ollama | src/lib/providers/ollama.ts | llama3.1:8b | OLLAMA_BASE_URL, OLLAMA_MODEL, OLLAMA_TIMEOUT, OLLAMA_OPENAI_COMPATIBLE |\n| SageMaker | src/lib/providers/amazonSagemaker.ts | Endpoint-based | SAGEMAKER_REGION, SAGEMAKER_DEFAULT_ENDPOINT, SAGEMAKER_MODEL, SAGEMAKER_MODEL_TYPE + 10 more |\n\nCritical SageMaker note: Streaming is explicitly NOT implemented (throws SageMakerError) — this is completely undisclosed.\n\nVerification: ls docs/getting-started/providers/ — confirm no openai.md, no ollama.md, no sagemaker.md.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"3.1 Providers With Zero Documentation","lvl3":""}}, {"objectID":"6beeffaa4ab8dfa96ee8367c0b97d7b7e1fd2814590b4b881cf11d110d9becca","title":"3.2 Providers With Incorrect Documentation","url":"/docs/DOCUMENTATION-AUDIT-REPORT#32-providers-with-incorrect-documentation","content":"LiteLLM (docs/getting-started/providers/litellm.md):\nThe entire doc explains how to install and configure the external LiteLLM proxy server\nIt NEVER explains the dedicated NeuroLink litellm provider (--provider litellm)\nActual env vars LITELLM_BASE_URL, LITELLM_API_KEY, LITELLM_MODEL are undocumented\nNo model list from LiteLLMModels enum\n\nHuggingFace (docs/getting-started/providers/huggingface.md):\nDocs use HUGGINGFACE_DEFAULT_MODEL but code reads HUGGINGFACE_MODEL\nDefault model documented as Mistral-7B-Instruct-v0.2 but code defaults to QWEN_2_5_72B_INSTRUCT\n\nVerification: Read src/lib/factories/providerRegistry.ts — find the HuggingFace and LiteLLM registration entries to confirm env var names and default models.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"3.2 Providers With Incorrect Documentation","lvl3":""}}, {"objectID":"668621a20fe12a5d44381574765cd1b03f363fc74fe507357f40e857ff611320","title":"3.3 Provider Documentation Gaps (per provider)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#33-provider-documentation-gaps-per-provider","content":"| Provider | Missing |\n| -------------------- | ---------------------------------------------------------------------------------- |\n| Google AI Studio | Embedding support (GOOGLE_AI_EMBEDDING_MODEL, gemini-embedding-001) |\n| Google Vertex | Embedding support (VERTEX_EMBEDDING_MODEL, text-embedding-004), TTS support |\n| Amazon Bedrock | BEDROCK_MODEL env var, ARN format examples, streaming behavior |\n| Azure OpenAI | Vision/image support for GPT-4o, embedding usage examples, 4 of 5 env var variants |\n| Mistral | Vision models (Pixtral), tool/function calling examples, correct embedding API |\n| Anthropic | CLAUDE_SONNET_4_6 (registry default) missing from model table |","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"3.3 Provider Documentation Gaps (per provider)","lvl3":""}}, {"objectID":"155df44da8425637128e4c5e38e2473925ea2413466795d360ddcf21976ccb60","title":"4.1 Entirely Undocumented Commands","url":"/docs/DOCUMENTATION-AUDIT-REPORT#41-entirely-undocumented-commands","content":"| Command | Source | Subcommands | Key Flags |\n| ---------------------------------------- | ----------------------------------- | --------------------------------------------------------- | ---------------------------------------------------- |\n| workflow | src/cli/commands/workflow.ts | list, info , execute | --provider, --model, --timeout, --verbose |\n| observability (aliases: obs, otel) | src/cli/commands/observability.ts | status, metrics, exporters, costs | — |\n| telemetry (alias: tel) | src/cli/commands/telemetry.ts | status, configure, list-exporters, flush, stats | 9 exporters |\n| docs | src/cli/commands/docs.ts | — | --transport (stdio/http), --port |\n| rag index | src/cli/commands/rag.ts:507 | — | --index-name, --strategy, --graph, --verbose |\n| rag query | src/cli/commands/rag.ts:740 | — | --index-name, --top-k, --hybrid, --graph |","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"4.1 Entirely Undocumented Commands","lvl3":""}}, {"objectID":"a936014804d0c28a29ae608119e15b84ce8d2f564ed6aab479c514fbdb092ebd","title":"4.2 Incorrect Flag Documentation","url":"/docs/DOCUMENTATION-AUDIT-REPORT#42-incorrect-flag-documentation","content":"| Issue | Docs Say | Code Says |\n| ------------------------------- | ------------------------------------- | ---------------------------------------------- |\n| rag chunk --chunk-size | --chunk-size | --maxSize (-m) |\n| rag chunk --chunk-overlap | --chunk-overlap | --overlap (-o) |\n| mcp add --url and --headers | Listed as flags | Not implemented as CLI flags |\n| --enable-beta | Defaults to true for OAuth | Code has default: false |\n| --dry-run | \"Returns mocked analytics/evaluation\" | \"Test command without making actual API calls\" |\n| --thinking-budget | \"Gemini 2.5+ models only\" | \"Anthropic Claude and Gemini 2.5+\" |","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"4.2 Incorrect Flag Documentation","lvl3":""}}, {"objectID":"4ac14b2b90d0c00fa7657e5207067f6c02992e80ed88f1fc6f3731fd5c20a62e","title":"4.3 Missing models Subcommands (5 of 6 undocumented)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#43-missing-models-subcommands-5-of-6-undocumented","content":"Only models list is documented. Missing from docs:\nmodels search [query] — --use-case, --max-cost, --min-context, --performance\nmodels best — --coding, --creative, --analysis, --fast, --require-vision, etc.\nmodels resolve — --fuzzy\nmodels compare \nmodels stats — --detailed","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"4.3 Missing models Subcommands (5 of 6 undocumented)","lvl3":""}}, {"objectID":"40fe47bbb1f12a261f46d6fa045a40e2711f89159c2ee27d60bb4a55d092996e","title":"4.4 PPT Generation Flags (6 flags, all absent)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#44-ppt-generation-flags-6-flags-all-absent","content":"--ppt-pages, --ppt-theme, --ppt-audience, --ppt-tone, --ppt-output, --ppt-aspect-ratio, --ppt-no-images — none documented. The --output-mode flag doesn't mention ppt as a valid choice.\n\nVerification: Read src/cli/factories/commandFactory.ts lines 340-410.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"4.4 PPT Generation Flags (6 flags, all absent)","lvl3":""}}, {"objectID":"33c69c0b345aa2245897c75cd722b1a8906675d2de0d8b6c45a37ab65a516726","title":"5.1 Undocumented Public Methods on NeuroLink Class","url":"/docs/DOCUMENTATION-AUDIT-REPORT#51-undocumented-public-methods-on-neurolink-class","content":"Source: /src/lib/neurolink.ts (10,249 lines)\n\nLifecycle (essential for production):\nshutdown() — Graceful shutdown (flushes OTEL, closes Redis, shuts down MCP servers). Line 2440.\ndispose() — Full resource disposal. Line 9954.\n\nContext Compaction API:\ncompactSession(sessionId, config?) — Manual 4-stage compaction. Line 10120.\ngetContextStats(sessionId, provider?, model?) — Token usage and compaction readiness. Line 10171.\nneedsCompaction(sessionId, provider?, model?) — Boolean check. Line 10213.\n\nProvider Diagnostics (11 methods):\ngetProviderStatus(options?) — Line 8372\ntestProvider(providerName) — Line 8558\ngetBestProvider(requestedProvider?) — Line 8590\ngetAvailableProviders() — Line 8599\nisValidProvider(providerName) — Line 8609\nhasProviderEnvVars(providerName) — Line 8748\ncheckProviderHealth(providerName, options?) — Line 8774\ncheckAllProvidersHealth(options) — Line 8820\ngetProviderHealthSummary() — Line 8864\nclearProviderHealthCache(providerName?) — Line 8911\ngetToolHealthReport() — Line 9037\n\nObservability/Metrics (5 methods):\ngetMetrics() — Returns MetricsSummary. Line 2346.\ngetSpans() — Returns SpanData[]. Line 2353.\ngetTraces() — Returns TraceView[]. Line 2360.\nresetMetrics() — Line 2367.\ninitializeLangfuseObservability() — Line 2414.\n\nMCP Management (14 methods):\nremoveExternalMCPServer(serverId, options?)","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"5.1 Undocumented Public Methods on NeuroLink Class","lvl3":""}}, {"objectID":"88ea8c21a5374508e90960c76c795bd5e3c1cf495d0da7e32b9d62124b14809d","title":"5.2 Missing generate()/stream() Parameters","url":"/docs/DOCUMENTATION-AUDIT-REPORT#52-missing-generatestream-parameters","content":"Parameters present in code but missing from API reference:\n\n| Parameter | Type | Purpose |\n| ------------------------- | -------------------------- | --------------------------------------------------------- |\n| tts | TTSOptions | Text-to-Speech configuration |\n| abortSignal | AbortSignal | External cancellation |\n| toolFilter | string[] | Whitelist of tools to include |\n| excludeTools | string[] | Blacklist of tools to exclude |\n| skipToolPromptInjection | boolean | Performance optimization (~30K tokens saved) |\n| thinkingConfig | object | Full thinking config (not just thinkingLevel shorthand) |\n| workflow | string | Predefined workflow ID |\n| workflowConfig | WorkflowConfig | Inline workflow configuration |\n| maxBudgetUsd | number | Per-session USD budget cap |\n| requestId | string | Observability correlation ID |\n| csvOptions | object | CSV processing options |\n| videoOptions | object | Video processing options |\n| factoryConfig | object | Factory configuration override |\n| middleware | MiddlewareFactoryOptions | Middleware configuration |\n| input.videoFiles | — | Video file input","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"5.2 Missing generate()/stream() Parameters","lvl3":""}}, {"objectID":"410df2929681189111a4849840232775f85ad2dc36fe3447684c2f2970f43c63","title":"5.3 Server Sub-Entry — Zero API Reference","url":"/docs/DOCUMENTATION-AUDIT-REPORT#53-server-sub-entry-zero-api-reference","content":"The @juspay/neurolink/server sub-entry (src/lib/server/index.ts) exports ~120 named exports with zero docs/api/ pages:\nFramework adapters (Hono, Express, Fastify, Koa)\nAll middleware functions (20+ functions)\nAll error classes\nAll route factories\nOpenAPI generation (OpenAPIGenerator, generateOpenAPISpec, createOpenApiRoutes)\nStream security (createStreamRedactor, redactStreamChunk)\nWebSocket utilities (WebStreamWriter, BaseDataStreamWriter)\nAll validation schemas","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"5.3 Server Sub-Entry — Zero API Reference","lvl3":""}}, {"objectID":"92df040b238ab6b9e94d516083392873136d6c023e6b8b705f05ce79473d798f","title":"6.1 Missing Files Linked from docs/api/","url":"/docs/DOCUMENTATION-AUDIT-REPORT#61-missing-files-linked-from-docsapi","content":"Missing docs/api/interfaces/ directory (entire directory absent — 6 interface files referenced):\nChunker.md, BM25Index.md, VectorStore.md, VectorQueryResult.md, Reranker.md, RerankerMetadata.md\n\nMissing docs/api/type-aliases/ files (20 files):\nBM25Result.md, MetadataFilter.md, RerankResult.md, HybridSearchResult.md\n9 chunker config types: CharacterChunkerConfig.md, RecursiveChunkerConfig.md, SentenceChunkerConfig.md, TokenChunkerConfig.md, MarkdownChunkerConfig.md, HTMLChunkerConfig.md, JSONChunkerConfig.md, LaTeXChunkerConfig.md, SemanticChunkerConfig.md\n5 metadata extractor configs: TitleExtractorConfig.md, SummaryExtractorConfig.md, KeywordExtractorConfig.md, QuestionExtractorConfig.md, CustomSchemaExtractorConfig.md\nRAGConfig.md (listed in docs/api/README.md index but file doesn't exist)\nRAGPreparedTool.md\n\nMissing docs/api/functions/ files (7 files):\nprepareRAGTool.md, formatContextWithCitations.md, summarizeContext.md\ngetRerankerMetadata.md, getRerankerDefaultConfig.md, getDefaultConfig.md, embed.md","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"6.1 Missing Files Linked from docs/api/","lvl3":""}}, {"objectID":"4f1269138fc9a6e69497bf671262d94f5d1b4fa9a4565cda58bcb84c7fcced82","title":"6.2 Broken Anchor Links (8 confirmed)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#62-broken-anchor-links-8-confirmed","content":"| Source File | Broken Link | Issue |\n| ------------------------------------------- | ------------------------------------------------- | ------------------------------------------------------------------ |\n| docs/api/type-aliases/GenerateOptions.md | sdk-custom-tools.md#-controlling-tool-execution | Section doesn't exist |\n| ~~docs/features/multimodal.md~~ | ~~#examples--recipes~~ | FALSE POSITIVE — & in heading correctly becomes -- in slug |\n| ~~docs/features/tts.md~~ | ~~#use-cases--examples~~ | FALSE POSITIVE — & in heading correctly becomes -- in slug |\n| ~~docs/guides/examples/code-patterns.md~~ | ~~#retry--backoff-strategies~~ | FALSE POSITIVE — & in heading correctly becomes -- in slug |\n| docs/troubleshooting.md | features/ppt-generation.md#error-handling | Closest: #error-handling-validation |\n| docs/troubleshooting.md | features/ppt-generation.md#file-output | No close match |\n| docs/troubleshooting.md | features/ppt-generation.md#ai-image-generation | Closest: #disable-ai-image-generation |","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"6.2 Broken Anchor Links (8 confirmed)","lvl3":""}}, {"objectID":"7f070e4daae591877f1455de0bae9c5389e3f8fcb00b5e28e5525adbb7fcbd34","title":"6.3 Broken Image References (2)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#63-broken-image-references-2","content":"| Source | Path | Likely Correct |\n| --------------------------- | ---------------------------------------- | ------------------------------------ |\n| docs/demos/index.md | ../assets/images/cli-demo.png | ../assets/images/cli-help-demo.png |\n| docs/demos/screenshots.md | ../assets/images/cli-help-overview.png | ../assets/images/cli-help-demo.png |","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"6.3 Broken Image References (2)","lvl3":""}}, {"objectID":"40e74b26b821eaa35c09ab1aa7ee92ec7ad71ff24a99fd3b14ea7a5d667716ca","title":"6.4 Placeholder Links (7)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#64-placeholder-links-7","content":"Links using (#) as URL: docs/tutorials/videos.md (1), docs/api/classes/NeuroLink.md (5), docs/api/type-aliases/AIProvider.md (1)","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"6.4 Placeholder Links (7)","lvl3":""}}, {"objectID":"6da1d179344d6183499e64a63711ae69b2e190e257ea5b272fe0f2cc71b89f5a","title":"6.5 Phantom API Documentation (5 files documenting non-existent code)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#65-phantom-api-documentation-5-files-documenting-non-existent-code","content":"| File | Phantom Class/API |\n| ------------------------------------ | ----------------------------------------- |\n| docs/health-monitoring-guide.md | HealthMonitor class |\n| docs/mcp-testing-guide.md | ContextManager class |\n| docs/advanced/mcp-testing-guide.md | ContextManager class (duplicate) |\n| docs/ai-orchestration-guide.md | DynamicOrchestrator class |\n| docs/mcp-concurrency-guide.md | SemaphoreManager class |\n| docs/features/enterprise-hitl.md | States features \"are not yet implemented\" |","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"6.5 Phantom API Documentation (5 files documenting non-existent code)","lvl3":""}}, {"objectID":"ae1a88bafdbd42a402c2387831bd163561a01726af3bfa2384660d38422d41d5","title":"6.6 \"Coming Soon\" Placeholder Content (40+ instances across 14 files)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#66-coming-soon-placeholder-content-40-instances-across-14-files","content":"Heaviest offenders:\ndocs/tutorials/videos.md — 15 \"Coming Soon\" sections with markers\ndocs/features/audio-input.md — 10+ \"Coming Soon\" entries for provider support\ndocs/mcp-integration.md — 3 \"Coming Soon\" sections\ndocs/changelog.md — 3 \"Coming Soon\" items (migration guides that never materialized)\ndocs/features/index.md — Video Generation marked \"Coming Soon\"","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"6.6 \"Coming Soon\" Placeholder Content (40+ instances across 14 files)","lvl3":""}}, {"objectID":"dc08115e2feffeba5d65c42e967ba4cfc7f2ddad4f41e69619bdd0d6b2708cfa","title":"6.7 Duplicate Documentation Files (16 pairs)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#67-duplicate-documentation-files-16-pairs","content":"| Root File | Duplicate(s) |\n| -------------------------------- | ------------------------------------------------------------------------------------------------------ |\n| docs/api-reference.md | docs/advanced/api-reference.md, docs/getting-started/api-reference.md, docs/sdk/api-reference.md |\n| docs/troubleshooting.md | docs/guides/troubleshooting.md, docs/reference/troubleshooting.md |\n| docs/configuration.md | docs/reference/configuration.md |\n| docs/mcp-integration.md | docs/advanced/mcp-integration.md |\n| docs/mcp-testing-guide.md | docs/advanced/mcp-testing-guide.md |\n| docs/cli-guide.md | docs/advanced/cli-guide.md |\n| docs/contributing.md | docs/development/contributing.md |\n| docs/dynamic-models.md | docs/advanced/dynamic-models.md |\n| docs/enterprise-proxy-setup.md | docs/getting-started/enterprise-proxy-setup.md |\n| docs/framework-integration.md | docs/sdk/framework-integration.md |\n| docs/middleware.md | docs/guides/server-adapters/middleware.md |\n| docs/provider-comparison.md | docs/reference/provider-comparison.md |\n| docs/testing.md | docs/development/testing.md","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"6.7 Duplicate Documentation Files (16 pairs)","lvl3":""}}, {"objectID":"045cb20a91c85b926ef43015cb4e0dd8a711c1141272453b91c0718ea9bd0bd6","title":"7.1 MkDocs Tabbed Syntax (~49 instances, NOT 351)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#71-mkdocs-tabbed-syntax-49-instances-not-351","content":"Correction: The original count of 351 was ~7x overstated. The grep included === inside fenced code blocks (e.g., Python comparisons, test assertions). Actual MkDocs tab syntax instances outside code blocks: ~49.\n\nThe === \"Tab Name\" syntax renders as raw text in Docusaurus. Affected files include:\ndocs/getting-started/installation.md\ndocs/sdk/index.md\ndocs/cli/index.md\ndocs/features/rag.md\ndocs/features/guardrails.md\nAnd additional files\n\nFix: Convert to Docusaurus component or use the sync-docs.ts script transformation (which may already handle some of this but is clearly missing many).","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"7.1 MkDocs Tabbed Syntax (~49 instances, NOT 351)","lvl3":""}}, {"objectID":"54c2f7ffbea0e2bfc454f31ca79988d67b2a4f5d525bd608614bb73acf048878","title":"7.2 MkDocs Material Icons (11 files)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#72-mkdocs-material-icons-11-files","content":"🖼️, 🔒, etc. render as literal text. Files include:\ndocs/features/index.md — all table entries\ndocs/guides/index.md","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"7.2 MkDocs Material Icons (11 files)","lvl3":""}}, {"objectID":"d3e3a8d7b438bbf88270c4f2f2d5c9bc856701879aeaad7680b7bf3ba54b219c","title":"7.3 MkDocs Admonitions (20+ files)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#73-mkdocs-admonitions-20-files","content":"!!! admonition syntax renders as raw text. Files include:\ndocs/features/guardrails.md\ndocs/features/hitl.md\ndocs/features/multimodal-chat.md","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"7.3 MkDocs Admonitions (20+ files)","lvl3":""}}, {"objectID":"26cdcb666c351c173f355ee13228badec6de1e1959c314e17b23065bddc413c7","title":"7.4 MkDocs Grid Cards (9 files)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#74-mkdocs-grid-cards-9-files","content":"
syntax renders as broken HTML. Files include:\ndocs/sdk/index.md","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"7.4 MkDocs Grid Cards (9 files)","lvl3":""}}, {"objectID":"229e35d809eb76b42f39c484ecf0d244287f89eafba2d1e7850c9cc9aa5af22d","title":"8.1 Sidebar Structure (from /docs-site/sidebars.ts)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#81-sidebar-structure-from-docs-sitesidebarsts","content":"16 top-level categories: Getting Started, SDK, CLI, Features, MCP, Memory, Workflows, Observability, Deployment, Guides, Cookbook, Tutorials, Examples, Reference, Demos, Development, Community\n\nKey problems:\nFeatures category: 31 flat items — Needs sub-grouping into: Input/Output (7), Generation (5), Conversation (5), Safety (3), Infrastructure (5), Special (6)\n3 duplicate entries: Migration guides (from-langchain, from-vercel-ai-sdk, migration-guide) appear in BOTH \"Getting Started > Migration Guides\" AND \"Guides > Migration\"\n8 cross-directory references: Pages from advanced/ placed in unrelated categories (e.g., advanced/streaming in Features, advanced/builtin-middleware in Workflows)\nCategory overlap: Cookbook vs Examples vs Tutorials (3 categories for usage patterns). MCP/Memory/Workflows/Observability are features but get separate top-level categories while 31 other features are in a flat list.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"8.1 Sidebar Structure (from /docs-site/sidebars.ts)","lvl3":""}}, {"objectID":"de3b54c2946987539d49541af54ab1b6488044af0875b06a97878604d52ecee0","title":"8.2 Orphaned Pages (70 total — not in sidebar)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#82-orphaned-pages-70-total-not-in-sidebar","content":"High-value orphaned content:\n\n| Page | Lines | Why It Matters |\n| --------------------------------- | ----- | ------------------------------------ |\n| features/context-compaction.md | 980 | Major feature with comprehensive doc |\n| features/video-director-mode.md | 1,144 | Major feature doc |\n| features/video-analysis.md | 150 | Feature doc with real content |\n| WORKFLOW-ENGINE-HLD.md | 847 | Workflow system design |\n| WORKFLOW-ENGINE-LLD.md | 2,024 | Workflow system detailed design |\n| connectors/index.md | 35 | Connector catalog |\n| connectors/tara.md | — | Connector doc |\n| connectors/yama.md | — | Connector doc |\n| playground/index.md | 133 | Interactive playground |\n| about/nervous-system-model.md | 62 | Architecture concept |\n| about/pipe-architecture.md | 113 | Architecture concept |\n| advanced/index.md | — | Advanced section landing page |\n| advanced/factory-patterns.md | — | Factory pattern guide |\n| sdk/custom-tools.md | 1,814 | Near-duplicate |\n\nOther orphans: Internal docs in plans/, phases/, tracking/, test-reports/, analysis/, visual-content/, rag/ — likely intentionally excluded.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"8.2 Orphaned Pages (70 total — not in sidebar)","lvl3":""}}, {"objectID":"e37433d115fecb3f053b1a740825e7c5f9ffcf91cc651bd4f52e4bcb366934e1","title":"9.1 Auto-Generated TypeDoc Status","url":"/docs/DOCUMENTATION-AUDIT-REPORT#91-auto-generated-typedoc-status","content":"Stale: All TypeDoc pages are pinned to an old commit (1be79595b7d7307795c98da4267c1728cb50033d). Fields added after that commit are missing from all auto-generated pages.\n\nVERSION constant: Hardcoded as \"1.0.0\" in docs/api/variables/VERSION.md — actual version is 9.26.1.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"9.1 Auto-Generated TypeDoc Status","lvl3":""}}, {"objectID":"299bc747324541392dd012ee4f894694b52a044835d9c5d290166b26abb18761","title":"9.2 Critical Types With No Documentation","url":"/docs/DOCUMENTATION-AUDIT-REPORT#92-critical-types-with-no-documentation","content":"| Type | File | Impact |\n| --------------------------------------- | ------------------ | ----------------------------------------------------------------------- |\n| StreamOptions | streamTypes.ts | Primary streaming input type — no TypeDoc page |\n| StreamResult | streamTypes.ts | Primary streaming output — no TypeDoc page, narrative omits 60%+ fields |\n| StreamChunk | streamTypes.ts | What stream yields — no docs, audio variant invisible |\n| ChatMessage | conversation.ts | Central message type — no TypeDoc page |\n| NeurolinkConstructorConfig | configTypes.ts | toolRegistry and modelAliasConfig fields missing |\n| NeuroLinkConfig | configTypes.ts | Runtime config — no page, no narrative |\n| RAGConfig | rag/types.ts | Broken link in API README |\n| TTSOptions / TTSResult / TTSChunk | ttsTypes.ts | Good in-source TSDoc but no TypeDoc pages |\n| HITLConfig | hitlTypes.ts | No TypeDoc page |\n| WorkflowConfig / WorkflowType | workflowTypes.ts | No TypeDoc page |\n| TokenUsage | analytics.ts | Used in every response — no page |\n| MCPTransportType | mcpTypes.ts | ws, tcp, unix values undocumented |\n| ExternalMCPServerConfig | externalMcp.ts | No TypeDoc page","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"9.2 Critical Types With No Documentation","lvl3":""}}, {"objectID":"0d16a2eaf1ca3dbc30168a1f4190ef672f1728b57bdd2b5695c5cbdf98a67e46","title":"9.3 Missing Model Enumerations","url":"/docs/DOCUMENTATION-AUDIT-REPORT#93-missing-model-enumerations","content":"Only AIProviderName, BedrockModels, OpenAIModels, VertexModels have TypeDoc pages. Missing:\nAnthropicModels, GoogleAIModels, MistralModels, AzureModels, OllamaModels, LiteLLMModels, HuggingFaceModels, OpenRouterModels","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"9.3 Missing Model Enumerations","lvl3":""}}, {"objectID":"d1c5ab59dcc999f4fff7c83960ca36c7d541467d40b8cc673fbd1c0bc871f101","title":"10.1 Nonsensical Migration Notes","url":"/docs/DOCUMENTATION-AUDIT-REPORT#101-nonsensical-migration-notes","content":"4+ files contain migration notes comparing generate() to itself:\ndocs/configuration.md line 12: \"Configuration remains identical for both generate() and generate().\"\ndocs/troubleshooting.md line 89: \"What's the difference between the new generate() and the legacy generate()?\"\n\nThis is leftover from a method rename where a previous name was globally replaced with generate().","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"10.1 Nonsensical Migration Notes","lvl3":""}}, {"objectID":"0d3742de15bb68fb838a45baa92121858924d262364c347cbb0cccdddf95dcf4","title":"10.2 Internal Status Banners in User-Facing Docs","url":"/docs/DOCUMENTATION-AUDIT-REPORT#102-internal-status-banners-in-user-facing-docs","content":"7+ files have IMPLEMENTATION STATUS: COMPLETE (2025-01-07) banners with checkbox items that read like developer notes, not documentation.\n\nFiles: troubleshooting.md, configuration.md, reference/troubleshooting.md, reference/configuration.md, advanced/mcp-integration.md, mcp-integration.md, cli-reference.md","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"10.2 Internal Status Banners in User-Facing Docs","lvl3":""}}, {"objectID":"883871b8cd3f46cff8cffa54ef289ae2ff2acbbdb54a03be3ee78132f2ec49a8","title":"10.3 Frontmatter Inconsistency","url":"/docs/DOCUMENTATION-AUDIT-REPORT#103-frontmatter-inconsistency","content":"~40% of feature docs lack YAML frontmatter (title, description, keywords)\nKeywords format varies: some inline keywords: rag, chunking, some YAML array\nOnly 9 of 31 feature docs have the > **Since**: vX.X.X | **Status**: Stable banner","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"10.3 Frontmatter Inconsistency","lvl3":""}}, {"objectID":"ab3a3ec3b7234442743482e0faa7641878a9b751581c09fd08118c7ed849e3a5","title":"10.4 Heading Structure Issues","url":"/docs/DOCUMENTATION-AUDIT-REPORT#104-heading-structure-issues","content":"3 files start with H2 instead of H1: video-director-mode.md, video-generation.md, multimodal-chat.md\nmultimodal-chat.md has no H1 at all\nEmoji usage in headings: present in getting-started/, sdk/, cli/; absent in features/","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"10.4 Heading Structure Issues","lvl3":""}}, {"objectID":"ec2f1059b0653f735bd45caa497e5866abd1077c6842c14be085c3322a20daaa","title":"10.5 Terminology Inconsistencies","url":"/docs/DOCUMENTATION-AUDIT-REPORT#105-terminology-inconsistencies","content":"| Term | Variations Found |\n| ----------------- | ------------------------------------------------------------------------ |\n| Provider count | \"9 providers\", \"12+ providers\", \"13 Providers\", \"14+ providers\" |\n| Tool count | \"58+ MCP Tools\", \"64+ built-in tools and MCP servers\" |\n| OpenRouter models | \"200+ Models\" (table) vs \"300+ models\" (text) |\n| Product name | \"NeuroLink\" (correct) vs \"Neurolink\" (wrong — in HLD/LLD, some API docs) |\n| Google AI | \"google-ai\", \"googleAiStudio\", \"Google AI\" used interchangeably |","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"10.5 Terminology Inconsistencies","lvl3":""}}, {"objectID":"b3ab3cef27015712f8852a27dc1ba2ad7337db8566a4751e6d216caff2b3033c","title":"10.6 Other Quality Issues","url":"/docs/DOCUMENTATION-AUDIT-REPORT#106-other-quality-issues","content":"Contributing guide (docs/development/contributing.md) uses npm instead of pnpm, and references npm run type-check (should be pnpm run check)\nDiscord badge in docs/index.md uses placeholder DISCORD_SERVER_ID\ndocs/troubleshooting.md references v7.47.0 (Sep 2025) — project is at v9.26.1\ndocs/reference/troubleshooting.md references v1.7.1 (Jan 2025)\ndocs/features/multimodal-chat.md references \"NeuroLink 7.47.0\"\ndocs/sdk/advanced-features.md is a 55-line stub with placeholder patterns (generateStream, generateBatch, cache)","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"10.6 Other Quality Issues","lvl3":""}}, {"objectID":"82985af2119d6af4170c668bd935e745590b7f776a860d9a00eaaf2586d9de64","title":"11.1 Missing Documentation Pages","url":"/docs/DOCUMENTATION-AUDIT-REPORT#111-missing-documentation-pages","content":"| Page Needed | Why |\n| --------------------------------------------- | ----------------------------------------------------------------------------------------- |\n| docs/features/embeddings.md | User-facing embedding feature guide |\n| docs/features/workflow-engine.md | User-facing workflow engine guide |\n| docs/features/streaming.md | Dedicated streaming feature guide (current advanced/streaming.md is enterprise-focused) |\n| docs/getting-started/providers/openai.md | Most popular provider |\n| docs/getting-started/providers/ollama.md | Local model execution |\n| docs/getting-started/providers/sagemaker.md | AWS SageMaker |\n| docs/guides/migration/from-openai-sdk.md | Most common migration path |","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"11.1 Missing Documentation Pages","lvl3":""}}, {"objectID":"f21d54464ba27915c4273276c842cc181f379ec0a6793fc072ea6c6909abc556","title":"11.2 Missing Quick Start Subsections in Feature Docs","url":"/docs/DOCUMENTATION-AUDIT-REPORT#112-missing-quick-start-subsections-in-feature-docs","content":"11 feature docs lack a Quick Start section: auto-evaluation.md, structured-output.md, thinking-configuration.md, mcp-tools-showcase.md, provider-orchestration.md, regional-streaming.md, video-analysis.md, file-processors.md, cli-loop-sessions.md, claude-subscription.md, claude-subscription-testing.md","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"11.2 Missing Quick Start Subsections in Feature Docs","lvl3":""}}, {"objectID":"133bd6bbb8bd5bda9d17499b648a790654a3a06f66de41ac3532be5b5d9faf9e","title":"11.3 Missing Runnable Examples","url":"/docs/DOCUMENTATION-AUDIT-REPORT#113-missing-runnable-examples","content":"No example files exist for: streaming, memory/conversation, embeddings, provider switching, middleware, observability/Langfuse, context compaction, guardrails.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"11.3 Missing Runnable Examples","lvl3":""}}, {"objectID":"f9fea3424d77c3ace497a6fe701941476ac1230a7712a96292d74b97e30f7dd5","title":"11.4 Missing Cookbook Recipes","url":"/docs/DOCUMENTATION-AUDIT-REPORT#114-missing-cookbook-recipes","content":"No recipes for: basic streaming, multimodal images, memory persistence, provider switching, embeddings, observability setup.","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"11.4 Missing Cookbook Recipes","lvl3":""}}, {"objectID":"d2ab33aca5ec4c48030328d84be2a007ffbf8590354a707089937c4959e65b32","title":"11.5 Missing Migration Guides","url":"/docs/DOCUMENTATION-AUDIT-REPORT#115-missing-migration-guides","content":"No major version migration guide (v7→v8, v8→v9)\nNo migration from OpenAI SDK directly\nNo migration from AWS SDK/Bedrock SDK","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"11.5 Missing Migration Guides","lvl3":""}}, {"objectID":"b2dfcb0ecc312a38dfd817b805fc0d8eec20c2254c248259116e90effa42219d","title":"12.2 Features Missing from README","url":"/docs/DOCUMENTATION-AUDIT-REPORT#122-features-missing-from-readme","content":"| Feature | Code Size | README Mention |\n| -------------------------------- | ------------------------------- | -------------------------------------------------------------------- |\n| Workflow System | 25 files, ~20K lines | Zero |\n| TTS | TTSProcessor + GoogleTTSHandler | Zero |\n| Full Observability (9 exporters) | ~6,800 lines | Brief \"OpenTelemetry\" mention |\n| Context Compaction | 12 files | Single buried bullet |\n| Audio/Video/Archive processors | 3 processor classes | Not in file processing table |\n| GraphRAG | graphRAG.ts | Not mentioned |\n| File Reference Tools (5 tools) | fileTools.ts | Not mentioned |\n| Embeddings API | 4 providers, server routes | Not mentioned |\n| Claude OAuth/Subscription | Auth command + types | Not mentioned |\n| ~~WebSocket Handler~~ | ~~WebSocketHandler.ts~~ | FALSE POSITIVE — WebSocket IS mentioned at 2 locations in README |","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"12.2 Features Missing from README","lvl3":""}}, {"objectID":"1fb6cee5e70cb70cc51574865ca92ec6ca83f470cd5563c635e4a351b3b4ce27","title":"12.3 README Inconsistencies","url":"/docs/DOCUMENTATION-AUDIT-REPORT#123-readme-inconsistencies","content":"OpenRouter: \"200+\" in table vs \"300+\" in feature bullets\n\"64+ built-in tools and MCP servers\" conflates two things (6 tools + 58 MCP servers)\nCore tools table shows 6 but omits analyzeCSV and conditional bashTool\n\"Platform Capabilities\" table has stale Q3/Q4 markers for features already shipped","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"12.3 README Inconsistencies","lvl3":""}}, {"objectID":"5114de920c90e46fc05cb1dee9097d0181f4f5f5aee40abd08b222c23d0fdcfa","title":"Verification Checklist for Agent (Historical — most items resolved)","url":"/docs/DOCUMENTATION-AUDIT-REPORT#verification-checklist-for-agent-historical-most-items-resolved","content":"A verification agent should confirm each category by:\nCode examples (Cat 1): Run grep -n \"prompt:\" docs/features/observability.md and verify prompt is used where input.text should be\nMissing features (Cat 2): Run ls docs/features/ and confirm no embeddings.md, workflow-engine.md exist\nMissing providers (Cat 3): Run ls docs/getting-started/providers/ and confirm no openai.md, ollama.md, sagemaker.md\nCLI commands (Cat 4): Run grep -r \"workflow\\|observability\\|telemetry\" docs/cli/commands.md and confirm 0 matches\nUndocumented methods (Cat 5): Run grep -n \"shutdown\\|dispose\\|compactSession\\|getContextStats\" docs/sdk/api-reference.md and confirm 0 matches\nBroken links (Cat 6): Run ls docs/api/interfaces/ and confirm directory doesn't exist\nMkDocs syntax (Cat 7): Run grep -c \"=== \" docs/getting-started/installation.md and confirm non-zero count\nSidebar (Cat 8): Read docs-site/sidebars.ts and confirm context-compaction is absent from the items list\nTypes (Cat 9): Run ls docs/api/type-aliases/StreamOptions.md and confirm file doesn't exist\nQuality (Cat 10): Read docs/configuration.md line 12 and confirm nonsensical migration note\nMissing guides (Cat 11): Run ls examples/ and confirm no streaming-basic.ts or embeddings.ts\nREADME (Cat 12): Read README.md lines 9-17 and confirm hero code uses wrong API","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"Verification Checklist for Agent (Historical — most items resolved)","lvl3":""}}, {"objectID":"6aa5e2ebf8c9eeb4a0e572825bcb355aacb4655be13a430ad1298668b8c1acd1","title":"Statistics","url":"/docs/DOCUMENTATION-AUDIT-REPORT#statistics","content":"| Metric | Count |\n| ------------------------------------------- | ------------------------------------------------------------------------ |\n| Total documentation files | 481 |\n| Total issues identified | 200+ |\n| Broken code examples | 32 |\n| Missing feature documentation pages | 7 |\n| Missing provider documentation pages | 3 |\n| Incorrect provider documentation | 2 |\n| Undocumented CLI commands | 5 |\n| Undocumented SDK public methods | ~50+ |\n| Broken internal links | 66 |\n| Broken anchor links | 5 (3 of original 8 were false positives — valid & → -- slugs) |\n| Orphaned pages (not in sidebar) | 70 |\n| Duplicate documentation files | 16 pairs |\n| MkDocs syntax instances (won't render) | ~49 tabs (corrected from overstated 351), 20+ admonitions, 11 icon files |\n| \"Coming Soon\" placeholders | 40+ |\n| Phantom API docs (non-existent","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"Statistics","lvl3":""}}, {"objectID":"df295b59a0ed50dd9112f4b9e63a240fa301d787e597634fc3ec6824ffd03e7c","title":"Audit Metadata","url":"/docs/DOCUMENTATION-AUDIT-REPORT#audit-metadata","content":"Date: 2026-03-17\nBranch: fix/documentation\nPackage version: 9.26.1\nAgents used: 16 parallel investigation agents\nAgent types: Explore (2), feature-dev:code-explorer (4), general-purpose (10)\nTotal investigation time: ~45 minutes wall clock (parallel execution)\nFiles examined: 481 documentation files + ~200 source files","hierarchy":{"lvl0":"DOCUMENTATION AUDIT REPORT","lvl1":"NeuroLink Documentation Audit Report","lvl2":"Audit Metadata","lvl3":""}}, {"objectID":"d7c4992bb9b8f0a9ff9962af9e8fb4c0b1f27c3789309f07ac27a86b0109afd1","title":"Migration Guide: Breaking Changes","url":"/docs/MIGRATION","content":"Migration Guide: Breaking Changes\n\nThis document tracks breaking changes shipped in specific NeuroLink\nreleases — changes to public SDK/CLI surface that require an update on the\nconsumer side, as opposed to the additive/backward-compatible changes covered\nby docs/guides/migration-guide.md and the\nauto-generated changelog (see the\nGitHub releases page).\n\nFour breaking changes have shipped (three across 9.94.x–9.95.x, one in 11.0.0),\nand a fifth is registered below before release. Each is intentional — this document exists so downstream\nconsumers know what changed and how to adapt.\n\nPolicy\n\nPer this repository's CLAUDE.md (Critical Rule 5), the public SDK API must\nnot break existing callers. The first four breaking changes below (9.94.x–9.95.x and 11.0.0) shipped\nwithout an accompanying migration path and are documented here retroactively. Going forward, any breaking change must ship its migration\npath in the same release that introduces it — not documented after the fact.\nMultimodal file/CSV processing is now fail-loud (v9.94.6)\n\nWhat changed: generate()/stream() calls that attach files via\ninput.files (auto-detected) or input.csvFiles (explicit CSV) now throw\non the first file that fails to process, instead of logging a warning and\nsilently continuing with the files that did succeed.\nprocessUnifiedFilesArray() (src/lib/utils/messageBuilder.ts) throws\n ErrorFactory.fileProcessingFailed(filename, originalError) on any file in\n input.files that fails detection/processing.\nprocessExplicitCsvFiles() (same file) throws\n ErrorFactory.csvProcessingFailed(filename, originalError) on any file in\n input.csvFiles that fails to parse.\n\nBoth errors are typed NeuroLinkErrors (ERROR_CODES.FILE_PROCESSING_FAILED\n/ ERROR_CODES.CSV_PROCESSING_FAILED, see src/lib/utils/errorHandling.ts)\nthat carry the original error as .originalError and the failing filename in\n.context.filename.\n\nWhy: the previous log-and-skip behavior silently produced a partial\nprompt — e.g. attaching 5 files and having one silently drop meant the model\nanswered as if that file never existed, with no signal to the caller or the\nend user that anything was missing. A model call that appears to succeed but\nis actually missing part of its input is worse than a call that fails loudly.\n\nBefore (9.94.5 and earlier):\n\nAfter (9.94.6+):\n\nHow to adapt:\nWrap multi-file generate()/stream() calls in try/catch if you were\n previously relying on partial success.\nIf you want the old best-effort behavior, pre-validate/pre-filter files\n yourself before passing them in input.files/input.csvFiles, or catch the\n typed error, drop the failing filename (error.context.filename), and\n retry without it.\nCSV-specific failures (input.csvFiles) and generic file failures\n (input.files) now throw different error codes — branch on\n error.code if you need to distinguish them.\nMessageContent dropped its open index signature (v9.94.6)\n\nWhat changed: the exported MessageContent type\n(src/lib/types/multimodal.ts, re-exported via src/lib/types/index.ts and\nthe package root) no longer has a [key: string]: unknown index signature.\nIt's now a closed shape with the concrete fields the pipeline actually reads:\ntype, text, image, mimeType, data, name, filename,\ntoolCallId, toolName, args, result, isError, providerOptions.\n\nWhy: the open index signature let any typo or unrelated key through\ntype-checking unchecked (e.g. { type: \"text\", txt: \"...\" } compiled fine\nand silently produced empty content). Closing it catches that class of bug at\ncompile time for everyone building MessageContent[] arrays directly.\n\nBefore:\n\nAfter:\n\nHow to adapt:\nIf you were attaching arbitrary metadata to a MessageContent item,\n use the dedicated providerOptions?: Record field instead\n — it's read by MessageBuilder when converting to ModelMessage[] and is\n the intended extension point.\nIf you genuinely need a field NeuroLink doesn't model, open an issue —\n MessageContent intentionally stays a closed, hand-maintained shape rather\n than reopening a blanket index signature (see the #325 comment on the\n type itself).\nRuntime behavior is unchanged — this is a compile-time-only break. Plain\n JavaScript callers, or TypeScript callers that never annotated a literal\n as MessageContent explicitly, are unaffected.\nBatchCommandArgs.file renamed to .promptsFile (v9.95.1)\n\nWhat changed: the public CLI argument type BatchCommandArgs\n(src/lib/types/cli.ts) renamed its file?: string property to\npromptsFile?: string.\n\nWhy: yargs implicitly registers a positional argument's key as a\nrecognized flag name too. Keeping the property named file collided with\nthe unrelated common --file auto-detect flag that the batch command\nintentionally does not register — --file on batch would silently pass\nstrictOptions() without actually doing anything. Renaming the positional's\nbacking property to promptsFile removes that collision.\n\nBefore:\n\nAfter:\n\nHow to adapt:\nUpdate any code that constructs or reads a BatchCommandArgs obj","hierarchy":{"lvl0":"MIGRATION","lvl1":"Migration Guide: Breaking Changes","lvl2":"","lvl3":""}}, {"objectID":"fdbab00295ca91868ff281f896eee90bf92853d0a6d787ba9742a6ed6308f77d","title":"Migration Guide: Breaking Changes","url":"/docs/MIGRATION#migration-guide-breaking-changes","content":"This document tracks breaking changes shipped in specific NeuroLink\nreleases — changes to public SDK/CLI surface that require an update on the\nconsumer side, as opposed to the additive/backward-compatible changes covered\nby docs/guides/migration-guide.md and the\nauto-generated changelog (see the\nGitHub releases page).\n\nFour breaking changes have shipped (three across 9.94.x–9.95.x, one in 11.0.0),\nand a fifth is registered below before release. Each is intentional — this document exists so downstream\nconsumers know what changed and how to adapt.","hierarchy":{"lvl0":"MIGRATION","lvl1":"Migration Guide: Breaking Changes","lvl2":"Migration Guide: Breaking Changes","lvl3":""}}, {"objectID":"409d93acb1401eea00338b9985911f29a71507864dfa8b6d6f5234ba733bf478","title":"Policy","url":"/docs/MIGRATION#policy","content":"Per this repository's CLAUDE.md (Critical Rule 5), the public SDK API must\nnot break existing callers. The first four breaking changes below (9.94.x–9.95.x and 11.0.0) shipped\nwithout an accompanying migration path and are documented here retroactively. Going forward, any breaking change must ship its migration\npath in the same release that introduces it — not documented after the fact.","hierarchy":{"lvl0":"MIGRATION","lvl1":"Migration Guide: Breaking Changes","lvl2":"Policy","lvl3":""}}, {"objectID":"fa51bc1764610ef23ef111c4abd683a88d6798907797c5f1d22c09fa46db2c08","title":"1. Multimodal file/CSV processing is now fail-loud (v9.94.6)","url":"/docs/MIGRATION#1-multimodal-filecsv-processing-is-now-fail-loud-v9946","content":"What changed: generate()/stream() calls that attach files via\ninput.files (auto-detected) or input.csvFiles (explicit CSV) now throw\non the first file that fails to process, instead of logging a warning and\nsilently continuing with the files that did succeed.\nprocessUnifiedFilesArray() (src/lib/utils/messageBuilder.ts) throws\n ErrorFactory.fileProcessingFailed(filename, originalError) on any file in\n input.files that fails detection/processing.\nprocessExplicitCsvFiles() (same file) throws\n ErrorFactory.csvProcessingFailed(filename, originalError) on any file in\n input.csvFiles that fails to parse.\n\nBoth errors are typed NeuroLinkErrors (ERROR_CODES.FILE_PROCESSING_FAILED\n/ ERROR_CODES.CSV_PROCESSING_FAILED, see src/lib/utils/errorHandling.ts)\nthat carry the original error as .originalError and the failing filename in\n.context.filename.\n\nWhy: the previous log-and-skip behavior silently produced a partial\nprompt — e.g. attaching 5 files and having one silently drop meant the model\nanswered as if that file never existed, with no signal to the caller or the\nend user that anything was missing. A model call that appears to succeed but\nis actually missing part of its input is worse than a call that fails loudly.\n\nBefore (9.94.5 and earlier):\n\nAfter (9.94.6+):\n\nHow to adapt:\nWrap multi-file generate()/stream() calls in try/catch if you were\n previously relying on partial success.\nIf you want the old best-effort behavior, pre-validate/pre-filter files\n yourself before passing them in input.files/input.csvFiles, or catch the\n typed error, drop the failing filename (error.context.filename), and\n retry without it.\nCSV-specific failures (input.csvFiles) and generic file failures\n (input.files) now throw different error codes — branch on\n error.code if you need to distinguish them.","hierarchy":{"lvl0":"MIGRATION","lvl1":"Migration Guide: Breaking Changes","lvl2":"1. Multimodal file/CSV processing is now fail-loud (v9.94.6)","lvl3":""}}, {"objectID":"495abd7312e110042baf6f38ccbea7a0e512ba343bf04ec04be67d85c44cea57","title":"2. MessageContent dropped its open index signature (v9.94.6)","url":"/docs/MIGRATION#2-messagecontent-dropped-its-open-index-signature-v9946","content":"What changed: the exported MessageContent type\n(src/lib/types/multimodal.ts, re-exported via src/lib/types/index.ts and\nthe package root) no longer has a [key: string]: unknown index signature.\nIt's now a closed shape with the concrete fields the pipeline actually reads:\ntype, text, image, mimeType, data, name, filename,\ntoolCallId, toolName, args, result, isError, providerOptions.\n\nWhy: the open index signature let any typo or unrelated key through\ntype-checking unchecked (e.g. { type: \"text\", txt: \"...\" } compiled fine\nand silently produced empty content). Closing it catches that class of bug at\ncompile time for everyone building MessageContent[] arrays directly.\n\nBefore:\n\nAfter:\n\nHow to adapt:\nIf you were attaching arbitrary metadata to a MessageContent item,\n use the dedicated providerOptions?: Record field instead\n — it's read by MessageBuilder when converting to ModelMessage[] and is\n the intended extension point.\nIf you genuinely need a field NeuroLink doesn't model, open an issue —\n MessageContent intentionally stays a closed, hand-maintained shape rather\n than reopening a blanket index signature (see the #325 comment on the\n type itself).\nRuntime behavior is unchanged — this is a compile-time-only break. Plain\n JavaScript callers, or TypeScript callers that never annotated a literal\n as MessageContent explicitly, are unaffected.","hierarchy":{"lvl0":"MIGRATION","lvl1":"Migration Guide: Breaking Changes","lvl2":"2. MessageContent dropped its open index signature (v9.94.6)","lvl3":""}}, {"objectID":"455b46e7ea05b11b8c11d3fb4a9c3265cffac0048da109b0c2482e1973b46185","title":"3. BatchCommandArgs.file renamed to .promptsFile (v9.95.1)","url":"/docs/MIGRATION#3-batchcommandargsfile-renamed-to-promptsfile-v9951","content":"What changed: the public CLI argument type BatchCommandArgs\n(src/lib/types/cli.ts) renamed its file?: string property to\npromptsFile?: string.\n\nWhy: yargs implicitly registers a positional argument's key as a\nrecognized flag name too. Keeping the property named file collided with\nthe unrelated common --file auto-detect flag that the batch command\nintentionally does not register — --file on batch would silently pass\nstrictOptions() without actually doing anything. Renaming the positional's\nbacking property to promptsFile removes that collision.\n\nBefore:\n\nAfter:\n\nHow to adapt:\nUpdate any code that constructs or reads a BatchCommandArgs object\n directly (custom CLI wrappers, tests) to use .promptsFile instead of\n .file.\nThe CLI's own positional argument and its behavior are\n unchanged — this is a type-only rename of the property NeuroLink's own\n batch command handler reads internally; end users invoking\n neurolink batch on the command line see no difference.","hierarchy":{"lvl0":"MIGRATION","lvl1":"Migration Guide: Breaking Changes","lvl2":"3. BatchCommandArgs.file renamed to .promptsFile (v9.95.1)","lvl3":""}}, {"objectID":"28f1dbfa91cf8d80727ab4e03c4433cd9eaa6aa433ee5af8a0a85768c084a435","title":"4. Unused provider-option types, ParameterNormalizer and OpenRouterConfig were removed (v11.0.0)","url":"/docs/MIGRATION#4-unused-provider-option-types-parameternormalizer-and-openrouterconfig-were-removed-v1100","content":"Who is affected: code that imports any of the ten names below from\n@juspay/neurolink. Nothing in the SDK or the CLI read these shapes, so code\nthat never imported them is unaffected and no generate()/stream() behavior\nchanged.\n\nWhat changed: the Phase-1 \"universal provider options\" abstraction\n(src/lib/types/universalProviderOptions.ts) was deleted and OpenRouterConfig\nwas removed from src/lib/types/providers.ts. The package root re-exports the\nwhole types barrel (export * from \"./types/index.js\" in src/lib/index.ts), so\nall ten names were importable from @juspay/neurolink through v10.12.9. They are\nabsent from v11.0.0 and from the current release branch. Importing one now\nfails: TS2305 in TypeScript, a missing named export in native ESM.\n\n| Removed export | Kind | What it was |\n| -------------------------- | --------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| UniversalProviderOptions | type | Optional prompt, model, temperature, maxTokens, systemPrompt, enableAnalytics, enableEvaluation, context, contextConfig, metadata, extensionOptions. |\n| GenericProviderOptions | type | UniversalProviderOptions without providerType. |\n| OpenAIProviderOptions | type | UniversalProviderOptions plus providerType: \"openai\", organization, seed, topP. |\n| GoogleAIProviderOptions | type | UniversalProviderOptions plus providerType: \"google-ai\", topK, candidateCount, stopSequences.","hierarchy":{"lvl0":"MIGRATION","lvl1":"Migration Guide: Breaking Changes","lvl2":"4. Unused provider-option types, ParameterNormalizer and OpenRouterConfig were removed (v11.0.0)","lvl3":""}}, {"objectID":"74241531632f160c856862e82caee0ef1f267748ea768817dcc8e63df4fbc268","title":"5. stream() synthesizes whenever tts.enabled is set (unreleased)","url":"/docs/MIGRATION#5-stream-synthesizes-whenever-ttsenabled-is-set-unreleased","content":"Who is affected: SDK callers using stream() with tts.enabled: true\nwithout setting useAiResponse: true.\n\nWhat changed: those callers now receive incremental tts_audio chunks and\nthe aggregate streamResult.audio. useAiResponse continues to choose input\nversus response synthesis for generate(); it no longer gates synthesis for\nstream().\n\nHow to adapt: disable TTS for a text-only stream by omitting tts or setting\ntts.enabled: false. Keep tts.enabled: true when streamed response audio is\ndesired; no useAiResponse value is required on that path.","hierarchy":{"lvl0":"MIGRATION","lvl1":"Migration Guide: Breaking Changes","lvl2":"5. stream() synthesizes whenever tts.enabled is set (unreleased)","lvl3":""}}, {"objectID":"a4124a7414cf9f4e06cac100556a4d178740dfa2037c518a1aef17f8a64105bb","title":"Workflow Engine - High-Level Design","url":"/docs/WORKFLOW-ENGINE-HLD","content":"Neurolink Workflow Engine - High-Level Design (HLD)\n\nVersion: 1.0 \nDate: November 28, 2025 \nStatus: Implementation Complete \nAuthor: Neurolink Team\n\n📋 Executive Summary\n\nThe Neurolink Workflow Engine is a new subsystem that enables advanced AI orchestration patterns through multi-model ensembles and judge-based scoring. It extends Neurolink's existing provider abstraction to support complex workflows where multiple AI models collaborate with evaluation for higher-quality outputs.\n\nCurrent Phase: Testing & Evaluation - workflows return original responses with scores for AB testing.\n\nKey Value Propositions\n🎯 Improved Accuracy: Leverage multiple models to cross-validate responses\n⚖️ Objective Evaluation: Use judge models to score and select best responses (0-100 scale)\n📊 Comprehensive Logging: Detailed metrics for AB testing and workflow evaluation\n🔧 Declarative Configuration: Define workflows as composable configs\n💰 Cost Transparency: Track ensemble performance and costs\n\n🎯 Goals & Non-Goals\n\nGoals (Testing Phase)\nEnable Multi-Model Workflows: Run N models in parallel for the same prompt\nIntelligent Evaluation: Use judge models to score (0-100) and rank responses\nComprehensive Logging: Detailed metrics for AB testing and evaluation\nOriginal Output: Return best response unchanged for production safety\nCost Transparency: Provide clear cost/performance metrics\nSeamless Integration: Work with existing Neurolink provider layer\n\nNon-Goals (Phase 1 - Testing)\n❌ Response conditioning/modification (deferred until testing validates workflows)\n❌ Streaming workflow execution (deferred to Phase 2)\n❌ Stateful/resumable workflows (deferred to Phase 2)\n❌ DAG-based workflow chaining (deferred to Phase 3)\n❌ Human-in-the-loop approval steps (deferred to Phase 3)\n❌ Workflow versioning/migration (deferred to Phase 3)\n\n🏗️ Architecture Overview\n\nSystem Context\n\nComponent Hierarchy\n\n🔄 Workflow Execution Flow\n\nHigh-Level Process\n\n🧩 Core Components\nWorkflow Runner\n\nPurpose: Main orchestrator that executes workflows end-to-end\n\nResponsibilities:\nLoad and validate workflow configurations\nCoordinate ensemble → judge → conditioning pipeline\nHandle errors and partial failures\nAggregate results with comprehensive metrics\n\nKey Methods:\nWorkflow Registry\n\nPurpose: Manage workflow templates (built-in + custom)\n\nResponsibilities:\nStore workflow configurations\nProvide workflow discovery API\nValidate configs before registration\nSupport workflow CRUD operations\n\nKey Methods:\nEnsemble Executor\n\nPurpose: Execute multiple models in parallel\n\nResponsibilities:\nCreate provider instances for each model\nExecute requests concurrently via Promise.all()\nCollect responses with timing/usage data\nHandle individual model failures gracefully\n\nKey Methods:\n\nIntegration Points:\nUses AIProviderFactory.createProvider() for model instantiation\nCalls BaseProvider.generate() for each model\nLeverages existing analytics from core/analytics.ts\nJudge Scorer\n\nPurpose: Evaluate and rank ensemble responses\n\nResponsibilities:\nFormat ensemble results for judge evaluation\nCall judge model with structured output schema\nParse scores/rankings from judge response\nSupport multiple scoring strategies (numeric, ranking, best-pick)\n\nKey Methods:\n\nScoring Strategies:\nNumeric Scoring: Return 0-10 scores for each response\nRanking: Order responses from best to worst\nBest Pick: Select single best response with reasoning\nMulti-Judge Voting: Average scores from multiple judges\nResponse Conditioner\n\nPurpose: Post-process responses based on confidence\n\nResponsibilities:\nCalculate overall confidence score\nAdjust tone based on confidence level\nAdd structured metadata\nFormat final user-facing response\n\nKey Methods:\n\nConditioning Rules:\nHigh confidence (>0.8): Direct, assertive language\nMedium confidence (0.5-0.8): Balanced, qualified language\nLow confidence (\\95% workflow completion\nCost Accuracy: ±5% cost estimation accuracy\nError Recovery: Handle 2/3 model failures gracefully\n\nDocumentation\nHigh-Level Design (this document)\nLow-Level Design with implementation details\nAPI Reference documentation\nTutorial with 5+ examples\nMigration guide for existing users\n\n🔮 Future Enhancements (Post-MVP)\n\nPhase 2: Streaming & Advanced Patterns\nStreaming Workflows: Progressive results with streamWorkflow()\nWorkflow State Management: Persistent workflow state\nAsync Workflows: Background execution with callbacks\nWorkflow Chaining: Connect workflows in pipelines\n\nPhase 3: Enterprise Features\nDAG-based Workflows: Complex multi-stage orchestration\nHuman-in-the-Loop: Manual approval/judging steps\nWorkflow Versioning: Manage workflow evolution\nA/B Testing: Compare workflow performance\nWorkflow Marketplace: Share and discover workflows\n\nPhase 4: Advanced Intelligence\nAdaptive Workflows: Auto-select models based on query\nSelf-Improving Workflows: Learn from past executions\nCost Optimization: Auto-route to cheapest viable models\nQuality Prediction: Predict confidence before execution\n\n📚 References\n\nInternal D","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"","lvl3":""}}, {"objectID":"ad7ea764218e2a457b6ecdfa0e7e9ec5d63e4f7fee0732433f7bc8572d6f2aaf","title":"Neurolink Workflow Engine - High-Level Design (HLD)","url":"/docs/WORKFLOW-ENGINE-HLD#neurolink-workflow-engine---high-level-design-hld","content":"Version: 1.0 \nDate: November 28, 2025 \nStatus: Implementation Complete \nAuthor: Neurolink Team","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"Neurolink Workflow Engine - High-Level Design (HLD)","lvl3":""}}, {"objectID":"4ee2552472dfa0529a5391867fcb9faba116539fd4d14576eca2e9af275891c3","title":"📋 Executive Summary","url":"/docs/WORKFLOW-ENGINE-HLD#-executive-summary","content":"The Neurolink Workflow Engine is a new subsystem that enables advanced AI orchestration patterns through multi-model ensembles and judge-based scoring. It extends Neurolink's existing provider abstraction to support complex workflows where multiple AI models collaborate with evaluation for higher-quality outputs.\n\nCurrent Phase: Testing & Evaluation - workflows return original responses with scores for AB testing.","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"📋 Executive Summary","lvl3":""}}, {"objectID":"f3bcb44760b0a6485325d143fc23b55c351877f602317058deeb44d539c6d0c2","title":"Key Value Propositions","url":"/docs/WORKFLOW-ENGINE-HLD#key-value-propositions","content":"🎯 Improved Accuracy: Leverage multiple models to cross-validate responses\n⚖️ Objective Evaluation: Use judge models to score and select best responses (0-100 scale)\n📊 Comprehensive Logging: Detailed metrics for AB testing and workflow evaluation\n🔧 Declarative Configuration: Define workflows as composable configs\n💰 Cost Transparency: Track ensemble performance and costs","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"Key Value Propositions","lvl3":""}}, {"objectID":"374705ef2f6a72babdbf60b2b374218fc29389080863223a82d5f79c03911d81","title":"Goals (Testing Phase)","url":"/docs/WORKFLOW-ENGINE-HLD#goals-testing-phase","content":"Enable Multi-Model Workflows: Run N models in parallel for the same prompt\nIntelligent Evaluation: Use judge models to score (0-100) and rank responses\nComprehensive Logging: Detailed metrics for AB testing and evaluation\nOriginal Output: Return best response unchanged for production safety\nCost Transparency: Provide clear cost/performance metrics\nSeamless Integration: Work with existing Neurolink provider layer","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"Goals (Testing Phase)","lvl3":""}}, {"objectID":"5aae7b3527e875d66062c66d020dc5a07b130c1648ebc9e253d16eed46c18a76","title":"Non-Goals (Phase 1 - Testing)","url":"/docs/WORKFLOW-ENGINE-HLD#non-goals-phase-1---testing","content":"❌ Response conditioning/modification (deferred until testing validates workflows)\n❌ Streaming workflow execution (deferred to Phase 2)\n❌ Stateful/resumable workflows (deferred to Phase 2)\n❌ DAG-based workflow chaining (deferred to Phase 3)\n❌ Human-in-the-loop approval steps (deferred to Phase 3)\n❌ Workflow versioning/migration (deferred to Phase 3)","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"Non-Goals (Phase 1 - Testing)","lvl3":""}}, {"objectID":"c655cefc9a72e252f5105150688cafc2f99c3f154b0493909b0d550787a40e0f","title":"1. 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Workflow Registry","lvl3":""}}, {"objectID":"401e1fd2b557ad5b72be7cdab6a8e53d8caaecc1f8acd37e03e3a68c336a89ab","title":"3. Ensemble Executor","url":"/docs/WORKFLOW-ENGINE-HLD#3-ensemble-executor","content":"Purpose: Execute multiple models in parallel\n\nResponsibilities:\nCreate provider instances for each model\nExecute requests concurrently via Promise.all()\nCollect responses with timing/usage data\nHandle individual model failures gracefully\n\nKey Methods:\n\nIntegration Points:\nUses AIProviderFactory.createProvider() for model instantiation\nCalls BaseProvider.generate() for each model\nLeverages existing analytics from core/analytics.ts","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"3. Ensemble Executor","lvl3":""}}, {"objectID":"220624ee3fafa1c6cb423e5556278ef963c883e388a96b69096b29ac2f4d984d","title":"4. Judge Scorer","url":"/docs/WORKFLOW-ENGINE-HLD#4-judge-scorer","content":"Purpose: Evaluate and rank ensemble responses\n\nResponsibilities:\nFormat ensemble results for judge evaluation\nCall judge model with structured output schema\nParse scores/rankings from judge response\nSupport multiple scoring strategies (numeric, ranking, best-pick)\n\nKey Methods:\n\nScoring Strategies:\nNumeric Scoring: Return 0-10 scores for each response\nRanking: Order responses from best to worst\nBest Pick: Select single best response with reasoning\nMulti-Judge Voting: Average scores from multiple judges","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"4. Judge Scorer","lvl3":""}}, {"objectID":"243106b9d7a2a70d14a7e26be8eab04fa49ce809bfeffff24b71bc45f1c8092b","title":"5. Response Conditioner","url":"/docs/WORKFLOW-ENGINE-HLD#5-response-conditioner","content":"Purpose: Post-process responses based on confidence\n\nResponsibilities:\nCalculate overall confidence score\nAdjust tone based on confidence level\nAdd structured metadata\nFormat final user-facing response\n\nKey Methods:\n\nConditioning Rules:\nHigh confidence (>0.8): Direct, assertive language\nMedium confidence (0.5-0.8): Balanced, qualified language\nLow confidence (\\<0.5): Tentative, exploratory language","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"5. Response Conditioner","lvl3":""}}, {"objectID":"b1866e7a6fe5eace19fbc8c3a61630101c0c646f0e3c34c06380160634606701","title":"1. 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Multi-Judge Workflow (multi-judge-5)","lvl3":""}}, {"objectID":"209883354730414a385e0f8bf10db9d40555e2da8f5254ad668298cbf90ed593","title":"Expected Latency","url":"/docs/WORKFLOW-ENGINE-HLD#expected-latency","content":"| Workflow Type | Models | Judge | Expected Latency | Cost Multiplier |\n| ------------- | ------ | ----- | ---------------- | --------------- |\n| Consensus-3 | 3 | 1 | 3-5 seconds | 4x |\n| Fast-Fallback | 1-2 | 0 | 1-3 seconds | 1-2x |\n| Quality-Max | 2 | 1 | 3-4 seconds | 3x |\n| Multi-Judge-5 | 3 | 2 | 4-6 seconds | 5x |","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"Expected Latency","lvl3":""}}, {"objectID":"c115debaca4b430728c0b04f09d23f6030303ed677eb598c630d27ffda606172","title":"Optimization Strategies","url":"/docs/WORKFLOW-ENGINE-HLD#optimization-strategies","content":"Parallel Execution: All ensemble models run concurrently\nTimeout Controls: Per-model timeout prevents hanging\nEarly Termination: Optional \"first N responses\" mode\nModel Selection: Lightweight models for speed, powerful for quality\nConcurrency Control: p-limit for controlled parallel execution","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - 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High-Level Design","lvl2":"Error Handling","lvl3":""}}, {"objectID":"d07e2ff877f214083e734bf66ce80857949e465f917904794520dc192c8a8a0b","title":"Metrics to Track","url":"/docs/WORKFLOW-ENGINE-HLD#metrics-to-track","content":"Execution Metrics\nTotal workflow execution time\nPer-model response time\nJudge scoring time\nEnsemble success rate\nQuality Metrics\nJudge scores distribution\nConsensus levels\nConfidence scores\nResponse variation\nCost Metrics\nTotal tokens used\nCost per workflow\nCost breakdown by model\nBudget utilization\nError Metrics\nModel failure rate\nTimeout frequency\nValidation errors\nRetry attempts","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"Metrics to Track","lvl3":""}}, {"objectID":"5809ec94a48280b190bf534eef6de648203c47f27d0a598fb58b3ff2f2d8f726","title":"Logging","url":"/docs/WORKFLOW-ENGINE-HLD#logging","content":"Structured JSON logs for all workflow executions\nDebug mode for detailed execution traces\nPerformance profiling for optimization\nAudit trail for compliance","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"Logging","lvl3":""}}, {"objectID":"d594a43685ee6b1c41183d0cce0181aa064712fa8bbf0bf1fe4176e1c3d7f137","title":"Phase 1 (MVP)","url":"/docs/WORKFLOW-ENGINE-HLD#phase-1-mvp","content":"✅ Support 3+ ensemble models running in parallel\n✅ Implement judge-based scoring with structured output\n✅ Response conditioning with confidence-based tone adjustment\n✅ 3 built-in workflows (consensus, fallback, quality-max)\n✅ Custom workflow registration API\n✅ Comprehensive analytics and metrics\n✅ Full TypeScript type safety\n✅ Integration tests with real providers","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - High-Level Design","lvl2":"Phase 1 (MVP)","lvl3":""}}, {"objectID":"b315505329a9729a0b1b49a4358c18f516c7a03d242e442c68c9986dfc72e603","title":"Performance Targets","url":"/docs/WORKFLOW-ENGINE-HLD#performance-targets","content":"Latency: \\95% workflow completion\nCost Accuracy: ±5% cost estimation accuracy\nError Recovery: Handle 2/3 model failures gracefully","hierarchy":{"lvl0":"WORKFLOW ENGINE HLD","lvl1":"Workflow Engine - 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Key Methods\n\nKey Algorithm: Judge Prompt Generation\nResponse Conditioner (workflow/core/responseConditioner.ts) - Key Methods\n\nTone Adjustment Algorithm:\nWorkflow Registry (workflow/core/workflowRegistry.ts) - Key Methods\nIntegration with NeuroLink Class\n\nModifications to src/lib/neurolink.ts\nTesting Strategy\n\nUnit Tests\n\nIntegration Tests\nError Handling Strategy\n\nError Hierarchy\n\nRetry Logic\nPerformance Optimizations\n\nParallel Execution Optimization\nObservability & Monitoring\n\nStructured Logging\n\nMetrics Collection\nSecurity Considerations\n\nInput Validation\n\n[^]*\n\nCost Controls\nBuilt-in Workflow Implementations\n\nConsensus Workflow\nAPI Usage Examples\n\nBasic Usage\n\nCustom Workflow\nMigration Path for Existing Users\n\nBackward Compatibility\n\nGradual Adoption\nPhase 1: Users can try workflows alongside existing methods\nPhase 2: Workflows become recommended for high-stakes queries\nPhase 3: Workflows are default with single-model as fallback\nPerformance Benchmarks (Expected)\n\n| Workflow | Models | Judge | Latency (p50) | Latency (p95) | Cost Multiplier |\n| ------------- | ------ | ----- | ------------- | ------------- | --------------- |\n| consensus-3 | 3 | 1 | 3.2s | 5.1s | 4.2x |\n| fast-fallback | 1-2 | 0 | 1.1s | 2.8s | 1.3x |\n| quality-max | 2 | 1 | 3.5s | 4.9s | 3.1x |\n| multi-judge-5 | 3 | 2 | 4.8s | 6.7s | 5.3x |\nFuture Enhancements\n\nPhase 2: Streaming Support\n\nPhase 3: Workflow Chaining\n\n📝 Implementation Checklist\n[ ] Create src/workflow/ directory structure\n[ ] Implement types.ts with all interfaces\n[ ] Implement config.ts with Zod schemas\n[ ] Implement ensembleExecutor.ts\n[ ] Implement judgeScorer.ts\n[ ] Implement responseConditioner.ts\n[ ] Implement workflowRegistry.ts\n[ ] Implement workflowRunner.ts\n[ ] Create built-in workflows (consensus, fallback, quality-max)\n[ ] Add methods to NeuroLink class\n[ ] Export types from src/lib/index.ts\n[ ] Write unit tests (80% coverage target)\n[ ] Write integration tests\n[ ] Add JSDoc documentation\n[ ] Create user guide with examples\n[ ] Add CLI support (optional Phase 2)\n\nDocument Status: ✅ Ready for Implementation \nNext Step: Code generation upon approval","hierarchy":{"lvl0":"WORKFLOW ENGINE LLD","lvl1":"Workflow Engine - 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Judge Scorer (workflow/core/judgeScorer.ts) - Key Methods","url":"/docs/WORKFLOW-ENGINE-LLD#5-judge-scorer-workflowcorejudgescorerts---key-methods","content":"Key Algorithm: Judge Prompt Generation","hierarchy":{"lvl0":"WORKFLOW ENGINE LLD","lvl1":"Workflow Engine - Low-Level Design","lvl2":"5. Judge Scorer (workflow/core/judgeScorer.ts) - Key Methods","lvl3":""}}, {"objectID":"3d87eccde8bef119b4cc440100bed3859f6e6ab35d8c6779cd4bf66f337826d7","title":"6. Response Conditioner (workflow/core/responseConditioner.ts) - Key Methods","url":"/docs/WORKFLOW-ENGINE-LLD#6-response-conditioner-workflowcoreresponseconditionerts---key-methods","content":"Tone Adjustment Algorithm:","hierarchy":{"lvl0":"WORKFLOW ENGINE LLD","lvl1":"Workflow Engine - Low-Level Design","lvl2":"6. Response Conditioner (workflow/core/responseConditioner.ts) - Key Methods","lvl3":""}}, {"objectID":"e171ea65deea448bb34b70ba170da90245765fb638ef81d5b29dfba71490b482","title":"Input Validation","url":"/docs/WORKFLOW-ENGINE-LLD#input-validation","content":"[^]*","hierarchy":{"lvl0":"WORKFLOW ENGINE LLD","lvl1":"Workflow Engine - Low-Level Design","lvl2":"Input Validation","lvl3":""}}, {"objectID":"b38e417fcd8ea5d3b983875efebb5b5a309883fdfe8bfdc80300e473bea5e081","title":"Gradual Adoption","url":"/docs/WORKFLOW-ENGINE-LLD#gradual-adoption","content":"Phase 1: Users can try workflows alongside existing methods\nPhase 2: Workflows become recommended for high-stakes queries\nPhase 3: Workflows are default with single-model as fallback","hierarchy":{"lvl0":"WORKFLOW ENGINE LLD","lvl1":"Workflow Engine - Low-Level Design","lvl2":"Gradual Adoption","lvl3":""}}, {"objectID":"2127ec4796813559375c700e37b4944f77c72f22a0410fe93bd6f5f7cff8a4c0","title":"17. Performance Benchmarks (Expected)","url":"/docs/WORKFLOW-ENGINE-LLD#17-performance-benchmarks-expected","content":"| Workflow | Models | Judge | Latency (p50) | Latency (p95) | Cost Multiplier |\n| ------------- | ------ | ----- | ------------- | ------------- | --------------- |\n| consensus-3 | 3 | 1 | 3.2s | 5.1s | 4.2x |\n| fast-fallback | 1-2 | 0 | 1.1s | 2.8s | 1.3x |\n| quality-max | 2 | 1 | 3.5s | 4.9s | 3.1x |\n| multi-judge-5 | 3 | 2 | 4.8s | 6.7s | 5.3x |","hierarchy":{"lvl0":"WORKFLOW ENGINE LLD","lvl1":"Workflow Engine - Low-Level Design","lvl2":"17. Performance Benchmarks (Expected)","lvl3":""}}, {"objectID":"2f54303255393f1b5c8695c6342964ce83cb786fd3ae00f535bf17c44cae16cd","title":"📝 Implementation Checklist","url":"/docs/WORKFLOW-ENGINE-LLD#-implementation-checklist","content":"[ ] Create src/workflow/ directory structure\n[ ] Implement types.ts with all interfaces\n[ ] Implement config.ts with Zod schemas\n[ ] Implement ensembleExecutor.ts\n[ ] Implement judgeScorer.ts\n[ ] Implement responseConditioner.ts\n[ ] Implement workflowRegistry.ts\n[ ] Implement workflowRunner.ts\n[ ] Create built-in workflows (consensus, fallback, quality-max)\n[ ] Add methods to NeuroLink class\n[ ] Export types from src/lib/index.ts\n[ ] Write unit tests (80% coverage target)\n[ ] Write integration tests\n[ ] Add JSDoc documentation\n[ ] Create user guide with examples\n[ ] Add CLI support (optional Phase 2)\n\nDocument Status: ✅ Ready for Implementation \nNext Step: Code generation upon approval","hierarchy":{"lvl0":"WORKFLOW ENGINE LLD","lvl1":"Workflow Engine - Low-Level Design","lvl2":"📝 Implementation Checklist","lvl3":""}}, {"objectID":"de72b620d608319199e3abf25b54298afc8fe5f294c73c538956a2feb63fe9b1","title":"The Nervous System Model","url":"/docs/about/nervous-system-model","content":"The Nervous System Model\n\nNeuroLink is built around a biological metaphor — not as decoration, but as a structural model that governs every architectural decision.\n\nThe Three Components\n\nNeurons — LLM Providers\n\nNeurons are where intelligence is generated. In NeuroLink, neurons are the AI providers, including: Anthropic, OpenAI, Google (AI Studio + Vertex), AWS (Bedrock + SageMaker), Azure, Mistral, LiteLLM, OpenRouter, Ollama, Hugging Face, DeepSeek, NVIDIA NIM, LM Studio, llama.cpp, OpenAI-compatible endpoints, TypeSafe Jev, Laya, XOR, Perplexity Decisions and Cloudflare Clef (decision-only) — plus voice neurons (OpenAI TTS, ElevenLabs, Google TTS, Azure TTS, Whisper, Deepgram, Azure STT, Google STT), realtime neurons (OpenAI Realtime, Gemini Live), and media-generation neurons (image, video, music, avatar).\n\nEach provider is a different type of neuron — different capabilities, different costs, different latency profiles. NeuroLink's ProviderRegistry gives you access to all of them through one interface, switchable with a single line.\n\nThe Pipe — NeuroLink\n\nThe pipe is the vascular layer that carries streams between neurons and organs. This is NeuroLink itself.\n\nWhat the pipe does every time you call generate() or stream():\nContext Building — RAG retrieval, memory lookup, file processing merge into the prompt\nBudget Check — BudgetChecker validates the assembled context fits the model's window\nProvider Dispatch — ProviderRegistry routes to the correct neuron\nStream Emission — Tokens flow as an async iterable\nTool Interception — When the model calls a tool, the stream pauses, MCP tool executes, result injects, stream continues\nObservability — Every stage emits OpenTelemetry spans\n\nOrgans — Connectors\n\nOrgans are the applications that consume the pipe. They connect to the vascular layer and open a gateway — a specific way for people or systems to interact with AI.\n\nEvery application built on NeuroLink is an organ. Production organs today:\nAutomatic — Shopify operations hub: address intelligence, RTO risk scoring\nTara — Slack engineering assistant: conversational AI with MCP tool access\nYama — Code review judge: automated PR analysis and governance\n\nWhy This Model Works\n\nThe metaphor enforces good architecture:\n\nSeparation of concerns: Neurons (generation) and organs (consumption) are completely decoupled. Changing AI provider doesn't touch the application. Changing the application doesn't touch the provider.\n\nSingle flow direction: Intelligence flows one way — neuron → pipe → organ. There's no confusion about where logic lives.\n\nObservable by default: A vascular system you can't monitor is dangerous. Every stage of the pipe emits telemetry by design.\n\nComposable: Multiple organs can share the same pipe. One NeuroLink instance serves many connectors.\n\nExtending the System\n\nThe nervous system model scales in three directions:\nAdd neurons — New AI provider? Register it in ProviderRegistry.\nExtend the pipe — New capability (chunking strategy, reranker, compaction stage)? Add it to the pipeline.\nBuild organs — New application? Import NeuroLink, connect to the pipe, open your gateway.\n\nSee Pipe Architecture → for the technical implementation.","hierarchy":{"lvl0":"About","lvl1":"The Nervous System Model","lvl2":"","lvl3":""}}, {"objectID":"27ef114b7e0cecc6ca45dbf3e9285980b691cd27d21e1912f2a27a7f309f68fe","title":"The Nervous System Model","url":"/docs/about/nervous-system-model#the-nervous-system-model","content":"NeuroLink is built around a biological metaphor — not as decoration, but as a structural model that governs every architectural decision.","hierarchy":{"lvl0":"About","lvl1":"The Nervous System Model","lvl2":"The Nervous System Model","lvl3":""}}, {"objectID":"58e5ab0de0df2a0949ae12798cd53ac3133b2c253880bcbf0c573938376fd84f","title":"Neurons — LLM Providers","url":"/docs/about/nervous-system-model#neurons-llm-providers","content":"Neurons are where intelligence is generated. In NeuroLink, neurons are the AI providers, including: Anthropic, OpenAI, Google (AI Studio + Vertex), AWS (Bedrock + SageMaker), Azure, Mistral, LiteLLM, OpenRouter, Ollama, Hugging Face, DeepSeek, NVIDIA NIM, LM Studio, llama.cpp, OpenAI-compatible endpoints, TypeSafe Jev, Laya, XOR, Perplexity Decisions and Cloudflare Clef (decision-only) — plus voice neurons (OpenAI TTS, ElevenLabs, Google TTS, Azure TTS, Whisper, Deepgram, Azure STT, Google STT), realtime neurons (OpenAI Realtime, Gemini Live), and media-generation neurons (image, video, music, avatar).\n\nEach provider is a different type of neuron — different capabilities, different costs, different latency profiles. NeuroLink's ProviderRegistry gives you access to all of them through one interface, switchable with a single line.","hierarchy":{"lvl0":"About","lvl1":"The Nervous System Model","lvl2":"Neurons — LLM Providers","lvl3":""}}, {"objectID":"96f94c64476fe5567b61e5374da6d1b5c5020e22654c6c082b8936ae3f72eb51","title":"The Pipe — NeuroLink","url":"/docs/about/nervous-system-model#the-pipe-neurolink","content":"The pipe is the vascular layer that carries streams between neurons and organs. This is NeuroLink itself.\n\nWhat the pipe does every time you call generate() or stream():\nContext Building — RAG retrieval, memory lookup, file processing merge into the prompt\nBudget Check — BudgetChecker validates the assembled context fits the model's window\nProvider Dispatch — ProviderRegistry routes to the correct neuron\nStream Emission — Tokens flow as an async iterable\nTool Interception — When the model calls a tool, the stream pauses, MCP tool executes, result injects, stream continues\nObservability — Every stage emits OpenTelemetry spans","hierarchy":{"lvl0":"About","lvl1":"The Nervous System Model","lvl2":"The Pipe — NeuroLink","lvl3":""}}, {"objectID":"1c74200e8d4572e84dca981b50bb162912b4c6700ea7495d60fe286cfb16347f","title":"Organs — Connectors","url":"/docs/about/nervous-system-model#organs-connectors","content":"Organs are the applications that consume the pipe. They connect to the vascular layer and open a gateway — a specific way for people or systems to interact with AI.\n\nEvery application built on NeuroLink is an organ. Production organs today:\nAutomatic — Shopify operations hub: address intelligence, RTO risk scoring\nTara — Slack engineering assistant: conversational AI with MCP tool access\nYama — Code review judge: automated PR analysis and governance","hierarchy":{"lvl0":"About","lvl1":"The Nervous System Model","lvl2":"Organs — Connectors","lvl3":""}}, {"objectID":"6c97c4e15e0de12e62fbb1994cc7b18328c9f2b0afa2b721d8f40f01e70d32f7","title":"Why This Model Works","url":"/docs/about/nervous-system-model#why-this-model-works","content":"The metaphor enforces good architecture:\n\nSeparation of concerns: Neurons (generation) and organs (consumption) are completely decoupled. Changing AI provider doesn't touch the application. Changing the application doesn't touch the provider.\n\nSingle flow direction: Intelligence flows one way — neuron → pipe → organ. There's no confusion about where logic lives.\n\nObservable by default: A vascular system you can't monitor is dangerous. Every stage of the pipe emits telemetry by design.\n\nComposable: Multiple organs can share the same pipe. One NeuroLink instance serves many connectors.","hierarchy":{"lvl0":"About","lvl1":"The Nervous System Model","lvl2":"Why This Model Works","lvl3":""}}, {"objectID":"c92dfd284a8cdc811c1830e1b07923ba451989e13139b52f5019c057a51b4005","title":"Extending the System","url":"/docs/about/nervous-system-model#extending-the-system","content":"The nervous system model scales in three directions:\nAdd neurons — New AI provider? Register it in ProviderRegistry.\nExtend the pipe — New capability (chunking strategy, reranker, compaction stage)? Add it to the pipeline.\nBuild organs — New application? Import NeuroLink, connect to the pipe, open your gateway.\n\nSee Pipe Architecture → for the technical implementation.","hierarchy":{"lvl0":"About","lvl1":"The Nervous System Model","lvl2":"Extending the System","lvl3":""}}, {"objectID":"706ecce11acffb2d9670464a5658a65f6c54f35cb226ce3bbce06e66a09f7ac3","title":"Pipe Architecture","url":"/docs/about/pipe-architecture","content":"Pipe Architecture\n\nEvery generate() and stream() call travels the same six-stage pipe. Understanding the pipe is understanding NeuroLink.\n\nThe Six Stages\nContext Building\n\nBefore any tokens are generated, the pipe assembles the full context:\nRAG retrieval — If rag: { files: [...] } is set, documents are chunked, embedded, and a search_knowledge_base tool is registered\nMemory lookup — Conversation history fetched from Redis or in-memory store\nFile processing — Attached files (images, PDFs, code, CSV) processed by ProcessorRegistry into provider-appropriate formats\nSystem prompt injection — Custom system prompts merged with NeuroLink defaults\nBudget Check\n\nBudgetChecker validates the assembled context fits within the model's context window before every LLM call.\nThreshold: triggers at 80% of context window\nIf over budget: runs ContextCompactor (5-stage pipeline): 0. Relevance drop — asks a decision model which earlier messages the current\n request still needs (skipped entirely without a decision provider)\nTool output pruning — replaces old tool results with placeholders\nFile read deduplication — keeps only latest read of each file\nLLM summarization — structured 10-section summary of oldest messages\nSliding window truncation — non-destructive tagging of oldest messages\n\nContext windows are tracked per-provider, per-model in src/lib/constants/contextWindows.ts.\nProvider Dispatch\n\nProviderRegistry resolves the provider name to a concrete implementation:\n\nSwitching providers requires one line — the rest of the pipe is unchanged.\nStream Emission\n\nTokens arrive as an async iterable. generate() is stream() collected — there is only stream().\n\nThe stream handles multiple event types: text deltas, tool calls, thinking blocks, usage statistics.\nTool Interception\n\nWhen the model emits a tool call, the stream pauses:\nTool call extracted from the stream\nMCPToolRegistry dispatches to the correct tool (built-in or external MCP server)\nTool result injected back into the conversation\nModel resumes generating from the tool result\nStream continues\n\nMCP transports supported: stdio (local), http (remote), sse, websocket.\nObservability\n\nEvery stage emits OpenTelemetry spans. The full trace covers:\nContext build duration\nToken counts (input + output)\nTool execution times\nMemory read/write latency\nProvider-specific attributes (model, temperature, finish reason)\n\nExporters: Langfuse, OTLP, Jaeger, Zipkin, Prometheus, Datadog, NewRelic, Honeycomb, Console.\n\nKey Files\n\n| File | Purpose |\n| --------------------------------------- | -------------------------------------------- |\n| src/lib/neurolink.ts | Main SDK class — orchestrates all six stages |\n| src/lib/factories/providerRegistry.ts | Provider registration with dynamic imports |\n| src/lib/context/budgetChecker.ts | Pre-generation context budget validation |\n| src/lib/context/contextCompactor.ts | Multi-stage compaction orchestrator |\n| src/lib/mcp/toolRegistry.ts | Tool management and MCP integration |\n| src/lib/rag/ragIntegration.ts | RAG auto-pipeline setup |\n| src/lib/telemetry/ | OpenTelemetry instrumentation |\n\nDesign Invariants\n\nThese never change regardless of provider, model, or connector:\nDynamic imports only — No static imports of providers in the registry (prevents circular deps)\nStream is the primitive for text — generate() is always stream collected; nothing in the text path bypasses stream(). decide() is a separate inference type that returns typed judgments and never enters this path.\nBudget checked before every text call — no generate()/stream() call without a budget check. A decide() call carries its own input ceilings instead, enforced by the provider.\nTools are always external — MCP protocol for all tool integrations, including built-ins\nMemory is scoped — Each conversation has isolated memory; no cross-contamination","hierarchy":{"lvl0":"About","lvl1":"Pipe Architecture","lvl2":"","lvl3":""}}, {"objectID":"597c3e847695870f8226e03f4389c7337a1cda648de5dd492bbad2d512996571","title":"Pipe Architecture","url":"/docs/about/pipe-architecture#pipe-architecture","content":"Every generate() and stream() call travels the same six-stage pipe. Understanding the pipe is understanding NeuroLink.","hierarchy":{"lvl0":"About","lvl1":"Pipe Architecture","lvl2":"Pipe Architecture","lvl3":""}}, {"objectID":"05d4793a793985068acbd8139dfa99f2daac3a61f19871782dd10af93b45cf7a","title":"1. Context Building","url":"/docs/about/pipe-architecture#1-context-building","content":"Before any tokens are generated, the pipe assembles the full context:\nRAG retrieval — If rag: { files: [...] } is set, documents are chunked, embedded, and a search_knowledge_base tool is registered\nMemory lookup — Conversation history fetched from Redis or in-memory store\nFile processing — Attached files (images, PDFs, code, CSV) processed by ProcessorRegistry into provider-appropriate formats\nSystem prompt injection — Custom system prompts merged with NeuroLink defaults","hierarchy":{"lvl0":"About","lvl1":"Pipe Architecture","lvl2":"1. Context Building","lvl3":""}}, {"objectID":"e4fa3308549f904194e558d2ee03d3137ee2795e69f7138885fa4881b6a6e57f","title":"2. Budget Check","url":"/docs/about/pipe-architecture#2-budget-check","content":"BudgetChecker validates the assembled context fits within the model's context window before every LLM call.\nThreshold: triggers at 80% of context window\nIf over budget: runs ContextCompactor (5-stage pipeline): 0. Relevance drop — asks a decision model which earlier messages the current\n request still needs (skipped entirely without a decision provider)\nTool output pruning — replaces old tool results with placeholders\nFile read deduplication — keeps only latest read of each file\nLLM summarization — structured 10-section summary of oldest messages\nSliding window truncation — non-destructive tagging of oldest messages\n\nContext windows are tracked per-provider, per-model in src/lib/constants/contextWindows.ts.","hierarchy":{"lvl0":"About","lvl1":"Pipe Architecture","lvl2":"2. Budget Check","lvl3":""}}, {"objectID":"235bb5cf69567dfb2888db922fd48a0de92a09890e37097382bbaaa44cb8fa6e","title":"3. Provider Dispatch","url":"/docs/about/pipe-architecture#3-provider-dispatch","content":"ProviderRegistry resolves the provider name to a concrete implementation:\n\nSwitching providers requires one line — the rest of the pipe is unchanged.","hierarchy":{"lvl0":"About","lvl1":"Pipe Architecture","lvl2":"3. Provider Dispatch","lvl3":""}}, {"objectID":"9cf833bf5d28040bc5655b79917c973480d7c5ae17ed4e0e9ffb5bf9961c3e06","title":"4. Stream Emission","url":"/docs/about/pipe-architecture#4-stream-emission","content":"Tokens arrive as an async iterable. generate() is stream() collected — there is only stream().\n\nThe stream handles multiple event types: text deltas, tool calls, thinking blocks, usage statistics.","hierarchy":{"lvl0":"About","lvl1":"Pipe Architecture","lvl2":"4. Stream Emission","lvl3":""}}, {"objectID":"fb4e76fb642bae17d7df8d9ab80f03631ff962688157312eae904919b6567dba","title":"5. Tool Interception","url":"/docs/about/pipe-architecture#5-tool-interception","content":"When the model emits a tool call, the stream pauses:\nTool call extracted from the stream\nMCPToolRegistry dispatches to the correct tool (built-in or external MCP server)\nTool result injected back into the conversation\nModel resumes generating from the tool result\nStream continues\n\nMCP transports supported: stdio (local), http (remote), sse, websocket.","hierarchy":{"lvl0":"About","lvl1":"Pipe Architecture","lvl2":"5. Tool Interception","lvl3":""}}, {"objectID":"556077ecd212a861d46a7b069bfe8243ccebb383fa394531e49c9abfafdcf890","title":"6. Observability","url":"/docs/about/pipe-architecture#6-observability","content":"Every stage emits OpenTelemetry spans. The full trace covers:\nContext build duration\nToken counts (input + output)\nTool execution times\nMemory read/write latency\nProvider-specific attributes (model, temperature, finish reason)\n\nExporters: Langfuse, OTLP, Jaeger, Zipkin, Prometheus, Datadog, NewRelic, Honeycomb, Console.","hierarchy":{"lvl0":"About","lvl1":"Pipe Architecture","lvl2":"6. Observability","lvl3":""}}, {"objectID":"9aafb7162a034e292934afb13bd6ce2ca9177be0a13fd292a734637840f23775","title":"Key Files","url":"/docs/about/pipe-architecture#key-files","content":"| File | Purpose |\n| --------------------------------------- | -------------------------------------------- |\n| src/lib/neurolink.ts | Main SDK class — orchestrates all six stages |\n| src/lib/factories/providerRegistry.ts | Provider registration with dynamic imports |\n| src/lib/context/budgetChecker.ts | Pre-generation context budget validation |\n| src/lib/context/contextCompactor.ts | Multi-stage compaction orchestrator |\n| src/lib/mcp/toolRegistry.ts | Tool management and MCP integration |\n| src/lib/rag/ragIntegration.ts | RAG auto-pipeline setup |\n| src/lib/telemetry/ | OpenTelemetry instrumentation |","hierarchy":{"lvl0":"About","lvl1":"Pipe Architecture","lvl2":"Key Files","lvl3":""}}, {"objectID":"a93f30940dbf4ce2d9ef1cc11bcfe70eb476e3b1e3ec734597880bbd699b5606","title":"Design Invariants","url":"/docs/about/pipe-architecture#design-invariants","content":"These never change regardless of provider, model, or connector:\nDynamic imports only — No static imports of providers in the registry (prevents circular deps)\nStream is the primitive for text — generate() is always stream collected; nothing in the text path bypasses stream(). decide() is a separate inference type that returns typed judgments and never enters this path.\nBudget checked before every text call — no generate()/stream() call without a budget check. A decide() call carries its own input ceilings instead, enforced by the provider.\nTools are always external — MCP protocol for all tool integrations, including built-ins\nMemory is scoped — Each conversation has isolated memory; no cross-contamination","hierarchy":{"lvl0":"About","lvl1":"Pipe Architecture","lvl2":"Design Invariants","lvl3":""}}, {"objectID":"1083b3420d2c0db59846347281059ffb8b3de773cbdcb879b080fe6d480cd572","title":"NeuroLink Vision & Roadmap","url":"/docs/about/vision","content":"NeuroLink Vision & Roadmap\n\nThe Future of AI: Edge-first execution and continuous streaming architectures\n\n🔮 The Future of AI: Edge-First & Streaming-Native\n\nThe Fundamental Shift\n\nA fundamental transformation is happening in AI: Edge-first execution makes LLM usage practically free.\n\nAs AI models move closer to users—running on edge devices, local machines, regional infrastructure, and in-browser—the marginal cost of inference approaches zero. This isn't incremental improvement. This changes everything.\n\n🌍 Edge-First AI: Run Anywhere, Pay Nothing\n\nThe Economics of Edge AI\n\nWhen LLMs run on user devices or regional edge, compute is free. Storage is free. Inference is free.\n\nWhy This Matters\n\n| Traditional Cloud AI | Edge-First AI |\n| ------------------------------- | ------------------------ |\n| $2,000/month for 1M requests | $0/month |\n| Network latency: 200-500ms | Local latency: \\ When AI runs at the edge, the marginal cost of inference becomes zero.\nWhen streams run continuously, the marginal cost of availability becomes zero.\nWhen both are true, AI becomes as ubiquitous as electricity.\n\nWhat This Enables\nReal-Time Everything\nLive translation in conversations\nInstant code completion while typing\nReal-time fraud detection in payments\nContinuous health monitoring\nAlways-on personal assistants\nUnlimited AI Interactions\nNo per-request costs to limit usage\nExperiment freely without budget concerns\nBuild AI-first products without economic constraints\nScale to billions of requests at zero marginal cost\nPerfect Privacy\nData processing happens on user devices\nNo cloud uploads, no third-party access\nGDPR/HIPAA compliant by design\nUsers own their data completely\nGovernment/regulatory compliance automatic\nOffline Capability\nAI works without internet\nEdge models run anywhere\nResilient to network issues\nNo cloud dependencies\nWorks in remote locations\nDeveloper Freedom\nBuild without provider lock-in\nSwitch models freely (all work the same way)\nDeploy anywhere (cloud, edge, device, browser)\nOwn your infrastructure\nNo vendor dependencies\n\n🚀 How to Participate in This Future\n\nUse NeuroLink Today\n\nStart building with NeuroLink:\nQuick Start Guide - Get running in \\<5 minutes\nProvider Setup - Configure supported providers\nSDK Integration - Build with TypeScript\nProduction Deployment - Enterprise setup\n\nContribute to Edge & Streaming Features\n\nHelp us build the future:\nEdge Deployment Kits: CloudFlare Workers, Lambda@Edge templates\nBrowser LLM Support: WebGPU integration\nStreaming Architecture: Protocol design and implementation\nExample Applications: Showcase edge + streaming patterns\n\nContributing Guide - How to contribute\n\nShare Your Use Cases\n\nTell us how you're using NeuroLink:\nEdge deployments: What works, what doesn't\nStreaming needs: Where continuous context matters\nPrivacy requirements: Compliance and security needs\nPerformance goals: Latency and cost targets\n\nGitHub Discussions - Join the conversation\n\n🎯 Join Us in Building This Future\n\nNeuroLink started as a production tool at Juspay to solve today's AI integration problems. But we're building for tomorrow—where AI is everywhere, costs nothing, and just works.\n\nIf You Believe in This Vision:\n\n✅ Use NeuroLink today for multi-provider AI\n✅ Contribute to edge-first and streaming features\n✅ Share your use cases to help us prioritize\n✅ Join the community to shape the future of AI infrastructure\n\nThe future of AI is edge-first, streaming-native, and practically free.\n\nNeuroLink is building the infrastructure to power that future.\n\nWelcome aboard.\n\nDocument maintained by: NeuroLink Core Team\nLast updated: March 2026\nNext review: Q3 2026 (after Phase 3 planning)","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"","lvl3":""}}, {"objectID":"5fcb53ab536f2cffaef276173e033cb9712a852233f3bbf6f04986ef826845db","title":"NeuroLink Vision & Roadmap","url":"/docs/about/vision#neurolink-vision-roadmap","content":"The Future of AI: Edge-first execution and continuous streaming architectures","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"NeuroLink Vision & Roadmap","lvl3":""}}, {"objectID":"179ed4cfa698f5619a89e45bef29ffb3dbb0a28269b2f3e501d95b5b06144ca0","title":"The Fundamental Shift","url":"/docs/about/vision#the-fundamental-shift","content":"A fundamental transformation is happening in AI: Edge-first execution makes LLM usage practically free.\n\nAs AI models move closer to users—running on edge devices, local machines, regional infrastructure, and in-browser—the marginal cost of inference approaches zero. This isn't incremental improvement. This changes everything.","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"The Fundamental Shift","lvl3":""}}, {"objectID":"2217000b17b8306979068110dadba347afaa519ac2716a3bead244060bdabade","title":"The Economics of Edge AI","url":"/docs/about/vision#the-economics-of-edge-ai","content":"When LLMs run on user devices or regional edge, compute is free. Storage is free. Inference is free.","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"The Economics of Edge AI","lvl3":""}}, {"objectID":"e4b932b5ac730cd27da895b8347cb9ca8e7fde63e26bb52749098c5b0d5b5bd2","title":"Why This Matters","url":"/docs/about/vision#why-this-matters","content":"| Traditional Cloud AI | Edge-First AI |\n| ------------------------------- | ------------------------ |\n| $2,000/month for 1M requests | $0/month |\n| Network latency: 200-500ms | Local latency: \\<100ms |\n| Data leaves your infrastructure | Data never leaves device |\n| Per-token billing limits usage | Unlimited usage |\n| Requires internet connectivity | Works offline |","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"Why This Matters","lvl3":""}}, {"objectID":"f33260a217406c1e0ea064707150d9e9fff6462ed188783f4c8a2514c8902509","title":"NeuroLink Already Supports Edge Deployment","url":"/docs/about/vision#neurolink-already-supports-edge-deployment","content":"NeuroLink is designed for edge-first AI from day one:\n🖥️ Local Execution: Ollama provider for complete privacy, zero latency, zero cost\n⚡ Edge Deployment: Compatible with CloudFlare Workers, AWS Lambda@Edge, Vercel Edge\n🌐 Regional Providers: Choose providers closest to users (Google US, AWS EU, Azure APAC)\n🔒 Private Infrastructure: Run on your own hardware with SageMaker or LiteLLM proxy\n\nThis Enables:\nReal-time AI responses without API costs\nComplete privacy (data never leaves user device)\nSub-100ms latency (no network round trip)\nUnlimited usage (no per-token billing)\nOffline capability (works without internet)","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"NeuroLink Already Supports Edge Deployment","lvl3":""}}, {"objectID":"40187e8b3a3a59c4c88673b975c2b6acef97a036ff8813777f9fb7489650878a","title":"The Problem with Request/Response AI","url":"/docs/about/vision#the-problem-with-requestresponse-ai","content":"Traditional Model:\n\nEvery request starts fresh. Context is limited by token windows. Expensive per-token costs add up. Stateless architecture forgets everything.","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"The Problem with Request/Response AI","lvl3":""}}, {"objectID":"bba7e1897313e3737e1e1d7c4285c0f0b41e1438560822e2301139f840a1f24f","title":"The Streaming Solution","url":"/docs/about/vision#the-streaming-solution","content":"Continuous Stream Model:\n\nInstead of starting fresh each time, maintain a continuous stream to your LLM that:\nRuns 24/7 on edge infrastructure (local machine, regional edge, user browser)\nMaintains perfect context across sessions (no context window limits)\nConnects/disconnects as needed (like WebSocket, but for AI)\nCosts nothing to keep alive (edge compute is free)","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"The Streaming Solution","lvl3":""}}, {"objectID":"7b2c2e4083a75c704054788f25145243cae6aaa053f4dc154f93a3770a9dce22","title":"How Continuous Streams Work","url":"/docs/about/vision#how-continuous-streams-work","content":"Traditional Request/Response:\n\nContinuous Streaming (NeuroLink's Vision):","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"How Continuous Streams Work","lvl3":""}}, {"objectID":"a321f88817ec635afc361ac9300f848af7c20c1a6130336b281db0c71deb4fc5","title":"Why Continuous Streams Change Everything","url":"/docs/about/vision#why-continuous-streams-change-everything","content":"| Traditional AI | Continuous Streaming AI |\n| ---------------------------------------- | ------------------------------- |\n| Cold start every request | Always warm, instant response |\n| Limited context window (200K tokens max) | Infinite context memory |\n| Expensive per-token costs | Free on edge |\n| Stateless, forgets everything | Stateful, remembers everything |\n| Batch processing | Real-time continuous processing |\n| High latency (network + cold start) | Sub-100ms responses |","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"Why Continuous Streams Change Everything","lvl3":""}}, {"objectID":"695b825ee1abb3d7dfa8560a77b79ae0bb07ff1da80fa2bd6069445886921195","title":"Phase 1: Universal Integration ✅ COMPLETE","url":"/docs/about/vision#phase-1-universal-integration-complete","content":"Status: Complete — in production use at Juspay\n\nWhat We Built:\n✅ AI providers unified under one API\n✅ Enterprise features (proxy, Redis, failover, telemetry)\n✅ SDK + CLI for any workflow\n✅ Real-time streaming with tool support\n✅ 6 built-in tools + any MCP-compliant server\n✅ Production deployment at scale (15M+ requests/month)\n\nYou can use this today.\n\nGet Started Now →","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"Phase 1: Universal Integration ✅ COMPLETE","lvl3":""}}, {"objectID":"03f6c09431a0abe505dab45cc757b528402676a09cb80dd3fb96c9806bafc3c7","title":"Phase 2: Edge-Native Execution 🚧 IN PROGRESS","url":"/docs/about/vision#phase-2-edge-native-execution-in-progress","content":"Goal: Make local/edge AI as easy as cloud AI\n\nWhat We're Building:\n✅ Ollama integration - Local LLMs, zero cost, complete privacy (Done)\n✅ LiteLLM proxy - 100+ models through one local endpoint (Done)\n🚧 Edge deployment kits - CloudFlare Workers, Lambda@Edge templates (In Progress)\n🚧 Browser LLM support - Run models entirely in-browser (WebGPU) (Research)\n🚧 Regional routing - Automatic provider selection based on user location (Design)\n\nTimeline: Q1-Q2 2025\n\nWhy It Matters: Every request runs \\<100ms, costs $0, never touches cloud","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"Phase 2: Edge-Native Execution 🚧 IN PROGRESS","lvl3":""}}, {"objectID":"8fc5d2489aaf7a90fa71f3826e623f395c8419e877654af6d66cf3d85c376b7c","title":"Phase 3: Continuous Streaming Architecture 📋 PLANNED","url":"/docs/about/vision#phase-3-continuous-streaming-architecture-planned","content":"Goal: Long-running, stateful LLM streams with infinite context\n\nWhat We're Building:\n📋 Stream management - Connect, disconnect, reconnect to persistent streams\n📋 Infinite context - No token limits, perfect memory across sessions\n📋 Edge orchestration - Streams run on user devices or regional edge\n📋 Automatic failover - Seamless cloud fallback if edge unavailable\n📋 Multi-stream coordination - Coordinate multiple specialized streams\n\nTimeline: Q3-Q4 2025\n\nWhy It Matters: AI becomes ambient, always available, costs nothing","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"Phase 3: Continuous Streaming Architecture 📋 PLANNED","lvl3":""}}, {"objectID":"3b18188089291551ff1079bc4c79b6ab38fce495ac239e65026bbcc71ad34b52","title":"Phase 4: AI-Powered Everything 🔮 FUTURE","url":"/docs/about/vision#phase-4-ai-powered-everything-future","content":"Vision: Every application has embedded AI, every user has personal AI assistants\n\nThe Future We're Building Toward:\nEvery App AI-Native: Embedded LLMs in all software\nPersonal AI Assistants: Running locally on your devices\nZero-Cost Inference: Edge execution makes AI practically free\nPerfect Memory: Continuous streams maintain infinite context\nInstant Responses: Edge compute = sub-100ms latency\nComplete Privacy: Your data never leaves your infrastructure","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"Phase 4: AI-Powered Everything 🔮 FUTURE","lvl3":""}}, {"objectID":"da9e3ffcff1870348667b8f6ceeb271321930c97a27bb97de2eb1b4e7ce88eb3","title":"The Fundamental Insight","url":"/docs/about/vision#the-fundamental-insight","content":"When AI runs at the edge, the marginal cost of inference becomes zero.\nWhen streams run continuously, the marginal cost of availability becomes zero.\nWhen both are true, AI becomes as ubiquitous as electricity.","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"The Fundamental Insight","lvl3":""}}, {"objectID":"3810596ced556602a9188acb8f49c79e655a74341d572122cd80aff9021771d1","title":"1. 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Perfect Privacy","url":"/docs/about/vision#3-perfect-privacy","content":"Data processing happens on user devices\nNo cloud uploads, no third-party access\nGDPR/HIPAA compliant by design\nUsers own their data completely\nGovernment/regulatory compliance automatic","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"3. Perfect Privacy","lvl3":""}}, {"objectID":"909e2fcfa686454a6e3e1cbaa3a5f82cde74701a7807c0e4990f3c612b398ac7","title":"4. Offline Capability","url":"/docs/about/vision#4-offline-capability","content":"AI works without internet\nEdge models run anywhere\nResilient to network issues\nNo cloud dependencies\nWorks in remote locations","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"4. Offline Capability","lvl3":""}}, {"objectID":"666161cdb9538fbd7517167d959520f3ce418b05b58ddce7e96708f23d17d67c","title":"5. Developer Freedom","url":"/docs/about/vision#5-developer-freedom","content":"Build without provider lock-in\nSwitch models freely (all work the same way)\nDeploy anywhere (cloud, edge, device, browser)\nOwn your infrastructure\nNo vendor dependencies","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"5. Developer Freedom","lvl3":""}}, {"objectID":"dbfd0458b9ad4cd41c504f56cf1dadb81a00fed1b6fdb1c4f711f1af0e919ebd","title":"Use NeuroLink Today","url":"/docs/about/vision#use-neurolink-today","content":"Start building with NeuroLink:\nQuick Start Guide - Get running in \\<5 minutes\nProvider Setup - Configure supported providers\nSDK Integration - Build with TypeScript\nProduction Deployment - Enterprise setup","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"Use NeuroLink Today","lvl3":""}}, {"objectID":"9e4a88e460a6fcfe5c4a5e9cb17b9abbd1e2eb71cc9024c7b1ac1904a56e070a","title":"Contribute to Edge & Streaming Features","url":"/docs/about/vision#contribute-to-edge-streaming-features","content":"Help us build the future:\nEdge Deployment Kits: CloudFlare Workers, Lambda@Edge templates\nBrowser LLM Support: WebGPU integration\nStreaming Architecture: Protocol design and implementation\nExample Applications: Showcase edge + streaming patterns\n\nContributing Guide - How to contribute","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"Contribute to Edge & Streaming Features","lvl3":""}}, {"objectID":"c47d3ea31b9350d8a7e39af83361be6a5c078cffb786893cc053a933cc4ca99b","title":"Share Your Use Cases","url":"/docs/about/vision#share-your-use-cases","content":"Tell us how you're using NeuroLink:\nEdge deployments: What works, what doesn't\nStreaming needs: Where continuous context matters\nPrivacy requirements: Compliance and security needs\nPerformance goals: Latency and cost targets\n\nGitHub Discussions - Join the conversation","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"Share Your Use Cases","lvl3":""}}, {"objectID":"a01f19466002f1a4decfb4e57a7976034cd068b42a0beb84e7cbda9e6ff6cefa","title":"🎯 Join Us in Building This Future","url":"/docs/about/vision#-join-us-in-building-this-future","content":"NeuroLink started as a production tool at Juspay to solve today's AI integration problems. But we're building for tomorrow—where AI is everywhere, costs nothing, and just works.","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"🎯 Join Us in Building This Future","lvl3":""}}, {"objectID":"6d274f87d0b1b39d152ef548c717b98a5ca0af1e46da78fa7976343ebc419f3a","title":"If You Believe in This Vision:","url":"/docs/about/vision#if-you-believe-in-this-vision","content":"✅ Use NeuroLink today for multi-provider AI\n✅ Contribute to edge-first and streaming features\n✅ Share your use cases to help us prioritize\n✅ Join the community to shape the future of AI infrastructure\n\nThe future of AI is edge-first, streaming-native, and practically free.\n\nNeuroLink is building the infrastructure to power that future.\n\nWelcome aboard.\n\nDocument maintained by: NeuroLink Core Team\nLast updated: March 2026\nNext review: Q3 2026 (after Phase 3 planning)","hierarchy":{"lvl0":"About","lvl1":"NeuroLink Vision & Roadmap","lvl2":"If You Believe in This Vision:","lvl3":""}}, {"objectID":"2c08ee20f6c6005a0d3d88aec0910caea8b54d8f9478d1e4bf7b7e73c9bbccf9","title":"Why the NeuroLink Core Stays Thin","url":"/docs/about/why-the-core-stays-thin","content":"Why the NeuroLink Core Stays Thin\n\nBreadth without bloat: what's actually in the package, and why unused capability costs you nothing at runtime\n\nNeuroLink's pitch is breadth: one install covers 17 capability domains — providers, MCP, evals,\nRAG, observability, voice, media, workflows, and more. Breadth claims like that earn a reasonable\nreflex: \"so it's another bloated framework that drags in everything whether I need it or not.\"\nThat reflex is fair, and it's the one framework abandonments over the last two years keep citing.\nSo instead of asking you to take breadth-without-bloat on faith, here's what's actually in the\npackage and why the surface doesn't have to cost you disk space, install time, or attack surface\nyou didn't ask for.\n\nProviders load on demand, not on import\n\nNeuroLink ships named provider integrations plus a generic OpenAI-compatible adapter. None\nof them run at import time. src/lib/factories/providerRegistry.ts resolves a provider name to a\ndynamic import() of that provider's module only when you actually request it — Anthropic's\nclient only loads if you call createBestAIProvider(\"anthropic\") or equivalent; Ollama, LiteLLM,\nHugging Face, Bedrock, Vertex, and the rest are each behind their own await import(\"../providers/.js\")\nline, one per provider, all in that same file. Ask for OpenAI and only the OpenAI\nprovider module executes — the rest never get evaluated.\n\nHeavy media/document deps are optional and lazy\n\nThe 35 packages that do real work in image, video, audio, and document processing —\nsharp, ffmpeg-static/fluent-ffmpeg, pdf-parse, mammoth, exceljs,\npptxgenjs, the LiveKit voice-agent plugins, bullmq, fastify/koa/express server adapters —\nare declared in optionalDependencies, not dependencies, in package.json. Being optional means\na package manager can skip them entirely if install fails or if you opt out; being lazy on top of\nthat means the code that needs them doesn't touch them until the feature runs. sharp is a good\nexample: NeuroLink's video processor only calls (await import(\"sharp\")).default inside the\nframe-resize path (src/lib/processors/media/VideoProcessor.ts), not anywhere near startup. If you\nnever touch video, sharp's native bindings never load into your process.\n\nSchema conversion loads on first use, not on import\n\nConverting between Zod schemas and JSON Schema — for a Zod 3 tool's parameters, or a plain\nJSON Schema parameters/inputSchema object — needs zod-to-json-schema or\njson-schema-to-zod respectively (src/lib/utils/schemaConversion.ts). Neither is a top-level\nimport: both load via require(), through createRequire(import.meta.url), the first time a\nconversion actually runs, so importing the SDK — or converting only Zod 4 schemas, which use\nZod's own built-in z.toJSONSchema() and never touch either package — never pulls them in. A\nrequire() made this way is not one a bundler follows, so nothing on the SDK's import graph\npulls either package in ahead of use.\n\nThe flip side is that a bundler cannot resolve this load for you. It does not see the\nrequire(), so bundling the SDK does not make these two packages available to it, and listing\nthem as external changes nothing about it. They have to be resolvable at run time from where\nthe bundle runs, so ship zod-to-json-schema and json-schema-to-zod in the node_modules\nnext to it. When they are not, the require() fails. That failure degrades rather than\ncrashes — a schema or tool that needed the missing package gets an empty object schema instead,\nlogged once per package at error level — but the model then sees no parameters for it, so treat\nthe error log as actionable, not just informational. The one bundle we ship ourselves, the\nbrowser artifact (dist/browser/neurolink.min.js), is handled in its build:\nscripts/build-browser.mjs bundles both packages in and serves them through its module stub.\n\nThe package exposes real subpath entry points, not one giant bundle\n\npackage.json's exports map lists separate entry points — ./voice, ./music, ./avatar,\n./image-gen, ./workflow, ./rag, ./files, ./processors/*, ./adapters/*, ./livekit —\nalongside the default . entry. That's what lets a bundler tree-shake: importing\n@juspay/neurolink/rag doesn't pull the voice stack into your bundle graph. The package also\ndeclares \"type\": \"module\" and a sideEffects array scoped to **/*.css plus the compiled\nvoice/music/avatar entry files — everything else is marked side-effect-free, which is the signal\nbundlers use to safely drop unused exports instead of keeping code \"just in case.\"\n\nOne interface, swappable implementations\n\nEvery provider — whether it's a first-party AI SDK wrapper or a bare HTTP client for something\nlike llama.cpp — implements the same AIProvider contract defined against BaseProvider\n(src/lib/core/baseProvider.ts). Generation, streaming, tool calls, and lifecycle hooks are all\nexpressed once, at the interface level, so adding provider #25 is additive (a new file + a new\nimport() branch in the registry), not a change to the","hierarchy":{"lvl0":"About","lvl1":"Why the NeuroLink Core Stays Thin","lvl2":"","lvl3":""}}, {"objectID":"aa2a97d1fbf62d32d4c2b89cedf6e8756457ec9552abdcc39df8e49353dc0a4a","title":"Why the NeuroLink Core Stays Thin","url":"/docs/about/why-the-core-stays-thin#why-the-neurolink-core-stays-thin","content":"Breadth without bloat: what's actually in the package, and why unused capability costs you nothing at runtime\n\nNeuroLink's pitch is breadth: one install covers 17 capability domains — providers, MCP, evals,\nRAG, observability, voice, media, workflows, and more. Breadth claims like that earn a reasonable\nreflex: \"so it's another bloated framework that drags in everything whether I need it or not.\"\nThat reflex is fair, and it's the one framework abandonments over the last two years keep citing.\nSo instead of asking you to take breadth-without-bloat on faith, here's what's actually in the\npackage and why the surface doesn't have to cost you disk space, install time, or attack surface\nyou didn't ask for.","hierarchy":{"lvl0":"About","lvl1":"Why the NeuroLink Core Stays Thin","lvl2":"Why the NeuroLink Core Stays Thin","lvl3":""}}, {"objectID":"ea24a1ae9ea7219b37355473fc345224ee1e97305283ff930e1f36b603bb38ea","title":"Providers load on demand, not on import","url":"/docs/about/why-the-core-stays-thin#providers-load-on-demand-not-on-import","content":"NeuroLink ships named provider integrations plus a generic OpenAI-compatible adapter. None\nof them run at import time. src/lib/factories/providerRegistry.ts resolves a provider name to a\ndynamic import() of that provider's module only when you actually request it — Anthropic's\nclient only loads if you call createBestAIProvider(\"anthropic\") or equivalent; Ollama, LiteLLM,\nHugging Face, Bedrock, Vertex, and the rest are each behind their own await import(\"../providers/.js\")\nline, one per provider, all in that same file. Ask for OpenAI and only the OpenAI\nprovider module executes — the rest never get evaluated.","hierarchy":{"lvl0":"About","lvl1":"Why the NeuroLink Core Stays Thin","lvl2":"Providers load on demand, not on import","lvl3":""}}, {"objectID":"8d37817e98f3351bfb570122b7aeeacd5673de163c03e3fdc2249f22ba153b01","title":"Heavy media/document deps are optional and lazy","url":"/docs/about/why-the-core-stays-thin#heavy-mediadocument-deps-are-optional-and-lazy","content":"The 35 packages that do real work in image, video, audio, and document processing —\nsharp, ffmpeg-static/fluent-ffmpeg, pdf-parse, mammoth, exceljs,\npptxgenjs, the LiveKit voice-agent plugins, bullmq, fastify/koa/express server adapters —\nare declared in optionalDependencies, not dependencies, in package.json. Being optional means\na package manager can skip them entirely if install fails or if you opt out; being lazy on top of\nthat means the code that needs them doesn't touch them until the feature runs. sharp is a good\nexample: NeuroLink's video processor only calls (await import(\"sharp\")).default inside the\nframe-resize path (src/lib/processors/media/VideoProcessor.ts), not anywhere near startup. If you\nnever touch video, sharp's native bindings never load into your process.","hierarchy":{"lvl0":"About","lvl1":"Why the NeuroLink Core Stays Thin","lvl2":"Heavy media/document deps are optional and lazy","lvl3":""}}, {"objectID":"eab25db03796822c25591a11b08f10b769de826cf0eefe3c09223afa3ee22adf","title":"Schema conversion loads on first use, not on import","url":"/docs/about/why-the-core-stays-thin#schema-conversion-loads-on-first-use-not-on-import","content":"Converting between Zod schemas and JSON Schema — for a Zod 3 tool's parameters, or a plain\nJSON Schema parameters/inputSchema object — needs zod-to-json-schema or\njson-schema-to-zod respectively (src/lib/utils/schemaConversion.ts). Neither is a top-level\nimport: both load via require(), through createRequire(import.meta.url), the first time a\nconversion actually runs, so importing the SDK — or converting only Zod 4 schemas, which use\nZod's own built-in z.toJSONSchema() and never touch either package — never pulls them in. A\nrequire() made this way is not one a bundler follows, so nothing on the SDK's import graph\npulls either package in ahead of use.\n\nThe flip side is that a bundler cannot resolve this load for you. It does not see the\nrequire(), so bundling the SDK does not make these two packages available to it, and listing\nthem as external changes nothing about it. They have to be resolvable at run time from where\nthe bundle runs, so ship zod-to-json-schema and json-schema-to-zod in the node_modules\nnext to it. When they are not, the require() fails. That failure degrades rather than\ncrashes — a schema or tool that needed the missing package gets an empty object schema instead,\nlogged once per package at error level — but the model then sees no parameters for it, so treat\nthe error log as actionable, not just informational. The one bundle we ship ourselves, the\nbrowser artifact (dist/browser/neurolink.min.js), is handled in its build:\nscripts/build-browser.mjs bundles both packages in and serves them through its module stub.","hierarchy":{"lvl0":"About","lvl1":"Why the NeuroLink Core Stays Thin","lvl2":"Schema conversion loads on first use, not on import","lvl3":""}}, {"objectID":"3539bbad3542a55c88f34d80ddd1bc1fce27889c57cf8aca66e6273e6f344bba","title":"The package exposes real subpath entry points, not one giant bundle","url":"/docs/about/why-the-core-stays-thin#the-package-exposes-real-subpath-entry-points-not-one-giant-bundle","content":"package.json's exports map lists separate entry points — ./voice, ./music, ./avatar,\n./image-gen, ./workflow, ./rag, ./files, ./processors/*, ./adapters/*, ./livekit —\nalongside the default . entry. That's what lets a bundler tree-shake: importing\n@juspay/neurolink/rag doesn't pull the voice stack into your bundle graph. The package also\ndeclares \"type\": \"module\" and a sideEffects array scoped to **/*.css plus the compiled\nvoice/music/avatar entry files — everything else is marked side-effect-free, which is the signal\nbundlers use to safely drop unused exports instead of keeping code \"just in case.\"","hierarchy":{"lvl0":"About","lvl1":"Why the NeuroLink Core Stays Thin","lvl2":"The package exposes real subpath entry points, not one giant bundle","lvl3":""}}, {"objectID":"becaa9cd1d3ca9248464018e639eac6f6c2b7cd66931086213e50c253aa4d607","title":"One interface, swappable implementations","url":"/docs/about/why-the-core-stays-thin#one-interface-swappable-implementations","content":"Every provider — whether it's a first-party AI SDK wrapper or a bare HTTP client for something\nlike llama.cpp — implements the same AIProvider contract defined against BaseProvider\n(src/lib/core/baseProvider.ts). Generation, streaming, tool calls, and lifecycle hooks are all\nexpressed once, at the interface level, so adding provider #25 is additive (a new file + a new\nimport() branch in the registry), not a change to the surface every existing provider has to pay\nfor.","hierarchy":{"lvl0":"About","lvl1":"Why the NeuroLink Core Stays Thin","lvl2":"One interface, swappable implementations","lvl3":""}}, {"objectID":"273fa6d89ab0536369235962641702748840e0eded987d1ca9520284907a5d1b","title":"Where we're heavy today, honestly","url":"/docs/about/why-the-core-stays-thin#where-were-heavy-today-honestly","content":"Lazy import() stops code from running until it's needed, but it doesn't stop npm install\nfrom downloading everything in dependencies (as opposed to optionalDependencies).\n@aws-sdk/client-bedrock, @aws-sdk/client-bedrock-runtime, @aws-sdk/client-sagemaker-runtime,\n@aws-sdk/credential-provider-node, @google-cloud/vertexai, @google-cloud/text-to-speech, and\n@huggingface/inference now live in optionalDependencies — matching the treatment\nmedia/document deps already get.\n\nOptional doesn't mean skipped by default: a plain npm install still downloads all seven, same as\nit always has for sharp or pdf-parse. What optional buys you is the ability to opt out —\nnpm install --omit=optional, or an npm client falling back gracefully when one of them fails to\nbuild on an unsupported platform, drops them without breaking anything else. We verified that\ndirectly: a clean --omit=optional install still runs import('@juspay/neurolink'),\nneurolink --version, and the CLI entry point directly (node dist/cli/index.js --version)\ncorrectly, and asking for a provider whose SDK got skipped — Bedrock or SageMaker, say — fails\nwith a plain, actionable error at the moment you request that provider, not a crash at import\ntime.\n\nThat safety only holds because all seven are genuinely lazy — nothing reachable from\nimport '@juspay/neurolink' (or from the CLI's startup path) touches them at load time.","hierarchy":{"lvl0":"About","lvl1":"Why the NeuroLink Core Stays Thin","lvl2":"Where we're heavy today, honestly","lvl3":""}}, {"objectID":"85a929d0294e7ca23cc2d3e0df0c30380ca5e8ce3344027310f9abbf16c0e9c9","title":"Bottom line","url":"/docs/about/why-the-core-stays-thin#bottom-line","content":"Thin doesn't mean small; it means the code you don't use doesn't run, and increasingly doesn't\neven load. Provider selection, heavy media/document processing, and now all of the named\ncloud-provider SDKs — including SageMaker's runtime client, the last holdout — prove that in the\nsource, not just in the pitch.","hierarchy":{"lvl0":"About","lvl1":"Why the NeuroLink Core Stays Thin","lvl2":"Bottom line","lvl3":""}}, {"objectID":"7c135d3992ca89c95420e0f50a535aef8ea147b193c563de92ff379d3c621cac","title":"Analytics & Evaluation","url":"/docs/advanced/analytics","content":"Analytics & Evaluation\n\nAdvanced analytics and AI response evaluation features for monitoring usage, performance, and quality.\n\n🎯 Overview\n\nNeuroLink provides comprehensive analytics and evaluation capabilities to help you monitor AI usage, track performance, and assess response quality. These features are essential for production applications and enterprise deployments.\n\n📊 Analytics Features\n\nUsage Analytics\n\nTrack detailed metrics about your AI interactions:\n\nCLI Analytics\n\nEnable analytics in CLI commands:\n\nTracked Metrics\nUsage Statistics: Request count, frequency, patterns\nPerformance Metrics: Response time, token usage, costs\nProvider Statistics: Success rates, error patterns, latency\nCost Analysis: Per-provider costs, budget tracking\nUser Analytics: Usage by user, team, or department\nQuality Metrics: Response evaluation scores\n\n🔍 Response Evaluation\n\nAI-Powered Quality Assessment\n\nCLI Evaluation\n\nEvaluation Domains\n\nSpecialized evaluation contexts:\nTechnical: Senior Software Engineer, DevOps Specialist, Data Scientist\nBusiness: Product Manager, Business Analyst, Marketing Manager\nCreative: Content Writer, UX Designer, Creative Director\nAcademic: Research Scientist, Technical Writer, Educator\n\n📈 Analytics Collection\n\nPer-Request Analytics\n\nAnalytics are collected on a per-request basis and included in each result:\n\nMiddleware-Based Analytics\n\nFor application-wide analytics collection, use the analytics middleware:\n\n🔧 Configuration\n\nEnvironment Variables\n\nPer-Request Configuration\n\nAnalytics and evaluation are configured on a per-request basis:\n\n📊 Available Methods\n\nThe following methods are fully available in the SDK for advanced analytics, performance monitoring, and cost calculations:\n\n| Method | Description |\n| -------------------------------------- | ------------------------------------------------- |\n| neurolink.getProviderMetrics() | Get aggregated provider metrics and performance |\n| neurolink.getCostAnalysis() | Get granular cost breakdown and projections |\n| neurolink.getTeamAnalytics() | Get team-wide usage, unique users, and quality |\n| neurolink.getProviderStatus() | Get provider availability status |\n| neurolink.getProviderHealthSummary() | Get health summary for all providers |\n| neurolink.getToolExecutionMetrics() | Get tool execution statistics |\n| getAnalyticsMetrics() | Standalone middleware function for analytics data |\n\n📊 Use Cases\n\nPerformance Monitoring\n\nCost Optimization\n\nQuality Assurance\n\n🚀 Enterprise Features\n\nTeam Analytics\n\nCustom Metrics\n\nCompliance Monitoring\n\n📚 Related Documentation\nCLI Commands - Analytics CLI commands\nEnvironment Variables - Configuration\nSDK Reference - Programmatic analytics\nEnterprise Setup - Enterprise features","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"","lvl3":""}}, {"objectID":"f5659f01905ca4dc4b1da82c0f86be31405fae6b5e8b1509ccaa804a63c5edc4","title":"Analytics & Evaluation","url":"/docs/advanced/analytics#analytics-evaluation","content":"Advanced analytics and AI response evaluation features for monitoring usage, performance, and quality.","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Analytics & Evaluation","lvl3":""}}, {"objectID":"85098a61e8239e65c18a6833c9dadbf8fdeac5fc7f4a050e26c72752f781db96","title":"🎯 Overview","url":"/docs/advanced/analytics#-overview","content":"NeuroLink provides comprehensive analytics and evaluation capabilities to help you monitor AI usage, track performance, and assess response quality. These features are essential for production applications and enterprise deployments.","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"🎯 Overview","lvl3":""}}, {"objectID":"fe222447e5b9084dc2fde7abe7ac68789085ec1e99b73bf4a09bf5880d549200","title":"Usage Analytics","url":"/docs/advanced/analytics#usage-analytics","content":"Track detailed metrics about your AI interactions:","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Usage Analytics","lvl3":""}}, {"objectID":"1e964f70987481087a3e163d52d81f293c8ca2fa89c563d7888367935a0b7290","title":"CLI Analytics","url":"/docs/advanced/analytics#cli-analytics","content":"Enable analytics in CLI commands:\n\n`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"CLI Analytics","lvl3":""}}, {"objectID":"593d7e717a98a7da6fc598aad0c262d8e13f0f181eea6dd8572179f0f3d64ca1","title":"Enable analytics for single command","url":"/docs/advanced/analytics#enable-analytics-for-single-command","content":"npx @juspay/neurolink gen \"Analyze data\" --enable-analytics","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Enable analytics for single command","lvl3":""}}, {"objectID":"d28f8fba3e8ca7e9f82dba9687264ebd3aed6428f847e8b8fa5cf32f269e259e","title":"With custom context","url":"/docs/advanced/analytics#with-custom-context","content":"npx @juspay/neurolink gen \"Business analysis\" \\\n --enable-analytics \\\n --context '{\"team\":\"product\",\"project\":\"dashboard\"}' \\\n --debug\n`","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"With custom context","lvl3":""}}, {"objectID":"139cdf41971f957afdfdb51cfa1b03ecea39c8f042bbfd73dbdf1e0d12c9d437","title":"Tracked Metrics","url":"/docs/advanced/analytics#tracked-metrics","content":"Usage Statistics: Request count, frequency, patterns\nPerformance Metrics: Response time, token usage, costs\nProvider Statistics: Success rates, error patterns, latency\nCost Analysis: Per-provider costs, budget tracking\nUser Analytics: Usage by user, team, or department\nQuality Metrics: Response evaluation scores","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Tracked Metrics","lvl3":""}}, {"objectID":"ee5c2ec6947e731e4d21ecc84b5226a958a346f2a77fd2ce3295fc989fa5bc67","title":"CLI Evaluation","url":"/docs/advanced/analytics#cli-evaluation","content":"`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"CLI Evaluation","lvl3":""}}, {"objectID":"f0194d0dec4b97d2b43ba46f0b0ec3ee23061bafe26e04ea35ed9ee93adcdd78","title":"Basic evaluation","url":"/docs/advanced/analytics#basic-evaluation","content":"npx @juspay/neurolink gen \"Write API documentation\" --enable-evaluation","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Basic evaluation","lvl3":""}}, {"objectID":"7ff4b9edec7541816f7397fd4929c90d50c6ebdcedf0b9252d3fb676ebd22720","title":"Domain-specific evaluation","url":"/docs/advanced/analytics#domain-specific-evaluation","content":"npx @juspay/neurolink gen \"Design system architecture\" \\\n --enable-evaluation \\\n --evaluation-domain \"Solutions Architect\"","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Domain-specific evaluation","lvl3":""}}, {"objectID":"8b63bf81a22c24ec4cba99c619a397c2fbc70af19839784909563873aaa96cac","title":"Combined analytics and evaluation","url":"/docs/advanced/analytics#combined-analytics-and-evaluation","content":"npx @juspay/neurolink gen \"Create test plan\" \\\n --enable-analytics \\\n --enable-evaluation \\\n --evaluation-domain \"QA Engineer\" \\\n --debug\n`","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Combined analytics and evaluation","lvl3":""}}, {"objectID":"fa6c1bf71f35ac5b9189c03b671d25fc17b796e743ce57ed1e2cba5ae6b28fe3","title":"Evaluation Domains","url":"/docs/advanced/analytics#evaluation-domains","content":"Specialized evaluation contexts:\nTechnical: Senior Software Engineer, DevOps Specialist, Data Scientist\nBusiness: Product Manager, Business Analyst, Marketing Manager\nCreative: Content Writer, UX Designer, Creative Director\nAcademic: Research Scientist, Technical Writer, Educator","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Evaluation Domains","lvl3":""}}, {"objectID":"e8c55de89b179783ec0c42903340054711167fd449afb082b0bf72079a143e5f","title":"Per-Request Analytics","url":"/docs/advanced/analytics#per-request-analytics","content":"Analytics are collected on a per-request basis and included in each result:","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Per-Request Analytics","lvl3":""}}, {"objectID":"de6eefd3b8090cbf83829a1786f801914e53feff25b84f2c8a8cf7ed02fc5e95","title":"Middleware-Based Analytics","url":"/docs/advanced/analytics#middleware-based-analytics","content":"For application-wide analytics collection, use the analytics middleware:","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Middleware-Based Analytics","lvl3":""}}, {"objectID":"660523ba61654a2c653fa8c82c6ee5c95eb20291b62fa1fae620cbf781d7df37","title":"Environment Variables","url":"/docs/advanced/analytics#environment-variables","content":"`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Environment Variables","lvl3":""}}, {"objectID":"0027d952a737f21f8d0cf860fff755361ca51ee7ecf1b93b08f82db06abec07b","title":"Evaluation Configuration","url":"/docs/advanced/analytics#evaluation-configuration","content":"NEUROLINKEVALUATIONPROVIDER=\"google-ai\"\nNEUROLINKEVALUATIONMODEL=\"gemini-2.5-flash\"\nNEUROLINKEVALUATIONTHRESHOLD=\"7\"\n`","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Evaluation Configuration","lvl3":""}}, {"objectID":"1e8995f6578e4aee77544bd1d32b867a0d6f234cd6292e67a16a441eb44360ac","title":"Per-Request Configuration","url":"/docs/advanced/analytics#per-request-configuration","content":"Analytics and evaluation are configured on a per-request basis:","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Per-Request Configuration","lvl3":""}}, {"objectID":"3356292a3c1bcd740822b137d2cf92399398b05e682751a9eefa8bbc3b179041","title":"📊 Available Methods","url":"/docs/advanced/analytics#-available-methods","content":"The following methods are fully available in the SDK for advanced analytics, performance monitoring, and cost calculations:\n\n| Method | Description |\n| -------------------------------------- | ------------------------------------------------- |\n| neurolink.getProviderMetrics() | Get aggregated provider metrics and performance |\n| neurolink.getCostAnalysis() | Get granular cost breakdown and projections |\n| neurolink.getTeamAnalytics() | Get team-wide usage, unique users, and quality |\n| neurolink.getProviderStatus() | Get provider availability status |\n| neurolink.getProviderHealthSummary() | Get health summary for all providers |\n| neurolink.getToolExecutionMetrics() | Get tool execution statistics |\n| getAnalyticsMetrics() | Standalone middleware function for analytics data |","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"📊 Available Methods","lvl3":""}}, {"objectID":"4db460e52b67c59ee069b6b33936c0c8d5eb062a6f917fd5a7e952a3f7de9c63","title":"Quality Assurance","url":"/docs/advanced/analytics#quality-assurance","content":"`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Quality Assurance","lvl3":""}}, {"objectID":"30471573c942bc8d0803c31d5dc99f479e04364a6145218edd8ac8b25df19f17","title":"Batch evaluate responses for quality","url":"/docs/advanced/analytics#batch-evaluate-responses-for-quality","content":"cat prompts.txt | while read prompt; do\n npx @juspay/neurolink gen \"$prompt\" \\\n --enable-evaluation \\\n --evaluation-domain \"Senior Engineer\" \\\n --json >> evaluations.json\ndone","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Batch evaluate responses for quality","lvl3":""}}, {"objectID":"313cacad005a4af784de05d98853cd9fd762b6612e0f4fa938bb21cb6398718c","title":"Analyze quality trends","url":"/docs/advanced/analytics#analyze-quality-trends","content":"jq '.evaluation.overall' evaluations.json | awk '{sum+=$1} END {print \"Average quality:\", sum/NR}'\n`","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Analyze quality trends","lvl3":""}}, {"objectID":"fe3c90bc005787cca05338517f8adea1c09e1aa2b238233358750b3f2a637053","title":"Compliance Monitoring","url":"/docs/advanced/analytics#compliance-monitoring","content":"`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Compliance Monitoring","lvl3":""}}, {"objectID":"66edd4140622c4bf5304eda3c3ecf9277050da3d579f6d590031bed39b4cfb63","title":"Audit trail with evaluation","url":"/docs/advanced/analytics#audit-trail-with-evaluation","content":"npx @juspay/neurolink gen \"Sensitive analysis\" \\\n --enable-analytics \\\n --enable-evaluation \\\n --context '{\"compliance\":\"required\",\"audit\":\"true\"}' \\\n --evaluation-domain \"Compliance Officer\"\n`","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"Audit trail with evaluation","lvl3":""}}, {"objectID":"40eda927dd396e3f496cac1f86cf5c908dbc639394b19ed6df187e8d5d6d8c49","title":"📚 Related Documentation","url":"/docs/advanced/analytics#-related-documentation","content":"CLI Commands - Analytics CLI commands\nEnvironment Variables - Configuration\nSDK Reference - Programmatic analytics\nEnterprise Setup - Enterprise features","hierarchy":{"lvl0":"Advanced","lvl1":"Analytics & Evaluation","lvl2":"📚 Related Documentation","lvl3":""}}, {"objectID":"0563cbb2f82b955ce7acdb3ffb8f70e55eb946b93fd1b04cce5c81d4f090c4f0","title":"Authentication Architecture","url":"/docs/advanced/auth-architecture","content":"Authentication Architecture\n\nAudience: Contributors and advanced users who need to understand how authentication is wired into NeuroLink's internals.\n\nDesign Principles\n\nNeuroLink's auth system follows the same architectural patterns used for AI providers:\nFactory + Registry -- providers are registered with factory functions and instantiated on demand via dynamic imports to avoid circular dependencies\nLazy initialization -- the auth provider is not created in the synchronous constructor; it is initialized on first use (generate/stream with auth.token)\nFail closed -- a valid token that does not resolve to a user identity is treated as an authentication failure\nToken-derived identity wins -- when both auth.token and requestContext are provided, token-derived fields (userId, userEmail, userRoles) override requestContext to prevent privilege escalation\n\nSystem Overview\n\nFactory + Registry Pattern\n\nAuthProviderFactory\n\nAuthProviderFactory extends BaseFactory and follows the singleton pattern. It registers 11 provider factory functions during initialization, each using dynamic imports:\n\nEach registration includes:\nType identifier -- canonical name (e.g., \"auth0\")\nFactory function -- async function that dynamically imports and instantiates the provider\nAliases -- alternative names for convenience (e.g., \"auth0-jwt\", \"auth0-oauth\")\nMetadata -- human-readable name, description, documentation URL\n\nThe create(type, config) method:\nCalls ensureInitialized() to lazily run registerAll() once\nResolves the name through alias lookup via resolveName()\nCalls the registered factory function with the provider config\nReturns the AuthProvider instance\n\nAuthProviderRegistry\n\nAuthProviderRegistry extends BaseRegistry and layers metadata and discovery on top of the factory:\nTracks provider capabilities (features like jwt-validation, session-management, sso)\nProvides discovery APIs (getProvidersByFeature(), getBuiltInProviders())\nRuns health checks by creating temporary provider instances\nCaches health status per provider type\n\nThe registry does not create providers directly; it delegates to AuthProviderFactory.create().\n\nError Factories\n\nBoth AuthProviderFactory and AuthProviderRegistry use createErrorFactory() from the core infrastructure to produce typed errors with unique codes:\n\n| Module | Code Prefix | Example |\n| -------- | ---------------- | ------------------------------- |\n| Factory | AUTH_FACTORY- | AUTH_FACTORY-001 (not found) |\n| Registry | AUTH_REGISTRY- | AUTH_REGISTRY-001 (not found) |\n\nProvider Interface\n\nAll providers implement the AuthProvider type, which defines:\n\nRequired Methods\n\n| Method | Purpose |\n| -------------------------- | ------------------------------------------------- |\n| authenticateToken() | Validate and decode a token, return user identity |\n| extractToken() | Extract token from request context |\n| authorizeUser() | Check if user has a specific permission |\n| authorizeRoles() | Check if user has any of the required roles |\n| authorizePermissions() | Check if user has all specified permissions |\n| createSession() | Create a new session for a user |\n| getSession() | Get an existing session by ID |\n| refreshSession() | Extend a session's expiration |\n| destroySession() | Invalidate a session |\n| getUserSessions() | Get all active sessions for a user |\n| destroyAllUserSessions() | Global logout |\n| authenticateRequest() | Full request authentication flow |\n| healthCheck() | Check provider connectivity |\n\nOptional Methods\n\n| Method | Purpose |\n| ------------------------- | ------------------------------- |\n| refreshToken() | Refresh an authentication token |\n| revokeToken() | Revoke a token (logout) |\n| getUser() | Get user by ID |\n| getUserByEmail() | Get user by email |\n| updateUserMetadata() | Update user metadata |\n| updateUserRoles() | Update user roles |\n| updateUserPermissions() | Update user permissions |\n| initialize() | Provider initialization |\n| cleanup() | Resource cleanup |\n\nBaseAuthProvider\n\nThe abstract BaseAuthProvider class provides default implementations for token extraction, authorization checks, and the full authenticateRequest() flow. Concrete providers only need to implement the abstract methods:\nauthenticateToken() -- provider-specific token validation\ncreateSession() / getSession() / refreshSession() / destroySession() -- session lifecycle\ngetUserSessions() / destroyAllUserS","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"","lvl3":""}}, {"objectID":"419854ed720ad0bc71739934836cbb8557e89225cf706fac5afb7871db64d195","title":"Authentication Architecture","url":"/docs/advanced/auth-architecture#authentication-architecture","content":"Audience: Contributors and advanced users who need to understand how authentication is wired into NeuroLink's internals.","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Authentication Architecture","lvl3":""}}, {"objectID":"3bc1aa980007416900e0fec664df12c77afb20629e6339fd452a79163028314c","title":"Design Principles","url":"/docs/advanced/auth-architecture#design-principles","content":"NeuroLink's auth system follows the same architectural patterns used for AI providers:\nFactory + Registry -- providers are registered with factory functions and instantiated on demand via dynamic imports to avoid circular dependencies\nLazy initialization -- the auth provider is not created in the synchronous constructor; it is initialized on first use (generate/stream with auth.token)\nFail closed -- a valid token that does not resolve to a user identity is treated as an authentication failure\nToken-derived identity wins -- when both auth.token and requestContext are provided, token-derived fields (userId, userEmail, userRoles) override requestContext to prevent privilege escalation","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Design Principles","lvl3":""}}, {"objectID":"a41372f6cab043e17ed8bef0f767ae3b570fd18cd08df6b55caf0fd5205829b2","title":"AuthProviderFactory","url":"/docs/advanced/auth-architecture#authproviderfactory","content":"AuthProviderFactory extends BaseFactory and follows the singleton pattern. It registers 11 provider factory functions during initialization, each using dynamic imports:\n\nEach registration includes:\nType identifier -- canonical name (e.g., \"auth0\")\nFactory function -- async function that dynamically imports and instantiates the provider\nAliases -- alternative names for convenience (e.g., \"auth0-jwt\", \"auth0-oauth\")\nMetadata -- human-readable name, description, documentation URL\n\nThe create(type, config) method:\nCalls ensureInitialized() to lazily run registerAll() once\nResolves the name through alias lookup via resolveName()\nCalls the registered factory function with the provider config\nReturns the AuthProvider instance","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"AuthProviderFactory","lvl3":""}}, {"objectID":"5945bdf296ab83edf71515a462ead30f360f81871f19aca34b06568465815c4f","title":"AuthProviderRegistry","url":"/docs/advanced/auth-architecture#authproviderregistry","content":"AuthProviderRegistry extends BaseRegistry and layers metadata and discovery on top of the factory:\nTracks provider capabilities (features like jwt-validation, session-management, sso)\nProvides discovery APIs (getProvidersByFeature(), getBuiltInProviders())\nRuns health checks by creating temporary provider instances\nCaches health status per provider type\n\nThe registry does not create providers directly; it delegates to AuthProviderFactory.create().","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"AuthProviderRegistry","lvl3":""}}, {"objectID":"e2a6095191e6bdeab1ea4ae0a7bb804441213dc1ca690703b1d47666cb84df43","title":"Error Factories","url":"/docs/advanced/auth-architecture#error-factories","content":"Both AuthProviderFactory and AuthProviderRegistry use createErrorFactory() from the core infrastructure to produce typed errors with unique codes:\n\n| Module | Code Prefix | Example |\n| -------- | ---------------- | ------------------------------- |\n| Factory | AUTH_FACTORY- | AUTH_FACTORY-001 (not found) |\n| Registry | AUTH_REGISTRY- | AUTH_REGISTRY-001 (not found) |","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Error Factories","lvl3":""}}, {"objectID":"88300f58c5be9d6da5558ae98432d52392c0799cbbb0e3d6c65348407f740135","title":"Provider Interface","url":"/docs/advanced/auth-architecture#provider-interface","content":"All providers implement the AuthProvider type, which defines:","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Provider Interface","lvl3":""}}, {"objectID":"a1ce21db9f39478b38cae2774ae8159845b373a512a6064b1b5c740fc4a923a0","title":"Required Methods","url":"/docs/advanced/auth-architecture#required-methods","content":"| Method | Purpose |\n| -------------------------- | ------------------------------------------------- |\n| authenticateToken() | Validate and decode a token, return user identity |\n| extractToken() | Extract token from request context |\n| authorizeUser() | Check if user has a specific permission |\n| authorizeRoles() | Check if user has any of the required roles |\n| authorizePermissions() | Check if user has all specified permissions |\n| createSession() | Create a new session for a user |\n| getSession() | Get an existing session by ID |\n| refreshSession() | Extend a session's expiration |\n| destroySession() | Invalidate a session |\n| getUserSessions() | Get all active sessions for a user |\n| destroyAllUserSessions() | Global logout |\n| authenticateRequest() | Full request authentication flow |\n| healthCheck() | Check provider connectivity |","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Required Methods","lvl3":""}}, {"objectID":"ceb430cf7028262df35f7eb22ed40362bc1ce5b08629a6bfee89d3e1d80f2b37","title":"Optional Methods","url":"/docs/advanced/auth-architecture#optional-methods","content":"| Method | Purpose |\n| ------------------------- | ------------------------------- |\n| refreshToken() | Refresh an authentication token |\n| revokeToken() | Revoke a token (logout) |\n| getUser() | Get user by ID |\n| getUserByEmail() | Get user by email |\n| updateUserMetadata() | Update user metadata |\n| updateUserRoles() | Update user roles |\n| updateUserPermissions() | Update user permissions |\n| initialize() | Provider initialization |\n| cleanup() | Resource cleanup |","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Optional Methods","lvl3":""}}, {"objectID":"d999dbc144093bc9fb741ce75000ea6502a3fc28d3622c9d4b2c627003778698","title":"BaseAuthProvider","url":"/docs/advanced/auth-architecture#baseauthprovider","content":"The abstract BaseAuthProvider class provides default implementations for token extraction, authorization checks, and the full authenticateRequest() flow. Concrete providers only need to implement the abstract methods:\nauthenticateToken() -- provider-specific token validation\ncreateSession() / getSession() / refreshSession() / destroySession() -- session lifecycle\ngetUserSessions() / destroyAllUserSessions() -- multi-session management\n\nThe base class also:\nDefaults token extraction to Authorization: Bearer header with case-insensitive lookup\nSupports hierarchical wildcard permissions (e.g., tools:* matches tools:execute)\nSupports role hierarchy via config.rbac.roleHierarchy\nEmits auth:unauthorized events via EventEmitter","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"BaseAuthProvider","lvl3":""}}, {"objectID":"ad286cbdc24af43655d83d9bd3ec1d4a99c853aeef2a7e827e0e37c246d2fe4b","title":"Per-Call Token Validation (generate/stream)","url":"/docs/advanced/auth-architecture#per-call-token-validation-generatestream","content":"When auth: { token } is passed to generate() or stream():","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Per-Call Token Validation (generate/stream)","lvl3":""}}, {"objectID":"4098a23526a6249e6b582eff3767de38e7297333fb62fff68928cb52acb707ce","title":"Server Middleware Flow","url":"/docs/advanced/auth-architecture#server-middleware-flow","content":"When using createAuthMiddleware():","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Server Middleware Flow","lvl3":""}}, {"objectID":"929c8ab52b3e6bcc5503063db18417137e3a202e8e70be3baf9044e54dac5374","title":"RBAC Enforcement Flow","url":"/docs/advanced/auth-architecture#rbac-enforcement-flow","content":"When using createRBACMiddleware():","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"RBAC Enforcement Flow","lvl3":""}}, {"objectID":"105008a1b6261f1f4e7d85357d2a44664d8f840d87fc414e5cdb617b57780ec2","title":"Storage Backends","url":"/docs/advanced/auth-architecture#storage-backends","content":"| Backend | Class | Characteristics |\n| --------- | -------------------------- | ----------------------------------------------------- |\n| In-memory | MemorySessionStorage | Single-instance, sessions lost on restart |\n| Redis | RedisSessionStorage | Distributed, TTL-based expiration, redis (node-redis) |\n| Custom | Implement SessionStorage | User-provided storage backend |\n\nSessionManager wraps the storage backend and adds:\nAutomatic session refresh when close to expiration (refreshThreshold)\nConfigurable session duration\nMetadata updates\nHealth checks","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Storage Backends","lvl3":""}}, {"objectID":"6648556047697a603719cc599fa7d7e1d0bee69b9c9b2280c82d4cc6e4f7fa24","title":"AsyncLocalStorage Context Propagation","url":"/docs/advanced/auth-architecture#asynclocalstorage-context-propagation","content":"NeuroLink uses Node.js AsyncLocalStorage to make the authenticated context available throughout the request lifecycle:\n\nFor environments where AsyncLocalStorage is not available (edge runtimes, etc.), the globalAuthContext singleton (AuthContextHolder) provides an imperative alternative with the same API surface.","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"AsyncLocalStorage Context Propagation","lvl3":""}}, {"objectID":"95e21afa10be6d5103c916c1020660884c17d37dfa1e85533fb8889f63731240","title":"NeuroLink SDK","url":"/docs/advanced/auth-architecture#neurolink-sdk","content":"| Method | Where | What it does |\n| ----------------------- | ------------------------- | ---------------------------------------------------------------- |\n| constructor({ auth }) | src/lib/neurolink.ts | Stores pendingAuthConfig for lazy init |\n| setAuthProvider() | src/lib/neurolink.ts | Creates or sets the auth provider |\n| getAuthProvider() | src/lib/neurolink.ts | Returns the current auth provider |\n| ensureAuthProvider() | src/lib/neurolink.ts | Lazy init on first use |\n| setAuthContext() | src/lib/neurolink.ts | Sets global auth context |\n| Per-call auth | generate() / stream() | Token validation, context merge, privilege escalation prevention |","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"NeuroLink SDK","lvl3":""}}, {"objectID":"beb5fcfde855faf38fb50fe1a5ae22bf7be1aad2bfebd4d236b473454c213fdb","title":"Server Routes","url":"/docs/advanced/auth-architecture#server-routes","content":"| Route | Where | Auth Integration |\n| -------------------------- | -------------------------------------- | ---------------------------------------------- |\n| POST /api/agent/generate | src/lib/server/routes/agentRoutes.ts | requestContext and authContext passthrough |\n| POST /api/agent/stream | src/lib/server/routes/agentRoutes.ts | requestContext and authContext passthrough |","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Server Routes","lvl3":""}}, {"objectID":"5892cb44c7ceddc9d60ce486f0be027d08d60ad9ec1c48d1aa005b02044d2a8d","title":"CLI","url":"/docs/advanced/auth-architecture#cli","content":"| Command | Where | What it does |\n| ---------------------------------- | ----------------------------------- | ----------------------------------- |\n| auth providers | src/cli/commands/authProviders.ts | List all 11 providers with metadata |\n| auth validate | src/cli/commands/authProviders.ts | Validate a token against a provider |\n| auth health | src/cli/commands/authProviders.ts | Health check a provider |\n| auth login/logout/status/refresh | src/cli/commands/auth.ts | Anthropic OAuth management |","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"CLI","lvl3":""}}, {"objectID":"7a941c944289cf59e1d7639b0c5fd8985de081b5db2c2ffeb931705c8b429972","title":"Tool Execution","url":"/docs/advanced/auth-architecture#tool-execution","content":"Authentication context can be passed to tool execution:","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Tool Execution","lvl3":""}}, {"objectID":"94830bee7b9c9a78feef510cefad183f05de510b7749f176219b9111fcd83cba","title":"Error Hierarchy","url":"/docs/advanced/auth-architecture#error-hierarchy","content":"Each error carries the provider type (error.provider) so error handlers can distinguish provider-specific failures.","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Error Hierarchy","lvl3":""}}, {"objectID":"99fa0b07493b3b32bf15921e8d79b9a66b98a4fca7af323bbe755781d03c7dae","title":"Rate Limiting Architecture","url":"/docs/advanced/auth-architecture#rate-limiting-architecture","content":"The rate limiter uses the token bucket algorithm:\nEach user gets a bucket with maxRequests tokens\nTokens are continuously refilled at maxRequests / windowMs rate\nEach request consumes one token\nWhen the bucket is empty, requests are rejected with 429 Too Many Requests","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Rate Limiting Architecture","lvl3":""}}, {"objectID":"fe222c7421b0e0c23c6bc4bd39d74038050fe3be5b2198181865d9d9ada05077","title":"Concurrency Safety","url":"/docs/advanced/auth-architecture#concurrency-safety","content":"In-memory: Single-threaded Node.js guarantees atomicity\nRedis: Uses a Lua script (atomicConsume) that performs refill-and-consume in a single atomic operation, preventing race conditions where parallel requests read the same token count","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Concurrency Safety","lvl3":""}}, {"objectID":"c63ea3a4c4aa6319f131d1f8178b30d7f3052ab09b5e6960e439ad76f9b8fa9b","title":"Role-Based Differentiation","url":"/docs/advanced/auth-architecture#role-based-differentiation","content":"roleLimits assigns per-role limits (highest limit wins for multi-role users)\nuserLimits assigns per-user overrides\nskipRoles bypasses rate limiting entirely for specified roles (e.g., super-admin)","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Role-Based Differentiation","lvl3":""}}, {"objectID":"1824c13c380df1f7461f4a8f7ce99478b257057b57adc5b2595a680ab923d094","title":"Adding a New Auth Provider","url":"/docs/advanced/auth-architecture#adding-a-new-auth-provider","content":"Create a provider class in src/lib/auth/providers/myProvider.ts that extends BaseAuthProvider\nImplement the abstract methods: authenticateToken(), createSession(), getSession(), refreshSession(), destroySession(), getUserSessions(), destroyAllUserSessions()\nRegister the provider in AuthProviderFactory.registerAll() with a dynamic import\nRegister metadata in AuthProviderRegistry.registerAll()\nAdd the type name to AuthProviderType union in src/lib/types/authTypes.ts\nAdd a typed config to src/lib/types/authTypes.ts and a discriminated union branch to NeuroLinkAuthConfig in src/lib/types/configTypes.ts\nAdd environment variable mappings to buildProviderConfig() in src/cli/commands/authProviders.ts\nExport the provider from src/lib/auth/index.ts\nAdd tests","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"Adding a New Auth Provider","lvl3":""}}, {"objectID":"a63ad442a5c17bcb83737eb96e06af21ce0d91127807b57bf333100ea11535c9","title":"See Also","url":"/docs/advanced/auth-architecture#see-also","content":"Authentication Providers Guide -- user-facing guide with configuration examples\nFactory Pattern Architecture -- how NeuroLink uses factory + registry across the codebase\nMiddleware Architecture -- the broader middleware system","hierarchy":{"lvl0":"Advanced","lvl1":"Authentication Architecture","lvl2":"See Also","lvl3":""}}, {"objectID":"4e93f12348468bad2ffb2c45eee31449794a11626f9ac799d5b5af33e79f742d","title":"Built-in Middleware Reference","url":"/docs/advanced/builtin-middleware","content":"Built-in Middleware Reference\n\nNeuroLink includes three built-in middleware components for common use cases: Analytics, Guardrails, and Auto-Evaluation. They ship ready to wire into your generate/stream calls.\n\nQuick Start\n\nEnable all built-in middleware with a single preset:\n\nOr enable specific middleware:\n\nAnalytics Middleware\n\nPurpose\n\nThe Analytics Middleware collects comprehensive usage metrics, timing data, and operational analytics for all AI operations. It's essential for monitoring production applications, tracking costs, and understanding usage patterns.\n\nKey Capabilities:\nToken usage tracking (input, output, total)\nResponse time measurement\nRequest success/failure tracking\nProvider and model information\nAutomatic metrics storage in response metadata\n\nConfiguration\n\nBasic Configuration:\n\nAdvanced Configuration:\n\nConditional Analytics (Production Only):\n\nCollected Metrics\n\n| Metric | Type | Description | Unit |\n| -------------- | ------ | ---------------------------------- | ------------ |\n| requestId | string | Unique identifier for this request | - |\n| timestamp | string | ISO 8601 timestamp | - |\n| responseTime | number | Total request duration | milliseconds |\n| usage.input | number | Input tokens consumed | tokens |\n| usage.output | number | Output tokens generated | tokens |\n| usage.total | number | Total tokens used | tokens |\n\nOutput Format\n\nAnalytics data is automatically added to the response metadata:\n\nGenerate Response:\n\nAnalytics Object Structure:\n\nStream Response:\n\nFor streaming responses, analytics are available in the rawResponse:\n\nStream Analytics Structure:\n\nUse Cases\nCost Tracking:\nPerformance Monitoring:\nUsage Analytics Dashboard:\n\nIntegration with External Systems\n\nSend to Datadog:\n\nSend to Prometheus:\n\nGuardrails Middleware\n\nPurpose\n\nThe Guardrails Middleware provides comprehensive content filtering and policy enforcement to block or redact unsafe content, prevent prompt injection attacks, and maintain compliance with content policies.\n\nKey Capabilities:\nBad word filtering (configurable word list)\nAI model-based content safety evaluation\nPrecall evaluation (block unsafe prompts before they reach the LLM)\nStream and generate support\nConfigurable filtering actions (block, redact, log)\n\nConfiguration\n\nBasic Configuration:\n\nAdvanced Configuration with Model-Based Filtering:\n\nPrecall Evaluation (Block Unsafe Prompts):\n\nBuilt-in Filters\n\n| Filter Type | Description | Action | Configuration |\n| ---------------------- | -------------------------------------- | ----------------- | --------------------------------- |\n| Bad Words | Block/redact specific words or phrases | Redact with *** | badWords: string[] |\n| Model-Based | Use AI to evaluate content safety | Block if unsafe | modelFilter.enabled: true |\n| Precall Evaluation | Block unsafe prompts before LLM call | Block request | precallEvaluation.enabled: true |\n\nBad Word Filtering\n\nHow It Works:\n\nThe bad word filter scans both requests and responses for prohibited terms and replaces them with ***.\n\nExample:\n\nConfiguration:\n\nModel-Based Filtering\n\nHow It Works:\n\nUses a separate AI model to evaluate whether content is safe. The filter sends the content to the model with a safety evaluation prompt.\n\nSafety Evaluation Prompt:\n\nExample:\n\nConfiguration:\n\nPrecall Evaluation\n\nHow It Works:\n\nEvaluates the safety of the input prompt before it reaches the main LLM. If the prompt is deemed unsafe, the request is blocked entirely, saving costs and preventing unsafe content generation.\n\nEvaluation Process:\nUser submits a prompt\nGuardrails middleware intercepts in transformParams\nSafety evaluation model scores the prompt (0-1 scale)\nIf score = threshold, request proceeds to main LLM\n\nBlocked Response:\n\nConfiguration:\n\nStreaming Support\n\nGuardrails work seamlessly with streaming responses:\n\nStream Filtering:\nBad words are replaced with *** in each text delta\nModel-based filtering is not applied to streams (too slow)\nPrecall evaluation works for streams\n\nUse Cases\nContent Moderation for User-Generated Prompts:\nCompliance with Content Policies:\nProtecting Against Prompt Injection:\n\nAuto-Evaluation Middleware\n\nPurpose\n\nThe Auto-Evaluation Middleware automatically evaluates AI response quality using configurable criteria. It can trigger retries for low-quality responses and provide quality metrics for monitoring.\n\nKey Capabilities:\nAutomatic quality evaluation after each response\nConfigurable evaluation criteria (relevance, accuracy, coherence, etc.)\nBlocking and non-blocking modes\nIntegration with custom evaluation providers\nQuality score thresholds\n\nConfiguration\n\nBasic Configuration:\n\nAdvanced Configuration:\n\nEvaluation Criteria\n\nDefault evaluation criteria (can be customized):\n\n","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"","lvl3":""}}, {"objectID":"ac9e7975939e43e8c0e52e2845fcd447f81788b86aafb183213385a1f69ee283","title":"Built-in Middleware Reference","url":"/docs/advanced/builtin-middleware#built-in-middleware-reference","content":"NeuroLink includes three built-in middleware components for common use cases: Analytics, Guardrails, and Auto-Evaluation. They ship ready to wire into your generate/stream calls.","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Built-in Middleware Reference","lvl3":""}}, {"objectID":"63a19a2a4b7e313ef69e05e0d386ce69144f3ddae6c5022b20dc7dd0c9be5cd5","title":"Quick Start","url":"/docs/advanced/builtin-middleware#quick-start","content":"Enable all built-in middleware with a single preset:\n\nOr enable specific middleware:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Quick Start","lvl3":""}}, {"objectID":"ba5e28fcd0f3518c621fa4ff5d661252622d1d582be75671f9246ba89b846b94","title":"Purpose","url":"/docs/advanced/builtin-middleware#purpose","content":"The Analytics Middleware collects comprehensive usage metrics, timing data, and operational analytics for all AI operations. It's essential for monitoring production applications, tracking costs, and understanding usage patterns.\n\nKey Capabilities:\nToken usage tracking (input, output, total)\nResponse time measurement\nRequest success/failure tracking\nProvider and model information\nAutomatic metrics storage in response metadata","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Purpose","lvl3":""}}, {"objectID":"f097d3aae3b2f08f80b1488048983d76d09a53d821e1249af7c8c2ef18425d68","title":"Configuration","url":"/docs/advanced/builtin-middleware#configuration","content":"Basic Configuration:\n\nAdvanced Configuration:\n\nConditional Analytics (Production Only):","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Configuration","lvl3":""}}, {"objectID":"0ed403ca8e213ef3e9c955b807bced0bca556ca4e388f7006bd855c14a3181a5","title":"Collected Metrics","url":"/docs/advanced/builtin-middleware#collected-metrics","content":"| Metric | Type | Description | Unit |\n| -------------- | ------ | ---------------------------------- | ------------ |\n| requestId | string | Unique identifier for this request | - |\n| timestamp | string | ISO 8601 timestamp | - |\n| responseTime | number | Total request duration | milliseconds |\n| usage.input | number | Input tokens consumed | tokens |\n| usage.output | number | Output tokens generated | tokens |\n| usage.total | number | Total tokens used | tokens |","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Collected Metrics","lvl3":""}}, {"objectID":"8caea477813509a12fa2ec697dd55809932ba384cd92d7f97968152515b9371e","title":"Output Format","url":"/docs/advanced/builtin-middleware#output-format","content":"Analytics data is automatically added to the response metadata:\n\nGenerate Response:\n\nAnalytics Object Structure:\n\nStream Response:\n\nFor streaming responses, analytics are available in the rawResponse:\n\nStream Analytics Structure:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Output Format","lvl3":""}}, {"objectID":"a6eab75cdd977cdba686031f66400da73b4f447cfea22ca74ad06fa867a5262c","title":"Use Cases","url":"/docs/advanced/builtin-middleware#use-cases","content":"Cost Tracking:\nPerformance Monitoring:\nUsage Analytics Dashboard:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Use Cases","lvl3":""}}, {"objectID":"9076a536f4f9ba60a6a30cf011dc0621aa48dd595f05fd3978851c4bc447907d","title":"Integration with External Systems","url":"/docs/advanced/builtin-middleware#integration-with-external-systems","content":"Send to Datadog:\n\nSend to Prometheus:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Integration with External Systems","lvl3":""}}, {"objectID":"0a93a01240d9e48fc450cac2f7488fd3bf188e290023d6b31523ee025eff6d28","title":"Purpose","url":"/docs/advanced/builtin-middleware#purpose-1","content":"The Guardrails Middleware provides comprehensive content filtering and policy enforcement to block or redact unsafe content, prevent prompt injection attacks, and maintain compliance with content policies.\n\nKey Capabilities:\nBad word filtering (configurable word list)\nAI model-based content safety evaluation\nPrecall evaluation (block unsafe prompts before they reach the LLM)\nStream and generate support\nConfigurable filtering actions (block, redact, log)","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Purpose","lvl3":""}}, {"objectID":"3e5982e2d7c84090b3d892cfbfab3f57bd476c9433d8b95ea9430b834cb58856","title":"Configuration","url":"/docs/advanced/builtin-middleware#configuration-1","content":"Basic Configuration:\n\nAdvanced Configuration with Model-Based Filtering:\n\nPrecall Evaluation (Block Unsafe Prompts):","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Configuration","lvl3":""}}, {"objectID":"daec1f88503e670629d89734a8659fe16da471cc93a7c6003741c4e64abc6692","title":"Built-in Filters","url":"/docs/advanced/builtin-middleware#built-in-filters","content":"| Filter Type | Description | Action | Configuration |\n| ---------------------- | -------------------------------------- | ----------------- | --------------------------------- |\n| Bad Words | Block/redact specific words or phrases | Redact with *** | badWords: string[] |\n| Model-Based | Use AI to evaluate content safety | Block if unsafe | modelFilter.enabled: true |\n| Precall Evaluation | Block unsafe prompts before LLM call | Block request | precallEvaluation.enabled: true |","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Built-in Filters","lvl3":""}}, {"objectID":"bb5a4d59cf38e8ff3e5c5af1631dac32fdcc4cf94ea96a0c2f89807b05a91be3","title":"Bad Word Filtering","url":"/docs/advanced/builtin-middleware#bad-word-filtering","content":"How It Works:\n\nThe bad word filter scans both requests and responses for prohibited terms and replaces them with ***.\n\nExample:\n\nConfiguration:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Bad Word Filtering","lvl3":""}}, {"objectID":"0b6ecb69b64f7786684bde751247277e3307a4ea5d6769df2f8fd04dfa929410","title":"Model-Based Filtering","url":"/docs/advanced/builtin-middleware#model-based-filtering","content":"How It Works:\n\nUses a separate AI model to evaluate whether content is safe. The filter sends the content to the model with a safety evaluation prompt.\n\nSafety Evaluation Prompt:\n\nExample:\n\nConfiguration:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Model-Based Filtering","lvl3":""}}, {"objectID":"f2f24ce610a7db1537dcbd86e033eff708a1cb0350736a4ae94aeb1c24c5b5b3","title":"Precall Evaluation","url":"/docs/advanced/builtin-middleware#precall-evaluation","content":"How It Works:\n\nEvaluates the safety of the input prompt before it reaches the main LLM. If the prompt is deemed unsafe, the request is blocked entirely, saving costs and preventing unsafe content generation.\n\nEvaluation Process:\nUser submits a prompt\nGuardrails middleware intercepts in transformParams\nSafety evaluation model scores the prompt (0-1 scale)\nIf score = threshold, request proceeds to main LLM\n\nBlocked Response:\n\nConfiguration:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Precall Evaluation","lvl3":""}}, {"objectID":"093de1c426f9bdb6d46b3761e4c5566f1ef6c868aed440d7a7e55eb5d40fce96","title":"Streaming Support","url":"/docs/advanced/builtin-middleware#streaming-support","content":"Guardrails work seamlessly with streaming responses:\n\nStream Filtering:\nBad words are replaced with *** in each text delta\nModel-based filtering is not applied to streams (too slow)\nPrecall evaluation works for streams","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Streaming Support","lvl3":""}}, {"objectID":"3554071ece46dd2dbd958906252d6ce063601d8647ea61f8679d0847c525cb00","title":"Use Cases","url":"/docs/advanced/builtin-middleware#use-cases-1","content":"Content Moderation for User-Generated Prompts:\nCompliance with Content Policies:\nProtecting Against Prompt Injection:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Use Cases","lvl3":""}}, {"objectID":"d9e6dcbb73334ba06488b0fd74aa3e6051e9d132f6ba965d7fa684ef71f7511b","title":"Purpose","url":"/docs/advanced/builtin-middleware#purpose-2","content":"The Auto-Evaluation Middleware automatically evaluates AI response quality using configurable criteria. It can trigger retries for low-quality responses and provide quality metrics for monitoring.\n\nKey Capabilities:\nAutomatic quality evaluation after each response\nConfigurable evaluation criteria (relevance, accuracy, coherence, etc.)\nBlocking and non-blocking modes\nIntegration with custom evaluation providers\nQuality score thresholds","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Purpose","lvl3":""}}, {"objectID":"6e7a01777f04abcb974725d235ad6c73fbb6714dceed504aa5f7e48d286cb560","title":"Configuration","url":"/docs/advanced/builtin-middleware#configuration-2","content":"Basic Configuration:\n\nAdvanced Configuration:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Configuration","lvl3":""}}, {"objectID":"ce43e4f6ff98db44546971741d4d79f9f94344541b70f5f605d8cd1dc11baf2f","title":"Evaluation Criteria","url":"/docs/advanced/builtin-middleware#evaluation-criteria","content":"Default evaluation criteria (can be customized):\n\n| Criterion | Description | Score Range |\n| --------------- | ---------------------------------- | ----------- |\n| Relevance | Response relevance to the prompt | 0-10 |\n| Accuracy | Factual accuracy and correctness | 0-10 |\n| Coherence | Logical structure and clarity | 0-10 |\n| Helpfulness | Value provided to the user | 0-10 |\n| Safety | Content safety and appropriateness | 0-10 |","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Evaluation Criteria","lvl3":""}}, {"objectID":"698acb984ee4c9d9dc5b80f4cac0f421f1107bd6e81afbc42a0a651a76febe21","title":"Blocking vs Non-Blocking Mode","url":"/docs/advanced/builtin-middleware#blocking-vs-non-blocking-mode","content":"Blocking Mode (blocking: true):\nEvaluation happens before response is returned\nUser waits for evaluation to complete\nCan retry or reject responses based on quality\nUse for critical applications where quality is paramount\n\nNon-Blocking Mode (blocking: false, default):\nEvaluation happens in background\nResponse returned immediately\nQuality metrics available via callback\nUse for most applications to maintain low latency","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Blocking vs Non-Blocking Mode","lvl3":""}}, {"objectID":"a297b29ba2c41d1373f199f1089b176fa1de22bc1d110eaabea75219e49e9af3","title":"Evaluation Output","url":"/docs/advanced/builtin-middleware#evaluation-output","content":"Evaluation Result Structure:\n\nExample Output:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Evaluation Output","lvl3":""}}, {"objectID":"76006d0fbf9fb118f6927e7d0cfa0eee3c0f7ebe1b05b60a1d2ec8ce45f052f0","title":"Streaming Support","url":"/docs/advanced/builtin-middleware#streaming-support-1","content":"Important: Auto-evaluation for streaming responses always runs in non-blocking mode, even if blocking: true is configured. This is because the stream needs to be returned to the user immediately.","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Streaming Support","lvl3":""}}, {"objectID":"2855271353dbf74a4b9afde700dbe3e93673c93463a37edd783dd2c1ba48f565","title":"Use Cases","url":"/docs/advanced/builtin-middleware#use-cases-2","content":"Quality Assurance for Customer-Facing AI:\nAutomatic Response Improvement:\nQuality Metrics Dashboard:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Use Cases","lvl3":""}}, {"objectID":"4aa7c15cbd561bf19d868429d78f4bc34a10159b731471c6dd2f1f01bc779bed","title":"Environment Variables","url":"/docs/advanced/builtin-middleware#environment-variables","content":"Configure auto-evaluation via environment variables:\n\n`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Environment Variables","lvl3":""}}, {"objectID":"638a1a4b61dc82f95d843b15585c9ec487ccbabb2555a642be510e0e94532141","title":"Set default threshold","url":"/docs/advanced/builtin-middleware#set-default-threshold","content":"NEUROLINKEVALUATIONTHRESHOLD=7","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Set default threshold","lvl3":""}}, {"objectID":"96537c53cd6962ca7eabae0b824ac8ff9b462c29495c9ba0dbc6c3f97a9b6a89","title":"Use in configuration","url":"/docs/advanced/builtin-middleware#use-in-configuration","content":"typescript\nconst factory = new MiddlewareFactory({\n middlewareConfig: {\n autoEvaluation: {\n enabled: true,\n config: {\n threshold: Number(process.env.NEUROLINKEVALUATIONTHRESHOLD) || 7,\n },\n },\n },\n});\n`","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Use in configuration","lvl3":""}}, {"objectID":"3f3ba7ad82ce55763c1578def0d38aeb515c8ef9e78effc1ad72b46e79983f04","title":"Recommended Execution Order","url":"/docs/advanced/builtin-middleware#recommended-execution-order","content":"Middleware executes in priority order (higher priority runs first). Here's the recommended order for combining built-in middleware:\n\nWhy This Order?\nAnalytics first: Capture metrics for all requests, even blocked ones\nGuardrails second: Block unsafe content before it's evaluated\nAuto-Evaluation last: Evaluate quality of safe responses","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Recommended Execution Order","lvl3":""}}, {"objectID":"e4558f00def8a56c6f8c3783450e2e4f60e468f28d88125a80041201d8b08a99","title":"Analytics","url":"/docs/advanced/builtin-middleware#analytics","content":"Overhead: Minimal (\\<5ms per request)\nImpact: None on latency (runs in request/response flow)\nRecommendation: Always enable in production","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Analytics","lvl3":""}}, {"objectID":"44f91010b5699675d4c299d8c7ad5a4760eff3ef0afeda434d4de900c9e8f7de","title":"Guardrails","url":"/docs/advanced/builtin-middleware#guardrails","content":"Bad Word Filtering: Very fast (\\<1ms)\nModel-Based Filtering: Adds 200-500ms (extra AI call)\nPrecall Evaluation: Adds 200-500ms (evaluated before main call)\nRecommendation: Use bad word filtering always, model-based filtering selectively","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Guardrails","lvl3":""}}, {"objectID":"be7731aceb5e7c5641d00acf260f8e811b483f86ed6b87c0dc985b05a3cb43d3","title":"Auto-Evaluation","url":"/docs/advanced/builtin-middleware#auto-evaluation","content":"Blocking Mode: Adds 200-1000ms to response time\nNon-Blocking Mode: No impact on response time\nRecommendation: Use non-blocking mode for most applications","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Auto-Evaluation","lvl3":""}}, {"objectID":"dde222bb5529019567904dc1128fdc938c1e5a50d9aace60914eda4b74e3b479","title":"Optimization Tips","url":"/docs/advanced/builtin-middleware#optimization-tips","content":"Use Conditional Execution: Only apply expensive middleware when needed\nUse Fast Models for Filtering: Use GPT-3.5 instead of GPT-4 for guardrails\nBatch Evaluations: For non-blocking auto-evaluation, batch multiple evaluations","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Optimization Tips","lvl3":""}}, {"objectID":"f85aae5f44c2d86ffa8a09f65b9aaadc483625986f73f5e8d4cc2d0e501b2897","title":"Analytics Not Appearing in Response","url":"/docs/advanced/builtin-middleware#analytics-not-appearing-in-response","content":"Problem: Analytics data is missing from response metadata.\n\nSolution:\nVerify analytics is enabled:\nCheck preset configuration:\nAccess analytics correctly:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Analytics Not Appearing in Response","lvl3":""}}, {"objectID":"38eadb19e438628d2f5577b60665da08e85998fa564d6feafef9cf97fd019825","title":"Guardrails Blocking Valid Content","url":"/docs/advanced/builtin-middleware#guardrails-blocking-valid-content","content":"Problem: Guardrails are blocking safe content.\n\nSolution:\nAdjust precall evaluation threshold:\nReview bad words list:\nCheck model-based filter:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Guardrails Blocking Valid Content","lvl3":""}}, {"objectID":"0a97e63cbe8d06a02bf8abaab850805edc8ec28bd2cb6f6d3a04a01606ff2ab6","title":"Auto-Evaluation Slowing Down Responses","url":"/docs/advanced/builtin-middleware#auto-evaluation-slowing-down-responses","content":"Problem: Responses are slower due to evaluation.\n\nSolution:\nUse non-blocking mode:\nReduce evaluation frequency:\nUse faster evaluation model:","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"Auto-Evaluation Slowing Down Responses","lvl3":""}}, {"objectID":"5a04f2372c821041d094c92e2d1dfae0814d5ba30b64ab861800b23e416722c5","title":"See Also","url":"/docs/advanced/builtin-middleware#see-also","content":"Middleware Architecture - Deep dive into middleware system design\nCustom Middleware Guide - Create your own middleware\nHITL Integration - Combine middleware with human approval workflows\nProvider Comparison - Which providers work best with middleware","hierarchy":{"lvl0":"Advanced","lvl1":"Built-in Middleware Reference","lvl2":"See Also","lvl3":""}}, {"objectID":"56b18445c10e437513d7a827766679fecdbda2a43f97895ad9c7fbfc7924f5fd","title":"Enterprise Features","url":"/docs/advanced/enterprise","content":"Enterprise Features\n\nNeuroLink provides comprehensive enterprise-grade features for production deployments.\n\nSecurity\n\nAuthentication\nAPI key management\nOAuth integration\nRole-based access control\n\nData Protection\nEncryption at rest and in transit\nData residency compliance\nAudit logging\n\nScalability\n\nHigh Availability\nLoad balancing\nFailover mechanisms\nMulti-region deployment\n\nPerformance\nCaching strategies\nConnection pooling\nRequest optimization\n\nMonitoring\n\nAnalytics\nUsage metrics\nPerformance monitoring\nError tracking\n\nAlerting\nReal-time notifications\nThreshold-based alerts\nCustom alert rules\n\nCompliance\n\nStandards\nSOC 2 compliance\nGDPR compliance\nIndustry-specific requirements\n\nGovernance\nData governance policies\nAccess controls\nAudit trails\n\nEnterprise Support\n\nService Level Agreements\n99.9% uptime guarantee\nResponse time commitments\nEscalation procedures\n\nProfessional Services\nImplementation consulting\nCustom development\nTraining and support\n\nFor setup instructions, see Enterprise Proxy Setup.","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"","lvl3":""}}, {"objectID":"66d5802be88a4af2366a720602932819245006a86d114fb1c3718f2cc456e8b1","title":"Enterprise Features","url":"/docs/advanced/enterprise#enterprise-features","content":"NeuroLink provides comprehensive enterprise-grade features for production deployments.","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"Enterprise Features","lvl3":""}}, {"objectID":"f370e09f226759d0b787d31da0d4fe33441f036992c0fa91a5e34686108547f2","title":"Authentication","url":"/docs/advanced/enterprise#authentication","content":"API key management\nOAuth integration\nRole-based access control","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"Authentication","lvl3":""}}, {"objectID":"59705a0e8aec7148d99c875b499aeed7756a11be0570c242e752bacabfe331ca","title":"Data Protection","url":"/docs/advanced/enterprise#data-protection","content":"Encryption at rest and in transit\nData residency compliance\nAudit logging","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"Data Protection","lvl3":""}}, {"objectID":"840fce62e6d104368c8ccfea50a0618c4f9cf83109b9cf9ba2f75f2e9cbbe983","title":"High Availability","url":"/docs/advanced/enterprise#high-availability","content":"Load balancing\nFailover mechanisms\nMulti-region deployment","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"High Availability","lvl3":""}}, {"objectID":"8e6ac8bc65752dc2602294ee6eefe6baf5470afd36da274991374599601d3ac5","title":"Performance","url":"/docs/advanced/enterprise#performance","content":"Caching strategies\nConnection pooling\nRequest optimization","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"Performance","lvl3":""}}, {"objectID":"4e38af5d8d853f837008ac66edf6edfc6af755ab00aebbc0a94e29df149fd882","title":"Analytics","url":"/docs/advanced/enterprise#analytics","content":"Usage metrics\nPerformance monitoring\nError tracking","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"Analytics","lvl3":""}}, {"objectID":"394c3c9e4b64f8eeb3749febc4133b0acdd4e557c973867f138b9501fe29addb","title":"Alerting","url":"/docs/advanced/enterprise#alerting","content":"Real-time notifications\nThreshold-based alerts\nCustom alert rules","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"Alerting","lvl3":""}}, {"objectID":"f151f3b3b1f355bd8991ae2677ff046a6058c125774e7c21c8aa2f794ec31122","title":"Standards","url":"/docs/advanced/enterprise#standards","content":"SOC 2 compliance\nGDPR compliance\nIndustry-specific requirements","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"Standards","lvl3":""}}, {"objectID":"c764f0eceb0e25db767986bd18db1f3c9503ab2af04214e1eb66cffa911d28f8","title":"Governance","url":"/docs/advanced/enterprise#governance","content":"Data governance policies\nAccess controls\nAudit trails","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"Governance","lvl3":""}}, {"objectID":"ba214500dc97e558fd89d2cb62e255be094b8404cfc3fe003c93f430bda6f6c1","title":"Service Level Agreements","url":"/docs/advanced/enterprise#service-level-agreements","content":"99.9% uptime guarantee\nResponse time commitments\nEscalation procedures","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"Service Level Agreements","lvl3":""}}, {"objectID":"12fae46ca9cd477efd9581cf32401cc4a8d289148d5286d02cb782af001037b4","title":"Professional Services","url":"/docs/advanced/enterprise#professional-services","content":"Implementation consulting\nCustom development\nTraining and support\n\nFor setup instructions, see Enterprise Proxy Setup.","hierarchy":{"lvl0":"Advanced","lvl1":"Enterprise Features","lvl2":"Professional Services","lvl3":""}}, {"objectID":"38452a84b70636b08970f7137dca5dc1059cded3c33ff6f78134d1b8099f7442","title":"NeuroLink Factory Patterns - Complete Implementation Guide","url":"/docs/advanced/factory-patterns-complete-guide","content":"NeuroLink Factory Patterns - Complete Implementation Guide\n\nOverview\n\nThe NeuroLink Factory Infrastructure provides a comprehensive, domain-agnostic framework for enhancing AI interactions with configurable patterns. This Phase 1 implementation delivers a complete factory system that works seamlessly with any domain (healthcare, finance, analytics, etc.) while maintaining 100% backward compatibility.\n\nQuick Start\n\nBasic Domain Enhancement\n\nAdvanced Enhancement Utilities\n\nCore Components\nDomain Configuration Factory\n\nThe DomainConfigurationFactory provides domain-specific configuration management:\nOptions Enhancement Utilities\n\nThe OptionsEnhancer provides intelligent enhancement of GenerateOptions:\nContext Conversion Utilities\n\nThe ContextConverter provides migration from legacy business contexts:\n\nIntegration Examples\n\nCLI Integration\n\nFactory patterns work seamlessly with the NeuroLink CLI:\n\nSDK Integration\n\nEvaluation and Analytics Integration\n\nDomain Configuration Reference\n\nPre-registered Domains\n\nHealthcare Domain\n\nAnalytics Domain\n\nCustom Domain Creation\n\nAdvanced Usage Patterns\n\nBatch Enhancement\n\nLegacy Migration Workflow\n\nPerformance Optimization\n\nError Handling and Validation\n\nGraceful Degradation\n\nValidation and Warnings\n\nTesting and Quality Assurance\n\nTest Coverage\n\nThe factory infrastructure includes comprehensive test suites:\nDomain Configuration Tests: 13 test suites, 50+ tests\nIntegration Tests: 11 test suites covering all interfaces\nStreaming Tests: 11 additional test suites with factory integration\nCLI Integration Tests: 14 test suites validating zero breaking changes\nEvaluation Integration: 6 test suites with domain-aware evaluation\nAnalytics Integration: 6 test suites with factory metadata tracking\n\nPerformance Benchmarks\nEnhancement Processing: \\<10ms per operation\nMemory Overhead: \\<5MB additional\nCLI Startup Time: No impact (2-3s maintained)\nStreaming Performance: \\<1% overhead\nBatch Operations: Linear scaling with minimal overhead\n\nMigration Guide\n\nFrom Legacy Business Context\n\nAdopting Factory Patterns Gradually\nPhase 1: Add optional analytics\nPhase 2: Add domain awareness\nPhase 3: Full factory workflow\n \n\nBest Practices\n\nDomain Design\nUse Descriptive Domain Names: Choose clear, specific domain names\nDefine Comprehensive Key Terms: Include domain-specific terminology\nSet Appropriate Thresholds: Adjust evaluation criteria for domain requirements\nInclude Tool Preferences: Specify domain-relevant tools\n\nPerformance Optimization\nCache Domain Configurations: Reuse domain configs across requests\nMonitor Enhancement Time: Track processing time in production\nUse Batch Enhancements: Combine multiple enhancements efficiently\nEnable Analytics Selectively: Only when needed for performance\n\nError Handling\nAlways Handle Enhancement Failures: Factory patterns should never break core functionality\nLog Enhancement Metadata: Track enhancement success/failure for monitoring\nUse Validation Judiciously: Enable validation in development, consider disabling in production for performance\n\nAPI Reference\n\nDomainConfigurationFactory\n\nOptionsEnhancer\n\nContextConverter\n\nConclusion\n\nThe NeuroLink Factory Infrastructure provides a comprehensive, production-ready framework for domain-agnostic AI enhancement. With zero breaking changes, extensive test coverage, and flexible enhancement patterns, it enables powerful domain-specific AI interactions while maintaining the simplicity and reliability of the existing NeuroLink SDK.\n\nThe factory patterns scale from simple domain configuration to complex multi-enhancement workflows, making them suitable for any application from basic chatbots to enterprise AI systems requiring sophisticated domain expertise and analytics tracking.","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"","lvl3":""}}, {"objectID":"c6cfe000c853e41d1ac4782608a909a22734ea7ab25b82096cf914c1d08a9b8e","title":"Overview","url":"/docs/advanced/factory-patterns-complete-guide#overview","content":"The NeuroLink Factory Infrastructure provides a comprehensive, domain-agnostic framework for enhancing AI interactions with configurable patterns. This Phase 1 implementation delivers a complete factory system that works seamlessly with any domain (healthcare, finance, analytics, etc.) while maintaining 100% backward compatibility.","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"Overview","lvl3":""}}, {"objectID":"f74cbce99947c7e9d944a58b5ccab484b638e52e2c4bd2cfdcf432a1149effbb","title":"1. Domain Configuration Factory","url":"/docs/advanced/factory-patterns-complete-guide#1-domain-configuration-factory","content":"The DomainConfigurationFactory provides domain-specific configuration management:","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"1. Domain Configuration Factory","lvl3":""}}, {"objectID":"e138bc114d5df2a07a6dce3458742ecd727e6e91e06328b539cc0bda496d4163","title":"2. Options Enhancement Utilities","url":"/docs/advanced/factory-patterns-complete-guide#2-options-enhancement-utilities","content":"The OptionsEnhancer provides intelligent enhancement of GenerateOptions:","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"2. Options Enhancement Utilities","lvl3":""}}, {"objectID":"233bac5e9e5e7c4549a068f25336f4cc3814ec58c656a7f94e220024f7edb1f1","title":"3. Context Conversion Utilities","url":"/docs/advanced/factory-patterns-complete-guide#3-context-conversion-utilities","content":"The ContextConverter provides migration from legacy business contexts:","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"3. Context Conversion Utilities","lvl3":""}}, {"objectID":"21a63b38e12319d2ca5d219d255373422c7ebf7c6d7cfe4a1652539dc3a9ce8e","title":"CLI Integration","url":"/docs/advanced/factory-patterns-complete-guide#cli-integration","content":"Factory patterns work seamlessly with the NeuroLink CLI:\n\n`bash","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"CLI Integration","lvl3":""}}, {"objectID":"838af151c1d293628445eca43716c7f77cc2de575af2d570c8f3b384519ff73e","title":"Basic usage (unchanged)","url":"/docs/advanced/factory-patterns-complete-guide#basic-usage-unchanged","content":"neurolink generate \"Analyze data trends\" --provider google-ai","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"Basic usage (unchanged)","lvl3":""}}, {"objectID":"931b70b9527e59179ba25f1bf7b282cb75ecad09b102b44f3f67206f4062fa12","title":"Enhanced with analytics","url":"/docs/advanced/factory-patterns-complete-guide#enhanced-with-analytics","content":"neurolink generate \"Healthcare analysis\" --enable-analytics --evaluation-domain healthcare","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"Enhanced with analytics","lvl3":""}}, {"objectID":"698f039455c6be86db542dfbf3d8ef56614d51ebc0cf0bfb1083ac638839b2fd","title":"Context integration","url":"/docs/advanced/factory-patterns-complete-guide#context-integration","content":"neurolink generate \"Custom analysis\" --context '{\"domain\":\"finance\",\"userId\":\"analyst123\"}'","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"Context integration","lvl3":""}}, {"objectID":"5c4ecbdfa85bb5b12bbaf4645a193834631f2e2bd91c3d9776d23711ae234707","title":"Streaming with domain awareness","url":"/docs/advanced/factory-patterns-complete-guide#streaming-with-domain-awareness","content":"neurolink stream \"Real-time analytics\" --enable-evaluation --evaluation-domain analytics\n`","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"Streaming with domain awareness","lvl3":""}}, {"objectID":"d56adf065afc2f832abfe73a2cc4e300bf7de5963c69768da7bc047d86c80bd6","title":"Test Coverage","url":"/docs/advanced/factory-patterns-complete-guide#test-coverage","content":"The factory infrastructure includes comprehensive test suites:\nDomain Configuration Tests: 13 test suites, 50+ tests\nIntegration Tests: 11 test suites covering all interfaces\nStreaming Tests: 11 additional test suites with factory integration\nCLI Integration Tests: 14 test suites validating zero breaking changes\nEvaluation Integration: 6 test suites with domain-aware evaluation\nAnalytics Integration: 6 test suites with factory metadata tracking","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - 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Complete Implementation Guide","lvl2":"Adopting Factory Patterns Gradually","lvl3":""}}, {"objectID":"db51b235cd510207d1ff31368fd483471518ff4a8d0fe2f1a913b38be4217660","title":"Domain Design","url":"/docs/advanced/factory-patterns-complete-guide#domain-design","content":"Use Descriptive Domain Names: Choose clear, specific domain names\nDefine Comprehensive Key Terms: Include domain-specific terminology\nSet Appropriate Thresholds: Adjust evaluation criteria for domain requirements\nInclude Tool Preferences: Specify domain-relevant tools","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"Domain Design","lvl3":""}}, {"objectID":"28619badc7e577a1d29dad93c9bf86d4b36629d0dd8bc1d1ea575e0f2cf259ee","title":"Performance Optimization","url":"/docs/advanced/factory-patterns-complete-guide#performance-optimization-1","content":"Cache Domain Configurations: Reuse domain configs across requests\nMonitor Enhancement Time: Track processing time in production\nUse Batch Enhancements: Combine multiple enhancements efficiently\nEnable Analytics Selectively: Only when needed for performance","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - 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With zero breaking changes, extensive test coverage, and flexible enhancement patterns, it enables powerful domain-specific AI interactions while maintaining the simplicity and reliability of the existing NeuroLink SDK.\n\nThe factory patterns scale from simple domain configuration to complex multi-enhancement workflows, making them suitable for any application from basic chatbots to enterprise AI systems requiring sophisticated domain expertise and analytics tracking.","hierarchy":{"lvl0":"Advanced","lvl1":"NeuroLink Factory Patterns - Complete Implementation Guide","lvl2":"Conclusion","lvl3":""}}, {"objectID":"c6195d69e8507af484f7b546748ef9e24e0a3f0830b302a621078695d30554b2","title":"Factory Pattern Migration Guide","url":"/docs/advanced/factory-patterns","content":"Factory Pattern Migration Guide\n\nOverview\n\nNeuroLink has been refactored to use a unified factory pattern architecture where providers inherit from a common BaseProvider class. This provides consistent tool support and behavior across supported AI providers.\n\nWhat Changed\nUnified BaseProvider Architecture\n\nProviders now inherit from BaseProvider, which provides:\nBuilt-in tool support (6 core tools)\nConsistent generate() and stream() methods\nAnalytics and evaluation capabilities\nStandardized error handling\nAutomatic Tool Support\n\nProviders automatically include these tools:\ngetCurrentTime - Get current date and time\nreadFile - Read file contents\nlistDirectory - List directory contents\ncalculateMath - Perform calculations\nwriteFile - Write to files\nsearchFiles - Search for files by pattern\nSimplified Provider Implementation\n\nProviders no longer need to implement their own tool handling - they inherit it from BaseProvider. This means:\nNo more executeGenerate methods in individual providers\nConsistent tool behavior across supported providers\nLess code duplication\n\nMigration Steps\n\nFor Users\n\nGood news! There are no breaking changes. Your existing code will continue to work exactly as before.\n\nTool Usage (No Changes Required)\n\nDisabling Tools (New Option)\n\nFor Provider Developers\n\nIf you've created custom providers, you'll need to update them to use the new pattern:\n\nBefore (Old Pattern)\n\nAfter (New Pattern)\n\nProvider Tool Support Status\n\nAfter the refactoring, here's the current status of tool support:\n\n| Provider | Status | Notes |\n| ------------ | ----------------- | ---------------------------------------------------- |\n| OpenAI | ✅ Full Support | All tools working correctly |\n| Google AI | ✅ Full Support | Excellent tool execution |\n| Anthropic | ✅ Full Support | Working after max_tokens fix |\n| Azure OpenAI | ✅ Full Support | Same as OpenAI |\n| Mistral | ✅ Full Support | Good tool support |\n| HuggingFace | ⚠️ Partial | Model sees tools but may describe instead of execute |\n| Vertex AI | ⚠️ Partial | Tools available but may not execute |\n| Ollama | ❌ Limited | Requires specific models (e.g., gemma3n) |\n| Bedrock | ✅ Full Support\\* | Requires valid AWS credentials |\n\nBenefits of the New Architecture\nConsistency: Providers behave the same way with tools\nMaintainability: Less code duplication, easier to update\nReliability: Centralized tool handling reduces bugs\nExtensibility: Easy to add new tools for providers at once\nTesting: Simplified testing with consistent behavior\n\nCommon Issues and Solutions\n\nIssue: Provider Not Using Tools\n\nSolution: Check if your model supports function calling. Some models (especially older or smaller ones) may not support tools.\n\nIssue: HuggingFace Describing Tools Instead of Using Them\n\nSolution: This is a model limitation. Use models that support function calling:\nmistralai/Mixtral-8x7B-Instruct-v0.1\nmistralai/Mistral-7B-Instruct-v0.2\n\nIssue: Ollama Returns Empty Content\n\nSolution: Use models that support tool calling:\n\nIssue: Vertex AI Not Using Tools\n\nSolution: This may require schema formatting adjustments. The Vertex provider needs to format tools according to Google's Gemini API schema.\n\nFuture Improvements\nDynamic Tool Loading: Ability to add custom tools at runtime\nProvider-Specific Tool Formatting: Automatic adaptation of tool schemas for each provider\nTool Usage Analytics: Detailed metrics on which tools are used most\nTool Caching: Cache tool results for better performance\n\nSupport\n\nIf you encounter any issues with the migration:\nCheck the provider status documentation\nReview the provider configuration guide\nOpen an issue on GitHub with details about your use case\n\nRemember: No breaking changes! Your existing code continues to work. The factory pattern refactoring improves the internal architecture while maintaining full backward compatibility.","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"","lvl3":""}}, {"objectID":"e1fbb3f6c4772e0c6b8fdab79ea987445b405779abd4e704b529a1c7027733a5","title":"Overview","url":"/docs/advanced/factory-patterns#overview","content":"NeuroLink has been refactored to use a unified factory pattern architecture where providers inherit from a common BaseProvider class. This provides consistent tool support and behavior across supported AI providers.","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"Overview","lvl3":""}}, {"objectID":"f01f032c4a2a3ab3836f627b474c1abe402de7bf927e4d802f874143b9af4235","title":"1. 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Automatic Tool Support","url":"/docs/advanced/factory-patterns#2-automatic-tool-support","content":"Providers automatically include these tools:\ngetCurrentTime - Get current date and time\nreadFile - Read file contents\nlistDirectory - List directory contents\ncalculateMath - Perform calculations\nwriteFile - Write to files\nsearchFiles - Search for files by pattern","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"2. Automatic Tool Support","lvl3":""}}, {"objectID":"aa2c23eaf2b0c2e2be76dc3084b8444c0b70d366332d093c32a84e2528261082","title":"3. Simplified Provider Implementation","url":"/docs/advanced/factory-patterns#3-simplified-provider-implementation","content":"Providers no longer need to implement their own tool handling - they inherit it from BaseProvider. This means:\nNo more executeGenerate methods in individual providers\nConsistent tool behavior across supported providers\nLess code duplication","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"3. Simplified Provider Implementation","lvl3":""}}, {"objectID":"86d70d915acdc3b44f307a6b520059845be38b4e81b2e52cce24bb9abc3edc83","title":"For Users","url":"/docs/advanced/factory-patterns#for-users","content":"Good news! There are no breaking changes. Your existing code will continue to work exactly as before.","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"For Users","lvl3":""}}, {"objectID":"bc63907d67e45dc5b0bf70d6f38f067bb5f6fb78caa9dbf87a931fcb08b25876","title":"For Provider Developers","url":"/docs/advanced/factory-patterns#for-provider-developers","content":"If you've created custom providers, you'll need to update them to use the new pattern:","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"For Provider Developers","lvl3":""}}, {"objectID":"d5baf3cc166eadb899b8b3ec96343a289e6578f97cfabe10f343e97967fc781b","title":"Provider Tool Support Status","url":"/docs/advanced/factory-patterns#provider-tool-support-status","content":"After the refactoring, here's the current status of tool support:\n\n| Provider | Status | Notes |\n| ------------ | ----------------- | ---------------------------------------------------- |\n| OpenAI | ✅ Full Support | All tools working correctly |\n| Google AI | ✅ Full Support | Excellent tool execution |\n| Anthropic | ✅ Full Support | Working after max_tokens fix |\n| Azure OpenAI | ✅ Full Support | Same as OpenAI |\n| Mistral | ✅ Full Support | Good tool support |\n| HuggingFace | ⚠️ Partial | Model sees tools but may describe instead of execute |\n| Vertex AI | ⚠️ Partial | Tools available but may not execute |\n| Ollama | ❌ Limited | Requires specific models (e.g., gemma3n) |\n| Bedrock | ✅ Full Support\\* | Requires valid AWS credentials |","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"Provider Tool Support Status","lvl3":""}}, {"objectID":"81adf8656716613f0b16078865a8f472548c274ff61064a79ed4bd5f0864effc","title":"Benefits of the New Architecture","url":"/docs/advanced/factory-patterns#benefits-of-the-new-architecture","content":"Consistency: Providers behave the same way with tools\nMaintainability: Less code duplication, easier to update\nReliability: Centralized tool handling reduces bugs\nExtensibility: Easy to add new tools for providers at once\nTesting: Simplified testing with consistent behavior","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"Benefits of the New Architecture","lvl3":""}}, {"objectID":"5a00f643d737c3d5f664f4d5343612485e7cbee65a7b25ba3385f5b2dc46c2f3","title":"Issue: Provider Not Using Tools","url":"/docs/advanced/factory-patterns#issue-provider-not-using-tools","content":"Solution: Check if your model supports function calling. Some models (especially older or smaller ones) may not support tools.","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"Issue: Provider Not Using Tools","lvl3":""}}, {"objectID":"5468d676003346b5b73b22385b4ba748b49c6104f7ef79e9d741ff91992fc60a","title":"Issue: HuggingFace Describing Tools Instead of Using Them","url":"/docs/advanced/factory-patterns#issue-huggingface-describing-tools-instead-of-using-them","content":"Solution: This is a model limitation. Use models that support function calling:\nmistralai/Mixtral-8x7B-Instruct-v0.1\nmistralai/Mistral-7B-Instruct-v0.2","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"Issue: HuggingFace Describing Tools Instead of Using Them","lvl3":""}}, {"objectID":"09138271807d52bd82973c6161251f23d0d534118286a356017d87d7f64b1c6b","title":"Issue: Ollama Returns Empty Content","url":"/docs/advanced/factory-patterns#issue-ollama-returns-empty-content","content":"Solution: Use models that support tool calling:\n\n`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"Issue: Ollama Returns Empty Content","lvl3":""}}, {"objectID":"9d03a301514e0254f20460908cc09fc551cfb025c48f2c35dc7b89ccadaae41c","title":"or","url":"/docs/advanced/factory-patterns#or","content":"`","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"or","lvl3":""}}, {"objectID":"a0353a0fc83eacfed2d573c32428534bf4824f08a75092ce1bd4a2fcae7cc7b9","title":"Issue: Vertex AI Not Using Tools","url":"/docs/advanced/factory-patterns#issue-vertex-ai-not-using-tools","content":"Solution: This may require schema formatting adjustments. The Vertex provider needs to format tools according to Google's Gemini API schema.","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"Issue: Vertex AI Not Using Tools","lvl3":""}}, {"objectID":"e31d552a5091a16fb62971fce68e39b71b394242112ababf43ecfedb532c2e7b","title":"Future Improvements","url":"/docs/advanced/factory-patterns#future-improvements","content":"Dynamic Tool Loading: Ability to add custom tools at runtime\nProvider-Specific Tool Formatting: Automatic adaptation of tool schemas for each provider\nTool Usage Analytics: Detailed metrics on which tools are used most\nTool Caching: Cache tool results for better performance","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"Future Improvements","lvl3":""}}, {"objectID":"c2e18fdce5df99dd5cf19bcb101833173bf787702e79ac85944fbaf9bfbd47ce","title":"Support","url":"/docs/advanced/factory-patterns#support","content":"If you encounter any issues with the migration:\nCheck the provider status documentation\nReview the provider configuration guide\nOpen an issue on GitHub with details about your use case\n\nRemember: No breaking changes! Your existing code continues to work. The factory pattern refactoring improves the internal architecture while maintaining full backward compatibility.","hierarchy":{"lvl0":"Advanced","lvl1":"Factory Pattern Migration Guide","lvl2":"Support","lvl3":""}}, {"objectID":"e250587b187d44b702c368a15efec9e8de9d13bea796da74ef451c1bcb45bf7f","title":"Advanced Features","url":"/docs/advanced","content":"Advanced Features\n\nExplore NeuroLink's enterprise-grade capabilities that set it apart from basic AI integration libraries.\n\n🎯 What Makes NeuroLink Advanced\n\nNeuroLink goes beyond simple API wrappers to provide a comprehensive AI development platform with:\nProduction-ready architecture with factory patterns\nBuilt-in tool ecosystem via Model Context Protocol (MCP)\nReal-time analytics and performance monitoring\nDynamic model management with cost optimization\nEnterprise streaming with multi-modal support\n\n🚀 Feature Overview\nMCP Integration — Model Context Protocol support: 6 built-in tools, plus connect any MCP-compliant external server.\nAnalytics & Evaluation — Built-in usage tracking, cost monitoring, performance metrics, and AI response quality evaluation.\nFactory Patterns — Unified provider architecture using the Factory Pattern for consistent interfaces and easy extensibility.\nDynamic Models — Self-updating model configurations, automatic cost optimization, and smart model resolution.\nStreaming — Real-time streaming architecture with analytics support and multi-modal readiness.\nMiddleware Architecture — Comprehensive middleware system for request/response processing, logging, and custom transformations.\nBuilt-in Middleware — Pre-built middleware for analytics, guardrails, and auto-evaluation.\n\n🛡️ Middleware System\n\nNeuroLink includes a powerful middleware architecture for extending functionality:\nMiddleware Architecture - Complete middleware lifecycle and factory patterns\nBuilt-in Middleware - Analytics, Guardrails, Auto-Evaluation middleware reference\nCustom Middleware Guide - Build your own middleware with examples\n\n🏭 Architecture Highlights\n\nFactory Pattern Implementation\n\nBuilt-in Tool System\n\nReal-time Analytics\n\n🔧 Enterprise Capabilities\n\nPerformance Optimization\n68% faster provider status checks (16s → 5s via parallel execution)\nAutomatic memory management for operations >50MB\nCircuit breakers and retry logic for resilience\nRate limiting to prevent API quota exhaustion\n\nEdge Case Handling\nInput validation with helpful error messages\nTimeout warnings for long-running operations\nNetwork resilience with automatic retries\nGraceful degradation when providers fail\n\nProduction Features\nComprehensive error handling with detailed logging\nType safety with full TypeScript support\nConfigurable timeouts and resource limits\nEnvironment-aware configuration loading\n\n🌟 Use Case Examples\n\n🔮 Future Roadmap\nReal-time WebSocket Infrastructure (in development)\nAdvanced caching strategies\n\n🔗 Deep Dive Resources\n\nEach advanced feature has comprehensive documentation with examples, best practices, and troubleshooting guides:\nFactory Pattern Migration Guide - Upgrade from older architectures\nMCP Testing Guide - Test tool integrations\nPerformance Tuning - Optimize for your use case\nProduction Deployment - Enterprise deployment patterns","hierarchy":{"lvl0":"Advanced","lvl1":"Advanced Features","lvl2":"","lvl3":""}}, {"objectID":"8a91f02a221342a27b475ce2aaba29aa18c0f934db93a8f96a7f27a4e4b6c50a","title":"Advanced Features","url":"/docs/advanced#advanced-features","content":"Explore NeuroLink's enterprise-grade capabilities that set it apart from basic AI integration libraries.","hierarchy":{"lvl0":"Advanced","lvl1":"Advanced Features","lvl2":"Advanced Features","lvl3":""}}, {"objectID":"4c8fa79318ca95646639528c432197c8664e04dc032a391e7095a0a906c41a3f","title":"🎯 What Makes NeuroLink Advanced","url":"/docs/advanced#-what-makes-neurolink-advanced","content":"NeuroLink goes beyond simple API wrappers to provide a comprehensive AI development platform with:\nProduction-ready architecture with factory patterns\nBuilt-in tool ecosystem via Model Context Protocol (MCP)\nReal-time analytics and performance monitoring\nDynamic model management with cost optimization\nEnterprise streaming with multi-modal support","hierarchy":{"lvl0":"Advanced","lvl1":"Advanced Features","lvl2":"🎯 What Makes NeuroLink Advanced","lvl3":""}}, {"objectID":"b967774c2132704356654ca9327de10af3123497f2beb9a468bf079a1732f5d2","title":"🚀 Feature Overview","url":"/docs/advanced#-feature-overview","content":"MCP Integration — Model Context Protocol support: 6 built-in tools, plus connect any MCP-compliant external server.\nAnalytics & Evaluation — Built-in usage tracking, cost monitoring, performance metrics, and AI response quality evaluation.\nFactory Patterns — Unified provider architecture using the Factory Pattern for consistent interfaces and easy extensibility.\nDynamic Models — Self-updating model configurations, automatic cost optimization, and smart model resolution.\nStreaming — Real-time streaming architecture with analytics support and multi-modal readiness.\nMiddleware Architecture — Comprehensive middleware system for request/response processing, logging, and custom transformations.\nBuilt-in Middleware — Pre-built middleware for analytics, guardrails, and auto-evaluation.","hierarchy":{"lvl0":"Advanced","lvl1":"Advanced Features","lvl2":"🚀 Feature Overview","lvl3":""}}, {"objectID":"7f2fd7418e7c3092d5f15705a3bd9be38f14125cd51ec91a6955146235ec8a13","title":"🛡️ Middleware System","url":"/docs/advanced#-middleware-system","content":"NeuroLink includes a powerful middleware architecture for extending functionality:\nMiddleware Architecture - Complete middleware lifecycle and factory patterns\nBuilt-in Middleware - Analytics, Guardrails, Auto-Evaluation middleware reference\nCustom Middleware Guide - Build your own middleware with examples","hierarchy":{"lvl0":"Advanced","lvl1":"Advanced Features","lvl2":"🛡️ Middleware System","lvl3":""}}, {"objectID":"36ad5a6b378e087fac78e3c8e979c26479a44677f36b4b98a039e25f156aaedd","title":"Performance Optimization","url":"/docs/advanced#performance-optimization","content":"68% faster provider status checks (16s → 5s via parallel execution)\nAutomatic memory management for operations >50MB\nCircuit breakers and retry logic for resilience\nRate limiting to prevent API quota exhaustion","hierarchy":{"lvl0":"Advanced","lvl1":"Advanced Features","lvl2":"Performance Optimization","lvl3":""}}, {"objectID":"00bc135828ae3322d4803cb85793af8b7b6ae11cbc5e010be2d6ffb10567576d","title":"Edge Case Handling","url":"/docs/advanced#edge-case-handling","content":"Input validation with helpful error messages\nTimeout warnings for long-running operations\nNetwork resilience with automatic retries\nGraceful degradation when providers fail","hierarchy":{"lvl0":"Advanced","lvl1":"Advanced Features","lvl2":"Edge Case Handling","lvl3":""}}, {"objectID":"c362da8f601fcff6d7c331084c5a33f38e61b84240c8b6d43787bb0523b9e8a6","title":"Production Features","url":"/docs/advanced#production-features","content":"Comprehensive error handling with detailed logging\nType safety with full TypeScript support\nConfigurable timeouts and resource limits\nEnvironment-aware configuration loading","hierarchy":{"lvl0":"Advanced","lvl1":"Advanced Features","lvl2":"Production Features","lvl3":""}}, {"objectID":"c78f7cd9211f40411dbea6872e3cdae150f84af5e000ce675695c1bb065a8b18","title":"🔮 Future Roadmap","url":"/docs/advanced#-future-roadmap","content":"Real-time WebSocket Infrastructure (in development)\nAdvanced caching strategies","hierarchy":{"lvl0":"Advanced","lvl1":"Advanced Features","lvl2":"🔮 Future Roadmap","lvl3":""}}, {"objectID":"324f4f4512f8e78de7950b82f99ce94587f18d8d9ccf818bdefa7ed2f7f876ee","title":"🔗 Deep Dive Resources","url":"/docs/advanced#-deep-dive-resources","content":"Each advanced feature has comprehensive documentation with examples, best practices, and troubleshooting guides:\nFactory Pattern Migration Guide - Upgrade from older architectures\nMCP Testing Guide - Test tool integrations\nPerformance Tuning - Optimize for your use case\nProduction Deployment - Enterprise deployment patterns","hierarchy":{"lvl0":"Advanced","lvl1":"Advanced Features","lvl2":"🔗 Deep Dive Resources","lvl3":""}}, {"objectID":"c8a718d15402c7dd9f510f4da761e273125b9e479996bb0af4221df37e0718fa","title":"Memory Integration with Hippocampus","url":"/docs/advanced/memory-integration","content":"Memory Integration with Hippocampus\n\nEnhance your AI applications with persistent, context-aware memory using NeuroLink's integrated @juspay/hippocampus support. This feature enables your AI to remember user preferences, context, and conversation history across sessions while maintaining complete user isolation.\n\nOverview\n\nNeuroLink's Hippocampus integration provides:\nCross-Session Memory: AI remembers context across different conversations and sessions\nUser Isolation: Complete separation of memory contexts between different users\nLLM-Powered Condensation: Memory is automatically summarized to stay within a configurable word limit\nMultiple Storage Backends: Support for S3, Redis, and SQLite\nNon-blocking Storage: Memory operations happen in the background without slowing down responses\nCrash-safe: Every SDK method is wrapped in try-catch — errors are logged, never thrown\n\nArchitecture\n\nThe memory system operates in three phases:\nMemory Retrieval: The user's condensed memory is fetched before generating a response\nContext Enhancement: Retrieved memory is prepended to the user's prompt\nMemory Storage: The new conversation turn is condensed and stored asynchronously\n\nInstallation\n\n@juspay/hippocampus is shipped as an optional peer dependency of NeuroLink. Memory features are off by default; install the package explicitly when you want them:\n\nIf a memory configuration is supplied without the package installed, NeuroLink logs a warning and proceeds with memory disabled — no exception is thrown and the rest of the SDK continues to work. This packaging change exists to keep NeuroLink's production dependency graph free of the deprecated @ai-sdk/google and @ai-sdk/google-vertex packages, which Hippocampus's own @juspay/neurolink peer was previously dragging in.\n\nQuick Start\n\nConfiguration\n\nStorage Backends\n\nS3 (Recommended for production)\n\nEach user's memory is stored as a single S3 object at {prefix}{userId}.\n\nRedis\n\nSQLite (Development)\n\nNote: SQLite requires the better-sqlite3 optional peer dependency: pnpm add better-sqlite3\n\nCondensation LLM\n\nThe neurolink field configures which AI provider and model is used to condense memory. You can use any provider registered with your NeuroLink instance:\n\nAdvanced Usage\n\nUser Isolation in Multi-Tenant Applications\n\nStreaming with Memory\n\nCustom Condensation Prompt\n\nControl exactly how memory is condensed by providing a custom prompt:\n\n| Placeholder | Replaced With |\n| ----------------- | -------------------------------------------------------- |\n| {{OLD_MEMORY}} | The user's existing condensed memory (may be empty) |\n| {{NEW_CONTENT}} | The new conversation turn: \"User: ...\\nAssistant: ...\" |\n| {{MAX_WORDS}} | The configured maxWords value |\n\nMemory Lifecycle\n\nWhen Memory Activates\n\nFor memory to activate on a call, all three conditions must be met:\nmemory.enabled is true in the config\noptions.context.userId is provided in the generate/stream call\nThe response has non-empty content (for storage)\n\nRetrieval Flow\nmemory.get(userId) fetches the condensed memory string\nIf memory exists, it is prepended to the prompt:\nThe LLM generates a response using the enhanced prompt\n\nStorage Flow\n\nAfter the LLM response completes:\nsetImmediate() schedules background storage (non-blocking)\nA conversation turn is formed: \"User: ...\\nAssistant: ...\"\nmemory.add(userId, content) sends the old memory + new turn to the condensation LLM\nThe condensed summary is written to the storage backend\n\nNamespace and Tenant Isolation\n\nFor multi-tenant apps, use tenant-scoped collection names or key prefixes:\n\nEnvironment Variables\n\n| Variable | Default | Description |\n| ------------------------ | -------- | ---------------------------------------------- |\n| HC_LOG_LEVEL | warn | Log level: debug, info, warn, error |\n| HC_CONDENSATION_PROMPT | built-in | Default prompt (overridden by config prompt) |\n\nError Handling\n\nMemory is designed to never crash the host application:\nEvery public method is wrapped in try-catch\nget() returns null on error — the call continues without memory context\nadd() silently fails on error — the generate/stream result is not affected\nStorage initialization errors disable memory for that instance\n\nType Reference\n\nProduction Checklist\n[ ] Use S3 or Redis storage (not SQLite) in production\n[ ] Set HC_LOG_LEVEL=warn or higher in production\n[ ] Ensure userId is stable and unique per user across sessions\n[ ] For multi-tenant: use tenant-scoped prefixes or collection names\n[ ] Monitor Memory retrieval failed and Memory storage failed warnings in logs\n[ ] Verify the condensation LLM provider is configured and has sufficient quota\n\nSee Also\nMemory Guide - Quick start and configuration reference\nConversation Memory - Session-based conversation history\nContext Compaction - Automatic context window management","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"","lvl3":""}}, {"objectID":"5a3b522396ea0882ab43472523859ca1e584e750e2f57a3f9ca5d80bd03fd534","title":"Memory Integration with Hippocampus","url":"/docs/advanced/memory-integration#memory-integration-with-hippocampus","content":"Enhance your AI applications with persistent, context-aware memory using NeuroLink's integrated @juspay/hippocampus support. This feature enables your AI to remember user preferences, context, and conversation history across sessions while maintaining complete user isolation.","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Memory Integration with Hippocampus","lvl3":""}}, {"objectID":"14c35ababf410f877019fa57fb596045195e124ec75899e31cfa1776ba0987b5","title":"Overview","url":"/docs/advanced/memory-integration#overview","content":"NeuroLink's Hippocampus integration provides:\nCross-Session Memory: AI remembers context across different conversations and sessions\nUser Isolation: Complete separation of memory contexts between different users\nLLM-Powered Condensation: Memory is automatically summarized to stay within a configurable word limit\nMultiple Storage Backends: Support for S3, Redis, and SQLite\nNon-blocking Storage: Memory operations happen in the background without slowing down responses\nCrash-safe: Every SDK method is wrapped in try-catch — errors are logged, never thrown","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Overview","lvl3":""}}, {"objectID":"db22b48fe3b79b84bb03bb59530a9758ace311f6af979528173d5a7fe73b7ccd","title":"Architecture","url":"/docs/advanced/memory-integration#architecture","content":"The memory system operates in three phases:\nMemory Retrieval: The user's condensed memory is fetched before generating a response\nContext Enhancement: Retrieved memory is prepended to the user's prompt\nMemory Storage: The new conversation turn is condensed and stored asynchronously","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Architecture","lvl3":""}}, {"objectID":"798aa6521bd88947c09ba97992108508b6ab094cbeb0e2b5845558432770c2c9","title":"Installation","url":"/docs/advanced/memory-integration#installation","content":"@juspay/hippocampus is shipped as an optional peer dependency of NeuroLink. Memory features are off by default; install the package explicitly when you want them:\n\n`bash\npnpm add @juspay/hippocampus","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Installation","lvl3":""}}, {"objectID":"5280524e2ed35837a70b93eac11dfdc5a96c7139ded884dea49e5586b16b0249","title":"or: npm install @juspay/hippocampus","url":"/docs/advanced/memory-integration#or-npm-install-juspayhippocampus","content":"If a memory configuration is supplied without the package installed, NeuroLink logs a warning and proceeds with memory disabled — no exception is thrown and the rest of the SDK continues to work. This packaging change exists to keep NeuroLink's production dependency graph free of the deprecated @ai-sdk/google and @ai-sdk/google-vertex packages, which Hippocampus's own @juspay/neurolink` peer was previously dragging in.","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"or: npm install @juspay/hippocampus","lvl3":""}}, {"objectID":"2866747d31ca23eb2d157ec567248b61cece58ce33f92c86cb41f934b851147a","title":"S3 (Recommended for production)","url":"/docs/advanced/memory-integration#s3-recommended-for-production","content":"Each user's memory is stored as a single S3 object at {prefix}{userId}.","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"S3 (Recommended for production)","lvl3":""}}, {"objectID":"6d3091ad8494d75e9c08cbd684d28ce299f095372193ef60d5b4493c1d724324","title":"SQLite (Development)","url":"/docs/advanced/memory-integration#sqlite-development","content":"Note: SQLite requires the better-sqlite3 optional peer dependency: pnpm add better-sqlite3","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"SQLite (Development)","lvl3":""}}, {"objectID":"14bdd854a85e0bff81736e1f6c8dace1d14b815bba74d915f9048de22f0550f0","title":"Condensation LLM","url":"/docs/advanced/memory-integration#condensation-llm","content":"The neurolink field configures which AI provider and model is used to condense memory. You can use any provider registered with your NeuroLink instance:","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Condensation LLM","lvl3":""}}, {"objectID":"0309f2b9d58752f41cf8593b526d1ee273fd3c701ff49c42dde6e809306404d3","title":"Custom Condensation Prompt","url":"/docs/advanced/memory-integration#custom-condensation-prompt","content":"Control exactly how memory is condensed by providing a custom prompt:\n\n| Placeholder | Replaced With |\n| ----------------- | -------------------------------------------------------- |\n| {{OLD_MEMORY}} | The user's existing condensed memory (may be empty) |\n| {{NEW_CONTENT}} | The new conversation turn: \"User: ...\\nAssistant: ...\" |\n| {{MAX_WORDS}} | The configured maxWords value |","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Custom Condensation Prompt","lvl3":""}}, {"objectID":"6c8dd2006dff845152900ea6a7da5290a25810c18a3653dbfb918bfb4363cba7","title":"When Memory Activates","url":"/docs/advanced/memory-integration#when-memory-activates","content":"For memory to activate on a call, all three conditions must be met:\nmemory.enabled is true in the config\noptions.context.userId is provided in the generate/stream call\nThe response has non-empty content (for storage)","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"When Memory Activates","lvl3":""}}, {"objectID":"11e629f433e8674b2b04660b950731f5d9408ad2c52eba0da6da7aa65168eed7","title":"Retrieval Flow","url":"/docs/advanced/memory-integration#retrieval-flow","content":"memory.get(userId) fetches the condensed memory string\nIf memory exists, it is prepended to the prompt:\nThe LLM generates a response using the enhanced prompt","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Retrieval Flow","lvl3":""}}, {"objectID":"33d83723aa978a43b78252f05d8843a738a37e0bba0c6ba94ea89177b243f4e9","title":"Storage Flow","url":"/docs/advanced/memory-integration#storage-flow","content":"After the LLM response completes:\nsetImmediate() schedules background storage (non-blocking)\nA conversation turn is formed: \"User: ...\\nAssistant: ...\"\nmemory.add(userId, content) sends the old memory + new turn to the condensation LLM\nThe condensed summary is written to the storage backend","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Storage Flow","lvl3":""}}, {"objectID":"063765a5ea3e28ac95703c0721215efc8672adfa78591f3abb3ffe2316ac37c1","title":"Namespace and Tenant Isolation","url":"/docs/advanced/memory-integration#namespace-and-tenant-isolation","content":"For multi-tenant apps, use tenant-scoped collection names or key prefixes:","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Namespace and Tenant Isolation","lvl3":""}}, {"objectID":"42511a552578f3244fd728ebffd2948f4253fcefc04d3123150de3e13de11a5c","title":"Environment Variables","url":"/docs/advanced/memory-integration#environment-variables","content":"| Variable | Default | Description |\n| ------------------------ | -------- | ---------------------------------------------- |\n| HC_LOG_LEVEL | warn | Log level: debug, info, warn, error |\n| HC_CONDENSATION_PROMPT | built-in | Default prompt (overridden by config prompt) |","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Environment Variables","lvl3":""}}, {"objectID":"83274029c906dfe570738346d3b0ee6d77dd8f4d2eb9dba30a0b6a8c25e1fb03","title":"Error Handling","url":"/docs/advanced/memory-integration#error-handling","content":"Memory is designed to never crash the host application:\nEvery public method is wrapped in try-catch\nget() returns null on error — the call continues without memory context\nadd() silently fails on error — the generate/stream result is not affected\nStorage initialization errors disable memory for that instance","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Error Handling","lvl3":""}}, {"objectID":"3652380f1c43e08d5d18dfa0862f10a3a51f30f48679a7635ce026df2e7bc51a","title":"Production Checklist","url":"/docs/advanced/memory-integration#production-checklist","content":"[ ] Use S3 or Redis storage (not SQLite) in production\n[ ] Set HC_LOG_LEVEL=warn or higher in production\n[ ] Ensure userId is stable and unique per user across sessions\n[ ] For multi-tenant: use tenant-scoped prefixes or collection names\n[ ] Monitor Memory retrieval failed and Memory storage failed warnings in logs\n[ ] Verify the condensation LLM provider is configured and has sufficient quota","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"Production Checklist","lvl3":""}}, {"objectID":"417bcc8fbbc98c4053d46139d169f1e6f165888a5dceb6e2327b15c8bb8473e4","title":"See Also","url":"/docs/advanced/memory-integration#see-also","content":"Memory Guide - Quick start and configuration reference\nConversation Memory - Session-based conversation history\nContext Compaction - Automatic context window management","hierarchy":{"lvl0":"Advanced","lvl1":"Memory Integration with Hippocampus","lvl2":"See Also","lvl3":""}}, {"objectID":"16f1c17ef8c078b33ae6aa26f8107300cca478f6d88a74dab2450a7762f3a54b","title":"Middleware System Architecture","url":"/docs/advanced/middleware-architecture","content":"Middleware System Architecture\n\nOverview\n\nNeuroLink's middleware system provides a powerful and flexible way to intercept, modify, and enhance AI requests and responses. Middleware enables you to implement cross-cutting concerns like authentication, logging, analytics, content filtering, and auto-evaluation without modifying your core application logic.\n\nWhy Middleware Matters:\nRequest Interception: Modify requests before they reach the AI provider\nResponse Processing: Transform, filter, or validate AI responses\nCross-Cutting Concerns: Implement authentication, logging, rate limiting, and caching in a centralized way\nComposability: Chain multiple middleware components together\nSeparation of Concerns: Keep business logic separate from infrastructure concerns\n\nKey Benefits:\nProduction-ready middleware for common use cases (analytics, guardrails, auto-evaluation)\nFactory pattern for easy middleware management\nPriority-based execution ordering\nProvider-specific conditional execution\nBuilt on NeuroLink's own wrapLanguageModel (src/lib/middleware/wrapLanguageModel.ts), which keeps the familiar wrap-a-model shape without depending on a third-party SDK\n\nArchitecture Diagram\n\nRequest Lifecycle\n\nThe middleware system processes requests through four distinct phases:\n\nPhase 1: Pre-Request (transformParams)\n\nMiddleware in this phase runs before the AI provider call, allowing you to:\nValidate input: Check request parameters for validity\nAuthenticate/Authorize: Verify user permissions\nTransform requests: Modify or enrich request parameters\nApply guardrails: Block requests with unsafe content using precall evaluation\nRate limiting: Enforce request quotas\n\nExample Use Cases:\nPrecall guardrails evaluation (blocking unsafe prompts)\nRequest parameter validation\nAdding authentication context\nModifying prompts based on user preferences\n\nPhase 2: Provider Execution\n\nThe actual AI provider call happens between middleware phases:\nRequest sent to configured provider (OpenAI, Anthropic, Vertex, etc.)\nProvider processes the request\nResponse received from provider\n\nThis phase is not middleware - it's the core AI operation that middleware wraps around.\n\nPhase 3: Post-Response (wrapGenerate/wrapStream)\n\nMiddleware in this phase runs after the AI provider responds, allowing you to:\nCollect analytics: Track token usage, response times, costs\nFilter content: Apply guardrails to block/redact unsafe responses\nEvaluate quality: Auto-evaluate response quality and trigger retries\nTransform responses: Modify or enrich the response\nCache results: Store responses for future use\n\nExample Use Cases:\nAnalytics and metrics collection\nContent filtering and safety checks\nResponse quality evaluation\nResponse caching\nLogging and auditing\n\nPhase 4: Error Handling\n\nIf an error occurs at any stage, error handling middleware can:\nLog errors: Record error details for debugging\nTransform errors: Convert provider errors to user-friendly messages\nImplement fallbacks: Retry with different providers\nAlert monitoring: Send alerts to monitoring systems\n\nExample Use Cases:\nError logging and tracking\nProvider fallback on failure\nRetry logic with exponential backoff\nUser-friendly error messages\n\nMiddleware Chain\n\nExecution Order\n\nMiddleware executes in priority order, where higher priority values run first:\n\nImportant Notes:\ntransformParams runs before wrapGenerate/wrapStream\nWithin the same priority, registration order determines execution\nMiddleware can be conditionally enabled based on provider, model, or custom logic\n\nChain Configuration\n\nConfigure which middleware to enable and their order:\n\nAvailable Presets\n\n| Preset | Middleware Enabled | Use Case |\n| ---------- | ---------------------- | ---------------------- |\n| default | Analytics only | Basic usage tracking |\n| all | Analytics + Guardrails | Production with safety |\n| security | Guardrails only | Security-focused |\n| Custom | Your choice | Define your own |\n\nFactory Pattern\n\nMiddlewareFactory Class\n\nThe MiddlewareFactory is the central component for managing middleware:\n\nCreating Middleware Instances\n\nBasic Usage:\n\nAdvanced Configuration:\n\nRegistry System\n\nRegistering Middleware\n\nThe MiddlewareRegistry manages all registered middleware:\n\nRegistration Example:\n\nDiscovering Middleware\n\nList all registered middleware:\n\nGet specific middleware:\n\nCheck if middleware is registered:\n\nMiddleware Metadata\n\nEvery middleware must provide metadata:\n\nExample:\n\nTypeScript Interfaces\n\nNeuroLinkMiddleware\n\nThe core middleware interface, which combines the model-middleware contract with NeuroLink metadata:\n\nLanguageModelMiddleware\n\nNeuroLink declares this contract itself — it no longer comes from the Vercel AI\nSDK, which is not a dependency. The shape follows the v3 model protocol:\n\nMiddlewareContext\n\nContext information passed to middleware:\n\nMiddlewareConfig\n\nConfiguration for individual middleware:\n\nMiddlewareFactoryOptions\n\nOptions for creating and ","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"","lvl3":""}}, {"objectID":"f59113cc43afb8aa74ec51f97fb491b40e5ac27d3bbdaecf1a226437ab0d13a4","title":"Overview","url":"/docs/advanced/middleware-architecture#overview","content":"NeuroLink's middleware system provides a powerful and flexible way to intercept, modify, and enhance AI requests and responses. Middleware enables you to implement cross-cutting concerns like authentication, logging, analytics, content filtering, and auto-evaluation without modifying your core application logic.\n\nWhy Middleware Matters:\nRequest Interception: Modify requests before they reach the AI provider\nResponse Processing: Transform, filter, or validate AI responses\nCross-Cutting Concerns: Implement authentication, logging, rate limiting, and caching in a centralized way\nComposability: Chain multiple middleware components together\nSeparation of Concerns: Keep business logic separate from infrastructure concerns\n\nKey Benefits:\nProduction-ready middleware for common use cases (analytics, guardrails, auto-evaluation)\nFactory pattern for easy middleware management\nPriority-based execution ordering\nProvider-specific conditional execution\nBuilt on NeuroLink's own wrapLanguageModel (src/lib/middleware/wrapLanguageModel.ts), which keeps the familiar wrap-a-model shape without depending on a third-party SDK","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Overview","lvl3":""}}, {"objectID":"60b4f50d9fe9e6299393c3a0d190cf5968e65731a535c66e5b17280f20b6bb4f","title":"Request Lifecycle","url":"/docs/advanced/middleware-architecture#request-lifecycle","content":"The middleware system processes requests through four distinct phases:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Request Lifecycle","lvl3":""}}, {"objectID":"d6648ed23071b3b4eaeeb161e0191b57f0aa1d3b718a1ea33c4472f4a822f91a","title":"Phase 1: Pre-Request (transformParams)","url":"/docs/advanced/middleware-architecture#phase-1-pre-request-transformparams","content":"Middleware in this phase runs before the AI provider call, allowing you to:\nValidate input: Check request parameters for validity\nAuthenticate/Authorize: Verify user permissions\nTransform requests: Modify or enrich request parameters\nApply guardrails: Block requests with unsafe content using precall evaluation\nRate limiting: Enforce request quotas\n\nExample Use Cases:\nPrecall guardrails evaluation (blocking unsafe prompts)\nRequest parameter validation\nAdding authentication context\nModifying prompts based on user preferences","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Phase 1: Pre-Request (transformParams)","lvl3":""}}, {"objectID":"a77c1a93477dec8da4b5222efc20e84d7bc8e8433e6c3f5989dc9265c1e82267","title":"Phase 2: Provider Execution","url":"/docs/advanced/middleware-architecture#phase-2-provider-execution","content":"The actual AI provider call happens between middleware phases:\nRequest sent to configured provider (OpenAI, Anthropic, Vertex, etc.)\nProvider processes the request\nResponse received from provider\n\nThis phase is not middleware - it's the core AI operation that middleware wraps around.","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Phase 2: Provider Execution","lvl3":""}}, {"objectID":"9102cd707518b942a0ad868617779dc90a863b85118cebe4f1e5ce2326224a55","title":"Phase 3: Post-Response (wrapGenerate/wrapStream)","url":"/docs/advanced/middleware-architecture#phase-3-post-response-wrapgeneratewrapstream","content":"Middleware in this phase runs after the AI provider responds, allowing you to:\nCollect analytics: Track token usage, response times, costs\nFilter content: Apply guardrails to block/redact unsafe responses\nEvaluate quality: Auto-evaluate response quality and trigger retries\nTransform responses: Modify or enrich the response\nCache results: Store responses for future use\n\nExample Use Cases:\nAnalytics and metrics collection\nContent filtering and safety checks\nResponse quality evaluation\nResponse caching\nLogging and auditing","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Phase 3: Post-Response (wrapGenerate/wrapStream)","lvl3":""}}, {"objectID":"20bf1eb219ac6b555aede3c4b1d49ae31ca8ce6f88ab65833ea58f0d563c68e0","title":"Phase 4: Error Handling","url":"/docs/advanced/middleware-architecture#phase-4-error-handling","content":"If an error occurs at any stage, error handling middleware can:\nLog errors: Record error details for debugging\nTransform errors: Convert provider errors to user-friendly messages\nImplement fallbacks: Retry with different providers\nAlert monitoring: Send alerts to monitoring systems\n\nExample Use Cases:\nError logging and tracking\nProvider fallback on failure\nRetry logic with exponential backoff\nUser-friendly error messages","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Phase 4: Error Handling","lvl3":""}}, {"objectID":"c1aab1346883c17f6e9da3cf125b3b05a5260dc96ec275774ec50e5e4e0f8671","title":"Execution Order","url":"/docs/advanced/middleware-architecture#execution-order","content":"Middleware executes in priority order, where higher priority values run first:\n\nImportant Notes:\ntransformParams runs before wrapGenerate/wrapStream\nWithin the same priority, registration order determines execution\nMiddleware can be conditionally enabled based on provider, model, or custom logic","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Execution Order","lvl3":""}}, {"objectID":"a8f35bb4df1cb02433a72127e19d70d13e65c37f39e82efcf3bcf6e8f87e527e","title":"Chain Configuration","url":"/docs/advanced/middleware-architecture#chain-configuration","content":"Configure which middleware to enable and their order:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Chain Configuration","lvl3":""}}, {"objectID":"4e2d305d305f23f07317c55ef429af7e4f49de1c6b555bf06943a1b82125ecfb","title":"Available Presets","url":"/docs/advanced/middleware-architecture#available-presets","content":"| Preset | Middleware Enabled | Use Case |\n| ---------- | ---------------------- | ---------------------- |\n| default | Analytics only | Basic usage tracking |\n| all | Analytics + Guardrails | Production with safety |\n| security | Guardrails only | Security-focused |\n| Custom | Your choice | Define your own |","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Available Presets","lvl3":""}}, {"objectID":"2ca1866088d786b0302554707dd19943bda8284588e3608359014cd37df2535b","title":"MiddlewareFactory Class","url":"/docs/advanced/middleware-architecture#middlewarefactory-class","content":"The MiddlewareFactory is the central component for managing middleware:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"MiddlewareFactory Class","lvl3":""}}, {"objectID":"1a9035a2cc3954f641addd49e21328c92c7328d84ffeea0eba439766fac54125","title":"Creating Middleware Instances","url":"/docs/advanced/middleware-architecture#creating-middleware-instances","content":"Basic Usage:\n\nAdvanced Configuration:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Creating Middleware Instances","lvl3":""}}, {"objectID":"b03a3da8a1b53b12b3c7e81e18e58528c3b4b32a517187311051c1ce954947c7","title":"Registering Middleware","url":"/docs/advanced/middleware-architecture#registering-middleware","content":"The MiddlewareRegistry manages all registered middleware:\n\nRegistration Example:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Registering Middleware","lvl3":""}}, {"objectID":"7ef687152b620a1afa40fc3fd92225b6a6bd8c27309d29ecec2eedc3fa1e1b51","title":"Discovering Middleware","url":"/docs/advanced/middleware-architecture#discovering-middleware","content":"List all registered middleware:\n\nGet specific middleware:\n\nCheck if middleware is registered:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Discovering Middleware","lvl3":""}}, {"objectID":"9e729cd9c2a3c3206f5ffd2f8170d6363c5223d962d7629b2ba8101fdeb188ec","title":"Middleware Metadata","url":"/docs/advanced/middleware-architecture#middleware-metadata","content":"Every middleware must provide metadata:\n\nExample:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Middleware Metadata","lvl3":""}}, {"objectID":"2031ee6e99a6d8050429c33d7477f17835d98cb956e380e38745821c4760984f","title":"NeuroLinkMiddleware","url":"/docs/advanced/middleware-architecture#neurolinkmiddleware","content":"The core middleware interface, which combines the model-middleware contract with NeuroLink metadata:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"NeuroLinkMiddleware","lvl3":""}}, {"objectID":"5bfbe2268c30c0e58dc806f21db460f650409ee316c96a3fd3d578d028955604","title":"LanguageModelMiddleware","url":"/docs/advanced/middleware-architecture#languagemodelmiddleware","content":"NeuroLink declares this contract itself — it no longer comes from the Vercel AI\nSDK, which is not a dependency. The shape follows the v3 model protocol:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"LanguageModelMiddleware","lvl3":""}}, {"objectID":"70cee76331a44128ce2833632c0e4fd2d0abc8c3b9b6fcd79e5bfdfff374c93f","title":"MiddlewareContext","url":"/docs/advanced/middleware-architecture#middlewarecontext","content":"Context information passed to middleware:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"MiddlewareContext","lvl3":""}}, {"objectID":"6ac64e4dc94936a7ff600d6a5a7b508c8b76e066ae7be6d36bec67c663f802cf","title":"MiddlewareConfig","url":"/docs/advanced/middleware-architecture#middlewareconfig","content":"Configuration for individual middleware:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"MiddlewareConfig","lvl3":""}}, {"objectID":"015599adac2534f21eb274d42c989df0f913b3a6276c4f7a231e6fcd4caa3edb","title":"MiddlewareFactoryOptions","url":"/docs/advanced/middleware-architecture#middlewarefactoryoptions","content":"Options for creating and configuring the factory:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"MiddlewareFactoryOptions","lvl3":""}}, {"objectID":"e487dd11e2c401b760e4a74ce77a33ab3562e210a7c1fb182f8682433d9ae4d1","title":"MiddlewareChainStats","url":"/docs/advanced/middleware-architecture#middlewarechainstats","content":"Statistics about middleware execution:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"MiddlewareChainStats","lvl3":""}}, {"objectID":"f2d7087b4f00bbaa68ef3b4c5afda3eb3753ebb251442b44d08874044019940d","title":"Conditional Execution","url":"/docs/advanced/middleware-architecture#conditional-execution","content":"Middleware can be configured to run only under specific conditions:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Conditional Execution","lvl3":""}}, {"objectID":"79aa425c822a716eaea7fee2c9b1db447f5103e712fc9e8056ae3122be26ad58","title":"Execution Statistics","url":"/docs/advanced/middleware-architecture#execution-statistics","content":"Track middleware performance:\n\nOutput Example:","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"Execution Statistics","lvl3":""}}, {"objectID":"db793df35d62dfc8776781786d02f5bb65c94ffb2b4a8786654c1b843a0fa860","title":"4. Keep Middleware Focused","url":"/docs/advanced/middleware-architecture#4-keep-middleware-focused","content":"Each middleware should have a single responsibility:\n✅ Good: Analytics middleware only collects metrics\n❌ Bad: Analytics middleware that also filters content and logs errors","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"4. Keep Middleware Focused","lvl3":""}}, {"objectID":"184d6f048167d671c86e7d6c13f51d48a9c08fdd3bf00a9055ad25d112689b1a","title":"See Also","url":"/docs/advanced/middleware-architecture#see-also","content":"Built-in Middleware Reference - Documentation for analytics, guardrails, and auto-evaluation\nCustom Middleware Guide - Step-by-step guide to creating custom middleware\nHITL Integration - Integrating middleware with Human-in-the-Loop workflows\nProvider Comparison - Which providers support which middleware features","hierarchy":{"lvl0":"Advanced","lvl1":"Middleware System Architecture","lvl2":"See Also","lvl3":""}}, {"objectID":"46990c8c101a6aca0019aaee88261605df6ef7fba2b73fc2afd8b10f35bac5d4","title":"Streaming Responses","url":"/docs/advanced/streaming","content":"Streaming Responses\n\nReal-time streaming capabilities for interactive AI applications with built-in analytics, evaluation, and enterprise-grade features.\n\n🌊 Overview\n\nNeuroLink supports real-time streaming for immediate response feedback, perfect for chat interfaces, live content generation, and interactive applications. Streaming works with all supported providers and includes advanced enterprise features:\nMulti-Model Streaming: Intelligent load balancing across multiple SageMaker endpoints\nRate Limiting & Backpressure: Enterprise-grade request management\nAdvanced Caching: Semantic caching with partial response matching\nReal-time Analytics: Comprehensive monitoring and alerting\nSecurity & Validation: Prompt injection detection, content filtering, and compliance\nTool Calling: Streaming function calls with structured output parsing\nError Recovery: Automatic failover and retry mechanisms\nPerformance Optimization: Adaptive rate limiting and circuit breakers\n\n🚀 Basic Streaming\n\nSDK Streaming\n\nBasic Streaming (Ready to Use)\n\nStreaming with Built-in Tools\n\nSimple Configuration\n\nCLI Streaming\n\n🔧 Advanced Features\n\nError Handling with Retry\n\nTimeout Handling\n\nCollecting Full Response\n\nAutomatic Provider Selection\n\nManual Provider Selection (Optional)\n\nSimple Rate Limiting\n\nBatch Processing\n\nSimple Caching Pattern\n\nCustom Configuration\n\nJSON Streaming Support\n\nError Handling & Recovery\n\nSecurity & Validation\n\ntypescript\n\nconst neurolink = new NeuroLink();\n\n// NeuroLink provides built-in analytics tracking\nasync function streamWithAnalytics(prompt: string) {\n const startTime = Date.now();\n let chunkCount = 0;\n let tokenCount = 0;\n\n try {\n const result = await neurolink.stream({\n input: { text: prompt },\n enableAnalytics: true, // Enable built-in analytics\n context: {\n userId: \"user-123\",\n sessionId: \"session-456\",\n requestType: \"interactive\",\n },\n });\n\n console.log(\"📊 Streaming with analytics enabled...\");\n\n for await (const chunk of result.stream) {\n const content = chunk.content || \"\";\n chunkCount++;\n tokenCount += Math.ceil(content.length / 4); // Rough token estimation\n\n process.stdout.write(content);\n\n // Access built-in analytics if available\n if (chunk.analytics) {\n console.log(\\n📈 Real-time: ${chunk.analytics.tokensPerSecond} tokens/sec);\n }\n }\n\n const totalTime = Date.now() - startTime;\n\n // Display session analytics\n console.log(\\n\\n📊 Session Analytics:);\n console.log(Total Time: ${totalTime}ms);\n console.log(Chunks Processed: ${chunkCount});\n console.log(Estimated Tokens: ${tokenCount});\n console.log(Throughput: ${(tokenCount / (totalTime / 1000)).toFixed(2)} tokens/sec);\n console.log(Provider: ${result.provider || \"auto-selected\"});\n\n // Access result analytics if available\n if (result.analytics) {\n console.log(Cost Estimate: $${result.analytics.estimatedCost || \"N/A\"});\n console.log(Model Used: ${result.analytics.model || \"N/A\"});\n }\n\n return {\n totalTime,\n chunkCount,\n tokenCount,\n provider: result.provider,\n analytics: result.analytics,\n };\n\n } catch (error) {\n const totalTime = Date.now() - startTime;\n console.error(\\n❌ Analytics Error: ${error.message});\n console.log(Failed after: ${totalTime}ms, ${chunkCount} chunks);\n throw error;\n }\n}\n\n// Usage with analytics\nstreamWithAnalytics(\"Generate a comprehensive business analysis\")\n .then((analytics) => {\n console.log(\"\\n✅ Streaming completed with analytics:\", analytics);\n })\n .catch((error) => {\n console.error(\"Streaming failed:\", error.message);\n });\ntypescript\nconst stream = await neurolink.stream({\n input: { text: \"Generate business report\" },\n analytics: {\n enabled: true,\n realTime: true,\n context: {\n userId: \"user123\",\n sessionId: \"session456\",\n feature: \"report_generation\",\n },\n },\n});\n\nfor await (const chunk of stream.stream) {\n if (\"content\" in chunk) {\n console.log(chunk.content);\n }\n}\n\n// Access analytics after streaming completes\nconst analytics = stream.analytics;\nif (analytics) {\n console.log(Tokens used: ${analytics.tokensUsed});\n console.log(Cost: $${analytics.estimatedCost});\n}\nbash\nStreaming with analytics\nnpx @juspay/neurolink stream \"Create documentation\" \\\n --enable-analytics \\\n --context '{\"project\":\"docs\",\"team\":\"engineering\"}' \\\n --debug\n\nWith evaluation\nnpx @juspay/neurolink stream \"Write production code\" \\\n --enable-analytics \\\n --enable-evaluation \\\n --evaluation-domain \"Senior Developer\" \\\n --debug\ntypescript\n\nfunction ChatComponent() {\n const [messages, setMessages] = useState([]);\n const [currentResponse, setCurrentResponse] = useState(\"\");\n const neurolink = new NeuroLink();\n\n const sendMessage = async (userMessage) => {\n setMessages(prev => [...prev, { role: \"user\", content: userMessage }]);\n setCurrentResponse(\"\");\n\n const result = await neurolink.stream({\n in","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"","lvl3":""}}, {"objectID":"c03d2de492e9662d0d34c8b1e223b6990f95df062abb6299d032650f03cbe175","title":"Streaming Responses","url":"/docs/advanced/streaming#streaming-responses","content":"Real-time streaming capabilities for interactive AI applications with built-in analytics, evaluation, and enterprise-grade features.","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Streaming Responses","lvl3":""}}, {"objectID":"33351cd0c15177eef080b1695af88ea48729d03f344cab9a96852add74777d8e","title":"🌊 Overview","url":"/docs/advanced/streaming#-overview","content":"NeuroLink supports real-time streaming for immediate response feedback, perfect for chat interfaces, live content generation, and interactive applications. Streaming works with all supported providers and includes advanced enterprise features:\nMulti-Model Streaming: Intelligent load balancing across multiple SageMaker endpoints\nRate Limiting & Backpressure: Enterprise-grade request management\nAdvanced Caching: Semantic caching with partial response matching\nReal-time Analytics: Comprehensive monitoring and alerting\nSecurity & Validation: Prompt injection detection, content filtering, and compliance\nTool Calling: Streaming function calls with structured output parsing\nError Recovery: Automatic failover and retry mechanisms\nPerformance Optimization: Adaptive rate limiting and circuit breakers","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"🌊 Overview","lvl3":""}}, {"objectID":"99854b86caa05020dd16a181e904068e810724966227af167d4d28d476585d36","title":"CLI Streaming","url":"/docs/advanced/streaming#cli-streaming","content":"`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"CLI Streaming","lvl3":""}}, {"objectID":"cb22cd28075fa08b814369b951ce579e9927c87ae087779937c6c37f767f225e","title":"Basic streaming with automatic provider selection","url":"/docs/advanced/streaming#basic-streaming-with-automatic-provider-selection","content":"npx @juspay/neurolink stream \"Tell me a story\"","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Basic streaming with automatic provider selection","lvl3":""}}, {"objectID":"8aa76b3c6c603fb4e6344926189f91ce776814950acd95516bda30bdd8f57b1d","title":"With specific provider (optional)","url":"/docs/advanced/streaming#with-specific-provider-optional","content":"npx @juspay/neurolink stream \"Explain quantum computing\" --provider google-ai","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"With specific provider (optional)","lvl3":""}}, {"objectID":"fc80343498d78bce19b8a12d896413dd10229a63c666ac4cc8e2cbb9faf2dcc4","title":"With debug output to see provider selection","url":"/docs/advanced/streaming#with-debug-output-to-see-provider-selection","content":"npx @juspay/neurolink stream \"Write a poem\" --debug","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"With debug output to see provider selection","lvl3":""}}, {"objectID":"b6be5af69c223474b75fd98a25124eb11759cb880899a16d8207d6a5398562eb","title":"JSON format streaming (future-ready)","url":"/docs/advanced/streaming#json-format-streaming-future-ready","content":"npx @juspay/neurolink stream \"Create structured data\" --format json --provider google-ai","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"JSON format streaming (future-ready)","lvl3":""}}, {"objectID":"ec59b6ecc68e95d1b02f794e527ae010067bdd7f4b16b9a8ee433bb09fd0da6d","title":"Streaming with tools enabled","url":"/docs/advanced/streaming#streaming-with-tools-enabled","content":"npx @juspay/neurolink stream \"What's the weather in New York?\" --enable-tools","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Streaming with tools enabled","lvl3":""}}, {"objectID":"6a1dd759cdd4e0acb3bf232eb04d4a6141b976026809599edc0e2985701520d2","title":"Specify streaming parameters","url":"/docs/advanced/streaming#specify-streaming-parameters","content":"npx @juspay/neurolink stream \"Analyze market trends\" \\\n --max-tokens 500 \\\n --temperature 0.7 \\\n --stream\n`","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Specify streaming parameters","lvl3":""}}, {"objectID":"678e8e78e290b5462a2923ad437a60c3844173d5a335bc39afdf8c3f02b0f266","title":"Security & Validation","url":"/docs/advanced/streaming#security-validation","content":"typescript\n\nconst neurolink = new NeuroLink();\n\n// NeuroLink includes built-in security and validation features\nasync function secureStreaming(prompt: string, userId: string) {\n // Basic input validation\n if (!prompt || prompt.length > 50000) {\n throw new Error(\"Invalid prompt: too long or empty\");\n }\n\n // Basic user authentication check\n if (!userId || userId.length < 3) {\n throw new Error(\"Invalid user ID\");\n }\n\n try {\n const result = await neurolink.stream({\n input: { text: prompt },\n provider: \"auto\", // NeuroLink automatically selects secure providers\n context: {\n userId,\n sessionId: session-${Date.now()},\n securityLevel: \"standard\",\n },\n });\n\n const chunks: string[] = [];\n for await (const chunk of result.stream) {\n // Basic output filtering\n const content = chunk.content || \"\";\n\n // Filter out potential PII (basic example)\n const sanitizedContent = content\n .replace(/\\b\\d{3}-\\d{2}-\\d{4}\\b/g, \"[SSN-REDACTED]\")\n .replace(/\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b/g, \"[EMAIL-REDACTED]\");\n\n chunks.push(sanitizedContent);\n process.stdout.write(sanitizedContent);\n }\n\n console.log(\\nSecure streaming completed for user: ${userId});\n console.log(Total chunks processed: ${chunks.length});\n\n return chunks.join(\"\");\n\n } catch (error) {\n console.error(Secure streaming failed for user ${userId}:, error.message);\n throw error;\n }\n}\n\n// Usage with built-in security\ntry {\n await secureStreaming(\"Generate a privacy-compliant financial report\", \"user-123\");\n} catch (error) {\n console.error(\"Secure streaming error:\", error.message);\n}","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Security & Validation","lvl3":""}}, {"objectID":"a82a5751ee53638cf9e3756111171e335a11a9b1d12015558f9e0cf3fbdcc7e3","title":"Built-in Analytics Support","url":"/docs/advanced/streaming#built-in-analytics-support","content":"`","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Built-in Analytics Support","lvl3":""}}, {"objectID":"844bb19a587921189512a717687f5dff89e3131325280c9873cea535e23fd7d7","title":"CLI Streaming with Analytics","url":"/docs/advanced/streaming#cli-streaming-with-analytics","content":"`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"CLI Streaming with Analytics","lvl3":""}}, {"objectID":"4e2956e6df85df89309865060727d1b043e87574708c315d7b53fd4606ec9569","title":"Streaming with analytics","url":"/docs/advanced/streaming#streaming-with-analytics","content":"npx @juspay/neurolink stream \"Create documentation\" \\\n --enable-analytics \\\n --context '{\"project\":\"docs\",\"team\":\"engineering\"}' \\\n --debug","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Streaming with analytics","lvl3":""}}, {"objectID":"0d2a52b32a4275d48d02fc8d3008327620fb44f9a87d7baccd60140b51bbd24e","title":"With evaluation","url":"/docs/advanced/streaming#with-evaluation","content":"npx @juspay/neurolink stream \"Write production code\" \\\n --enable-analytics \\\n --enable-evaluation \\\n --evaluation-domain \"Senior Developer\" \\\n --debug\n`","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"With evaluation","lvl3":""}}, {"objectID":"4981ceccb38a4c47e995936f71d6f98f37c836498023b2228242d7b2e5b5bf68","title":"Interactive Documentation","url":"/docs/advanced/streaming#interactive-documentation","content":"`bash\n#!/bin/bash","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Interactive Documentation","lvl3":""}}, {"objectID":"aeb23776e26d5ffd0d4d42d22f576f265aa1e9fcd254d5e6f01c9af45a9df97b","title":"Interactive documentation generator","url":"/docs/advanced/streaming#interactive-documentation-generator","content":"echo \"📚 Interactive Documentation Generator\"\necho \"Enter topic (or 'quit' to exit):\"\n\nwhile read -r topic; do\n if [ \"$topic\" = \"quit\" ]; then\n break\n fi\n\n echo \"🔄 Generating documentation for: $topic\"\n npx @juspay/neurolink stream \"\n Create comprehensive technical documentation for: $topic\n\n Include:\nOverview and purpose\nInstallation/setup instructions\nUsage examples\nBest practices\nTroubleshooting\n \" --provider google-ai --enable-analytics\n\n echo -e \"\\n\\n📝 Documentation complete! Enter next topic:\"\ndone\n`","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Interactive documentation generator","lvl3":""}}, {"objectID":"eb429895ef0d253628f4149718e7222b631f09b9712425a52e055f6dc9a33a41","title":"Production Environment Variables","url":"/docs/advanced/streaming#production-environment-variables","content":"For production deployments, configure these environment variables:\n\n`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Production Environment Variables","lvl3":""}}, {"objectID":"d8e78dd4a6d9bd702b5842adbe7ce3efee191e55fb4517f6a1c8ae5ab5999a18","title":"Optional: Performance Settings","url":"/docs/advanced/streaming#optional-performance-settings","content":"`","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Optional: Performance Settings","lvl3":""}}, {"objectID":"5e8d7a89c1a5d3b15e6b3a106f6a39cf17dac3a4b8c946b5c1cdb392167b92b9","title":"Production Configuration File","url":"/docs/advanced/streaming#production-configuration-file","content":"Create neurolink.config.js in your project root:","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Production Configuration File","lvl3":""}}, {"objectID":"3122dbef923a24bc4ddc39c4ebbea6d5fc53ec35636e555d22aa63f5263fb72a","title":"CLI Monitoring Commands","url":"/docs/advanced/streaming#cli-monitoring-commands","content":"`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"CLI Monitoring Commands","lvl3":""}}, {"objectID":"a02d9e80997a11fb2219922cfde3c99f0ecc73ebc92a6250951b64cd28e78f43","title":"Real-time streaming monitor","url":"/docs/advanced/streaming#real-time-streaming-monitor","content":"npx @juspay/neurolink sagemaker stream-monitor \\\n --endpoint production-endpoint \\\n --duration 3600 \\\n --alerts \\\n --export prometheus \\\n --export cloudwatch","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Real-time streaming monitor","lvl3":""}}, {"objectID":"d1b1185cc60b9dcd59e59528f226d853447c8d8a4917f1f7fd4917f92f13e08d","title":"System health check","url":"/docs/advanced/streaming#system-health-check","content":"npx @juspay/neurolink sagemaker diagnose \\\n --endpoint production-endpoint \\\n --check-models \\\n --check-cache \\\n --check-security \\\n --check-rate-limits","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"System health check","lvl3":""}}, {"objectID":"f3759fae278db1615c5d7c5f8478f9ae4690c1f0f007dbf89480e7b048f4c937","title":"Performance benchmarking","url":"/docs/advanced/streaming#performance-benchmarking","content":"npx @juspay/neurolink sagemaker stream-benchmark \\\n --endpoint production-endpoint \\\n --concurrent 50 \\\n --requests 1000 \\\n --duration 300 \\\n --enable-analytics \\\n --enable-caching \\\n --model-selection performance_based","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Performance benchmarking","lvl3":""}}, {"objectID":"cfdd8efb425cfcfb8e205a1dd3739269dc722db35aca80b5ae34380a625c01ee","title":"Security audit","url":"/docs/advanced/streaming#security-audit","content":"npx @juspay/neurolink sagemaker security-audit \\\n --endpoint production-endpoint \\\n --hours 24 \\\n --export-report \\\n --include-recommendations","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Security audit","lvl3":""}}, {"objectID":"71f8fb982c6f43bfad42ea293d016bf83b005d59d9a71ed51ffb324a28482262","title":"Cache analysis","url":"/docs/advanced/streaming#cache-analysis","content":"npx @juspay/neurolink sagemaker cache-analyze \\\n --endpoint production-endpoint \\\n --strategy semantic \\\n --optimize \\\n --report\n`","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Cache analysis","lvl3":""}}, {"objectID":"79f1a06ffd0e2361da6f7b9f543d44e4e1db2a1afd7db2addbe8a946c6a5a234","title":"Stream Debugging","url":"/docs/advanced/streaming#stream-debugging","content":"`bash","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Stream Debugging","lvl3":""}}, {"objectID":"ea54881f84be78361c0774fbb3b39f8f56202656213106922bf3ca887af2c484","title":"Enable verbose streaming debug","url":"/docs/advanced/streaming#enable-verbose-streaming-debug","content":"npx @juspay/neurolink stream \"Debug this response\" \\\n --provider openai \\\n --debug \\\n --timeout 30000","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Enable verbose streaming debug","lvl3":""}}, {"objectID":"76633f490201d209978c28dc10c6f2d067abdbcdd3430cbc4d0b397a9397df5e","title":"Monitor stream performance","url":"/docs/advanced/streaming#monitor-stream-performance","content":"npx @juspay/neurolink stream \"Performance test\" \\\n --enable-analytics \\\n --debug \\\n --provider google-ai","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Monitor stream performance","lvl3":""}}, {"objectID":"69db4ce505981ad10925dd8190caa70bfd97286bd72250e28b89dd5cb5722ae1","title":"Debug streaming with the unified NeuroLink API","url":"/docs/advanced/streaming#debug-streaming-with-the-unified-neurolink-api","content":"npx @juspay/neurolink stream \"Complex analysis task\" \\\n --provider sagemaker \\\n --debug \\\n --max-tokens 500 \\\n --temperature 0.7\n`","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Debug streaming with the unified NeuroLink API","lvl3":""}}, {"objectID":"790cb310c92ec7d2023d00c5199aa7f745a82c3a33bd5493dafd1111f549edcc","title":"Enterprise Configuration Template","url":"/docs/advanced/streaming#enterprise-configuration-template","content":"`yaml","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"Enterprise Configuration Template","lvl3":""}}, {"objectID":"904a0dd535800e074d6a4fd5fdd36d8f014c5e683a6e2b70abda4f894920a14a","title":"neurolink-enterprise-streaming.yaml","url":"/docs/advanced/streaming#neurolink-enterprise-streamingyaml","content":"streaming:\n sagemaker:\n endpoints:\n production:\n name: \"production-multi-model\"\n models:\nid: \"llama-3-70b\"\n name: \"LLaMA 3 70B\"\n type: \"llama\"\n weight: 3\n specializations: [\"reasoning\", \"analysis\"]\n thresholds:\n max_latency: 5000\n maxerrorrate: 2\n min_throughput: 20\nid: \"claude-3-5-sonnet\"\n name: \"Claude 3.5 Sonnet\"\n type: \"anthropic\"\n weight: 4\n specializations: [\"functioncalling\", \"structuredoutput\"]\n thresholds:\n max_latency: 3000\n maxerrorrate: 1\n min_throughput: 25\n\n load_balancing:\n strategy: \"performance_based\"\n health_check:\n enabled: true\n interval: 30000\n timeout: 5000\n\n failover:\n enabled: true\n max_retries: 3\n strategies: [\"modelswitch\", \"endpointswitch\"]\n circuit_breaker:\n threshold: 5\n timeout: 60000\n\n rate_limiting:\n preset: \"enterprise\"\n requestspersecond: 100\n burst_capacity: 200\n adaptive: true\n targetresponsetime: 1000\n strategy: \"queue\"\n maxqueuesize: 1000\n priority_queue: true\n\n caching:\n preset: \"enterprise\"\n storage: \"hybrid\"\n maxsizemb: 5000\n ttl: 21600000 # 6 hours\n strategy: \"fuzzy\"\n compression:\n enabled: true\n algorithm: \"brotli\"\n partial_hits: true\n warming: \"scheduled\"\n\n security:\n preset: \"enterprise\"\n input_validation:\n enabled: true\n maxpromptlength: 100000\n injection_detection: true\n content_policy: true\n output_filtering:\n enabled: true\n pii_redaction: true\n toxicity_filtering: true\n compliance: true\n access_control:\n enabled: true\n authentication: true\n apikeyvalidation: true\n monitoring:","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"neurolink-enterprise-streaming.yaml","lvl3":""}}, {"objectID":"57a8574ce6a5d78b67ba3f51b728e603baa23697be52a70b33468a88aac46169","title":"📚 Related Documentation","url":"/docs/advanced/streaming#-related-documentation","content":"CLI Commands - Streaming CLI commands\nSDK Reference - Complete streaming API\nAnalytics - Streaming analytics features\nDynamic Models - Multi-model endpoint setup\nEnterprise Features - Enterprise security features\nPerformance Optimization - Optimization strategies\nAnalytics & Monitoring - Comprehensive monitoring\nProvider Setup - Provider configuration\nDevelopment Guide - Development and deployment guide","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"📚 Related Documentation","lvl3":""}}, {"objectID":"c5973e1b2dff383fb60d43c9fdf32b2df41acd4bc9e76881f6caefb598ef50a7","title":"🎆 What's Next","url":"/docs/advanced/streaming#-whats-next","content":"With Phase 2 complete, NeuroLink now offers enterprise-grade streaming capabilities:\n✅ Multi-Model Streaming: Intelligent load balancing and automatic failover\n✅ Enterprise Security: Comprehensive validation, filtering, and compliance\n✅ Advanced Caching: Semantic caching with partial response matching\n✅ Real-time Analytics: Complete monitoring and alerting system\n✅ Rate Limiting: Sophisticated backpressure handling and circuit breakers\n✅ Tool Integration: Streaming function calls with structured output\n\nUpcoming in Phase 3:\nMulti-Provider Streaming: Seamless streaming across different AI providers\nEdge Deployment: CDN-based streaming for global latency optimization\nAdvanced Tool Orchestration: Complex multi-step tool workflows\nCustom Model Integration: Support for proprietary and fine-tuned models","hierarchy":{"lvl0":"Advanced","lvl1":"Streaming Responses","lvl2":"🎆 What's Next","lvl3":""}}, {"objectID":"6954075dd03fea926b437d961f675293e248e12e8dfe28d0e3fdb9fde400b997","title":"Updated Provider Test Results","url":"/docs/advanced/updated-provider-test-results","content":"Updated Provider Test Results\n\nThis document contains the latest test results for all supported providers.\n\nTest Summary\n\nProvider Status\n✅ OpenAI: All tests passing\n✅ Amazon Bedrock: All tests passing\n✅ Google Vertex AI: All tests passing\n✅ Anthropic: All tests passing\n✅ LiteLLM: All tests passing\n\nPerformance Metrics\nAverage response time: 2.3s\nSuccess rate: 99.7%\nError rate: 0.3%\n\nTest Coverage\nUnit tests: 95%\nIntegration tests: 87%\nEnd-to-end tests: 92%\n\nDetailed Results\n\nOpenAI Provider\nText generation: ✅ Pass\nStreaming: ✅ Pass\nError handling: ✅ Pass\n\nAmazon Bedrock Provider\nText generation: ✅ Pass\nStreaming: ✅ Pass\nError handling: ✅ Pass\n\nGoogle Vertex AI Provider\nText generation: ✅ Pass\nStreaming: ✅ Pass\nError handling: ✅ Pass\n\nFor more details, see the Testing Guide.","hierarchy":{"lvl0":"Advanced","lvl1":"Updated Provider Test Results","lvl2":"","lvl3":""}}, {"objectID":"bc34a26427c9a19deb48ea9798e55ea4a1b5dc4c2d4a0d6215c4fde27020828b","title":"Updated Provider Test Results","url":"/docs/advanced/updated-provider-test-results#updated-provider-test-results","content":"This document contains the latest test results for all supported providers.","hierarchy":{"lvl0":"Advanced","lvl1":"Updated Provider Test Results","lvl2":"Updated Provider Test Results","lvl3":""}}, {"objectID":"eb827015fb1ff760e186e029a33110b5b5e3557372825dcb930df5f5662a5a66","title":"Provider Status","url":"/docs/advanced/updated-provider-test-results#provider-status","content":"✅ OpenAI: All tests passing\n✅ Amazon Bedrock: All tests passing\n✅ Google Vertex AI: All tests passing\n✅ Anthropic: All tests passing\n✅ LiteLLM: All tests passing","hierarchy":{"lvl0":"Advanced","lvl1":"Updated Provider Test Results","lvl2":"Provider Status","lvl3":""}}, {"objectID":"e68d1095c6ecee164faa886c8b991be0559c95b18c949be8bc8ac445160ec35d","title":"Performance Metrics","url":"/docs/advanced/updated-provider-test-results#performance-metrics","content":"Average response time: 2.3s\nSuccess rate: 99.7%\nError rate: 0.3%","hierarchy":{"lvl0":"Advanced","lvl1":"Updated Provider Test Results","lvl2":"Performance Metrics","lvl3":""}}, {"objectID":"47d1d7bdc36bcfe18c41c6673bd85bee3fd3bb4f538a21be36921a9fdc095abc","title":"Test Coverage","url":"/docs/advanced/updated-provider-test-results#test-coverage","content":"Unit tests: 95%\nIntegration tests: 87%\nEnd-to-end tests: 92%","hierarchy":{"lvl0":"Advanced","lvl1":"Updated Provider Test Results","lvl2":"Test Coverage","lvl3":""}}, {"objectID":"ccceabc5546c52a9d49d046d7dd07133945ee02cf29749438c1e07fe621442c4","title":"OpenAI Provider","url":"/docs/advanced/updated-provider-test-results#openai-provider","content":"Text generation: ✅ Pass\nStreaming: ✅ Pass\nError handling: ✅ Pass","hierarchy":{"lvl0":"Advanced","lvl1":"Updated Provider Test Results","lvl2":"OpenAI Provider","lvl3":""}}, {"objectID":"8573afda3dd6e8605272e38db3ed153881b5d90a1f582c88473bfa38853dba94","title":"Amazon Bedrock Provider","url":"/docs/advanced/updated-provider-test-results#amazon-bedrock-provider","content":"Text generation: ✅ Pass\nStreaming: ✅ Pass\nError handling: ✅ Pass","hierarchy":{"lvl0":"Advanced","lvl1":"Updated Provider Test Results","lvl2":"Amazon Bedrock Provider","lvl3":""}}, {"objectID":"da1f6bae0ca274bddf32c0b18647eea7d7f50de68805b1665c235750edb4153b","title":"Google Vertex AI Provider","url":"/docs/advanced/updated-provider-test-results#google-vertex-ai-provider","content":"Text generation: ✅ Pass\nStreaming: ✅ Pass\nError handling: ✅ Pass\n\nFor more details, see the Testing Guide.","hierarchy":{"lvl0":"Advanced","lvl1":"Updated Provider Test Results","lvl2":"Google Vertex AI Provider","lvl3":""}}, {"objectID":"e9e796bc5107a73803dd4663059f6e1341e9cb3daff90f497209e94f88a9e007","title":"Artifact Banking (`bankArtifact` / `readArtifact`)","url":"/docs/agents/ARTIFACT-BANKING","content":"Artifact Banking (bankArtifact / readArtifact)\n\nA long-running agent produces outputs that do not fit in a conversation: a\nworker's full report, a build log, a stage's structured result. The tempting\nanswer is to truncate one and send the head — but the discarded bytes are gone,\nand nothing records that they ever existed.\n\nBanking is the other answer. The payload is written to disk whole, and what\ngoes into the conversation is a bounded preview plus the exact call that reads\nthe rest. Context cost stays flat, evidence stays complete, and compaction can\nevict the preview without destroying anything.\n\nRead-back is the tool that already existed: retrieve_context. No new tool,\nno new name for the model to learn.\n\nQuick start\n\nThe model reads the same artifact with the tool it already has:\n\nretrieve_context is registered automatically the first time anything banks —\nyou do not have to configure mcp.outputLimits to get it.\n\nAPI\n\n| Member | Returns | Notes |\n| -------------------------------- | ------------------- | ------------------------------------------------------------ |\n| bankArtifact(payload, options) | BankedArtifactRef | Stores the payload whole; returns id + bounded preview |\n| readArtifact(id, page?) | string \\| null | Full payload when page is omitted; null if unknown |\n| getArtifactStore() | ArtifactStore | Creates the store on demand and registers retrieve_context |\n| setArtifactStore(store) | void | Swaps the backend for every path, MCP normalizer included |\n\nBankArtifactOptions:\n\n| Field | Required | Default | Notes |\n| -------------- | -------- | -------- | ------------------------------------------------------------- |\n| kind | yes | — | worker-report · command-output · stage-output · other |\n| label | yes | — | Short human label, e.g. delegate:auth-review |\n| sessionId | no | — | Recorded on the artifact metadata |\n| contentType | no | \"text\" | \"json\" stores with a .json extension |\n| previewChars | no | 1000 | Hard cap 4000 — a preview is a pointer |\n\nBankedArtifactRef: { artifactId, label, kind, sizeBytes, preview, readBackHint }.\nsizeBytes is UTF-8 bytes; preview is characters.\n\nWhat it is built on\n\nOne store, not two. Banking writes into the same artifact store the MCP output\nnormalizer externalizes into — local temp by default, Redis when you say so,\nsee Storage backends — so an oversized MCP tool output and\na banked worker report read back through exactly the same call.\n\nThe only thing banking adds to the store's lifecycle: previously it existed only\nwhen mcp.outputLimits.strategy === \"externalize\", which meant a caller who\njust wanted to bank a report had to configure MCP output limits it never used.\ngetArtifactStore() now creates one on first use, and registers\nretrieve_context with it — so a banked payload is reachable by the model,\nnot only by host code.\n\nCross-process reads\n\nEvery payload gets a .meta.json sidecar written beside it. The in-memory\nindex stays the fast path, but an id it does not know is resolved from the\nsidecar — so an artifact banked by one process is readable by another, and by\nthe same process after a restart. If the sidecar is missing, the payload file\nitself is probed and its metadata recovered from stat.\n\nIds that reach that probe come from the model, so they are validated first:\nanything containing a path separator or a dot is refused before the filesystem\nis touched. Real ids are UUIDs.\n\ncleanup(olderThanMs) stays index-scoped on purpose — it expires what this\nprocess banked, and never walks the directory deleting another process's work.\n\nStorage backends\n\nWhere artifacts live is chosen the way conversation memory's storage is chosen,\nand by the same switch:\n\n| Backend | Select with | Survives a redeploy | Visible to other replicas | Expiry |\n| --------- | ---------------------------------------------- | ------------------- | ------------------------- | ---------------------------------------- |\n| \"local\" | default | no | only on the same machine | never, unless the host calls cleanup() |\n| \"redis\" | STORAGE_TYPE=redis or artifacts.storage | yes | yes | TTL, 24 h by default |\n| custom | artifacts.store or setArtifactStore(store) | up to you | up to you | up to you |\nNothing to do. STORAGE_TYPE=redis already moves sessions to Redis,\nand now moves artifacts with them, on the same pooled connection.\nExplicit, with its own connection.\nAnything else","hierarchy":{"lvl0":"Agents","lvl1":"Artifact Banking (`bankArtifact` / `readArtifact`)","lvl2":"","lvl3":""}}, {"objectID":"42a7905d2c08e1105cc5e0e58ca9df314a8e9f255f7d1128eb891ea5a6f52eab","title":"Artifact Banking (bankArtifact / readArtifact)","url":"/docs/agents/ARTIFACT-BANKING#artifact-banking-bankartifact-readartifact","content":"A long-running agent produces outputs that do not fit in a conversation: a\nworker's full report, a build log, a stage's structured result. The tempting\nanswer is to truncate one and send the head — but the discarded bytes are gone,\nand nothing records that they ever existed.\n\nBanking is the other answer. The payload is written to disk whole, and what\ngoes into the conversation is a bounded preview plus the exact call that reads\nthe rest. Context cost stays flat, evidence stays complete, and compaction can\nevict the preview without destroying anything.\n\nRead-back is the tool that already existed: retrieve_context. No new tool,\nno new name for the model to learn.","hierarchy":{"lvl0":"Agents","lvl1":"Artifact Banking (`bankArtifact` / `readArtifact`)","lvl2":"Artifact Banking (bankArtifact / readArtifact)","lvl3":""}}, {"objectID":"c92a773ea9f728a0d9753ea02175723dc0e8e47336eebda135cf4ce258e20ced","title":"Quick start","url":"/docs/agents/ARTIFACT-BANKING#quick-start","content":"The model reads the same artifact with the tool it already has:\n\nretrieve_context is registered automatically the first time anything banks —\nyou do not have to configure mcp.outputLimits to get it.","hierarchy":{"lvl0":"Agents","lvl1":"Artifact Banking (`bankArtifact` / `readArtifact`)","lvl2":"Quick start","lvl3":""}}, {"objectID":"3f7e51498530fc00a8f1356d1954e01bb1c2c968508b89fc1374efc21a86e423","title":"API","url":"/docs/agents/ARTIFACT-BANKING#api","content":"| Member | Returns | Notes |\n| -------------------------------- | ------------------- | ------------------------------------------------------------ |\n| bankArtifact(payload, options) | BankedArtifactRef | Stores the payload whole; returns id + bounded preview |\n| readArtifact(id, page?) | string \\| null | Full payload when page is omitted; null if unknown |\n| getArtifactStore() | ArtifactStore | Creates the store on demand and registers retrieve_context |\n| setArtifactStore(store) | void | Swaps the backend for every path, MCP normalizer included |\n\nBankArtifactOptions:\n\n| Field | Required | Default | Notes |\n| -------------- | -------- | -------- | ------------------------------------------------------------- |\n| kind | yes | — | worker-report · command-output · stage-output · other |\n| label | yes | — | Short human label, e.g. delegate:auth-review |\n| sessionId | no | — | Recorded on the artifact metadata |\n| contentType | no | \"text\" | \"json\" stores with a .json extension |\n| previewChars | no | 1000 | Hard cap 4000 — a preview is a pointer |\n\nBankedArtifactRef: { artifactId, label, kind, sizeBytes, preview, readBackHint }.\nsizeBytes is UTF-8 bytes; preview is characters.","hierarchy":{"lvl0":"Agents","lvl1":"Artifact Banking (`bankArtifact` / `readArtifact`)","lvl2":"API","lvl3":""}}, {"objectID":"360ceb83b82c551378721612f7a121c846317852013f2e8bff993c16c80cb795","title":"What it is built on","url":"/docs/agents/ARTIFACT-BANKING#what-it-is-built-on","content":"One store, not two. Banking writes into the same artifact store the MCP output\nnormalizer externalizes into — local temp by default, Redis when you say so,\nsee Storage backends — so an oversized MCP tool output and\na banked worker report read back through exactly the same call.\n\nThe only thing banking adds to the store's lifecycle: previously it existed only\nwhen mcp.outputLimits.strategy === \"externalize\", which meant a caller who\njust wanted to bank a report had to configure MCP output limits it never used.\ngetArtifactStore() now creates one on first use, and registers\nretrieve_context with it — so a banked payload is reachable by the model,\nnot only by host code.","hierarchy":{"lvl0":"Agents","lvl1":"Artifact Banking (`bankArtifact` / `readArtifact`)","lvl2":"What it is built on","lvl3":""}}, {"objectID":"b96faed1bc023dcc2613c300631a83ff7bb86c75c921a6e7eb47feb9ad20a27d","title":"Cross-process reads","url":"/docs/agents/ARTIFACT-BANKING#cross-process-reads","content":"Every payload gets a .meta.json sidecar written beside it. The in-memory\nindex stays the fast path, but an id it does not know is resolved from the\nsidecar — so an artifact banked by one process is readable by another, and by\nthe same process after a restart. If the sidecar is missing, the payload file\nitself is probed and its metadata recovered from stat.\n\nIds that reach that probe come from the model, so they are validated first:\nanything containing a path separator or a dot is refused before the filesystem\nis touched. Real ids are UUIDs.\n\ncleanup(olderThanMs) stays index-scoped on purpose — it expires what this\nprocess banked, and never walks the directory deleting another process's work.","hierarchy":{"lvl0":"Agents","lvl1":"Artifact Banking (`bankArtifact` / `readArtifact`)","lvl2":"Cross-process reads","lvl3":""}}, {"objectID":"8dd3e5fe06486b3b6e1637d191f31c9a8d850d242c0730038fc7a81f7ec2241c","title":"Storage backends","url":"/docs/agents/ARTIFACT-BANKING#storage-backends","content":"Where artifacts live is chosen the way conversation memory's storage is chosen,\nand by the same switch:\n\n| Backend | Select with | Survives a redeploy | Visible to other replicas | Expiry |\n| --------- | ---------------------------------------------- | ------------------- | ------------------------- | ---------------------------------------- |\n| \"local\" | default | no | only on the same machine | never, unless the host calls cleanup() |\n| \"redis\" | STORAGE_TYPE=redis or artifacts.storage | yes | yes | TTL, 24 h by default |\n| custom | artifacts.store or setArtifactStore(store) | up to you | up to you | up to you |\nNothing to do. STORAGE_TYPE=redis already moves sessions to Redis,\nand now moves artifacts with them, on the same pooled connection.\nExplicit, with its own connection.\nAnything else. Implement the ArtifactStore type (store /\nretrieve / delete / cleanup / generatePreview, plus the optional\nretrieveRange / close) and hand it in. NeuroLink ships only \"local\" and\n\"redis\".\n\nResolution order for the backend: artifacts.store → artifacts.storage →\nSTORAGE_TYPE → \"local\". For the Redis connection: artifacts.redisConfig →\nconversationMemory.redisConfig → REDIS_URL / REDIS_HOST and friends. The\nkey prefix is never inherited: artifacts get neurolink:artifact: even when\nthe connection came from the conversation config, so the two keyspaces cannot\ncollide. RedisArtifactStore, LocalTempArtifactStore and\ncreateArtifactStore are exported if you want to build or wrap one yourself.\n\nTwo things to know before flipping the switch on a running system:\nsetArtifactStore() replaces, it does not migrate. Artifacts already in\n the previous store stop resolving through the instance.","hierarchy":{"lvl0":"Agents","lvl1":"Artifact Banking (`bankArtifact` / `readArtifact`)","lvl2":"Storage backends","lvl3":""}}, {"objectID":"33ab5ee48608cb06f54b7febf39e926ac13ba5826c54901134eb7954eef6135d","title":"Range reads","url":"/docs/agents/ARTIFACT-BANKING#range-reads","content":"retrieve_context({ artifactId, offset, limit }) and readArtifact(id, page)\nask the backend for one window when it can produce one. ArtifactStore has\nan optional retrieveRange(id, { offset, limit }) returning\n{ content, offset, totalLength }; when a backend implements it, only the\nwindow crosses the wire and hasMore / totalSize come from totalLength,\nnever from the payload. A backend without it is read whole and sliced, exactly\nas before.\n\nUnits are characters (UTF-16 code units), the same unit offset and\nlimit always used. RedisArtifactStore records the payload's character\nlength beside its byte length and uses GETRANGE only when the two are equal\n— pure ASCII, which is what JSON tool output and logs almost always are.\nAnything else falls back to a whole read, which is slower and still correct.\nA window never starts on the wrong character.","hierarchy":{"lvl0":"Agents","lvl1":"Artifact Banking (`bankArtifact` / `readArtifact`)","lvl2":"Range reads","lvl3":""}}, {"objectID":"7085ebf9cc51233991e0bb7d5684b0afaa26d6845f9e2a9d140fcbab1c406fd3","title":"Searching an artifact","url":"/docs/agents/ARTIFACT-BANKING#searching-an-artifact","content":"retrieve_context({ artifactId, search }) finds literal, case-insensitive text\nin an artifact and returns where it is, so the model can jump instead of\npaging to it:\n\nSnippets are bounded (about 120 characters each side of the hit) rather than\nwhole lines, because an MCP artifact is usually one compact JSON line and \"the\nmatching line\" would be the entire payload. Up to 50 matches come back per\ncall; totalMatches counts the rest and nextSearchOffset is the offset to\npass to see them. Regex metacharacters are matched literally — the model's\ninput is never compiled as a pattern — and an empty or over-long pattern is an\nexplicit error, never a silently unfiltered read.","hierarchy":{"lvl0":"Agents","lvl1":"Artifact Banking (`bankArtifact` / `readArtifact`)","lvl2":"Searching an artifact","lvl3":""}}, {"objectID":"2861dd40f080ccdf046d8ed7779d4fa805e22593f21066888f1d6065b82e1c06","title":"Rules of thumb","url":"/docs/agents/ARTIFACT-BANKING#rules-of-thumb","content":"Bank first, summarize second. Write the payload, then decide what the\n conversation sees. Never the other way round.\nHand the model preview _and_ readBackHint together. A preview with no\n way back is just a truncation with extra steps.\nDo not raise previewChars to avoid a read-back. Past a few thousand\n characters the preview recreates the context pressure banking removes; that\n is why the cap exists.\nsizeBytes is the honest number. Show it when you show a preview, so\n \"there is more\" is visible rather than inferred.","hierarchy":{"lvl0":"Agents","lvl1":"Artifact Banking (`bankArtifact` / `readArtifact`)","lvl2":"Rules of thumb","lvl3":""}}, {"objectID":"d8109a96295f93f460ef7aa2be72fb5043666f7d2024b7baff67e14c90d5584d","title":"Async Delegation (`delegate_task` / `collect_results`)","url":"/docs/agents/ASYNC-DELEGATION","content":"Async Delegation (delegate_task / collect_results)\n\nDelegation through registerAgentTool is synchronous: the supervising\nagent's loop blocks on each worker, so four investigations cost four times one\ninvestigation and the supervisor sits idle while each runs.\n\nThese tools change only when the caller waits. delegate_task returns a\nworkerId immediately and the agent keeps working; collect_results hands back\nwhichever worker finished first, which has nothing to do with which was\nspawned first.\n\nOpt-in and additive: nothing registers these tools until you ask.\n\nQuick start\n\nOr drive it from host code:\n\nThe tools\n\n| Tool | Input | Behaviour |\n| ----------------- | ----------------------------------------------- | ---------------------------------------------------------------------------------------------- |\n| delegate_task | { task, scope?, context?, tools?, model? } | Starts a background worker. Returns { workerId, spawnedAt, queued, pending, ready } at once. |\n| collect_results | { mode?: \"any\" \\| \"all\", workerId?, waitMs? } | Claims finished workers in completion order, each exactly once. |\n\nA collected outcome:\n\nsummary and report are not alternatives: the summary is what the\nconversation carries, the report is where the evidence lives. The full\nreport — narrative, structured data, a tool digest, and every tool execution\nrecord in full — is banked to a file, never truncated into the conversation.\n\nOut-of-order collection\n\nwaitMs: 0 polls (whatever is ready right now); omitting it waits up to five\nminutes. timedOut means work was still outstanding when the call returned —\nthe signal to come back later, not an error.\n\nKnowing a worker landed, without polling\n\nEvery ChecklistToolResult carries delegatesPending and delegatesReady, so\na model that calls tasks_list for any reason learns that a worker finished:\n\nThat is the whole notification channel. The core generate loop is untouched —\nthere is no injection point to get wrong and nothing to poll.\n\nConcurrency, depth and cancellation\nOne pool. Concurrency uses the same process-wide delegation pool as\n registerAgentTool. maxConcurrent raises it and never lowers it — it is\n not a per-agent throttle. Spawns past capacity queue; they are never\n refused. DelegateHandle.queued says which happened.\nDepth. maxDepth defaults to 1: a background worker does not spawn\n background workers, because its delegates would outlive it with nobody left to\n collect them. At the ceiling delegate_task refuses, in the registrar's own\n wording, and names what to do instead.\nCancellation. cancelDelegates(workerId?) aborts one worker or every\n worker this instance spawned; an abortSignal passed to spawnDelegate does\n the same when the parent aborts. A cancelled worker still settles into a\n claimable outcome with ok: false and a banked report of whatever it had —\n silence would strand the supervisor waiting for a worker that is never coming.\nAn uncollected outcome is retained until claimed. Collection is what\n frees a job's registry entry; a supervisor that spawns and never collects\n accumulates settled outcomes for the life of the process. Collect what you\n spawn.\nmaxConcurrent: Infinity maps to a finite stand-in (1024) — effectively\n unbounded, without letting a non-finite number into the pool arithmetic\n (NaN once deadlocked it permanently).\n\nSessions\n\nCollection is scoped to the caller's session, resolved the same way the task\nchecklist resolves it: execution context first, then the instance's\nsetToolContext({ sessionId }), then one default per instance. Session A can\nnever collect session B's worker.\n\nA worker gets its own session (the run id), deliberately: its checklist and\nits own delegate counters must not merge into its supervisor's.\n\nHost API\n\nTypes live in src/lib/types/delegation.ts and are exported from the package\nbarrel.\n\nWhat it is built on\n\nNothing here is a second implementation of anything:\nrunIsolatedAgent — fresh session on a worker instance that shares this\n host's tool registry, so live MCP connections are reused; waste detection,\n honest stop reasons, continuation handles;\nthe delegation pool in agentToolRegistrar — one pool, raised never lowered;\nbankArtifact (N3) — the full report on disk, a pointer in the conversation;\nthe task checklist (N1) — the counters that make completion visible.\n\nTests\n\npnpm run build && pnpm run test:agent-delegation —\ntest/continuous-test-suite-agent-delegation.ts.\n\nThe timing claims are proved against a loopback chat server that answers\nDELAY: after exactly that many milliseconds, so \"which worker finished\nfirst\" is a property of the test rather than of a provider's mood. Only the live\ncase needs credentials, and it skips without them.","hierarchy":{"lvl0":"Agents","lvl1":"Async Delegation (`delegate_task` / `collect_results`)","lvl2":"","lvl3":""}}, {"objectID":"afb7f7ea69e16ed38d8524c55061a475066a9e125156564d1a79fa3dbdb98523","title":"Async Delegation (delegate_task / collect_results)","url":"/docs/agents/ASYNC-DELEGATION#async-delegation-delegate_task-collect_results","content":"Delegation through registerAgentTool is synchronous: the supervising\nagent's loop blocks on each worker, so four investigations cost four times one\ninvestigation and the supervisor sits idle while each runs.\n\nThese tools change only when the caller waits. delegate_task returns a\nworkerId immediately and the agent keeps working; collect_results hands back\nwhichever worker finished first, which has nothing to do with which was\nspawned first.\n\nOpt-in and additive: nothing registers these tools until you ask.","hierarchy":{"lvl0":"Agents","lvl1":"Async Delegation (`delegate_task` / `collect_results`)","lvl2":"Async Delegation (delegate_task / collect_results)","lvl3":""}}, {"objectID":"205057b267b584b2c25ef4c83e0dc259ba04063e13ec9da8f71969dcc4889257","title":"Quick start","url":"/docs/agents/ASYNC-DELEGATION#quick-start","content":"Or drive it from host code:","hierarchy":{"lvl0":"Agents","lvl1":"Async Delegation (`delegate_task` / `collect_results`)","lvl2":"Quick start","lvl3":""}}, {"objectID":"225a13455cf22c535832c74508bf9494e67091749df9845222b58af72e63d15b","title":"The tools","url":"/docs/agents/ASYNC-DELEGATION#the-tools","content":"| Tool | Input | Behaviour |\n| ----------------- | ----------------------------------------------- | ---------------------------------------------------------------------------------------------- |\n| delegate_task | { task, scope?, context?, tools?, model? } | Starts a background worker. Returns { workerId, spawnedAt, queued, pending, ready } at once. |\n| collect_results | { mode?: \"any\" \\| \"all\", workerId?, waitMs? } | Claims finished workers in completion order, each exactly once. |\n\nA collected outcome:\n\nsummary and report are not alternatives: the summary is what the\nconversation carries, the report is where the evidence lives. The full\nreport — narrative, structured data, a tool digest, and every tool execution\nrecord in full — is banked to a file, never truncated into the conversation.","hierarchy":{"lvl0":"Agents","lvl1":"Async Delegation (`delegate_task` / `collect_results`)","lvl2":"The tools","lvl3":""}}, {"objectID":"d66463c5f2b9d6f4ae44f462ad41325f10fe5980732c9254b5f96599ee2f0f6d","title":"Out-of-order collection","url":"/docs/agents/ASYNC-DELEGATION#out-of-order-collection","content":"waitMs: 0 polls (whatever is ready right now); omitting it waits up to five\nminutes. timedOut means work was still outstanding when the call returned —\nthe signal to come back later, not an error.","hierarchy":{"lvl0":"Agents","lvl1":"Async Delegation (`delegate_task` / `collect_results`)","lvl2":"Out-of-order collection","lvl3":""}}, {"objectID":"056a587dae310ef9141bf390701f5f994b90ec0e953bca26bc66ff651bd10ace","title":"Knowing a worker landed, without polling","url":"/docs/agents/ASYNC-DELEGATION#knowing-a-worker-landed-without-polling","content":"Every ChecklistToolResult carries delegatesPending and delegatesReady, so\na model that calls tasks_list for any reason learns that a worker finished:\n\nThat is the whole notification channel. The core generate loop is untouched —\nthere is no injection point to get wrong and nothing to poll.","hierarchy":{"lvl0":"Agents","lvl1":"Async Delegation (`delegate_task` / `collect_results`)","lvl2":"Knowing a worker landed, without polling","lvl3":""}}, {"objectID":"63bcb00cc8a92b97f7642b1bc002685fa0bd97afc717378b8e426a107754bf2d","title":"Concurrency, depth and cancellation","url":"/docs/agents/ASYNC-DELEGATION#concurrency-depth-and-cancellation","content":"One pool. Concurrency uses the same process-wide delegation pool as\n registerAgentTool. maxConcurrent raises it and never lowers it — it is\n not a per-agent throttle. Spawns past capacity queue; they are never\n refused. DelegateHandle.queued says which happened.\nDepth. maxDepth defaults to 1: a background worker does not spawn\n background workers, because its delegates would outlive it with nobody left to\n collect them. At the ceiling delegate_task refuses, in the registrar's own\n wording, and names what to do instead.\nCancellation. cancelDelegates(workerId?) aborts one worker or every\n worker this instance spawned; an abortSignal passed to spawnDelegate does\n the same when the parent aborts. A cancelled worker still settles into a\n claimable outcome with ok: false and a banked report of whatever it had —\n silence would strand the supervisor waiting for a worker that is never coming.\nAn uncollected outcome is retained until claimed. Collection is what\n frees a job's registry entry; a supervisor that spawns and never collects\n accumulates settled outcomes for the life of the process. Collect what you\n spawn.\nmaxConcurrent: Infinity maps to a finite stand-in (1024) — effectively\n unbounded, without letting a non-finite number into the pool arithmetic\n (NaN once deadlocked it permanently).","hierarchy":{"lvl0":"Agents","lvl1":"Async Delegation (`delegate_task` / `collect_results`)","lvl2":"Concurrency, depth and cancellation","lvl3":""}}, {"objectID":"de8796fe6a029f9d1b2973c6aba250d1eb33fa9ba7aa4a3994a48822b27886d7","title":"Sessions","url":"/docs/agents/ASYNC-DELEGATION#sessions","content":"Collection is scoped to the caller's session, resolved the same way the task\nchecklist resolves it: execution context first, then the instance's\nsetToolContext({ sessionId }), then one default per instance. Session A can\nnever collect session B's worker.\n\nA worker gets its own session (the run id), deliberately: its checklist and\nits own delegate counters must not merge into its supervisor's.","hierarchy":{"lvl0":"Agents","lvl1":"Async Delegation (`delegate_task` / `collect_results`)","lvl2":"Sessions","lvl3":""}}, {"objectID":"29f6f5edb20c907e3434385f62d0389c6de8d63f088b787771d91b79cda151db","title":"Host API","url":"/docs/agents/ASYNC-DELEGATION#host-api","content":"Types live in src/lib/types/delegation.ts and are exported from the package\nbarrel.","hierarchy":{"lvl0":"Agents","lvl1":"Async Delegation (`delegate_task` / `collect_results`)","lvl2":"Host API","lvl3":""}}, {"objectID":"7687a1f43203ce2b79dbd58588c0e866de27b4c153ce4a11b20e84a6d759ac67","title":"What it is built on","url":"/docs/agents/ASYNC-DELEGATION#what-it-is-built-on","content":"Nothing here is a second implementation of anything:\nrunIsolatedAgent — fresh session on a worker instance that shares this\n host's tool registry, so live MCP connections are reused; waste detection,\n honest stop reasons, continuation handles;\nthe delegation pool in agentToolRegistrar — one pool, raised never lowered;\nbankArtifact (N3) — the full report on disk, a pointer in the conversation;\nthe task checklist (N1) — the counters that make completion visible.","hierarchy":{"lvl0":"Agents","lvl1":"Async Delegation (`delegate_task` / `collect_results`)","lvl2":"What it is built on","lvl3":""}}, {"objectID":"c31b7ef4deb0d5c74cf2a92930ffc893c74f78cf4a5352c088536793d327487f","title":"Tests","url":"/docs/agents/ASYNC-DELEGATION#tests","content":"pnpm run build && pnpm run test:agent-delegation —\ntest/continuous-test-suite-agent-delegation.ts.\n\nThe timing claims are proved against a loopback chat server that answers\nDELAY: after exactly that many milliseconds, so \"which worker finished\nfirst\" is a property of the test rather than of a provider's mood. Only the live\ncase needs credentials, and it skips without them.","hierarchy":{"lvl0":"Agents","lvl1":"Async Delegation (`delegate_task` / `collect_results`)","lvl2":"Tests","lvl3":""}}, {"objectID":"49bfb17b7da8b8887501ef22f4b6e140b84c469bf5df2b06ea91ff662e47a3e5","title":"Background Commands (`run_command_bg` / `command_status` / `command_output` / `command_kill`)","url":"/docs/agents/BACKGROUND-COMMANDS","content":"Background Commands (run_command_bg / command_status / command_output / command_kill)\n\nA long-running agent has to run real commands — a build, a test suite, a linter\nwhose output is the evidence for a finding. The two obvious shapes both\nfail: bashTool blocks the loop, hands the model a shell, and truncates its own\noutput at 100 KB; a child_process call with a command string is a shell\ninjection with extra steps.\n\nThese tools keep three promises instead.\n\n| Promise | What it means |\n| ------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Detached | run_command_bg returns a taskId at once; the agent keeps working and asks about the command when it wants to. |\n| Nothing discarded | Both streams go straight to files as they arrive, and the COMPLETE files are banked as artifacts when the command settles. The conversation gets a bounded tail plus a read-back call. |\n| Hardened by contract | argv arrays with no shell, an exact-match executable allowlist, a realpath cwd sandbox, and a timeout that escalates SIGTERM → SIGKILL. |\n\nOpt-in and additive: nothing registers these tools until you ask, and the\npolicy is required — until one is set, every start is refused.\n\nQuick start\n\nOr drive it from host code:\n\nThe policy is the contract\nargv arrays only. spawn(argv[0], argv.slice(1), { shell: false }). There\n is no string form and no shell, ever. An argv[0] containing whitespace or a\n shell metacharacter is refused with a message saying so — because a caller\n that passed \"pnpm run lint && echo done\" believed it was writing a shell\n line, and spawning an executable with that literal name would be a worse\n answer than a refusal.\nExact-match allowlist. argv[0] must appear verbatim in\n allowedExecutables. There is deliberately no basename fallback: allowlisting\n git must never permit /tmp/evil/git. Name absolute paths when you can.\nallowlist has the final say, after the allowlist and the sandbox pass.\n Return true, or a string that becomes the refusal — so put the recovery step\n in it.\ncwd sandbox. resolveWithinRoot(cwd, cwdRoot) resolves both sides\n through the filesystem before comparing them, so a symlink inside the root\n pointing at /etc is refused rather than followed. A lexical comparison is\n not a sandbox.\nTimeout kill. SIGTERM at timeoutMs, SIGKILL five seconds later, state\n timeout. A process that ignores SIGTERM does not get to outlive its budget.\n\nenv deserves its own note: omit it and the command inherits the parent\nenvironment (what a repository's own checks normally need); pass it and it\nREPLACES the parent environment entirely — the child gets exactly those\nvariables and nothing else, which is how you keep the host's credentials out of\na third-party build.\n\nOutput: bounded previews, unbounded files\n\nBoth streams are written to\njoin(tmpdir(), \"neurolink-commands\", )/{stdout,stderr}.log as they\narrive, and banked with bankArtifact({ kind: \"command-output\" }) when the\ncommand settles. That means:\nstatus.tailPreview is ≤ 2000 characters — orientation, never evidence.\nstatus.stdout / status.stderr are BankedArtifactRefs once settled;\n retrieve_context({ artifactId }) pages them like any other artifact.\ncommand_output({ taskId, stream, offset, limit }) reads the log file\n directly, while the command is still running as well as after. Character\n offsets, totalSize and hasMore match retrieve_context exactly, so paging\n code written for one works on the other.\n\nmaxOutputBytes is the single bound, and reaching it is loud: the command is\nkilled, the state is output-limit, and everything written up to the cap\nstays on disk in full. A capped command is a different fact from a failed one,\nand both are different from a truncated one — which is why there is a state for\nit rather than a silent cut.\n\nLearning that a command finished\n\nThe core generate loop is not modified. Completion surfaces the same way a\ndelegate's does (N2.3): counters ride along on results.\nEvery command tool result carries running / finished for the session.\nEvery ChecklistToolResult carries commandsRunning / commandsFinished, so\n a tasks_list tells the agent a build landed with no polling machinery.\n\nfinished means finished and not yet looked at. Reading a settled command's\nstatus or output clears it, so a non-zero finished always means there is\nsomething new to read. Nothing is discarded when it clears — the job, its logs\nand its artifacts stay exactly where they were.\n\nRead-only git toolset\n\nSix bounded tools — git_log, git_show, git_diff, git_blame,\ngit_m","hierarchy":{"lvl0":"Agents","lvl1":"Background Commands (`run_command_bg` / `command_status` / `command_output` / `command_kill`)","lvl2":"","lvl3":""}}, {"objectID":"37947dabf3ae65ea803029b5512489b699d1e57f96c2f6bac290dff24545848f","title":"Background Commands (run_command_bg / command_status / command_output / command_kill)","url":"/docs/agents/BACKGROUND-COMMANDS#background-commands-run_command_bg-command_status-command_output-command_kill","content":"A long-running agent has to run real commands — a build, a test suite, a linter\nwhose output is the evidence for a finding. The two obvious shapes both\nfail: bashTool blocks the loop, hands the model a shell, and truncates its own\noutput at 100 KB; a child_process call with a command string is a shell\ninjection with extra steps.\n\nThese tools keep three promises instead.\n\n| Promise | What it means |\n| ------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Detached | run_command_bg returns a taskId at once; the agent keeps working and asks about the command when it wants to. |\n| Nothing discarded | Both streams go straight to files as they arrive, and the COMPLETE files are banked as artifacts when the command settles. The conversation gets a bounded tail plus a read-back call. |\n| Hardened by contract | argv arrays with no shell, an exact-match executable allowlist, a realpath cwd sandbox, and a timeout that escalates SIGTERM → SIGKILL. |\n\nOpt-in and additive: nothing registers these tools until you ask, and the\npolicy is required — until one is set, every start is refused.","hierarchy":{"lvl0":"Agents","lvl1":"Background Commands (`run_command_bg` / `command_status` / `command_output` / `command_kill`)","lvl2":"Background Commands (run_command_bg / command_status / command_output / command_kill)","lvl3":""}}, {"objectID":"a381651c37e0faf28b38e7f49e867f4e7a1dc6c8df54ddf23aa6cfba2ef018d0","title":"Quick start","url":"/docs/agents/BACKGROUND-COMMANDS#quick-start","content":"Or drive it from host code:","hierarchy":{"lvl0":"Agents","lvl1":"Background Commands (`run_command_bg` / `command_status` / `command_output` / `command_kill`)","lvl2":"Quick start","lvl3":""}}, {"objectID":"7483221358760e7764502b71af53a0d446b05a1dfa55dfa04ffaa3fc677f1665","title":"The policy is the contract","url":"/docs/agents/BACKGROUND-COMMANDS#the-policy-is-the-contract","content":"argv arrays only. spawn(argv[0], argv.slice(1), { shell: false }). There\n is no string form and no shell, ever. An argv[0] containing whitespace or a\n shell metacharacter is refused with a message saying so — because a caller\n that passed \"pnpm run lint && echo done\" believed it was writing a shell\n line, and spawning an executable with that literal name would be a worse\n answer than a refusal.\nExact-match allowlist. argv[0] must appear verbatim in\n allowedExecutables. There is deliberately no basename fallback: allowlisting\n git must never permit /tmp/evil/git. Name absolute paths when you can.\nallowlist has the final say, after the allowlist and the sandbox pass.\n Return true, or a string that becomes the refusal — so put the recovery step\n in it.\ncwd sandbox. resolveWithinRoot(cwd, cwdRoot) resolves both sides\n through the filesystem before comparing them, so a symlink inside the root\n pointing at /etc is refused rather than followed. A lexical comparison is\n not a sandbox.\nTimeout kill. SIGTERM at timeoutMs, SIGKILL five seconds later, state\n timeout. A process that ignores SIGTERM does not get to outlive its budget.\n\nenv deserves its own note: omit it and the command inherits the parent\nenvironment (what a repository's own checks normally need); pass it and it\nREPLACES the parent environment entirely — the child gets exactly those\nvariables and nothing else, which is how you keep the host's credentials out of\na third-party build.","hierarchy":{"lvl0":"Agents","lvl1":"Background Commands (`run_command_bg` / `command_status` / `command_output` / `command_kill`)","lvl2":"The policy is the contract","lvl3":""}}, {"objectID":"6dcc916c89b1bed7daaeefdd0b97b8b9fc04bffcd5db5bdd7948ff151de8386e","title":"Output: bounded previews, unbounded files","url":"/docs/agents/BACKGROUND-COMMANDS#output-bounded-previews-unbounded-files","content":"Both streams are written to\njoin(tmpdir(), \"neurolink-commands\", )/{stdout,stderr}.log as they\narrive, and banked with bankArtifact({ kind: \"command-output\" }) when the\ncommand settles. That means:\nstatus.tailPreview is ≤ 2000 characters — orientation, never evidence.\nstatus.stdout / status.stderr are BankedArtifactRefs once settled;\n retrieve_context({ artifactId }) pages them like any other artifact.\ncommand_output({ taskId, stream, offset, limit }) reads the log file\n directly, while the command is still running as well as after. Character\n offsets, totalSize and hasMore match retrieve_context exactly, so paging\n code written for one works on the other.\n\nmaxOutputBytes is the single bound, and reaching it is loud: the command is\nkilled, the state is output-limit, and everything written up to the cap\nstays on disk in full. A capped command is a different fact from a failed one,\nand both are different from a truncated one — which is why there is a state for\nit rather than a silent cut.","hierarchy":{"lvl0":"Agents","lvl1":"Background Commands (`run_command_bg` / `command_status` / `command_output` / `command_kill`)","lvl2":"Output: bounded previews, unbounded files","lvl3":""}}, {"objectID":"4c4818d7cb6fd212933cd0153a4dc016b849e98a95d0671ea05ace19f09d823d","title":"Learning that a command finished","url":"/docs/agents/BACKGROUND-COMMANDS#learning-that-a-command-finished","content":"The core generate loop is not modified. Completion surfaces the same way a\ndelegate's does (N2.3): counters ride along on results.\nEvery command tool result carries running / finished for the session.\nEvery ChecklistToolResult carries commandsRunning / commandsFinished, so\n a tasks_list tells the agent a build landed with no polling machinery.\n\nfinished means finished and not yet looked at. Reading a settled command's\nstatus or output clears it, so a non-zero finished always means there is\nsomething new to read. Nothing is discarded when it clears — the job, its logs\nand its artifacts stay exactly where they were.","hierarchy":{"lvl0":"Agents","lvl1":"Background Commands (`run_command_bg` / `command_status` / `command_output` / `command_kill`)","lvl2":"Learning that a command finished","lvl3":""}}, {"objectID":"09a59458b786fa4298d1013992731a36588ed1c5afd2fda6a9d5ef6513f830f6","title":"Read-only git toolset","url":"/docs/agents/BACKGROUND-COMMANDS#read-only-git-toolset","content":"Six bounded tools — git_log, git_show, git_diff, git_blame,\ngit_merge_base, git_ls_files — built on the same runner.\n\nThey take values, never flags. The model supplies a ref, a path, a line\nrange or a count; each tool validates them and assembles a fixed argv. That is\nwhat keeps them read-only: a free-form argument string would carry\n--output= (which writes) and diff.external (which executes) straight\nthrough. A value beginning with - is refused outright, paths must resolve\ninside repoRoot, and every invocation runs with --no-pager -c color.ui=false\n-c diff.external= -c core.fsmonitor=false and a replaced environment.\n\nRegistering them widens nothing else: they run under a private\none-executable policy rooted at repoRoot, so run_command_bg still cannot\nexecute git, and no general command policy is required.\n\nOutput follows the same rule as everything else here — bounded preview, full\noutput banked, readBackHint spelling out the retrieve_context call.","hierarchy":{"lvl0":"Agents","lvl1":"Background Commands (`run_command_bg` / `command_status` / `command_output` / `command_kill`)","lvl2":"Read-only git toolset","lvl3":""}}, {"objectID":"c1bfb5f21ce8cc7e0b0082fced1395491608feb7d3f78a5ce14686dc85506035","title":"Host API","url":"/docs/agents/BACKGROUND-COMMANDS#host-api","content":"Host-side calls throw where the tool returns a refusal (no policy, malformed\nargv, a non-allowlisted executable, a vetoed command, a cwd escape, an unknown\ntaskId), so host code should catch rather than inspect a result.\nawaitBackgroundCommand's timeoutMs bounds the wait, not the command: when\nit elapses you get the current status back rather than an exception, so a caller\ncan poll in bounded steps and never lose the job.","hierarchy":{"lvl0":"Agents","lvl1":"Background Commands (`run_command_bg` / `command_status` / `command_output` / `command_kill`)","lvl2":"Host API","lvl3":""}}, {"objectID":"0cc831e21aad900e7fede07f43166c50db3f46ede198aff7c68abb826c832c9a","title":"Things worth knowing","url":"/docs/agents/BACKGROUND-COMMANDS#things-worth-knowing","content":"Settled jobs are never evicted. Their logs and artifacts are the run's\n evidence, and a command_status that answers \"unknown taskId\" for a command\n that ran is exactly the information loss this primitive exists to prevent. The\n registry is process-local; the log files live under the OS temp directory.\nA command that could not start still settles. An allowlisted executable\n that is not installed produces a settled job with error explaining why —\n never a job the agent waits on forever.\nKilling discards the process, never its output. Whatever a command printed\n before it was killed is banked and still readable.\nBoth toolsets are registered with cacheable: false. Their results are a\n function of live process state, not of their arguments; a cached\n command_status would report a finished build as still running for the whole\n TTL.\nBackground children keep the event loop alive, as child processes normally\n do. A host that wants to exit while commands are outstanding should kill them\n first.\nThe allowlist is a NAME allowlist, not a binary allowlist. The policy\n matches argv[0] exactly; the OS then resolves that name through PATH, so\n the policy controls the name and the environment controls which binary runs.\n Pin the binary by allowlisting an absolute path. And choose entries knowing\n that anything with an escape hatch grants general execution: node runs\n arbitrary code, pnpm runs any package.json script, find has -exec.\nThe cwd sandbox is checked at start time. resolveWithinRoot realpaths\n and validates before the spawn; a process able to replace path components\n with symlinks between check and spawn can race it. Known limitation — the\n sandbox is a guard against mistakes and model-supplied paths, not against a\n hostile local writer inside the root.\nThe registry grows one entry per command per process lifetime. Per-entry\n memory is bounded (an 8 KB tail per stream; full output lives on disk), but\n the count is not, and there is no TTL.","hierarchy":{"lvl0":"Agents","lvl1":"Background Commands (`run_command_bg` / `command_status` / `command_output` / `command_kill`)","lvl2":"Things worth knowing","lvl3":""}}, {"objectID":"e8345a1034fca3d0a58232d651c03ca1976415f92c1e26e4acb7c00526fa13ce","title":"Tests","url":"/docs/agents/BACKGROUND-COMMANDS#tests","content":"pnpm run test:background-commands →\ntest/continuous-test-suite-background-commands.ts. No credentials are\nneeded — every case is mechanical and nothing in the suite can SKIP. It covers\na long-running command polled to completion, 3 MB banked and paged back\nbyte-exact, the byte cap landing mid-chunk, kill, timeout, SIGKILL escalation\nagainst a process that ignores SIGTERM, every refusal (no policy, shell string,\nnon-allowlisted executable, basename bypass, cwd escape, symlink escape,\nsibling-prefix escape, policy veto), env replacement, the checklist counters,\nand the git toolset including its argument-injection refusals.","hierarchy":{"lvl0":"Agents","lvl1":"Background Commands (`run_command_bg` / `command_status` / `command_output` / `command_kill`)","lvl2":"Tests","lvl3":""}}, {"objectID":"9411cf27affec786fc7f935bff98b3ca439293112791806f34512ff7063005e3","title":"Multi-Agent Networks CLI Coverage Report","url":"/docs/agents/CLI-COVERAGE","content":"Multi-Agent Networks CLI Coverage Report\n\nSummary\n\nThe Multi-Agent Networks feature has full CLI coverage. All agent and network\ncommands are implemented in src/cli/commands/agent.ts via the\nAgentCommandFactory class.\n\nSDK Coverage\n\nThe SDK provides full programmatic access to Multi-Agent Networks:\n\nCLI Coverage\n\nAgent Commands\n\nneurolink agent create\n\nCreate a new agent definition from inline flags or a JSON file.\n\nStatus: Implemented\n\nneurolink agent list\n\nList all agents registered in the current session.\n\nStatus: Implemented\n\nneurolink agent execute / neurolink agent run\n\nExecute a registered agent. run is an alias for execute.\n\nStatus: Implemented\n\nNetwork Commands\n\nneurolink network create\n\nCreate an agent network from a JSON configuration file.\n\nStatus: Implemented\n\nneurolink network list\n\nList all networks registered in the current session.\n\nStatus: Implemented\n\nneurolink network execute / neurolink network run\n\nExecute a registered network. run is an alias for execute.\n\nStatus: Implemented\n\nShared Flags\n\nAll agent and network subcommands support:\n\n| Flag | Type | Default | Description |\n| ---------------- | ------------------- | ------- | ----------------------------- |\n| --format | text\\|json\\|table | text | Output format |\n| --output | string | — | Save output to file |\n| --quiet / -q | boolean | false | Suppress non-essential output |\n| --debug | boolean | false | Enable debug output |\n\nagent execute / agent run and network execute / network run also accept:\n\n| Flag | Type | Default | Description |\n| ------------ | --------- | -------- | -------------------------------------- |\n| --stream | boolean | false | Stream output in real-time |\n| --context | string | — | Additional context as JSON |\n| --maxSteps | number | 10 | Maximum execution steps |\n| --timeout | number | 120000 | Timeout in milliseconds (network only) |\n\nCommands Not Implemented\n\nThe following commands are out of scope for the current implementation:\nneurolink agent show — show individual agent details\nneurolink agent delete — remove a registered agent\nneurolink network show — show individual network details\nneurolink network delete — remove a registered network\nneurolink network status — live network health/load status\nneurolink message send / neurolink message broadcast — direct messaging\n\nSession state (registered agents and networks) is in-memory and does not\npersist across CLI invocations.\n\nSource Reference\nImplementation: src/cli/commands/agent.ts\nCommand factory class: AgentCommandFactory\nAgent subcommands: create, list, execute, run\nNetwork subcommands: create, list, execute, run","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"","lvl3":""}}, {"objectID":"f70048f1aebd25b85ee614c93f42b8cb6b356b8c39e6e4d1f896830f453acf8f","title":"Summary","url":"/docs/agents/CLI-COVERAGE#summary","content":"The Multi-Agent Networks feature has full CLI coverage. All agent and network\ncommands are implemented in src/cli/commands/agent.ts via the\nAgentCommandFactory class.","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"Summary","lvl3":""}}, {"objectID":"01e865a4aae8272d66c594b6b83c59bc3ca13034fed58fd33f542f59ccd2b089","title":"SDK Coverage","url":"/docs/agents/CLI-COVERAGE#sdk-coverage","content":"The SDK provides full programmatic access to Multi-Agent Networks:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"SDK Coverage","lvl3":""}}, {"objectID":"4196d1625729f8ca3a6f2987a91291fe79721c514fb37f7d1dffc559cde29a9f","title":"neurolink agent create","url":"/docs/agents/CLI-COVERAGE#neurolink-agent-create","content":"Create a new agent definition from inline flags or a JSON file.\n\n`bash","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"neurolink agent create","lvl3":""}}, {"objectID":"e2e198520ad8acf32f8b857ecd3e1f9da4138037499b4bd3bd2b695372647827","title":"Inline flags","url":"/docs/agents/CLI-COVERAGE#inline-flags","content":"neurolink agent create \\\n --id researcher \\\n --name \"Research Agent\" \\\n --description \"Searches and analyzes information\" \\\n --instructions \"You are a research assistant...\" \\\n --provider anthropic \\\n --model claude-3-5-sonnet-20241022","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"Inline flags","lvl3":""}}, {"objectID":"f49f5e509c49c8c9ff0f7072c5bac8961a96db82df5ddc9ec29177f93edf0126","title":"From a JSON file","url":"/docs/agents/CLI-COVERAGE#from-a-json-file","content":"neurolink agent create --file agent-config.json\n`\n\nStatus: Implemented","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"From a JSON file","lvl3":""}}, {"objectID":"a0090da311cd09f4b3db6eaf0929ec55a34d5ef217aaf934f2a3e299731b05f7","title":"neurolink agent list","url":"/docs/agents/CLI-COVERAGE#neurolink-agent-list","content":"List all agents registered in the current session.\n\nStatus: Implemented","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"neurolink agent list","lvl3":""}}, {"objectID":"153b618c52e378a14a48d057495f9fa9d25597c4e0039b4afe5ee3b13af6311a","title":"neurolink agent execute / neurolink agent run","url":"/docs/agents/CLI-COVERAGE#neurolink-agent-execute-neurolink-agent-run","content":"Execute a registered agent. run is an alias for execute.\n\nStatus: Implemented","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"neurolink agent execute / neurolink agent run","lvl3":""}}, {"objectID":"966b1519ea8b77728c9b5dc277ae549833c4e18f4f755ece71dbd08edbf5b5ea","title":"neurolink network create","url":"/docs/agents/CLI-COVERAGE#neurolink-network-create","content":"Create an agent network from a JSON configuration file.\n\n`bash\nneurolink network create \\\n --name \"Content Team\" \\\n --file network-config.json","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"neurolink network create","lvl3":""}}, {"objectID":"7b33f5ffe35377169df42a9bd1c8b58a762dd2f578709d5d27f08d042df4643d","title":"Override router settings","url":"/docs/agents/CLI-COVERAGE#override-router-settings","content":"neurolink network create \\\n --name \"Content Team\" \\\n --file network-config.json \\\n --routerProvider anthropic \\\n --routerModel claude-3-5-sonnet-20241022\n`\n\nStatus: Implemented","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"Override router settings","lvl3":""}}, {"objectID":"11866b198011e253c27565fd4d624d2f4423948dfdfd13d1be6c4d0e16199176","title":"neurolink network list","url":"/docs/agents/CLI-COVERAGE#neurolink-network-list","content":"List all networks registered in the current session.\n\nStatus: Implemented","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"neurolink network list","lvl3":""}}, {"objectID":"b9b5c0778da1959357c96414f41e336762011ec8a751417604c81a7c67ea2c52","title":"neurolink network execute / neurolink network run","url":"/docs/agents/CLI-COVERAGE#neurolink-network-execute-neurolink-network-run","content":"Execute a registered network. run is an alias for execute.\n\nStatus: Implemented","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"neurolink network execute / neurolink network run","lvl3":""}}, {"objectID":"7846ce91b5fbd0d5605cf8f554f6e43badcfbc55146ee9e2f872853b5cbac863","title":"Shared Flags","url":"/docs/agents/CLI-COVERAGE#shared-flags","content":"All agent and network subcommands support:\n\n| Flag | Type | Default | Description |\n| ---------------- | ------------------- | ------- | ----------------------------- |\n| --format | text\\|json\\|table | text | Output format |\n| --output | string | — | Save output to file |\n| --quiet / -q | boolean | false | Suppress non-essential output |\n| --debug | boolean | false | Enable debug output |\n\nagent execute / agent run and network execute / network run also accept:\n\n| Flag | Type | Default | Description |\n| ------------ | --------- | -------- | -------------------------------------- |\n| --stream | boolean | false | Stream output in real-time |\n| --context | string | — | Additional context as JSON |\n| --maxSteps | number | 10 | Maximum execution steps |\n| --timeout | number | 120000 | Timeout in milliseconds (network only) |","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"Shared Flags","lvl3":""}}, {"objectID":"7d941e9de60729a03092a7905c68e3421ca7dc72238aff474ea595837d7550c9","title":"Commands Not Implemented","url":"/docs/agents/CLI-COVERAGE#commands-not-implemented","content":"The following commands are out of scope for the current implementation:\nneurolink agent show — show individual agent details\nneurolink agent delete — remove a registered agent\nneurolink network show — show individual network details\nneurolink network delete — remove a registered network\nneurolink network status — live network health/load status\nneurolink message send / neurolink message broadcast — direct messaging\n\nSession state (registered agents and networks) is in-memory and does not\npersist across CLI invocations.","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"Commands Not Implemented","lvl3":""}}, {"objectID":"80a860270dce7b491a8662d16c7bfe68125e548a8e65af393294781c7a509e77","title":"Source Reference","url":"/docs/agents/CLI-COVERAGE#source-reference","content":"Implementation: src/cli/commands/agent.ts\nCommand factory class: AgentCommandFactory\nAgent subcommands: create, list, execute, run\nNetwork subcommands: create, list, execute, run","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks CLI Coverage Report","lvl2":"Source Reference","lvl3":""}}, {"objectID":"da34ca766b418827f40f27ef2e846b60ff5702acd5a8ae48182e4ea721f94694","title":"Multi-Agent Networks Configuration Guide","url":"/docs/agents/CONFIGURATION","content":"Multi-Agent Networks Configuration Guide\n\nOverview\n\nThis document describes all configuration options for the Multi-Agent Networks\nfeature in NeuroLink.\n\nAgent Configuration\n\nAgentDefinition\n\nThe core configuration for creating an agent:\n\nExample Agent Configurations\n\nBasic Agent\n\nSpecialized Agent with Tools\n\nAgent with Schema Validation\n\nNetwork Configuration\n\nAgentNetworkConfig\n\nConfiguration for creating a multi-agent network:\n\nRouterConfig\n\nThe router is a system prompt plus provider settings that the AI SDK uses to\nselect which agent tool to invoke. There is no separate RouterAgent class —\nrouting is performed by the AI SDK's built-in generate loop.\n\nExample:\n\nNetworkDefaults\n\nDefault settings for network execution:\n\nTopology Configurations\n\nHub-Spoke Topology\n\nCentral hub agent coordinates with spoke agents:\n\nMesh Topology\n\nAll agents can communicate directly:\n\nHierarchical Topology\n\nTree-structured agent organization:\n\nMessageBus Configuration\n\nMessageBusConfig\n\nConfiguration for inter-agent messaging:\n\nPriority Levels\n\nExecution Options\n\nAgentExecutionOptions\n\nOptions for executing an agent:\n\nNetworkExecutionOptions\n\nOptions for executing a network:\n\nEnvironment Variables\n\nConfigure behavior via environment variables:\n\nConfiguration Best Practices\n\nAgent Descriptions\n\nWrite clear, detailed descriptions — they are critical for router selection:\n\nTool Selection\n\nOnly include tools the agent actually needs:\n\nTemperature Settings\n\nMatch temperature to task type:\n\nTimeout Configuration\n\nSet appropriate timeouts based on task complexity:\n\nRelated Documentation\nTESTING.md - Testing guide\nVERIFICATION.md - Verification checklist","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"","lvl3":""}}, {"objectID":"0b23f2e0c29643ffd040497ec9620de6a6b5797cd9c91fdcedc77b564dabcbab","title":"Overview","url":"/docs/agents/CONFIGURATION#overview","content":"This document describes all configuration options for the Multi-Agent Networks\nfeature in NeuroLink.","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Overview","lvl3":""}}, {"objectID":"1e54e98df82ba9800e117b126e0d3c23d64fcdaae7e6a9a3e3a382b0f596e610","title":"AgentDefinition","url":"/docs/agents/CONFIGURATION#agentdefinition","content":"The core configuration for creating an agent:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"AgentDefinition","lvl3":""}}, {"objectID":"ed90acfb56dc1564517348607e2d6401289d83dde007c57577d1c9122bdd1490","title":"AgentNetworkConfig","url":"/docs/agents/CONFIGURATION#agentnetworkconfig","content":"Configuration for creating a multi-agent network:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"AgentNetworkConfig","lvl3":""}}, {"objectID":"542223cc7d8bf7328dad113976d717979a271d2f63a1fec97054c032be5edf4c","title":"RouterConfig","url":"/docs/agents/CONFIGURATION#routerconfig","content":"The router is a system prompt plus provider settings that the AI SDK uses to\nselect which agent tool to invoke. There is no separate RouterAgent class —\nrouting is performed by the AI SDK's built-in generate loop.\n\nExample:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"RouterConfig","lvl3":""}}, {"objectID":"b9d663a4a02b83a771e1dd20430df8a5cba5e1e919d77ea15372d0cd3259ce27","title":"NetworkDefaults","url":"/docs/agents/CONFIGURATION#networkdefaults","content":"Default settings for network execution:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"NetworkDefaults","lvl3":""}}, {"objectID":"64ac34e531be1cb9124f5c60a2391fb3a8fbcdf7dfa4b921c2ef7c8768be5a4d","title":"Hub-Spoke Topology","url":"/docs/agents/CONFIGURATION#hub-spoke-topology","content":"Central hub agent coordinates with spoke agents:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Hub-Spoke Topology","lvl3":""}}, {"objectID":"9f8825e075d5d00e6b20fde5f440f8bcf3d39e2092c6b305fbb9c94fb41b23ed","title":"Mesh Topology","url":"/docs/agents/CONFIGURATION#mesh-topology","content":"All agents can communicate directly:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Mesh Topology","lvl3":""}}, {"objectID":"fa9e048c1fa1b3672562342aef6effc899535069be29e435359e86a8d0d3ba5f","title":"Hierarchical Topology","url":"/docs/agents/CONFIGURATION#hierarchical-topology","content":"Tree-structured agent organization:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Hierarchical Topology","lvl3":""}}, {"objectID":"faccd933f900118096df78440ece3b57e559e7d5bc575c03b114cf8dc354b9cb","title":"MessageBusConfig","url":"/docs/agents/CONFIGURATION#messagebusconfig","content":"Configuration for inter-agent messaging:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"MessageBusConfig","lvl3":""}}, {"objectID":"6b10e856853b1774fbbb7af2dfc87690baa13b8572abc80b7e311d4f98eb0749","title":"AgentExecutionOptions","url":"/docs/agents/CONFIGURATION#agentexecutionoptions","content":"Options for executing an agent:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"AgentExecutionOptions","lvl3":""}}, {"objectID":"5e2dcb386170e08af49b7d63d832b24a1cda5b4bcb7641be5f9782e1dfffacec","title":"NetworkExecutionOptions","url":"/docs/agents/CONFIGURATION#networkexecutionoptions","content":"Options for executing a network:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"NetworkExecutionOptions","lvl3":""}}, {"objectID":"32d1dcab994f06b949cd61dcbbf9b011b11d995e1a274a2fd5b093d49625737f","title":"Environment Variables","url":"/docs/agents/CONFIGURATION#environment-variables","content":"Configure behavior via environment variables:\n\n`bash","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Environment Variables","lvl3":""}}, {"objectID":"09181a21fbdfc8bcbe63c0932c7eab07da6ca5c68fcd7df83a063385ff18d34c","title":"Default provider for agents without explicit provider","url":"/docs/agents/CONFIGURATION#default-provider-for-agents-without-explicit-provider","content":"NEUROLINKDEFAULTPROVIDER=vertex","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Default provider for agents without explicit provider","lvl3":""}}, {"objectID":"174d08f13f2e298edb1ebf73fddbc40e4f7679d351cd2e1c754ec658c312d082","title":"Default model","url":"/docs/agents/CONFIGURATION#default-model","content":"NEUROLINKDEFAULTMODEL=gemini-2.0-flash","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Default model","lvl3":""}}, {"objectID":"cf3d11a30f174bbe352909cfa6ce2b9a7b6d382fd7e3b4ec06a9a76ae7cf142d","title":"Maximum concurrent agent executions","url":"/docs/agents/CONFIGURATION#maximum-concurrent-agent-executions","content":"NEUROLINKMAXCONCURRENT_AGENTS=10","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Maximum concurrent agent executions","lvl3":""}}, {"objectID":"aaaceea553c31bfd61d4f5620320a1b38d6d42fb7b642f136f689ae16cc4e395","title":"Default execution timeout (ms)","url":"/docs/agents/CONFIGURATION#default-execution-timeout-ms","content":"NEUROLINKAGENTTIMEOUT=30000","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Default execution timeout (ms)","lvl3":""}}, {"objectID":"d4f0cfd8a165ae177cedba28940e684049a29f6750e408dfaca7b91c6c815d5e","title":"Enable agent execution tracing","url":"/docs/agents/CONFIGURATION#enable-agent-execution-tracing","content":"NEUROLINKAGENTTRACING=true","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Enable agent execution tracing","lvl3":""}}, {"objectID":"3b8ea3e4353b7c57a2a130625fc183668fcfcec0d797158054c45a951b41fcbf","title":"MessageBus persistence","url":"/docs/agents/CONFIGURATION#messagebus-persistence","content":"NEUROLINKMESSAGEBUSPERSISTENCE=memory","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"MessageBus persistence","lvl3":""}}, {"objectID":"9dbe019f8386982dae833e9d0d374e7e5c7a10f48db668b33b89a6ba3d8e02ae","title":"Routing confidence threshold","url":"/docs/agents/CONFIGURATION#routing-confidence-threshold","content":"NEUROLINKROUTINGTHRESHOLD=0.7\n`","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Routing confidence threshold","lvl3":""}}, {"objectID":"348e65f1c63cf72e083838f2b63b344b02af82abea000e1b73ff9eaf8446fe9f","title":"Agent Descriptions","url":"/docs/agents/CONFIGURATION#agent-descriptions","content":"Write clear, detailed descriptions — they are critical for router selection:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Agent Descriptions","lvl3":""}}, {"objectID":"7f7c7d09a3aaa7d50f36e71bcd7a52345fbd258b85f4050b0fed1eebc8eaf0f1","title":"Tool Selection","url":"/docs/agents/CONFIGURATION#tool-selection","content":"Only include tools the agent actually needs:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Tool Selection","lvl3":""}}, {"objectID":"2214baea9af110316bfc22362c3d3e9971a0ae184b38634166a8e46f1aa9d985","title":"Temperature Settings","url":"/docs/agents/CONFIGURATION#temperature-settings","content":"Match temperature to task type:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Configuration Guide","lvl2":"Temperature Settings","lvl3":""}}, {"objectID":"14e2b92e7e2c9c9f704932a8e0cbc8ea25687c61144b6f80761f43be661f3f0a","title":"Timeout Configuration","url":"/docs/agents/CONFIGURATION#timeout-configuration","content":"Set appropriate timeouts based on task 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The task checklist keeps the plan out of the\nmessage list: it is session state the model edits through three tools and the\nhost reads synchronously — so \"did this run actually finish everything?\" is a\nquestion code can answer, with no LLM in the loop.\n\nOpt-in and additive: nothing registers these tools until you ask.\n\nQuick start\n\nThe tools\n\n| Tool | Input | Behaviour |\n| -------------- | ----------------------- | ------------------------------------------------------------------------------------------------------------------- |\n| tasks_create | { titles: string[] } | Appends tasks. The engine assigns the ids (t1, t2, …); the model never picks one. Blank titles are dropped. |\n| tasks_update | { id, status, note? } | pending → in_progress → done, or closed for work that will not be done. |\n| tasks_list | {} | Reads the checklist. Cheap, always current. |\n\nAll three return the whole checklist:\n\nTwo refusals, each carrying its own recovery step in the error text:\nan unknown id is refused and the valid ids are listed;\nstatus: \"closed\" without a note is refused — closing a task unfinished\n requires saying why.\n\nWhy it survives compaction\n\nChecklist state lives in a module-level map keyed by sessionId, never in the\nconversation. Compaction rewrites messages; it cannot touch a module map. And\nbecause every tool result returns the full list, the first tasks_* call after\na compaction re-anchors the model for free — there is no re-injection mechanism\nto get wrong.\n\nSessions\n\nThe checklist is keyed by the sessionId on the tool execution context:\nthe session stamped on the executing agent (workers created by\n runIsolatedAgent get their own, so a worker cannot edit its parent's plan);\notherwise the instance's setToolContext({ sessionId });\notherwise a single default checklist for that instance — so a host that\n never declared a session still gets one list rather than one per call.\n\nA direct executeTool(\"tasks_create\", …) call should pass\nauthContext: { sessionId }; without it the tool registry mints a fresh id for\nthat one call.\n\nTwo consequences of the keying worth knowing: checklist state is\nprocess-global by sessionId, with no per-instance isolation — two\nNeuroLink instances in one process that use the same session id share one\nchecklist, and getTaskState(sessionId) on either reads it. And entries have\nno TTL — state lives until clearTaskState(sessionId); a long-lived server\nminting many session ids should clear sessions it is done with.\n\nHost API\n\ngetTaskState() returns a copy — mutating it does not touch the live\nchecklist. Omit sessionId to read whichever session the tools would currently\nwrite to.\n\nTypes (ChecklistItem, ChecklistItemStatus, ChecklistState,\nChecklistToolResult, …) are exported from the package barrel. They carry the\nChecklist prefix because the unrelated scheduler in\nsrc/lib/types/task.ts already owns Task, TaskStatus and friends —\nregisterTaskTools() (checklist) and the tasks getter (scheduler) are\ndifferent subsystems.\n\nTests\n\ntest/continuous-test-suite-agent-tasks.ts — registration, id assignment,\nstatus transitions, both refusals, session scoping, the host read, and one live\ntwo-turn model run that skips without provider credentials.","hierarchy":{"lvl0":"Agents","lvl1":"Task Checklist (`tasks_create` / `tasks_update` / `tasks_list`)","lvl2":"","lvl3":""}}, {"objectID":"eddb5ba1e09e5ee777633b7da2e77b681ad2b3b1aad665a79f322d6564e5e56b","title":"Task Checklist (tasks_create / tasks_update / tasks_list)","url":"/docs/agents/TASK-CHECKLIST#task-checklist-tasks_create-tasks_update-tasks_list","content":"A long-running agent that plans in prose loses the plan the moment the\nconversation is summarized. The task checklist keeps the plan out of the\nmessage list: it is session state the model edits through three tools and the\nhost reads synchronously — so \"did this run actually finish everything?\" is a\nquestion code can answer, with no LLM in the loop.\n\nOpt-in and additive: nothing registers these tools until you ask.","hierarchy":{"lvl0":"Agents","lvl1":"Task Checklist (`tasks_create` / `tasks_update` / `tasks_list`)","lvl2":"Task Checklist (tasks_create / tasks_update / tasks_list)","lvl3":""}}, {"objectID":"859d93f92aff3e395969103e8769e12184a305d51a23b3a5e55e80ad373de4b4","title":"The tools","url":"/docs/agents/TASK-CHECKLIST#the-tools","content":"| Tool | Input | Behaviour |\n| -------------- | ----------------------- | ------------------------------------------------------------------------------------------------------------------- |\n| tasks_create | { titles: string[] } | Appends tasks. The engine assigns the ids (t1, t2, …); the model never picks one. Blank titles are dropped. |\n| tasks_update | { id, status, note? } | pending → in_progress → done, or closed for work that will not be done. |\n| tasks_list | {} | Reads the checklist. Cheap, always current. |\n\nAll three return the whole checklist:\n\nTwo refusals, each carrying its own recovery step in the error text:\nan unknown id is refused and the valid ids are listed;\nstatus: \"closed\" without a note is refused — closing a task unfinished\n requires saying why.","hierarchy":{"lvl0":"Agents","lvl1":"Task Checklist (`tasks_create` / `tasks_update` / `tasks_list`)","lvl2":"The tools","lvl3":""}}, {"objectID":"7ce5428b2d3d517212b504564f692d944cd8c43677388472d6f5a62199a23e3c","title":"Why it survives compaction","url":"/docs/agents/TASK-CHECKLIST#why-it-survives-compaction","content":"Checklist state lives in a module-level map keyed by sessionId, never in the\nconversation. Compaction rewrites messages; it cannot touch a module map. And\nbecause every tool result returns the full list, the first tasks_* call after\na compaction re-anchors the model for free — there is no re-injection mechanism\nto get wrong.","hierarchy":{"lvl0":"Agents","lvl1":"Task Checklist (`tasks_create` / `tasks_update` / `tasks_list`)","lvl2":"Why it survives compaction","lvl3":""}}, {"objectID":"aaf51cb7c5ad06e957f68f6e1bded34561ea32c403c916834adfd375a3a95c48","title":"Sessions","url":"/docs/agents/TASK-CHECKLIST#sessions","content":"The checklist is keyed by the sessionId on the tool execution context:\nthe session stamped on the executing agent (workers created by\n runIsolatedAgent get their own, so a worker cannot edit its parent's plan);\notherwise the instance's setToolContext({ sessionId });\notherwise a single default checklist for that instance — so a host that\n never declared a session still gets one list rather than one per call.\n\nA direct executeTool(\"tasks_create\", …) call should pass\nauthContext: { sessionId }; without it the tool registry mints a fresh id for\nthat one call.\n\nTwo consequences of the keying worth knowing: checklist state is\nprocess-global by sessionId, with no per-instance isolation — two\nNeuroLink instances in one process that use the same session id share one\nchecklist, and getTaskState(sessionId) on either reads it. 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Tests verify:\nCorrect agent tool is selected for given input\nRouting completes with finishReason: stop\nRouterConfig fields (provider, model, instructions, maxAttempts,\n confidenceThreshold) take effect\nMessageBus Tests\n\nTests for inter-agent communication:\nPublish/subscribe patterns\nRequest-response patterns\nBroadcast messages\nPriority queue ordering\nMessage delivery guarantees\n\nWriting New Tests\n\nIntegration Test Pattern\n\nAll agent tests follow the continuous test suite pattern — not vitest describe\nblocks. Add new tests by pushing results to the suite's result array:\n\nImporting Agent in Tests\n\nUse the correct import path — the module is singular and lowercase:\n\nDo not use ../../src/lib/agents/Agent.js (plural directory, capitalized file)\n— that path does not exist.\n\nMock SDK Creation\n\nDebugging Tests\n\nEnable Verbose Logging\n\nIsolate a Single Test\n\nBecause the suite is a plain script, wrap the test in a standalone file or add\na name filter variable and short-circuit other tests:\n\nTest Coverage Goals\n\n| Component | Target Coverage |\n| ------------ | --------------- |\n| Agent | 90% |\n| AgentNetwork | 85% |\n| MessageBus | 90% |\n| Topologies | 80% |\n\nKnown Limitations\nReal Provider Tests: Require API keys and may incur costs\nStreaming Tests: May be sensitive to timing\n\nTroubleshooting\n\nImport Errors\n\nEnsure the project is built before running the suite:\n\nTests Timing Out\n\nSet a longer timeout via the environment, or check provider rate limits:\n\nRelated Documentation\nCONFIGURATION.md - Configuration options\nVERIFICATION.md - Manual verification checklist\nCLI-COVERAGE.md - CLI coverage report","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"","lvl3":""}}, {"objectID":"adcf2a06d5866eb50f04ff372c2fd803f201227ae0738423b96e552ad191e598","title":"Overview","url":"/docs/agents/TESTING#overview","content":"This document provides comprehensive guidance for testing the Multi-Agent\nNetworks feature in NeuroLink.","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Overview","lvl3":""}}, {"objectID":"feab2465a25e7678954293ce194e6927340dd774c22ae5696d398709c11ce484","title":"Environment Setup","url":"/docs/agents/TESTING#environment-setup","content":"Node.js: Ensure Node.js 18+ is installed\npnpm: Install pnpm package manager\nDependencies: Install project dependencies\n\n`bash","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing 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Environment Variables","lvl3":""}}, {"objectID":"fae9afd214b112e7ef259c7364992e6429c00710e86bdc58a05e7c83053531a7","title":"Test configuration","url":"/docs/agents/TESTING#test-configuration","content":"`","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Test configuration","lvl3":""}}, {"objectID":"f4f9b2dfa4bb2f3ddb350a4c59044a78e1c5fcc247598d393739553bab4bf602","title":"Test Structure","url":"/docs/agents/TESTING#test-structure","content":"The agent feature uses a single continuous test suite rather than individual\nvitest unit test files.","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Test Structure","lvl3":""}}, {"objectID":"bb0809e10a0936b387c230f0c5a8f1f30fe1a66b913cb2fbd754fe039b6d0b76","title":"Continuous Test Suite","url":"/docs/agents/TESTING#continuous-test-suite","content":"Located at test/continuous-test-suite-agents.ts:\nSelf-contained TypeScript script run directly with tsx\nCovers all components: Agent, AgentNetwork, MessageBus, topologies\nUses fixture files from test/fixtures/agents/\nReports pass/fail per test case with timing","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Continuous Test Suite","lvl3":""}}, {"objectID":"9378c85153bc792c6985247c6481a74462cd909f7aa8866cf8006af574206e8d","title":"Test Fixtures","url":"/docs/agents/TESTING#test-fixtures","content":"Located in test/fixtures/agents/:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Test Fixtures","lvl3":""}}, {"objectID":"752e107d219b5f63d84b71f95561fa19aa47967a2349f4d13f037a7daab1cfd2","title":"Run the Agent Test Suite","url":"/docs/agents/TESTING#run-the-agent-test-suite","content":"`bash","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Run the Agent Test Suite","lvl3":""}}, {"objectID":"d6e34580393db12a3f61c41be4ed431f566f732d076bd61e8e4f4828bb1f731f","title":"Run the continuous integration test 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provider","lvl3":""}}, {"objectID":"e60fbe35ccc4af952d6e56984c298c9a46dd73dd0c581f6d01ad0fd15c87e447","title":"Run All NeuroLink Tests (includes agent suite)","url":"/docs/agents/TESTING#run-all-neurolink-tests-includes-agent-suite","content":"`bash\npnpm test","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Run All NeuroLink Tests (includes agent suite)","lvl3":""}}, {"objectID":"e7979060edec65188ed753e0381cc297a4f89d780b9028c189acb1d5a54b4434","title":"With coverage","url":"/docs/agents/TESTING#with-coverage","content":"pnpm run test:coverage\n`","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"With coverage","lvl3":""}}, {"objectID":"fec8e7806308d45ab1ed524c3cedffaee411cce46520a9838987952daa4f6c0c","title":"Run in CI","url":"/docs/agents/TESTING#run-in-ci","content":"`yaml","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Run in CI","lvl3":""}}, 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MessageBus Tests","url":"/docs/agents/TESTING#4-messagebus-tests","content":"Tests for inter-agent communication:\nPublish/subscribe patterns\nRequest-response patterns\nBroadcast messages\nPriority queue ordering\nMessage delivery guarantees","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"4. MessageBus Tests","lvl3":""}}, {"objectID":"35aa74af8bbbc1c4cb49a683c531ad320eb1035deeb5712deaa4159576bb5634","title":"Integration Test Pattern","url":"/docs/agents/TESTING#integration-test-pattern","content":"All agent tests follow the continuous test suite pattern — not vitest describe\nblocks. Add new tests by pushing results to the suite's result array:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Integration Test Pattern","lvl3":""}}, {"objectID":"6310ac0a6bad376a45f2a91fb1c818b9e9e72fd0fc90b21a7165fd7ccc8c492b","title":"Importing Agent in Tests","url":"/docs/agents/TESTING#importing-agent-in-tests","content":"Use the correct import path — the module is singular and lowercase:\n\nDo not use ../../src/lib/agents/Agent.js (plural directory, capitalized file)\n— that path does not exist.","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Importing Agent in Tests","lvl3":""}}, {"objectID":"6082a5a47c16d629092bd356fce1cb8f982975c91af989c22e4862eda33c66f3","title":"Isolate a Single Test","url":"/docs/agents/TESTING#isolate-a-single-test","content":"Because the suite is a plain script, wrap the test in a standalone file or add\na name filter variable and short-circuit other tests:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Isolate a Single Test","lvl3":""}}, {"objectID":"54d05a5cdf4bea22f4160405b7616ab23f98cd8bad1960d05d03f183a38814d8","title":"Test Coverage Goals","url":"/docs/agents/TESTING#test-coverage-goals","content":"| Component | Target Coverage |\n| ------------ | --------------- |\n| Agent | 90% |\n| AgentNetwork | 85% |\n| MessageBus | 90% |\n| Topologies | 80% |","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Test Coverage Goals","lvl3":""}}, {"objectID":"be8210ed0d39bf83933a8160cb45a64992fc813f255973d81add15170d112adb","title":"Known Limitations","url":"/docs/agents/TESTING#known-limitations","content":"Real Provider Tests: Require API keys and may incur costs\nStreaming Tests: May be sensitive to timing","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Known Limitations","lvl3":""}}, {"objectID":"7bf7d92c906696262912f35b2914f64880ca6661919e32f3a42bc754838b55cd","title":"Import Errors","url":"/docs/agents/TESTING#import-errors","content":"Ensure the project is built before running the suite:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Import Errors","lvl3":""}}, {"objectID":"1612dd4b70bc0da7b58acca67a35a5d956493e8c6aa2e8f20273381be869c5b6","title":"Tests Timing Out","url":"/docs/agents/TESTING#tests-timing-out","content":"Set a longer timeout via the environment, or check provider rate limits:","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Tests Timing Out","lvl3":""}}, {"objectID":"62c73177ce92a0d1d7e4dd3fdf7c8f6ba21a4fab7560425da6c8e03e808aa983","title":"Related Documentation","url":"/docs/agents/TESTING#related-documentation","content":"CONFIGURATION.md - Configuration options\nVERIFICATION.md - Manual verification checklist\nCLI-COVERAGE.md - CLI coverage report","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Testing Guide","lvl2":"Related Documentation","lvl3":""}}, {"objectID":"f87e3b0bab6246febbd6f1082aa0ca5219573a8a1a2502cf48b1b8f6725d09c1","title":"Multi-Agent Networks Verification Checklist","url":"/docs/agents/VERIFICATION","content":"Multi-Agent Networks Verification Checklist\n\nOverview\n\nThis document provides a manual verification checklist for the Multi-Agent\nNetworks feature.\n\nPre-Verification Setup\nEnvironment Preparation\n[ ] Node.js 18+ installed\n[ ] pnpm installed\n[ ] Project dependencies installed (pnpm install)\n[ ] Project built (pnpm run build)\n[ ] At least one provider API key configured\nRequired API Keys\n\nConfigure at least one of:\n[ ] OPENAI_API_KEY\n[ ] ANTHROPIC_API_KEY\n[ ] GOOGLE_AI_STUDIO_API_KEY\n[ ] GOOGLE_CLOUD_PROJECT (for Vertex AI)\n\nIntegration Test Verification\n\nRun: pnpm exec tsx test/continuous-test-suite-agents.ts\n\nAgent Class Integration\n[ ] Fixtures load correctly\n[ ] All agent definitions valid\n[ ] Multiple providers configured\n[ ] Tool configurations correct\n[ ] Mock SDK works\n\nNetwork Topology Integration\n[ ] Hub-spoke config valid\n[ ] Mesh config valid\n[ ] Hierarchical config valid\n[ ] Router configs valid\n[ ] Network defaults valid\n\nRouting Rules Integration\n[ ] All rules defined\n[ ] Pattern matching works\n[ ] Confidence thresholds set\n[ ] Fallback behavior defined\n[ ] Priority ordering correct\n\nMessageBus Integration\n[ ] All message types defined\n[ ] Test messages valid\n[ ] Subscription patterns work\n[ ] Priority levels correct\n[ ] Test scenarios execute\n\nFunctional Verification\n\nBasic Agent Operations\n[ ] Create agent programmatically\n[ ] Execute agent with text input\n[ ] Execute agent with structured input\n[ ] Stream agent output\n[ ] Handle agent errors\n\nNetwork Operations\n[ ] Create network with multiple agents\n[ ] Execute network task\n[ ] Observe routing decisions\n[ ] Track execution traces\n[ ] Handle network failures\n\nMessaging Operations\n[ ] Publish message\n[ ] Subscribe and receive\n[ ] Request-response works\n[ ] Broadcast reaches all\n[ ] Priority respected\n\nPerformance Verification\n\nResponse Time\n[ ] Single agent < 5s\n[ ] Network routing < 1s\n[ ] Message delivery < 100ms\n\nConcurrency\n[ ] 10 concurrent agents\n[ ] 100 messages/second\n[ ] No memory leaks\n\nError Handling Verification\n\nAgent Errors\n[ ] Invalid input handled\n[ ] Provider errors caught\n[ ] Timeout errors handled\n[ ] Schema validation errors\n\nNetwork Errors\n[ ] Routing failures handled\n[ ] Agent unavailable handled\n[ ] Network timeout handled\n\nMessage Errors\n[ ] Subscriber errors isolated\n[ ] Timeout errors reported\n[ ] Invalid message rejected\n\nDocumentation Verification\n[ ] README complete\n[ ] API documented\n[ ] Examples provided\n[ ] Error messages clear\n\nCLI Coverage Verification\n\nAll agent and network CLI commands are implemented in\nsrc/cli/commands/agent.ts.\n\nAgent Commands\n[ ] neurolink agent create - available\n[ ] neurolink agent list - available\n[ ] neurolink agent execute - available\n[ ] neurolink agent run (alias for execute) - available\n\nNetwork Commands\n[ ] neurolink network create - available\n[ ] neurolink network list - available\n[ ] neurolink network execute - available\n[ ] neurolink network run (alias for execute) - available\n\nSee CLI-COVERAGE.md for full flag reference and usage\nexamples.\n\nFinal Verification Summary\n\n| Category | Method | Status |\n| -------------------- | ---------------------------------------------------- | ------ |\n| Agent Class | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| AgentNetwork | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| MessageBus | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| HubSpokeTopology | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| MeshTopology | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| HierarchicalTopology | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| CLI Commands | Manual smoke test (neurolink agent --help) | ? |\n| Integration | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n\nSign-Off\n[ ] Integration tests passing\n[ ] CLI commands smoke-tested\n[ ] Performance acceptable\n[ ] Documentation complete\n\nVerified by: \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ Date: \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"","lvl3":""}}, {"objectID":"598c1fb23f9f6357fe9f90c06c346c20378de9a6bd81420e453a10e1451332d9","title":"Overview","url":"/docs/agents/VERIFICATION#overview","content":"This document provides a manual verification checklist for the Multi-Agent\nNetworks feature.","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Overview","lvl3":""}}, {"objectID":"b99acef6b83c34129be0d828393780af67434a2cb5b9f20be7d6c546080a711d","title":"1. Environment Preparation","url":"/docs/agents/VERIFICATION#1-environment-preparation","content":"[ ] Node.js 18+ installed\n[ ] pnpm installed\n[ ] Project dependencies installed (pnpm install)\n[ ] Project built (pnpm run build)\n[ ] At least one provider API key configured","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"1. Environment Preparation","lvl3":""}}, {"objectID":"b5c94878620d4b16bceec685ce93fb2c13e34338172350e9a46254c95c1e9419","title":"2. Required API Keys","url":"/docs/agents/VERIFICATION#2-required-api-keys","content":"Configure at least one of:\n[ ] OPENAI_API_KEY\n[ ] ANTHROPIC_API_KEY\n[ ] GOOGLE_AI_STUDIO_API_KEY\n[ ] GOOGLE_CLOUD_PROJECT (for Vertex AI)","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"2. 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Integration","url":"/docs/agents/VERIFICATION#network-topology-integration","content":"[ ] Hub-spoke config valid\n[ ] Mesh config valid\n[ ] Hierarchical config valid\n[ ] Router configs valid\n[ ] Network defaults valid","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Network Topology Integration","lvl3":""}}, {"objectID":"cae13fe2596c0d7f1918467cfc1d2af7d52136be248ccdb5712b0a18e760e116","title":"Routing Rules Integration","url":"/docs/agents/VERIFICATION#routing-rules-integration","content":"[ ] All rules defined\n[ ] Pattern matching works\n[ ] Confidence thresholds set\n[ ] Fallback behavior defined\n[ ] Priority ordering correct","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Routing Rules Integration","lvl3":""}}, {"objectID":"422f7b7c798018550a49d495f5ca8c818a3843adb85713e429756d336d47f4f8","title":"MessageBus Integration","url":"/docs/agents/VERIFICATION#messagebus-integration","content":"[ ] All message types defined\n[ ] Test messages valid\n[ ] Subscription patterns work\n[ ] Priority levels correct\n[ ] Test scenarios execute","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"MessageBus Integration","lvl3":""}}, {"objectID":"bdfafb79165f7e2f1c17dbe1fe1242cf8b54b2a065b2bb8fa09b7d275a2800f7","title":"Basic Agent Operations","url":"/docs/agents/VERIFICATION#basic-agent-operations","content":"[ ] Create agent programmatically\n[ ] Execute agent with text input\n[ ] Execute agent with structured input\n[ ] Stream agent output\n[ ] Handle agent errors","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Basic Agent Operations","lvl3":""}}, {"objectID":"755c527324f8a158240cd6b93e350b57b4d1f0e1d0f42c3aab3fd4feafaffb28","title":"Network Operations","url":"/docs/agents/VERIFICATION#network-operations","content":"[ ] Create network with multiple agents\n[ ] Execute network task\n[ ] Observe routing decisions\n[ ] Track execution traces\n[ ] Handle network failures","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Network Operations","lvl3":""}}, {"objectID":"854fd66f549071b88397743d0410e41b0efff81acbd29da674fc1f3e9abc9252","title":"Messaging Operations","url":"/docs/agents/VERIFICATION#messaging-operations","content":"[ ] Publish message\n[ ] Subscribe and receive\n[ ] Request-response works\n[ ] Broadcast reaches all\n[ ] Priority respected","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Messaging Operations","lvl3":""}}, {"objectID":"df93cc0a7418ba6099f177e1bd0d2fa3e2acfc464128889ff3e7486c0bd4a1a5","title":"Response Time","url":"/docs/agents/VERIFICATION#response-time","content":"[ ] Single agent < 5s\n[ ] Network routing < 1s\n[ ] Message delivery < 100ms","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Response Time","lvl3":""}}, 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handled","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Network Errors","lvl3":""}}, {"objectID":"63d402dac874483fa215b8579c6de155fcc3f328b72288cfbc02a810b8dc8845","title":"Message Errors","url":"/docs/agents/VERIFICATION#message-errors","content":"[ ] Subscriber errors isolated\n[ ] Timeout errors reported\n[ ] Invalid message rejected","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Message Errors","lvl3":""}}, {"objectID":"ae5546d412659375f86fc6a3f2800c1abdfc112da73797106dea44c1e10e4704","title":"Documentation Verification","url":"/docs/agents/VERIFICATION#documentation-verification","content":"[ ] README complete\n[ ] API documented\n[ ] Examples provided\n[ ] Error messages clear","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Documentation Verification","lvl3":""}}, {"objectID":"dfd3f12818baa5d2ade933d516cb4d6996922ad67baacea5455c2dc1513a6368","title":"CLI Coverage Verification","url":"/docs/agents/VERIFICATION#cli-coverage-verification","content":"All agent and network CLI commands are implemented in\nsrc/cli/commands/agent.ts.","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"CLI Coverage Verification","lvl3":""}}, {"objectID":"385f0936e6517b08b28a51d2d64f1b43b808201ee8c99eb4be694dbaa947a534","title":"Agent Commands","url":"/docs/agents/VERIFICATION#agent-commands","content":"[ ] neurolink agent create - available\n[ ] neurolink agent list - available\n[ ] neurolink agent execute - available\n[ ] neurolink agent run (alias for execute) - available","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Agent Commands","lvl3":""}}, {"objectID":"8808ba67debf1606303ecc0aaa21f46aad53b23e0545e852ae816da0f8d1b70e","title":"Network Commands","url":"/docs/agents/VERIFICATION#network-commands","content":"[ ] neurolink network create - available\n[ ] neurolink network list - available\n[ ] neurolink network execute - available\n[ ] neurolink network run (alias for execute) - available\n\nSee CLI-COVERAGE.md for full flag reference and usage\nexamples.","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Network Commands","lvl3":""}}, {"objectID":"3bf417c52feb21b5fcbad8ff3162a8bae02e1245c62819c46ed375c3d373c2ee","title":"Final Verification Summary","url":"/docs/agents/VERIFICATION#final-verification-summary","content":"| Category | Method | Status |\n| -------------------- | ---------------------------------------------------- | ------ |\n| Agent Class | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| AgentNetwork | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| MessageBus | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| HubSpokeTopology | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| MeshTopology | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| HierarchicalTopology | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |\n| CLI Commands | Manual smoke test (neurolink agent --help) | ? |\n| Integration | pnpm exec tsx test/continuous-test-suite-agents.ts | ? |","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Final Verification Summary","lvl3":""}}, {"objectID":"0e38ebba0b49d2df35a436aec7b1df78eb32193aba379664f377a9d1029ec49a","title":"Sign-Off","url":"/docs/agents/VERIFICATION#sign-off","content":"[ ] Integration tests passing\n[ ] CLI commands smoke-tested\n[ ] Performance acceptable\n[ ] Documentation complete\n\nVerified by: \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ Date: \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\","hierarchy":{"lvl0":"Agents","lvl1":"Multi-Agent Networks Verification Checklist","lvl2":"Sign-Off","lvl3":""}}, {"objectID":"1dcbe493b12b60692a8a9f9e2f47b6789f2a5f0cb86b6fb64bf93493eb0d6c64","title":"🧠 AI Analysis Tools","url":"/docs/ai-analysis-tools","content":"🧠 AI Analysis Tools\n\nNeuroLink features 3 specialized AI Analysis Tools for AI optimization and workflow enhancement. These tools work seamlessly behind our factory method interface, providing enterprise-grade AI analysis capabilities.\n\n🏆 Production Status\n\nProduction Ready: 20/20 Tests Passing (100% Success Rate)\n✅ 3 AI Analysis Tools Implemented: Complete AI optimization and analysis capabilities\n✅ Enterprise Integration: Professional web interface with full API endpoints\n✅ Performance Validated: All tools execute under 1ms individually, 7 seconds total for full suite\n✅ Production Infrastructure: Rich context, permissions, error handling, comprehensive validation\n\n🔧 Available Tools\nAI Usage Analysis - analyzeAIUsage()\n\nAnalyze AI usage patterns, token consumption, and cost optimization across your configured providers.\n\nFeatures:\nToken Usage Analytics: Detailed breakdown by provider and time period\nCost Optimization: Identify most cost-effective providers for your workload\nUsage Patterns: Detect peak usage times and optimization opportunities\nProvider Comparison: Side-by-side cost and performance analysis\nProvider Performance Benchmarking - benchmarkProviders()\n\nAdvanced benchmarking with latency, quality, and cost metrics across configured AI providers.\n\nFeatures:\nLatency Testing: Measure real response times across providers\nQuality Assessment: Evaluate output quality for different prompt types\nCost Efficiency: Calculate cost per token and value metrics\nProvider Rankings: Automatic ranking by performance criteria\nPrompt Parameter Optimization - optimizePrompt()\n\nOptimize prompt parameters (temperature, max tokens, style) for better output quality.\n\nFeatures:\nParameter Tuning: Automatic optimization of temperature, max tokens, style\nQuality Prediction: Estimate quality improvements from parameter changes\nAlternative Suggestions: Multiple parameter sets for different use cases\nStyle Optimization: Adjust parameters for specific writing styles\n\n🎯 Business Benefits\n\nCost Optimization\nProvider Cost Analysis: Identify most cost-effective providers for your workload\nUsage Pattern Insights: Detect opportunities to reduce token consumption\nBudget Planning: Predict costs based on historical usage patterns\n\nPerformance Enhancement\nReal-time Benchmarking: Continuous performance monitoring across providers\nQuality Metrics: Measure and improve output quality over time\nLatency Optimization: Choose fastest providers for time-sensitive applications\n\nParameter Intelligence\nAutomated Tuning: Remove guesswork from prompt parameter selection\nQuality Prediction: Understand impact of parameter changes before implementation\nStyle Adaptation: Optimize parameters for different content types\n\n🌐 Interactive Web Interface\n\nAll AI Analysis Tools are available through our unified demo application with professional UI:\n\nFeatures\n✅ Real-time Analysis: Interactive forms for all 3 analysis tools\n✅ API Endpoints: Full REST API at /api/ai/analyze-usage, /api/ai/benchmark-performance, /api/ai/optimize-parameters\n✅ JSON Results: Comprehensive analysis results with visual feedback\n✅ Simulation Mode: Fallback to realistic simulated responses for demonstration\n\nAPI Endpoints\n\nAnalyze AI Usage\n\nBenchmark Performance\n\nOptimize Parameters\n\n🎬 Visual Documentation\n\nScreenshots\nAI Usage Analysis Interface: Interactive form with real-time token analysis\nPerformance Benchmarking: Provider comparison with latency and quality metrics\nParameter Optimization: Prompt tuning interface with multiple suggestions\n\nDemo Videos\n\nAll analysis tools are demonstrated in our comprehensive demo videos:\nVisual Demos - Real-time analysis and optimization demonstrations\n\n🔧 Technical Implementation\n\nMCP Integration\n\nAI Analysis Tools are implemented as MCP (Model Context Protocol) tools that work internally behind our factory methods:\n\nError Handling\nGraceful Fallback: Tools fall back to simulation mode if AI providers unavailable\nComprehensive Validation: Input validation and error reporting\nProduction Logging: Detailed logging for debugging and monitoring\n\nPerformance Metrics\nTool Execution: Individual tools execute under 1ms\nSuite Execution: Complete analysis suite runs in ~7 seconds\nAPI Response: REST endpoints respond within 2-5 seconds\nError Recovery: Automatic fallback to simulation mode on provider failures\n\n🚀 Getting Started\nInstall NeuroLink: npm install @juspay/neurolink\nSet up providers: Configure at least one AI provider (see Provider Configuration) (now with authentication and model availability checks)\nTry the tools: Use factory methods or visit the demo application\nIntegrate APIs: Use REST endpoints for web applications\n\n📚 Related Documentation\nMain README - Project overview and quick start\nAI Workflow Tools - Development lifecycle tools\nMCP Foundation - Technical architecture details\nAPI Reference - Complete TypeScript API\nVisual Demos - Screenshots and videos\n\nEnterprise AI Analysis - Transform your AI development workflow with data-driven insight","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"","lvl3":""}}, {"objectID":"66690692e64f192b8fce4bc12064c12cd35dcbeaf9fb1990c69ccda1b84f0a58","title":"🧠 AI Analysis Tools","url":"/docs/ai-analysis-tools#-ai-analysis-tools","content":"NeuroLink features 3 specialized AI Analysis Tools for AI optimization and workflow enhancement. These tools work seamlessly behind our factory method interface, providing enterprise-grade AI analysis capabilities.","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"🧠 AI Analysis Tools","lvl3":""}}, {"objectID":"ea8da74b416b1adbb43bbb8d705549eb806e256ee9a12f302e0b8812cfa100ef","title":"🏆 Production Status","url":"/docs/ai-analysis-tools#-production-status","content":"Production Ready: 20/20 Tests Passing (100% Success Rate)\n✅ 3 AI Analysis Tools Implemented: Complete AI optimization and analysis capabilities\n✅ Enterprise Integration: Professional web interface with full API endpoints\n✅ Performance Validated: All tools execute under 1ms individually, 7 seconds total for full suite\n✅ Production Infrastructure: Rich context, permissions, error handling, comprehensive validation","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"🏆 Production Status","lvl3":""}}, {"objectID":"b5bb3d3ac6141db777c7b58baac837ce10d4a35cc79794fb84a1a7c9f03c0d00","title":"1. AI Usage Analysis - analyzeAIUsage()","url":"/docs/ai-analysis-tools#1-ai-usage-analysis---analyzeaiusage","content":"Analyze AI usage patterns, token consumption, and cost optimization across your configured providers.\n\nFeatures:\nToken Usage Analytics: Detailed breakdown by provider and time period\nCost Optimization: Identify most cost-effective providers for your workload\nUsage Patterns: Detect peak usage times and optimization opportunities\nProvider Comparison: Side-by-side cost and performance analysis","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"1. AI Usage Analysis - analyzeAIUsage()","lvl3":""}}, {"objectID":"e56c57d9e2c4bebca178d66bdb3825da39c6a402aa5cb3e360375dca3780f568","title":"2. Provider Performance Benchmarking - benchmarkProviders()","url":"/docs/ai-analysis-tools#2-provider-performance-benchmarking---benchmarkproviders","content":"Advanced benchmarking with latency, quality, and cost metrics across configured AI providers.\n\nFeatures:\nLatency Testing: Measure real response times across providers\nQuality Assessment: Evaluate output quality for different prompt types\nCost Efficiency: Calculate cost per token and value metrics\nProvider Rankings: Automatic ranking by performance criteria","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"2. Provider Performance Benchmarking - benchmarkProviders()","lvl3":""}}, {"objectID":"ccc0ec328fa4e1bf6569b75db1aab6acded275c4c3724465fdae50fb7658c641","title":"3. Prompt Parameter Optimization - optimizePrompt()","url":"/docs/ai-analysis-tools#3-prompt-parameter-optimization---optimizeprompt","content":"Optimize prompt parameters (temperature, max tokens, style) for better output quality.\n\nFeatures:\nParameter Tuning: Automatic optimization of temperature, max tokens, style\nQuality Prediction: Estimate quality improvements from parameter changes\nAlternative Suggestions: Multiple parameter sets for different use cases\nStyle Optimization: Adjust parameters for specific writing styles","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"3. Prompt Parameter Optimization - optimizePrompt()","lvl3":""}}, {"objectID":"b4acf2c29169709cf647011258869b62fcc281a8966741775480b1d545212969","title":"Cost Optimization","url":"/docs/ai-analysis-tools#cost-optimization","content":"Provider Cost Analysis: Identify most cost-effective providers for your workload\nUsage Pattern Insights: Detect opportunities to reduce token consumption\nBudget Planning: Predict costs based on historical usage patterns","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"Cost Optimization","lvl3":""}}, {"objectID":"6fa479f08fe4d34a1987229dc9244c0aea3fbeb096a2b900b7ac4de2c543ffd9","title":"Performance Enhancement","url":"/docs/ai-analysis-tools#performance-enhancement","content":"Real-time Benchmarking: Continuous performance monitoring across providers\nQuality Metrics: Measure and improve output quality over time\nLatency Optimization: Choose fastest providers for time-sensitive applications","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"Performance Enhancement","lvl3":""}}, {"objectID":"0c7b19978cbb947bead07c17ec00136548a93ecd335c486433cd3998186aace9","title":"Parameter Intelligence","url":"/docs/ai-analysis-tools#parameter-intelligence","content":"Automated Tuning: Remove guesswork from prompt parameter selection\nQuality Prediction: Understand impact of parameter changes before implementation\nStyle Adaptation: Optimize parameters for different content types","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"Parameter Intelligence","lvl3":""}}, {"objectID":"f954c4280234e0775054552324a3c34457e180e5b309f05bd7bd0765f2f6c8ae","title":"🌐 Interactive Web Interface","url":"/docs/ai-analysis-tools#-interactive-web-interface","content":"All AI Analysis Tools are available through our unified demo application with professional UI:\n\n`bash\ncd neurolink-demo && node server.js","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"🌐 Interactive Web Interface","lvl3":""}}, {"objectID":"554fb998875680f0b851fc0e06b0450b646380c55c9d4c62de5254a9719f6c48","title":"Visit http://localhost:9876 to see AI Analysis Tools in action","url":"/docs/ai-analysis-tools#visit-httplocalhost9876-to-see-ai-analysis-tools-in-action","content":"`","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"Visit http://localhost:9876 to see AI Analysis Tools in action","lvl3":""}}, {"objectID":"b47021413ce4b06e5890942582058f149911e47c3801ada4d5b333ddf7b25de0","title":"Features","url":"/docs/ai-analysis-tools#features","content":"✅ Real-time Analysis: Interactive forms for all 3 analysis tools\n✅ API Endpoints: Full REST API at /api/ai/analyze-usage, /api/ai/benchmark-performance, /api/ai/optimize-parameters\n✅ JSON Results: Comprehensive analysis results with visual feedback\n✅ Simulation Mode: Fallback to realistic simulated responses for demonstration","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"Features","lvl3":""}}, {"objectID":"d655ecf85bc1d8386ed211b289a815abe97562716fac1c26d9e0b17aad178a52","title":"Screenshots","url":"/docs/ai-analysis-tools#screenshots","content":"AI Usage Analysis Interface: Interactive form with real-time token analysis\nPerformance Benchmarking: Provider comparison with latency and quality metrics\nParameter Optimization: Prompt tuning interface with multiple suggestions","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"Screenshots","lvl3":""}}, {"objectID":"880bda95f9a1b6fb1534647ec3559b2d6b66683902701a12362735dead5cb185","title":"Demo Videos","url":"/docs/ai-analysis-tools#demo-videos","content":"All analysis tools are demonstrated in our comprehensive demo videos:\nVisual Demos - Real-time analysis and optimization demonstrations","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"Demo Videos","lvl3":""}}, {"objectID":"160edc60d2c9e281489f6e93cef72282452b7090ee0600383d4f426ac23c4167","title":"MCP Integration","url":"/docs/ai-analysis-tools#mcp-integration","content":"AI Analysis Tools are implemented as MCP (Model Context Protocol) tools that work internally behind our factory methods:","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"MCP Integration","lvl3":""}}, {"objectID":"7e04dc696464363c2bea6210f6c444d3410fa01ae08e0cb9f61f11ffc80a9d11","title":"Error Handling","url":"/docs/ai-analysis-tools#error-handling","content":"Graceful Fallback: Tools fall back to simulation mode if AI providers unavailable\nComprehensive Validation: Input validation and error reporting\nProduction Logging: Detailed logging for debugging and monitoring","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"Error Handling","lvl3":""}}, {"objectID":"2d85636dfd7fae3624eb9a194ab51b1de91702965d5482f2986a28e36143cb7d","title":"Performance Metrics","url":"/docs/ai-analysis-tools#performance-metrics","content":"Tool Execution: Individual tools execute under 1ms\nSuite Execution: Complete analysis suite runs in ~7 seconds\nAPI Response: REST endpoints respond within 2-5 seconds\nError Recovery: Automatic fallback to simulation mode on provider failures","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"Performance Metrics","lvl3":""}}, {"objectID":"de9e31f569da491c78051955d3cb4e157104b14ead5fd10a21447a96b7e65f36","title":"🚀 Getting Started","url":"/docs/ai-analysis-tools#-getting-started","content":"Install NeuroLink: npm install @juspay/neurolink\nSet up providers: Configure at least one AI provider (see Provider Configuration) (now with authentication and model availability checks)\nTry the tools: Use factory methods or visit the demo application\nIntegrate APIs: Use REST endpoints for web applications","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"🚀 Getting Started","lvl3":""}}, {"objectID":"dfec0c50580feab838da9596a104c686eb1d154f090d71a99bf556dab7c7b721","title":"📚 Related Documentation","url":"/docs/ai-analysis-tools#-related-documentation","content":"Main README - Project overview and quick start\nAI Workflow Tools - Development lifecycle tools\nMCP Foundation - Technical architecture details\nAPI Reference - Complete TypeScript API\nVisual Demos - Screenshots and videos\n\nEnterprise AI Analysis - Transform your AI development workflow with data-driven insights and optimization recommendations.","hierarchy":{"lvl0":"Ai Analysis Tools","lvl1":"🧠 AI Analysis Tools","lvl2":"📚 Related Documentation","lvl3":""}}, {"objectID":"0c3ca4d7eec12de7fee3b64b95a83e48bfeaac676547551c8fc6ad41ad074f0d","title":"🚀 NeuroLink AI Enhancements - Complete Documentation","url":"/docs/ai-enhancements","content":"🚀 NeuroLink AI Enhancements - Complete Documentation\n\nOverview\n\nNeuroLink v3.1.0 introduces 6 powerful AI enhancement features that transform it from a basic AI SDK into a comprehensive AI development platform with quality monitoring and analytics capabilities.\n\n🆕 New Features\nResponse Quality Evaluation ⭐\n\nAI-powered quality scoring using fast, cost-effective models to evaluate response quality on multiple dimensions.\n\nMetrics:\nRelevance (1-10): How well the response addresses the prompt\nAccuracy (1-10): Factual correctness of the information\nCompleteness (1-10): Whether the response fully answers the question\nOverall (1-10): Combined quality assessment\n\nConfiguration:\nUsage Analytics 📊\n\nComprehensive tracking of AI usage patterns, costs, and performance metrics.\n\nMetrics Captured:\nToken usage (input, output, total)\nEstimated costs (based on provider pricing)\nResponse time\nProvider and model used\nCustom context data\nTimestamp\n\nSupported Cost Estimation:\nOpenAI (GPT-4, GPT-4 Turbo, GPT-3.5 Turbo)\nAnthropic (Claude 3 Opus, Sonnet, Haiku)\nGoogle AI (Gemini Pro, Gemini 2.5 Flash)\nGeneric Context Flow 🔄\n\nPass custom context objects through the entire AI request lifecycle for domain-specific tracking and analytics.\n\nUse Cases:\nUser identification (userId, sessionId)\nDomain-specific metadata (department, project)\nRequest categorization (priority, type)\nCustom business logic data\nQuality Monitoring 📈\n\nAnalytics and evaluation data returned in response objects for user-controlled alerting and monitoring.\n\nNo External Dependencies:\nAll data stays within NeuroLink ecosystem\nUsers control what to do with the data\nNo forced external endpoints or webhooks\n\n🛠️ SDK Usage\n\nBasic Usage with Analytics\n\nUsage with Quality Evaluation\n\nCombined Analytics and Evaluation\n\n🖥️ CLI Usage\n\nAnalytics Tracking\n\nQuality Evaluation\n\nCustom Context\n\nAll Features Combined\nUniversal Evaluation System 🌐\n\nEnterprise-grade multi-provider evaluation with intelligent fallback, cost optimization, and performance tuning.\n\nKey Features:\nMulti-Provider Support: Google AI, OpenAI, Anthropic, Vertex, Bedrock, Azure, Ollama, Hugging Face, Mistral\nIntelligent Fallback: Automatic provider selection when primary fails\nCost Optimization: Provider-specific cost calculations and budget awareness\nPerformance Modes: Fast, balanced, and quality evaluation options\nRetry Logic: Robust error handling with exponential backoff\n\nConfiguration:\n\nUsage:\n\nCLI Usage:\nLighthouse Enhanced Evaluation 🎯\n\nDomain-aware evaluation with 6-dimensional scoring based on Lighthouse AI platform patterns.\n\nEnhanced Scoring Dimensions:\nRelevance Score (1-10): How well response addresses the prompt\nAccuracy Score (1-10): Factual correctness of information\nCompleteness Score (1-10): Whether response fully answers question\nDomain Alignment (1-10): Expertise alignment with specified domain\nTerminology Accuracy (1-10): Proper use of domain-specific terms\nTool Effectiveness (1-10): How well MCP tools were utilized\n\nAdvanced Features:\nContext Integration: Tool usage tracking and conversation history\nDomain Expertise: Specialized evaluation prompts for specific domains\nEnterprise Telemetry: Structured logging with OpenTelemetry patterns\nBackward Compatibility: Full compatibility with Universal Evaluation System\n\nCLI Usage:\n\nSDK Usage:\n\n📋 Interface Reference\n\nEnhanced TextGenerationOptions\n\nAnalyticsData Structure\n\nEvaluationData Structure\n\n🔧 Configuration\n\nEnvironment Variables\n\nCost Estimation Configuration\n\nBuilt-in pricing for major providers (updated regularly):\n\n🚀 Performance Considerations\n\nPerformance Impact\nFeatures Disabled (default): Zero overhead\nAnalytics Only: \\<5ms additional processing\nEvaluation Only: Depends on evaluation model (recommend fast models)\nBoth Enabled: Minimal combined impact\n\nCost Optimization\nAnalytics: No additional API costs (local processing)\nEvaluation: Additional API calls to evaluation model\nRecommendation: Use fast, cheap models like Gemini 2.5 Flash for evaluation\n\nScaling Recommendations\nUse analytics for all production requests\nUse evaluation for critical or customer-facing content\nImplement sampling for high-volume applications\nCache evaluation results for similar prompts\n\n🛡️ Security & Privacy\nNo External Transmission: All data stays within NeuroLink ecosystem\nUser Control: You decide what to do with analytics/evaluation data\nContext Security: Context objects support any data format you control\nProvider Security: Same security model as existing NeuroLink providers\n\n🔄 Migration Guide\n\nFrom v3.0.x to v3.1.x\n\nZero Breaking Changes! All existing code continues to work unchanged.\n\nCLI Migration\n\n📚 Examples & Use Cases\n\nCustomer Support Analytics\n\nContent Generation Pipeline\n\nCost Monitoring Dashboard\n\n🎯 Best Practices\nEnable Analytics by Default: Track all production usage\nSelective Evaluation: Use for critical or customer-facing content\nMeaningful Context: Include user/session IDs for tracking\nQuality Thresholds: Set minimum quality score","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"","lvl3":""}}, {"objectID":"512ceed0db78e7f0d674d5e37f90436adc5b945cda65d883131596e99e69a0a6","title":"Overview","url":"/docs/ai-enhancements#overview","content":"NeuroLink v3.1.0 introduces 6 powerful AI enhancement features that transform it from a basic AI SDK into a comprehensive AI development platform with quality monitoring and analytics capabilities.","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Overview","lvl3":""}}, {"objectID":"d2122d692220c6532895d03b0cc8014c1663ff0479ac29ae7a4012c2d41eae55","title":"1. Response Quality Evaluation ⭐","url":"/docs/ai-enhancements#1-response-quality-evaluation-","content":"AI-powered quality scoring using fast, cost-effective models to evaluate response quality on multiple dimensions.\n\nMetrics:\nRelevance (1-10): How well the response addresses the prompt\nAccuracy (1-10): Factual correctness of the information\nCompleteness (1-10): Whether the response fully answers the question\nOverall (1-10): Combined quality assessment\n\nConfiguration:\n\n`bash","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"1. Response Quality Evaluation ⭐","lvl3":""}}, {"objectID":"9136704b56c2ed8a21fe9b3f36eeeecd37ea82d47ad8e42e087c3e6876baa285","title":"Optional environment variables","url":"/docs/ai-enhancements#optional-environment-variables","content":"NEUROLINKEVALUATIONMODEL=gemini-2.5-flash\nNEUROLINKEVALUATIONPROVIDER=google-ai\n`","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Optional environment variables","lvl3":""}}, {"objectID":"e2caa987b1774e4c74e82515ee86080befdc094b8db6cbbed2ee3a898fbf285b","title":"2. Usage Analytics 📊","url":"/docs/ai-enhancements#2-usage-analytics-","content":"Comprehensive tracking of AI usage patterns, costs, and performance metrics.\n\nMetrics Captured:\nToken usage (input, output, total)\nEstimated costs (based on provider pricing)\nResponse time\nProvider and model used\nCustom context data\nTimestamp\n\nSupported Cost Estimation:\nOpenAI (GPT-4, GPT-4 Turbo, GPT-3.5 Turbo)\nAnthropic (Claude 3 Opus, Sonnet, Haiku)\nGoogle AI (Gemini Pro, Gemini 2.5 Flash)","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"2. Usage Analytics 📊","lvl3":""}}, {"objectID":"add63411cc6134e07905954dac7d95e6ec2af232cad1f4fe0f5195e29a8caf52","title":"3. Generic Context Flow 🔄","url":"/docs/ai-enhancements#3-generic-context-flow-","content":"Pass custom context objects through the entire AI request lifecycle for domain-specific tracking and analytics.\n\nUse Cases:\nUser identification (userId, sessionId)\nDomain-specific metadata (department, project)\nRequest categorization (priority, type)\nCustom business logic data","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"3. Generic Context Flow 🔄","lvl3":""}}, {"objectID":"8c8afb326c743aae10af44a7383520c5303659de50b0f7fd5b71b3ec25388a89","title":"4. Quality Monitoring 📈","url":"/docs/ai-enhancements#4-quality-monitoring-","content":"Analytics and evaluation data returned in response objects for user-controlled alerting and monitoring.\n\nNo External Dependencies:\nAll data stays within NeuroLink ecosystem\nUsers control what to do with the data\nNo forced external endpoints or webhooks","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"4. Quality Monitoring 📈","lvl3":""}}, {"objectID":"8f897b9e1cc0850887c235b8e80df4c4b82e23434e8f5cda0cc8d921f5bd95a1","title":"Analytics Tracking","url":"/docs/ai-enhancements#analytics-tracking","content":"`bash","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Analytics Tracking","lvl3":""}}, {"objectID":"206ad8f0cab3677690587507ba8c491362d411dcc4e9d3e287428f8c27a852fd","title":"Enable analytics with debug output","url":"/docs/ai-enhancements#enable-analytics-with-debug-output","content":"npx @juspay/neurolink generate \"Explain quantum computing\" \\\n --enable-analytics \\\n --debug","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Enable analytics with debug output","lvl3":""}}, {"objectID":"4da140acf339b07fc9c38c87e31cdaeab63a6178d0d59a9cea405990e5142aeb","title":"- Provider information","url":"/docs/ai-enhancements#--provider-information","content":"`","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"- Provider information","lvl3":""}}, {"objectID":"8d71322b339986bf6954f84c3f69031040fb3958977b33ab4836c645851677ed","title":"Quality Evaluation","url":"/docs/ai-enhancements#quality-evaluation","content":"`bash","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Quality Evaluation","lvl3":""}}, {"objectID":"435f8d20d9ea614a0e22fcb8e5e09cccd6cce1a1aff12d83e412724c1ffa5c50","title":"Enable response quality scoring","url":"/docs/ai-enhancements#enable-response-quality-scoring","content":"npx @juspay/neurolink generate \"Write a business proposal\" \\\n --enable-evaluation \\\n --debug","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Enable response quality scoring","lvl3":""}}, {"objectID":"254e467c6ce041ef0a4f29970a1122355070e974b8658d2c171483e9788d68e7","title":"- Evaluation time","url":"/docs/ai-enhancements#--evaluation-time","content":"`","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"- Evaluation time","lvl3":""}}, {"objectID":"5f05e97f8f9f87ebb38655b67ff24693ac4f89a38b57ac295145c6c58530730c","title":"Custom Context","url":"/docs/ai-enhancements#custom-context","content":"`bash","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Custom Context","lvl3":""}}, {"objectID":"b65cbafe5841729c29a9c7b2e371d401f54cf7c4ff4036c985cab215c0573a54","title":"Pass custom context data","url":"/docs/ai-enhancements#pass-custom-context-data","content":"npx @juspay/neurolink generate \"Help with customer issue\" \\\n --context '{\"userId\":\"support-001\",\"priority\":\"high\",\"department\":\"customer-service\"}' \\\n --enable-analytics \\\n --debug","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Pass custom context data","lvl3":""}}, {"objectID":"4ec3cef712d6cca4a3c2482777fad60bfb686d5b4c65e8be94e8c055df8f666b","title":"Context appears in analytics data for tracking","url":"/docs/ai-enhancements#context-appears-in-analytics-data-for-tracking","content":"`","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Context appears in analytics data for tracking","lvl3":""}}, {"objectID":"99797b075d2fa6f8219510f2bfa2c37028a1ee86c0f1690395f469dfbe3e492f","title":"All Features Combined","url":"/docs/ai-enhancements#all-features-combined","content":"`bash","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"All Features Combined","lvl3":""}}, {"objectID":"54a1be91463ed5aba200f5bcd864f4f09ef3c2691eaac2867069de2876f32252","title":"Use all enhancement features together","url":"/docs/ai-enhancements#use-all-enhancement-features-together","content":"npx @juspay/neurolink generate \"Generate marketing copy for AI product\" \\\n --enable-analytics \\\n --enable-evaluation \\\n --context '{\"campaign\":\"q1-2025\",\"target\":\"developers\",\"budget\":\"high\"}' \\\n --provider openai \\\n --temperature 0.8 \\\n --debug\n`","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Use all enhancement features together","lvl3":""}}, {"objectID":"f73ce5cc4748212c04a2d0ecb918e6e913ba8b463da3666a8301dd9364ff4ae1","title":"5. Universal Evaluation System 🌐","url":"/docs/ai-enhancements#5-universal-evaluation-system-","content":"Enterprise-grade multi-provider evaluation with intelligent fallback, cost optimization, and performance tuning.\n\nKey Features:\nMulti-Provider Support: Google AI, OpenAI, Anthropic, Vertex, Bedrock, Azure, Ollama, Hugging Face, Mistral\nIntelligent Fallback: Automatic provider selection when primary fails\nCost Optimization: Provider-specific cost calculations and budget awareness\nPerformance Modes: Fast, balanced, and quality evaluation options\nRetry Logic: Robust error handling with exponential backoff\n\nConfiguration:\n\n`bash","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"5. Universal Evaluation System 🌐","lvl3":""}}, {"objectID":"c5b8c93c2a78edddf2837a27279c62d890f91fddf57ba40e54f1c5971a3d929d","title":"Primary evaluation setup","url":"/docs/ai-enhancements#primary-evaluation-setup","content":"NEUROLINKEVALUATIONPROVIDER=google-ai\nNEUROLINKEVALUATIONMODE=fast\nNEUROLINKEVALUATIONFALLBACK_ENABLED=true\nNEUROLINKEVALUATIONFALLBACK_PROVIDERS=openai,anthropic,vertex","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Primary evaluation setup","lvl3":""}}, {"objectID":"b036f6d4fafc2138e4d4fd6f1f26e45a2fd64a7c9b1970f22b6fb4779f5728cf","title":"Cost optimization","url":"/docs/ai-enhancements#cost-optimization","content":"NEUROLINKEVALUATIONPREFER_CHEAP=true\nNEUROLINKEVALUATIONMAXCOSTPER_EVAL=0.01","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Cost optimization","lvl3":""}}, {"objectID":"49ea85018663a897b47d6278ee3285e382241cb631986b270e86295e75d8c60d","title":"Performance tuning","url":"/docs/ai-enhancements#performance-tuning","content":"NEUROLINKEVALUATIONTIMEOUT=10000\nNEUROLINKEVALUATIONRETRY_ATTEMPTS=2\ntypescript\n// Automatic provider selection\nconst result = await sdk.generate({\n input: { text: \"Explain quantum computing\" },\n enableEvaluation: true, // Uses configured evaluation system\n});\n\n// Will try: google-ai → openai → anthropic → vertex (if primary fails)\nbash","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Performance tuning","lvl3":""}}, {"objectID":"2205edbf9a32d89e08ac341baceb7dbf7689dd22fc859a1c95f3446378c06389","title":"Uses Universal Evaluation System automatically","url":"/docs/ai-enhancements#uses-universal-evaluation-system-automatically","content":"npx @juspay/neurolink generate \"What is machine learning?\" --enable-evaluation","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Uses Universal Evaluation System automatically","lvl3":""}}, {"objectID":"7ce05ea6db7675a28cb21a4473168f76aa34fbeed820e89463405d78a21d8c7b","title":"With debug to see provider selection","url":"/docs/ai-enhancements#with-debug-to-see-provider-selection","content":"npx @juspay/neurolink generate \"Explain AI\" --enable-evaluation --debug\n`","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"With debug to see provider selection","lvl3":""}}, {"objectID":"4a37505f2499fdb6bde89dd419d5849070d7fca13f9851519a7d264e1cdeb904","title":"6. Lighthouse Enhanced Evaluation 🎯","url":"/docs/ai-enhancements#6-lighthouse-enhanced-evaluation-","content":"Domain-aware evaluation with 6-dimensional scoring based on Lighthouse AI platform patterns.\n\nEnhanced Scoring Dimensions:\nRelevance Score (1-10): How well response addresses the prompt\nAccuracy Score (1-10): Factual correctness of information\nCompleteness Score (1-10): Whether response fully answers question\nDomain Alignment (1-10): Expertise alignment with specified domain\nTerminology Accuracy (1-10): Proper use of domain-specific terms\nTool Effectiveness (1-10): How well MCP tools were utilized\n\nAdvanced Features:\nContext Integration: Tool usage tracking and conversation history\nDomain Expertise: Specialized evaluation prompts for specific domains\nEnterprise Telemetry: Structured logging with OpenTelemetry patterns\nBackward Compatibility: Full compatibility with Universal Evaluation System\n\nCLI Usage:\n\n`bash","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"6. Lighthouse Enhanced Evaluation 🎯","lvl3":""}}, {"objectID":"e206655af8fc70ab6beedb2bdb4d38800927b7a2bb530b3e681edf88aeea0b7b","title":"Basic Lighthouse-style evaluation","url":"/docs/ai-enhancements#basic-lighthouse-style-evaluation","content":"npx @juspay/neurolink generate \"Fix this Python code\" \\\n --lighthouse-style \\\n --evaluation-domain \"Python coding assistant\"","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Basic Lighthouse-style evaluation","lvl3":""}}, {"objectID":"d1218405c2090e20f435ed39933e12510a2506927903a14a3de500d6bb2084be","title":"Enterprise evaluation with full context","url":"/docs/ai-enhancements#enterprise-evaluation-with-full-context","content":"npx @juspay/neurolink generate \"Analyze sales performance\" \\\n --lighthouse-style \\\n --evaluation-domain \"Business data analyst\" \\\n --tool-usage-context \"Used sales-data and analytics MCP tools\" \\\n --context '{\"role\":\"senior_analyst\",\"department\":\"sales\"}'\ntypescript\n\n performEnhancedEvaluation,\n createEnhancedContext,\n} from \"@juspay/neurolink\";\n\n// Create enhanced evaluation context\nconst enhancedContext = createEnhancedContext(\n \"Write a business proposal for Q1 expansion\",\n result.text,\n {\n domain: \"Business development\",\n role: \"Business proposal assistant\",\n toolsUsed: [\"generate\", \"analytics-helper\"],\n conversationHistory: [\n { role: \"user\", content: \"I need help with our Q1 business plan\" },\n {\n role: \"assistant\",\n content: \"I can help you create a comprehensive plan\",\n },\n ],\n },\n);\n\n// Perform enhanced evaluation\nconst domainEvaluation = await performEnhancedEvaluation(enhancedContext);\nconsole.log(\"🎯 Enhanced Evaluation:\", domainEvaluation);\n// {\n// relevanceScore: 9, accuracyScore: 8, completenessScore: 9,\n// domainAlignment: 9, terminologyAccuracy: 8, toolEffectiveness: 9,\n// overall: 8.7, alertSeverity: 'none'\n// }\n`","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Enterprise evaluation with full context","lvl3":""}}, {"objectID":"47736aa116b113e26461b0b4f1e08bed68cc873fecc66e2fad4be53aeb8802fa","title":"Environment Variables","url":"/docs/ai-enhancements#environment-variables","content":"`bash","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Environment Variables","lvl3":""}}, {"objectID":"08251c48a6d84d15d488e4c3beba99850f3118b5e6635b63552b5848480796b7","title":"Response Quality Evaluation (optional)","url":"/docs/ai-enhancements#response-quality-evaluation-optional","content":"NEUROLINKEVALUATIONMODEL=gemini-2.5-flash\nNEUROLINKEVALUATIONPROVIDER=google-ai","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Response Quality Evaluation (optional)","lvl3":""}}, {"objectID":"9d0cc51a8eeb9f1f015de840f47e787b001ce1060314810affee9cda3b72d7a7","title":"Provider API Keys (existing)","url":"/docs/ai-enhancements#provider-api-keys-existing","content":"OPENAIAPIKEY=sk-your-openai-key\nGOOGLEAIAPI_KEY=AIza-your-google-ai-key\nAWSACCESSKEY_ID=your-aws-access-key","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Provider API Keys (existing)","lvl3":""}}, {"objectID":"ec9e808266891da33fa188d7601be7efa9768bcd3644aef6108e734bfe0afdb4","title":"... other provider keys","url":"/docs/ai-enhancements#-other-provider-keys","content":"`","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"... other provider keys","lvl3":""}}, {"objectID":"1ec24996dc22df5ac6840ef51869cb40f24670fd0dacddf45727c400cf6703ba","title":"Cost Estimation Configuration","url":"/docs/ai-enhancements#cost-estimation-configuration","content":"Built-in pricing for major providers (updated regularly):","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Cost Estimation Configuration","lvl3":""}}, {"objectID":"b2e2b35a6dcd2bb2787de0d8755f45f7568f0f1582999a96d902cf585a3b4a21","title":"Performance Impact","url":"/docs/ai-enhancements#performance-impact","content":"Features Disabled (default): Zero overhead\nAnalytics Only: \\<5ms additional processing\nEvaluation Only: Depends on evaluation model (recommend fast models)\nBoth Enabled: Minimal combined impact","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Performance Impact","lvl3":""}}, {"objectID":"7c341a448b6332ea307d1eb7abfa0d40b0c631756f6d330f18e28646e4431fec","title":"Cost Optimization","url":"/docs/ai-enhancements#cost-optimization-1","content":"Analytics: No additional API costs (local processing)\nEvaluation: Additional API calls to evaluation model\nRecommendation: Use fast, cheap models like Gemini 2.5 Flash for evaluation","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Cost Optimization","lvl3":""}}, {"objectID":"62fca298f806211e7abcb99f45d610f291355d553b12a1155509a6ce6c3cce69","title":"Scaling Recommendations","url":"/docs/ai-enhancements#scaling-recommendations","content":"Use analytics for all production requests\nUse evaluation for critical or customer-facing content\nImplement sampling for high-volume applications\nCache evaluation results for similar prompts","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Scaling Recommendations","lvl3":""}}, {"objectID":"acd250ccbc67054fd1b93f7ddd87e6055f7dabf658be2548b5dfa04791d6224f","title":"🛡️ Security & Privacy","url":"/docs/ai-enhancements#-security-privacy","content":"No External Transmission: All data stays within NeuroLink ecosystem\nUser Control: You decide what to do with analytics/evaluation data\nContext Security: Context objects support any data format you control\nProvider Security: Same security model as existing NeuroLink providers","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"🛡️ Security & Privacy","lvl3":""}}, {"objectID":"5413ba5994a2c22b56a802cd4612fae5b7952b837e89fcec3574f18483e17b37","title":"From v3.0.x to v3.1.x","url":"/docs/ai-enhancements#from-v30x-to-v31x","content":"Zero Breaking Changes! All existing code continues to work unchanged.","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"From v3.0.x to v3.1.x","lvl3":""}}, {"objectID":"2eaec8502d6f4adfacd34c66ddf29ad187b2ed1bff1d446803bda3793d287c76","title":"CLI Migration","url":"/docs/ai-enhancements#cli-migration","content":"`bash","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"CLI Migration","lvl3":""}}, {"objectID":"38669524f132577059ef7e31ff7aacd27c3cd65195b02b2a3cdbd5a85a7093fb","title":"Existing commands (unchanged)","url":"/docs/ai-enhancements#existing-commands-unchanged","content":"npx @juspay/neurolink generate \"Hello world\"","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Existing commands (unchanged)","lvl3":""}}, {"objectID":"5a759f5d7e2461669fbcebfb277025d1eb692b6e16ffbaf1649cbe08ab229858","title":"Enhanced commands (new flags)","url":"/docs/ai-enhancements#enhanced-commands-new-flags","content":"npx @juspay/neurolink generate \"Hello world\" --enable-analytics\nnpx @juspay/neurolink generate \"Hello world\" --enable-evaluation\nnpx @juspay/neurolink generate \"Hello world\" --context '{\"key\":\"value\"}'\n`","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"Enhanced commands (new flags)","lvl3":""}}, {"objectID":"222a12c5a25ae5c1156c921ce118cc4500693f9914b8ac73d4f8d2c0fb5b7ca3","title":"🎯 Best Practices","url":"/docs/ai-enhancements#-best-practices","content":"Enable Analytics by Default: Track all production usage\nSelective Evaluation: Use for critical or customer-facing content\nMeaningful Context: Include user/session IDs for tracking\nQuality Thresholds: Set minimum quality scores for auto-publish\nCost Alerts: Monitor spending with custom thresholds\nPerformance Monitoring: Track response times and token usage\nA/B Testing: Use context to track different prompt strategies\n\nNeuroLink AI Enhancements v3.1.0 - Transform your AI applications with comprehensive quality monitoring and analytics.","hierarchy":{"lvl0":"Ai Enhancements","lvl1":"🚀 NeuroLink AI Enhancements - Complete Documentation","lvl2":"🎯 Best Practices","lvl3":""}}, {"objectID":"e49c633cd6c41e779fe83198bcca809db2d36cec8210501159f293fc5c9e7642","title":"🛠️ AI Development Workflow Tools","url":"/docs/ai-workflow-tools","content":"🛠️ AI Development Workflow Tools\n\nNeuroLink features 4 specialized AI Development Workflow Tools for comprehensive AI development lifecycle support. These tools work seamlessly behind our factory method interface, providing enterprise-grade development assistance.\n\n🏆 Production Status\n\nProduction Ready: 24/24 Tests Passing (100% Success Rate)\n✅ 4 AI Workflow Tools Implemented: Complete development lifecycle support\n✅ Platform Evolution: NeuroLink now features 10 specialized tools (3 core + 3 analysis + 4 workflow)\n✅ Performance Validated: All tools designed for \\<100ms execution individually\n✅ Demo Integration: Professional web interface with complete API backend\n\n🔧 Available Tools\nTest Case Generation - generateTestCases()\n\nGenerate comprehensive test cases for code and AI applications with multiple testing strategies.\n\nFeatures:\nUnit Test Generation: Comprehensive unit test coverage for functions and classes\nEdge Case Detection: Identify and test boundary conditions and error scenarios\nIntegration Testing: Generate tests for component interactions and API endpoints\nFramework Support: Jest, Mocha, Vitest, and other popular testing frameworks\nRealistic Data: Generate meaningful test data and mock scenarios\nCode Refactoring - refactorCode()\n\nAI-powered code refactoring and optimization with performance and maintainability improvements.\n\nFeatures:\nModern JavaScript: Upgrade legacy code to ES6+, TypeScript, modern patterns\nPerformance Optimization: Identify and fix performance bottlenecks\nCode Quality: Improve readability, maintainability, and best practices\nPattern Recognition: Detect and apply appropriate design patterns\nSecurity Enhancements: Identify and fix potential security vulnerabilities\nDocumentation Generation - generateDocumentation()\n\nAutomatic documentation generation from code, APIs, and AI outputs with multiple formats.\n\nFeatures:\nAPI Documentation: Automatic generation of API reference documentation\nUser Guides: Create user-friendly tutorials and getting-started guides\nCode Examples: Generate working examples and usage patterns\nMultiple Formats: Markdown, HTML, PDF, and other documentation formats\nInteractive Examples: Create runnable code snippets and demos\nAI Output Debugging - debugAIOutput()\n\nAI output analysis and debugging assistance with issue identification and correction suggestions.\n\nFeatures:\nFormat Validation: Detect and fix JSON, XML, CSV, and other format issues\nLogic Analysis: Identify logical inconsistencies and data validation problems\nCompleteness Check: Ensure all required fields and information are present\nType Corrections: Fix data type mismatches and conversion errors\nStructure Optimization: Improve data organization and schema compliance\n\n🎯 Development Lifecycle Benefits\n\nAutomated Testing\nComprehensive Coverage: Generate tests for unit, integration, and edge cases\nFramework Agnostic: Support for popular testing frameworks and patterns\nRealistic Scenarios: Create meaningful test data and user scenarios\nContinuous Integration: Generate tests suitable for CI/CD pipelines\n\nCode Quality Enhancement\nModern Standards: Upgrade legacy code to current best practices\nPerformance Optimization: Identify and fix performance bottlenecks\nSecurity Improvements: Detect and remediate security vulnerabilities\nMaintainability: Improve code readability and long-term maintainability\n\nDocumentation Automation\nConsistent Documentation: Maintain up-to-date documentation automatically\nMultiple Audiences: Generate both technical and user-facing documentation\nInteractive Examples: Create runnable code examples and tutorials\nVersion Synchronization: Keep documentation in sync with code changes\n\nDebug Assistance\nAI Output Quality: Improve reliability of AI-generated content\nFormat Compliance: Ensure outputs meet required specifications\nError Prevention: Catch and fix issues before they reach production\nQuality Assurance: Validate AI outputs against expected standards\n\nStep 5: Debug Analysis - Acceptance Criteria\n\nThe debug analysis step (Step 5) in the complete workflow integration must meet these acceptance criteria:\n\nFunctional Requirements:\nIssue Detection: Must identify logical inconsistencies, format problems, and data validation issues\nRecommendation Generation: Must provide actionable suggestions for improvement\nAnalysis Depth: Must support \"detailed\", \"quick\", and \"comprehensive\" analysis modes\nMulti-format Support: Must handle JSON, XML, CSV, and other structured data formats\n\nQuality Standards:\nIssue Count Reporting: Must report exact number of issues found\nCategorized Issues: Must group issues by type (format, logic, completeness, type mismatches)\nSeverity Assessment: Must indicate issue severity and priority for fixes\nImprovement Suggestions: Must provide specific, implementable recommendations\n\nIntegration Requirements:\nWorkflow Continuity: Must accept output from previous workflow steps (refactored code)\nContext Preservation: Must maintain original prompt context for accurate analys","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"","lvl3":""}}, {"objectID":"8cb177ee9264fd6ffd3383d06bdf38a3a47d49f74227315d47a70ca15729ecc7","title":"🛠️ AI Development Workflow Tools","url":"/docs/ai-workflow-tools#-ai-development-workflow-tools","content":"NeuroLink features 4 specialized AI Development Workflow Tools for comprehensive AI development lifecycle support. These tools work seamlessly behind our factory method interface, providing enterprise-grade development assistance.","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"🛠️ AI Development Workflow Tools","lvl3":""}}, {"objectID":"bc8231c953ac56d42cd3bc8933cffc7b647d684eac44843a281df062507d366a","title":"🏆 Production Status","url":"/docs/ai-workflow-tools#-production-status","content":"Production Ready: 24/24 Tests Passing (100% Success Rate)\n✅ 4 AI Workflow Tools Implemented: Complete development lifecycle support\n✅ Platform Evolution: NeuroLink now features 10 specialized tools (3 core + 3 analysis + 4 workflow)\n✅ Performance Validated: All tools designed for \\<100ms execution individually\n✅ Demo Integration: Professional web interface with complete API backend","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"🏆 Production Status","lvl3":""}}, {"objectID":"cbeccdc76c39579edac175ec09134baf0f8209cca2085f37a22dae7fc8a14d79","title":"1. Test Case Generation - generateTestCases()","url":"/docs/ai-workflow-tools#1-test-case-generation---generatetestcases","content":"Generate comprehensive test cases for code and AI applications with multiple testing strategies.\n\nFeatures:\nUnit Test Generation: Comprehensive unit test coverage for functions and classes\nEdge Case Detection: Identify and test boundary conditions and error scenarios\nIntegration Testing: Generate tests for component interactions and API endpoints\nFramework Support: Jest, Mocha, Vitest, and other popular testing frameworks\nRealistic Data: Generate meaningful test data and mock scenarios","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"1. Test Case Generation - generateTestCases()","lvl3":""}}, {"objectID":"0a8a6bf03195733e7633af076c28a4c28026dc8380e26a321fe11fd72921bcda","title":"2. Code Refactoring - refactorCode()","url":"/docs/ai-workflow-tools#2-code-refactoring---refactorcode","content":"AI-powered code refactoring and optimization with performance and maintainability improvements.\n\nFeatures:\nModern JavaScript: Upgrade legacy code to ES6+, TypeScript, modern patterns\nPerformance Optimization: Identify and fix performance bottlenecks\nCode Quality: Improve readability, maintainability, and best practices\nPattern Recognition: Detect and apply appropriate design patterns\nSecurity Enhancements: Identify and fix potential security vulnerabilities","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"2. Code Refactoring - refactorCode()","lvl3":""}}, {"objectID":"327439fe201af422903a0e17dd7cfaba6a0714a67ec392d7e3a0fd34905c933b","title":"3. Documentation Generation - generateDocumentation()","url":"/docs/ai-workflow-tools#3-documentation-generation---generatedocumentation","content":"Automatic documentation generation from code, APIs, and AI outputs with multiple formats.\n\nFeatures:\nAPI Documentation: Automatic generation of API reference documentation\nUser Guides: Create user-friendly tutorials and getting-started guides\nCode Examples: Generate working examples and usage patterns\nMultiple Formats: Markdown, HTML, PDF, and other documentation formats\nInteractive Examples: Create runnable code snippets and demos","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"3. Documentation Generation - generateDocumentation()","lvl3":""}}, {"objectID":"fc50abe3f1a40d1b7a8df6e19df1b22495a40201341486744e3a91e8cc0a7c76","title":"4. AI Output Debugging - debugAIOutput()","url":"/docs/ai-workflow-tools#4-ai-output-debugging---debugaioutput","content":"AI output analysis and debugging assistance with issue identification and correction suggestions.\n\nFeatures:\nFormat Validation: Detect and fix JSON, XML, CSV, and other format issues\nLogic Analysis: Identify logical inconsistencies and data validation problems\nCompleteness Check: Ensure all required fields and information are present\nType Corrections: Fix data type mismatches and conversion errors\nStructure Optimization: Improve data organization and schema compliance","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"4. AI Output Debugging - debugAIOutput()","lvl3":""}}, {"objectID":"0597dc765e277da9ad83ee391e62977eb85328cadb1833e481a041b28a0305a2","title":"Automated Testing","url":"/docs/ai-workflow-tools#automated-testing","content":"Comprehensive Coverage: Generate tests for unit, integration, and edge cases\nFramework Agnostic: Support for popular testing frameworks and patterns\nRealistic Scenarios: Create meaningful test data and user scenarios\nContinuous Integration: Generate tests suitable for CI/CD pipelines","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Automated Testing","lvl3":""}}, {"objectID":"bc3cb1b45cec10c0089966a1c98292bd2a8599eab4de5a30329fe9d0345d90f0","title":"Code Quality Enhancement","url":"/docs/ai-workflow-tools#code-quality-enhancement","content":"Modern Standards: Upgrade legacy code to current best practices\nPerformance Optimization: Identify and fix performance bottlenecks\nSecurity Improvements: Detect and remediate security vulnerabilities\nMaintainability: Improve code readability and long-term maintainability","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Code Quality Enhancement","lvl3":""}}, {"objectID":"3544e10e3a2c02347948e5eeae999578036095a1f3434d387e9421e4406ea53a","title":"Documentation Automation","url":"/docs/ai-workflow-tools#documentation-automation","content":"Consistent Documentation: Maintain up-to-date documentation automatically\nMultiple Audiences: Generate both technical and user-facing documentation\nInteractive Examples: Create runnable code examples and tutorials\nVersion Synchronization: Keep documentation in sync with code changes","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Documentation Automation","lvl3":""}}, {"objectID":"daf5561763d01ab5b1413ad1c23ae19f4251af86523669a6836784ac259255e5","title":"Debug Assistance","url":"/docs/ai-workflow-tools#debug-assistance","content":"AI Output Quality: Improve reliability of AI-generated content\nFormat Compliance: Ensure outputs meet required specifications\nError Prevention: Catch and fix issues before they reach production\nQuality Assurance: Validate AI outputs against expected standards","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Debug Assistance","lvl3":""}}, {"objectID":"c478bd6598f7203e8894988db0ccdbcbca15704405b0ffd38dfbc6e4a4e3391e","title":"Step 5: Debug Analysis - Acceptance Criteria","url":"/docs/ai-workflow-tools#step-5-debug-analysis---acceptance-criteria","content":"The debug analysis step (Step 5) in the complete workflow integration must meet these acceptance criteria:\n\nFunctional Requirements:\nIssue Detection: Must identify logical inconsistencies, format problems, and data validation issues\nRecommendation Generation: Must provide actionable suggestions for improvement\nAnalysis Depth: Must support \"detailed\", \"quick\", and \"comprehensive\" analysis modes\nMulti-format Support: Must handle JSON, XML, CSV, and other structured data formats\n\nQuality Standards:\nIssue Count Reporting: Must report exact number of issues found\nCategorized Issues: Must group issues by type (format, logic, completeness, type mismatches)\nSeverity Assessment: Must indicate issue severity and priority for fixes\nImprovement Suggestions: Must provide specific, implementable recommendations\n\nIntegration Requirements:\nWorkflow Continuity: Must accept output from previous workflow steps (refactored code)\nContext Preservation: Must maintain original prompt context for accurate analysis\nError Handling: Must gracefully handle malformed or incomplete AI outputs\nPerformance: Must complete analysis within reasonable time limits\n\nOutput Format:","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Step 5: Debug Analysis - Acceptance Criteria","lvl3":""}}, {"objectID":"e55184a8db044f20df7f47cff80ba9dd4ecc053327a292cc7ef9d0c3764bdf94","title":"🌐 Interactive Web Interface","url":"/docs/ai-workflow-tools#-interactive-web-interface","content":"All AI Development Workflow Tools are available through our unified demo application:\n\n`bash\ncd neurolink-demo && node server.js","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"🌐 Interactive Web Interface","lvl3":""}}, {"objectID":"82b70d75989b2b56ecaf920f4ad5252ca79a9d0b1d1f5ab81f17c45f806334c6","title":"Visit http://localhost:9876 to see all 10 AI tools in action","url":"/docs/ai-workflow-tools#visit-httplocalhost9876-to-see-all-10-ai-tools-in-action","content":"`","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Visit http://localhost:9876 to see all 10 AI tools in action","lvl3":""}}, {"objectID":"2d9a4c63f7daf9d4816d4b87e6073a99b5fdc5d46f4d133874b3c3352b3860e0","title":"Features","url":"/docs/ai-workflow-tools#features","content":"✅ Complete Tool Suite: Interactive forms for all 10 specialized tools (3 core + 3 analysis + 4 workflow)\n✅ Full API Coverage: REST endpoints for all AI Analysis and Workflow tools\n✅ Professional Results: Comprehensive output with structured JSON responses\n✅ Demonstration Mode: Realistic examples for immediate evaluation","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Features","lvl3":""}}, {"objectID":"5b054992f65162260295931a616fffbcb1ce3185ddb2024cf1fc461887cb05cd","title":"Screenshots","url":"/docs/ai-workflow-tools#screenshots","content":"Test Case Generation: Interactive form showing comprehensive test generation\nCode Refactoring: Before/after code comparison with optimization suggestions\nDocumentation Generator: Automatic API documentation creation interface\nDebug Assistant: AI output analysis with issue identification and fixes","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Screenshots","lvl3":""}}, {"objectID":"db324117dbb50da2ebedb3a92812c06d4bd67ca65908d8fcc7a5ca5b7610ec9c","title":"Demo Videos","url":"/docs/ai-workflow-tools#demo-videos","content":"All workflow tools are demonstrated in our comprehensive demo videos:\nVisual Demos - Complete workflow demonstrations and technical applications","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Demo Videos","lvl3":""}}, {"objectID":"d95cfeab416b70bd4945633e605e6ad75a904d3e2784280e7518ab9bfe6b2818","title":"MCP Integration","url":"/docs/ai-workflow-tools#mcp-integration","content":"AI Workflow Tools are implemented as MCP (Model Context Protocol) tools that work internally behind our factory methods:","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"MCP Integration","lvl3":""}}, {"objectID":"8686a7ad6bdcebc28a69fd8ff06e24d1352ea05ed23c71dfc4cf843c47b6aae2","title":"Real AI Integration","url":"/docs/ai-workflow-tools#real-ai-integration","content":"Enhanced AI Generation: All tools now use real AI generation instead of mock data\nNeuroLink Integration: Tools leverage actual NeuroLink class with automatic fallback\nGraceful Fallback: AI tools fall back to mock data only if AI parsing fails\nProvider Tracking: Tools report which AI provider was actually used","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Real AI Integration","lvl3":""}}, {"objectID":"363386733e11c691f8e592d949eef6e7a8e571fe736c9610225dc1bee4edd6a9","title":"Error Handling","url":"/docs/ai-workflow-tools#error-handling","content":"Comprehensive Validation: Input validation and error reporting for all tools\nProduction Logging: Detailed logging for debugging and monitoring\nGraceful Degradation: Fallback to simulation mode when AI providers unavailable\nContext Preservation: Maintain context across tool execution chains","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Error Handling","lvl3":""}}, {"objectID":"3db20ae5caae87e88caa064497bece3e4103c9454dcd47d8d11d1752a24530ab","title":"Performance Metrics","url":"/docs/ai-workflow-tools#performance-metrics","content":"Tool Execution: Individual tools designed for \\<100ms execution\nAPI Response: REST endpoints respond within 2-5 seconds\nError Recovery: Automatic fallback mechanisms for reliability\nResource Management: Efficient handling of large code bases and outputs","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Performance Metrics","lvl3":""}}, {"objectID":"d4b25f250422d56866a25aedfbb1fc5b30038f7facefcacd877f4c7fd4cc9982","title":"Prerequisites","url":"/docs/ai-workflow-tools#prerequisites","content":"Install NeuroLink: npm install @juspay/neurolink\nConfigure Providers: Set up at least one AI provider (see Provider Configuration) (now with authentication and model availability checks)\nVerify Setup: Run npx @juspay/neurolink status to check connectivity","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Prerequisites","lvl3":""}}, {"objectID":"e91453744d1d406e678319c5066110d117e8437cb45b8e3648a2a48e80e04b16","title":"CI/CD Integration","url":"/docs/ai-workflow-tools#cicd-integration","content":"`yaml","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"CI/CD Integration","lvl3":""}}, {"objectID":"8e4f251fb04312d355842ff0c42d1678030a4595c240801693a7525607860ab6","title":"GitHub Actions example","url":"/docs/ai-workflow-tools#github-actions-example","content":"name: Generate Tests\n run: npx @juspay/neurolink generate-test-cases --input src/ --output tests/\n`","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"GitHub Actions example","lvl3":""}}, {"objectID":"d1dd748884898f0bc86358bccf65ab19ce58d10bbda60e6e3394cf783cb6152e","title":"Development Workflow","url":"/docs/ai-workflow-tools#development-workflow","content":"`bash","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Development Workflow","lvl3":""}}, {"objectID":"e955fa0ee405bf061dee4fec361bc843f9b383f3f92bf3bfd640ebe22f20cda5","title":"Local development commands","url":"/docs/ai-workflow-tools#local-development-commands","content":"neurolink refactor-code --file legacy.js --target modern\nneurolink generate-docs --input src/ --output docs/\nneurolink debug-output --file ai-response.json --format json\n`","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"Local development commands","lvl3":""}}, {"objectID":"746b58428d221e9cc8248212b61b336a2cfc48f600d630e585e609059e47fa1a","title":"📊 Current Integration Status","url":"/docs/ai-workflow-tools#-current-integration-status","content":"Total Workflow Tools: 4 specialized development tools\nTest Generation: Comprehensive test case creation for all code types\nCode Refactoring: AI-powered optimization and modernization\nDocumentation: Automatic generation of API docs and guides\nDebug Assistance: AI output validation and correction\n\nPlatform Achievement: NeuroLink has successfully evolved into a Comprehensive AI Development Platform with complete development lifecycle support.","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"📊 Current Integration Status","lvl3":""}}, {"objectID":"d42bf8e384aebf97f37fabcf6547721cdafaa70fe927ff57e01d90a7bb991b7d","title":"📚 Related Documentation","url":"/docs/ai-workflow-tools#-related-documentation","content":"Main README - Project overview and quick start\nAI Analysis Tools - AI optimization and analysis tools\nMCP Foundation - Technical architecture details\nAPI Reference - Complete TypeScript API\nCLI Guide - Command-line interface documentation\nVisual Demos - Screenshots and videos\n\nAI-Powered Development - Accelerate your development workflow with intelligent code generation, optimization, and quality assurance tools.","hierarchy":{"lvl0":"Ai Workflow Tools","lvl1":"🛠️ AI Development Workflow Tools","lvl2":"📚 Related Documentation","lvl3":""}}, {"objectID":"c6fc1242c1856e1cbd006c63df51ffffa23d0f46ba342dc3cd6ad732c4a693b4","title":"Proxy dashboard accounting","url":"/docs/assets/dashboards/README","content":"Proxy dashboard accounting\n\nImport neurolink-proxy-observability-dashboard.json into OpenObserve after\nthe matching proxy release emits the indexed accounting and pricing fields.\nThe template is an artifact; changing it does not update an installed dashboard\nor activate a proxy release. Request panels query the metadata log stream.\nBulk body captures can use a separate stream.\n\nClient traffic, errors, and latency belong to the outer request. An internal\nbridge request owns provider usage when proxy_usage_owner_request_id names\nthat child; the parent contributes no second token charge. Cached input is\nincluded once according to ai_input_includes_cached_tokens.\n\nLog queries first collapse exact producer retries, ignoring ingestion-time\nchanges. The stable identity combines service instance and producer event ID;\nolder records fall back to request ID where available. Contradictory payloads\nunder one identity are excluded from aggregate totals. The Traffic & Health\nconflict counter reports those exclusions. A nonzero counter means totals\ncover only the verified subset; use telemetry doctor for the conflicting raw\npayloads. Rows without any stable identity cannot establish retry deduplication.\n\nCost panels sum ai_cost_api_equivalent_usd only when\nai_pricing_status='exact'. These are estimates from this release's API price\ntable and complete provider usage, not subscription charges or quota usage.\nPrefix-inferred prices, missing rates, partial token usage, and a bridge parent's\ndelegated usage do not receive an invented dollar amount. Pricing-gap panels\nshow the excluded usage owners; a blank known-cost total is not zero spending.\nCache savings compare cache reads with the same table's uncached input rate.\nThe offline analyzer's historical estimates separately retain their existing\nrequestsPricedByPrefix and modelsPricedByPrefix disclosures.\n\nThe deterministic capture-pipeline suite executes the shipped request, token,\ncost, and conflict SQL on an in-memory database and records exact price-table,\npartial-usage, and unknown-model OTLP fixtures. SQLite validates the standard SQL\nlogic; verify field availability and query compatibility in the target\nOpenObserve version when installing the dashboard. Metric panels retain their\nscrape/window semantics and are not a substitute for reconciled request logs.","hierarchy":{"lvl0":"Assets","lvl1":"Proxy dashboard accounting","lvl2":"","lvl3":""}}, {"objectID":"45b2fbb88c8b8f79b76d62f72e62e2fc1f3b67e24adc7efd0d63e2b2fd4b2f49","title":"Proxy dashboard accounting","url":"/docs/assets/dashboards/README#proxy-dashboard-accounting","content":"Import neurolink-proxy-observability-dashboard.json into OpenObserve after\nthe matching proxy release emits the indexed accounting and pricing fields.\nThe template is an artifact; changing it does not update an installed dashboard\nor activate a proxy release. Request panels query the metadata log stream.\nBulk body captures can use a separate stream.\n\nClient traffic, errors, and latency belong to the outer request. An internal\nbridge request owns provider usage when proxy_usage_owner_request_id names\nthat child; the parent contributes no second token charge. Cached input is\nincluded once according to ai_input_includes_cached_tokens.\n\nLog queries first collapse exact producer retries, ignoring ingestion-time\nchanges. The stable identity combines service instance and producer event ID;\nolder records fall back to request ID where available. Contradictory payloads\nunder one identity are excluded from aggregate totals. The Traffic & Health\nconflict counter reports those exclusions. A nonzero counter means totals\ncover only the verified subset; use telemetry doctor for the conflicting raw\npayloads. Rows without any stable identity cannot establish retry deduplication.\n\nCost panels sum ai_cost_api_equivalent_usd only when\nai_pricing_status='exact'. These are estimates from this release's API price\ntable and complete provider usage, not subscription charges or quota usage.\nPrefix-inferred prices, missing rates, partial token usage, and a bridge parent's\ndelegated usage do not receive an invented dollar amount. Pricing-gap panels\nshow the excluded usage owners; a blank known-cost total is not zero spending.\nCache savings compare cache reads with the same table's uncached input rate.\nThe offline analyzer's historical estimates separately retain their existing\nrequestsPricedByPrefix and modelsPricedByPrefix disclosures.","hierarchy":{"lvl0":"Assets","lvl1":"Proxy dashboard accounting","lvl2":"Proxy dashboard accounting","lvl3":""}}, {"objectID":"e15c93aac2a801b035f46a8175503c313d89270bbe22d31954441b3e83362e72","title":"🚀 Automated Publishing Guide (Semantic Release)","url":"/docs/automated-publishing-guide","content":"🚀 Automated Publishing Guide (Semantic Release)\n\nComplete step-by-step guide to set up semantic-release for automated GitHub releases, tags, and NPM publishing for NeuroLink.\n\n🎯 Current Status\n\n✅ GitHub Workflow - .github/workflows/release.yml configured with semantic-release\n✅ Semantic Release Config - .releaserc.json configured\n✅ Dependencies Added - All semantic-release packages in package.json\n⏳ NPM Token Setup - Required for NPM publishing\n⏳ First Release - Ready to trigger after NPM token\n\n📋 Step-by-Step Setup\n\nStep 1: Create NPM Automation Token\nLogin to NPM:\n\n \n\n Use your NPM account credentials\nCreate Automation Token:\nCopy the token (starts with npm_...)\n\nStep 2: Add NPM Token to GitHub Secrets\nGo to: https://github.com/juspay/neurolink/settings/secrets/actions\nClick \"New repository secret\"\nName: NPM_TOKEN\nValue: Paste your NPM automation token\nClick \"Add secret\"\n\nStep 3: Use Conventional Commits\n\nSemantic-release uses conventional commits to determine version bumps:\n\nStep 4: Trigger Automatic Release\n\n🎉 Just push to release branch with conventional commits!\n\n🔧 How Semantic Release Works\n\nCommit Analysis:\nfix: → Patch release (1.7.0 → 1.7.1)\nfeat: → Minor release (1.7.0 → 1.8.0)\nBREAKING CHANGE or ! → Major release (1.7.0 → 2.0.0)\ndocs:, style:, refactor:, test:, chore: → No release\n\nGenerated Assets:\n🏷️ Git Tag: v1.8.0 (automatically created)\n📝 CHANGELOG.md (automatically generated and committed)\n🐙 GitHub Release (with professional release notes)\n📦 NPM Package: https://www.npmjs.com/package/@juspay/neurolink\n📚 GitHub Package: https://github.com/juspay/neurolink/packages\n\nAutomatic Updates:\n✅ package.json version updated and committed\n✅ CHANGELOG.md generated and committed\n✅ Git tags created automatically\n✅ Release notes generated from commits\n\n🎉 Expected Results\n\nAfter pushing conventional commits to release branch:\n\nAutomatic Process:\n🔍 Analyzes commits since last release\n📊 Determines version based on conventional commits\n📝 Generates CHANGELOG.md from commit messages\n🏷️ Creates Git tag (e.g., v1.8.0)\n🐙 Creates GitHub release with generated notes\n📦 Publishes to NPM registry\n📚 Publishes to GitHub Packages\n💾 Commits changes back to release branch\n\nGitHub Repository:\n✅ Tags: Automatically created (v1.8.0)\n✅ Releases: Professional release notes from commits\n✅ Packages: Available on GitHub Packages\n✅ CHANGELOG.md: Auto-generated and updated\n\nNPM Registry:\n✅ Published Package: @juspay/neurolink@1.8.0\n✅ Installation: npm install @juspay/neurolink\n\n🚨 Troubleshooting\n\nCommon Issues:\n\n\"No release published\"\nCause: No conventional commits since last release\nSolution: Use proper conventional commit format (feat:, fix:, etc.)\n\n\"NPM_TOKEN not found\"\nSolution: Add NPM token to GitHub repository secrets\nCheck: Repository → Settings → Secrets and variables → Actions\n\n\"Permission denied to publish\"\nSolution: Ensure NPM token has publishing permissions\nFix: Create new automation token with correct permissions\n\n\"CHANGELOG.md conflicts\"\nSolution: Semantic-release handles this automatically\nInfo: Don't manually edit CHANGELOG.md - it's auto-generated\n\nVerification Commands:\n\n📚 Conventional Commit Examples\n\nFeature Examples:\n\nBug Fix Examples:\n\nBreaking Change Examples:\n\nOther Types:\n\n🔄 Future Releases\n\nFully Automated Process:\nWrite code with conventional commits\nPush to release branch\nThat's it! Semantic-release handles everything else\n\nNo Manual Steps Required:\n❌ No manual version bumping\n❌ No manual changelog writing\n❌ No manual tag creation\n❌ No manual release creation\n❌ No manual NPM publishing\n\nProfessional Results:\n✅ Consistent versioning with SemVer\n✅ Professional changelogs from commits\n✅ Comprehensive release notes\n✅ Zero human error in releases\n\n✅ Next Steps\nComplete Step 1-2: NPM token setup\nUse conventional commits: Follow the format above\nPush to release branch: Automatic release triggered\nVerify: Check all platforms have packages\nCelebrate: You now have industry-standard automation! 🎉\n\n📞 Need Help?\nCheck the workflow logs in GitHub Actions\nEnsure NPM_TOKEN is properly configured\nUse conventional commit format\nTest with npx semantic-release --dry-run locally\n\nThe semantic-release workflow is the industry standard used by thousands of open-source projects. Once set up, you'll have bulletproof, professional-grade release automation! 🚀\n\n🔗 References\nSemantic Release Documentation\nConventional Commits Specification\nGitHub Actions for Semantic Release","hierarchy":{"lvl0":"Automated Publishing Guide","lvl1":"🚀 Automated Publishing Guide (Semantic Release)","lvl2":"","lvl3":""}}, {"objectID":"8c6f70d26d379aa7a0b6df4af95fba63b133f8b99cf24d9bc6069cefef6de4d2","title":"🚀 Automated Publishing Guide (Semantic Release)","url":"/docs/automated-publishing-guide#-automated-publishing-guide-semantic-release","content":"Complete step-by-step guide to set up semantic-release for automated GitHub releases, tags, and NPM publishing for NeuroLink.","hierarchy":{"lvl0":"Automated Publishing Guide","lvl1":"🚀 Automated Publishing Guide (Semantic Release)","lvl2":"🚀 Automated Publishing Guide (Semantic Release)","lvl3":""}}, {"objectID":"e31d1f414e78bc6c2e2660a68c29e974c3df6607a3f966ab338bd4124257b0fc","title":"🎯 Current 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Release)","lvl2":"MINOR version (1.7.0 → 1.8.0) - New features","lvl3":""}}, {"objectID":"ea892ed21741c939c79a760c958a07586d8dda9f402e04f04c4105834d51272b","title":"MAJOR version (1.7.0 → 2.0.0) - Breaking changes","url":"/docs/automated-publishing-guide#major-version-170-200---breaking-changes","content":"git commit -m \"feat!: remove deprecated API methods\"\ngit commit -m \"fix!: change provider interface signature\"","hierarchy":{"lvl0":"Automated Publishing Guide","lvl1":"🚀 Automated Publishing Guide (Semantic Release)","lvl2":"MAJOR version (1.7.0 → 2.0.0) - Breaking changes","lvl3":""}}, {"objectID":"d7f8a1dce845efff8ac01b1ccd54c99d9362d7818abac6eefefbc7c61d2feebe","title":"Alternative major version syntax","url":"/docs/automated-publishing-guide#alternative-major-version-syntax","content":"git commit -m \"feat: add new authentication\n\nBREAKING CHANGE: Previous auth methods no longer supported\"\n`","hierarchy":{"lvl0":"Automated Publishing Guide","lvl1":"🚀 Automated Publishing Guide (Semantic Release)","lvl2":"Alternative major version syntax","lvl3":""}}, {"objectID":"2aa82e2a4c727cca83a14b40b3828c687b4c98e0ccd0fccdae499d22640b566f","title":"Step 4: Trigger Automatic Release","url":"/docs/automated-publishing-guide#step-4-trigger-automatic-release","content":"🎉 Just push to release branch with conventional commits!\n\n`bash","hierarchy":{"lvl0":"Automated Publishing Guide","lvl1":"🚀 Automated Publishing Guide (Semantic Release)","lvl2":"Step 4: Trigger Automatic Release","lvl3":""}}, {"objectID":"bd9b70d65e28aeaa075cd009398048eaf56c771a168b86e91bcaa4d7712d58e8","title":"Make your changes with conventional commits","url":"/docs/automated-publishing-guide#make-your-changes-with-conventional-commits","content":"git add .\ngit commit -m \"feat: add Google AI Studio integration\"","hierarchy":{"lvl0":"Automated Publishing Guide","lvl1":"🚀 Automated Publishing Guide (Semantic Release)","lvl2":"Make your changes with conventional commits","lvl3":""}}, 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(1.7.0 → 1.7.1)\nfeat: → Minor release (1.7.0 → 1.8.0)\nBREAKING CHANGE or ! → Major release (1.7.0 → 2.0.0)\ndocs:, style:, refactor:, test:, chore: → No release","hierarchy":{"lvl0":"Automated Publishing Guide","lvl1":"🚀 Automated Publishing Guide (Semantic Release)","lvl2":"Commit Analysis:","lvl3":""}}, {"objectID":"e77ab844912203475b0e63d4ae3f26a7927649f9eaf4a8f6466c3f722e2d9d5d","title":"Generated Assets:","url":"/docs/automated-publishing-guide#generated-assets","content":"🏷️ Git Tag: v1.8.0 (automatically created)\n📝 CHANGELOG.md (automatically generated and committed)\n🐙 GitHub Release (with professional release notes)\n📦 NPM Package: https://www.npmjs.com/package/@juspay/neurolink\n📚 GitHub Package: https://github.com/juspay/neurolink/packages","hierarchy":{"lvl0":"Automated Publishing Guide","lvl1":"🚀 Automated Publishing Guide (Semantic Release)","lvl2":"Generated Assets:","lvl3":""}}, 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quality consistency achieved","hierarchy":{"lvl0":"Business Documentation","lvl1":"💼 Business Documentation Hub","lvl2":"⭐ Quality Improvement","lvl3":""}}, {"objectID":"0c58c7f1ed7ae9f35308dc39c56f8414b036d086759289d9341cdb283bd323af","title":"📈 Productivity Gains","url":"/docs/business-documentation#-productivity-gains","content":"Immediate: Real-time cost and quality visibility\nWeek 2: Automated quality control reduces manual review\nMonth 1: 50-75% reduction in content review time\nMonth 3: 10x faster content creation with quality assurance","hierarchy":{"lvl0":"Business Documentation","lvl1":"💼 Business Documentation Hub","lvl2":"📈 Productivity Gains","lvl3":""}}, {"objectID":"7683a459dd6490affedff35137b19696309725dac5819753933138d85ffdc722","title":"E-commerce Company","url":"/docs/business-documentation#e-commerce-company","content":"Challenge: 50,000 product descriptions monthly\nSolution: Analytics-driven model selection + quality gates\nResults: 65% cost reduction, 90% quality consistency, 10x faster creation","hierarchy":{"lvl0":"Business Documentation","lvl1":"💼 Business Documentation Hub","lvl2":"E-commerce Company","lvl3":""}}, {"objectID":"2cea9c9162bc78f18560adb7c17c1f391f2fb25970f0baae5cc2a4521934eaf0","title":"Healthcare Organization","url":"/docs/business-documentation#healthcare-organization","content":"Challenge: Regulatory compliance for patient education\nSolution: Strict evaluation thresholds + medical review workflows\nResults: 100% compliance, 75% faster creation, 40% better comprehension","hierarchy":{"lvl0":"Business Documentation","lvl1":"💼 Business Documentation Hub","lvl2":"Healthcare Organization","lvl3":""}}, {"objectID":"1e16ed0db45f4ab32d360d4ba72a1668bbf017ebec1b5cb6b5630d9ebf1c9751","title":"SaaS Company","url":"/docs/business-documentation#saas-company","content":"Challenge: Scale customer support while maintaining quality\nSolution: Tiered quality control + response time optimization\nResults: 88% satisfaction, 60% cost reduction, 10x volume handling","hierarchy":{"lvl0":"Business Documentation","lvl1":"💼 Business Documentation Hub","lvl2":"SaaS Company","lvl3":""}}, {"objectID":"8da9b11ec1a68985353b1b28593026a1db42bb93af2947d1cd37afa1da7a41d8","title":"Financial Services","url":"/docs/business-documentation#financial-services","content":"Challenge: Accurate investment reports with regulatory compliance\nSolution: Compliance frameworks + fact-checking requirements\nResults: Zero violations, 5x faster reports, 45% better ratings","hierarchy":{"lvl0":"Business Documentation","lvl1":"💼 Business Documentation Hub","lvl2":"Financial Services","lvl3":""}}, {"objectID":"07b959a14b3f6045463c6a9e729350096fd2929fa26dd4368117d0370e172bcd","title":"Core Components","url":"/docs/business-documentation#core-components","content":"Analytics System: Real-time usage tracking and cost analysis\nEvaluation System: AI-powered response quality scoring\nContext Flow: Custom business data through request chains\nQuality Gates: Automated quality control and review workflows\nCost Optimization: Intelligent provider and model selection","hierarchy":{"lvl0":"Business Documentation","lvl1":"💼 Business Documentation Hub","lvl2":"Core Components","lvl3":""}}, {"objectID":"6b6c1cb86a7396f314b720f925ade3f41d8e544a2586cd93a4e871cc9a2c5b18","title":"Getting Help","url":"/docs/business-documentation#getting-help","content":"Technical Issues: GitHub Issues\nFeature Requests: GitHub Discussions\nDocumentation: Complete API Reference\nExamples: Working Code Examples","hierarchy":{"lvl0":"Business Documentation","lvl1":"💼 Business Documentation Hub","lvl2":"Getting Help","lvl3":""}}, {"objectID":"367147dfe5de97d6eda73f934c37e7fa00027689e1d97ce413e4f05abefad739","title":"Community","url":"/docs/business-documentation#community","content":"NPM Package: @juspay/neurolink\nGitHub Repository: juspay/neurolink\nLicense: MIT (Production-friendly)","hierarchy":{"lvl0":"Business Documentation","lvl1":"💼 Business Documentation Hub","lvl2":"Community","lvl3":""}}, {"objectID":"652b45647c135cdcce501745db41fc0b12a9c31d84fee904d3d73f16df7690d2","title":"🎯 Next Steps","url":"/docs/business-documentation#-next-steps","content":"Assess Your Needs: Review Industry Use Cases for your sector\nCalculate ROI: Use examples in Business Value Guide\nStart Implementation: Follow the Integration Tutorials\nValidate Results: Use Testing & Validation\nOptimize & Scale: Monitor analytics and optimize based on data\n\nReady to transform your AI operations?\n\nStart with the Business Value Guide to understand the ROI potential, then move to Industry Use Cases to see how organizations like yours are achieving success.\n\nThe analytics and evaluation features typically deliver 300-1000% ROI within 3-6 months through cost optimization, quality improvement, and productivity gains.","hierarchy":{"lvl0":"Business Documentation","lvl1":"💼 Business Documentation Hub","lvl2":"🎯 Next Steps","lvl3":""}}, {"objectID":"3bcbc23420d7b0bf8f9c82cdce6002060f1c725c0c8787ec663e1bc5779d3c00","title":"💰 Business Value Guide: Analytics & Evaluation Features","url":"/docs/business-value","content":"💰 Business Value Guide: Analytics & Evaluation Features\n✅ Performance Monitoring Achieved:\n🎯 Next Steps\n\nNeuroLink's analytics and evaluation features deliver measurable business value through cost optimization, quality improvement, and performance monitoring. This guide shows real-world examples of business impact and ROI.\n\n📊 Cost Optimization\n\nProblem: Uncontrolled AI Spending\n\nBefore NeuroLink Analytics:\nNo visibility into AI provider costs\nUsing expensive models for simple tasks\nNo department-level cost tracking\nEstimated monthly spend: $5,000-$8,000\n\nAfter NeuroLink Analytics:\nReal-time cost tracking by provider, model, department\nAutomatic model selection based on task complexity\nCost optimization alerts and recommendations\nActual monthly spend: $3,200-$4,500 (35-40% reduction)\n\nROI Example: E-commerce Company\n\nDepartment-Level Cost Tracking\n\n⭐ Quality Improvement\n\nProblem: Inconsistent AI Response Quality\n\nBefore NeuroLink Evaluation:\nNo automated quality assessment\nManual review required for all content\nInconsistent response quality (60-75% satisfaction)\nHigh review overhead (2-3 hours daily)\n\nAfter NeuroLink Evaluation:\nAutomated quality scoring (relevance, accuracy, completeness)\nQuality gates prevent low-quality content\nConsistent high-quality responses (85-95% satisfaction)\nReduced review time (30 minutes daily)\n\nROI Example: Customer Support\n\nContent Quality Monitoring\n\n📈 Performance Monitoring\n\nReal-Time Business Intelligence\n\nPerformance Optimization Dashboard\n\n🎯 Industry-Specific Value\n\nE-commerce\n\nUse Case: Product description generation\nVolume: 50,000 products/month\nCost Savings: $2,400/month (optimized model selection)\nQuality Improvement: 85% consistency (vs 60% manual)\nTime Savings: 200 hours/month human writing\n\nHealthcare\n\nUse Case: Patient education content\nCompliance: 98% accuracy requirement met\nReview Time: 75% reduction in medical review\nPatient Satisfaction: +30% comprehension scores\nRisk Mitigation: Zero compliance violations\n\nFinancial Services\n\nUse Case: Investment report generation\nAccuracy: 95% fact-checking score required\nCompliance: Automated regulatory review\nClient Satisfaction: +40% report quality ratings\nProductivity: 3x faster report generation\n\nSaaS Companies\n\nUse Case: Customer communication\nResponse Time: 90% under 30 seconds\nQuality: 88% customer satisfaction\nCost: 60% reduction vs human-only support\nScalability: Handle 10x volume with same team\n\n📊 ROI Calculation Framework\n\nCost Savings Calculator\n\nQuality Improvement Metrics\n\n🚀 Getting Started with Business Value\n\nWeek 1: Baseline Measurement\n\nWeek 2: Enable Analytics\n\nWeek 3: Add Quality Control\n\nWeek 4: Optimize Based on Data\n\n📋 Business Value Checklist\n\n✅ Cost Optimization Achieved:\n[ ] Real-time cost tracking implemented\n[ ] Department-level cost allocation setup\n[ ] Model optimization based on task complexity\n[ ] Monthly cost reduction of 25-40%\n[ ] Automated cost alerts configured\n\n✅ Quality Improvement Achieved:\n[ ] Automated quality scoring implemented\n[ ] Quality gates prevent low-quality content\n[ ] Customer satisfaction increased 20%+\n[ ] Manual review time reduced 70%+\n[ ] Compliance requirements met consistently\n\nPerformance Monitoring Achieved:\n[ ] Real-time performance dashboards\n[ ] Quality trend analysis\n[ ] Cost optimization recommendations\n[ ] Provider reliability monitoring\n[ ] Business intelligence reporting\n\nNext Steps\nImplement Analytics: Start with cost tracking\nAdd Quality Control: Implement evaluation scoring\nMeasure Baseline: Document current costs/quality\nOptimize Based on Data: Use insights for improvement\nScale Across Organization: Roll out to all teams\n\nThe combination of analytics and evaluation features typically delivers 300-1000% ROI within 3-6 months through cost optimization, quality improvement, and productivity gains.","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"","lvl3":""}}, {"objectID":"87475b41e80e743967bf399f7f36ee1453de9da0572bd7af0a44a0029b3bee15","title":"💰 Business Value Guide: Analytics & Evaluation Features","url":"/docs/business-value#-business-value-guide-analytics-evaluation-features","content":"✅ Performance Monitoring Achieved:\n🎯 Next Steps\n\nNeuroLink's analytics and evaluation features deliver measurable business value through cost optimization, quality improvement, and performance monitoring. This guide shows real-world examples of business impact and ROI.","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"💰 Business Value Guide: Analytics & Evaluation Features","lvl3":""}}, {"objectID":"d2981a598a11bc80ebadad9f1ae44afeeaea998158b8b2fe86687688c6bf0021","title":"Problem: Uncontrolled AI Spending","url":"/docs/business-value#problem-uncontrolled-ai-spending","content":"Before NeuroLink Analytics:\nNo visibility into AI provider costs\nUsing expensive models for simple tasks\nNo department-level cost tracking\nEstimated monthly spend: $5,000-$8,000\n\nAfter NeuroLink Analytics:\nReal-time cost tracking by provider, model, department\nAutomatic model selection based on task complexity\nCost optimization alerts and recommendations\nActual monthly spend: $3,200-$4,500 (35-40% reduction)","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Problem: Uncontrolled AI 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Intelligence","url":"/docs/business-value#real-time-business-intelligence","content":"`bash","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Real-Time Business Intelligence","lvl3":""}}, {"objectID":"411280da65d68f3f7bad8e6f9048cc1dbc5b62d0960afb94b10103ddfe72a7fb","title":"Daily analytics reporting","url":"/docs/business-value#daily-analytics-reporting","content":"npx @juspay/neurolink generate \"Daily report summary\" \\\n --enable-analytics --enable-evaluation \\\n --context '{\"report_type\":\"daily\",\"department\":\"analytics\"}' \\\n --debug","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Daily analytics reporting","lvl3":""}}, {"objectID":"783944396520d1ed723bcf11aa9da04faa8fdd322148b9e258af3da3ee618f47","title":"⭐ Evaluation: Overall: 9/10, Accuracy: 9/10, Completeness: 8/10","url":"/docs/business-value#-evaluation-overall-910-accuracy-910-completeness-810","content":"`","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"⭐ Evaluation: Overall: 9/10, Accuracy: 9/10, Completeness: 8/10","lvl3":""}}, {"objectID":"1b32e52292ff192b043470f0251d15b90a60fa2d18d383940c9b198a0e6fbaa1","title":"E-commerce","url":"/docs/business-value#e-commerce","content":"Use Case: Product description generation\nVolume: 50,000 products/month\nCost Savings: $2,400/month (optimized model selection)\nQuality Improvement: 85% consistency (vs 60% manual)\nTime Savings: 200 hours/month human writing","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"E-commerce","lvl3":""}}, {"objectID":"da98e1d8413ad6ac2b3de24103352b3bdf1c7e1cba98b5150d85b14eb8eb1642","title":"Healthcare","url":"/docs/business-value#healthcare","content":"Use Case: Patient education content\nCompliance: 98% accuracy requirement met\nReview Time: 75% reduction in medical review\nPatient Satisfaction: +30% comprehension scores\nRisk Mitigation: Zero compliance violations","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Healthcare","lvl3":""}}, {"objectID":"d7759a2fafe4bcc67813c85548ad97d1eb217967a3b28eb884d59c7b9ccb8823","title":"Financial Services","url":"/docs/business-value#financial-services","content":"Use Case: Investment report generation\nAccuracy: 95% fact-checking score required\nCompliance: Automated regulatory review\nClient Satisfaction: +40% report quality ratings\nProductivity: 3x faster report generation","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Financial Services","lvl3":""}}, {"objectID":"5b9a48a263fe26ace667945f5f9e2be67f379013c7c63690a37726f56073ae8c","title":"SaaS Companies","url":"/docs/business-value#saas-companies","content":"Use Case: Customer communication\nResponse Time: 90% under 30 seconds\nQuality: 88% customer satisfaction\nCost: 60% reduction vs human-only support\nScalability: Handle 10x volume with same team","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"SaaS Companies","lvl3":""}}, {"objectID":"15186043efe07f099d5f02f0973505727ddbfd0d9c7ec5185d9c992c17911b22","title":"Week 1: Baseline Measurement","url":"/docs/business-value#week-1-baseline-measurement","content":"`bash","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Week 1: Baseline Measurement","lvl3":""}}, {"objectID":"e1880c37b957ee2061130355ee98d4d90dc5a9c1c949c41345d3be487f98535a","title":"Measure current costs without analytics","url":"/docs/business-value#measure-current-costs-without-analytics","content":"npx @juspay/neurolink generate \"Business content\" --provider openai","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Measure current costs without analytics","lvl3":""}}, {"objectID":"31619a5a3385aeaab316a76f9c07ed288c7e1ff0ce6a3d12d94a66cc011a5972","title":"Note: No cost tracking, no quality metrics","url":"/docs/business-value#note-no-cost-tracking-no-quality-metrics","content":"`","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Note: No cost tracking, no quality metrics","lvl3":""}}, {"objectID":"1eb4166ef7d7cda909be066bba8946966f63baf91e5feacd8c4bb62988177ac4","title":"Week 2: Enable Analytics","url":"/docs/business-value#week-2-enable-analytics","content":"`bash","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Week 2: Enable Analytics","lvl3":""}}, {"objectID":"38d59c35242c0fd108062d22f5d448d575f8c327b34e28763826f6bd214f756e","title":"Start tracking costs and usage","url":"/docs/business-value#start-tracking-costs-and-usage","content":"npx @juspay/neurolink generate \"Business content\" \\\n --provider openai --enable-analytics --debug","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Start tracking costs and usage","lvl3":""}}, {"objectID":"91ea0241694c3c57ae92ce64debc4f9ee1bf0d5d7108d23c9eaaae2e8f78a20d","title":"Result: Immediate cost visibility","url":"/docs/business-value#result-immediate-cost-visibility","content":"`","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Result: Immediate cost visibility","lvl3":""}}, {"objectID":"db31df30e8f203e8581a5b54ae6f9a773a841f36090f3d3e5dc53fc732f457ca","title":"Week 3: Add Quality Control","url":"/docs/business-value#week-3-add-quality-control","content":"`bash","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Week 3: Add Quality Control","lvl3":""}}, {"objectID":"b1fb94efa7859dafbde202279c303bcf63a4717bbedc5eee45dbeb2e46c19c79","title":"Add automated quality assessment","url":"/docs/business-value#add-automated-quality-assessment","content":"npx @juspay/neurolink generate \"Business content\" \\\n --provider openai --enable-analytics --enable-evaluation --debug","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Add automated quality assessment","lvl3":""}}, {"objectID":"3ed5f9e11826dc048ed4aedeb255d29214b2e223c6629e65dd750da863d4120b","title":"Result: Quality scores + cost tracking","url":"/docs/business-value#result-quality-scores-cost-tracking","content":"`","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Result: Quality scores + cost tracking","lvl3":""}}, {"objectID":"6601f16e391674bc86aec56eb47eb582c7bb6639bd83072c9af42b11f7334324","title":"Week 4: Optimize Based on Data","url":"/docs/business-value#week-4-optimize-based-on-data","content":"`bash","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Week 4: Optimize Based on Data","lvl3":""}}, {"objectID":"71a483add4f18df9dc7b77b9a658da9c34291599bb77a8b0138b1eb4993a92c4","title":"Use analytics data to optimize provider/model selection","url":"/docs/business-value#use-analytics-data-to-optimize-providermodel-selection","content":"npx @juspay/neurolink generate \"Business content\" \\\n --provider google-ai --model gemini-2.5-flash \\\n --enable-analytics --enable-evaluation --debug","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Use analytics data to optimize provider/model 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{"objectID":"9663b783d19b790ddb8392314481ee6c06b74437afe4b33d1ad0bf003298e8c4","title":"✅ Quality Improvement Achieved:","url":"/docs/business-value#-quality-improvement-achieved","content":"[ ] Automated quality scoring implemented\n[ ] Quality gates prevent low-quality content\n[ ] Customer satisfaction increased 20%+\n[ ] Manual review time reduced 70%+\n[ ] Compliance requirements met consistently","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"✅ Quality Improvement Achieved:","lvl3":""}}, {"objectID":"c40e433e414dc6eabce1d9374adfc34a7141a15cb43b381b2fe5bff3f5ad5ff7","title":"Performance Monitoring Achieved:","url":"/docs/business-value#performance-monitoring-achieved","content":"[ ] Real-time performance dashboards\n[ ] Quality trend analysis\n[ ] Cost optimization recommendations\n[ ] Provider reliability monitoring\n[ ] Business intelligence reporting","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Performance Monitoring Achieved:","lvl3":""}}, {"objectID":"dd6a9e3dca7e8a44de3072bb3a70b8701a5b4ffd8da6415a3ef010444f89621d","title":"Next Steps","url":"/docs/business-value#next-steps","content":"Implement Analytics: Start with cost tracking\nAdd Quality Control: Implement evaluation scoring\nMeasure Baseline: Document current costs/quality\nOptimize Based on Data: Use insights for improvement\nScale Across Organization: Roll out to all teams\n\nThe combination of analytics and evaluation features typically delivers 300-1000% ROI within 3-6 months through cost optimization, quality improvement, and productivity gains.","hierarchy":{"lvl0":"Business Value","lvl1":"💰 Business Value Guide: Analytics & Evaluation Features","lvl2":"Next Steps","lvl3":""}}, {"objectID":"45854d1b5ea2cba96ae1f1ccb9f8bfbf775daf32685fd5b33afb5155ee05dfa1","title":"Advanced CLI Usage","url":"/docs/cli/advanced","content":"Advanced CLI Usage\n\nPower user features, optimization techniques, and advanced workflows for the NeuroLink CLI.\n\n🚀 Advanced Generation Techniques\n\nMulti-Provider Strategies\n\nDynamic Provider Selection\n\n📊 Analytics and Monitoring\n\nAdvanced Analytics Usage\n\nPerformance Monitoring\n\nReal-time Monitoring Dashboard\n\n🔧 Configuration Management\n\nAdvanced Configuration\n\nDynamic Configuration\n\n🎯 Specialized Workflows\n\nCode Analysis Pipeline\n\nDocumentation Generation Pipeline\n\n🔄 Batch Processing Optimization\n\nParallel Processing\n\nSmart Rate Limiting\n\n🔐 Security and Compliance\n\nSecure API Key Management\n\nAudit Logging\n\n🚀 Performance Optimization\n\nCaching Strategies\n\nConnection Pooling\n\n🔧 Custom Tool Development\n\nMCP Server Integration\n\nTool Chain Automation\n\n📈 Metrics and Reporting\n\nAdvanced Reporting\n\n🎯 Specialized Use Cases\n\nCI/CD Integration\n\nContent Management System\n\nThis advanced CLI usage guide provides sophisticated patterns and techniques for power users who want to maximize the capabilities of NeuroLink CLI in production environments.\n\n📚 Related Documentation\nCLI Commands Reference - Complete command documentation\nCLI Examples - Practical usage examples\nEnvironment Variables - Configuration\nSDK Advanced Features - Programmatic equivalents\nTroubleshooting - Common issues","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"","lvl3":""}}, {"objectID":"0b52850902058d02583744f0d52031f634e239495abbac19fc4bc94678b797f6","title":"Advanced CLI Usage","url":"/docs/cli/advanced#advanced-cli-usage","content":"Power user features, optimization techniques, and advanced workflows for the NeuroLink CLI.","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Advanced CLI Usage","lvl3":""}}, {"objectID":"ddb9ea6bd29d0fa5ff758b1968db8ec99a29645be37a1bba482c0dc7d856c0c8","title":"Multi-Provider Strategies","url":"/docs/cli/advanced#multi-provider-strategies","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Multi-Provider Strategies","lvl3":""}}, {"objectID":"149b5176cf88a260209bb29240026b7721d7ca50b4b3d6848459ffca3d2f15ba","title":"Provider fallback chain","url":"/docs/cli/advanced#provider-fallback-chain","content":"generatewithfallback() {\n local prompt=\"$1\"\n local providers=(\"google-ai\" \"openai\" \"anthropic\")\n\n for provider in \"${providers[@]}\"; do\n if result=$(npx @juspay/neurolink gen \"$prompt\" --provider $provider 2>/dev/null); then\n echo \"✅ Success with $provider\"\n echo \"$result\"\n return 0\n fi\n done\n\n echo \"❌ All providers failed\"\n return 1\n}","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Provider fallback chain","lvl3":""}}, {"objectID":"fa900495d3227b44444c441ab8678274eeb9f1894976769b48df45bf45e24f09","title":"Usage","url":"/docs/cli/advanced#usage","content":"generatewithfallback \"Complex technical analysis\"\n`","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Usage","lvl3":""}}, {"objectID":"296464e917d548d69fc0f212d35fb9e57ce99ce04bf42aea1910f3c2b0acd2e7","title":"Dynamic Provider Selection","url":"/docs/cli/advanced#dynamic-provider-selection","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Dynamic Provider Selection","lvl3":""}}, {"objectID":"7cb9f5053b174364b3853f22f47fcd43b9aec568d0063eec2be8edacf57df17f","title":"Select provider based on task type","url":"/docs/cli/advanced#select-provider-based-on-task-type","content":"selectproviderby_task() {\n local task_type=\"$1\"\n\n case $task_type in\n \"code\")\n echo \"anthropic\" # Best for code analysis\n ;;\n \"creative\")\n echo \"openai\" # Best for creative content\n ;;\n \"fast\")\n echo \"google-ai\" # Fastest responses\n ;;\n *)\n echo \"auto\" # Let NeuroLink decide\n ;;\n esac\n}","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Select provider based on task type","lvl3":""}}, {"objectID":"4a2d5cda46386675e8951937750a4999a68c232bca5b4bec84c864f64bae1bb9","title":"Usage","url":"/docs/cli/advanced#usage-1","content":"provider=$(selectproviderby_task \"code\")\nnpx @juspay/neurolink gen \"Write a Python class\" --provider $provider\n`","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Usage","lvl3":""}}, {"objectID":"d786a50779242cce67030690360be30559dbf80e2e1098c7d986185adbb176e1","title":"Advanced Analytics Usage","url":"/docs/cli/advanced#advanced-analytics-usage","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Advanced Analytics Usage","lvl3":""}}, {"objectID":"f25c5fc7a0497db969db5950c534401a64623097c1b16ea790bf4a1fc24520e6","title":"Context-aware analytics","url":"/docs/cli/advanced#context-aware-analytics","content":"npx @juspay/neurolink gen \"Design microservices architecture\" \\\n --enable-analytics \\\n --context '{\n \"user_id\": \"dev123\",\n \"project\": \"ecommerce-platform\",\n \"team\": \"backend\",\n \"environment\": \"development\",\n \"sessionid\": \"sess456\"\n }' \\\n --debug","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Context-aware analytics","lvl3":""}}, {"objectID":"3ad8c39e2a564319d0c35752ec29e86f51dbccc9dde48a2fca1669cdb288213f","title":"Business intelligence tracking","url":"/docs/cli/advanced#business-intelligence-tracking","content":"npx @juspay/neurolink gen \"Create marketing strategy\" \\\n --enable-analytics \\\n --enable-evaluation \\\n --evaluation-domain \"Marketing Director\" \\\n --context '{\n \"department\": \"marketing\",\n \"campaign\": \"Q1-launch\",\n \"budget\": \"high\",\n \"target_audience\": \"enterprise\"\n }' \\\n --debug\n`","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Business intelligence tracking","lvl3":""}}, {"objectID":"47f2b2b5c5483471725cc58f55a82c4f6bc09736989df4d829d3f94c8ea7fb89","title":"Performance 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\"===============================\"\n date\n echo\n\n # Check provider status\n status=$(npx @juspay/neurolink status --json 2>/dev/null)\n\n if [ $? -eq 0 ]; then\n echo \"📊 Provider Status:\"\n echo \"$status\" | jq -r '.[] | \" \\(.name): \\(.status) (\\(.responseTime)ms)\"'\n\n # Count working providers\n working=$(echo \"$status\" | jq '[.[] | select(.status == \"working\")] | length')\n total=$(echo \"$status\" | jq 'length')\n\n echo\n echo \"📈 Summary: $working/$total providers working\"\n else\n echo \"❌ Failed to get provider status\"\n fi\n\n echo\n echo \"Press Ctrl+C to exit\"\n sleep 30\n done\n}","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"provider-dashboard.sh - Real-time provider monitoring","lvl3":""}}, {"objectID":"f32925ba57afa6ef720fd047fffeaa6e12cf96045ff559b40501e6c7a956a392","title":"Run monitoring","url":"/docs/cli/advanced#run-monitoring","content":"monitor_providers\n`","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Run monitoring","lvl3":""}}, {"objectID":"8303203b7dd39f45f0a85995311401f9fadda6bec14ec33c9a181923ae58a92c","title":"Advanced Configuration","url":"/docs/cli/advanced#advanced-configuration","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Advanced Configuration","lvl3":""}}, {"objectID":"20afa582b8fbcf4d549f53d2bf53967f2ac5d5b839cf99f58aca4c7b863219b1","title":"Environment-specific configs","url":"/docs/cli/advanced#environment-specific-configs","content":"setup_environment() {\n local env=\"$1\"\n\n case $env in\n \"development\")\n export NEUROLINKLOGLEVEL=\"debug\"\n export NEUROLINKCACHEENABLED=\"false\"\n export NEUROLINK_TIMEOUT=\"60000\"\n ;;\n \"staging\")\n export NEUROLINKLOGLEVEL=\"info\"\n export NEUROLINKCACHEENABLED=\"true\"\n export NEUROLINK_TIMEOUT=\"30000\"\n ;;\n \"production\")\n export NEUROLINKLOGLEVEL=\"warn\"\n export NEUROLINKCACHEENABLED=\"true\"\n export NEUROLINK_TIMEOUT=\"15000\"\n export NEUROLINKANALYTICSENABLED=\"true\"\n ;;\n esac\n\n echo \"✅ Environment set to: $env\"\n}","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Environment-specific configs","lvl3":""}}, {"objectID":"5d83ec5377baa276da3214162f8d1675b386636364102055c6409f07b6f9aecc","title":"Usage","url":"/docs/cli/advanced#usage-3","content":"setup_environment \"production\"\nnpx @juspay/neurolink gen \"Production prompt\"\n`","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Usage","lvl3":""}}, {"objectID":"6d69a50b8109e5a4dacfebaa6fe5dfe0ea9542a2a6e937cd9eaf2b7179cdbb81","title":"Dynamic Configuration","url":"/docs/cli/advanced#dynamic-configuration","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Dynamic Configuration","lvl3":""}}, {"objectID":"7788c1dc495f52fcef85cf3ae470ffa79ad9acd5587343d693d83415357f341a","title":"Load configuration from external source","url":"/docs/cli/advanced#load-configuration-from-external-source","content":"loadremoteconfig() {\n local config_url=\"$1\"\n\n # Fetch configuration\n config=$(curl -s \"$config_url\")\n\n if [ $? -eq 0 ]; then\n # Export environment variables\n echo \"$config\" | jq -r 'to_entries[] | \"export \\(.key)=\\(.value)\"' | source /dev/stdin\n echo \"✅ Configuration loaded from $config_url\"\n else\n echo \"❌ Failed to load configuration\"\n return 1\n fi\n}","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Load configuration from external source","lvl3":""}}, {"objectID":"548bb2d45efc0414e545b93e9b8d109a3b807e7fc08a7274ab1262beb2b764c5","title":"load_remote_config \"https://config.company.com/neurolink.json\"","url":"/docs/cli/advanced#load_remote_config-httpsconfigcompanycomneurolinkjson","content":"`","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"load_remote_config \"https://config.company.com/neurolink.json\"","lvl3":""}}, {"objectID":"ae4f0086da2c989ed5340f2a7147b75bec8b21504ee5bd3896e578788df67b56","title":"Code Analysis Pipeline","url":"/docs/cli/advanced#code-analysis-pipeline","content":"`bash\n#!/bin/bash","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Code Analysis Pipeline","lvl3":""}}, {"objectID":"b0702472f46884f1285758e81bc844d0f702b018e8209b88662f4951d30d4232","title":"code-analyzer.sh - Comprehensive code analysis","url":"/docs/cli/advanced#code-analyzersh---comprehensive-code-analysis","content":"analyze_codebase() {\n local project_path=\"$1\"\n local output_dir=\"$2\"\n\n mkdir -p \"$output_dir\"\n\n echo \"🔍 Analyzing codebase at: $project_path\"\n\n # Find code files\n find \"$project_path\" -name \".ts\" -o -name \".js\" -o -name \"*.py\" | while read file; do\n echo \"Analyzing: $file\"\n\n # Code review\n npx @juspay/neurolink gen \"\n Perform comprehensive code review:\nCode quality and best-practice adherence\nSecurity vulnerabilities\nPerformance optimizations\nMaintainability improvements\n\n File: $(basename $file)\n \" --enable-evaluation \\\n --evaluation-domain \"Senior Software Architect\" \\\n --context \"{\\\"file\\\":\\\"$file\\\",\\\"project\\\":\\\"$project_path\\\"}\" \\\n > \"$output_dir/review-$(basename $file).md\"\n\n # Generate tests\n npx @juspay/neurolink gen \"\n Generate comprehensive unit tests for this code.\n Include edge cases and error scenarios.\n\n File: $(basename $file)\n \" --provider anthropic \\\n > \"$output_dir/tests-$(basename $file).md\"\n\n sleep 2 # Rate limiting\n done\n\n echo \"✅ Analysis complete. Results in: $output_dir\"\n}","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"code-analyzer.sh - Comprehensive code analysis","lvl3":""}}, {"objectID":"4e1b61150c234704d85f43e7ff9280a6ae865fef1c29249a686942248064f736","title":"analyze_codebase \"./src\" \"./analysis-results\"","url":"/docs/cli/advanced#analyze_codebase-src-analysis-results","content":"`","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"analyze_codebase \"./src\" \"./analysis-results\"","lvl3":""}}, {"objectID":"a15c494d3724690a861e60aab2bdde733ae3d25b2122245f4033a355ad6e6f17","title":"Documentation Generation Pipeline","url":"/docs/cli/advanced#documentation-generation-pipeline","content":"`bash\n#!/bin/bash","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Documentation Generation Pipeline","lvl3":""}}, {"objectID":"5ed492df45cd7fa9633cc3a1c75b55e2887a01f4c0c80ba4c4a337b9a72b7cfc","title":"docs-generator.sh - Automated documentation generation","url":"/docs/cli/advanced#docs-generatorsh---automated-documentation-generation","content":"generateprojectdocs() {\n local project_path=\"$1\"\n local docs_dir=\"$2\"\n\n mkdir -p \"$docs_dir\"\n\n echo \"📚 Generating documentation for: $project_path\"\n\n # API documentation\n npx @juspay/neurolink gen \"\n Generate comprehensive API documentation for this project.\n Include:\nEndpoint descriptions\nRequest/response examples\nAuthentication methods\nError codes and handling\n\n Project path: $project_path\n \" --enable-analytics \\\n --context \"{\\\"project\\\":\\\"$project_path\\\",\\\"type\\\":\\\"api-docs\\\"}\" \\\n --max-tokens 2000 \\\n > \"$docs_dir/api-reference.md\"\n\n # User guide\n npx @juspay/neurolink gen \"\n Create a comprehensive user guide for this project.\n Include:\nGetting started\nInstallation instructions\nUsage examples\nTroubleshooting\n\n Project path: $project_path\n \" --enable-evaluation \\\n --evaluation-domain \"Technical Writer\" \\\n --max-tokens 1500 \\\n > \"$docs_dir/user-guide.md\"\n\n # Developer guide\n npx @juspay/neurolink gen \"\n Write a developer guide for contributing to this project.\n Include:\nDevelopment setup\nArchitecture overview\nCoding standards\nTesting guidelines\n\n Project path: $project_path\n \" --provider anthropic \\\n --max-tokens 1500 \\\n > \"$docs_dir/developer-guide.md\"\n\n echo \"✅ Documentation generated in: $docs_dir\"\n}","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"docs-generator.sh - Automated documentation generation","lvl3":""}}, {"objectID":"1a353c92d9df1493af319a95c693f4903105ddf1b8a2cb69f0e0f44a41ef61d2","title":"generate_project_docs \"./my-project\" \"./docs\"","url":"/docs/cli/advanced#generate_project_docs-my-project-docs","content":"`","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"generate_project_docs \"./my-project\" \"./docs\"","lvl3":""}}, {"objectID":"785c78ac20737b7940758432e9182deb794c3fcee085124d2d2e5e887a35f0d1","title":"Parallel Processing","url":"/docs/cli/advanced#parallel-processing","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Parallel Processing","lvl3":""}}, {"objectID":"2358aa5d3d5596e3bf650cc97e121c538db42995c3203296b47143f6baca8459","title":"parallel-batch.sh - Optimized batch processing","url":"/docs/cli/advanced#parallel-batchsh---optimized-batch-processing","content":"parallel_generate() {\n local prompts_file=\"$1\"\n local max_jobs=\"${2:-4}\"\n local output_dir=\"${3:-./results}\"\n\n mkdir -p \"$output_dir\"\n\n echo \"🚀 Processing prompts in parallel (max jobs: $max_jobs)\"\n\n # Use GNU parallel for concurrent processing\n cat \"$promptsfile\" | parallel -j \"$maxjobs\" --line-buffer \\\n 'echo \"Processing: {}\" &&\n npx @juspay/neurolink gen \"{}\" \\\n --enable-analytics \\\n --json > \"'\"$output_dir\"'/result-{#}.json\" &&\n echo \"✅ Completed: {}\"'\n\n echo \"✅ All prompts processed. Results in: $output_dir\"\n}","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"parallel-batch.sh - Optimized batch processing","lvl3":""}}, {"objectID":"e61a7a6472f56b7b314d0b6ceadf673ac67d7b93c8e1aab2b0c355d2ef35569d","title":"parallel_generate \"prompts.txt\" 6 \"./batch-results\"","url":"/docs/cli/advanced#parallel_generate-promptstxt-6-batch-results","content":"`","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"parallel_generate \"prompts.txt\" 6 \"./batch-results\"","lvl3":""}}, {"objectID":"f31e3ec37923bb8632b3a1d79bd01c16434833e4e812ec8c804870a41c113e4c","title":"Smart Rate Limiting","url":"/docs/cli/advanced#smart-rate-limiting","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"Smart Rate Limiting","lvl3":""}}, {"objectID":"0306e175d2bb2052ae65c604b8b0a67519e4063bfd50d4b064a0041e769c9903","title":"rate-limited-batch.sh - Intelligent rate limiting","url":"/docs/cli/advanced#rate-limited-batchsh---intelligent-rate-limiting","content":"smartbatchprocess() {\n local prompts_file=\"$1\"\n local provider=\"$2\"\n local output_file=\"${3:-batch-results.json}\"\n\n echo \"🎯 Smart batch processing with $provider\"\n\n # Determine optimal delay based on provider\n case $provider in\n \"openai\")\n delay=3000 # Conservative for OpenAI rate limits\n ;;\n \"google-ai\")\n delay=1000 # Google AI has generous limits\n ;;\n \"anthropic\")\n delay=2000 # Moderate delay for Claude\n ;;\n *)\n delay=2000 # Default safe delay\n ;;\n esac\n\n echo \"Using ${delay}ms delay between requests\"\n\n # Process with adaptive delay\n npx @juspay/neurolink batch \"$prompts_file\" \\\n --provider \"$provider\" \\\n --delay \"$delay\" \\\n --output \"$output_file\" \\\n --enable-analytics\n\n echo \"✅ Batch processing complete\"\n}","hierarchy":{"lvl0":"Cli","lvl1":"Advanced CLI Usage","lvl2":"rate-limited-batch.sh - 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Every command shares consistent options and outputs so you can prototype in the terminal and port the workflow to code later.\n\nInstall or Run Ad-hoc\n\nCommand Map\n\n| Command | Description | Example |\n| --------------------- | ------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------- |\n| generate / gen | One-shot content generation with optional multimodal input. | npx @juspay/neurolink generate \"Draft release notes\" --image ./before.png |\n| stream | Real-time streaming output with tool support. | npx @juspay/neurolink stream \"Narrate sprint demo\" --enableAnalytics |\n| decide [state] | Typed, calibrated judgements from a decision model (no free text). | npx @juspay/neurolink decide \"Refund request\" --questions '{\"urgent\":{\"type\":\"boolean\",\"instructions\":\"Is this urgent?\"}}' |\n| batch | Process multiple prompts from a file. | npx @juspay/neurolink batch prompts.txt --format json |\n| loop | Interactive session with persistent variables & memory. | npx @juspay/neurolink loop --auto-redis |\n| auth | Manage provider authentication (API key or OAuth). | npx @juspay/neurolink auth login anthropic --method oauth |\n| setup / s | Guided provider onboarding and validation. | npx @juspay/neurolink setup --provider openai |\n| status | Health check for configured providers. | npx @juspay/neurolink status --verbose |\n| get-best-provider | Show the best available AI provider. | npx @juspay/neurolink get-best-provider --format json |\n| models list | Inspect available models and capabilities. | npx @juspay/neurolink models list --capability vision |\n| config | Initialise, validate, export, or reset configuration. | npx @juspay/neurolink config validate |\n| memory | View, export, or clear conversation history. | npx @juspay/neurolink memory history NL_x3yr --format json |\n| mcp | Manage Model Context Protocol servers/tools. | npx @juspay/neurolink mcp list |\n| ollama | Manage Ollama local AI models. | npx @juspay/neurolink ollama list-models |\n| sagemaker | Manage Amazon SageMaker endpoints and models. | npx @juspay/neurolink sagemaker status |\n| server | Manage NeuroLink HTTP server | npx @juspay/neurolink server start --port 3000 |\n| serve | Start server in foreground mode | npx @juspay/neurolink serve --port 3000 |\n| proxy | Manage the Claude multi-account proxy and its local telemetry. | npx @juspay/neurolink proxy telemetry setup |\n| rag | RAG document processing (chunk, index, query). | npx @juspay/neurolink rag chunk ./docs/guide.md |\n| workflow | Manage and execute AI workflows. | npx @juspay/neurolink workflow list ","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"","lvl3":""}}, {"objectID":"f94196010727fad589b3435c8d55133e9a350fcdcfea6aba103c1246986694c7","title":"CLI Command Reference","url":"/docs/cli/commands#cli-command-reference","content":"The NeuroLink CLI mirrors the SDK. 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Covers Office documents (Word .docx, Excel .xlsx/.xls, PowerPoint .pptx, RTF, OpenDocument), audio (.mp3, .wav, .m4a, … — transcribed automatically), video (.mp4, .webm, .mov, .mkv — keyframes plus metadata and any embedded subtitles), archives, JSON, YAML, XML, HTML, SVG, Markdown, and 50+ code languages. Repeatable. Not available on batch, where it would collide with the prompts-file positional — use generate or stream.\n--temperature, -t – creativity (default 0.7).\n--maxTokens, --max – response limit (default 1000).\n--system, -s – system prompt.\n--format, -f, --output-format – text (default), json, or table. With json, stdout carries only the JSON result; debug and info logs go to stderr, so the output stays parseable even with --debug.\n--output, -o – write response to file.\n--imageOutput, --image-output – custom path for generated image (default: generated-images/image-.png).\n--enableAnalytics / --enableEvaluation – capture metrics & quality scores.\n--evaluationDomain – domain hint for the judge model.\n--domainAware – use domain-aware evaluation (default false).\n--context – JSON string appended to analytics/evaluation context.\n--domain, -d – domain type for specialized processing: healthcare, finance, analytics, ecommerce, education, legal, technology, generic, auto.\n--disableTools – bypass MCP tools for this call.\n--timeout – seconds before aborting the request (default 120).\n--region, -r – Vertex AI region (e.g., us-central1, europe-west1, asia-northeast1).\n--debug, -v, --verbose – verbose logging and full JSON payloads.\n--quiet, -q – suppress non-essential output (default true).","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"generate ","lvl3":""}}, {"objectID":"621800e7302c71eae24c4286bcb9e05a8e110cf656e2a2d339bd8ecbe998d32e","title":"Generate with explicit subscription tier","url":"/docs/cli/commands#generate-with-explicit-subscription-tier","content":"npx @juspay/neurolink generate \"Explain quantum computing\" \\\n --provider anthropic --subscription-tier pro","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Generate with explicit subscription tier","lvl3":""}}, {"objectID":"ad3c3b350fe961788a9aad769e12752ba4a17c25fbc14e3aeec6374cfb865d0c","title":"Generate with OAuth auth method","url":"/docs/cli/commands#generate-with-oauth-auth-method","content":"npx @juspay/neurolink generate \"Write a poem\" \\\n --provider anthropic --authMethod oauth --enableBeta","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Generate with OAuth auth method","lvl3":""}}, {"objectID":"e10205dca3731c8d19a1bad6da55856d73395d3512b695b335cffbb198d8498b","title":"Stream with max tier","url":"/docs/cli/commands#stream-with-max-tier","content":"npx @juspay/neurolink stream \"Tell me a story\" \\\n --provider anthropic --subscriptionTier max\nbash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Stream with max tier","lvl3":""}}, {"objectID":"2f2211a1cbe2b22f3e6e0792c6ac7f5d04203955bff37c132ba1496abe867828","title":"Attach multiple file types","url":"/docs/cli/commands#attach-multiple-file-types","content":"npx @juspay/neurolink generate \"Analyze this data\" \\\n --file ./report.xlsx \\\n --file ./config.yaml \\\n --file ./diagram.svg","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Attach multiple file types","lvl3":""}}, {"objectID":"db8dc9b54b861c9f87d0645f64741006110a0f8cbd78e2414ae2d93af34323ab","title":"Mix file types with images and PDFs","url":"/docs/cli/commands#mix-file-types-with-images-and-pdfs","content":"npx @juspay/neurolink generate \"Compare architecture\" \\\n --file ./main.ts \\\n --pdf ./spec.pdf \\\n --image ./screenshot.png\nbash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Mix file types with images and PDFs","lvl3":""}}, {"objectID":"ced310f1b6c269511afb6ce948650a980f7ad27c98508884bfe3f90b3759a09f","title":"Generate a presentation","url":"/docs/cli/commands#generate-a-presentation","content":"npx @juspay/neurolink generate \"Quarterly business review for Q4 2025\" \\\n --outputMode ppt --pptPages 15 --pptTheme corporate --pptOutput ./q4-review.pptx","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Generate a presentation","lvl3":""}}, {"objectID":"50f5ba2e2435cf33ad1ded331cfb0f7c8eae5ef3ebc37417af7abf317760a2ce","title":"Generate with audience and tone","url":"/docs/cli/commands#generate-with-audience-and-tone","content":"npx @juspay/neurolink generate \"Introduction to machine learning\" \\\n --pptPages 20 --pptAudience students --pptTone educational --pptOutput ./ml-intro.pptx","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Generate with audience and tone","lvl3":""}}, {"objectID":"f2af75a1446c83bb27c1724b2e40e0fb3e603b2e831beb84891b10e1215af416","title":"Minimal presentation without AI images","url":"/docs/cli/commands#minimal-presentation-without-ai-images","content":"npx @juspay/neurolink generate \"Project status update\" \\\n --outputMode ppt --pptNoImages --pptOutput ./status.pptx\n\n\ngen` is a short alias with the same options.","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Minimal presentation without AI images","lvl3":""}}, {"objectID":"7ec85e0656bd3eb179cbc2ba3cc248046c955160bd6193952a4918f84537a931","title":"stream ","url":"/docs/cli/commands#stream","content":"stream shares the same flags as generate and adds chunked output for live UIs. 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storage:**\n\n- API keys are saved to the project .env file as ANTHROPICAPIKEY.\n- OAuth tokens are stored in ~/.neurolink/-credentials.json with restricted file permissions.\n- The logout subcommand clears both stored credentials and offers to remove the key from .env`.\n\nSee also: Claude Subscription Guide | Provider Setup Guide","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Clear all stored credentials for Anthropic","lvl3":""}}, {"objectID":"2245b7c69e29c2a9f708bb64776697ca5793cc2152da50441611a8147a8482ac","title":"setup","url":"/docs/cli/commands#setup","content":"Interactive provider configuration wizard that guides you through API key setup, credential validation, and recommended model selection.\n\n`bash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"setup","lvl3":""}}, {"objectID":"94d079ddc93dc4b84f44b53a5b121706274d6e686b8c57da46a9ab57686b15a0","title":"Launch interactive setup 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------------------------------------------------------------------------------------------------------------------------- |\n| --use-case | string | | Filter by primary use case: coding, creative, analysis, conversation, reasoning, translation, summarization |\n| --max-cost | number | | Maximum cost per 1K tokens (USD) |\n| --min-context | number | | Minimum context window size (tokens) |\n| --max-context | number | | Maximum context window size (tokens) |\n| --performance | string | | Required performance level: fast, medium, slow, high, low |","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"models search [query]","lvl3":""}}, {"objectID":"39162f243ad65ce4d8bb64dc37fb31f5a584b5f9d787da6e02ab2326294d2637","title":"models best","url":"/docs/cli/commands#models-best","content":"Get the best model recommendation for your use case.\n\n| Option | Type | Description |\n| ---------------------------- | ------- | -------------------------------------------- |\n| --coding | boolean | Optimize for code generation and programming |\n| --creative | boolean | Optimize for creative writing and content |\n| --analysis | boolean | Optimize for data analysis and research |\n| --conversation | boolean | Optimize for conversational interactions |\n| --reasoning | boolean | Optimize for logical reasoning tasks |\n| --translation | boolean | Optimize for language translation |\n| --summarization | boolean | Optimize for text summarization |\n| --cost-effective | boolean | Prioritize cost-effectiveness |\n| --high-quality | boolean | Prioritize output quality over cost |\n| --fast | boolean | Prioritize response speed |\n| --require-vision | boolean | Require vision/image processing capability |\n| --require-function-calling | boolean | Require function calling capability |\n| --exclude-providers | array | Exclude specific providers |\n| --prefer-local | boolean | Prefer local/offline models |","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"models 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statistics and insights.\n\n| Option | Type | Default | Description |\n| ------------ | ------- | ------- | ---------------------------------------- |\n| --detailed | boolean | false | Show detailed statistics with breakdowns |","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"models stats","lvl3":""}}, {"objectID":"9b1d8f2e5d0157573441885a79bdbb39c6158a595f27235076750d40729acad0","title":"config","url":"/docs/cli/commands#config","content":"Manage persistent configuration stored in the NeuroLink config directory.","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"config","lvl3":""}}, {"objectID":"3b37bf3c2f2acc80b89df79c434c7a0ea9f520ba28953ee7aeb5f68aa4f39f4f","title":"memory","url":"/docs/cli/commands#memory","content":"Manage conversation history stored in Redis. 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{"objectID":"12c5b993ec83812890c1d004439eea2b65b1fa656a6a9455a64fd5da65fc7c2f","title":"Get specific value","url":"/docs/cli/commands#get-specific-value","content":"neurolink server config --get defaultPort","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Get specific value","lvl3":""}}, {"objectID":"ac43578a5dc2e121362d36d10d3c4226d9e69348d57e2bc00180db88b5e42709","title":"Set a value","url":"/docs/cli/commands#set-a-value","content":"neurolink server config --set defaultPort=8080","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Set a value","lvl3":""}}, {"objectID":"eee54dd6fe04f1e25ab449070b6360db76e95147873d564c02bb2addfc7ebd4d","title":"Reset to defaults","url":"/docs/cli/commands#reset-to-defaults","content":"neurolink server config --reset\n`","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Reset to defaults","lvl3":""}}, {"objectID":"bdaaadd22f16ee540144d0a827a7e16df691784882ed6fed0340855c200422ee","title":"server 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the Claude multi-account proxy in the foreground |\n| status | Show live proxy status, including attempts vs completed requests |\n| telemetry | Manage the local OpenObserve stack and maintained dashboard for the proxy |\n| setup | One-command onboarding: auth + service install + Claude Code configuration |\n| install | Install the proxy as a persistent background service |\n| uninstall` | Remove the persistent background service |","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"Bring up OpenObserve + OTEL collector + dashboard","lvl3":""}}, {"objectID":"b008b4a3a629278995cff6232a410dea89bfe2e36313cf20764e8392c1df5bdc","title":"proxy start","url":"/docs/cli/commands#proxy-start","content":"| Option | Alias | Type | Default | Description |\n| ------------------- | ----- | ------- | -------------------------------- | --------------------------------------------------------- |\n| --port | -p | number | 55669 | Port to listen on |\n| --host | -H | string | 127.0.0.1 | Host to bind to |\n| --strategy | -s | string | fill-first | Account selection strategy: fill-first or round-robin |\n| --health-interval | | number | 30 | Health check interval in seconds |\n| --config | -c | string | ~/.neurolink/proxy-config.yaml | Path to proxy config file |\n| --env-file | | string | | Path to proxy provider env file |\n| --passthrough | | boolean | false | Transparent forwarding: no retry, rotation, or polyfill |\n| --debug | -d | boolean | false | Enable debug output |\n| --quiet | -q | boolean | false | Suppress non-essential output |","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"proxy start","lvl3":""}}, {"objectID":"69cb017d91d3c8e02af64a37c7e132f63db1d12f8a46ca0cdc4d0b3e6b912100","title":"proxy status","url":"/docs/cli/commands#proxy-status","content":"| Option | Alias | Type | Default | Description |\n| ---------- | ----- | ------- | ------- | ------------------------------- |\n| --format | | string | text | Output format: text or json |\n| --quiet | -q | boolean | false | Suppress non-essential output |","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"proxy status","lvl3":""}}, {"objectID":"ae006b9912af00810cb404e3eebb898c372cb8511f416ecd63de2ef212afa2d0","title":"proxy telemetry \\","url":"/docs/cli/commands#proxy-telemetry-action","content":"Manage the repo-owned local OpenObserve stack in scripts/observability/.\n\n| Action | Description |\n| ------------------ | ----------------------------------------------------------------------- |\n| setup | Start OpenObserve + OTEL collector and import the maintained dashboard |\n| start | Start the local telemetry stack without re-importing the dashboard |\n| stop | Stop the local telemetry stack |\n| status | Show local stack health and endpoint info |\n| logs | Follow OpenObserve and collector logs |\n| import-dashboard | Re-import the dashboard and dedupe older dashboards with the same title |\n\n| Option | Alias | Type | Default | Description |\n| --------- | ----- | ------- | ------- | ---------------------------------------------------- |\n| --quiet | -q | boolean | false | Suppress the local CLI spinner and delegate directly |","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"proxy telemetry \\","lvl3":""}}, {"objectID":"d827e581a1b45a14f3a3633e94d694c6d48351c2b379c6680c5fe106f31654e8","title":"proxy setup","url":"/docs/cli/commands#proxy-setup","content":"| Option | Alias | Type | Default | Description |\n| -------------- | ----- | ------- | ------- | -------------------------------------------------------- |\n| --port | -p | number | 55669 | Proxy port |\n| --method | | string | oauth | Auth method: oauth or api-key |\n| --no-service | | boolean | false | Skip service installation and start in foreground |\n| --env-file | | string | | Path to proxy provider env file to persist for the proxy |","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"proxy setup","lvl3":""}}, 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directly with generate and stream commands via --rag-files:\n\n| Flag | Type | Default | Description |\n| --------------------- | -------- | ------------- | ----------------------------------- |\n| --rag-files | string[] | - | File paths to load for RAG context |\n| --rag-strategy | string | auto-detected | Chunking strategy for RAG documents |\n| --rag-chunk-size | number | 1000 | Maximum chunk size in characters |\n| --rag-chunk-overlap | number | 200 | Overlap between adjacent chunks |\n| --rag-top-k | number | 5 | Number of top results to retrieve |","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"RAG Flags on generate/stream","lvl3":""}}, {"objectID":"5a7423689987a8d716e1f9883bd9a18b043710d7c09205646dc2671754b357ca","title":"workflow \\","url":"/docs/cli/commands#workflow-subcommand","content":"Manage and execute AI workflows (consensus, fallback, adaptive, multi-judge).\n\n`bash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Command Reference","lvl2":"workflow 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Testing\n\nTemperature Experiments\n\nToken Limit Testing\n\n🔗 Related Resources\nCLI Commands Reference - Complete command documentation\nAdvanced Usage - Power user features\nInstallation Guide - Setup instructions\nEnvironment Variables - Configuration\nTroubleshooting - Common issues","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"","lvl3":""}}, {"objectID":"03625aa61bb31e6c0ba861895a08bad39ca4b9494198f5fceb841ad8bd5cbf84","title":"CLI Examples","url":"/docs/cli/examples#cli-examples","content":"Practical examples and usage patterns for the NeuroLink CLI.","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"CLI Examples","lvl3":""}}, {"objectID":"cbaa79c5e583835d23dcaab7a2e23df19c9e3ec43b92ce0878117717f67f0c5e","title":"Basic Text Generation","url":"/docs/cli/examples#basic-text-generation","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Basic Text Generation","lvl3":""}}, 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that:\nAccepts POST requests to /api/users\nCreates new user accounts\nReturns user ID and status\n\" --max-tokens 1000","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Generate API documentation","lvl3":""}}, {"objectID":"e1ed816eac4736a79e00f6a8bab5d0c1e904f59d1e8ec1afc0451d3da71c6f0c","title":"Generate README sections","url":"/docs/cli/examples#generate-readme-sections","content":"npx @juspay/neurolink gen \"\nWrite a 'Getting Started' section for a Node.js CLI tool\nthat processes CSV files. Include installation and basic usage.\n\"\n`","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Generate README sections","lvl3":""}}, {"objectID":"5a3defa2cef472b0ecc396a5a207e7b56c0bba5a4b14f8e6b9e6df98858a1f74","title":"Content Creation","url":"/docs/cli/examples#content-creation","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Content Creation","lvl3":""}}, {"objectID":"37aecaf7651b41309e983adb5154e291fba0fa6033be80c6c11d8bef2d6eed5d","title":"Marketing copy","url":"/docs/cli/examples#marketing-copy","content":"npx @juspay/neurolink gen \"\nWrite compelling product description for an AI development platform\nthat supports multiple providers and has built-in tools.\n\" --temperature 0.8","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Marketing copy","lvl3":""}}, {"objectID":"5a29728c257f2c10b64e7d98fdedf6fb5b0fb9be6227d6f68fe78c30cccca405","title":"Email templates","url":"/docs/cli/examples#email-templates","content":"npx @juspay/neurolink gen \"\nCreate a professional email template for announcing\nnew API features to enterprise customers.\n\"","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Email templates","lvl3":""}}, {"objectID":"56c5fdaa7b6fb56b732dc983f25ba2e7023cdf0f19b71b457a0739292cd4692f","title":"Social media content","url":"/docs/cli/examples#social-media-content","content":"npx @juspay/neurolink gen \"\nWrite 3 Twitter posts about AI automation benefits\nfor software development teams. Keep under 280 characters each.\n\"\n`","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Social media content","lvl3":""}}, {"objectID":"b0771dba4b490402be6d6a52b87b0c04627bf42a3b53c83b34e990159ea45381","title":"Business Analysis","url":"/docs/cli/examples#business-analysis","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Business Analysis","lvl3":""}}, {"objectID":"6636639eb2f9022774e6c3cb4b4bd6e1c65e9d4c7429fa31f01889fea8fe0236","title":"Market research","url":"/docs/cli/examples#market-research","content":"npx @juspay/neurolink gen \"\nAnalyze the current trends in AI development tools.\nFocus on developer experience and enterprise adoption.\n\" --provider anthropic --max-tokens 1500","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Market research","lvl3":""}}, {"objectID":"69f97662cda6d511efd15ad2bc1dc0d8e1e360095694c1f43d42218c53f5080e","title":"Competitive analysis","url":"/docs/cli/examples#competitive-analysis","content":"npx @juspay/neurolink gen \"\nCompare the advantages of multi-provider AI platforms\nversus single-provider solutions for enterprise use.\n\"\n`","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Competitive analysis","lvl3":""}}, {"objectID":"2bfa88a6698d70a6ab66cd8580ff88243ca51145ea07f17fe5bf25ab8ffd94f4","title":"Content Pipeline","url":"/docs/cli/examples#content-pipeline","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Content Pipeline","lvl3":""}}, {"objectID":"8930a281a5e59b295e1a2fc2162f61d17c32a0851f773187a8c5217229cef780","title":"Create prompts file","url":"/docs/cli/examples#create-prompts-file","content":"cat > content-prompts.txt << EOF\nWrite a blog post title about AI automation\nCreate a product announcement for new features\nDraft a technical overview of our platform\nGenerate FAQ answers about pricing\nEOF","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Create prompts file","lvl3":""}}, 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{"objectID":"e070867e06bb6207fc02822c7ba10f19b31ce20aa8a41988970f413393ea484a","title":"Create review prompts","url":"/docs/cli/examples#create-review-prompts","content":"cat > review-prompts.txt << EOF\nReview this TypeScript code for best practices and potential issues\nSuggest improvements for error handling and performance\nCheck for security vulnerabilities in API endpoints\nAnalyze code maintainability and documentation needs\nEOF","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Create review prompts","lvl3":""}}, {"objectID":"af1519d82d24ad32314faf72aca400d6453e8de4b95c1722d94b7979d3cfc660","title":"Run reviews with different providers","url":"/docs/cli/examples#run-reviews-with-different-providers","content":"npx @juspay/neurolink batch review-prompts.txt \\\n --provider anthropic \\\n --output code-reviews.json \\\n --delay 3000\n`","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Run reviews with different providers","lvl3":""}}, 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@juspay/neurolink gen \"Help with API design\" \\\n --enable-analytics \\\n --context '{\"userId\":\"dev123\",\"project\":\"ecommerce\",\"role\":\"backend\"}' \\\n --debug","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"User session context","lvl3":""}}, {"objectID":"e597c6077256dec56d89aa8a7f2e25635f821ba6f4e74e4f6ef292c82d579422","title":"Business context","url":"/docs/cli/examples#business-context","content":"npx @juspay/neurolink gen \"Create project timeline\" \\\n --context '{\"company\":\"TechCorp\",\"department\":\"engineering\",\"quarter\":\"Q1\"}' \\\n --enable-evaluation \\\n --evaluation-domain \"Project Manager\"\n`","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Business context","lvl3":""}}, {"objectID":"a655fe0cc6fef3e400d6f30414b044d3fa7abb20b866a9cd3471e0080504b2c0","title":"Provider Diagnostics","url":"/docs/cli/examples#provider-diagnostics","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Provider Diagnostics","lvl3":""}}, {"objectID":"cab574c6b8597337cf5e91b05bc3e0c40e2f7717c7e32ba20aec5fd5b962cf65","title":"Verbose status check","url":"/docs/cli/examples#verbose-status-check","content":"npx @juspay/neurolink status --verbose","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Verbose status check","lvl3":""}}, {"objectID":"33fd3a3df2feaa37922e5215db4cae0c3f0af990d32f99867fbec3f4382c4717","title":"Debug generation","url":"/docs/cli/examples#debug-generation","content":"npx @juspay/neurolink gen \"Test prompt\" --debug","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Debug generation","lvl3":""}}, {"objectID":"dd11218f5afd74e3759f3ccc2341d4dd629f9502462e564794913287d2d02ccb","title":"Check configuration","url":"/docs/cli/examples#check-configuration","content":"npx @juspay/neurolink config show","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Check configuration","lvl3":""}}, {"objectID":"2e279c6dafee9a338489e13545d4be214dcc45bf2c31435355349ade790db914","title":"Validate setup","url":"/docs/cli/examples#validate-setup","content":"npx @juspay/neurolink doctor\n`","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Validate setup","lvl3":""}}, {"objectID":"c92499a419f752604d67308ac03e30b63a7cb729e8e905be9bfc25cb7279c578","title":"Performance Testing","url":"/docs/cli/examples#performance-testing","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Performance Testing","lvl3":""}}, {"objectID":"302a7f177eb9a9d6afc0a497b31a4e0a5108693414bf5152b16feba7c7305d5b","title":"Test response times","url":"/docs/cli/examples#test-response-times","content":"time npx @juspay/neurolink gen \"Quick test\" --provider openai\ntime npx @juspay/neurolink gen \"Quick test\" --provider google-ai","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Test response times","lvl3":""}}, {"objectID":"0360b96aa92756acc7e997fbe6873f2ae1d743149df2859e1f26fce3df234a87","title":"Batch performance 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messages","url":"/docs/cli/examples#ai-powered-git-commit-messages","content":"diff=$(git diff --cached --name-only)\nif [ -z \"$diff\" ]; then\n echo \"No staged changes\"\n exit 1\nfi\n\ncommit_msg=$(npx @juspay/neurolink gen \\\n \"Generate concise git commit message for: $diff\" \\\n --max-tokens 50 \\\n --temperature 0.3)\n\necho \"Suggested: $commit_msg\"\nread -p \"Use this message? (y/N): \" -n 1 -r\nif [[ $REPLY =~ ^[Yy]$ ]]; then\n git commit -m \"$commit_msg\"\nfi\n`","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"AI-powered git commit messages","lvl3":""}}, {"objectID":"8dfa907a183e8dee8b6b18eb64f3404fad6456cc35d0ed06c27fd6394c6c29cb","title":"Content Management","url":"/docs/cli/examples#content-management","content":"`bash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Content Management","lvl3":""}}, {"objectID":"e2b40b1123ae75f4f4d1ec0b83826c4cba803c63c79f8053151b653f99cec231","title":"Daily content generation","url":"/docs/cli/examples#daily-content-generation","content":"#!/bin/bash\nDATE=$(date +\"%Y-%m-%d\")","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Daily content generation","lvl3":""}}, {"objectID":"77f6ab92d8a1c2442eb176aece9aa775be23cd0f222547cfcae405ee66d16efa","title":"Generate daily summary","url":"/docs/cli/examples#generate-daily-summary","content":"npx @juspay/neurolink gen \"\nCreate a daily engineering summary for $DATE.\nInclude: progress updates, blockers, next steps.\n\" --enable-analytics > reports/daily-$DATE.md","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Generate daily summary","lvl3":""}}, {"objectID":"4ffcf3e5a53fbb9342704f588ce5d67f2369cc7f523acf5239cf48ee382f6f91","title":"Generate team updates","url":"/docs/cli/examples#generate-team-updates","content":"npx @juspay/neurolink gen \"\nWrite team update email template for weekly standup.\nInclude sections for achievements, challenges, goals.\n\" > templates/weekly-update.md\n`","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Generate team updates","lvl3":""}}, {"objectID":"8247ee80bc16fcf34cd4e0a83bff4c02b7088f9f4f05280a630dceb34f19d6cd","title":"Code Review Pipeline","url":"/docs/cli/examples#code-review-pipeline","content":"`bash\n#!/bin/bash","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Code Review Pipeline","lvl3":""}}, {"objectID":"44fb1be41482b38388c144160e0e7aaa9eddb87fe9bb5bf892316d1d8e49d28d","title":"Get changed files","url":"/docs/cli/examples#get-changed-files","content":"files=$(git diff --name-only HEAD~1)","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Get changed files","lvl3":""}}, {"objectID":"c67fea9574d11bf5307bb008cca04a867644a9fbfc448e539569eb9796a618f4","title":"Review each file","url":"/docs/cli/examples#review-each-file","content":"for file in $files; do\n if [[ $file == .ts ]] || [[ $file == .js ]]; then\n echo \"Reviewing $file...\"\n npx @juspay/neurolink gen \"\n Review this code for:\nBest practices\nSecurity issues\nPerformance optimizations\nMaintainability\n\n File: $file\n \" --enable-evaluation \\\n --evaluation-domain \"Senior Code Reviewer\" \\\n > reviews/review-$(basename $file).md\n fi\ndone\n`","hierarchy":{"lvl0":"Cli","lvl1":"CLI Examples","lvl2":"Review each file","lvl3":""}}, 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Examples\nAnalytics Domain Examples\nFinance Domain Examples\nE-commerce Domain Examples\nContext Integration Examples\nEvaluation and Analytics\nProvider-Specific Examples\nStreaming with Domains\nConfiguration Management\nAdvanced Use Cases\n\nBasic Domain Usage\n\nSimple Domain Generation\n\nDomain-Specific Streaming\n\nHealthcare Domain Examples\n\nMedical Diagnosis Support\n\nTreatment Planning\n\nMedical Research Analysis\n\nAnalytics Domain Examples\n\nBusiness Intelligence\n\nData Science Insights\n\nPredictive Analytics\n\nFinance Domain Examples\n\nInvestment Analysis\n\nFinancial Planning\n\nMarket Analysis\n\nE-commerce Domain Examples\n\nConversion Optimization\n\nCustomer Experience\n\nMarketing Campaign Analysis\n\nContext Integration Examples\n\nComplex Organizational Context\n\nMulti-Domain Context\n\nEvaluation and Analytics\n\nComprehensive Evaluation Setup\n\nAnalytics-Only Mode\n\nEvaluation-Only Mode\n\nProvider-Specific Examples\n\nOpenAI with Healthcare Domain\n\nAnthropic with Finance Domain\n\nGoogle AI with Analytics Domain\n\nStreaming with Domains\n\nInteractive Healthcare Consultation\n\nReal-time Financial Analysis\n\nLive Business Intelligence\n\nConfiguration Management\n\nSetting Domain Defaults\n\nDomain-Specific Configuration\n\nCustom Domain Setup\n\nAdvanced Use Cases\n\nMulti-Step Analysis Pipeline\n\nCross-Domain Analysis\n\nCompliance-Aware Generation\n\nPerformance-Optimized Commands\n\nBest Practices\nDomain Selection Guidelines\nHealthcare: Medical analysis, diagnosis support, treatment planning, regulatory compliance\nAnalytics: Data analysis, business intelligence, predictive modeling, performance metrics\nFinance: Investment analysis, risk assessment, financial planning, market analysis\nE-commerce: Conversion optimization, customer experience, marketing campaigns, sales analytics\nContext Structure Best Practices\nOutput Format Selection\nUse --format json for structured analysis and integration\nUse --format text for human-readable reports\nUse --format table for comparative data presentation\nPerformance Optimization\nUse --max-tokens to control response length\nEnable --enable-analytics for detailed performance metrics\nUse appropriate providers for specific domains\nStructure context data efficiently\nEvaluation Best Practices\nAlways enable evaluation for critical domain applications\nUse domain-specific evaluation criteria\nMonitor evaluation scores for quality assurance\nCombine evaluation with analytics for comprehensive insights\n\nTroubleshooting\n\nCommon Issues and Solutions\nUnknown domain error\nContext parsing errors\nPerformance issues\nProvider compatibility\n \n\nAdditional Resources\nCLI Reference\nConfiguration Guide\nPerformance Optimization\nAPI Documentation\n\nFor more examples and advanced usage patterns, visit the NeuroLink Examples Repository.","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"","lvl3":""}}, {"objectID":"d4abfa1de9f0f5750c41cca056b24de18116d6b921a241751706fc7f12f43929","title":"Domain Configuration Examples for NeuroLink CLI","url":"/docs/cli-domain-examples#domain-configuration-examples-for-neurolink-cli","content":"This document provides comprehensive examples of using domain-specific features with the NeuroLink CLI, showcasing the Phase 1 Factory Infrastructure capabilities.","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Domain Configuration Examples for NeuroLink CLI","lvl3":""}}, {"objectID":"57e8725bfb34eba4e1613a799e9654a4afe57c4a5fb887e7ba7b9103464aa203","title":"Table of Contents","url":"/docs/cli-domain-examples#table-of-contents","content":"Basic Domain Usage\nHealthcare Domain Examples\nAnalytics Domain Examples\nFinance Domain Examples\nE-commerce Domain Examples\nContext Integration Examples\nEvaluation and Analytics\nProvider-Specific Examples\nStreaming with Domains\nConfiguration Management\nAdvanced Use Cases","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Table of Contents","lvl3":""}}, {"objectID":"3c230b253db94f8ea13efe436874fd6d406e509aeafeaaf87fed5daae344ae08","title":"Simple Domain Generation","url":"/docs/cli-domain-examples#simple-domain-generation","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Simple Domain Generation","lvl3":""}}, {"objectID":"903120e89149d98e71039ee16f05325857e94820233995a9d12a436f880a8319","title":"Basic healthcare domain usage","url":"/docs/cli-domain-examples#basic-healthcare-domain-usage","content":"neurolink generate \"Analyze patient symptoms: fever, headache, fatigue\" \\\n --evaluationDomain healthcare \\\n --enable-evaluation \\\n --format json","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Basic healthcare domain usage","lvl3":""}}, {"objectID":"801034aaaf80e28c04fb8f04aad9139f7cf0ecf7df6969ed79fe91df5e46fb9e","title":"Basic analytics domain usage","url":"/docs/cli-domain-examples#basic-analytics-domain-usage","content":"neurolink generate \"Calculate quarterly revenue growth trends\" \\\n --evaluationDomain analytics \\\n --enable-evaluation \\\n --enable-analytics \\\n --format json\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Basic analytics domain usage","lvl3":""}}, {"objectID":"8a76107f90542d406f9f34da974ff9004bc6590d35d16f4ee1c79f6510eab1c7","title":"Domain-Specific Streaming","url":"/docs/cli-domain-examples#domain-specific-streaming","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Domain-Specific Streaming","lvl3":""}}, {"objectID":"24642b1ea9f175bc3c6eea990ae1c73299f80f33dc1105aa21ba49d151f38359","title":"Stream with finance domain evaluation","url":"/docs/cli-domain-examples#stream-with-finance-domain-evaluation","content":"neurolink stream \"Assess investment portfolio risk for retirement planning\" \\\n --evaluationDomain finance \\\n --enable-evaluation","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Stream with finance domain evaluation","lvl3":""}}, {"objectID":"1fbcbfaf0bb5218e58a002e7dd062c1a6ae3cc91e8f3fdd0aeb09f2ddc53b716","title":"Stream with ecommerce domain evaluation","url":"/docs/cli-domain-examples#stream-with-ecommerce-domain-evaluation","content":"neurolink stream \"Optimize conversion funnel for online retail store\" \\\n --evaluationDomain ecommerce \\\n --enable-evaluation\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Stream with ecommerce domain evaluation","lvl3":""}}, {"objectID":"f7e90afc3c12f45a0aa8d1d02b36af9d5cd46a6d377f949adb6f38c65302a75b","title":"Medical Diagnosis Support","url":"/docs/cli-domain-examples#medical-diagnosis-support","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Medical Diagnosis Support","lvl3":""}}, {"objectID":"25d4424ee66ca4ec773bc2c0bb0d830a1618353f6af9c449c007e06e50be94aa","title":"Comprehensive symptom analysis","url":"/docs/cli-domain-examples#comprehensive-symptom-analysis","content":"neurolink generate \"Patient presents with: chest pain (8/10), shortness of breath, elevated heart rate (110 BPM), diaphoresis. History: hypertension, diabetes. Age 65. Provide differential diagnosis and recommended tests.\" \\\n --evaluationDomain healthcare \\\n --enable-evaluation \\\n --enable-analytics \\\n --provider google-ai \\\n --max-tokens 800 \\\n --format json\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Comprehensive symptom analysis","lvl3":""}}, {"objectID":"d396b6c06206c509fea3723394d93c6621fd613248f4268bf7de8d5d853f9fe2","title":"Treatment Planning","url":"/docs/cli-domain-examples#treatment-planning","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Treatment Planning","lvl3":""}}, {"objectID":"e39d8369bc06348a481dc154dca535d8ab1bb53da775c210bd64198c2532adb5","title":"Treatment recommendation with context","url":"/docs/cli-domain-examples#treatment-recommendation-with-context","content":"neurolink generate \"Develop treatment plan for Type 2 diabetes patient\" \\\n --context '{\"patientAge\":55,\"comorbidities\":[\"hypertension\",\"obesity\"],\"allergies\":[\"penicillin\"],\"currentMedications\":[\"metformin\",\"lisinopril\"]}' \\\n --evaluationDomain healthcare \\\n --enable-evaluation \\\n --format json\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Treatment recommendation with context","lvl3":""}}, {"objectID":"4e820d5b55f00fd6b44083ec1682d16f8032ef1427382668ea1e26977ce623e5","title":"Medical Research Analysis","url":"/docs/cli-domain-examples#medical-research-analysis","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Medical Research Analysis","lvl3":""}}, {"objectID":"38cf8a13161482d4f8263c89fa6aa92cde9a7634f0652faec61bdf163692f3af","title":"Clinical trial data analysis","url":"/docs/cli-domain-examples#clinical-trial-data-analysis","content":"neurolink stream \"Analyze clinical trial results for new cardiovascular drug\" \\\n --context '{\"studyType\":\"randomized-controlled\",\"sampleSize\":2000,\"primaryEndpoint\":\"MACE reduction\",\"duration\":\"24-months\"}' \\\n --evaluationDomain healthcare \\\n --enable-evaluation \\\n --enable-analytics \\\n --provider anthropic\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Clinical trial data analysis","lvl3":""}}, {"objectID":"be78e4a06446d119bfa9a3acec58a26fd8be0aae17265f5029dea8259819a940","title":"Business Intelligence","url":"/docs/cli-domain-examples#business-intelligence","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Business Intelligence","lvl3":""}}, {"objectID":"c227f4dba1d6497f8556aec2c9c6b225a3138c2c8ce0e9a7a544af8bf9fd8444","title":"Quarterly business analysis","url":"/docs/cli-domain-examples#quarterly-business-analysis","content":"neurolink generate \"Analyze Q3 performance metrics and identify growth opportunities\" \\\n --context '{\"revenue\":\"$2.5M\",\"growth\":\"15%\",\"customerAcquisition\":450,\"churnRate\":\"3.2%\",\"marketSegment\":\"B2B-SaaS\"}' \\\n --evaluationDomain analytics \\\n --enable-evaluation \\\n --enable-analytics \\\n --format json \\\n --max-tokens 1000\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Quarterly business analysis","lvl3":""}}, {"objectID":"e612c2bf4bca85ed3f337d70e2e7f15527505fa4bfc608e84ceeebd01bfb95f6","title":"Data Science Insights","url":"/docs/cli-domain-examples#data-science-insights","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Data Science Insights","lvl3":""}}, {"objectID":"603e5d360ea1b10f0e23f9470d5f1ede4088716a403a138cf1dcc38df395474b","title":"Machine learning model performance analysis","url":"/docs/cli-domain-examples#machine-learning-model-performance-analysis","content":"neurolink generate \"Evaluate ML model performance and recommend optimizations\" \\\n --context '{\"modelType\":\"gradient-boosting\",\"accuracy\":0.87,\"precision\":0.83,\"recall\":0.91,\"f1Score\":0.87,\"trainingData\":\"50k-samples\",\"features\":42}' \\\n --evaluationDomain analytics \\\n --enable-evaluation \\\n --provider openai \\\n --format json\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Machine learning model performance analysis","lvl3":""}}, {"objectID":"dfb58f925b1bb083b34bddf2f0058d673f76793d6c8edc80e4d5020c3e63e028","title":"Predictive Analytics","url":"/docs/cli-domain-examples#predictive-analytics","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Predictive Analytics","lvl3":""}}, {"objectID":"f471955cdfb2b50ff8d6e543ed9223837a4291ef3544e4e97434c90580757e28","title":"Sales forecasting with streaming","url":"/docs/cli-domain-examples#sales-forecasting-with-streaming","content":"neurolink stream \"Generate sales forecast for next quarter based on historical trends\" \\\n --context '{\"historicalData\":\"3-years\",\"seasonality\":\"high\",\"marketTrends\":\"positive\",\"competitiveAnalysis\":\"included\"}' \\\n --evaluationDomain analytics \\\n --enable-evaluation \\\n --enable-analytics\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Sales forecasting with streaming","lvl3":""}}, {"objectID":"b1a9a42622979ff6a8ff1e3be97b83f58690b7f8b3453c2cfb83d3e90a6de3ac","title":"Investment Analysis","url":"/docs/cli-domain-examples#investment-analysis","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Investment Analysis","lvl3":""}}, {"objectID":"77b92c36d9aece1a46390787ec3a4f52f9b8e7aa85e26195663420a8cc2ebee7","title":"Portfolio risk assessment","url":"/docs/cli-domain-examples#portfolio-risk-assessment","content":"neurolink generate \"Assess risk profile of diversified investment portfolio\" \\\n --context '{\"assetAllocation\":{\"stocks\":0.60,\"bonds\":0.30,\"alternatives\":0.10},\"totalValue\":\"$500k\",\"timeHorizon\":\"10-years\",\"riskTolerance\":\"moderate\"}' \\\n --evaluationDomain finance \\\n --enable-evaluation \\\n --enable-analytics \\\n --format json\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Portfolio risk assessment","lvl3":""}}, {"objectID":"6833b1815da277e49c9b5bb048e447f9c9c440aa9e5edb27b716a039a450a2e9","title":"Financial Planning","url":"/docs/cli-domain-examples#financial-planning","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Financial Planning","lvl3":""}}, {"objectID":"0e663a4dd35203943c4ad52fe9eb112add50c2c3428d70cbfad2138cb483f840","title":"Retirement planning analysis","url":"/docs/cli-domain-examples#retirement-planning-analysis","content":"neurolink generate \"Create comprehensive retirement savings strategy\" \\\n --context '{\"currentAge\":35,\"retirementAge\":65,\"currentSavings\":\"$75k\",\"annualIncome\":\"$120k\",\"savingsRate\":\"15%\",\"expectedReturns\":\"7%\"}' \\\n --evaluationDomain finance \\\n --enable-evaluation \\\n --provider vertex \\\n --max-tokens 1200\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Retirement planning analysis","lvl3":""}}, {"objectID":"6078bc0754f1e7887817693ae6b8dac5f0e05bad201577c5515b250164d734cf","title":"Market Analysis","url":"/docs/cli-domain-examples#market-analysis","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Market Analysis","lvl3":""}}, {"objectID":"4757f484335cf7e7dff1c0d674f4da589cb176c1724ab5272533a662c380619d","title":"Economic trend analysis with streaming","url":"/docs/cli-domain-examples#economic-trend-analysis-with-streaming","content":"neurolink stream \"Analyze current market conditions and economic indicators\" \\\n --context '{\"inflationRate\":\"3.2%\",\"unemploymentRate\":\"3.8%\",\"fedFundsRate\":\"5.25%\",\"gdpGrowth\":\"2.1%\",\"marketVolatility\":\"elevated\"}' \\\n --evaluationDomain finance \\\n --enable-evaluation\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Economic trend analysis with streaming","lvl3":""}}, {"objectID":"30693c2cfd22d8c7f4ddf2ed21f36e3455c49e83cd716a50e578cedbe3a91363","title":"Conversion Optimization","url":"/docs/cli-domain-examples#conversion-optimization","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Conversion Optimization","lvl3":""}}, {"objectID":"d77ab840680e6292b1a56ef60d9efb297f742860c72bef0d8a161675a30bcc45","title":"E-commerce funnel analysis","url":"/docs/cli-domain-examples#e-commerce-funnel-analysis","content":"neurolink generate \"Optimize checkout process to reduce cart abandonment\" \\\n --context '{\"cartAbandonmentRate\":\"68%\",\"checkoutSteps\":4,\"averageLoadTime\":\"3.2s\",\"mobileUsers\":\"75%\",\"paymentOptions\":[\"card\",\"paypal\",\"apple-pay\"]}' \\\n --evaluationDomain ecommerce \\\n --enable-evaluation \\\n --enable-analytics \\\n --format json\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"E-commerce funnel analysis","lvl3":""}}, {"objectID":"4e9517a78ea7ee63176c1b2417b177212051440de99b9a4a4fa89520bd62b53b","title":"Customer Experience","url":"/docs/cli-domain-examples#customer-experience","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Customer Experience","lvl3":""}}, {"objectID":"bc5a91a71c17b8540c2816110f87567a8ca4ee8d3ea5c436a0d3d99b8b4e7d25","title":"Product recommendation strategy","url":"/docs/cli-domain-examples#product-recommendation-strategy","content":"neurolink generate \"Develop personalized product recommendation engine\" \\\n --context '{\"userBase\":\"50k-active\",\"purchaseHistory\":\"available\",\"browsingData\":\"tracked\",\"categoryCount\":25,\"averageOrderValue\":\"$85\"}' \\\n --evaluationDomain ecommerce \\\n --enable-evaluation \\\n --provider google-ai\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Product recommendation strategy","lvl3":""}}, {"objectID":"f988d39f90cf051972aec81703543ec4081ef25b561e7d03fc7d5582003a0bda","title":"Marketing Campaign Analysis","url":"/docs/cli-domain-examples#marketing-campaign-analysis","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Marketing Campaign Analysis","lvl3":""}}, {"objectID":"6a50e11036bc693b83e991d896b50a793343e7669caf05e8caff26497bf93bb2","title":"Campaign performance optimization","url":"/docs/cli-domain-examples#campaign-performance-optimization","content":"neurolink stream \"Analyze digital marketing campaign performance and ROI\" \\\n --context '{\"channels\":[\"social\",\"email\",\"ppc\",\"seo\"],\"budget\":\"$50k\",\"duration\":\"3-months\",\"conversions\":1250,\"cac\":\"$40\",\"ltv\":\"$300\"}' \\\n --evaluationDomain ecommerce \\\n --enable-evaluation \\\n --enable-analytics\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Campaign performance optimization","lvl3":""}}, {"objectID":"26b6b10280e933a7d24b0e9bedfe9cc5945e0f2bc4fc43ffae4d52a5a1da8d09","title":"Complex Organizational Context","url":"/docs/cli-domain-examples#complex-organizational-context","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Complex Organizational Context","lvl3":""}}, {"objectID":"c930113588c5e885041ee87c6f8d0d5e3e98b2c0db85fa2c105a7cf243ebdbb4","title":"Enterprise analytics with comprehensive context","url":"/docs/cli-domain-examples#enterprise-analytics-with-comprehensive-context","content":"neurolink generate \"Analyze operational efficiency across multiple departments\" \\\n --context '{\n \"organization\": {\n \"id\": \"acme-corp-2024\",\n \"industry\": \"technology\",\n \"size\": \"mid-market\",\n \"locations\": [\"us-east\", \"eu-west\", \"apac-south\"]\n },\n \"departments\": {\n \"engineering\": {\"headcount\": 120, \"budget\": \"$8M\", \"kpis\": [\"velocity\", \"quality\", \"innovation\"]},\n \"sales\": {\"headcount\": 45, \"budget\": \"$2M\", \"kpis\": [\"revenue\", \"pipeline\", \"conversion\"]},\n \"marketing\": {\"headcount\": 25, \"budget\": \"$1.5M\", \"kpis\": [\"leads\", \"brand\", \"engagement\"]}\n },\n \"timeframe\": \"Q3-2024\",\n \"objectives\": [\"growth\", \"efficiency\", \"scalability\"]\n }' \\\n --evaluationDomain analytics \\\n --enable-evaluation \\\n --enable-analytics \\\n --format json \\\n --max-tokens 1500\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Enterprise analytics with comprehensive context","lvl3":""}}, {"objectID":"6c6b5862a6f90fe657459a7cda5131c6432406c918730f0def710d8240825531","title":"Multi-Domain Context","url":"/docs/cli-domain-examples#multi-domain-context","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Multi-Domain Context","lvl3":""}}, {"objectID":"5c29d79562a77fa9708920d97a22f274c7e886aeb2fe5c896f63fe04708e0cf6","title":"Healthcare analytics with regulatory context","url":"/docs/cli-domain-examples#healthcare-analytics-with-regulatory-context","content":"neurolink generate \"Analyze patient outcomes while ensuring HIPAA compliance\" \\\n --context '{\n \"healthcare\": {\n \"facilityType\": \"hospital\",\n \"specialties\": [\"cardiology\", \"oncology\", \"emergency\"],\n \"patientVolume\": \"daily-500\"\n },\n \"compliance\": {\n \"frameworks\": [\"HIPAA\", \"SOX\", \"FDA\"],\n \"auditStatus\": \"current\",\n \"dataClassification\": \"sensitive\"\n },\n \"analytics\": {\n \"metricsTracked\": [\"readmission-rates\", \"patient-satisfaction\", \"treatment-outcomes\"],\n \"reportingFrequency\": \"monthly\",\n \"stakeholders\": [\"medical-staff\", \"administration\", \"regulators\"]\n }\n }' \\\n --evaluationDomain healthcare \\\n --enable-evaluation \\\n --enable-analytics \\\n --provider anthropic\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Healthcare analytics with regulatory context","lvl3":""}}, {"objectID":"193cbfc365ed473e30b23b847959ebc5cb1d414424bed65202d4420280c38615","title":"Comprehensive Evaluation Setup","url":"/docs/cli-domain-examples#comprehensive-evaluation-setup","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Comprehensive Evaluation Setup","lvl3":""}}, {"objectID":"cb13c1e62f61e0e43dd7e87b04322084c0855ceedb83ad986ec92a157393b300","title":"Full evaluation with custom domain","url":"/docs/cli-domain-examples#full-evaluation-with-custom-domain","content":"neurolink generate \"Develop AI strategy for enterprise transformation\" \\\n --evaluationDomain analytics \\\n --enable-evaluation \\\n --enable-analytics \\\n --context '{\"industry\":\"manufacturing\",\"aiMaturity\":\"beginner\",\"budget\":\"$2M\",\"timeline\":\"18-months\"}' \\\n --provider google-ai \\\n --format json \\\n --max-tokens 2000\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Full evaluation with custom domain","lvl3":""}}, {"objectID":"ad0bf016e979f03b2615719c86c6f4f2f4ffd1fb846e8f33c2fc43deb32202de","title":"Analytics-Only Mode","url":"/docs/cli-domain-examples#analytics-only-mode","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Analytics-Only Mode","lvl3":""}}, {"objectID":"0e473870a3362ed241ae7cf74281a5567eaf720f1b78909d608b8cf4900cf87d","title":"Analytics without evaluation","url":"/docs/cli-domain-examples#analytics-without-evaluation","content":"neurolink generate \"Create quarterly performance report\" \\\n --enable-analytics \\\n --context '{\"quarter\":\"Q3\",\"metrics\":[\"revenue\",\"growth\",\"efficiency\"],\"stakeholders\":[\"executives\",\"board\",\"investors\"]}' \\\n --format json\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Analytics without evaluation","lvl3":""}}, {"objectID":"8e5610cfc4dca6b141fbf7e81e0e7bc41415fada8a206994d4c7ff74be8696b8","title":"Evaluation-Only Mode","url":"/docs/cli-domain-examples#evaluation-only-mode","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Evaluation-Only Mode","lvl3":""}}, {"objectID":"a675298451694be9a127d471ee0bd36efea6c5981a3ef4ca0f9b66b326652f51","title":"Evaluation without analytics","url":"/docs/cli-domain-examples#evaluation-without-analytics","content":"neurolink generate \"Review software architecture decisions\" \\\n --evaluationDomain analytics \\\n --enable-evaluation \\\n --context '{\"architecture\":\"microservices\",\"scale\":\"enterprise\",\"complexity\":\"high\"}'\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Evaluation without analytics","lvl3":""}}, {"objectID":"606f3cb2cc41cea659f494ba7633edc4822e0204652c7dce168c2bc0cf83d282","title":"Interactive Healthcare Consultation","url":"/docs/cli-domain-examples#interactive-healthcare-consultation","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Interactive Healthcare Consultation","lvl3":""}}, {"objectID":"eba0af8680cf8fdecd8c3084ca9e79813bf7f8af27e231ade8018f6b3920ed00","title":"Stream medical case analysis","url":"/docs/cli-domain-examples#stream-medical-case-analysis","content":"neurolink stream \"Walk through differential diagnosis process for complex case\" \\\n --evaluationDomain healthcare \\\n --enable-evaluation \\\n --context '{\"setting\":\"emergency-room\",\"urgency\":\"high\",\"resources\":\"full-diagnostic\"}' \\\n --provider anthropic\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Stream medical case analysis","lvl3":""}}, {"objectID":"d79a585abb7959848dcd118ac6e6970cea7bec078012c79a83d6db858bce51c2","title":"Real-time Financial Analysis","url":"/docs/cli-domain-examples#real-time-financial-analysis","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Real-time Financial Analysis","lvl3":""}}, {"objectID":"22c5b26dc4c07975926efb6686e2b3d5d8d21bc8a7718518a15c58d89b4c1204","title":"Stream market analysis","url":"/docs/cli-domain-examples#stream-market-analysis","content":"neurolink stream \"Provide real-time analysis of market volatility impact\" \\\n --evaluationDomain finance \\\n --enable-evaluation \\\n --enable-analytics \\\n --context '{\"marketConditions\":\"volatile\",\"portfolio\":\"balanced\",\"clientRisk\":\"moderate\"}'\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Stream market analysis","lvl3":""}}, {"objectID":"64bc5c42e84c30432376d947b204a512fe75bbf9c525e9e2e6bed11261d4c63a","title":"Live Business Intelligence","url":"/docs/cli-domain-examples#live-business-intelligence","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Live Business Intelligence","lvl3":""}}, {"objectID":"78fa761a575583d4df39a10df0ca8b0d8cbf12aa1fefe402887f1e73a03ddede","title":"Stream business insights","url":"/docs/cli-domain-examples#stream-business-insights","content":"neurolink stream \"Generate actionable insights from real-time business metrics\" \\\n --evaluationDomain analytics \\\n --enable-evaluation \\\n --enable-analytics \\\n --context '{\"dataSource\":\"live-dashboard\",\"updateFrequency\":\"real-time\",\"stakeholder\":\"c-suite\"}'\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Stream business insights","lvl3":""}}, 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Examples for NeuroLink CLI","lvl2":"- Enable Evaluation by Default: yes","lvl3":""}}, {"objectID":"7e0bf13fed38202d7f829288718c890567fa58b2750f510507733dbb82398253","title":"Domain-Specific Configuration","url":"/docs/cli-domain-examples#domain-specific-configuration","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Domain-Specific Configuration","lvl3":""}}, {"objectID":"6104c0a2251e482a1fd19758a35c75833025f7b4c2b454e7cba6f242b14b838e","title":"Show current domain configuration","url":"/docs/cli-domain-examples#show-current-domain-configuration","content":"neurolink config show","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Show current domain configuration","lvl3":""}}, {"objectID":"a998b07bfdcea8c23fdddb35db98d21d3fb9e006b0dc8861b5d0d3b0e50afa9a","title":"Export configuration with domain settings","url":"/docs/cli-domain-examples#export-configuration-with-domain-settings","content":"neurolink config export --format json > neurolink-domain-config.json","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Export configuration with domain settings","lvl3":""}}, {"objectID":"71630108a9aaeca92863dd04859a5e0162d18dc13b0030f842e7c20ea03b0c8d","title":"Validate domain configuration","url":"/docs/cli-domain-examples#validate-domain-configuration","content":"neurolink config validate\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Validate domain configuration","lvl3":""}}, {"objectID":"eb747d76d37ae3557f52da5b1eb419f651ee9bd29ec7255bdd8cd5a17921e301","title":"Custom Domain Setup","url":"/docs/cli-domain-examples#custom-domain-setup","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Custom Domain Setup","lvl3":""}}, {"objectID":"0b6f2b543a7003181a6f55294edef1b9495fecc090794bba7d7aafc62d63a743","title":"Initialize with custom domain preferences","url":"/docs/cli-domain-examples#initialize-with-custom-domain-preferences","content":"neurolink config init","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Initialize with custom domain preferences","lvl3":""}}, {"objectID":"a74ea4a18340a31dc5dd0d0a214f62b269abf61a7785ef12f6a2132865b8f43d","title":"Enable treatment outcomes tracking","url":"/docs/cli-domain-examples#enable-treatment-outcomes-tracking","content":"`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Enable treatment outcomes tracking","lvl3":""}}, {"objectID":"d2f37aeadda778302a8d80ade982e0af24cfece3936ad1eda78beb31a5470741","title":"Multi-Step Analysis Pipeline","url":"/docs/cli-domain-examples#multi-step-analysis-pipeline","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Multi-Step Analysis Pipeline","lvl3":""}}, {"objectID":"1ded3cecff516ac8bf841b1be59b5e46f8f006c24030274918a0083a235254a0","title":"Step 1: Initial analysis","url":"/docs/cli-domain-examples#step-1-initial-analysis","content":"neurolink generate \"Conduct preliminary market research analysis\" \\\n --evaluationDomain analytics \\\n --enable-evaluation \\\n --context '{\"market\":\"fintech\",\"stage\":\"preliminary\",\"scope\":\"competitive-landscape\"}' \\\n --output step1-analysis.json \\\n --format json","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Step 1: Initial analysis","lvl3":""}}, {"objectID":"cc4d7397e244e7526ee0e5cd6cf46846adc18eb4eccc790e3a80fd06583dec9e","title":"Step 2: Deep dive based on initial findings","url":"/docs/cli-domain-examples#step-2-deep-dive-based-on-initial-findings","content":"neurolink generate \"Deep dive into identified market opportunities\" \\\n --evaluationDomain analytics \\\n --enable-evaluation \\\n --enable-analytics \\\n --context '{\"previousAnalysis\":\"step1-analysis.json\",\"focus\":\"opportunity-sizing\",\"methodology\":\"bottom-up\"}' \\\n --format json\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Step 2: Deep dive based on initial findings","lvl3":""}}, {"objectID":"85c32108bc4b6a826accc9701e574a52eede7ef8f1a7eab3ce9145e8d7ffc67b","title":"Cross-Domain Analysis","url":"/docs/cli-domain-examples#cross-domain-analysis","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Cross-Domain Analysis","lvl3":""}}, {"objectID":"d93183b62f2c86aa32f762d74c14fbec29bb3ec4bcaeb4c6f509d5eaaa259361","title":"Healthcare + Analytics combined analysis","url":"/docs/cli-domain-examples#healthcare-analytics-combined-analysis","content":"neurolink generate \"Analyze healthcare cost optimization using data analytics\" \\\n --evaluationDomain healthcare \\\n --enable-evaluation \\\n --enable-analytics \\\n --context '{\n \"healthcare\": {\"costs\":\"rising\",\"quality\":\"maintained\",\"patient-satisfaction\":\"high\"},\n \"analytics\": {\"dataAvailable\":[\"claims\",\"outcomes\",\"satisfaction\"],\"methodology\":\"predictive-modeling\"}\n }' \\\n --format json \\\n --max-tokens 2000\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Healthcare + Analytics combined analysis","lvl3":""}}, {"objectID":"e3de82fa54acc2b56169043bd3af44d30a82d453244b61649f5c872cb8875062","title":"Compliance-Aware Generation","url":"/docs/cli-domain-examples#compliance-aware-generation","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Compliance-Aware Generation","lvl3":""}}, {"objectID":"861b12cac90076148ace65411a2cd131bb831e52171122af4affd087262fafae","title":"Finance with regulatory compliance","url":"/docs/cli-domain-examples#finance-with-regulatory-compliance","content":"neurolink generate \"Develop investment strategy complying with fiduciary standards\" \\\n --evaluationDomain finance \\\n --enable-evaluation \\\n --context '{\n \"regulatory\": {\"framework\":\"DOL-fiduciary\",\"state\":\"california\",\"clientType\":\"retirement-plan\"},\n \"investment\": {\"universe\":\"mutual-funds\",\"fees\":\"low-cost\",\"diversification\":\"required\"}\n }' \\\n --format json\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Finance with regulatory compliance","lvl3":""}}, {"objectID":"d2814f6a9caed9e8ba4af03c48b820c3b49a615d3242d9a1f492f0ee5c84a23b","title":"Performance-Optimized Commands","url":"/docs/cli-domain-examples#performance-optimized-commands","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Performance-Optimized Commands","lvl3":""}}, {"objectID":"f5864f7437d14e207e225a3e4bcd77b4013a34ca3cf83c60e518ca5959726594","title":"High-performance analytics processing","url":"/docs/cli-domain-examples#high-performance-analytics-processing","content":"neurolink generate \"Process large dataset for business insights\" \\\n --evaluationDomain analytics \\\n --enable-analytics \\\n --provider vertex \\\n --max-tokens 1000 \\\n --timeout 180 \\\n --context '{\"dataSize\":\"100GB\",\"processing\":\"distributed\",\"latency\":\"low\",\"accuracy\":\"high\"}' \\\n --format json\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"High-performance analytics processing","lvl3":""}}, {"objectID":"e00e15169c86ee75ec631c1040bd6a022588b739f26311c35c7d70d95b0d47ec","title":"1. Domain Selection Guidelines","url":"/docs/cli-domain-examples#1-domain-selection-guidelines","content":"Healthcare: Medical analysis, diagnosis support, treatment planning, regulatory compliance\nAnalytics: Data analysis, business intelligence, predictive modeling, performance metrics\nFinance: Investment analysis, risk assessment, financial planning, market analysis\nE-commerce: Conversion optimization, customer experience, marketing campaigns, sales analytics","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"1. Domain Selection Guidelines","lvl3":""}}, {"objectID":"ad1ff512aab37f6f2e766eef0de0846f5be32d1c8cd9e76b48fc1a2d1bb6523b","title":"2. Context Structure Best Practices","url":"/docs/cli-domain-examples#2-context-structure-best-practices","content":"`bash","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"2. Context Structure Best Practices","lvl3":""}}, {"objectID":"f870e9dae8b3886b311dfee3e6b89b9a5f776c34e267a9490ad21fbef6f87b92","title":"Well-structured context example","url":"/docs/cli-domain-examples#well-structured-context-example","content":"neurolink generate \"Your analysis request\" \\\n --context '{\n \"domain_specific\": {\n \"key_metrics\": [\"metric1\", \"metric2\"],\n \"constraints\": [\"constraint1\", \"constraint2\"]\n },\n \"organizational\": {\n \"size\": \"enterprise\",\n \"industry\": \"technology\"\n },\n \"temporal\": {\n \"timeframe\": \"Q3-2024\",\n \"urgency\": \"high\"\n }\n }' \\\n --evaluationDomain analytics \\\n --enable-evaluation\n`","hierarchy":{"lvl0":"Cli Domain Examples","lvl1":"Domain Configuration Examples for NeuroLink CLI","lvl2":"Well-structured context example","lvl3":""}}, {"objectID":"614fb79712c89d9060f084db1250eb94e64abfcf32f613582be97c7ef23ad9c5","title":"3. 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provider","url":"/docs/cli-reference#with-specific-provider","content":"npx @juspay/neurolink generate \"Explain quantum computing\" --provider openai","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"With specific provider","lvl3":""}}, {"objectID":"e451221842e511d028df2b409bd3a3f78548436bed739acadeea035344bae911","title":"With model selection","url":"/docs/cli-reference#with-model-selection","content":"npx @juspay/neurolink generate \"Write code\" --provider google-ai --model gemini-2.5-pro\n`","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"With model selection","lvl3":""}}, {"objectID":"21fd70df76997f607472bf3f223d5bd0972e92e4eb8414879757e37ab242309d","title":"Enhanced Analytics & Evaluation","url":"/docs/cli-reference#enhanced-analytics-evaluation","content":"`bash","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Enhanced Analytics & Evaluation","lvl3":""}}, {"objectID":"3efba52e8384b17110e444ca25bdb41245b0e6be79d8f7651676958982b75b74","title":"Basic analytics","url":"/docs/cli-reference#basic-analytics","content":"npx @juspay/neurolink generate \"What is machine learning?\" --enable-analytics","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Basic analytics","lvl3":""}}, {"objectID":"5779f4e338cd947cdcbe35ed778efce3da557d03e648b1ce5672b18d5b9c7eea","title":"Analytics + evaluation","url":"/docs/cli-reference#analytics-evaluation","content":"npx @juspay/neurolink generate \"Explain AI ethics\" --enable-analytics --enable-evaluation","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Analytics + evaluation","lvl3":""}}, {"objectID":"a79a86088bf05ab2d092daf1e6a3eff6f80e60ddd3fa2792c9a136bdf474e86a","title":"With custom context","url":"/docs/cli-reference#with-custom-context","content":"npx @juspay/neurolink generate \"Create a proposal\" \\\n --enable-analytics --enable-evaluation \\\n --context '{\"company\":\"TechCorp\",\"department\":\"AI\"}'\n`","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"With custom context","lvl3":""}}, {"objectID":"d3f75a4c2f17403b9dc7881c544594d8a5ca2424db9f83f3649ccc3f6a46a2e5","title":"Domain-Aware Evaluation","url":"/docs/cli-reference#domain-aware-evaluation","content":"`bash","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Domain-Aware Evaluation","lvl3":""}}, {"objectID":"90e967716144518a0aa871803afa61ad358cce8c882e80a1a3977ff760b4beac","title":"Basic domain evaluation","url":"/docs/cli-reference#basic-domain-evaluation","content":"npx @juspay/neurolink generate \"Fix this Python code\" \\\n --enable-evaluation --evaluation-domain \"Python coding assistant\"","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Basic domain evaluation","lvl3":""}}, {"objectID":"821aa5db0b1e017c2c18c4155ce566e963d9a02d90055507a517cff54c98806d","title":"Lighthouse-style evaluation","url":"/docs/cli-reference#lighthouse-style-evaluation","content":"npx @juspay/neurolink generate \"Create a business plan\" \\\n --lighthouse-style --evaluation-domain \"Business consultant\" \\\n --tool-usage-context \"Used market-research and financial-analysis tools\"","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Lighthouse-style evaluation","lvl3":""}}, {"objectID":"3890d13beaadea721683ad15c152ec0e33fa1e8788a8c26fb78a97cf96b3a532","title":"Enterprise evaluation with context","url":"/docs/cli-reference#enterprise-evaluation-with-context","content":"npx @juspay/neurolink generate \"Analyze sales data\" \\\n --enable-analytics --lighthouse-style \\\n --evaluation-domain \"Data analyst\" \\\n --context '{\"role\":\"senioranalyst\",\"accesslevel\":\"full\"}'\n`","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Enterprise evaluation with context","lvl3":""}}, {"objectID":"f411852362ba864c69c12f6596a819f93dedf483a5a5963d0d693be2030ef1a9","title":"Debug & Development","url":"/docs/cli-reference#debug-development","content":"`bash","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Debug & Development","lvl3":""}}, {"objectID":"ca3cbcb5a4a0e29b8bc46eb9766c270a16d38e8c747fb56bfacddaf2682b1709","title":"Debug mode with full output","url":"/docs/cli-reference#debug-mode-with-full-output","content":"npx @juspay/neurolink generate \"Test prompt\" --debug","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Debug mode with full output","lvl3":""}}, {"objectID":"51ef4052b76782da4b7b5beaa519385a6d219387f65d0b007d46dbd648e50d75","title":"Debug with enhancements","url":"/docs/cli-reference#debug-with-enhancements","content":"npx @juspay/neurolink generate \"Test analytics\" \\\n --enable-analytics --enable-evaluation --debug","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference 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openai --model gpt-4o \\\n --enable-analytics --lighthouse-style \\\n --evaluation-domain \"AI strategy consultant\" \\\n --tool-usage-context \"Market research, competitor analysis, financial modeling\" \\\n --context '{\"company\":\"Fortune500\",\"quarter\":\"Q1-2025\",\"budget\":\"$5M\"}' \\\n --debug","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Enterprise AI assistant with full features","lvl3":""}}, {"objectID":"12c135a2c7cb09c38dffcfbb6ba9106103a37752e9828bdd0f5c16e2c9871269","title":"Cost-optimized evaluation","url":"/docs/cli-reference#cost-optimized-evaluation","content":"npx @juspay/neurolink generate \"Quick code review\" \\\n --provider google-ai --model gemini-2.5-flash \\\n --enable-evaluation --evaluation-domain \"Code reviewer\" \\\n --max-tokens 500","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Cost-optimized evaluation","lvl3":""}}, {"objectID":"d4b63173f7b8061f799a40857ebe80c380cfc11f7e106e8b7a84af75e887ba2b","title":"High-quality content generation","url":"/docs/cli-reference#high-quality-content-generation","content":"npx @juspay/neurolink generate \"Write technical documentation\" \\\n --provider anthropic --model claude-3-opus \\\n --enable-analytics --enable-evaluation \\\n --evaluation-domain \"Technical writer\" \\\n --temperature 0.3 --max-tokens 2000\n`","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"High-quality content generation","lvl3":""}}, {"objectID":"cc00dd0ef2d64bf026e6fd024ca4e9a7cae6f0e2197e7c4040c8cfb09f1ab4c3","title":"Common Errors & Solutions","url":"/docs/cli-reference#common-errors-solutions","content":"Provider not available:\n\nInvalid context JSON:\n\nModel not found:\n\nEvaluation failed:","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Common Errors & Solutions","lvl3":""}}, {"objectID":"f364f3c4e96e66099b41139b859b00553ce14c1d5d302a051603ae68cc7531c2","title":"Performance Tips","url":"/docs/cli-reference#performance-tips","content":"Fast Evaluation: Use --model gemini-2.5-flash for quick, cost-effective evaluation\nQuality Content: Use --provider anthropic --model claude-3-opus for high-quality generation\nCost Optimization: Set NEUROLINK_EVALUATION_PREFER_CHEAP=true for automatic cost optimization\nDebug Efficiently: Use --debug only when troubleshooting to avoid verbose output\nContext Size: Keep --context objects small to minimize token usage","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Performance Tips","lvl3":""}}, {"objectID":"15ff11687d9541132e455e6b7f2ed513384cd1de7bd70acf5514904aa27ad17e","title":"Video Generation Examples","url":"/docs/cli-reference#video-generation-examples","content":"Generate videos from images using Veo 3.1 via Vertex AI:\n\n`bash","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Video Generation Examples","lvl3":""}}, {"objectID":"337cc791ca5f4667e8bb0fa18c278c22af5a26bf1715ff06c7facbfc17c6ff36","title":"Basic video generation","url":"/docs/cli-reference#basic-video-generation","content":"npx @juspay/neurolink generate \"Product showcase with smooth camera movement\" \\\n --image ./product.jpg \\\n --outputMode video \\\n --videoOutput ./output.mp4","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Basic video generation","lvl3":""}}, {"objectID":"1c8cfa5c3ed8b49022b140b6cf15f3db656741cec700451e02190d2b4b82bf07","title":"Full options","url":"/docs/cli-reference#full-options","content":"npx @juspay/neurolink generate \"Cinematic reveal with dramatic lighting\" \\\n --image ./hero-image.png \\\n --provider vertex \\\n --model veo-3.1 \\\n --outputMode video \\\n --videoResolution 1080p \\\n --videoLength 8 \\\n --videoAspectRatio 16:9 \\\n --videoOutput ./cinematic.mp4","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Full options","lvl3":""}}, {"objectID":"d4eaf3657b28a3bcd62a5e8bc1924fe7668653681d1251ad2bb1c4c590d4462a","title":"Portrait video for social media","url":"/docs/cli-reference#portrait-video-for-social-media","content":"npx @juspay/neurolink generate \"Vertical scroll animation\" \\\n --image ./mobile-screenshot.jpg \\\n --outputMode video \\\n --videoResolution 720p \\\n --videoAspectRatio 9:16 \\\n --videoOutput ./story.mp4\n`\n\nNote: Video generation requires Vertex AI credentials. See Video Generation Guide.","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Portrait video for social media","lvl3":""}}, {"objectID":"5f0dbba1fce39b7a0267d6d749e8814cf5ee5c28315189258cbde1d554be20ad","title":"PPT Generation Examples","url":"/docs/cli-reference#ppt-generation-examples","content":"Generate AI-powered PowerPoint presentations:\n\n`bash","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"PPT Generation Examples","lvl3":""}}, {"objectID":"e7c733ef56ab5bd88812dbf83a7e65b59ddf7e22e0c1d6d909e6423e8aefc5d5","title":"Basic PPT generation","url":"/docs/cli-reference#basic-ppt-generation","content":"npx @juspay/neurolink generate \"Introduction to Machine Learning\" \\\n --outputMode ppt \\\n --pptPages 10 \\\n --pptOutput ./ml-presentation.pptx","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Basic PPT generation","lvl3":""}}, {"objectID":"8f3debd2a64e1ddbdaf433d97aa1353650470438ac5d8a6956e4b844b8cbe78d","title":"With theme and audience customization","url":"/docs/cli-reference#with-theme-and-audience-customization","content":"npx @juspay/neurolink generate \"Quarterly Sales Report Q4 2025\" \\\n --provider vertex \\\n --model gemini-2.5-pro \\\n --outputMode ppt \\\n --pptPages 15 \\\n --pptTheme corporate \\\n --pptAudience business \\\n --pptTone professional \\\n --pptOutput ./q4-report.pptx","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"With theme and audience customization","lvl3":""}}, {"objectID":"e23df944e1038de0b8367480eb7376b58eca9afed1ea79c4b11c194fc0cbe86c","title":"Creative presentation with AI-generated images","url":"/docs/cli-reference#creative-presentation-with-ai-generated-images","content":"npx @juspay/neurolink generate \"Future of Space Tourism\" \\\n --outputMode ppt \\\n --pptPages 12 \\\n --pptTheme creative \\\n --pptOutput ./space-tourism.pptx","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Creative presentation with AI-generated images","lvl3":""}}, {"objectID":"2f7dd0e372e8f39bea6dcb172f2d23fb4fe8ee44254d12f39cd1d5c185726c58","title":"Technical documentation with dark theme","url":"/docs/cli-reference#technical-documentation-with-dark-theme","content":"npx @juspay/neurolink generate \"Kubernetes Architecture Deep Dive\" \\\n --provider anthropic \\\n --model claude-3-5-sonnet \\\n --outputMode ppt \\\n --pptPages 20 \\\n --pptTheme dark \\\n --pptAudience technical \\\n --pptTone educational \\\n --pptOutput ./k8s-architecture.pptx","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Technical documentation with dark theme","lvl3":""}}, {"objectID":"1f647659fde2338543421d6442d5cc5b67b6501cac8219cbc04c853c82160f92","title":"Disable AI image generation","url":"/docs/cli-reference#disable-ai-image-generation","content":"npx @juspay/neurolink generate \"Company Brand Guidelines\" \\\n --outputMode ppt \\\n --pptPages 8 \\\n --pptTheme minimal \\\n --pptNoImages \\\n --pptOutput ./brand-guidelines.pptx\n`\n\nNote: PPT generation works with multiple AI providers. See PPT Generation Guide.","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Disable AI image generation","lvl3":""}}, {"objectID":"971a7e5368674e3cd806ce777cd2eb8778e508a6bfa075f479d8848f32528ff4","title":"Environment Variables","url":"/docs/cli-reference#environment-variables","content":"See the Environment Variables documentation for complete configuration options.","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"Environment Variables","lvl3":""}}, {"objectID":"cc420fe1d5b9a089ca4aca964660ff8f49b9f178bc5ffb1ebd86291ce9c950b2","title":"API Integration","url":"/docs/cli-reference#api-integration","content":"For programmatic usage, see the API Reference documentation.","hierarchy":{"lvl0":"Cli Reference","lvl1":"🖥️ CLI Reference Guide","lvl2":"API Integration","lvl3":""}}, {"objectID":"a26ede10a817fc130076d98adc18544a9e8d4031cc7469053596a5e7d1590098","title":"Changelog","url":"/docs/community/changelog","content":"Changelog\n\nThe current release notes live on GitHub Releases:\ngithub.com/juspay/neurolink/releases — generated\nautomatically by semantic-release on every publish, so they are always current. An RSS/Atom feed\nis available at releases.atom.\n\nCHANGELOG.md in the repository is not a reliable source of the current version.\n@semantic-release/git was deliberately removed so that nothing pushes back to the release\nbranch (branch protection rejects pushes that carry no check runs), which means the committed\nCHANGELOG.md stops at the last version that was committed by hand. It still ships inside the\npublished npm package, where it is generated at publish time and is correct.\n\nRelease highlights\n\nThe entries below are hand-written highlights for selected past releases. They are an archive,\nnot a complete or current list — use GitHub Releases above for that.\n\nv9.14.0 (February 28, 2026)\n\nFeatures:\n(providers): Add Claude Subscription Support with OAuth 2.0 PKCE authentication for Claude Pro/Max subscriptions\n\nWhat's New:\nOAuth 2.0 with PKCE authentication for Claude Pro, Max, and Team subscription users, enabling API access without separate API keys\nAutomatic token refresh before every generate() and stream() call, ensuring uninterrupted sessions\nModel tier access enforcement with six subscription tiers: free, pro, max, max_5, max_20, and api, restricting model access based on the user's plan\nNew auth CLI command with four subcommands: login (browser-based OAuth flow), status (display current authentication state), refresh (manually refresh tokens), and logout (revoke and clear credentials)\nSecure token storage at ~/.neurolink/anthropic-credentials.json with filesystem-level permissions\nBeta feature support for claude-code, interleaved-thinking, and fine-grained-tool-streaming via Anthropic beta headers\n99 integration tests covering authentication flows, token lifecycle, tier enforcement, and CLI command behavior\n\nv8.26.1 (December 31, 2025)\n\nBug Fixes:\n(providers): Resolve Gemini 3 issues, add utilities, improve tests (270ef6f)\n\nWhat's New:\nEnhanced Gemini 3 provider stability\nImproved test coverage for Google AI providers\nAdded new provider utility functions\n\nv8.26.0 (December 30, 2025)\n\nFeatures:\n(types): Add video output types (VIDEO-GEN-001) (1b1b5c2)\n\nWhat's New:\nVideo generation type support\nEnhanced multimodal capabilities\nNew type definitions for video outputs\n\nv8.25.0 (December 30, 2025)\n\nFeatures:\n(observability): Add support for custom metadata in Context (b175249)\n\nWhat's New:\nCustom metadata support for observability\nEnhanced context tracking capabilities\nImproved telemetry integration\n\nRecent Notable Releases\n\nv8.24.0 - OpenRouter Integration\nAdded OpenRouter provider with 300+ model support\nEnhanced provider ecosystem\nExpanded model availability\n\nv8.23.0 - CSV Enhancements\nAdded file extension field to CSV metadata\nImproved CSV processing capabilities\n\nv8.22.0 - CI/CD Improvements\nAdded ffmpeg installation and verification to CI/CD pipeline\nEnhanced multimedia processing support\n\nv8.21.0 - Office Documents\nAdded office document type definitions\nComprehensive document handling tests\nEnhanced multimodal support\n\nv8.20.0 - Memory Improvements\nImplemented token-based summarization\nEnhanced conversation memory management\nOptimized context handling\n\nv8.19.0 - TTS Integration\nIntegrated Text-to-Speech (TTS) into BaseProvider.generate()\nEnhanced audio generation capabilities\nGoogle TTS handler improvements\n\nVersion Support Policy\n\n| Version | Status | Support Level | End of Life |\n| ------- | ----------- | -------------------------------------------------------- | ------------ |\n| 8.x | Active | Full support - Security updates, bug fixes, new features | - |\n| 7.x | Maintenance | Security updates and critical bug fixes only | June 1, 2026 |\n| 6.x | End of Life | No support | June 1, 2025 |\n\nSupport Levels Explained:\nActive: Full support including new features, enhancements, bug fixes, and security updates\nMaintenance: Security patches and critical bug fixes only, no new features\nEnd of Life: No updates or support, upgrade recommended\n\nUpgrade Guides\n\nMigrating between major versions? Check out our comprehensive upgrade guides:\n\nMajor Version Upgrades\nv8 to v9 Migration Guide\n\n > This guide is planned for a future release.\nv7 to v8 Migration Guide\n\n > This guide is planned for a future release.\nv6 to v7 Migration Guide\n > This guide is planned for a future release.\n\nMigrating from Other SDKs\n\nAlready using another AI SDK? We have migration guides:\nFrom LangChain\nFeature comparison\nAPI mapping\nTool/chain equivalents\nFrom Vercel AI SDK\nProvider migration\nStreaming API changes\nUI integration patterns\n\nRelease Highlights by Feature Area\n\nProviders (v8.20.0 - v9.14.0)\nv9.14.0: Claude Subscription Support with OAuth 2.0 PKCE authentication\nv8.26.1: Gemini 3 stability improvements\nv8.24.0: Op","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"","lvl3":""}}, {"objectID":"35efabfd884699a53fad5778677ed8dea10200414522b12dcc22699baecaeb1c","title":"Changelog","url":"/docs/community/changelog#changelog","content":"The current release notes live on GitHub Releases:\ngithub.com/juspay/neurolink/releases — generated\nautomatically by semantic-release on every publish, so they are always current. An RSS/Atom feed\nis available at releases.atom.\n\nCHANGELOG.md in the repository is not a reliable source of the current version.\n@semantic-release/git was deliberately removed so that nothing pushes back to the release\nbranch (branch protection rejects pushes that carry no check runs), which means the committed\nCHANGELOG.md stops at the last version that was committed by hand. It still ships inside the\npublished npm package, where it is generated at publish time and is correct.","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Changelog","lvl3":""}}, {"objectID":"f81147fb9c1eae671fd1e0080c158f888ef56203f0d955c28fb3dbf4d028fd28","title":"Release highlights","url":"/docs/community/changelog#release-highlights","content":"The entries below are hand-written highlights for selected past releases. 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We have migration guides:\nFrom LangChain\nFeature comparison\nAPI mapping\nTool/chain equivalents\nFrom Vercel AI SDK\nProvider migration\nStreaming API changes\nUI integration patterns","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Migrating from Other SDKs","lvl3":""}}, {"objectID":"ba9e7943186f8db2c074c587773da0fe947b3d15212c83d039ccc8f8701f0061","title":"Providers (v8.20.0 - v9.14.0)","url":"/docs/community/changelog#providers-v8200---v9140","content":"v9.14.0: Claude Subscription Support with OAuth 2.0 PKCE authentication\nv8.26.1: Gemini 3 stability improvements\nv8.24.0: OpenRouter provider (300+ models)\nv8.20.0: Enhanced provider error handling","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Providers (v8.20.0 - v9.14.0)","lvl3":""}}, {"objectID":"a11370de368d49cbb425338260a14239b71eba2e46da593fe173a37ab379698d","title":"Multimodal (v8.19.0 - v8.26.0)","url":"/docs/community/changelog#multimodal-v8190---v8260","content":"v8.26.0: Video output types\nv8.23.0: CSV metadata enhancements\nv8.21.0: Office document support\nv8.19.0: TTS integration","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Multimodal (v8.19.0 - v8.26.0)","lvl3":""}}, {"objectID":"eb94c009b5b28af316e5a3f249caf413dbcbaa32b2413468795faadc9ca8a8fa","title":"Memory & Context (v8.20.0 - v8.25.0)","url":"/docs/community/changelog#memory-context-v8200---v8250","content":"v8.25.0: Custom metadata in Context\nv8.20.0: Token-based summarization","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Memory & Context (v8.20.0 - v8.25.0)","lvl3":""}}, {"objectID":"4062e4ba3e9bd381fbd66ca2da562e7943986d367d4cdc45a2a91106c4dc959e","title":"Developer Experience (v8.22.0 - v8.23.1)","url":"/docs/community/changelog#developer-experience-v8220---v8231","content":"v8.23.1: Blocked tool support\nv8.22.0: Enhanced CI/CD pipeline","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Developer Experience (v8.22.0 - v8.23.1)","lvl3":""}}, {"objectID":"108b249c6701b97cbfd1fd8b6e2a4cd5aea1e45a726d7aa053fbf6314489d685","title":"v8.x Series","url":"/docs/community/changelog#v8x-series","content":"No major breaking changes in v8.x patch releases. 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We follow Semantic Versioning:\nMajor (x.0.0): Breaking changes\nMinor (8.x.0): New features, backward compatible\nPatch (8.26.x): Bug fixes, backward compatible","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Future Breaking Changes","lvl3":""}}, {"objectID":"470d9b1a644a3a3ae340112f387f5249b04095355db3819b809b78d3c7e8f0ba","title":"Release Schedule","url":"/docs/community/changelog#release-schedule","content":"NeuroLink follows a continuous release schedule:\nPatch Releases: As needed for bug fixes and minor improvements\nMinor Releases: Every 1-2 weeks for new features\nMajor Releases: Annually or when significant architecture changes are needed","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Release Schedule","lvl3":""}}, {"objectID":"2216718968b2bf3ad3fe2489acb7300628838c40cc02b7d8bdd0d7ea0669aad3","title":"Release Notifications","url":"/docs/community/changelog#release-notifications","content":"Stay updated with new releases:\nGitHub Releases: Watch the NeuroLink repository for release notifications\nNPM: Follow @juspay/neurolink on npm\nChangelog: Monitor this page or the full CHANGELOG.md\nGitHub Discussions: Join discussions for release announcements","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Release Notifications","lvl3":""}}, {"objectID":"923ebd577e631768d0f77100cec9a4bc96ba595b27b97203f0fea84025726f59","title":"Contribution to Changelog","url":"/docs/community/changelog#contribution-to-changelog","content":"Found a bug or want to contribute? Here's how:\nReport Issues: GitHub Issues\nSubmit PRs: Contributing Guide\nDiscuss Features: GitHub Discussions\n\nAll contributions are automatically included in the changelog via our automated release process using semantic-release.","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Contribution to Changelog","lvl3":""}}, {"objectID":"45a2bbe1e9f8410018490525cbe717674c5276e04cd26a1e90607943a6ee3462","title":"Historical Releases","url":"/docs/community/changelog#historical-releases","content":"For a complete history of all releases including detailed commit information, see:\n\nComplete CHANGELOG.md","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Historical Releases","lvl3":""}}, {"objectID":"68d4399466b114a3628e88efe593a1506d0b406785ff6b27ae99e2493ca9c401","title":"Related Documentation","url":"/docs/community/changelog#related-documentation","content":"Installation Guide - Install the latest version\nQuick Start - Get up and running quickly\nMigration Guides - Upgrade from older versions\nBreaking Changes - Detailed breaking changes documentation is planned for a future release\n\nLast Updated: February 28, 2026\nCurrent Version: v9.14.0","hierarchy":{"lvl0":"Community","lvl1":"Changelog","lvl2":"Related Documentation","lvl3":""}}, {"objectID":"d8062a4693d3416edc0e9c88f92c9e53bddb3b6202eb49fcfe18fa651f8b1b00","title":"Contributor Covenant Code of Conduct","url":"/docs/community/code-of-conduct","content":"Contributor Covenant Code of Conduct\n\nOur Pledge\n\nWe as members, contributors, and leaders pledge to make participation in our\ncommunity a harassment-free experience for everyone, regardless of age, body\nsize, visible or invisible disability, ethnicity, sex characteristics, gender\nidentity and expression, level of experience, education, socio-economic status,\nnationality, personal appearance, race, religion, or sexual identity\nand orientation.\n\nWe pledge to act and interact in ways that contribute to an open, welcoming,\ndiverse, inclusive, and healthy community.\n\nOur Standards\n\nExamples of behavior that contributes to a positive environment for our\ncommunity include:\nDemonstrating empathy and kindness toward other people\nBeing respectful of differing opinions, viewpoints, and experiences\nGiving and gracefully accepting constructive feedback\nAccepting responsibility and apologizing to those affected by our mistakes,\n and learning from the experience\nFocusing on what is best not just for us as individuals, but for the\n overall community\n\nExamples of unacceptable behavior include:\nThe use of sexualized language or imagery, and sexual attention or\n advances of any kind\nTrolling, insulting or derogatory comments, and personal or political attacks\nPublic or private harassment\nPublishing others' private information, such as a physical or email\n address, without their explicit permission\nOther conduct which could reasonably be considered inappropriate in a\n professional setting\n\nEnforcement Responsibilities\n\nProject maintainers are responsible for clarifying and enforcing our standards of\nacceptable behavior and will take appropriate and fair corrective action in\nresponse to any behavior that they deem inappropriate, threatening, offensive,\nor harmful.\n\nProject maintainers have the right and responsibility to remove, edit, or reject\ncomments, commits, code, wiki edits, issues, and other contributions that are\nnot aligned to this Code of Conduct, and will communicate reasons for moderation\ndecisions when appropriate.\n\nScope\n\nThis Code of Conduct applies within all community spaces, and also applies when\nan individual is officially representing the community in public spaces.\nExamples of representing our community include using an official e-mail address,\nposting via an official social media account, or acting as an appointed\nrepresentative at an online or offline event.\n\nEnforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported to the project team at support@juspay.in.\nAll complaints will be reviewed and investigated promptly and fairly.\n\nAll project maintainers are obligated to respect the privacy and security of the\nreporter of any incident.\n\nEnforcement Guidelines\n\nProject maintainers will follow these Community Impact Guidelines in determining\nthe consequences for any action they deem in violation of this Code of Conduct:\nCorrection\n\nCommunity Impact: Use of inappropriate language or other behavior deemed\nunprofessional or unwelcome in the community.\n\nConsequence: A private, written warning from project maintainers, providing\nclarity around the nature of the violation and an explanation of why the\nbehavior was inappropriate. A public apology may be requested.\nWarning\n\nCommunity Impact: A violation through a single incident or series\nof actions.\n\nConsequence: A warning with consequences for continued behavior. No\ninteraction with the people involved, including unsolicited interaction with\nthose enforcing the Code of Conduct, for a specified period of time. This\nincludes avoiding interactions in community spaces as well as external channels\nlike social media. Violating these terms may lead to a temporary or\npermanent ban.\nTemporary Ban\n\nCommunity Impact: A serious violation of community standards, including\nsustained inappropriate behavior.\n\nConsequence: A temporary ban from any sort of interaction or public\ncommunication with the community for a specified period of time. No public or\nprivate interaction with the people involved, including unsolicited interaction\nwith those enforcing the Code of Conduct, is allowed during this period.\nViolating these terms may lead to a permanent ban.\nPermanent Ban\n\nCommunity Impact: Demonstrating a pattern of violation of community\nstandards, including sustained inappropriate behavior, harassment of an\nindividual, or aggression toward or disparagement of classes of individuals.\n\nConsequence: A permanent ban from any sort of public interaction within\nthe community.\n\nAttribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage],\nversion 2.0, available at\nhttps://www.contributor-covenant.org/version/2/0/codeofconduct.html.\n\nCommunity Impact Guidelines were inspired by Mozilla's code of conduct\nenforcement ladder.\n\n[homepage]: https://www.contributor-covenant.org\n\nFor answers to common questions about this code of conduct, see the FAQ at\nhttps://www.contributor-covenant.org/faq. Translations are available at\nhttps:/","hierarchy":{"lvl0":"Community","lvl1":"Contributor Covenant Code of Conduct","lvl2":"","lvl3":""}}, {"objectID":"7cb50259bf99de43fef298990559b4ea7259fdec26a1e0a1d506b53ad323d577","title":"Our Pledge","url":"/docs/community/code-of-conduct#our-pledge","content":"We as members, contributors, and leaders pledge to make participation in our\ncommunity a harassment-free experience for everyone, regardless of age, body\nsize, visible or invisible disability, ethnicity, sex characteristics, gender\nidentity and expression, level of experience, education, socio-economic status,\nnationality, personal appearance, race, religion, or sexual identity\nand orientation.\n\nWe pledge to act and interact in ways that contribute to an open, welcoming,\ndiverse, inclusive, and healthy community.","hierarchy":{"lvl0":"Community","lvl1":"Contributor Covenant Code of Conduct","lvl2":"Our Pledge","lvl3":""}}, {"objectID":"579e74e98d390b706021c0d954cd2ed2675443622e00611827d6171d089e5424","title":"Our Standards","url":"/docs/community/code-of-conduct#our-standards","content":"Examples of behavior that contributes to a positive environment for our\ncommunity include:\nDemonstrating empathy and kindness toward other people\nBeing respectful of differing opinions, viewpoints, and experiences\nGiving and gracefully accepting constructive feedback\nAccepting responsibility and apologizing to those affected by our mistakes,\n and learning from the experience\nFocusing on what is best not just for us as individuals, but for the\n overall community\n\nExamples of unacceptable behavior include:\nThe use of sexualized language or imagery, and sexual attention or\n advances of any kind\nTrolling, insulting or derogatory comments, and personal or political attacks\nPublic or private harassment\nPublishing others' private information, such as a physical or email\n address, without their explicit permission\nOther conduct which could reasonably be considered inappropriate in a\n professional setting","hierarchy":{"lvl0":"Community","lvl1":"Contributor Covenant Code of Conduct","lvl2":"Our Standards","lvl3":""}}, {"objectID":"0bd1982a85b793b0fcc70dc6bdb2879d1d79fd0c21b6561d74f2c3b577ab3c84","title":"Enforcement Responsibilities","url":"/docs/community/code-of-conduct#enforcement-responsibilities","content":"Project maintainers are responsible for clarifying and enforcing our standards of\nacceptable behavior and will take appropriate and fair corrective action in\nresponse to any behavior that they deem inappropriate, threatening, offensive,\nor harmful.\n\nProject maintainers have the right and responsibility to remove, edit, or reject\ncomments, commits, code, wiki edits, issues, and other contributions that are\nnot aligned to this Code of Conduct, and will communicate reasons for moderation\ndecisions when appropriate.","hierarchy":{"lvl0":"Community","lvl1":"Contributor Covenant Code of Conduct","lvl2":"Enforcement Responsibilities","lvl3":""}}, {"objectID":"17a0f908a00b36008808ad26fb3c5dbe467b82c55a1f6952ee597c21c7900736","title":"Scope","url":"/docs/community/code-of-conduct#scope","content":"This Code of Conduct applies within all community spaces, and also applies when\nan individual is officially representing the community in public spaces.\nExamples of representing our community include using an official e-mail address,\nposting via an official social media account, or acting as an appointed\nrepresentative at an online or offline event.","hierarchy":{"lvl0":"Community","lvl1":"Contributor Covenant Code of Conduct","lvl2":"Scope","lvl3":""}}, {"objectID":"4bcb8bec2fed465373a126415f975500dee083b244752d6877edc611218134b8","title":"Enforcement","url":"/docs/community/code-of-conduct#enforcement","content":"Instances of abusive, harassing, or otherwise unacceptable behavior may be\nreported to the project team at support@juspay.in.\nAll complaints will be reviewed and investigated promptly and fairly.\n\nAll project maintainers are obligated to respect the privacy and security of the\nreporter of any incident.","hierarchy":{"lvl0":"Community","lvl1":"Contributor Covenant Code of Conduct","lvl2":"Enforcement","lvl3":""}}, {"objectID":"79a00e3d04f3d01cdf315e1e72e3982e6faee059c244cc9d3bb70b8e03b39d55","title":"Enforcement Guidelines","url":"/docs/community/code-of-conduct#enforcement-guidelines","content":"Project maintainers will follow these Community Impact Guidelines in determining\nthe consequences for any action they deem in violation of this Code of Conduct:","hierarchy":{"lvl0":"Community","lvl1":"Contributor Covenant Code of Conduct","lvl2":"Enforcement Guidelines","lvl3":""}}, {"objectID":"8b54275a410b10ff2f1bae41b6cb07c5e7fc818cf9e9b507e539f1a8ef06b062","title":"1. 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This\nincludes avoiding interactions in community spaces as well as external channels\nlike social media. Violating these terms may lead to a temporary or\npermanent ban.","hierarchy":{"lvl0":"Community","lvl1":"Contributor Covenant Code of Conduct","lvl2":"2. Warning","lvl3":""}}, {"objectID":"06f5df2cdfb856a21ec330064592c68d0d71c4126190de9c2c2780a8e1c2a2a3","title":"3. Temporary Ban","url":"/docs/community/code-of-conduct#3-temporary-ban","content":"Community Impact: A serious violation of community standards, including\nsustained inappropriate behavior.\n\nConsequence: A temporary ban from any sort of interaction or public\ncommunication with the community for a specified period of time. No public or\nprivate interaction with the people involved, including unsolicited interaction\nwith those enforcing the Code of Conduct, is allowed during this period.\nViolating these terms may lead to a permanent ban.","hierarchy":{"lvl0":"Community","lvl1":"Contributor Covenant Code of Conduct","lvl2":"3. Temporary Ban","lvl3":""}}, {"objectID":"dd2444cb2763295df9ec833a07c84817879e44015a71e57383070932c87cfc59","title":"4. Permanent Ban","url":"/docs/community/code-of-conduct#4-permanent-ban","content":"Community Impact: Demonstrating a pattern of violation of community\nstandards, including sustained inappropriate behavior, harassment of an\nindividual, or aggression toward or disparagement of classes of individuals.\n\nConsequence: A permanent ban from any sort of public interaction within\nthe community.","hierarchy":{"lvl0":"Community","lvl1":"Contributor Covenant Code of Conduct","lvl2":"4. 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Ask questions and share ideas\nIssues - Report bugs and request features\nDiscord - Community chat is planned for the future","hierarchy":{"lvl0":"Community","lvl1":"🤝 Contributing to NeuroLink","lvl2":"Getting Help","lvl3":""}}, {"objectID":"0c4d02d883bee471806e4e49d6cc64f38c2a813279d81c437bc013f9b9715bc5","title":"Ways to Contribute","url":"/docs/community/contributing#ways-to-contribute","content":"Code - Fix bugs, add features\nDocumentation - Improve guides and examples\nTesting - Add test coverage\nDesign - UI/UX improvements\nCommunity - Help others, answer questions","hierarchy":{"lvl0":"Community","lvl1":"🤝 Contributing to NeuroLink","lvl2":"Ways to Contribute","lvl3":""}}, {"objectID":"f5f73efdd4286e5fadbc8e565606028eaaf1d527e37beeaaeec1910817bb072c","title":"Recognition","url":"/docs/community/contributing#recognition","content":"We value all contributions! Contributors are:\nListed in our Contributors page\nMentioned in release notes\nGiven credit in the changelog","hierarchy":{"lvl0":"Community","lvl1":"🤝 Contributing to NeuroLink","lvl2":"Recognition","lvl3":""}}, {"objectID":"4bb17f9c985c0b3f6575474dc003fc5a5bb94668d98a8071d6852c14dad1796f","title":"🎯 Current Focus Areas","url":"/docs/community/contributing#-current-focus-areas","content":"We're particularly interested in contributions for:\nProvider Support - Adding new AI providers\nTool Integration - MCP external server activation\nPerformance - Optimization and benchmarking\nDocumentation - Tutorials and guides\nTesting - Increasing test coverage","hierarchy":{"lvl0":"Community","lvl1":"🤝 Contributing to NeuroLink","lvl2":"🎯 Current Focus Areas","lvl3":""}}, {"objectID":"f88b7ad80d1f2c3206d04b9cbcae88e51d4e58e1d133a35f909db9a3504d8535","title":"📝 License","url":"/docs/community/contributing#-license","content":"By contributing to NeuroLink, you agree that your contributions will be licensed under the MIT License.\n\nThank you for contributing to NeuroLink! 🚀","hierarchy":{"lvl0":"Community","lvl1":"🤝 Contributing to NeuroLink","lvl2":"📝 License","lvl3":""}}, {"objectID":"e79e61b456a7d08d82af7d1db7a9208b9742297f6704005da3bb2c755d4a47c7","title":"Automatic","url":"/docs/connectors/automatic","content":"Automatic\n\nStatus: ✅ Production\nType: Consumer intelligence · Operations hub\nStack: SvelteKit + NeuroLink SDK + Shopify API\n\nPurpose\n\nAutomatic is a Shopify merchant operations connector. It routes order and address data through NeuroLink's pipe to surface address quality grades and return-to-origin (RTO) risk scores — giving merchants AI-driven insight on every order before fulfillment.\n\nStream Types\n\n| Stream | Direction | Description |\n| ----------------------- | --------- | ----------------------------------------------------------- |\n| Shopify order data | → Pipe | Orders fetched via Shopify GraphQL and Vayu backend |\n| Address strings | → Pipe | Delivery addresses sent to external validation service |\n| Pincode + order context | → Pipe | COD flag, order value, address score for RTO model |\n| Validation results | Pipe → | Grade (A–E), score (0–100), spam flag, missing fields |\n| RTO assessment | Pipe → | Risk level (HIGH/MEDIUM/LOW), probability %, reason factors |\n\nInput / Output Contract\n\nPOST /automatic/analytics\n\nReturns: Array of Shopify order nodes with full order data.\n\nPOST /automatic/address\n\nReturns:\n\nPOST /automatic/rto\n\nReturns:\n\nPOST /automatic/rto/pincode\n\nReturns: { \"isRtoPincode\": true }\n\nNeuroLink Integration\n\nAutomatic uses NeuroLink for MCP tool registry, HITL conversation types, and OpenTelemetry observability. Provider selection happens at generation time in other parts of the application.\n\nFeatures used:\nMCP tool registry and execution\nOpenTelemetry logging (DEBUG / INFO / WARN / ERROR severity)\nHITL types from @juspay/neurolink\nConversation memory configuration types\n\nGateway Unlocked\n\nAutomatic gives Shopify merchants:\nAddress intelligence — grade every delivery address before shipping\nRTO prediction — flag high-risk orders (COD + bad pincode + low address score)\nOperational clarity — every order carries an AI risk signal, no manual review needed\n\nOperational Notes\nAddress validation and 2-second UX delay run concurrently via Promise.all()\nRequest bodies validated with type decoders and Zod schemas\nAll endpoints return createFailureResponse() (400) on error with descriptive message\nNeuroLink instance lazy-initialized — ensureToolsRegistered() called on first use\nSession auth: Shopify session tokens via validateAuth() middleware","hierarchy":{"lvl0":"Connectors","lvl1":"Automatic","lvl2":"","lvl3":""}}, {"objectID":"de5dd97b5cff947efcff1a1224ddf4b64296bb3ce017a2c627ab88120a6d63b6","title":"Automatic","url":"/docs/connectors/automatic#automatic","content":"Status: ✅ Production\nType: Consumer intelligence · Operations hub\nStack: SvelteKit + NeuroLink SDK + Shopify API","hierarchy":{"lvl0":"Connectors","lvl1":"Automatic","lvl2":"Automatic","lvl3":""}}, {"objectID":"80d1ddf5f066c98190b83e53fc449a51c4d4f24646ae3428c02a7648166d4ce5","title":"Purpose","url":"/docs/connectors/automatic#purpose","content":"Automatic is a Shopify merchant operations connector. It routes order and address data through NeuroLink's pipe to surface address quality grades and return-to-origin (RTO) risk scores — giving merchants AI-driven insight on every order before fulfillment.","hierarchy":{"lvl0":"Connectors","lvl1":"Automatic","lvl2":"Purpose","lvl3":""}}, {"objectID":"c2b720f9555688be2e4bb98a65c88136c15d11e8fc8aec9c002858fed0060764","title":"Stream Types","url":"/docs/connectors/automatic#stream-types","content":"| Stream | Direction | Description |\n| ----------------------- | --------- | ----------------------------------------------------------- |\n| Shopify order data | → Pipe | Orders fetched via Shopify GraphQL and Vayu backend |\n| Address strings | → Pipe | Delivery addresses sent to external validation service |\n| Pincode + order context | → Pipe | COD flag, order value, address score for RTO model |\n| Validation results | Pipe → | Grade (A–E), score (0–100), spam flag, missing fields |\n| RTO assessment | Pipe → | Risk level (HIGH/MEDIUM/LOW), probability %, reason factors |","hierarchy":{"lvl0":"Connectors","lvl1":"Automatic","lvl2":"Stream Types","lvl3":""}}, {"objectID":"096c39cbafe6b528de0bff65d2a66f77f899be7fd43ceb4ef659f8636a65a05c","title":"POST /automatic/analytics","url":"/docs/connectors/automatic#post-automaticanalytics","content":"Returns: Array of Shopify order nodes with full order data.","hierarchy":{"lvl0":"Connectors","lvl1":"Automatic","lvl2":"POST /automatic/analytics","lvl3":""}}, {"objectID":"ae1ffeb7537ba19800e5cd2c90236a2beaf6c1764670b4735ddcab468c7bed2b","title":"POST /automatic/address","url":"/docs/connectors/automatic#post-automaticaddress","content":"Returns:","hierarchy":{"lvl0":"Connectors","lvl1":"Automatic","lvl2":"POST /automatic/address","lvl3":""}}, {"objectID":"57befbc7f7f6e32d1815c6b35496d8212cfe8b98ded1a2d89117e2b0fd2775f9","title":"POST /automatic/rto","url":"/docs/connectors/automatic#post-automaticrto","content":"Returns:","hierarchy":{"lvl0":"Connectors","lvl1":"Automatic","lvl2":"POST /automatic/rto","lvl3":""}}, {"objectID":"71d1f27e3a563db07f1dee966ee2e5790e43d95d426da6c6721cfe8a226289b8","title":"POST /automatic/rto/pincode","url":"/docs/connectors/automatic#post-automaticrtopincode","content":"Returns: { \"isRtoPincode\": true }","hierarchy":{"lvl0":"Connectors","lvl1":"Automatic","lvl2":"POST /automatic/rto/pincode","lvl3":""}}, {"objectID":"6a46accc0bc04e2dbe7f1408f66bcc70d671a0be83f15d8e9106dde2839f1e8a","title":"NeuroLink Integration","url":"/docs/connectors/automatic#neurolink-integration","content":"Automatic uses NeuroLink for MCP tool registry, HITL conversation types, and OpenTelemetry observability. 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The connector decides what flows through it and what happens at each end.\n\nProduction Connectors\n\n| Connector | Status | Gateway | Provider |\n| --------------------------------------- | ------------- | ----------------------------------------- | ----------------------- |\n| Automatic | ✅ Production | Consumer intelligence · Operations risk | NeuroLink SDK |\n| Tara | ✅ Production | Engineering assistance · Self-improvement | Vertex AI via NeuroLink |\n| Yama | ✅ Production | Code quality · Automated governance | LiteLLM via NeuroLink |\n\nBuilding Your Own Connector\n\nAny application that imports NeuroLink and connects it to a data source or action surface is a connector. 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The connector decides what flows through it and what happens at each end.","hierarchy":{"lvl0":"Connectors","lvl1":"Connector Catalog","lvl2":"Connector Catalog","lvl3":""}}, {"objectID":"233db02e714a05b2a1a0e0a18a69caf331cea31541c961aa30e518e739702033","title":"Production Connectors","url":"/docs/connectors#production-connectors","content":"| Connector | Status | Gateway | Provider |\n| --------------------------------------- | ------------- | ----------------------------------------- | ----------------------- |\n| Automatic | ✅ Production | Consumer intelligence · Operations risk | NeuroLink SDK |\n| Tara | ✅ Production | Engineering assistance · Self-improvement | Vertex AI via NeuroLink |\n| Yama | ✅ Production | Code quality · Automated governance | LiteLLM via NeuroLink |","hierarchy":{"lvl0":"Connectors","lvl1":"Connector Catalog","lvl2":"Production Connectors","lvl3":""}}, {"objectID":"5306737a197d884dd98075ad80575abdb72fda79646a3d8cbf5a66b027f44fd9","title":"Building Your Own Connector","url":"/docs/connectors#building-your-own-connector","content":"Any application that imports NeuroLink and connects it to a data source or action surface is a connector. Study the production connectors above — then build yours.\n\nStart with: Quick Start →","hierarchy":{"lvl0":"Connectors","lvl1":"Connector Catalog","lvl2":"Building Your Own Connector","lvl3":""}}, {"objectID":"cbd11bd0cda3b8eefbd7d317d82088c39a2cd0af631421bc647af672a9e215ca","title":"Tara","url":"/docs/connectors/tara","content":"Tara\n\nStatus: ✅ Production\nType: Engineering assistant · Self-improving AI agent\nStack: Slack Bolt + NeuroLink SDK + Vertex AI + Redis + PostgreSQL\n\nPurpose\n\nTara is a Slack-native AI assistant for engineering teams. She receives messages and file attachments in Slack, routes them through NeuroLink's pipe with full MCP tool access (Bitbucket, JIRA, GitHub, Figma, OpenObserve), and responds with streaming answers or structured PDF reports. Each Slack thread gets its own isolated NeuroLink instance with Redis-backed conversation memory.\n\nStream Types\n\n| Stream | Direction | Description |\n| ----------------- | ------------ | --------------------------------------------------------------------- |\n| Slack DM messages | → Pipe | User text + file attachments via Slack Assistant API |\n| @mention events | → Pipe | Channel messages where user mentions @tara |\n| Attached files | → Pipe | PDFs, images, code files, CSV, Word — 16+ types via ProcessorRegistry |\n| Streaming tokens | Pipe → | Real-time token stream posted to Slack thread |\n| PDF reports | Pipe → | Structured JSON → PDF, uploaded to Slack |\n| Tool results | Pipe → Slack | PR data, JIRA tickets, code search, Figma assets |\n\nInput / Output Contract\n\nInput (Slack Events)\n\nOutput (Slack messages)\n\nNeuroLink Integration\n\nFeatures used:\nneurolink.stream() — streaming Slack responses\nneurolink.generate() — structured JSON for PDF reports\nRedis conversation memory (per-thread isolation)\nMultimodal file processing (FileDetector + ProcessorRegistry — 16+ file types)\nMCP servers: Bitbucket Server, JIRA, GitHub, Figma, OpenObserve\nLangfuse observability with session/user/conversation context enrichment\n\nMCP Tools\n\n| Server | Tools Available |\n| ---------------- | ------------------------------------------------------- |\n| Bitbucket Server | PR review, branch ops, code search, file content, diffs |\n| JIRA | Ticket ops, issue search, project queries |\n| GitHub | Repository data |\n| Figma | Design files, component inspection |\n| OpenObserve | Traces, logs, metrics queries |\n\nGateway Unlocked\n\nTara gives engineering teams:\nCodebase Q&A — ask questions, get answers with code references\nPR assistance — review PRs, generate descriptions, search context\nJIRA integration — create, update, query tickets from Slack\nAutonomous tasks — multi-step coding tasks via tool loops\nPDF reports — long-form analysis delivered as downloadable documents\n\nOperational Notes\nLatency: 6.9s–70.3s — exceeds Slack's 3s ACK deadline. Handled via unhandledRequestHandler returning 200 immediately, then posting response when ready\nConcurrency: LRU pool of 100 NeuroLink instances, one per active Slack thread\nFile processing: All files downloaded and processed locally before sending to AI — unsupported formats return helpful error messages\nAsync tasks: BullMQ task queue for long-running operations (title generation, DB sync)\nSession persistence: Thread titles auto-generated and stored in PostgreSQL; conversation state in Redis\nObservability: Full OpenTelemetry traces, Langfuse AI spans with userId + sessionId + conversationId context","hierarchy":{"lvl0":"Connectors","lvl1":"Tara","lvl2":"","lvl3":""}}, {"objectID":"132058e896d092934432fcc626a2998a0edbb5d03c3be79d7a785736786fbc11","title":"Tara","url":"/docs/connectors/tara#tara","content":"Status: ✅ Production\nType: Engineering assistant · Self-improving AI agent\nStack: Slack Bolt + NeuroLink SDK + Vertex AI + Redis + PostgreSQL","hierarchy":{"lvl0":"Connectors","lvl1":"Tara","lvl2":"Tara","lvl3":""}}, {"objectID":"70b504be979b07e7630bbfd89ad7b4f491eb507c36e730e739ed6176a4d5f327","title":"Purpose","url":"/docs/connectors/tara#purpose","content":"Tara is a Slack-native AI assistant for engineering teams. She receives messages and file attachments in Slack, routes them through NeuroLink's pipe with full MCP tool access (Bitbucket, JIRA, GitHub, Figma, OpenObserve), and responds with streaming answers or structured PDF reports. Each Slack thread gets its own isolated NeuroLink instance with Redis-backed conversation memory.","hierarchy":{"lvl0":"Connectors","lvl1":"Tara","lvl2":"Purpose","lvl3":""}}, {"objectID":"2f8c1bdd5e2a5ae20831d85a89ca2fb33a2575e40cce4291bc858102bac7cb30","title":"Stream Types","url":"/docs/connectors/tara#stream-types","content":"| Stream | Direction | Description |\n| ----------------- | ------------ | --------------------------------------------------------------------- |\n| Slack DM messages | → Pipe | User text + file attachments via Slack Assistant API |\n| @mention events | → Pipe | Channel messages where user mentions @tara |\n| Attached files | → Pipe | PDFs, images, code files, CSV, Word — 16+ types via ProcessorRegistry |\n| Streaming tokens | Pipe → | Real-time token stream posted to Slack thread |\n| PDF reports | Pipe → | Structured JSON → PDF, uploaded to Slack |\n| Tool results | Pipe → Slack | PR data, JIRA tickets, code search, Figma assets |","hierarchy":{"lvl0":"Connectors","lvl1":"Tara","lvl2":"Stream Types","lvl3":""}}, {"objectID":"9ecdf8f1723dc73315ba1b3ae3a753820e9bc2078731350cc425bab00034b918","title":"NeuroLink Integration","url":"/docs/connectors/tara#neurolink-integration","content":"Features used:\nneurolink.stream() — streaming Slack responses\nneurolink.generate() — structured JSON for PDF reports\nRedis conversation memory (per-thread isolation)\nMultimodal file processing (FileDetector + ProcessorRegistry — 16+ file types)\nMCP servers: Bitbucket Server, JIRA, GitHub, Figma, OpenObserve\nLangfuse observability with session/user/conversation context enrichment","hierarchy":{"lvl0":"Connectors","lvl1":"Tara","lvl2":"NeuroLink Integration","lvl3":""}}, {"objectID":"de63acc503224b098a76bcd2a9b1b786c80728a0cd1e907670a25a949bf5eff2","title":"MCP Tools","url":"/docs/connectors/tara#mcp-tools","content":"| Server | Tools Available |\n| ---------------- | ------------------------------------------------------- |\n| Bitbucket Server | PR review, branch ops, code search, file content, diffs |\n| JIRA | Ticket ops, issue search, project queries |\n| GitHub | Repository data |\n| Figma | Design files, component inspection |\n| OpenObserve | Traces, logs, metrics queries |","hierarchy":{"lvl0":"Connectors","lvl1":"Tara","lvl2":"MCP Tools","lvl3":""}}, {"objectID":"5ec48f4c79d04ace58e7a668f756596c8bc898011e01426a234442a1ad798ace","title":"Gateway Unlocked","url":"/docs/connectors/tara#gateway-unlocked","content":"Tara gives engineering teams:\nCodebase Q&A — ask questions, get answers with code references\nPR assistance — review PRs, generate descriptions, search context\nJIRA integration — create, update, query tickets from Slack\nAutonomous tasks — multi-step coding tasks via tool loops\nPDF reports — long-form analysis delivered as downloadable documents","hierarchy":{"lvl0":"Connectors","lvl1":"Tara","lvl2":"Gateway Unlocked","lvl3":""}}, {"objectID":"bf5cc357053eb3bf59d7feb843d8e49a0f6c5332e845bb90856d89e9fb8c7021","title":"Operational Notes","url":"/docs/connectors/tara#operational-notes","content":"Latency: 6.9s–70.3s — exceeds Slack's 3s ACK deadline. Handled via unhandledRequestHandler returning 200 immediately, then posting response when ready\nConcurrency: LRU pool of 100 NeuroLink instances, one per active Slack thread\nFile processing: All files downloaded and processed locally before sending to AI — unsupported formats return helpful error messages\nAsync tasks: BullMQ task queue for long-running operations (title generation, DB sync)\nSession persistence: Thread titles auto-generated and stored in PostgreSQL; conversation state in Redis\nObservability: Full OpenTelemetry traces, Langfuse AI spans with userId + sessionId + conversationId context","hierarchy":{"lvl0":"Connectors","lvl1":"Tara","lvl2":"Operational Notes","lvl3":""}}, {"objectID":"7a2203f21d81aa0e9bd63366f2e96ceaba4a6c9673afafd24c524c3f09dbf81b","title":"Yama","url":"/docs/connectors/yama","content":"Yama\n\nStatus: ✅ Production\nType: Code review judge · Automated governance\nStack: CLI (pnpm yama) + NeuroLink SDK + LiteLLM + Bitbucket Server MCP\n\nPurpose\n\nYama is a CLI-driven code review connector. It fetches PR diffs from Bitbucket Server via MCP, routes them through NeuroLink with a LiteLLM provider, and posts structured inline comments grouped by focus area: Security, Runtime Correctness, Performance, and Code Quality. Yama also enhances PR descriptions with structured summaries.\n\nStream Types\n\n| Stream | Direction | Description |\n| --------------- | --------- | ----------------------------------------------------------- |\n| PR diffs | → Pipe | File-by-file diffs fetched via Bitbucket MCP |\n| Code context | → Pipe | Code search, file content, repo structure |\n| Memory bank | → Pipe | Project standards from memory-bank/*.md |\n| Inline comments | Pipe → | Posted to Bitbucket PR with line references |\n| PR description | Pipe → | Structured summary appended to PR description |\n| Analytics | Pipe → | Token usage, tool calls, cost tracked to .yama/analytics/ |\n\nInput / Output Contract\n\nInvocation\n\nInput (from Bitbucket MCP)\n\nOutput (to Bitbucket via MCP)\n\nFocus Areas and Severity\n\n| Focus Area | Priority | What It Checks |\n| ------------------- | -------- | --------------------------------------------- |\n| Security Analysis | CRITICAL | Injection, auth bypass, data exposure |\n| Runtime Correctness | MAJOR | Null refs, race conditions, wrong assumptions |\n| Performance Review | MAJOR | N+1 queries, blocking ops, memory leaks |\n| Code Quality | MAJOR | Duplication, naming, maintainability |\n\nNeuroLink Integration\n\nFeatures used:\nLiteLLM provider (NeuroLink's gateway to private model deployments)\nBitbucket Server MCP (read-only tool set)\nFile-based memory bank (memory-bank/*.md for project context)\nKnowledge base (.yama/knowledge-base.md — max 50 entries, auto-summarized)\nAnalytics export to .yama/analytics/\n\nGateway Unlocked\n\nYama gives engineering teams:\nSecurity gate — CRITICAL findings block merge (configurable)\nConsistent reviews — every PR gets the same analysis, no reviewer fatigue\nContext-aware — reads memory bank for project standards before reviewing\nLow noise — excludes lock files, images, minified assets automatically\nCost-bounded — hard limits on review duration (15min) and cost ($2)\n\nOperational Notes\nFile strategy: Reviews files one-by-one (not the entire diff at once) — more accurate, fits within context limits\nSmart filtering: Auto-excludes lock files, images, minified JS, source maps\nPR guard: Calls get_branch() first — skips review if no open PR or if target branch is not main\nMemory bank: Reads memory-bank/project-overview.md, architecture.md, coding-standards.md, security-guidelines.md before each review\nKnowledge base: Accumulates review learnings in .yama/knowledge-base.md; auto-summarized when > 50 entries\nCost guard: Warning at $1.50, hard stop at $2.00 per review\nAnalytics: Tool calls, AI decisions, and token usage exported to .yama/analytics/ as JSON","hierarchy":{"lvl0":"Connectors","lvl1":"Yama","lvl2":"","lvl3":""}}, {"objectID":"f73cbd4b86c7ceb95f2a60179ae4e0bab8687231d16456b89fa5ddb5a577b879","title":"Yama","url":"/docs/connectors/yama#yama","content":"Status: ✅ Production\nType: Code review judge · Automated governance\nStack: CLI (pnpm yama) + NeuroLink SDK + LiteLLM + Bitbucket Server MCP","hierarchy":{"lvl0":"Connectors","lvl1":"Yama","lvl2":"Yama","lvl3":""}}, {"objectID":"1006529294594a2e03c8e59946e3de9d36dd7655dfade9c2b7324b7b8e1923f1","title":"Purpose","url":"/docs/connectors/yama#purpose","content":"Yama is a CLI-driven code review connector. It fetches PR diffs from Bitbucket Server via MCP, routes them through NeuroLink with a LiteLLM provider, and posts structured inline comments grouped by focus area: Security, Runtime Correctness, Performance, and Code Quality. Yama also enhances PR descriptions with structured summaries.","hierarchy":{"lvl0":"Connectors","lvl1":"Yama","lvl2":"Purpose","lvl3":""}}, {"objectID":"1d67633f0713371a8f4f42610bcd3b09bc8b4a9214039364e407337b11340aac","title":"Stream Types","url":"/docs/connectors/yama#stream-types","content":"| Stream | Direction | Description |\n| --------------- | --------- | ----------------------------------------------------------- |\n| PR diffs | → Pipe | File-by-file diffs fetched via Bitbucket MCP |\n| Code context | → Pipe | Code search, file content, repo structure |\n| Memory bank | → Pipe | Project standards from memory-bank/*.md |\n| Inline comments | Pipe → | Posted to Bitbucket PR with line references |\n| PR description | Pipe → | Structured summary appended to PR description |\n| Analytics | Pipe → | Token usage, tool calls, cost tracked to .yama/analytics/ |","hierarchy":{"lvl0":"Connectors","lvl1":"Yama","lvl2":"Stream Types","lvl3":""}}, {"objectID":"2b14cdc378e0c437104d797e0b602e5105b2c0cd9ceb0cca98861eae5f40c133","title":"Focus Areas and Severity","url":"/docs/connectors/yama#focus-areas-and-severity","content":"| Focus Area | Priority | What It Checks |\n| ------------------- | -------- | --------------------------------------------- |\n| Security Analysis | CRITICAL | Injection, auth bypass, data exposure |\n| Runtime Correctness | MAJOR | Null refs, race conditions, wrong assumptions |\n| Performance Review | MAJOR | N+1 queries, blocking ops, memory leaks |\n| Code Quality | MAJOR | Duplication, naming, maintainability |","hierarchy":{"lvl0":"Connectors","lvl1":"Yama","lvl2":"Focus Areas and Severity","lvl3":""}}, {"objectID":"a47e5e14159068d21360e6285d07afda53eef0794d49a3446798a53bdfc3f61f","title":"NeuroLink Integration","url":"/docs/connectors/yama#neurolink-integration","content":"Features used:\nLiteLLM provider (NeuroLink's gateway to private model deployments)\nBitbucket Server MCP (read-only tool set)\nFile-based memory bank (memory-bank/*.md for project context)\nKnowledge base (.yama/knowledge-base.md — max 50 entries, auto-summarized)\nAnalytics export to .yama/analytics/","hierarchy":{"lvl0":"Connectors","lvl1":"Yama","lvl2":"NeuroLink Integration","lvl3":""}}, {"objectID":"296378ae0b5ffa480fb59449ce9148cf7497671c2c4fdd76817b295a54f0ab80","title":"Gateway Unlocked","url":"/docs/connectors/yama#gateway-unlocked","content":"Yama gives engineering teams:\nSecurity gate — CRITICAL findings block merge (configurable)\nConsistent reviews — every PR gets the same analysis, no reviewer fatigue\nContext-aware — reads memory bank for project standards before reviewing\nLow noise — excludes lock files, images, minified assets automatically\nCost-bounded — hard limits on review duration (15min) and cost ($2)","hierarchy":{"lvl0":"Connectors","lvl1":"Yama","lvl2":"Gateway Unlocked","lvl3":""}}, {"objectID":"3c2c8c47c4bc2f188f28fc89fc930b2930d1d792aa19db29e3b69c3fe941d30e","title":"Operational Notes","url":"/docs/connectors/yama#operational-notes","content":"File strategy: Reviews files one-by-one (not the entire diff at once) — more accurate, fits within context limits\nSmart filtering: Auto-excludes lock files, images, minified JS, source maps\nPR guard: Calls get_branch() first — skips review if no open PR or if target branch is not main\nMemory bank: Reads memory-bank/project-overview.md, architecture.md, coding-standards.md, security-guidelines.md before each review\nKnowledge base: Accumulates review learnings in .yama/knowledge-base.md; auto-summarized when > 50 entries\nCost guard: Warning at $1.50, hard stop at $2.00 per review\nAnalytics: Tool calls, AI decisions, and token usage exported to .yama/analytics/ as JSON","hierarchy":{"lvl0":"Connectors","lvl1":"Yama","lvl2":"Operational Notes","lvl3":""}}, {"objectID":"97e432655392f70ff51f3b644e1f4eb5b8ce4f6307296f11d84d263e4fa7f775","title":"AutoResearch Quickstart","url":"/docs/cookbook/autoresearch-quickstart","content":"AutoResearch Quickstart\n\nProblem\n\nYou have a training script and a metric you want to optimize (e.g., validation loss), and you want an AI agent to autonomously iterate on the code — proposing changes, running experiments, keeping improvements, and reverting failures — without manual intervention.\n\nSolution\n\nUse NeuroLink's AutoResearch engine. Initialize a config pointing at your repo, define the metric to optimize, and run experiment cycles. Each cycle: the AI reads your code, proposes a change, commits it to a branch, runs the experiment, parses the metric, and keeps or reverts the change.\n\nCode\n\nCLI — Single Experiment Cycle\n\nSDK — Single Experiment Cycle\n\nSDK — Scheduled via TaskManager\n\nNote: saveTask() only persists the task definition. You must call startTaskWorker() (SDK) or run neurolink task start (CLI) to begin execution.\n\nExplanation\nInitialization\n\nautoresearch init (CLI) or createResearchWorker() (SDK) sets up the config:\nmutablePaths — Files the AI is allowed to edit (your training script)\nimmutablePaths — Files the AI can read but not modify (research program, dataset configs)\nrunCommand — Shell command to execute the experiment\nmetric — Name, regex pattern to extract the value from stdout, and optimization direction (lower or higher)\ntimeoutMs — Max wall-clock time per experiment run\n\nThe CLI writes this to .autoresearch/config.json and creates a dedicated git branch.\nExperiment Cycle\n\nEach runExperimentCycle() call goes through 9 phases:\nbootstrap — Read the research program and understand the codebase\nanalyze — Study current results and identify improvement opportunities\nplan — Propose a specific code change\nimplement — Apply the change to mutable files\nvalidate — Verify the code is syntactically valid\ncommit — Git-commit the candidate change\nexecute — Run the experiment command\nevaluate — Parse the metric from stdout using the regex pattern\ndecide — Keep the commit if the metric improved, revert otherwise\nArtifacts\n\nAfter running, check .autoresearch/ in your repo:\n\n| File | Contents |\n| ------------- | ------------------------------------------------------------------------------ |\n| config.json | Persisted configuration |\n| state.json | Current best metric, cycle count, phase, branch name |\n| results.tsv | Tab-separated log: commit, metric name, memory_gb, status, description |\n| runs.jsonl | Full JSON audit log — one JSON object per completed cycle |\nEvents\n\nThe SDK emits 10 typed events via neurolink.getEventEmitter():\n\n| Event | Fired when |\n| ------------------------------ | ----------------------------------- |\n| autoresearch:cycle:start | A cycle begins |\n| autoresearch:cycle:end | A cycle completes (success or fail) |\n| autoresearch:phase:enter | Worker enters a new phase |\n| autoresearch:phase:exit | Worker exits a phase |\n| autoresearch:metric:recorded | A metric value is parsed |\n| autoresearch:commit:created | A candidate commit is made |\n| autoresearch:commit:reverted | A candidate commit is reverted |\n| autoresearch:error | An error occurs |\n| autoresearch:timeout | Experiment exceeds time limit |\n| autoresearch:stopped | Worker stops (manual or max-runs) |\n\nVariations\n\nUse a Different Provider\n\nReplace the provider/model in the config. AutoResearch works with any NeuroLink-supported provider:\n\nOr via CLI:\n\nOptimize a Higher-is-Better Metric\n\nSet direction: \"higher\" for metrics like accuracy:\n\nReset and Start Over\n\nPause and Resume (TaskManager)\n\nNote: pause, resume, and stop update the stored task status but do not interact with the TaskManager runtime directly. The task worker checks status before each cycle.\n\nSee Also\nAutoResearch Feature Guide — Full reference with phase diagrams, configuration, and architecture\nTool Chaining — AutoResearch uses phase-gated tool chaining internally\nStructured Output with JSON Schema — Extract structured data from experiment outputs\nAPI Reference","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"","lvl3":""}}, {"objectID":"91e8ee4260b2834f556661f4704b999b57a6f12f6a9a523f64cfc360de8bd32e","title":"Problem","url":"/docs/cookbook/autoresearch-quickstart#problem","content":"You have a training script and a metric you want to optimize (e.g., validation loss), and you want an AI agent to autonomously iterate on the code — proposing changes, running experiments, keeping improvements, and reverting failures — without manual intervention.","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"Problem","lvl3":""}}, {"objectID":"689e68fb0668bf0c621c7ec5c845596bc3858a1b9fe24fa4cad27d843b3ebb7f","title":"Solution","url":"/docs/cookbook/autoresearch-quickstart#solution","content":"Use NeuroLink's AutoResearch engine. Initialize a config pointing at your repo, define the metric to optimize, and run experiment cycles. Each cycle: the AI reads your code, proposes a change, commits it to a branch, runs the experiment, parses the metric, and keeps or reverts the change.","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"Solution","lvl3":""}}, {"objectID":"56114b15cdfe5a456bc2be2adb1647403e4a23f3dd91dc85b81f2d4cb0072e4f","title":"CLI — Single Experiment Cycle","url":"/docs/cookbook/autoresearch-quickstart#cli-single-experiment-cycle","content":"`bash","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"CLI — Single Experiment Cycle","lvl3":""}}, {"objectID":"4db16dfc9b8a014290d64b57443821d873c60140b52307d1bc3c91eec9eb09a7","title":"1. Initialize AutoResearch in your repo","url":"/docs/cookbook/autoresearch-quickstart#1-initialize-autoresearch-in-your-repo","content":"neurolink autoresearch init /path/to/repo \\\n --tag \"run1\" \\\n --target \"train.py\" \\\n --immutable \"program.md\" \\\n --run-command \"python3 train.py\" \\\n --metric-name val_bpb \\\n --metric-pattern \"^val_bpb:\\\\s+([\\\\d.]+)\" \\\n --metric-direction lower \\\n --timeout 120","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"1. Initialize AutoResearch in your repo","lvl3":""}}, {"objectID":"ffb51179e59d6e9405aa7dcb33e0ce9070fa0ec7bea84a3cf1b4446e984a8d6c","title":"2. Run one cycle (propose → execute → evaluate → keep/revert)","url":"/docs/cookbook/autoresearch-quickstart#2-run-one-cycle-propose-execute-evaluate-keeprevert","content":"neurolink autoresearch run-once /path/to/repo","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"2. Run one cycle (propose → execute → evaluate → keep/revert)","lvl3":""}}, {"objectID":"c18bc40162224e29bba22390a339ecbc8c69f360e425c883926f244e50c9fc8e","title":"3. Check results","url":"/docs/cookbook/autoresearch-quickstart#3-check-results","content":"neurolink autoresearch status /path/to/repo\nneurolink autoresearch results /path/to/repo\n`","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"3. Check results","lvl3":""}}, {"objectID":"5717d08a9d39494089e504ea264e42e8a7eeba9e9636b248b8fb2643e1878026","title":"SDK — Scheduled via TaskManager","url":"/docs/cookbook/autoresearch-quickstart#sdk-scheduled-via-taskmanager","content":"Note: saveTask() only persists the task definition. You must call startTaskWorker() (SDK) or run neurolink task start (CLI) to begin execution.","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"SDK — Scheduled via TaskManager","lvl3":""}}, {"objectID":"7cee02de46fd8368e76cbc9071b91158131d031fc5034e157e30ef3d9d8b23f1","title":"1. Initialization","url":"/docs/cookbook/autoresearch-quickstart#1-initialization","content":"autoresearch init (CLI) or createResearchWorker() (SDK) sets up the config:\nmutablePaths — Files the AI is allowed to edit (your training script)\nimmutablePaths — Files the AI can read but not modify (research program, dataset configs)\nrunCommand — Shell command to execute the experiment\nmetric — Name, regex pattern to extract the value from stdout, and optimization direction (lower or higher)\ntimeoutMs — Max wall-clock time per experiment run\n\nThe CLI writes this to .autoresearch/config.json and creates a dedicated git branch.","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"1. Initialization","lvl3":""}}, {"objectID":"c3429e15cb82ee310f2c1e8a2d142b6c68630b3c5744833a05fe6d8423fc6181","title":"2. Experiment Cycle","url":"/docs/cookbook/autoresearch-quickstart#2-experiment-cycle","content":"Each runExperimentCycle() call goes through 9 phases:\nbootstrap — Read the research program and understand the codebase\nanalyze — Study current results and identify improvement opportunities\nplan — Propose a specific code change\nimplement — Apply the change to mutable files\nvalidate — Verify the code is syntactically valid\ncommit — Git-commit the candidate change\nexecute — Run the experiment command\nevaluate — Parse the metric from stdout using the regex pattern\ndecide — Keep the commit if the metric improved, revert otherwise","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"2. Experiment Cycle","lvl3":""}}, {"objectID":"a7fbb04d65ab40f22ca92e6c93f77400db68136ab69039e05e5fa6830189ecc0","title":"3. Artifacts","url":"/docs/cookbook/autoresearch-quickstart#3-artifacts","content":"After running, check .autoresearch/ in your repo:\n\n| File | Contents |\n| ------------- | ------------------------------------------------------------------------------ |\n| config.json | Persisted configuration |\n| state.json | Current best metric, cycle count, phase, branch name |\n| results.tsv | Tab-separated log: commit, metric name, memory_gb, status, description |\n| runs.jsonl | Full JSON audit log — one JSON object per completed cycle |","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"3. Artifacts","lvl3":""}}, {"objectID":"21cab0b2c003cc954d9deb08ec2b6481b574559d3b37bc2ee52725d55ccd22a0","title":"4. Events","url":"/docs/cookbook/autoresearch-quickstart#4-events","content":"The SDK emits 10 typed events via neurolink.getEventEmitter():\n\n| Event | Fired when |\n| ------------------------------ | ----------------------------------- |\n| autoresearch:cycle:start | A cycle begins |\n| autoresearch:cycle:end | A cycle completes (success or fail) |\n| autoresearch:phase:enter | Worker enters a new phase |\n| autoresearch:phase:exit | Worker exits a phase |\n| autoresearch:metric:recorded | A metric value is parsed |\n| autoresearch:commit:created | A candidate commit is made |\n| autoresearch:commit:reverted | A candidate commit is reverted |\n| autoresearch:error | An error occurs |\n| autoresearch:timeout | Experiment exceeds time limit |\n| autoresearch:stopped | Worker stops (manual or max-runs) |","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"4. Events","lvl3":""}}, {"objectID":"a20b4135ed6c40134e76db4dd1fcaa1ddd2e36f461c39acada1b02e7758754e2","title":"Use a Different Provider","url":"/docs/cookbook/autoresearch-quickstart#use-a-different-provider","content":"Replace the provider/model in the config. AutoResearch works with any NeuroLink-supported provider:\n\nOr via CLI:","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"Use a Different Provider","lvl3":""}}, {"objectID":"feab9bdcb7bb4d800d47eaa83199235f1136259d8853ed3531f424f94aabb9d2","title":"Optimize a Higher-is-Better Metric","url":"/docs/cookbook/autoresearch-quickstart#optimize-a-higher-is-better-metric","content":"Set direction: \"higher\" for metrics like accuracy:","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"Optimize a Higher-is-Better Metric","lvl3":""}}, {"objectID":"b4631e1ea89f85ce6dfda4dbc040560531dcbf16f4fafe7a08392f533bb1fba3","title":"Reset and Start Over","url":"/docs/cookbook/autoresearch-quickstart#reset-and-start-over","content":"`bash","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"Reset and Start Over","lvl3":""}}, {"objectID":"d2b7941207ddda2f4bc1720dcf688c7c0db0ad1a2aea9e5c9fb01047bce02165","title":"Deletes entire .autoresearch/ directory (config, state, results)","url":"/docs/cookbook/autoresearch-quickstart#deletes-entire-autoresearch-directory-config-state-results","content":"neurolink autoresearch reset /path/to/repo\n`","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"Deletes entire .autoresearch/ directory (config, state, results)","lvl3":""}}, {"objectID":"40039a96b3e2b64627cf01cde3871329430363268e06bc74cd00c91144b16b3e","title":"Pause and Resume (TaskManager)","url":"/docs/cookbook/autoresearch-quickstart#pause-and-resume-taskmanager","content":"`bash\nneurolink autoresearch pause /path/to/repo","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"Pause and Resume (TaskManager)","lvl3":""}}, {"objectID":"42ef4814587f7ec949982abb497889c8338d16e6fc8d95e6ec4dfe2570c05f6d","title":"... later ...","url":"/docs/cookbook/autoresearch-quickstart#-later-","content":"neurolink autoresearch resume /path/to/repo\n\n\n> **Note:** pause, resume, and stop` update the stored task status but do not interact with the TaskManager runtime directly. The task worker checks status before each cycle.","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"... later ...","lvl3":""}}, {"objectID":"54d6c9f6848998038e0fa95741b46ece35591dc56e696b8b5cc46e6bd652b3b0","title":"See Also","url":"/docs/cookbook/autoresearch-quickstart#see-also","content":"AutoResearch Feature Guide — Full reference with phase diagrams, configuration, and architecture\nTool Chaining — AutoResearch uses phase-gated tool chaining internally\nStructured Output with JSON Schema — Extract structured data from experiment outputs\nAPI Reference","hierarchy":{"lvl0":"Cookbook","lvl1":"AutoResearch Quickstart","lvl2":"See Also","lvl3":""}}, {"objectID":"36e3d79b039b91486e7b8df5aa28b16cfe586e45ce9a0eebdf4febf89b9b0dcd","title":"Basic Streaming","url":"/docs/cookbook/basic-streaming","content":"Basic Streaming\n\nProblem\n\nWaiting for a complete AI response before displaying anything creates a sluggish user experience. Users see nothing for seconds, then the entire response appears at once. For long responses, this delay is especially painful.\n\nSolution\n\nUse neurolink.stream() to receive the response in real time, chunk by chunk. The result contains a .stream async iterable that yields content objects as they arrive from the provider.\n\nCode\n\nExplanation\nCalling neurolink.stream()\n\nThe stream() method accepts the same input object as generate(). The key difference is the return type: instead of a single content string, you get a StreamResult with a .stream async iterable.\nConsuming the Stream\n\nThe .stream property is an AsyncIterable that yields objects with a content field. Use a for await...of loop to process each chunk as it arrives:\n\nThe \"content\" in chunk guard handles the discriminated union -- stream chunks can be text, audio, or image types depending on your configuration.\nAccessing Metadata After Completion\n\nToken usage, provider name, model name, and finish reason are available on the result object. Some fields (like usage) resolve after the stream finishes.\nStream Options\n\nstream() accepts the same core options as generate():\n\n| Option | Description |\n| -------------- | ------------------------------------- |\n| provider | AI provider name (e.g., \"openai\") |\n| model | Specific model (e.g., \"gpt-4\") |\n| temperature | Response randomness (0.0 - 1.0) |\n| maxTokens | Maximum tokens in the response |\n| systemPrompt | System-level instructions |\n| timeout | Request timeout (number or string) |\n| abortSignal | External cancellation via AbortSignal |\n\nVariations\n\nAccumulate the Full Response\n\nCollect all chunks into a single string while still displaying them in real time:\n\nStream with a System Prompt\n\nSet instructions that guide the model's behavior:\n\nCancel a Stream with AbortSignal\n\nStop a long-running stream programmatically:\n\nStream to a Web Response (Server-Side)\n\nPipe the stream to an HTTP response for real-time delivery to a browser:\n\nSee Also\nStreaming with Retry Logic\nError Recovery Patterns\nMulti-Provider Fallback\nAPI Reference","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"","lvl3":""}}, {"objectID":"3fb1ae65eb0acde7a0357daffcdebbe41c5979c4fcdc842b1d58bbf8e4ad6a00","title":"Problem","url":"/docs/cookbook/basic-streaming#problem","content":"Waiting for a complete AI response before displaying anything creates a sluggish user experience. Users see nothing for seconds, then the entire response appears at once. For long responses, this delay is especially painful.","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"Problem","lvl3":""}}, {"objectID":"5517cd63c040da902bb42e09fc12309bcc88e6b404595f527f4a1ed7bd370615","title":"Solution","url":"/docs/cookbook/basic-streaming#solution","content":"Use neurolink.stream() to receive the response in real time, chunk by chunk. The result contains a .stream async iterable that yields content objects as they arrive from the provider.","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"Solution","lvl3":""}}, {"objectID":"d8ff284175b151b0ffc5af8da088fabdfdf29e04acde38b13e2e493b201fdf77","title":"1. Calling neurolink.stream()","url":"/docs/cookbook/basic-streaming#1-calling-neurolinkstream","content":"The stream() method accepts the same input object as generate(). The key difference is the return type: instead of a single content string, you get a StreamResult with a .stream async iterable.","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"1. Calling neurolink.stream()","lvl3":""}}, {"objectID":"51dbd4e80948ba0d202cf92196cc535a1be1dd60c42954289658fe775beee7e1","title":"2. Consuming the Stream","url":"/docs/cookbook/basic-streaming#2-consuming-the-stream","content":"The .stream property is an AsyncIterable that yields objects with a content field. Use a for await...of loop to process each chunk as it arrives:\n\nThe \"content\" in chunk guard handles the discriminated union -- stream chunks can be text, audio, or image types depending on your configuration.","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"2. Consuming the Stream","lvl3":""}}, {"objectID":"9f27ca6e6b122d2b44d2f684d31ff730cbfbe83da52a8a7113b26396a758f49e","title":"3. Accessing Metadata After Completion","url":"/docs/cookbook/basic-streaming#3-accessing-metadata-after-completion","content":"Token usage, provider name, model name, and finish reason are available on the result object. Some fields (like usage) resolve after the stream finishes.","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"3. Accessing Metadata After Completion","lvl3":""}}, {"objectID":"e46e398eb09f4b4eea71c35281a329b721f0d4fb06f23bf798ccc1a62e4bf0e8","title":"4. Stream Options","url":"/docs/cookbook/basic-streaming#4-stream-options","content":"stream() accepts the same core options as generate():\n\n| Option | Description |\n| -------------- | ------------------------------------- |\n| provider | AI provider name (e.g., \"openai\") |\n| model | Specific model (e.g., \"gpt-4\") |\n| temperature | Response randomness (0.0 - 1.0) |\n| maxTokens | Maximum tokens in the response |\n| systemPrompt | System-level instructions |\n| timeout | Request timeout (number or string) |\n| abortSignal | External cancellation via AbortSignal |","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"4. Stream Options","lvl3":""}}, {"objectID":"1d0e59ee842878abc29d836052fe686f4c12e1f3d0b5a37747097ef1908652f3","title":"Accumulate the Full Response","url":"/docs/cookbook/basic-streaming#accumulate-the-full-response","content":"Collect all chunks into a single string while still displaying them in real time:","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"Accumulate the Full Response","lvl3":""}}, {"objectID":"849fb9993d61719d27a176e265e51c3c9909617cdb39c5ba681c1a7aaff4d82a","title":"Stream with a System Prompt","url":"/docs/cookbook/basic-streaming#stream-with-a-system-prompt","content":"Set instructions that guide the model's behavior:","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"Stream with a System Prompt","lvl3":""}}, {"objectID":"a01083220a55d926da60bd582a3fcfb9df05a4636d1278d8293ead349f8cebac","title":"Cancel a Stream with AbortSignal","url":"/docs/cookbook/basic-streaming#cancel-a-stream-with-abortsignal","content":"Stop a long-running stream programmatically:","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"Cancel a Stream with AbortSignal","lvl3":""}}, {"objectID":"3fb12768d885c196fc72d8ea3bba6a81fb4a9431a1c00cfbf7eab4c9308be382","title":"Stream to a Web Response (Server-Side)","url":"/docs/cookbook/basic-streaming#stream-to-a-web-response-server-side","content":"Pipe the stream to an HTTP response for real-time delivery to a browser:","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"Stream to a Web Response (Server-Side)","lvl3":""}}, {"objectID":"87f9a8e815acb3b2499d8f19aa1eab85b1a7ea5125145777c71a5592f98b0ca0","title":"See Also","url":"/docs/cookbook/basic-streaming#see-also","content":"Streaming with Retry Logic\nError Recovery Patterns\nMulti-Provider Fallback\nAPI Reference","hierarchy":{"lvl0":"Cookbook","lvl1":"Basic Streaming","lvl2":"See Also","lvl3":""}}, {"objectID":"1c96c36bcd20f20f4cf01d5119d6ef70388c06bfe4d96d98cf4bad0f35b256ee","title":"Batch Processing","url":"/docs/cookbook/batch-processing","content":"Batch Processing\n\nProblem\n\nProcessing many requests sequentially is slow and inefficient:\nHigh latency (wait for each request)\nUnderutilized rate limits\nPoor resource usage\nSlow time-to-completion\n\nApplications often need to process:\nMultiple documents\nLarge datasets\nUser-generated content\nBatch analytics\n\nSolution\n\nImplement efficient batch processing with:\nConcurrent request handling\nRate limit awareness\nProgress tracking\nError recovery\nResult aggregation\n\nCode\n\nExplanation\nConcurrency Control\n\nProcess multiple requests simultaneously:\n\nBenefits:\n5x faster than sequential\nEfficient resource usage\nRespects provider limits\nRate Limiting\n\nPrevent exceeding provider rate limits:\nProgress Tracking\n\nMonitor batch processing in real-time:\nError Handling\n\nIndividual failures don't stop the batch:\nRetry Logic\n\nAutomatically retry failed items:\n\nVariations\n\nChunked Batch Processing\n\nProcess very large datasets in chunks:\n\nPriority Queue\n\nProcess high-priority items first:\n\nResult Streaming\n\nStream results as they complete:\n\nCost Tracking\n\nTrack costs per batch:\n\nPerformance Comparison\n\n| Approach | 100 Items | 1000 Items | Notes |\n| ------------------- | --------- | ---------- | ------------------- |\n| Sequential | 200s | 2000s | Baseline |\n| Concurrency: 5 | 40s | 400s | 5x faster |\n| Concurrency: 10 | 20s | 200s | 10x faster |\n| Concurrency: 20 | 15s | 150s | May hit rate limits |\n\nBest Practices\nStart conservative: Begin with low concurrency (3-5)\nMonitor rate limits: Track 429 errors\nImplement retries: Handle transient failures\nTrack progress: Show completion status\nUse cheap models: Batch processing doesn't need GPT-4\nCache results: Save completed work\nHandle partial failures: Don't block on errors\n\nSee Also\nRate Limit Handling\nCost Optimization\nError Recovery\nStructured Output","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"","lvl3":""}}, {"objectID":"9b26ae4c8df94e13f9c4a4d87fcbe7dbe128edafd09af03dd3ac473f612f5846","title":"Problem","url":"/docs/cookbook/batch-processing#problem","content":"Processing many requests sequentially is slow and inefficient:\nHigh latency (wait for each request)\nUnderutilized rate limits\nPoor resource usage\nSlow time-to-completion\n\nApplications often need to process:\nMultiple documents\nLarge datasets\nUser-generated content\nBatch analytics","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"Problem","lvl3":""}}, {"objectID":"2fdd7a51a7a90da3266c583d1b5fedc62a7f5b2eac332aca55c9bd7bc6d6ea69","title":"Solution","url":"/docs/cookbook/batch-processing#solution","content":"Implement efficient batch processing with:\nConcurrent request handling\nRate limit awareness\nProgress tracking\nError recovery\nResult aggregation","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"Solution","lvl3":""}}, {"objectID":"08faf6b600b9be7ee35f84173cdd8d2c6c3b854beeddc480e6b31056303d3c3f","title":"1. Concurrency Control","url":"/docs/cookbook/batch-processing#1-concurrency-control","content":"Process multiple requests simultaneously:\n\nBenefits:\n5x faster than sequential\nEfficient resource usage\nRespects provider limits","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"1. Concurrency Control","lvl3":""}}, {"objectID":"2ff4da961da60b440ca9bc8a24f5127d1f0134b61a7d84af54fb69ed070ba750","title":"2. Rate Limiting","url":"/docs/cookbook/batch-processing#2-rate-limiting","content":"Prevent exceeding provider rate limits:","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"2. Rate Limiting","lvl3":""}}, {"objectID":"08f70dbb1e0a30f6fafa8055adbbd118bdee1101cf6dbe89e4251a74be5ebd2c","title":"3. Progress Tracking","url":"/docs/cookbook/batch-processing#3-progress-tracking","content":"Monitor batch processing in real-time:","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"3. Progress Tracking","lvl3":""}}, {"objectID":"9a4435d56c8e246dbc1395bc000210013b90079ffe97d9f05ee79dacd607759d","title":"4. Error Handling","url":"/docs/cookbook/batch-processing#4-error-handling","content":"Individual failures don't stop the batch:","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"4. Error Handling","lvl3":""}}, {"objectID":"2b1c9d99a5190b49bfca46d47c05c5c33cd6f065a6c949abde284afa6a860b28","title":"5. Retry Logic","url":"/docs/cookbook/batch-processing#5-retry-logic","content":"Automatically retry failed items:","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"5. Retry Logic","lvl3":""}}, {"objectID":"03453b7e109264d7be23307b252edc5e2c169da532e5072aa447bfc24874f30f","title":"Chunked Batch Processing","url":"/docs/cookbook/batch-processing#chunked-batch-processing","content":"Process very large datasets in chunks:","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"Chunked Batch Processing","lvl3":""}}, {"objectID":"79764b30a2b4f82d9d2a968c374a9eed1bf7558d77cec6452268b120708a2055","title":"Priority Queue","url":"/docs/cookbook/batch-processing#priority-queue","content":"Process high-priority items first:","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"Priority Queue","lvl3":""}}, {"objectID":"ed134834b31a25a8f33e20ffb3551663dc47b82b85aac56796ee3377e2589ef8","title":"Result Streaming","url":"/docs/cookbook/batch-processing#result-streaming","content":"Stream results as they complete:","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"Result Streaming","lvl3":""}}, {"objectID":"58d26fdbd13722d6bc07c270aad664b7d756aeadbc92e9d6f59942992294ec11","title":"Cost Tracking","url":"/docs/cookbook/batch-processing#cost-tracking","content":"Track costs per batch:","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"Cost Tracking","lvl3":""}}, {"objectID":"d572e71ec178ac351d3b3419e24643c7a60ea7e86ce2109e7315c97f088e0f75","title":"Performance Comparison","url":"/docs/cookbook/batch-processing#performance-comparison","content":"| Approach | 100 Items | 1000 Items | Notes |\n| ------------------- | --------- | ---------- | ------------------- |\n| Sequential | 200s | 2000s | Baseline |\n| Concurrency: 5 | 40s | 400s | 5x faster |\n| Concurrency: 10 | 20s | 200s | 10x faster |\n| Concurrency: 20 | 15s | 150s | May hit rate limits |","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"Performance Comparison","lvl3":""}}, {"objectID":"4b9d722c6f39d321c50a02f902b3233f92f2370960b4e1496f20f9bfbfafde73","title":"Best Practices","url":"/docs/cookbook/batch-processing#best-practices","content":"Start conservative: Begin with low concurrency (3-5)\nMonitor rate limits: Track 429 errors\nImplement retries: Handle transient failures\nTrack progress: Show completion status\nUse cheap models: Batch processing doesn't need GPT-4\nCache results: Save completed work\nHandle partial failures: Don't block on errors","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"Best Practices","lvl3":""}}, {"objectID":"fdc61fb4c007d654da8b040dbbbc6adcee98e84f69968cedcbca0b3815f9576f","title":"See Also","url":"/docs/cookbook/batch-processing#see-also","content":"Rate Limit Handling\nCost Optimization\nError Recovery\nStructured Output","hierarchy":{"lvl0":"Cookbook","lvl1":"Batch Processing","lvl2":"See Also","lvl3":""}}, {"objectID":"26fca7ec17c021d87cbfa3e6dae26bc4a1ce6f2f7d96fd31632514db3ac7c61e","title":"Context Window Management","url":"/docs/cookbook/context-window-management","content":"Context Window Management\n\nProblem\n\nAI models have limited context windows (token limits):\nGPT-4o: 128K tokens (~96K words)\nClaude 4 Sonnet: 200K tokens (~150K words)\nGemini 2.5 Flash: 1M tokens (~750K words)\nGPT-4.1: 1M tokens (~750K words)\n\nLong conversations exceed these limits, causing:\nTruncated context\nLost conversation history\nInconsistent responses\nAPI errors\n\nSolution\n\nImplement intelligent context management:\nTrack token usage\nSliding window approach\nAutomatic summarization\nStrategic message pruning\nContext compression\n\nCode\n\nExplanation\nToken Estimation\n\nEstimate tokens before sending to API:\n\nThis is approximate but sufficient for context management.\nSliding Window\n\nKeep most recent messages, discard oldest:\nSystem message: Always preserved\nRecent messages: Keep in full\nOld messages: Remove or summarize\nAutomatic Pruning\n\nWhen reaching 100% capacity:\nRemove oldest messages\nTarget 80% capacity (leave buffer)\nPreserve conversation coherence\nIntelligent Summarization\n\nInstead of discarding, summarize old messages:\n\nPreserves context while reducing tokens.\nProgressive Strategy\n\nVariations\n\nKeep Important Messages\n\nTag and preserve important messages:\n\nSemantic Compression\n\nUse embeddings to identify redundant messages:\n\nProvider-Specific Limits\n\nDifferent models, different limits:\n\nRolling Summary\n\nMaintain a rolling summary that updates:\n\nToken Budgets by Use Case\n\n| Use Case | Recommended Limit | Reasoning |\n| ----------------- | ----------------- | ------------------------------- |\n| Chatbot | 4K-8K tokens | Quick responses, recent context |\n| Code assistant | 16K-32K tokens | Need file context |\n| Document analysis | 32K-100K tokens | Large documents |\n| Long-form writing | 8K-16K tokens | Story continuity |\n| Customer support | 4K tokens | Short interactions |\n\nUsing Built-in Context Compaction\n\nThe manual patterns shown above (token estimation, sliding windows, summarization)\nare now available as built-in components in NeuroLink. See\nContext Compaction Guide for full details.\nContextCompactor (src/lib/context/contextCompactor.ts) implements a 5-stage\n pipeline: relevance drop (Stage 0 — asks a decision model which earlier messages\n the current request still needs; skipped entirely without a decision provider),\n tool-output pruning, file-read deduplication, LLM summarization, and\n sliding-window truncation. It replaces the need to build custom\n ContextWindowManager classes.\nBudgetChecker (src/lib/context/budgetChecker.ts) validates context size against\n per-model token limits before every generation call. Compaction is triggered\n automatically when usage exceeds the configured threshold.\ngetContextStats() provides live token counts, remaining capacity, and a\n shouldCompact flag -- a production-grade replacement for the manual\n getStats() helper shown in this cookbook.\ncompactSession() runs the full 5-stage pipeline on demand and returns\n a CompactionResult with the compacted messages and token savings.\n\nProvider-specific context window sizes are maintained in\nsrc/lib/constants/contextWindows.ts, removing the need for hard-coded\nCONTEXT_LIMITS maps.\n\nConfiguration\n\nEnable context compaction through the conversationMemory.contextCompaction\nconfig when creating a NeuroLink instance:\n\nChecking Context Usage\n\nUse getContextStats() to inspect how much of the context window a session\nis consuming. The method returns token estimates, a usage ratio, and a\nshouldCompact flag based on the configured threshold:\n\nManual Compaction\n\nWhen shouldCompact is true, or at any time you want to free up context\nspace, call compactSession():\n\nFull Example: Auto-Monitoring Loop\n\nCombining the APIs above into a conversation loop that monitors context\nusage and compacts automatically:\n\nSee Also\nConversation Summarization\nCost Optimization\nMemory Management Guide\nProvider Comparison","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"","lvl3":""}}, {"objectID":"a7d7d3b74607b75d16daff6c9db3516982adf7fee77dbbe61d45d08196c1a214","title":"Problem","url":"/docs/cookbook/context-window-management#problem","content":"AI models have limited context windows (token limits):\nGPT-4o: 128K tokens (~96K words)\nClaude 4 Sonnet: 200K tokens (~150K words)\nGemini 2.5 Flash: 1M tokens (~750K words)\nGPT-4.1: 1M tokens (~750K words)\n\nLong conversations exceed these limits, causing:\nTruncated context\nLost conversation history\nInconsistent responses\nAPI errors","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Problem","lvl3":""}}, {"objectID":"418b22afc5d8682ec79b5e82250b89240572b08c1fc305e1991cf2768401eaed","title":"Solution","url":"/docs/cookbook/context-window-management#solution","content":"Implement intelligent context management:\nTrack token usage\nSliding window approach\nAutomatic summarization\nStrategic message pruning\nContext compression","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Solution","lvl3":""}}, {"objectID":"39bc167ba7616ae8b6d26e222a0efad2ffe6fc7f656054b1dd6818349c9b5626","title":"1. Token Estimation","url":"/docs/cookbook/context-window-management#1-token-estimation","content":"Estimate tokens before sending to API:\n\nThis is approximate but sufficient for context management.","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"1. Token Estimation","lvl3":""}}, {"objectID":"6aff5003f25ccc95d193c6c56c79e493d55e1cea046c869c910fa0ded0733105","title":"2. Sliding Window","url":"/docs/cookbook/context-window-management#2-sliding-window","content":"Keep most recent messages, discard oldest:\nSystem message: Always preserved\nRecent messages: Keep in full\nOld messages: Remove or summarize","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"2. Sliding Window","lvl3":""}}, {"objectID":"d88ab5b8516e257809b355dadf303ecf5aab189add6efe5980d216346948a0aa","title":"3. Automatic Pruning","url":"/docs/cookbook/context-window-management#3-automatic-pruning","content":"When reaching 100% capacity:\nRemove oldest messages\nTarget 80% capacity (leave buffer)\nPreserve conversation coherence","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"3. Automatic Pruning","lvl3":""}}, {"objectID":"db9fcda865ffb4223e12c7ba152155ff0878f3b633900d8ff60f132205f647c8","title":"4. Intelligent Summarization","url":"/docs/cookbook/context-window-management#4-intelligent-summarization","content":"Instead of discarding, summarize old messages:\n\nPreserves context while reducing tokens.","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"4. Intelligent Summarization","lvl3":""}}, {"objectID":"dca22174f856aa1cb6136b7145861701dd4e414235a8e131bb5d11872325a5ba","title":"Keep Important Messages","url":"/docs/cookbook/context-window-management#keep-important-messages","content":"Tag and preserve important messages:","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Keep Important Messages","lvl3":""}}, {"objectID":"4cb43bfa3e8ded1626967812dae3533bf932a10f5a62a117d7a082d356406985","title":"Semantic Compression","url":"/docs/cookbook/context-window-management#semantic-compression","content":"Use embeddings to identify redundant messages:","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Semantic Compression","lvl3":""}}, {"objectID":"2054fe62d445c8c7215e25aee5edf59e7aede526cfec1f2da12dd2f3252708c5","title":"Provider-Specific Limits","url":"/docs/cookbook/context-window-management#provider-specific-limits","content":"Different models, different limits:","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Provider-Specific Limits","lvl3":""}}, {"objectID":"9989fa6736e8979b950d86bf904df55cd5ca3a8a99b744917f9e53e92abff2e8","title":"Rolling Summary","url":"/docs/cookbook/context-window-management#rolling-summary","content":"Maintain a rolling summary that updates:","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Rolling Summary","lvl3":""}}, {"objectID":"56af11db66f5f8e03447348434134d22df4e3d67c80b291395b41e1a8dedad4f","title":"Token Budgets by Use Case","url":"/docs/cookbook/context-window-management#token-budgets-by-use-case","content":"| Use Case | Recommended Limit | Reasoning |\n| ----------------- | ----------------- | ------------------------------- |\n| Chatbot | 4K-8K tokens | Quick responses, recent context |\n| Code assistant | 16K-32K tokens | Need file context |\n| Document analysis | 32K-100K tokens | Large documents |\n| Long-form writing | 8K-16K tokens | Story continuity |\n| Customer support | 4K tokens | Short interactions |","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Token Budgets by Use Case","lvl3":""}}, {"objectID":"c999025023c5bdde7461c0813bf09bc6d175d5ce924c25e62c20e4abeedc3fc6","title":"Using Built-in Context Compaction","url":"/docs/cookbook/context-window-management#using-built-in-context-compaction","content":"The manual patterns shown above (token estimation, sliding windows, summarization)\nare now available as built-in components in NeuroLink. See\nContext Compaction Guide for full details.\nContextCompactor (src/lib/context/contextCompactor.ts) implements a 5-stage\n pipeline: relevance drop (Stage 0 — asks a decision model which earlier messages\n the current request still needs; skipped entirely without a decision provider),\n tool-output pruning, file-read deduplication, LLM summarization, and\n sliding-window truncation. It replaces the need to build custom\n ContextWindowManager classes.\nBudgetChecker (src/lib/context/budgetChecker.ts) validates context size against\n per-model token limits before every generation call. Compaction is triggered\n automatically when usage exceeds the configured threshold.\ngetContextStats() provides live token counts, remaining capacity, and a\n shouldCompact flag -- a production-grade replacement for the manual\n getStats() helper shown in this cookbook.\ncompactSession() runs the full 5-stage pipeline on demand and returns\n a CompactionResult with the compacted messages and token savings.\n\nProvider-specific context window sizes are maintained in\nsrc/lib/constants/contextWindows.ts, removing the need for hard-coded\nCONTEXT_LIMITS maps.","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Using Built-in Context Compaction","lvl3":""}}, {"objectID":"676cc2f71ad18d7a376c1f5a74899696c22d6f0f83cfc914efc0c3e0df5fc745","title":"Configuration","url":"/docs/cookbook/context-window-management#configuration","content":"Enable context compaction through the conversationMemory.contextCompaction\nconfig when creating a NeuroLink instance:","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Configuration","lvl3":""}}, {"objectID":"afb96c50a0418aafc41bfb71af82a75ba42677109656cce8beb8dbc91878361f","title":"Checking Context Usage","url":"/docs/cookbook/context-window-management#checking-context-usage","content":"Use getContextStats() to inspect how much of the context window a session\nis consuming. The method returns token estimates, a usage ratio, and a\nshouldCompact flag based on the configured threshold:","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Checking Context Usage","lvl3":""}}, {"objectID":"9505ce2923a9560aacc2c5e0f955c46d01a162de4fbc57276061ee01c45942cc","title":"Manual Compaction","url":"/docs/cookbook/context-window-management#manual-compaction","content":"When shouldCompact is true, or at any time you want to free up context\nspace, call compactSession():","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Manual Compaction","lvl3":""}}, {"objectID":"bc7ce25b0bdb5808a5a75f042ca769de644807dd38ee6457079fc3a46b2f3605","title":"Full Example: Auto-Monitoring Loop","url":"/docs/cookbook/context-window-management#full-example-auto-monitoring-loop","content":"Combining the APIs above into a conversation loop that monitors context\nusage and compacts automatically:","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"Full Example: Auto-Monitoring Loop","lvl3":""}}, {"objectID":"27ba227d048ba0e5e5e66f1b4762741a8a13698cd3d304242b5c775456222f7b","title":"See Also","url":"/docs/cookbook/context-window-management#see-also","content":"Conversation Summarization\nCost Optimization\nMemory Management Guide\nProvider Comparison","hierarchy":{"lvl0":"Cookbook","lvl1":"Context Window Management","lvl2":"See Also","lvl3":""}}, {"objectID":"a0d5b1497b1e09fed14328aa399335988e1b592ec8d9dc9a73da53c99563aa11","title":"Conversation Summarization","url":"/docs/cookbook/conversation-summarization","content":"Conversation Summarization\n\nProblem\n\nLong conversations consume excessive tokens and costs:\nContext window fills quickly\nAPI costs scale with message count\nResponse quality degrades with very long context\nImportant information gets buried\n\nSolution\n\nAutomatically summarize conversation history to:\nPreserve key information\nReduce token usage\nMaintain context continuity\nEnable indefinite conversations\n\nCode\n\nExplanation\nTrigger Threshold\n\nSummarization triggers when message count exceeds threshold:\nPreserve Important Messages\n\nMark critical messages to preserve:\nSplit Strategy\nFirst half: Summarize\nSecond half: Keep in full\nImportant: Always keep\nHierarchical Summaries\n\nCombine summaries over time:\nCost Optimization\n\nUse cheap model for summarization:\nClaude Haiku: $0.00025/1K tokens\nGemini Pro: $0.00025/1K tokens\n\nVariations\n\nProgressive Summarization\n\nSummarize at multiple levels:\n\nTopic-Based Summarization\n\nOrganize summaries by topic:\n\nTime-Based Summarization\n\nSummarize by time windows:\n\nExtractive Summarization\n\nKeep actual message excerpts:\n\nSummarization Strategies\n\n| Strategy | When to Use | Token Savings | Context Preservation |\n| --------------------------------------- | ------------------------- | ------------- | -------------------- |\n| Simple: Remove old messages | Short conversations | 90% | Low |\n| Abstractive: AI-generated summary | Long conversations | 80% | Medium |\n| Extractive: Key sentence selection | Factual conversations | 60% | High |\n| Hierarchical: Multi-level summaries | Very long conversations | 85% | Medium-High |\n| Topic-based: Group by subject | Multi-topic conversations | 75% | High |\n\nBest Practices\nSummarize early: Don't wait until context is full\nPreserve decisions: Mark important messages\nUse cheap models: Summarization doesn't need GPT-4\nTest summaries: Verify important info isn't lost\nExport regularly: Save full conversation for debugging\n\nSee Also\nContext Window Management\nCost Optimization\nMemory Management Guide\nRedis Persistence","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"","lvl3":""}}, {"objectID":"3fbcb793068f43424b121eaa7783215e8278f8d706f529d33cb7ef18d0109444","title":"Problem","url":"/docs/cookbook/conversation-summarization#problem","content":"Long conversations consume excessive tokens and costs:\nContext window fills quickly\nAPI costs scale with message count\nResponse quality degrades with very long context\nImportant information gets buried","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"Problem","lvl3":""}}, {"objectID":"5dc72d5ecfde3765fc8640218428dbcf11820fdf54f12beccadf5b1d4a14bf61","title":"Solution","url":"/docs/cookbook/conversation-summarization#solution","content":"Automatically summarize conversation history to:\nPreserve key information\nReduce token usage\nMaintain context continuity\nEnable indefinite conversations","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"Solution","lvl3":""}}, {"objectID":"25eadeaa22d6a7fcc86126df5eb5963dcd9178bb17f7f1dff6f50ba62e2cf5bc","title":"1. Trigger Threshold","url":"/docs/cookbook/conversation-summarization#1-trigger-threshold","content":"Summarization triggers when message count exceeds threshold:","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"1. Trigger Threshold","lvl3":""}}, {"objectID":"478d2879f85243c01dd582294893fe11ac1ab1ed9dcf464eaea77f5c189c171f","title":"2. Preserve Important Messages","url":"/docs/cookbook/conversation-summarization#2-preserve-important-messages","content":"Mark critical messages to preserve:","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"2. Preserve Important Messages","lvl3":""}}, {"objectID":"96598e4d0d27669ba23a0ac5d7158f49827d00d232591abf79c444185d112b9f","title":"3. Split Strategy","url":"/docs/cookbook/conversation-summarization#3-split-strategy","content":"First half: Summarize\nSecond half: Keep in full\nImportant: Always keep","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"3. Split Strategy","lvl3":""}}, {"objectID":"bbeb807e843e1618e785dd44309a5c45f8d59fa84a9898d16857d51eb56309cc","title":"4. Hierarchical Summaries","url":"/docs/cookbook/conversation-summarization#4-hierarchical-summaries","content":"Combine summaries over time:","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"4. Hierarchical Summaries","lvl3":""}}, {"objectID":"21675a3d1549d788fd8f5d49e11a2aae58352cda79a57daf2a57396d65804637","title":"5. Cost Optimization","url":"/docs/cookbook/conversation-summarization#5-cost-optimization","content":"Use cheap model for summarization:\nClaude Haiku: $0.00025/1K tokens\nGemini Pro: $0.00025/1K tokens","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"5. Cost Optimization","lvl3":""}}, {"objectID":"e1e1a59d7274c977dd2211a256aed48e4bbc01d2b718e9c7bf59f8275c54becc","title":"Progressive Summarization","url":"/docs/cookbook/conversation-summarization#progressive-summarization","content":"Summarize at multiple levels:","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"Progressive Summarization","lvl3":""}}, {"objectID":"443de43740891f025bdfa28898ac5ee6fd3c852334a3f643aa975dc9e915e7d1","title":"Topic-Based Summarization","url":"/docs/cookbook/conversation-summarization#topic-based-summarization","content":"Organize summaries by topic:","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"Topic-Based Summarization","lvl3":""}}, {"objectID":"8dc5b8d88afe0c6d54f041196a910d48a3c6ac61000ee276a710d338d4682aa7","title":"Time-Based Summarization","url":"/docs/cookbook/conversation-summarization#time-based-summarization","content":"Summarize by time windows:","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"Time-Based Summarization","lvl3":""}}, {"objectID":"0ea1be723597473120ad9be4f029264a7926a6bd2095cad34ce2b93eb191744d","title":"Extractive Summarization","url":"/docs/cookbook/conversation-summarization#extractive-summarization","content":"Keep actual message excerpts:","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"Extractive Summarization","lvl3":""}}, {"objectID":"f46f93cc7a0a4f279329331fe98f218bbc4fd257f9e6f29e4b3da0b70a75244b","title":"Summarization Strategies","url":"/docs/cookbook/conversation-summarization#summarization-strategies","content":"| Strategy | When to Use | Token Savings | Context Preservation |\n| --------------------------------------- | ------------------------- | ------------- | -------------------- |\n| Simple: Remove old messages | Short conversations | 90% | Low |\n| Abstractive: AI-generated summary | Long conversations | 80% | Medium |\n| Extractive: Key sentence selection | Factual conversations | 60% | High |\n| Hierarchical: Multi-level summaries | Very long conversations | 85% | Medium-High |\n| Topic-based: Group by subject | Multi-topic conversations | 75% | High |","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"Summarization Strategies","lvl3":""}}, {"objectID":"91581b8e016e9e59fcc2e3c467d13bd834c6d1ab4cf1eeb2906cfd4fd6a28ae6","title":"Best Practices","url":"/docs/cookbook/conversation-summarization#best-practices","content":"Summarize early: Don't wait until context is full\nPreserve decisions: Mark important messages\nUse cheap models: Summarization doesn't need GPT-4\nTest summaries: Verify important info isn't lost\nExport regularly: Save full conversation for debugging","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"Best Practices","lvl3":""}}, {"objectID":"ff6426f94d8a3b7505d3bfdcbc50107135252872b1243ee9626af0e14ce969e1","title":"See Also","url":"/docs/cookbook/conversation-summarization#see-also","content":"Context Window Management\nCost Optimization\nMemory Management Guide\nRedis Persistence","hierarchy":{"lvl0":"Cookbook","lvl1":"Conversation Summarization","lvl2":"See Also","lvl3":""}}, {"objectID":"1da757abcd16ab4158a5aef554f5a39efbbf0910a66a197c6728b06f6a7f073c","title":"Cost Optimization","url":"/docs/cookbook/cost-optimization","content":"Cost Optimization\n\nProblem\n\nAI API costs can accumulate quickly, especially with:\nLarge context windows\nFrequent API calls\nExpensive models (GPT-4, Claude Opus)\nInefficient prompt engineering\n\nSolution\n\nImplement cost optimization strategies:\nUse cheaper models when appropriate\nMinimize context size\nCache responses\nImplement token counting\nUse model routing based on complexity\n\nCode\n\nExplanation\nSmart Model Routing\n\nThe selectModel() method analyzes the prompt to choose the most cost-effective model:\nSimple queries → Claude Haiku ($0.00025/1K input tokens)\nComplex queries → Claude Sonnet ($0.003/1K input tokens)\nComplex + Creative → GPT-4 ($0.03/1K input tokens)\nResponse Caching\n\nIdentical prompts return cached responses at zero cost. Perfect for:\nRepeated queries\nDevelopment/testing\nCommon questions in production\nToken Limiting\n\nSet maxTokens to prevent unexpectedly long (expensive) responses:\nSummaries: 200-300 tokens\nExplanations: 500-1000 tokens\nCreative content: 1000-2000 tokens\nCost Tracking\n\nEstimate costs per request to monitor spending:\nPrompt Truncation\n\nVery long prompts increase costs without adding value. Truncate to essential context.\n\nVariations\n\nContext Window Compression\n\nCompress conversation history to reduce tokens:\n\nModel Tier System\n\nExplicitly define cost tiers:\n\nBudget Enforcement\n\nSet spending limits:\n\nCost Comparison\n\n| Task Type | Best Model | Cost (per 1K tokens) | Use Case |\n| ---------------- | ------------- | -------------------- | ---------------------------- |\n| Simple Q&A | Claude Haiku | $0.00025 | FAQs, basic queries |\n| Data extraction | GPT-3.5 Turbo | $0.0015 | JSON parsing, classification |\n| Analysis | Claude Sonnet | $0.003 | Summaries, explanations |\n| Deep reasoning | GPT-4 | $0.03 | Complex problem-solving |\n| Creative writing | GPT-4 | $0.03 | Stories, marketing copy |\n\nSee Also\nBatch Processing\nContext Window Management\nProvider Selection Guide\nRate Limit Handling","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"","lvl3":""}}, {"objectID":"e105860be075181eba2bb24ffac29cdbaa962d8f58ff212663f3697aef38d1ff","title":"Problem","url":"/docs/cookbook/cost-optimization#problem","content":"AI API costs can accumulate quickly, especially with:\nLarge context windows\nFrequent API calls\nExpensive models (GPT-4, Claude Opus)\nInefficient prompt engineering","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"Problem","lvl3":""}}, {"objectID":"1caa80de7d02f34051b11a7fbbc87666321efadeb6460e5ffb6766107bacc78c","title":"Solution","url":"/docs/cookbook/cost-optimization#solution","content":"Implement cost optimization strategies:\nUse cheaper models when appropriate\nMinimize context size\nCache responses\nImplement token counting\nUse model routing based on complexity","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"Solution","lvl3":""}}, {"objectID":"4a2c3011c9110cfb265a03095e905e6bfd2bc79d06128b522e4ccd320f8ce2a5","title":"1. Smart Model Routing","url":"/docs/cookbook/cost-optimization#1-smart-model-routing","content":"The selectModel() method analyzes the prompt to choose the most cost-effective model:\nSimple queries → Claude Haiku ($0.00025/1K input tokens)\nComplex queries → Claude Sonnet ($0.003/1K input tokens)\nComplex + Creative → GPT-4 ($0.03/1K input tokens)","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"1. Smart Model Routing","lvl3":""}}, {"objectID":"d59dda53a975ffaf6d9becda2195629f7712f2445dc5525234f0ccd7e39648f0","title":"2. Response Caching","url":"/docs/cookbook/cost-optimization#2-response-caching","content":"Identical prompts return cached responses at zero cost. Perfect for:\nRepeated queries\nDevelopment/testing\nCommon questions in production","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"2. Response Caching","lvl3":""}}, {"objectID":"3c3c3fcb7a9a96bc3d337a76045adcd5fad794c9b15f993ad37c0032c96bcb2e","title":"3. Token Limiting","url":"/docs/cookbook/cost-optimization#3-token-limiting","content":"Set maxTokens to prevent unexpectedly long (expensive) responses:\nSummaries: 200-300 tokens\nExplanations: 500-1000 tokens\nCreative content: 1000-2000 tokens","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"3. Token Limiting","lvl3":""}}, {"objectID":"31e1dda0ec3582fc42fee29b7fec724b6fd71b7e2e5824870b9fafe5552eec0a","title":"4. Cost Tracking","url":"/docs/cookbook/cost-optimization#4-cost-tracking","content":"Estimate costs per request to monitor spending:","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"4. Cost Tracking","lvl3":""}}, {"objectID":"ccd96e9a8bc40119b0efaaac3a72fe7e23ce385a4f5cd6576ff52d52c45eeb25","title":"5. Prompt Truncation","url":"/docs/cookbook/cost-optimization#5-prompt-truncation","content":"Very long prompts increase costs without adding value. Truncate to essential context.","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"5. Prompt Truncation","lvl3":""}}, {"objectID":"ba9589992088212769dffdb3c92234f9294fff8454917f679f8a8e08e83656eb","title":"Context Window Compression","url":"/docs/cookbook/cost-optimization#context-window-compression","content":"Compress conversation history to reduce tokens:","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"Context Window Compression","lvl3":""}}, {"objectID":"a895b52c51b3a4751450fb03d889b90eedea7dd3359f873bf967536a84c9a50c","title":"Model Tier System","url":"/docs/cookbook/cost-optimization#model-tier-system","content":"Explicitly define cost tiers:","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"Model Tier System","lvl3":""}}, {"objectID":"411e9a17d161082ac1b956361541e97f67c5c63ecb5475d930578be0f746832e","title":"Budget Enforcement","url":"/docs/cookbook/cost-optimization#budget-enforcement","content":"Set spending limits:","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"Budget Enforcement","lvl3":""}}, {"objectID":"407bfdc6fe9b34eb12d90812c933df7b7078cec5e43aab6b9a0a9680d500fcb2","title":"Cost Comparison","url":"/docs/cookbook/cost-optimization#cost-comparison","content":"| Task Type | Best Model | Cost (per 1K tokens) | Use Case |\n| ---------------- | ------------- | -------------------- | ---------------------------- |\n| Simple Q&A | Claude Haiku | $0.00025 | FAQs, basic queries |\n| Data extraction | GPT-3.5 Turbo | $0.0015 | JSON parsing, classification |\n| Analysis | Claude Sonnet | $0.003 | Summaries, explanations |\n| Deep reasoning | GPT-4 | $0.03 | Complex problem-solving |\n| Creative writing | GPT-4 | $0.03 | Stories, marketing copy |","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"Cost Comparison","lvl3":""}}, {"objectID":"36b482657194f3c5acd8f63328af04dc010334cd4804e63116c55dbfd95a2ca2","title":"See Also","url":"/docs/cookbook/cost-optimization#see-also","content":"Batch Processing\nContext Window Management\nProvider Selection Guide\nRate Limit Handling","hierarchy":{"lvl0":"Cookbook","lvl1":"Cost Optimization","lvl2":"See Also","lvl3":""}}, {"objectID":"28a7e145291ee648d731fcd74aa567ed274f847064ce1ebbd74e4b00d7ff8db7","title":"Embeddings Basics","url":"/docs/cookbook/embeddings-basics","content":"Embeddings Basics\n\nProblem\n\nMany AI applications need to compare text semantically -- finding similar documents, powering search, clustering content, or building recommendation systems. Raw text comparison (string matching) misses synonyms, paraphrases, and conceptual similarity.\n\nSolution\n\nUse NeuroLink's embed() and embedMany() provider methods to generate vector embeddings. These fixed-length number arrays capture the semantic meaning of text, enabling similarity comparisons with cosine similarity or dot product.\n\nCode\n\nExplanation\nProvider Setup for Embeddings\n\nEmbedding models are accessed through the provider directly via ProviderFactory. Nine providers implement embed() / embedMany() natively, each with its own default embedding model:\n\n| Provider | Default Embedding Model | Dimensions |\n| ---------------- | ------------------------------ | ---------- |\n| OpenAI | text-embedding-3-small | 1536 |\n| Google AI Studio | gemini-embedding-001 | 3072 |\n| Google Vertex | text-embedding-004 | 768 |\n| Amazon Bedrock | amazon.titan-embed-text-v2:0 | 1024 |\n| Cohere | embed-english-v3.0 | 1024 |\n| Voyage AI | voyage-3.5 | 1024 |\n| Jina AI | jina-embeddings-v3 | 1024 |\n| Ollama | nomic-embed-text | 768 |\n| LiteLLM | proxied to the upstream model | varies |\n\nNine providers implement embed() / embedMany() natively. Voyage AI and Jina AI are embedding-focused and don't serve generate() / stream() chat completions — Voyage is embedding-only, and Jina also does reranking. Cohere additionally implements embed() / embedMany(), but its default model (command-r-plus) is a full chat model, so unlike Voyage and Jina, Cohere also serves generate() / stream().\n\nNote: Google's text-embedding-004 is being retired. The recommended replacement is gemini-embedding-001 (3072 dimensions). Override the default with VERTEX_EMBEDDING_MODEL=gemini-embedding-001.\nembed() vs embedMany()\nembed(text): Generates a single embedding vector. Use for one-off queries.\nembedMany(texts): Generates embeddings for an array of texts in one API call. More efficient for batches.\nCosine Similarity\n\nCosine similarity measures the angle between two vectors. Values range from -1 to 1:\n1.0: Identical meaning\n0.0: Unrelated\n-1.0: Opposite meaning (rare with embedding models)\n\nIn practice, similar texts score above 0.7 and unrelated texts score below 0.4.\nSemantic Search Pattern\n\nThe core semantic search pattern is:\nEmbed all documents once (store the vectors)\nEmbed the user's query at search time\nCompute cosine similarity between the query and each document\nReturn the top K highest-scoring documents\n\nVariations\n\nCache Embeddings for Repeated Searches\n\nAvoid re-embedding documents on every search:\n\nUse with Google AI Studio\n\nSwitch to Google's embedding model:\n\nCombine Embeddings with RAG\n\nUse embeddings as the foundation for RAG (Retrieval-Augmented Generation):\n\nClustering Documents\n\nGroup similar documents together using embeddings:\n\nTips\nEmbed once, query many times: Embedding documents is the expensive step. Store embeddings in a database or vector store for fast repeated searches.\nUse embedMany() for batches: It is significantly faster than calling embed() in a loop because it makes a single API call.\nMatch embedding and search models: Always use the same model to embed both documents and queries. Vectors from different models are incompatible.\nConsider dimensions: text-embedding-3-small (1536d) is a good balance of quality and size. For storage-constrained systems, OpenAI also offers a 256d variant.\n\nSee Also\nBatch Processing\nCost Optimization\nProvider Switching\nAPI Reference","hierarchy":{"lvl0":"Cookbook","lvl1":"Embeddings Basics","lvl2":"","lvl3":""}}, {"objectID":"ffa5e2ffdf6de0e4548a0d63c7e75efa0bc12c704f8e88ce8b0bc39a633d73af","title":"Problem","url":"/docs/cookbook/embeddings-basics#problem","content":"Many AI applications need to compare text semantically -- finding similar documents, powering search, clustering content, or building recommendation systems. Raw text comparison (string matching) misses synonyms, paraphrases, and conceptual similarity.","hierarchy":{"lvl0":"Cookbook","lvl1":"Embeddings Basics","lvl2":"Problem","lvl3":""}}, {"objectID":"d08f294aa5051c755c3234dc1d7affda833eac6d6761361d94ab0ee27ebdfcb6","title":"Solution","url":"/docs/cookbook/embeddings-basics#solution","content":"Use NeuroLink's embed() and embedMany() provider methods to generate vector embeddings. These fixed-length number arrays capture the semantic meaning of text, enabling similarity comparisons with cosine similarity or dot product.","hierarchy":{"lvl0":"Cookbook","lvl1":"Embeddings Basics","lvl2":"Solution","lvl3":""}}, {"objectID":"ca1a56f9d0033a85bd3fd930dfaab88cce83340d8a0e12784ea0b17f9654320c","title":"1. Provider Setup for Embeddings","url":"/docs/cookbook/embeddings-basics#1-provider-setup-for-embeddings","content":"Embedding models are accessed through the provider directly via ProviderFactory. Nine providers implement embed() / embedMany() natively, each with its own default embedding model:\n\n| Provider | Default Embedding Model | Dimensions |\n| ---------------- | ------------------------------ | ---------- |\n| OpenAI | text-embedding-3-small | 1536 |\n| Google AI Studio | gemini-embedding-001 | 3072 |\n| Google Vertex | text-embedding-004 | 768 |\n| Amazon Bedrock | amazon.titan-embed-text-v2:0 | 1024 |\n| Cohere | embed-english-v3.0 | 1024 |\n| Voyage AI | voyage-3.5 | 1024 |\n| Jina AI | jina-embeddings-v3 | 1024 |\n| Ollama | nomic-embed-text | 768 |\n| LiteLLM | proxied to the upstream model | varies |\n\nNine providers implement embed() / embedMany() natively. Voyage AI and Jina AI are embedding-focused and don't serve generate() / stream() chat completions — Voyage is embedding-only, and Jina also does reranking. Cohere additionally implements embed() / embedMany(), but its default model (command-r-plus) is a full chat model, so unlike Voyage and Jina, Cohere also serves generate() / stream().\n\nNote: Google's text-embedding-004 is being retired. The recommended replacement is gemini-embedding-001 (3072 dimensions). Override the default with VERTEX_EMBEDDING_MODEL=gemini-embedding-001.","hierarchy":{"lvl0":"Cookbook","lvl1":"Embeddings Basics","lvl2":"1. Provider Setup for Embeddings","lvl3":""}}, {"objectID":"6323248b0a78882c82ef4c3c58cb3be07d17c56d52f5b9dcebd001d667ad078b","title":"2. embed() vs embedMany()","url":"/docs/cookbook/embeddings-basics#2-embed-vs-embedmany","content":"embed(text): Generates a single embedding vector. Use for one-off queries.\nembedMany(texts): Generates embeddings for an array of texts in one API call. More efficient for batches.","hierarchy":{"lvl0":"Cookbook","lvl1":"Embeddings Basics","lvl2":"2. embed() vs embedMany()","lvl3":""}}, {"objectID":"faf2868cb51151353460133dcff555a59b5e7b3e7390751027f94c786555b4c2","title":"3. Cosine Similarity","url":"/docs/cookbook/embeddings-basics#3-cosine-similarity","content":"Cosine similarity measures the angle between two vectors. Values range from -1 to 1:\n1.0: Identical meaning\n0.0: Unrelated\n-1.0: Opposite meaning (rare with embedding models)\n\nIn practice, similar texts score above 0.7 and unrelated texts score below 0.4.","hierarchy":{"lvl0":"Cookbook","lvl1":"Embeddings Basics","lvl2":"3. Cosine Similarity","lvl3":""}}, {"objectID":"b0cbe556ce97890d4b5feaa1996739f0cda8012fc76aae130850e56523e64c6c","title":"4. 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Store embeddings in a database or vector store for fast repeated searches.\nUse embedMany() for batches: It is significantly faster than calling embed() in a loop because it makes a single API call.\nMatch embedding and search models: Always use the same model to embed both documents and queries. Vectors from different models are incompatible.\nConsider dimensions: text-embedding-3-small (1536d) is a good balance of quality and size. 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Error Classification","url":"/docs/cookbook/error-recovery#1-error-classification","content":"Errors fall into three categories:\n\nRetryable: Temporary issues that may resolve\nNetwork timeouts\nConnection resets\nTemporary service issues\n\nFallback: Use alternative provider\nRate limits\nService overload\nProvider outages\n\nFatal: Don't retry\nInvalid API keys\nMalformed requests\nUnauthorized access","hierarchy":{"lvl0":"Cookbook","lvl1":"Error Recovery Patterns","lvl2":"1. Error Classification","lvl3":""}}, {"objectID":"cc092ce7d655999b3f9b7a8fce037c2137af84349e96c9f337e5aff8f345dd05","title":"2. Retry Strategy","url":"/docs/cookbook/error-recovery#2-retry-strategy","content":"Exponential backoff: 1s, 2s, 4s, 8s (max 10s)\nMax retries: 3 attempts by default\nSmart delays: Longer delays for repeated failures","hierarchy":{"lvl0":"Cookbook","lvl1":"Error Recovery Patterns","lvl2":"2. 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This collection of recipes provides practical, copy-paste ready solutions for common use cases and challenges when building with NeuroLink.\n\nWhat's in the Cookbook?\n\nEach recipe follows a consistent structure:\nProblem: What challenge does this solve?\nSolution: High-level approach\nCode: Complete, working TypeScript example\nExplanation: Step-by-step breakdown\nVariations: Alternative approaches\nSee Also: Related recipes and documentation\n\nRecipe Categories\n\nGetting Started\nBasic Streaming - Stream AI responses in real time with the result.stream pattern\nMultimodal Images - Send images to vision models for analysis, OCR, and comparison\nProvider Switching - Switch providers at runtime, compare outputs, and implement fallback\nEmbeddings Basics - Generate embeddings, compare similarity, and build semantic search\n\nReliability & Error Handling\nStreaming with Retry Logic - Handle network interruptions and implement automatic retry for streaming responses\nError Recovery Patterns - Graceful degradation and error handling strategies\nMulti-Provider Fallback - Automatically switch providers when one fails\n\nPerformance & Optimization\nCost Optimization - Minimize token usage and API costs\nRate Limit Handling - Manage rate limits across providers\nBatch Processing - Efficiently process multiple requests\n\nContext Management\nContext Window Management - Handle large conversations within token limits\nConversation Summarization - Automatically summarize long conversations\n\nAdvanced Features\nStructured Output with JSON Schema - Extract structured data with type safety\nTool Chaining - Chain multiple MCP tool calls together\nAutoResearch Quickstart - Set up an autonomous AI experiment loop in under 5 minutes\n\nHow to Use These Recipes\nFind your use case: Browse the categories above\nCopy the code: All examples are production-ready\nCustomize: Adapt the code to your specific needs\nTest: Verify the solution works in your environment\n\nPrerequisites\n\nMost recipes assume you have:\nNeuroLink installed: npm install @juspay/neurolink\nAt least one provider configured (API keys in .env)\nBasic TypeScript/JavaScript knowledge\n\nContributing\n\nFound a common pattern not covered here? 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Contribute a recipe!","hierarchy":{"lvl0":"Cookbook","lvl1":"NeuroLink Cookbook","lvl2":"Contributing","lvl3":""}}, {"objectID":"9a9bbafa70428c19967e4be8e20f5138fe1acbcbacd7797e73b7a82776d16d56","title":"See Also","url":"/docs/cookbook#see-also","content":"Getting Started Guide\nAPI Reference\nTroubleshooting Guide","hierarchy":{"lvl0":"Cookbook","lvl1":"NeuroLink Cookbook","lvl2":"See Also","lvl3":""}}, {"objectID":"9764e2f0028c46c8544f9ee76f3af6995b9eaa229a37d6f58b7414de4a76e87d","title":"Multi-Provider Fallback","url":"/docs/cookbook/multi-provider-fallback","content":"Multi-Provider Fallback\n\nProblem\n\nRelying on a single AI provider creates a single point of failure:\nProvider outages affect your entire application\nRate limits halt all operations\nRegional availability issues block access\nModel deprecation requires code changes\n\nSolution\n\nImplement automatic fallback across multiple providers:\nPrimary → Secondary → Tertiary provider chain\nHealth monitoring for each provider\nAutomatic failover on errors\nLoad balancing across providers\nCost-aware routing\n\nCode\n\nExplanation\nProvider Priority\n\nProviders are ordered by priority (1 = highest). The default getBestProvider() order prioritizes self-hosted providers first (no rate limits, no external costs):\nHealth Monitoring\n\nTrack provider health automatically:\nHealthy: Available for requests\nUnhealthy: Temporarily skipped (auto-recovers after 60s)\nFailure triggers: 503, 502, connection errors\nAutomatic Failover\n\nOn error, automatically try next provider:\nError Classification\n\nNot all errors trigger failover:\n503, 502: Provider issue → Mark unhealthy, try next\n401, 403: Auth issue → Try next (may have different credentials)\n400: Bad request → Don't retry (same error on all providers)\nTimeout Protection\n\nSet timeouts to prevent hanging on slow providers:\n\nVariations\n\nCost-Aware Routing\n\nPrefer cheaper providers when quality is similar:\n\nRegion-Aware Routing\n\nChoose provider based on region:\n\nLoad Balancing\n\nDistribute load across providers:\n\nModel-Specific Fallback\n\nDifferent models for different tasks:\n\nHealth Check Endpoint\n\nProactive health checking:\n\nProvider Comparison\n\n| Provider | Availability | Rate Limits | Global Regions | Cost |\n| ------------ | ------------ | ------------ | -------------- | ---- |\n| OpenAI | 99.9% | 3500 req/min | Yes | $$$ |\n| Anthropic | 99.9% | 1000 req/min | Limited | $$ |\n| Google AI | 99.5% | 60 req/min | Yes | $ |\n| Azure OpenAI | 99.95% | Custom | Global | $$$ |\n\nBest Practices\nConfigure at least 2 providers: Minimum for true failover\nMix provider types: Different infrastructure = better reliability\nMonitor health actively: Don't wait for failures\nSet appropriate timeouts: Balance speed vs reliability\nLog all failovers: Track patterns for optimization\n\nSee Also\nError Recovery Patterns\nRate Limit Handling\nCost Optimization\nProvider Comparison Guide","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"","lvl3":""}}, {"objectID":"ae1e5f8f35ed67fc33f194dc75bcf58d75f1fe9ffd0826b50c6b887c296a92f2","title":"Problem","url":"/docs/cookbook/multi-provider-fallback#problem","content":"Relying on a single AI provider creates a single point of failure:\nProvider outages affect your entire application\nRate limits halt all operations\nRegional availability issues block access\nModel deprecation requires code changes","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"Problem","lvl3":""}}, {"objectID":"ae742769b7ba9878164641a8313ca8f8eec47b774997fc0568733e8404b4a2c3","title":"Solution","url":"/docs/cookbook/multi-provider-fallback#solution","content":"Implement automatic fallback across multiple providers:\nPrimary → Secondary → Tertiary provider chain\nHealth monitoring for each provider\nAutomatic failover on errors\nLoad balancing across providers\nCost-aware routing","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"Solution","lvl3":""}}, {"objectID":"3dd17f63cc7bab9eba4d8959501e7c4efa4965ef8568abcebd3a9a9bd57740f1","title":"1. 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Automatic Failover","url":"/docs/cookbook/multi-provider-fallback#3-automatic-failover","content":"On error, automatically try next provider:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"3. Automatic Failover","lvl3":""}}, {"objectID":"7752ff9202f68646f4e4ed70f6177259f84e5df5758ce11817dd775c7cea833d","title":"4. Error Classification","url":"/docs/cookbook/multi-provider-fallback#4-error-classification","content":"Not all errors trigger failover:\n503, 502: Provider issue → Mark unhealthy, try next\n401, 403: Auth issue → Try next (may have different credentials)\n400: Bad request → Don't retry (same error on all providers)","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"4. Error Classification","lvl3":""}}, {"objectID":"5ef46f94680281c60ce152d50fcbd474b32be55e48987ce61a137785f5524679","title":"5. Timeout Protection","url":"/docs/cookbook/multi-provider-fallback#5-timeout-protection","content":"Set timeouts to prevent hanging on slow providers:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"5. Timeout Protection","lvl3":""}}, {"objectID":"4f54edcc38f819d6a90d83908f9f4f4957a4979c50961cbad7ec43b221a32cd1","title":"Cost-Aware Routing","url":"/docs/cookbook/multi-provider-fallback#cost-aware-routing","content":"Prefer cheaper providers when quality is similar:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"Cost-Aware Routing","lvl3":""}}, {"objectID":"0ef8adbea2207ee0dc493fac8d3d106b3b75834fb8b87125e908ab590be25450","title":"Region-Aware Routing","url":"/docs/cookbook/multi-provider-fallback#region-aware-routing","content":"Choose provider based on region:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"Region-Aware Routing","lvl3":""}}, {"objectID":"b2b80f02c0954f2c935ab2c5a280974fc52d48fab1543a58b16827f7df16dd25","title":"Load Balancing","url":"/docs/cookbook/multi-provider-fallback#load-balancing","content":"Distribute load across providers:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"Load Balancing","lvl3":""}}, {"objectID":"34c8601cf010c667f8d2cfceccd9a2f708c12d357414e776a0df52a33092294d","title":"Model-Specific Fallback","url":"/docs/cookbook/multi-provider-fallback#model-specific-fallback","content":"Different models for different tasks:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"Model-Specific Fallback","lvl3":""}}, {"objectID":"b91a578792b895df67acc94d5a83c81cc1d4e4744ce8d7e6b8d293ca2781c956","title":"Health Check Endpoint","url":"/docs/cookbook/multi-provider-fallback#health-check-endpoint","content":"Proactive health checking:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"Health Check Endpoint","lvl3":""}}, {"objectID":"d847869ffc227359af381832544623bdaa7ef584122435e15af03abb4eea8c6b","title":"Provider Comparison","url":"/docs/cookbook/multi-provider-fallback#provider-comparison","content":"| Provider | Availability | Rate Limits | Global Regions | Cost |\n| ------------ | ------------ | ------------ | -------------- | ---- |\n| OpenAI | 99.9% | 3500 req/min | Yes | $$$ |\n| Anthropic | 99.9% | 1000 req/min | Limited | $$ |\n| Google AI | 99.5% | 60 req/min | Yes | $ |\n| Azure OpenAI | 99.95% | Custom | Global | $$$ |","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"Provider Comparison","lvl3":""}}, {"objectID":"e9aba718ef8c2c10c40a61a432294ed4d237d95ae3818474d14576e132218e0a","title":"Best Practices","url":"/docs/cookbook/multi-provider-fallback#best-practices","content":"Configure at least 2 providers: Minimum for true failover\nMix provider types: Different infrastructure = better reliability\nMonitor health actively: Don't wait for failures\nSet appropriate timeouts: Balance speed vs reliability\nLog all failovers: Track patterns for optimization","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"Best Practices","lvl3":""}}, {"objectID":"344d922f243a623e9feb66d5aeab101f97416ebed8cdd8c264b401d9f58f107b","title":"See Also","url":"/docs/cookbook/multi-provider-fallback#see-also","content":"Error Recovery Patterns\nRate Limit Handling\nCost Optimization\nProvider Comparison Guide","hierarchy":{"lvl0":"Cookbook","lvl1":"Multi-Provider Fallback","lvl2":"See Also","lvl3":""}}, {"objectID":"43bdef285c949d39de7ce6e4e29aad7dcfe338226a8d1d3fd8e0061dc2d1351f","title":"Multimodal Images","url":"/docs/cookbook/multimodal-images","content":"Multimodal Images\n\nProblem\n\nMany AI tasks require visual understanding -- analyzing screenshots, describing photos, comparing diagrams, or extracting text from images. Text-only prompts cannot handle these use cases.\n\nSolution\n\nPass images to NeuroLink via the input.images array in generate() or stream(). NeuroLink handles the encoding and provider-specific formatting automatically. Images can be URLs, base64 strings, or Buffers.\n\nCode\n\nExplanation\nThe input.images Array\n\nThe images field accepts an array of image sources. Each element can be:\n\n| Type | Example | When to Use |\n| -------- | ---------------------------------- | ------------------------------ |\n| string | \"https://example.com/photo.jpg\" | Public image URLs |\n| string | \"data:image/png;base64,iVBOR...\" | Base64-encoded data URIs |\n| Buffer | readFileSync(\"./image.png\") | Local files loaded into memory |\n\nNeuroLink's MessageBuilder and ProviderImageAdapter handle the conversion to each provider's required format automatically.\nVision Model Requirements\n\nNot all models support images. You must use a vision-capable model:\n\n| Provider | Vision Models |\n| --------- | -------------------------------------------------------------------- |\n| OpenAI | gpt-4o, gpt-4o-mini, gpt-4-turbo |\n| Anthropic | claude-sonnet-4-20250514, claude-3-5-sonnet-*, claude-3-opus-* |\n| Google AI | gemini-2.5-flash, gemini-2.5-pro, gemini-2.0-flash |\n| Vertex AI | gemini-2.5-flash, gemini-2.5-pro |\n| Bedrock | anthropic.claude-3-*, anthropic.claude-sonnet-4-* |\nMultiple Images\n\nPass multiple images and reference them in your prompt. The model sees them in order:\nAlt Text for Accessibility\n\nFor production applications, provide alt text with the ImageWithAltText format:\n\nVariations\n\nStream Image Analysis\n\nStream the response while analyzing an image:\n\nExtract Text from an Image (OCR)\n\nUse a vision model as an OCR tool:\n\nBase64 String Input\n\nWhen you already have a base64-encoded image (e.g., from a database or API):\n\nBatch Image Classification\n\nClassify multiple images in sequence:\n\nTips\nUse the right model for cost: gpt-4o-mini and gemini-2.5-flash are cheaper for simple image tasks. Reserve gpt-4o and claude-sonnet-4-20250514 for complex visual reasoning.\nResize large images: Very large images consume more tokens. Resize to the minimum resolution needed before sending.\nBe specific in your prompt: Instead of \"describe this image\", ask \"list all the text visible in the top-right corner of this screenshot.\"\nOne image or many: Some tasks work better with a single detailed image; comparison tasks benefit from passing 2-3 images in one request.\n\nSee Also\nBasic Streaming\nStructured Output with JSON Schema\nCost Optimization\nAPI Reference","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"","lvl3":""}}, {"objectID":"e514f7d517ef48a0dbaf5b9dc63b3ddd491b090ce5c066a94a76ddadf8d3ade1","title":"Problem","url":"/docs/cookbook/multimodal-images#problem","content":"Many AI tasks require visual understanding -- analyzing screenshots, describing photos, comparing diagrams, or extracting text from images. Text-only prompts cannot handle these use cases.","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"Problem","lvl3":""}}, {"objectID":"eae56ad24a46f34aa3de0a2039db4520e454fb4cd1e14440ca25123459a93aa2","title":"Solution","url":"/docs/cookbook/multimodal-images#solution","content":"Pass images to NeuroLink via the input.images array in generate() or stream(). NeuroLink handles the encoding and provider-specific formatting automatically. Images can be URLs, base64 strings, or Buffers.","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"Solution","lvl3":""}}, {"objectID":"66b7f3ed168648d31b3b9559185a2dff5e7e07dc75bdab0eeec0cbe7386bc6ee","title":"1. The input.images Array","url":"/docs/cookbook/multimodal-images#1-the-inputimages-array","content":"The images field accepts an array of image sources. Each element can be:\n\n| Type | Example | When to Use |\n| -------- | ---------------------------------- | ------------------------------ |\n| string | \"https://example.com/photo.jpg\" | Public image URLs |\n| string | \"data:image/png;base64,iVBOR...\" | Base64-encoded data URIs |\n| Buffer | readFileSync(\"./image.png\") | Local files loaded into memory |\n\nNeuroLink's MessageBuilder and ProviderImageAdapter handle the conversion to each provider's required format automatically.","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"1. The input.images Array","lvl3":""}}, {"objectID":"c51005216f47462dfa9713061e1eb348fb0be0847c212e2215c4efdbcb856d6b","title":"2. Vision Model Requirements","url":"/docs/cookbook/multimodal-images#2-vision-model-requirements","content":"Not all models support images. You must use a vision-capable model:\n\n| Provider | Vision Models |\n| --------- | -------------------------------------------------------------------- |\n| OpenAI | gpt-4o, gpt-4o-mini, gpt-4-turbo |\n| Anthropic | claude-sonnet-4-20250514, claude-3-5-sonnet-*, claude-3-opus-* |\n| Google AI | gemini-2.5-flash, gemini-2.5-pro, gemini-2.0-flash |\n| Vertex AI | gemini-2.5-flash, gemini-2.5-pro |\n| Bedrock | anthropic.claude-3-*, anthropic.claude-sonnet-4-* |","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"2. Vision Model Requirements","lvl3":""}}, {"objectID":"c835a4c9741df03d1d277f6bd3d5203f16d1afe5e9d523eb06954653dc862ad9","title":"3. Multiple Images","url":"/docs/cookbook/multimodal-images#3-multiple-images","content":"Pass multiple images and reference them in your prompt. The model sees them in order:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"3. Multiple Images","lvl3":""}}, {"objectID":"1afe6e77b9b35d343aba56c1698b7ef08c2ad73e5aba491f12a3d75de05a8f02","title":"4. Alt Text for Accessibility","url":"/docs/cookbook/multimodal-images#4-alt-text-for-accessibility","content":"For production applications, provide alt text with the ImageWithAltText format:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"4. Alt Text for Accessibility","lvl3":""}}, {"objectID":"74f7bbde0879a70902ce40aa13ed06b5f30decaccf8ffb9d139ca8c503589647","title":"Stream Image Analysis","url":"/docs/cookbook/multimodal-images#stream-image-analysis","content":"Stream the response while analyzing an image:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"Stream Image Analysis","lvl3":""}}, {"objectID":"113c13222f642dd3f47ff313ea8196eca81e016fb93f6ea931a731c4127d8c70","title":"Extract Text from an Image (OCR)","url":"/docs/cookbook/multimodal-images#extract-text-from-an-image-ocr","content":"Use a vision model as an OCR tool:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"Extract Text from an Image (OCR)","lvl3":""}}, {"objectID":"4b453131e8f72c89515905a23a64e0d23a5e234dc2bebbdff5d3ce26bb88e648","title":"Base64 String Input","url":"/docs/cookbook/multimodal-images#base64-string-input","content":"When you already have a base64-encoded image (e.g., from a database or API):","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"Base64 String Input","lvl3":""}}, {"objectID":"7467cf34a0a9e04232f8ef72809e3f92929d36d29e666e3530e827fa525392df","title":"Batch Image Classification","url":"/docs/cookbook/multimodal-images#batch-image-classification","content":"Classify multiple images in sequence:","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"Batch Image Classification","lvl3":""}}, {"objectID":"8b456cca9d550991b03e4d8ae6a9090f937a2b4ffaa4819089a69d5602b76f87","title":"Tips","url":"/docs/cookbook/multimodal-images#tips","content":"Use the right model for cost: gpt-4o-mini and gemini-2.5-flash are cheaper for simple image tasks. Reserve gpt-4o and claude-sonnet-4-20250514 for complex visual reasoning.\nResize large images: Very large images consume more tokens. Resize to the minimum resolution needed before sending.\nBe specific in your prompt: Instead of \"describe this image\", ask \"list all the text visible in the top-right corner of this screenshot.\"\nOne image or many: Some tasks work better with a single detailed image; comparison tasks benefit from passing 2-3 images in one request.","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"Tips","lvl3":""}}, {"objectID":"b1d7e0b142c4bab3fa090a377db3fdffa3d13c57942c4dff4935c9da417ee43b","title":"See Also","url":"/docs/cookbook/multimodal-images#see-also","content":"Basic Streaming\nStructured Output with JSON Schema\nCost Optimization\nAPI Reference","hierarchy":{"lvl0":"Cookbook","lvl1":"Multimodal Images","lvl2":"See Also","lvl3":""}}, {"objectID":"f8b90373bc6935fc0bce56316babb83784cf5127c644f8d5576cb7ed4e736286","title":"Provider Switching","url":"/docs/cookbook/provider-switching","content":"Provider Switching\n\nProblem\n\nDifferent AI providers have different strengths, costs, and availability. You may need to:\nCompare outputs across providers for quality evaluation\nSwitch providers at runtime based on user preference or task type\nImplement graceful fallback when a provider is down\nOptimize cost by routing different workloads to different providers\n\nSolution\n\nNeuroLink's unified API makes provider switching a one-line change. The provider and model fields on generate() and stream() accept any registered provider name. This recipe shows how to leverage that for comparison, runtime switching, and basic fallback.\n\nCode\n\nExplanation\nUnified API Across Providers\n\nNeuroLink abstracts away provider-specific APIs. The same input object works with every provider:\nProvider Names and Aliases\n\nNeuroLink supports both canonical names and aliases:\n\n| Canonical Name | Aliases |\n| -------------- | ------------------------------------ |\n| openai | gpt, chatgpt |\n| anthropic | claude |\n| google-ai | googleAiStudio, google, gemini |\n| vertex | googleVertex, google-vertex |\n| bedrock | amazonBedrock, aws-bedrock |\n| azure | azureOpenai, azure-openai |\n| mistral | mistralai |\n| litellm | lite-llm, lite |\n| ollama | (none) |\nParallel Comparison with Promise.allSettled\n\nUse Promise.allSettled (not Promise.all) so that one provider's failure does not cancel the others:\n\nEach result is either { status: \"fulfilled\", value: ... } or { status: \"rejected\", reason: ... }.\nTimeout for Fallback\n\nSet a timeout to prevent a slow provider from blocking the fallback chain:\n\nVariations\n\nTask-Based Routing\n\nRoute different tasks to the best provider for each:\n\nStream with Provider Switching\n\nThe same pattern works for streaming:\n\nA/B Testing Providers\n\nRun a percentage of traffic through different providers:\n\nTips\nDefault models: If you omit the model field, each provider uses its default model. This is fine for quick testing but specify the model explicitly in production.\nEnvironment variables: Each provider reads its API key from standard environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_AI_API_KEY, etc.). Configure only the providers you need.\nCost awareness: Provider pricing varies significantly. Use cheaper models (e.g., gemini-2.5-flash, gpt-4o-mini) for simple tasks and reserve expensive models for complex reasoning.\nConsistency: Different providers may produce different response styles. If you need consistent formatting, use a systemPrompt to enforce structure.\n\nSee Also\nMulti-Provider Fallback\nCost Optimization\nError Recovery Patterns\nStreaming with Retry Logic","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"","lvl3":""}}, {"objectID":"9686d4d00ec1b5fb7f9727c252255ff9d31aad9c0e180b78e2895a3e439785fa","title":"Problem","url":"/docs/cookbook/provider-switching#problem","content":"Different AI providers have different strengths, costs, and availability. You may need to:\nCompare outputs across providers for quality evaluation\nSwitch providers at runtime based on user preference or task type\nImplement graceful fallback when a provider is down\nOptimize cost by routing different workloads to different providers","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"Problem","lvl3":""}}, {"objectID":"ccce343ffa37a3b66ea3aa11df4d245da8d86dd9a24a4138b98d8b4fe33df1f8","title":"Solution","url":"/docs/cookbook/provider-switching#solution","content":"NeuroLink's unified API makes provider switching a one-line change. The provider and model fields on generate() and stream() accept any registered provider name. This recipe shows how to leverage that for comparison, runtime switching, and basic fallback.","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"Solution","lvl3":""}}, {"objectID":"05e02ec145a9c13c95383b023e1f2fb52c09c74c00ecb82f5b10625ac2f7c1dd","title":"1. Unified API Across Providers","url":"/docs/cookbook/provider-switching#1-unified-api-across-providers","content":"NeuroLink abstracts away provider-specific APIs. The same input object works with every provider:","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"1. Unified API Across Providers","lvl3":""}}, {"objectID":"c87340c29b7a4c34009ef3afc8b58911ca7c795d5f03b46aed207cbc014e478a","title":"2. Provider Names and Aliases","url":"/docs/cookbook/provider-switching#2-provider-names-and-aliases","content":"NeuroLink supports both canonical names and aliases:\n\n| Canonical Name | Aliases |\n| -------------- | ------------------------------------ |\n| openai | gpt, chatgpt |\n| anthropic | claude |\n| google-ai | googleAiStudio, google, gemini |\n| vertex | googleVertex, google-vertex |\n| bedrock | amazonBedrock, aws-bedrock |\n| azure | azureOpenai, azure-openai |\n| mistral | mistralai |\n| litellm | lite-llm, lite |\n| ollama | (none) |","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"2. Provider Names and Aliases","lvl3":""}}, {"objectID":"4ab6048e41d061f4856c7f5aebaba89d2a0092d80f66fb7cf84ea277acb0ae91","title":"3. Parallel Comparison with Promise.allSettled","url":"/docs/cookbook/provider-switching#3-parallel-comparison-with-promiseallsettled","content":"Use Promise.allSettled (not Promise.all) so that one provider's failure does not cancel the others:\n\nEach result is either { status: \"fulfilled\", value: ... } or { status: \"rejected\", reason: ... }.","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"3. Parallel Comparison with Promise.allSettled","lvl3":""}}, {"objectID":"e966e97be71488e7c007ad805e6bbbecbb4fbbfe018a4773121cecb09835b282","title":"4. Timeout for Fallback","url":"/docs/cookbook/provider-switching#4-timeout-for-fallback","content":"Set a timeout to prevent a slow provider from blocking the fallback chain:","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"4. Timeout for Fallback","lvl3":""}}, {"objectID":"8ed16e4021175043a705036be676b9a53be8d8b64b922e50f381c67c02654e61","title":"Task-Based Routing","url":"/docs/cookbook/provider-switching#task-based-routing","content":"Route different tasks to the best provider for each:","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"Task-Based Routing","lvl3":""}}, {"objectID":"6937a637aa647cdf4beae5435fa3c7163b6412d615dd01a6aefe1571c8f5d807","title":"Stream with Provider Switching","url":"/docs/cookbook/provider-switching#stream-with-provider-switching","content":"The same pattern works for streaming:","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"Stream with Provider Switching","lvl3":""}}, {"objectID":"a0957ef2d73041767523e11488b49e30e1d36e2663d6e28d2c803ae6c7f567e7","title":"A/B Testing Providers","url":"/docs/cookbook/provider-switching#ab-testing-providers","content":"Run a percentage of traffic through different providers:","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"A/B Testing Providers","lvl3":""}}, {"objectID":"2cd20a5960671a3514066e66457e6de830371dc1ca15586868bed767819b6c7a","title":"Tips","url":"/docs/cookbook/provider-switching#tips","content":"Default models: If you omit the model field, each provider uses its default model. This is fine for quick testing but specify the model explicitly in production.\nEnvironment variables: Each provider reads its API key from standard environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_AI_API_KEY, etc.). Configure only the providers you need.\nCost awareness: Provider pricing varies significantly. Use cheaper models (e.g., gemini-2.5-flash, gpt-4o-mini) for simple tasks and reserve expensive models for complex reasoning.\nConsistency: Different providers may produce different response styles. If you need consistent formatting, use a systemPrompt to enforce structure.","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"Tips","lvl3":""}}, {"objectID":"b2a9e7594f1e54ca57c19952dadf9503b931e1d8df79501ec3a52af2f24c28d9","title":"See Also","url":"/docs/cookbook/provider-switching#see-also","content":"Multi-Provider Fallback\nCost Optimization\nError Recovery Patterns\nStreaming with Retry Logic","hierarchy":{"lvl0":"Cookbook","lvl1":"Provider Switching","lvl2":"See Also","lvl3":""}}, {"objectID":"fdcacdbb4be556ad82bb3c68721fa9c8b2f5070484903930213169cca3fded66","title":"Rate Limit Handling","url":"/docs/cookbook/rate-limit-handling","content":"Rate Limit Handling\n\nProblem\n\nAI providers enforce rate limits to prevent abuse and ensure fair usage. Exceeding these limits results in:\nHTTP 429 errors\nRequest failures\nService disruption\nTemporary bans\n\nDifferent providers have different limits:\nOpenAI: 3,500 requests/min (paid tier)\nAnthropic: 50 requests/min (free tier)\nGoogle AI: 60 requests/min\n\nSolution\n\nImplement intelligent rate limiting with:\nToken bucket algorithm\nRequest queuing\nAutomatic backoff\nPer-provider limits\nRequest prioritization\n\nCode\n\nExplanation\nToken Bucket Algorithm\n\nThe rate limiter uses a token bucket:\nBucket capacity: burstSize (max requests in burst)\nRefill rate: requestsPerMinute / 60 tokens per second\nToken consumption: 1 token per request\n\nThis allows bursts while maintaining average rate.\nAutomatic Refill\n\nTokens refill continuously based on elapsed time:\nWait Strategy\n\nWhen no tokens available:\nCalculate time until next token\nSleep for that duration\nConsume token and proceed\n429 Error Handling\n\nWhen provider returns 429:\nRead Retry-After header\nReset token bucket\nWait and retry automatically\nPer-Provider Configuration\n\nDifferent providers have different limits. Configure each separately:\n\n| Provider | Free Tier | Paid Tier | Burst Size |\n| --------- | ---------- | ------------ | ---------- |\n| OpenAI | 3 req/min | 3500 req/min | 100 |\n| Anthropic | 50 req/min | 1000 req/min | 10 |\n| Google AI | 60 req/min | 1000 req/min | 15 |\n\nVariations\n\nPriority Queue\n\nPrioritize important requests:\n\nAdaptive Rate Limiting\n\nAdjust limits based on errors:\n\nDistributed Rate Limiting with Redis\n\nFor multi-instance deployments:\n\nBest Practices\nSet conservative limits: Start with 80% of provider's limit\nMonitor usage: Track request patterns to optimize limits\nUse burst capacity: Allow occasional spikes while maintaining average rate\nImplement backoff: Exponential backoff on repeated rate limit errors\nCache responses: Reduce duplicate requests (see Cost Optimization)\n\nSee Also\nCost Optimization\nBatch Processing\nError Recovery\nStreaming with Retry","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"","lvl3":""}}, {"objectID":"da9d2397233db936ff7700016301ef203ffab71f51333ef0582be2ab2709c3ae","title":"Problem","url":"/docs/cookbook/rate-limit-handling#problem","content":"AI providers enforce rate limits to prevent abuse and ensure fair usage. Exceeding these limits results in:\nHTTP 429 errors\nRequest failures\nService disruption\nTemporary bans\n\nDifferent providers have different limits:\nOpenAI: 3,500 requests/min (paid tier)\nAnthropic: 50 requests/min (free tier)\nGoogle AI: 60 requests/min","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"Problem","lvl3":""}}, {"objectID":"fe504c44384edfd4ee616a9c52d5f47081870d4b61a3af539397baa3aac51818","title":"Solution","url":"/docs/cookbook/rate-limit-handling#solution","content":"Implement intelligent rate limiting with:\nToken bucket algorithm\nRequest queuing\nAutomatic backoff\nPer-provider limits\nRequest prioritization","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"Solution","lvl3":""}}, {"objectID":"33f108b25aca5552fd0c5da430202007727c5094485837a70316e9e3e9aa1309","title":"1. Token Bucket Algorithm","url":"/docs/cookbook/rate-limit-handling#1-token-bucket-algorithm","content":"The rate limiter uses a token bucket:\nBucket capacity: burstSize (max requests in burst)\nRefill rate: requestsPerMinute / 60 tokens per second\nToken consumption: 1 token per request\n\nThis allows bursts while maintaining average rate.","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"1. Token Bucket Algorithm","lvl3":""}}, {"objectID":"3785470d2dc3a12844352306efcefc0c74dc8dd84103af001a8a453d8ab595ca","title":"2. Automatic Refill","url":"/docs/cookbook/rate-limit-handling#2-automatic-refill","content":"Tokens refill continuously based on elapsed time:","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"2. Automatic Refill","lvl3":""}}, {"objectID":"466cd44f2bd1d3d7549afd7da0d1ca277c8315c552fdfba1f03246cceb83b4ad","title":"3. Wait Strategy","url":"/docs/cookbook/rate-limit-handling#3-wait-strategy","content":"When no tokens available:\nCalculate time until next token\nSleep for that duration\nConsume token and proceed","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"3. Wait Strategy","lvl3":""}}, {"objectID":"f578b36fefaf84723b2436536bb263ce6d624b232ccd4d5e8a3e061e358f71b6","title":"4. 429 Error Handling","url":"/docs/cookbook/rate-limit-handling#4-429-error-handling","content":"When provider returns 429:\nRead Retry-After header\nReset token bucket\nWait and retry automatically","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"4. 429 Error Handling","lvl3":""}}, {"objectID":"3ca9ab0d074d0b966e20f84450428059ff485fb4d36381298d9d9335fd9889f1","title":"5. Per-Provider Configuration","url":"/docs/cookbook/rate-limit-handling#5-per-provider-configuration","content":"Different providers have different limits. Configure each separately:\n\n| Provider | Free Tier | Paid Tier | Burst Size |\n| --------- | ---------- | ------------ | ---------- |\n| OpenAI | 3 req/min | 3500 req/min | 100 |\n| Anthropic | 50 req/min | 1000 req/min | 10 |\n| Google AI | 60 req/min | 1000 req/min | 15 |","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"5. Per-Provider Configuration","lvl3":""}}, {"objectID":"0046de004b8639fec77c475e17b8926846dbebc70b7201c1f2be0cde8bca6c85","title":"Priority Queue","url":"/docs/cookbook/rate-limit-handling#priority-queue","content":"Prioritize important requests:","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"Priority Queue","lvl3":""}}, {"objectID":"e17026944d0c7ee5bff31ee55b15b4db5aaa0d210c62a5fb2ecf343fd74104f2","title":"Adaptive Rate Limiting","url":"/docs/cookbook/rate-limit-handling#adaptive-rate-limiting","content":"Adjust limits based on errors:","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"Adaptive Rate Limiting","lvl3":""}}, {"objectID":"d98dc8888c6b1b9c2181c08fe15c1d7f0545d665022f285f5349ecc5a7dedbe7","title":"Distributed Rate Limiting with Redis","url":"/docs/cookbook/rate-limit-handling#distributed-rate-limiting-with-redis","content":"For multi-instance deployments:","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"Distributed Rate Limiting with Redis","lvl3":""}}, {"objectID":"ebb9c835006b8a4ebb952c6342112e445f5a584bc0f24287fc5886e240c8f375","title":"Best Practices","url":"/docs/cookbook/rate-limit-handling#best-practices","content":"Set conservative limits: Start with 80% of provider's limit\nMonitor usage: Track request patterns to optimize limits\nUse burst capacity: Allow occasional spikes while maintaining average rate\nImplement backoff: Exponential backoff on repeated rate limit errors\nCache responses: Reduce duplicate requests (see Cost Optimization)","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"Best Practices","lvl3":""}}, {"objectID":"122f6a4b4882fe77a0403e20ab2c3032e58546c210d38e58d571da33ced23320","title":"See Also","url":"/docs/cookbook/rate-limit-handling#see-also","content":"Cost Optimization\nBatch Processing\nError Recovery\nStreaming with Retry","hierarchy":{"lvl0":"Cookbook","lvl1":"Rate Limit Handling","lvl2":"See Also","lvl3":""}}, {"objectID":"feb0e8fdd5a55ed39cd3eeb932df920702f17bf519b947650e61547319e7cc11","title":"Streaming with Retry Logic","url":"/docs/cookbook/streaming-with-retry","content":"Streaming with Retry Logic\n\nProblem\n\nNetwork interruptions, temporary provider outages, and transient errors can cause streaming responses to fail mid-stream. Without retry logic, users experience incomplete responses and poor reliability.\n\nSolution\n\nImplement automatic retry with exponential backoff for streaming responses. Handle different failure scenarios:\nNetwork timeouts\nConnection drops\nProvider rate limits\nTransient API errors\n\nCode\n\nExplanation\nRetry Configuration\n\nThe RetryConfig interface defines retry behavior:\nmaxRetries: Maximum number of retry attempts\ninitialDelay: Starting delay between retries (milliseconds)\nmaxDelay: Maximum delay to prevent excessive waiting\nbackoffMultiplier: How quickly delays increase (exponential backoff)\nRetry Loop\n\nThe while loop attempts streaming up to maxRetries + 1 times (initial attempt + retries).\nError Classification\n\nNot all errors should trigger retries:\nRetryable: Network errors, rate limits, temporary service issues\nNon-retryable: Authentication errors, invalid requests, missing models\nExponential Backoff\n\nEach retry waits longer than the previous:\nFirst retry: 1000ms\nSecond retry: 2000ms\nThird retry: 4000ms\nFourth retry: 8000ms (capped at maxDelay)\n\nThis prevents overwhelming the provider and gives transient issues time to resolve.\nStream Consumption\n\nThe code accumulates chunks to provide a complete response even if earlier attempts partially succeeded.\n\nVariations\n\nResume from Last Position\n\nFor very long streams, resume from the last received position:\n\nCircuit Breaker Pattern\n\nPrevent repeated failures with a circuit breaker:\n\nProvider Fallback on Retry\n\nTry different providers on subsequent retries:\n\nSee Also\nError Recovery Patterns\nMulti-Provider Fallback\nRate Limit Handling\nStreaming API Reference","hierarchy":{"lvl0":"Cookbook","lvl1":"Streaming with Retry Logic","lvl2":"","lvl3":""}}, {"objectID":"472ceb0e39b287f287bd570b5250f75c2c2d8bc15e868b791e472374d26b1e02","title":"Problem","url":"/docs/cookbook/streaming-with-retry#problem","content":"Network interruptions, temporary provider outages, and transient errors can cause streaming responses to fail mid-stream. Without retry logic, users experience incomplete responses and poor reliability.","hierarchy":{"lvl0":"Cookbook","lvl1":"Streaming with Retry Logic","lvl2":"Problem","lvl3":""}}, {"objectID":"6bcb7cd2ca9e53a0aa68e99dc609654017ddd5f23b6067e390140b05461edeca","title":"Solution","url":"/docs/cookbook/streaming-with-retry#solution","content":"Implement automatic retry with exponential backoff for streaming responses. Handle different failure scenarios:\nNetwork timeouts\nConnection drops\nProvider rate limits\nTransient API errors","hierarchy":{"lvl0":"Cookbook","lvl1":"Streaming with Retry Logic","lvl2":"Solution","lvl3":""}}, {"objectID":"698239032e7c166416bea4bc349591a3c45b04a69856f94b368c51661929c507","title":"1. Retry Configuration","url":"/docs/cookbook/streaming-with-retry#1-retry-configuration","content":"The RetryConfig interface defines retry behavior:\nmaxRetries: Maximum number of retry attempts\ninitialDelay: Starting delay between retries (milliseconds)\nmaxDelay: Maximum delay to prevent excessive waiting\nbackoffMultiplier: How quickly delays increase (exponential backoff)","hierarchy":{"lvl0":"Cookbook","lvl1":"Streaming with Retry Logic","lvl2":"1. Retry Configuration","lvl3":""}}, {"objectID":"5c86ad31f040c5162dc4dd7b52c1e42f84a44ebfe6a7ff9709e10979f1d8e248","title":"2. Retry Loop","url":"/docs/cookbook/streaming-with-retry#2-retry-loop","content":"The while loop attempts streaming up to maxRetries + 1 times (initial attempt + retries).","hierarchy":{"lvl0":"Cookbook","lvl1":"Streaming with Retry Logic","lvl2":"2. Retry Loop","lvl3":""}}, {"objectID":"718825ea30baf3d66847139cbd9931c250d2c3d23e13e7a78cb7aa3e7abade9f","title":"3. Error Classification","url":"/docs/cookbook/streaming-with-retry#3-error-classification","content":"Not all errors should trigger retries:\nRetryable: Network errors, rate limits, temporary service issues\nNon-retryable: Authentication errors, invalid requests, missing models","hierarchy":{"lvl0":"Cookbook","lvl1":"Streaming with Retry Logic","lvl2":"3. Error Classification","lvl3":""}}, {"objectID":"71b3a98c748fd3916b7132c882dbdb478c21cbe7e2c4d0fc1d8d9291dc168a6b","title":"4. Exponential Backoff","url":"/docs/cookbook/streaming-with-retry#4-exponential-backoff","content":"Each retry waits longer than the previous:\nFirst retry: 1000ms\nSecond retry: 2000ms\nThird retry: 4000ms\nFourth retry: 8000ms (capped at maxDelay)\n\nThis prevents overwhelming the provider and gives transient issues time to resolve.","hierarchy":{"lvl0":"Cookbook","lvl1":"Streaming with Retry Logic","lvl2":"4. 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Stream Consumption","lvl3":""}}, {"objectID":"1b2c93baf7234f018e57efe0b00e23a0056234e1d42655d7a6bb689078891407","title":"Resume from Last Position","url":"/docs/cookbook/streaming-with-retry#resume-from-last-position","content":"For very long streams, resume from the last received position:","hierarchy":{"lvl0":"Cookbook","lvl1":"Streaming with Retry Logic","lvl2":"Resume from Last Position","lvl3":""}}, {"objectID":"25f9d71b36b14d1cef3249c4be7fb3df71a118a47495b09cbe8dee7b4e2deedb","title":"Circuit Breaker Pattern","url":"/docs/cookbook/streaming-with-retry#circuit-breaker-pattern","content":"Prevent repeated failures with a circuit breaker:","hierarchy":{"lvl0":"Cookbook","lvl1":"Streaming with Retry Logic","lvl2":"Circuit Breaker Pattern","lvl3":""}}, {"objectID":"bbec39e5870e087dc3604e1bfb04794acaf2fbde13cbc347865afec465455aa3","title":"Provider Fallback on Retry","url":"/docs/cookbook/streaming-with-retry#provider-fallback-on-retry","content":"Try different providers on subsequent retries:","hierarchy":{"lvl0":"Cookbook","lvl1":"Streaming with Retry Logic","lvl2":"Provider Fallback on Retry","lvl3":""}}, {"objectID":"c7cda85f39f50813b5652cbc2612e002a9b6f8610c50516af29cf80bc8aba053","title":"See Also","url":"/docs/cookbook/streaming-with-retry#see-also","content":"Error Recovery Patterns\nMulti-Provider Fallback\nRate Limit Handling\nStreaming API Reference","hierarchy":{"lvl0":"Cookbook","lvl1":"Streaming with Retry Logic","lvl2":"See Also","lvl3":""}}, {"objectID":"baed42db388b2898eacf91304b0a8be0fd1109fdb3a7184e7e61ac088b7edd19","title":"Structured Output with JSON Schema","url":"/docs/cookbook/structured-output","content":"Structured Output with JSON Schema\n\nProblem\n\nAI models return unstructured text by default:\nInconsistent formatting\nManual parsing required\nType safety missing\nError-prone extraction\nDifficult validation\n\nApplications need structured, typed data:\nJSON objects for APIs\nType-safe TypeScript interfaces\nDatabase records\nForm data\n\nSolution\n\nUse JSON schema to enforce structured output:\nDefine TypeScript interfaces\nGenerate JSON schemas\nValidate responses\nType-safe parsing\nError handling\n\nCode\n\nExplanation\nJSON Schema Definition\n\nDefine structure upfront:\nType Safety\n\nUse TypeScript interfaces for compile-time checking:\nValidation\n\nValidate parsed JSON against schema:\nRequired fields present\nCorrect types\nEnum values valid\nNumber ranges respected\nError Handling\n\nRetry with enhanced prompt on validation failure:\nProvider Selection\n\nDifferent providers handle structured output differently:\nOpenAI: Excellent JSON mode\nAnthropic: Good with clear schemas\nGoogle AI: NOTE - Cannot use tools with structured output\n\nVariations\n\nNested Objects\n\nHandle complex nested structures:\n\nStreaming Structured Output\n\nStream and validate incrementally:\n\nUnion Types\n\nHandle multiple possible schemas:\n\nSchema from TypeScript\n\nAuto-generate schemas from interfaces:\n\nUse Cases\n\n| Use Case | Schema Complexity | Recommended Provider |\n| ------------------ | ----------------- | -------------------- |\n| Data extraction | Simple | OpenAI, Anthropic |\n| Form filling | Medium | OpenAI |\n| API responses | Medium | OpenAI, Google AI |\n| Database records | Complex | OpenAI |\n| Classification | Simple | Any provider |\n| Sentiment analysis | Simple | Anthropic |\n\nBest Practices\nDefine schemas upfront: Don't rely on prompt engineering alone\nUse TypeScript types: Compile-time safety prevents runtime errors\nValidate responses: Don't trust AI output blindly\nRetry on failure: Validation errors can be recovered\nTest schemas: Verify with sample data before production\nKeep schemas simple: Complex nesting reduces accuracy\n\nSee Also\nBatch Processing\nError Recovery\nAPI Reference - Generate Method\nProvider Comparison","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"","lvl3":""}}, {"objectID":"8a4040f56bc938825665a0e67ed36a926a8cb7cd257fb8215c2118abb660770f","title":"Problem","url":"/docs/cookbook/structured-output#problem","content":"AI models return unstructured text by default:\nInconsistent formatting\nManual parsing required\nType safety missing\nError-prone extraction\nDifficult validation\n\nApplications need structured, typed data:\nJSON objects for APIs\nType-safe TypeScript interfaces\nDatabase records\nForm data","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"Problem","lvl3":""}}, {"objectID":"071ca3adf5507307b8e2c7a651550acefedf5cd4a772467e0e73da854e92c460","title":"Solution","url":"/docs/cookbook/structured-output#solution","content":"Use JSON schema to enforce structured output:\nDefine TypeScript interfaces\nGenerate JSON schemas\nValidate responses\nType-safe parsing\nError handling","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"Solution","lvl3":""}}, {"objectID":"b97834870aff6f9d925c54b3a7b8ff91dfb647ff939d126dab531b7134706d85","title":"1. 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Validation","lvl3":""}}, {"objectID":"1c9a21ae88ad44ad2a3ccf704f6f9c655b78891bd42dc27ae597a84f1b2fe8df","title":"4. Error Handling","url":"/docs/cookbook/structured-output#4-error-handling","content":"Retry with enhanced prompt on validation failure:","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"4. Error Handling","lvl3":""}}, {"objectID":"c9237a026e3d7f7661653bc7e6fcffa8cbdcee55da45d2fb2b7b17c8242b10fe","title":"5. Provider Selection","url":"/docs/cookbook/structured-output#5-provider-selection","content":"Different providers handle structured output differently:\nOpenAI: Excellent JSON mode\nAnthropic: Good with clear schemas\nGoogle AI: NOTE - Cannot use tools with structured output","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"5. Provider Selection","lvl3":""}}, {"objectID":"0905fdbbee4e8e2cf72b9f17a8d8ac117f8d6996cb8c06ebbd3f14375891eba3","title":"Nested Objects","url":"/docs/cookbook/structured-output#nested-objects","content":"Handle complex nested structures:","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"Nested Objects","lvl3":""}}, {"objectID":"2a0051f9d4ae1f4f1ceb6025061379703b164fe3abf853356b461de9f33e3b88","title":"Streaming Structured Output","url":"/docs/cookbook/structured-output#streaming-structured-output","content":"Stream and validate incrementally:","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"Streaming Structured Output","lvl3":""}}, {"objectID":"fb744d3faf508c30c9b5e4d9ad903d3d3b084959dee80dbdfda9823bb256192a","title":"Union Types","url":"/docs/cookbook/structured-output#union-types","content":"Handle multiple possible schemas:","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"Union Types","lvl3":""}}, {"objectID":"4ff82a9c7783eb8860d3c620a848b62792703cd2d5ebe74c9c0d6060a8e33cee","title":"Schema from TypeScript","url":"/docs/cookbook/structured-output#schema-from-typescript","content":"Auto-generate schemas from interfaces:","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"Schema from TypeScript","lvl3":""}}, {"objectID":"79de584ea48953fd39c1acc309d8531d0770c673af08f447aea2e2c139dc885f","title":"Use Cases","url":"/docs/cookbook/structured-output#use-cases","content":"| Use Case | Schema Complexity | Recommended Provider |\n| ------------------ | ----------------- | -------------------- |\n| Data extraction | Simple | OpenAI, Anthropic |\n| Form filling | Medium | OpenAI |\n| API responses | Medium | OpenAI, Google AI |\n| Database records | Complex | OpenAI |\n| Classification | Simple | Any provider |\n| Sentiment analysis | Simple | Anthropic |","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"Use Cases","lvl3":""}}, {"objectID":"9802592728dce71fffb774ed9c1306a6dcd06684b03b29f06f2604f5d4278259","title":"Best Practices","url":"/docs/cookbook/structured-output#best-practices","content":"Define schemas upfront: Don't rely on prompt engineering alone\nUse TypeScript types: Compile-time safety prevents runtime errors\nValidate responses: Don't trust AI output blindly\nRetry on failure: Validation errors can be recovered\nTest schemas: Verify with sample data before production\nKeep schemas simple: Complex nesting reduces accuracy","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"Best Practices","lvl3":""}}, {"objectID":"7eafc9edc9fb410ed60639fed38417abeef125a2e5a714efdbc918c681b172c0","title":"See Also","url":"/docs/cookbook/structured-output#see-also","content":"Batch Processing\nError Recovery\nAPI Reference - Generate Method\nProvider Comparison","hierarchy":{"lvl0":"Cookbook","lvl1":"Structured Output with JSON Schema","lvl2":"See Also","lvl3":""}}, {"objectID":"12977500073ab87f23145f1aa57f89f0dbd65142ea9a06aae171f22b27daaf44","title":"Tool Chaining with MCP","url":"/docs/cookbook/tool-chaining","content":"Tool Chaining with MCP\n\nProblem\n\nComplex tasks require multiple MCP tool calls in sequence:\nSearch → Read → Analyze → Write\nQuery database → Process → Store results\nFetch data → Transform → Send notification\n\nManually orchestrating tool calls is:\nError-prone\nDifficult to manage state\nHard to handle failures\nNot reusable\n\nSolution\n\nImplement intelligent tool chaining with:\nAutomatic tool selection\nState management\nError recovery\nResult validation\nChain composition\n\nCode\n\nExplanation\nFluent Interface\n\nChain steps with method chaining:\nResult References\n\nReference previous step results:\nValidation\n\nValidate step results:\nError Handling\n\nControl flow on errors:\n\"abort\": Stop chain\n\"retry\": Retry current step\n\"skip\": Continue to next step\nReusable Templates\n\nPre-built chains for common patterns:\n\nVariations\n\nConditional Chains\n\nBranch based on results:\n\nParallel Chains\n\nExecute independent chains in parallel:\n\nLoop Chains\n\nRepeat steps until condition met:\n\nChain Composition\n\nCombine multiple chains:\n\nCommon Patterns\n\nData Processing Pipeline\n\nContent Workflow\n\nGitHub Automation\n\nMonitoring Pipeline\n\nBest Practices\nKeep chains short: 3-5 steps maximum\nValidate early: Check results at each step\nHandle errors: Define recovery strategy\nUse templates: Standardize common patterns\nLog extensively: Track chain execution\nTest chains: Verify each step independently\nDocument dependencies: Clear step relationships\n\nSee Also\nMCP Integration Guide\nError Recovery\nBatch Processing\nSDK Custom Tools","hierarchy":{"lvl0":"Cookbook","lvl1":"Tool Chaining with MCP","lvl2":"","lvl3":""}}, {"objectID":"aba5dff9b6f58c061f171f701d5691f7ad9b67eeffc4b06b58b6fdc71ef29f30","title":"Problem","url":"/docs/cookbook/tool-chaining#problem","content":"Complex tasks require multiple MCP tool calls in sequence:\nSearch → Read → Analyze → Write\nQuery database → Process → Store results\nFetch data → Transform → Send notification\n\nManually orchestrating tool calls is:\nError-prone\nDifficult to manage state\nHard to handle failures\nNot reusable","hierarchy":{"lvl0":"Cookbook","lvl1":"Tool Chaining with MCP","lvl2":"Problem","lvl3":""}}, {"objectID":"7269191a16757115d306b10e6207d4796872e84d8611dcb016d6f740318057ec","title":"Solution","url":"/docs/cookbook/tool-chaining#solution","content":"Implement intelligent tool chaining with:\nAutomatic tool selection\nState management\nError recovery\nResult validation\nChain composition","hierarchy":{"lvl0":"Cookbook","lvl1":"Tool Chaining with MCP","lvl2":"Solution","lvl3":""}}, {"objectID":"2a5a387ec0145f55aa390443b84a7943ebba68dd9146db35f7d95e1706d7f3b3","title":"1. Fluent Interface","url":"/docs/cookbook/tool-chaining#1-fluent-interface","content":"Chain steps with method chaining:","hierarchy":{"lvl0":"Cookbook","lvl1":"Tool Chaining with MCP","lvl2":"1. Fluent Interface","lvl3":""}}, {"objectID":"367ecdea1786d14b48f0a3ab50488dee5a34b858128f9a4d706124035fba5d0b","title":"2. Result References","url":"/docs/cookbook/tool-chaining#2-result-references","content":"Reference previous step results:","hierarchy":{"lvl0":"Cookbook","lvl1":"Tool Chaining with MCP","lvl2":"2. Result References","lvl3":""}}, {"objectID":"e4d2e684fcfeae37678af02cd54fbe3ae077ea2fd67d1b452cb3cfb8014db7f0","title":"3. Validation","url":"/docs/cookbook/tool-chaining#3-validation","content":"Validate step results:","hierarchy":{"lvl0":"Cookbook","lvl1":"Tool Chaining with MCP","lvl2":"3. Validation","lvl3":""}}, {"objectID":"fd80a6aeac4bcc00fe3d9c96ae6eaf3f2647188275380648877f0b1a2d4bb823","title":"4. Error Handling","url":"/docs/cookbook/tool-chaining#4-error-handling","content":"Control flow on errors:\n\"abort\": Stop chain\n\"retry\": Retry current step\n\"skip\": Continue to next step","hierarchy":{"lvl0":"Cookbook","lvl1":"Tool Chaining with MCP","lvl2":"4. Error Handling","lvl3":""}}, {"objectID":"10e7cb831bf3b928ee13c19207d36b132ab600f16b548a145c055ded1041b05f","title":"5. Reusable Templates","url":"/docs/cookbook/tool-chaining#5-reusable-templates","content":"Pre-built chains for common patterns:","hierarchy":{"lvl0":"Cookbook","lvl1":"Tool Chaining with MCP","lvl2":"5. 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API keys:\nMock provider setup\nEnvironment configuration\nTesting workflows\nError handling examples\n\nCustom Integration Builder\n\nIntegration Builder →\n\nBuild custom integrations visually:\nDrag-and-drop workflow design\nCode generation\nTesting environment\nExport capabilities\n\n📊 Analytics Dashboard Demo\n\nReal-time Metrics\n\nAnalytics Demo →\n\nExplore analytics capabilities:\nUsage Tracking: Monitor API calls and performance\nCost Analysis: Understand provider costs\nQuality Metrics: View evaluation scores\nPerformance: Response times and success rates\n\nCustom Reports\n\nReport Builder →\n\nCreate custom analytics reports:\nDrag-and-drop interface\nMultiple chart types\nData filtering options\nExport capabilities\n\n🎯 Use Case Simulators\n\nIndustry-Specific Demos\n\nSoftware Development\n\nDeveloper Tools Demo →\n\nInteractive development workflow:\nCode generation requests\nDocumentation automation\nBug analysis\nTesting assistance\n\nTry these scenarios:\nGenerate a REST API endpoint\nCreate unit tests\nWrite technical documentation\nDebug code issues\n\nMarketing & Content\n\nMarketing Suite Demo →\n\nContent creation workflow:\nBlog post generation\nSocial media content\nEmail campaigns\nSEO optimization\n\nInteractive features:\nBrand voice customization\nTarget audience selection\nContent performance prediction\nA/B testing simulation\n\nBusiness Intelligence\n\nBI Dashboard Demo →\n\nBusiness analysis capabilities:\nData interpretation\nReport generation\nTrend analysis\nDecision support\n\nSample datasets:\nSales performance data\nCustomer behavior metrics\nMarket research findings\nFinancial projections\n\n🔄 Comparison Tools\n\nProvider Performance Comparison\n\nProvider Benchmark →\n\nCompare providers in real-time:\nSide-by-side generation\nPerformance metrics\nQuality evaluation\nCost analysis\n\nTest Scenarios:\nCreative writing tasks\nTechnical documentation\nCode generation\nData analysis\n\nFeature Comparison Matrix\n\nFeature Matrix →\n\nInteractive feature comparison:\nProvider capabilities\nModel availability\nPricing comparison\nPerformance metrics\n\n🎓 Interactive Tutorials\n\nStep-by-Step Learning\n\nTutorial Series →\n\nProgressive learning experience:\nBeginner Level\nBasic concepts\nSimple examples\nGuided exercises\nIntermediate Level\nAdvanced features\nIntegration patterns\nBest practices\nExpert Level\nComplex workflows\nCustom solutions\nPerformance optimization\n\nHands-On Exercises\n\nPractice Exercises →\n\nInteractive coding challenges:\nComplete real-world tasks\nGet instant feedback\nProgress tracking\nCertificate of completion\n\n🛠️ Development Tools\n\nAPI Explorer\n\nAPI Explorer →\n\nInteractive API documentation:\nLive endpoint testing\nRequest/response examples\nParameter customization\nCode generation\n\nSDK Playground\n\nSDK Tester →\n\nTest SDK features directly:\n\n📱 Mobile Experience\n\nProgressive Web App\n\nMobile Demo →\n\nMobile-optimized interface:\nTouch-friendly design\nOffline capabilities\nPush notifications\nNative app feel\n\nResponsive Testing\n\nDevice Simulator →\n\nTest across devices:\nPhone layouts\nTablet interfaces\nDesktop views\nCustom viewports\n\n🎨 Customization Studio\n\nTheme Builder\n\nTheme Studio →\n\nCustomize the interface:\nColor schemes\nLayout options\nComponent styles\nExport themes\n\nWidget C","hierarchy":{"lvl0":"Demos","lvl1":"Interactive Demo","lvl2":"","lvl3":""}}, {"objectID":"3041aa482c30636a44543bb5b69779166869e189d75f5a614ef6d4fac7a6596b","title":"Interactive Demo","url":"/docs/demos/interactive#interactive-demo","content":"Try NeuroLink directly in your browser with our interactive demonstrations and live examples.","hierarchy":{"lvl0":"Demos","lvl1":"Interactive Demo","lvl2":"Interactive Demo","lvl3":""}}, {"objectID":"176686fe1750cf0640462453580f0a0c63f5623a807217202c6650b22fcaf53b","title":"Try NeuroLink Now","url":"/docs/demos/interactive#try-neurolink-now","content":"Launch Interactive Demo →\n\nExperience NeuroLink's capabilities without any installation:\nReal AI Generation: Test with live AI providers\nProvider Comparison: See performance differences\nAnalytics Dashboard: View usage metrics in real-time\nMCP Integration: Explore tool capabilities\n\nDemo Features:\n✅ No registration required\n✅ Free usage limits\n✅ Real provider responses\n✅ Interactive tutorials","hierarchy":{"lvl0":"Demos","lvl1":"Interactive Demo","lvl2":"Try NeuroLink Now","lvl3":""}}, {"objectID":"6c967ef5e9119e48b0b282820ae92b1e1b90359f1b5e0b394959f506578b379e","title":"Guided Walkthrough","url":"/docs/demos/interactive#guided-walkthrough","content":"Guided Tour →\n\nStep-by-step interactive tutorial covering:\nBasic Text Generation\nSimple prompt input\nProvider selection\nResponse analysis\nAdvanced Features\nAnalytics tracking\nQuality evaluation\nStreaming responses\nBusiness Applications\nContent creation\nCode generation\nData analysis","hierarchy":{"lvl0":"Demos","lvl1":"Interactive Demo","lvl2":"Guided 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discoverability:","hierarchy":{"lvl0":"Demos","lvl1":"Screenshots Gallery","lvl2":"File Naming Standards","lvl3":""}}, {"objectID":"10e21be0acae1fb90868abd62c10fd8d98039c0729987e3630c4e7a4a3d2209f","title":"Category Codes","url":"/docs/demos/screenshots#category-codes","content":"cli- - Command Line Interface screenshots\nweb- - Web interface screenshots\nui- - User interface components\nworkflow- - Multi-step workflow demonstrations\nanalytics- - Performance and analytics dashboards\nsetup- - Configuration and setup processes\nerror- - Error states and troubleshooting\ndemo- - General demonstration screenshots\ncomparison- - Before/after or side-by-side comparisons\nmobile- - Mobile or responsive design screenshots","hierarchy":{"lvl0":"Demos","lvl1":"Screenshots Gallery","lvl2":"Category Codes","lvl3":""}}, {"objectID":"83c247baed03abfb4f04c95bba92431a4deb8153274abbb342eff7ad0b8d9ddf","title":"Feature Descriptors","url":"/docs/demos/screenshots#feature-descriptors","content":"help - Help commands and documentation\nstatus - Provider status and connectivity\ngenerate - Text generation features\nconfigure - Configuration processes\nmonitor - Monitoring and analytics\ntools - Tool integration and MCP features\nauth - Authentication and security\nperformance - Performance metrics and optimization","hierarchy":{"lvl0":"Demos","lvl1":"Screenshots Gallery","lvl2":"Feature Descriptors","lvl3":""}}, {"objectID":"3bb2e6ff581cd42893b0e9e6e7491750d239e7abe0a6fa2a13555404cacd89bb","title":"Context Descriptifiers","url":"/docs/demos/screenshots#context-descriptifiers","content":"overview - General overview or main view\ndetail - Detailed/close-up view\nflow - Sequential workflow steps\nresult - Output or results view\nsettings - Settings or configuration view\nerror - Error state or troubleshooting\nsuccess - Successful completion state","hierarchy":{"lvl0":"Demos","lvl1":"Screenshots Gallery","lvl2":"Context Descriptifiers","lvl3":""}}, 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{"objectID":"5718da6ca6b346e832a2ebdeed3ca193be140fe78ea153fd4048817f61de1594","title":"Technical Requirements","url":"/docs/demos/screenshots#technical-requirements","content":"Resolution: Minimum 1920x1080 for desktop, 375x812 for mobile\nFormat: PNG for UI screenshots, JPG for photographic content\nColor Depth: 24-bit color minimum\nCompression: Optimize for web without sacrificing clarity\nFile Size: Target \\<500KB per image, \\<1MB maximum","hierarchy":{"lvl0":"Demos","lvl1":"Screenshots Gallery","lvl2":"Technical Requirements","lvl3":""}}, {"objectID":"bded34f952834930fd6ef03e7afd46f9296553787f90dc0927614d456aea8548","title":"Visual Standards","url":"/docs/demos/screenshots#visual-standards","content":"Consistent Terminal Theme: Use same color scheme across CLI screenshots\nClean Interface: Hide personal information, use placeholder data\nClear Focus: Highlight relevant areas, blur sensitive information\nProper Cropping: Include sufficient context without unnecessary 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filename=$(basename \"$file\")\n\n # Check naming pattern\n if [[ ! $filename =~ ^[a-z]+-[a-z]+-[a-z]+(-[a-z0-9]+)?\\.(png|jpg|webp)$ ]]; then\n echo \"❌ Invalid naming: $filename\"\n echo \" Expected: category-feature-context[-variant].extension\"\n else\n echo \"✅ Valid naming: $filename\"\n fi\ndone\n`","hierarchy":{"lvl0":"Demos","lvl1":"Screenshots Gallery","lvl2":"Validates screenshot naming convention compliance","lvl3":""}}, {"objectID":"9c0a5457220ae71d3f3fbd590dd365a7f571193fc8deaa4da5ef163d58c87397","title":"Large Asset Management","url":"/docs/demos/screenshots#large-asset-management","content":"For screenshots larger than 100KB, use Git LFS:\n\n`bash","hierarchy":{"lvl0":"Demos","lvl1":"Screenshots Gallery","lvl2":"Large Asset Management","lvl3":""}}, {"objectID":"8cb23ab35d4c14fb89eefa822fe753253095e2abd4a7ae67c3a5deff11af2b9d","title":"Track screenshot files with Git LFS","url":"/docs/demos/screenshots#track-screenshot-files-with-git-lfs","content":"git lfs track \"docs/assets/images//*.png\"\ngit lfs track \"docs/assets/images//*.jpg\"\ngit lfs track \"docs/visual-content//*.png\"","hierarchy":{"lvl0":"Demos","lvl1":"Screenshots Gallery","lvl2":"Track screenshot files with Git LFS","lvl3":""}}, {"objectID":"c617dd5bd190238f64725f853779ecf6bb12a4d5c30811a4fa1e8b8075e792f3","title":"Add LFS patterns to .gitattributes","url":"/docs/demos/screenshots#add-lfs-patterns-to-gitattributes","content":"echo \"docs/assets/images//*.png filter=lfs diff=lfs merge=lfs -text\" >> .gitattributes\necho \"docs/assets/images//*.jpg filter=lfs diff=lfs merge=lfs -text\" >> .gitattributes\n`","hierarchy":{"lvl0":"Demos","lvl1":"Screenshots Gallery","lvl2":"Add LFS patterns to .gitattributes","lvl3":""}}, {"objectID":"25df5a07c0cbf16b0b11d45c0945f4ef7b2942a1a2c03dc014f183cecfa33315","title":"Reference Template","url":"/docs/demos/screenshots#reference-template","content":"`markdown","hierarchy":{"lvl0":"Demos","lvl1":"Screenshots Gallery","lvl2":"Reference 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Meets technical and visual standards\n[ ] File Size: Optimized for web delivery\n[ ] Privacy: No sensitive information visible\n[ ] Consistency: Matches existing screenshot style\n[ ] Git LFS: Large files tracked with LFS if needed\n[ ] Documentation: Properly integrated into relevant docs\n\nAll screenshots are captured from live NeuroLink implementations and demonstrate real functionality. 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MP4\n\nComplete walkthrough of NeuroLink CLI capabilities:\nCommand structure and syntax\nAvailable options and flags\nProvider selection and configuration\nHelp system navigation\n\nKey Highlights:\nProfessional CLI interface\nComprehensive command reference\nReal-time help and examples\nIntuitive user experience\n\nProvider Management\n\nProvider Status Check\nDuration: 1:45 | Format: MP4\n\nDemonstrates provider connectivity and health monitoring:\nMulti-provider status checking\nResponse time measurement\nError detection and reporting\nProvider comparison metrics\n\nAuto Provider Selection\nDuration: 2:15 | Format: MP4\n\nShows intelligent provider selection algorithm:\nAutomatic best provider detection\nFallback mechanisms\nPerformance-based routing\nReliability optimization\n\nText Generation Workflows\n\nReal-time Text Generation\nDuration: 3:20 | Format: MP4\n\nLive demonstration of AI content generation:\nMultiple provider comparison\nQuality evaluation in action\nAnalytics tracking\nResponse time analysis\n\nStreaming Responses\nDuration: 2:45 | Format: MP4\n\nReal-time streaming capabilities:\nLive content generation\nProgressive response display\nStream error handling\nPerformance monitoring\n\nAdvanced Features\n\nAdvanced CLI Features\nDuration: 4:10 | Format: MP4\n\nComprehensive advanced functionality:\nBatch processing capabilities\nAnalytics and evaluation features\nCustom configuration options\nIntegration patterns\n\n🔧 MCP Integration Videos\n\nMCP Server Management\n\nMCP Help & Commands\nDuration: 2:00 | Format: MP4\n\nComplete MCP command reference:\nMCP server discovery\nTool inventory management\nServer configuration\nIntegration workflows\n\nMCP Server Listing\nDuration: 1:30 | Format: MP4\n\nDemonstrates MCP server discovery:\nAutomatic server detection\nConfiguration file parsing\nServer status monitoring\nTool availability checking\n\nAI Workflow Tools\n\nAI Workflow Tools Demo\nDuration: 5:25 | Format: MP4\n\nComprehensive workflow automation demonstration:\nEnd-to-end development workflows\nAI-powered code assistance\nDocumentation generation\nQuality assurance integration\n\nFeatures Demonstrated:\nCode generation and review\nAutomated testing\nDocumentation creation\nPerformance optimization\n\n📊 Business Application Videos\n\nExecutive Decision Support\n\nBusiness Applications Demo (General Business Demo)\nDuration: 4:15 | Format: MP4\n\nGeneral business use cases demonstration covering strategic analysis, sales intelligence, and financial planning:\nMarket opportunity analysis\nCompetitive intelligence\nRisk assessment frameworks\nROI projections\n\nMarketing & Sales\n\nContent Creation Workflow\nDuration: 3:45 | Format: MP4\n\nMarketing content generation pipeline:\nBlog post creation\nSocial media content\nEmail campaign development\nSEO optimization\n\nSame Business Demo - Sales Focus\nDuration: 3:20 | Format: MP4\n\nSales-focused section of the business applications demo:\nPipeline analysis\nCompetitive positioning\nPricing strategy development\nCustomer segmentation\n\nOperations & Analytics\n\nProcess Optimization\nDuration: 4:00 | Format: MP4\n\nBusiness process analysis and improvement:\nWorkflow efficiency analysis\nBottleneck identification\nAutomation opportunities\nCost-benefit analysis\n\n🎯 Industry-Specific Demonstrations\n\nSoftware Development\n\nDeveloper Tools Demo (General Developer Demo)\nDuration: 5:30 | Format: MP4\n\nGeneral developer workflow demonstration covering multiple development scenarios:\nCode generation and review\nDocumentation automation\nTesting assistance\nDeployment optimization\n\nKey Workflows:\nFeature development\nBug fixing assistance\nCode quality improvement\nTechnical documentation\n\nHealthcare & Research\n\nMedical Documentation Demo\nDuration: 3:15 | Format: MP4\n\nHealthcare-specific applications:\nClinical documentation\nResearch analysis\nPatient education materials\nCompliance reporting\n\nFinancial Services\n\nBusiness Demo - Financial Focus\nDuration: 4:30 | Format: MP4\n\nFinancial applications from the business use cases demo:\nRisk assessment modeling\nRegulatory compliance\nInvestment analysis\nPortfolio optimization\n\n🔍 Technical Deep Dives\n\nArchitecture & Scalability\n\nDeveloper Demo - Architecture Focus\nDuration: 6:00 | Format: MP4\n\nArchitecture-focused section of the developer tools demo:\nMulti-provider infrastructure\nScalability patterns\nReliability mechanisms\nPerformance optimization\n\nIntegration Patterns\n\nDeveloper Demo - Framework Integration\nDuration: 4:45 | Format: MP4\n\nFramework integration portion of the developer tools demo:\nReact/Next.js integration\nNode.js backend setup\nAPI integration patterns\nError handling strategies\n\nSecurity & Compliance\n\nSecurity Implementation\nDuration: 3:30 | Format: MP4\n\nSecurity and compliance features:\nAPI key management\nAudit logging\nAccess control\nCompliance reporting\n\n📈 Performance & Benchmarking\n\nSpeed 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merge=lfs -text\ndocs//*.webm filter=lfs diff=lfs merge=lfs -text\ndocs//*.mov filter=lfs diff=lfs merge=lfs -text\ndocs//*.avi filter=lfs diff=lfs merge=lfs -text","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Video files - always use LFS","lvl3":""}}, {"objectID":"456aba83dc285979bcc7476ab9c3a9f8e81bd033de726b12e8742e51d58a0897","title":"Audio files - use LFS for large files","url":"/docs/demos/videos#audio-files---use-lfs-for-large-files","content":"docs//*.wav filter=lfs diff=lfs merge=lfs -text\ndocs//*.flac filter=lfs diff=lfs merge=lfs -text","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Audio files - use LFS for large files","lvl3":""}}, {"objectID":"5beb90636c430f4183614f4101c6fb8c09ef54c9121489214e11a7b0dcd73705","title":"Other large assets","url":"/docs/demos/videos#other-large-assets","content":"docs//*.zip filter=lfs diff=lfs merge=lfs -text\ndocs//*.tar.gz filter=lfs diff=lfs merge=lfs -text\nbash","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Other large assets","lvl3":""}}, {"objectID":"e25f6874a758c230f95a52db53d80f1e557dd7a5a3436a0d113ccf33333916c9","title":"Add and commit LFS files normally","url":"/docs/demos/videos#add-and-commit-lfs-files-normally","content":"git add docs/demos/videos/new-demo.mp4\ngit commit -m \"Add new demo video\"","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Add and commit LFS files normally","lvl3":""}}, {"objectID":"54f59e9be3d482051c3697c585e479705c70392393a39f2cdd31936a1a2816ee","title":"Push LFS files to remote","url":"/docs/demos/videos#push-lfs-files-to-remote","content":"git push origin main","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Push LFS files to remote","lvl3":""}}, {"objectID":"16ff50b2601b8c1fc7c794618af682ce3aa81a3f9865fa61f4ae283a4dc94ec1","title":"Pull LFS files on clone","url":"/docs/demos/videos#pull-lfs-files-on-clone","content":"git clone --recursive","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Pull LFS files on clone","lvl3":""}}, {"objectID":"168ec8d6cda5e324bd017e1de6e60cff7475b929b55a526d46cbb07d85eb863a","title":"Check LFS status","url":"/docs/demos/videos#check-lfs-status","content":"git lfs status\ngit lfs ls-files","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Check LFS status","lvl3":""}}, {"objectID":"e593d142df0ebe2d94a8d94b0d0de2c3f993c3aa21f516bde5bb34b0a2b40fd2","title":"Track LFS bandwidth usage","url":"/docs/demos/videos#track-lfs-bandwidth-usage","content":"git lfs env\n`","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Track LFS bandwidth usage","lvl3":""}}, {"objectID":"93994c682cebe6f937109d763d08abbd85d9972e74ed654ae5bb2012c4f1ab96","title":"Content Quality Checklist","url":"/docs/demos/videos#content-quality-checklist","content":"[ ] Audio Quality: Clear narration, no background noise\n[ ] Visual Quality: Sharp text, readable UI elements\n[ ] Pacing: Appropriate speed for comprehension\n[ ] Content Accuracy: Up-to-date features and interfaces\n[ ] Professional Presentation: Consistent branding and style","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Content Quality Checklist","lvl3":""}}, {"objectID":"1d335414b409b2150297cddfa2ad9d56f9f14ceb05888bc4a02388fe171d5c9a","title":"Technical Quality Validation","url":"/docs/demos/videos#technical-quality-validation","content":"`bash\n#!/bin/bash","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Technical Quality Validation","lvl3":""}}, {"objectID":"1360da299ce616ec4651842968606190036e463ec22ff5dbc0d88fdc91740a95","title":"Validates video technical specifications","url":"/docs/demos/videos#validates-video-technical-specifications","content":"checkvideospecs() {\n local file=\"$1\"\n\n # Get video information\n duration=$(ffprobe -v quiet -show_entries format=duration -of csv=\"p=0\" \"$file\")\n resolution=$(ffprobe -v quiet -selectstreams v:0 -showentries stream=width,height -of csv=\"s=x:p=0\" \"$file\")\n bitrate=$(ffprobe -v quiet -showentries format=bitrate -of csv=\"p=0\" \"$file\")\n\n echo \"File: $file\"\n echo \"Duration: ${duration}s\"\n echo \"Resolution: $resolution\"\n echo \"Bitrate: $bitrate bps\"\n\n # Size validation\n size=$(stat -f%z \"$file\" 2>/dev/null || stat -c%s \"$file\")\n size_mb=$((size / 1024 / 1024))\n\n echo \"File Size: ${size_mb}MB\"\n\n # Check if file should use LFS\n if [ $size_mb -gt 50 ]; then\n if ! git lfs ls-files | grep -q \"$file\"; then\n echo \"⚠️ Warning: Large file not tracked by Git LFS\"\n else\n echo \"✅ File properly tracked by Git LFS\"\n fi\n fi\n}","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Validates video technical specifications","lvl3":""}}, {"objectID":"070493af6aedc254517a3feadaaf06b615a50c0331256249dbf625fc583dc4c5","title":"Check all video files","url":"/docs/demos/videos#check-all-video-files","content":"find docs/ -name \".mp4\" -o -name \".webm\" | while read file; do\n checkvideospecs \"$file\"\n echo \"---\"\ndone\n`","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Check all video files","lvl3":""}}, {"objectID":"5a01eee5acb3294808db4be731c82cabb7e976525af4a1d5cea0c7858d792a59","title":"Required Accessibility Features","url":"/docs/demos/videos#required-accessibility-features","content":"[ ] Closed Captions: SRT or VTT subtitle files\n[ ] Audio Descriptions: Narrated descriptions of visual elements\n[ ] Keyboard Navigation: Video player must be keyboard accessible\n[ ] Screen Reader Compatibility: Proper ARIA labels and descriptions\n[ ] Transcript Files: Text transcripts for each video","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Required Accessibility Features","lvl3":""}}, {"objectID":"106437bbceb56152adef86f61f265dcfce19c950a628fca5a19c34c44d3f6f11","title":"Markdown Embedding","url":"/docs/demos/videos#markdown-embedding","content":"`markdown","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Markdown Embedding","lvl3":""}}, {"objectID":"30ac21be2e4a7c768dd8f979f90b06ea8e0ba4cdbbeda1424fda808fa31a435d","title":"Video Title","url":"/docs/demos/videos#video-title","content":"Video Description\nDuration: X:XX | Format: MP4 | Size: XXMb\n\nBrief description of video content and key features demonstrated.\n\nKey Features Shown:\nFeature 1: Description\nFeature 2: Description\nFeature 3: Description\n\nAccessibility:\nCaptions\nTranscript\nAudio Description\n`","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Video Title","lvl3":""}}, {"objectID":"149a7e09de657390464012292d4824acde30d1b624954bd15c5f8e22d81f177c","title":"Web Delivery Optimization","url":"/docs/demos/videos#web-delivery-optimization","content":"Progressive Download: Use faststart flag for immediate playback\nMultiple Quality Levels: Provide 480p, 720p, and 1080p versions\nAdaptive Streaming: Consider HLS or DASH for long videos\nThumbnail Generation: Create poster images for video previews\nCDN Distribution: Use content delivery networks for global access","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Web Delivery Optimization","lvl3":""}}, {"objectID":"2f5623962e0d88148b5495c10e1c5537059346bbeffe42452c8671653739432b","title":"Bandwidth Considerations","url":"/docs/demos/videos#bandwidth-considerations","content":"`bash","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Bandwidth Considerations","lvl3":""}}, {"objectID":"673fe1398a175b868af36ec974ae0ec16a1b11f3cae34b9002673546db8ff013","title":"Generate multiple quality versions","url":"/docs/demos/videos#generate-multiple-quality-versions","content":"createvideovariants() {\n local input=\"$1\"\n local base=\"${input%.*}\"\n\n # HD version (original quality)\n ffmpeg -i \"$input\" -c:v libx264 -crf 23 -preset medium -c:a aac -b:a 192k \"${base}-hd.mp4\"\n\n # Standard version (720p)\n ffmpeg -i \"$input\" -vf scale=1280:720 -c:v libx264 -crf 25 -preset medium -c:a aac -b:a 128k \"${base}-std.mp4\"\n\n # Mobile version (480p)\n ffmpeg -i \"$input\" -vf scale=854:480 -c:v libx264 -crf 28 -preset medium -c:a aac -b:a 96k \"${base}-mobile.mp4\"\n\n # Generate poster image\n ffmpeg -i \"$input\" -ss 00:00:03 -vframes 1 \"${base}-poster.jpg\"\n}\n`","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Generate multiple quality versions","lvl3":""}}, {"objectID":"0f7c3f2868dd7d15f6062eabc730bcc5d92df8db62d4929a276ec79cc3a8d858","title":"Pre-Commit Validation","url":"/docs/demos/videos#pre-commit-validation","content":"`bash\n#!/bin/bash","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Pre-Commit Validation","lvl3":""}}, {"objectID":"8922f450718a779ebc278b400e9040ca644a3ae230fc36c6526411ca7ead7efb","title":"pre-commit-video-check.sh","url":"/docs/demos/videos#pre-commit-video-checksh","content":"echo \"Validating video assets...\"","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"pre-commit-video-check.sh","lvl3":""}}, {"objectID":"66d6428f35c7c026d26083b8d9d123a0c42ea8b532eafa6e6ecf9dfad2cc9745","title":"Check for large files not in LFS","url":"/docs/demos/videos#check-for-large-files-not-in-lfs","content":"find docs/ -name \".mp4\" -o -name \".webm\" | while read file; do\n size=$(stat -f%z \"$file\" 2>/dev/null || stat -c%s \"$file\")\n size_mb=$((size / 1024 / 1024))\n\n if [ $size_mb -gt 50 ] && ! git lfs ls-files | grep -q \"$file\"; then\n echo \"❌ Error: $file (${size_mb}MB) must be tracked by Git LFS\"\n exit 1\n fi\ndone","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Check for large files not in LFS","lvl3":""}}, {"objectID":"5c634242e774e42de72f6f3770dc6381c03f0d1c101085165e060b3855501ac4","title":"Check for required accessibility files","url":"/docs/demos/videos#check-for-required-accessibility-files","content":"find docs/ -name \"*.mp4\" | while read video; do\n base=\"${video%.*}\"\n\n if [ ! -f \"${base}.vtt\" ] && [ ! -f \"${base}-captions.vtt\" ]; then\n echo \"⚠️ Warning: Missing captions for $video\"\n fi\ndone\n\necho \"✅ Video asset validation complete\"\n`","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Check for required accessibility files","lvl3":""}}, {"objectID":"560a1c36c1b3c760ebff16143237f9d1c834f6f4c49c2b0857012ad8f46267a7","title":"Moving Existing Videos to LFS","url":"/docs/demos/videos#moving-existing-videos-to-lfs","content":"`bash\n#!/bin/bash","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Moving Existing Videos to LFS","lvl3":""}}, {"objectID":"cc8b061aeb092947a260d589fde9b6f8986f764ae9e4cab0e01fae4df9d09401","title":"Setup LFS tracking","url":"/docs/demos/videos#setup-lfs-tracking","content":"git lfs track \"docs//*.mp4\"\ngit lfs track \"docs//*.webm\"","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Setup LFS tracking","lvl3":""}}, {"objectID":"ff5586d65df77c690d315a0e82b61de9a1f917f8a7ae9c9a94f9fb85b3f6853e","title":"Find and migrate existing videos","url":"/docs/demos/videos#find-and-migrate-existing-videos","content":"find docs/ -name \".mp4\" -o -name \".webm\" | while read file; do\n echo \"Migrating $file to LFS...\"\n\n # Remove from Git history (if already committed)\n git rm --cached \"$file\"\n\n # Re-add with LFS\n git add \"$file\"\ndone","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Find and migrate existing videos","lvl3":""}}, {"objectID":"5030ce8f5ea40133a0e072117634c723ea9f30411ee178d035359cf2546603cf","title":"Commit LFS migration","url":"/docs/demos/videos#commit-lfs-migration","content":"git commit -m \"Migrate video assets to Git LFS\"\n\necho \"Migration complete. Videos now tracked by Git LFS.\"\n`","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Commit LFS migration","lvl3":""}}, {"objectID":"0ddcb996fe595d69667732dc7c3286314bb67787587ec8c5de0406b728229ce6","title":"Viewing Options","url":"/docs/demos/videos#viewing-options","content":"Streaming Quality:\n4K (2160p) - Ultra HD viewing\n1080p - Standard HD viewing\n720p - Mobile-optimized\n480p - Low bandwidth option\n\nDownload Options:\nMP4 format for offline viewing\nWebM format for web optimization\nMobile-optimized versions\nAudio-only versions available","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Viewing Options","lvl3":""}}, {"objectID":"2243e0beb5ac2fad5c7e1a8bbd2d8ec66e68087990029f17be6ef7357118bb23","title":"Playlist Organization","url":"/docs/demos/videos#playlist-organization","content":"Getting Started (4 videos, 12 minutes)\nCLI Mastery (6 videos, 18 minutes)\nBusiness Applications (8 videos, 30 minutes)\nTechnical Deep Dives (5 videos, 25 minutes)\nAdvanced Features (7 videos, 28 minutes)","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Playlist Organization","lvl3":""}}, {"objectID":"0f8f378dac788bf75d18949dc854e07dac4cb6488d4b9b3830030ba565bdc5ec","title":"Interactive Elements","url":"/docs/demos/videos#interactive-elements","content":"Chapter navigation for long videos\nTimestamped bookmarks for key features\nRelated video suggestions\nTranscript search capability\n\nAll videos are professionally produced with clear audio, high-quality visuals, and detailed explanations. Each video includes timestamps, captions, and related documentation links.","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"Interactive Elements","lvl3":""}}, {"objectID":"63d09e959666dec2069b3276277a5b7fb753db780e80184e1bc5af8590e34904","title":"📚 Related Resources","url":"/docs/demos/videos#-related-resources","content":"Screenshots Gallery - Static visual examples\nInteractive Demo - Try it yourself\nCLI Examples - Command-line patterns\nComplete Visual Guide - Full documentation","hierarchy":{"lvl0":"Demos","lvl1":"Video Demonstrations","lvl2":"📚 Related Resources","lvl3":""}}, {"objectID":"f409c05efc8e41246057bb9b199353479e4afaa2923a524e0754e7edf24f41fd","title":"NeuroLink Dependency Upgrade Report","url":"/docs/dependency-upgrade-report","content":"NeuroLink Dependency Upgrade Report\n\nDescribes the pre-removal dependency state. The Vercel AI SDK sections\nbelow (§4.1–4.7, §4.25) analyse ai and @ai-sdk/* packages that have since\nbeen removed from this repo — see\ndocs/plans/2026-09-03-completing-the-ai-sdk-removal.md. Kept as a record;\nthose upgrade recommendations are no longer actionable.\n\nDate: 2026-02-27\nVersion: 9.12.1\nBranch: fix/security-fixes\nExecutive Summary\n\n25 packages were analyzed across 4 categories (AI SDK, AWS SDK, Core Libraries, Dev Dependencies). All 25 packages have available upgrades.\n\nOverall Risk Assessment: LOW-MEDIUM\n\nThe vast majority of upgrades are low-risk patch and minor version bumps. Only 2 packages carry elevated risk:\nTypeScript 5.0.0 -> 5.9.3 (HIGH risk) -- 10 minor versions spanning 2.5 years with cumulative stricter type checks\ntslib 2.4.1 -> 2.8.1 (MEDIUM risk) -- large jump with decorator hook order change and new exports structure\n\nKey Highlights\n\n| Category | Details |\n| ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Security Fixes | 1 direct fix (download size limits in @ai-sdk/provider-utils), 1 dev dependency fix (Fastify CVE-2026-25224), 2 already-patched CVEs in baseline versions |\n| Critical Bug Fixes | Azure streaming tool calls fixed (@ai-sdk/openai 3.0.36), duplicate tool part creation fixed (ai 6.0.101) |\n| New Features | Anthropic code execution tool, Gemini 3.1 image model, gpt-5.3-codex phase parameter, Google server-side MCP, GenAI telemetry conventions |\n| Breaking Changes | Zero breaking changes in production dependencies; TypeScript 5.9 will surface new type errors |\nUpgrade Priority Matrix\n\n| # | Package | Current | Latest | Risk | Priority | Category |\n| --- | ------------------------------------- | -------- | ------- | ------ | ------------ | ----------- |\n| 1 | @ai-sdk/openai | 3.0.34 | 3.0.36 | Low | Critical | Bug Fix |\n| 2 | @ai-sdk/azure | 3.0.35 | 3.0.37 | Low | Critical | Bug Fix |\n| 3 | @ai-sdk/mistral | 3.0.12 | 3.0.20 | Low | Critical | Security |\n| 4 | fastify | 5.7.2 | 5.7.4 | Low | High | Security |\n| 5 | ai | 6.0.101 | 6.0.103 | Low | High | Bug Fix |\n| 6 | @ai-sdk/anthropic | 3.0.47 | 3.0.48 | Low | High | Feature |\n| 7 | @ai-sdk/google | 3.0.31 | 3.0.33 | Low | High | Feature |\n| 8 | @ai-sdk/google-vertex | 4.0.63 | 4.0.66 | Low | High | Feature |\n| 9 | @google/genai | 1.42.0 | 1.43.0 | Low | High | Feature |\n| 10 | undici | >=7.18.2 | 7.22.0 | Low | Medium | Maintenance |\n| 11 | @opentelemetry/semantic-conventions | 1.39.0 | 1.40.0 | Low | Medium | Feature |\n| 12 | hono | 4.12.2 | 4.12.3 | Low | Medium | Maintenance |\n| 13 | nanoid | 5.1.5 | 5.1.6 | Low | Low | Maintenance |\n| 14 | @aws-sdk/client-bedrock | 3.998.0 | 3.999.0 | Low | Low | Maintenance |\n| 15 | @aws-sdk/client-bedrock-runtime | 3.998.0 | 3.999.0 | Low | Low | Maintenance |\n| 16 | @aws-sdk/client-sagemaker | 3.998.0 | 3.999.0 | Low | Low | Maintenance |\n| 17 | @aws-sdk/client-sagemaker-runtime | 3.998.0 | 3.999.0 | Low | Low | Maintenance |\n| 18 | @semantic-release/npm | 13.1.2 | 13.1.4 | Low | Low | Maintenance |\n| 19 | @sveltejs/kit | 2.53.2 | 2.53.3 | Low | Low | Maintenance |\n| 20 | @types/node | 25.3.1 | 25.3.2 | Low | Low | Maintenance |\n| 21 | svelte-check | 4.4.3 | 4.4.4 | Low | Low | Maintenance |\n| 22 | @ai-sdk/provider | 3.0.8 | 3.0.8 | None | None | Up to date |\n| 23 | tslib | 2.4.1 | 2.8.1 | Medium | Medium | Maintenance |\n| 24 | typescript | 5.0.0 | 5.9.3 | High | Medium | Maintenance |\n| 25 | @langfuse/otel | 4.6.1 | latest | Low | Low | Maintenance |\nSecurity Fixes\n\nDirect Security Fix: Download Size Limits (provider-utils 4.0.15)\n\n| Field | Value ","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"","lvl3":""}}, {"objectID":"0d586a6ec841b313bdf61b0740bdd92d4e0db031d75b9b9e1723ccd7e38c1d26","title":"NeuroLink Dependency Upgrade Report","url":"/docs/dependency-upgrade-report#neurolink-dependency-upgrade-report","content":"Describes the pre-removal dependency state. The Vercel AI SDK sections\nbelow (§4.1–4.7, §4.25) analyse ai and @ai-sdk/* packages that have since\nbeen removed from this repo — see\ndocs/plans/2026-09-03-completing-the-ai-sdk-removal.md. Kept as a record;\nthose upgrade recommendations are no longer actionable.\n\nDate: 2026-02-27\nVersion: 9.12.1\nBranch: fix/security-fixes","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"NeuroLink Dependency Upgrade Report","lvl3":""}}, {"objectID":"5058786e71c18e20b1e8781b9ebb86757f4b697d6d0accde18e4429d3dbb4aa3","title":"1. Executive Summary","url":"/docs/dependency-upgrade-report#1-executive-summary","content":"25 packages were analyzed across 4 categories (AI SDK, AWS SDK, Core Libraries, Dev Dependencies). All 25 packages have available upgrades.","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"1. Executive Summary","lvl3":""}}, {"objectID":"6217427d9dcfc60e1991e79fa183c4b4105faccc8c220dc01114fe68bf8cb473","title":"Overall Risk Assessment: LOW-MEDIUM","url":"/docs/dependency-upgrade-report#overall-risk-assessment-low-medium","content":"The vast majority of upgrades are low-risk patch and minor version bumps. Only 2 packages carry elevated risk:\nTypeScript 5.0.0 -> 5.9.3 (HIGH risk) -- 10 minor versions spanning 2.5 years with cumulative stricter type checks\ntslib 2.4.1 -> 2.8.1 (MEDIUM risk) -- large jump with decorator hook order change and new exports structure","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Overall Risk Assessment: LOW-MEDIUM","lvl3":""}}, {"objectID":"9f1fbe7911b4703049ec9846ce42fd18c63f6fc26c87c41b5d37375d5dfad126","title":"Key Highlights","url":"/docs/dependency-upgrade-report#key-highlights","content":"| Category | Details |\n| ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Security Fixes | 1 direct fix (download size limits in @ai-sdk/provider-utils), 1 dev dependency fix (Fastify CVE-2026-25224), 2 already-patched CVEs in baseline versions |\n| Critical Bug Fixes | Azure streaming tool calls fixed (@ai-sdk/openai 3.0.36), duplicate tool part creation fixed (ai 6.0.101) |\n| New Features | Anthropic code execution tool, Gemini 3.1 image model, gpt-5.3-codex phase parameter, Google server-side MCP, GenAI telemetry conventions |\n| Breaking Changes | Zero breaking changes in production dependencies; TypeScript 5.9 will surface new type errors |","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Key Highlights","lvl3":""}}, {"objectID":"34fc7b8a2e9fafda41f4e8852ebf3ed4b4cb8f2746747f16fd0bb01f29b40cb3","title":"2. Upgrade Priority Matrix","url":"/docs/dependency-upgrade-report#2-upgrade-priority-matrix","content":"| # | Package | Current | Latest | Risk | Priority | Category |\n| --- | ------------------------------------- | -------- | ------- | ------ | ------------ | ----------- |\n| 1 | @ai-sdk/openai | 3.0.34 | 3.0.36 | Low | Critical | Bug Fix |\n| 2 | @ai-sdk/azure | 3.0.35 | 3.0.37 | Low | Critical | Bug Fix |\n| 3 | @ai-sdk/mistral | 3.0.12 | 3.0.20 | Low | Critical | Security |\n| 4 | fastify | 5.7.2 | 5.7.4 | Low | High | Security |\n| 5 | ai | 6.0.101 | 6.0.103 | Low | High | Bug Fix |\n| 6 | @ai-sdk/anthropic | 3.0.47 | 3.0.48 | Low | High | Feature |\n| 7 | @ai-sdk/google | 3.0.31 | 3.0.33 | Low | High | Feature |\n| 8 | @ai-sdk/google-vertex | 4.0.63 | 4.0.66 | Low | High | Feature |\n| 9 | @google/genai | 1.42.0 | 1.43.0 | Low | High | Feature |\n| 10 | undici | >=7.18.2 | 7.22.0 | Low | Medium | Maintenance |\n| 11 | @opentelemetry/semantic-conventions | 1.39.0 | 1.40.0 | Low | Medium | Feature |\n| 12 | hono | 4.12.2 | 4.12.3 | Low | Medium | Maintenance |\n| 13 | nanoid | 5.1.5 | 5.1.6 | Low | Low | Maintenance |\n| 14 | @aws-sdk/client-bedrock | 3.998.0 | 3.999.0 | Low | Low | Maintenance |\n| 15 | @aws-sdk/client-bedrock-runtime | 3.998.0 | 3.999.0 | Low | Low | Maintenance |\n| 16 | @aws-sdk/client-sagemaker | 3.998.0 | 3.999.0 | Low | Low | Maintenance |\n| 17 | @aws-sdk/client-sagemaker-runtime | 3.998.0 | 3.999.0 | Low | Low | Maintenance |\n| 18 | @semantic-release/npm | 13.1.2 | 13.1.4 |","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"2. Upgrade Priority Matrix","lvl3":""}}, {"objectID":"99841f5450fda274fc760b0414bc4757a2b042217b410951d0991a3b50479b2c","title":"Direct Security Fix: Download Size Limits (provider-utils 4.0.15)","url":"/docs/dependency-upgrade-report#direct-security-fix-download-size-limits-provider-utils-4015","content":"| Field | Value |\n| -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Severity | Medium (DoS prevention) |\n| Package | @ai-sdk/provider-utils 4.0.15 (transitive, pulled in via @ai-sdk/mistral 3.0.20) |\n| Description | download() and downloadBlob() now enforce a default 2 GiB size limit on user-provided URLs. Downloads exceeding the limit abort with DownloadError. abortSignal properly passed to fetch(). |\n| Are we affected? | Yes. NeuroLink passes user-provided URLs to generateText()/streamText() (e.g., image URLs). Without this fix, a malicious URL could cause memory exhaustion (DoS). |\n| Fix | Upgrade @ai-sdk/mistral to 3.0.20. All other AI SDK packages will transitively receive this fix. |","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Direct Security Fix: Download Size Limits (provider-utils 4.0.15)","lvl3":""}}, {"objectID":"a506fde63d98817de558e996c21c7f5e6d52400ed1ff4154322c325031d147b1","title":"Dev Dependency Security Fix: Fastify CVE-2026-25224","url":"/docs/dependency-upgrade-report#dev-dependency-security-fix-fastify-cve-2026-25224","content":"| Field | Value |\n| -------------------- | --------------------------------------------------------------------------------------------------------------------- |\n| CVE | CVE-2026-25224 (GHSA-mrq3-vjjr-p77c) |\n| Severity | Not yet scored |\n| Package | fastify 5.7.3+ |\n| Description | Security fix related to Reply.send() string serialization. |\n| Are we affected? | Fastify is a devDependency used as a server adapter (src/lib/server/). Low production risk, but important to patch. |\n| Fix | Upgrade fastify to 5.7.4. |","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Dev Dependency Security Fix: Fastify CVE-2026-25224","lvl3":""}}, {"objectID":"6b7a7d09c10efcf4648ab13bd1d1a20d96306996481b8829b14ec891b3bd557c","title":"Already Patched (in current baseline versions)","url":"/docs/dependency-upgrade-report#already-patched-in-current-baseline-versions","content":"| CVE | Severity | Package | Description | Status |\n| -------------- | --------------- | ------- | -------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- |\n| CVE-2025-48985 | Low | AI SDK | Input validation bypass allowing URL-to-data substitution in multimodal prompts | Fixed in versions prior to our baseline (6.0.101+). Not a concern. |\n| CVE-2026-22036 | Low (CVSS 3.7) | undici | Unbounded decompression chain in HTTP Content-Encoding causing CPU/memory exhaustion | Fixed in 7.18.2 (our current minimum). Not a concern. |\n| CVE-2026-27700 | High (CVSS 8.2) | hono | Authentication bypass by IP spoofing in AWS Lambda ALB conninfo | Fixed in 4.12.2 (our current version). 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Single import. |\n| Risk level | Low |\n| Recommendation | Upgrade alongside OpenTelemetry packages. |","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"4.24 @langfuse/otel (4.6.1 -> latest)","lvl3":""}}, {"objectID":"87e3bab4b51024aee013224ec7f8d953d6833c4752dd0dcb5e112fd636833c27","title":"4.25 @ai-sdk/provider (3.0.8 -- already current)","url":"/docs/dependency-upgrade-report#425-ai-sdkprovider-308----already-current","content":"| Field | Details |\n| ------------------- | ------------------------------------------------------------------------------------ |\n| What changed | N/A -- already at latest. |\n| Codebase impact | src/lib/types/evaluationTypes.ts -- LanguageModelV3CallOptions type-only import. |\n| Risk level | None |\n| Recommendation | No action needed. |","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"4.25 @ai-sdk/provider (3.0.8 -- already current)","lvl3":""}}, {"objectID":"f5d8cc7e34fc0d17db699bb90eeef6a786bc107fd19bc666a380a7fb62ccde43","title":"5. 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Resolves existing user-facing failures. |\n| Download size limit (DoS prevention) | @ai-sdk/provider-utils | 4.0.15 | 2 GiB default size limit prevents memory exhaustion from malicious URLs. Security hardening for production. |\n| gpt-5.3-codex phase parameter | @ai-sdk/openai | 3.0.34 | Required for correct gpt-5.3-codex behavior. Dropping phase causes performance degradation. |\n| Google server-side MCP | @google/genai | 1.43.0 | Pass MCP server configs directly to Google API instead of client-side tool handling. Aligns with NeuroLink's MCP architecture. |\n| Anthropic code execution tool | @ai-sdk/anthropic | 3.0.48 | Sandbox code execution. Can be exposed as a built-in tool option for Anthropic users. |","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"High Value","lvl3":""}}, {"objectID":"72c71ef018c34f748a61bf4f67ef551c4ed39d6a720f0679555e79ed3c1a9146","title":"Medium Value","url":"/docs/dependency-upgrade-report#medium-value","content":"| Feature | Package | Version | Description |\n| ------------------------------------ | ------------------------------------- | --------------- | ----------------------------------------------------------------------------------------------- |\n| Gemini 3.1 models | @ai-sdk/google, @google/genai | 3.0.32+, 1.43.0 | gemini-3.1-flash-image-preview and gemini-3.1-pro-preview models. Add to model definitions. |\n| Image Grounding for GoogleSearch | @google/genai | 1.43.0 | Multimodal search capabilities via GoogleSearch tool. |\n| GenAI telemetry conventions | @opentelemetry/semantic-conventions | 1.40.0 | Cache token attributes, MCP protocol attributes, tool call support for richer observability. |\n| Proxy/fetch improvements | undici | 7.19-7.22 | 401 loop fix, proxy agent enhancements, HTTP/2 flow-control, stack trace preservation. |","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Medium Value","lvl3":""}}, {"objectID":"fbd9e897639bfaeaa3929c5f0f6abeec16686124085a2055679cb57fceb21a33","title":"Lower Value (Future)","url":"/docs/dependency-upgrade-report#lower-value-future","content":"| Feature | Package | Version | Description |\n| ------------------------------ | ------------------ | ------- | -------------------------------------------------------------------------- |\n| Inferred type predicates | typescript | 5.5+ | .filter() calls now properly narrow types. Catches bugs. |\n| NoInfer | typescript | 5.4+ | Useful in factory/registry generics. |\n| using/await using | typescript | 5.2+ | Explicit resource management for MCP connections, Redis, etc. |\n| import defer | typescript | 5.9 | Deferred module evaluation aligns with NeuroLink's dynamic import pattern. |\n| Experimental video support | @ai-sdk/provider | 3.0.7+ | generateVideo support in provider interface (experimental). |","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Lower Value (Future)","lvl3":""}}, {"objectID":"83910f8b3822b09ace1c7e59f3f541a7404f218c4b6e638426cf4705260c6e02","title":"Phase 1: Zero-Risk Patches (Do Immediately)","url":"/docs/dependency-upgrade-report#phase-1-zero-risk-patches-do-immediately","content":"Estimated effort: 30 minutes\nStrategy: Batch update, run tests once\n\n| Package | From | To | Reason |\n| ----------------------- | ------- | ------- | -------------------------------- |\n| @ai-sdk/openai | 3.0.34 | 3.0.36 | Fixes Azure streaming failures |\n| @ai-sdk/azure | 3.0.35 | 3.0.37 | Dependency alignment |\n| @ai-sdk/anthropic | 3.0.47 | 3.0.48 | Code execution tool |\n| @ai-sdk/google | 3.0.31 | 3.0.33 | Image model support |\n| @ai-sdk/google-vertex | 4.0.63 | 4.0.66 | Dependency alignment |\n| @ai-sdk/mistral | 3.0.12 | 3.0.20 | Security fix |\n| ai | 6.0.101 | 6.0.103 | Bug fix for duplicate tool parts |\n| nanoid | 5.1.5 | 5.1.6 | Trivial patch |\n| hono | 4.12.2 | 4.12.3 | Memory leak fix |\n\nVerification:","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Phase 1: Zero-Risk Patches (Do Immediately)","lvl3":""}}, {"objectID":"f7953b7ec0827fbbbd3e3ec06b183250184902866389a80a81d658076ef6c1c0","title":"Phase 2: Low-Risk Upgrades (Batch Together)","url":"/docs/dependency-upgrade-report#phase-2-low-risk-upgrades-batch-together","content":"Estimated effort: 1 hour\nStrategy: Batch update by group, run targeted tests\n\nGroup A -- AWS SDK (upgrade together):\n\n| Package | From | To |\n| ----------------------------------- | ------- | ------- |\n| @aws-sdk/client-bedrock | 3.998.0 | 3.999.0 |\n| @aws-sdk/client-bedrock-runtime | 3.998.0 | 3.999.0 |\n| @aws-sdk/client-sagemaker | 3.998.0 | 3.999.0 |\n| @aws-sdk/client-sagemaker-runtime | 3.998.0 | 3.999.0 |\n\nGroup B -- Core libs & Google:\n\n| Package | From | To |\n| ------------------------------------- | -------- | ------ |\n| @google/genai | 1.42.0 | 1.43.0 |\n| undici | >=7.18.2 | 7.22.0 |\n| @opentelemetry/semantic-conventions | 1.39.0 | 1.40.0 |\n\nGroup C -- Dev dependencies:\n\n| Package | From | To |\n| ----------------------- | ------ | ------ |\n| @semantic-release/npm | 13.1.2 | 13.1.4 |\n| @sveltejs/kit | 2.53.2 | 2.53.3 |\n| @types/node | 25.3.1 | 25.3.2 |\n| fastify | 5.7.2 | 5.7.4 |\n| svelte-check | 4.4.3 | 4.4.4 |\n| @langfuse/otel | 4.6.1 | latest |\n\nVerification:","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Phase 2: Low-Risk Upgrades (Batch Together)","lvl3":""}}, {"objectID":"1c30b0a4b1f10e5177b776e9f91465ce9c224519d2821fef944328e8bbc354c9","title":"Phase 3: Medium-Risk Upgrades (Test Carefully)","url":"/docs/dependency-upgrade-report#phase-3-medium-risk-upgrades-test-carefully","content":"Estimated effort: 2 hours\nStrategy: Upgrade one at a time, test after each\n\n| Package | From | To | Key Concern |\n| ------- | ----- | ----- | ---------------------------------------------------------------------------------------------------- |\n| tslib | 2.4.1 | 2.8.1 | Decorator hook order, exports field changes. Likely unused at runtime (importHelpers not enabled). |\n\nVerification:","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Phase 3: Medium-Risk Upgrades (Test Carefully)","lvl3":""}}, {"objectID":"364ecab936b388f80e5711b41a077e9b65e48f9348a06465e07fe7200c777026","title":"Phase 4: High-Risk Upgrades (Dedicated Effort)","url":"/docs/dependency-upgrade-report#phase-4-high-risk-upgrades-dedicated-effort","content":"Estimated effort: 1-2 days\nStrategy: Separate branch, dedicated type error resolution\n\n| Package | From | To | Key Concern |\n| ------------ | ----- | ----- | -------------------------------------------------------------------------------- |\n| typescript | 5.0.0 | 5.9.3 | 10 minor versions with cumulative stricter checks. Will surface new type errors. |\n\nMigration steps:\nCreate a dedicated branch: chore/typescript-5.9-upgrade\nUpdate typescript to 5.9.3 in package.json\nRun pnpm install\nRun tsc --noEmit and catalog all new errors\nFix errors in order of severity (type errors first, then new warnings)\nPay special attention to:\nAlways-truthy/nullish checks (TS 5.6) -- likely the most common new errors\nUninitialized variable checks (TS 5.7)\nStricter conditional return types (TS 5.8)\nGeneric constraint null checks (TS 5.9)\nArrayBuffer/Buffer relationship changes (TS 5.9)\nRun full test suite: pnpm test\nRun full type check: pnpm run check\nRun full build: pnpm run build:complete\nConsider using --noCheck flag (TS 5.6) as a temporary escape hatch if needed during migration","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Phase 4: High-Risk Upgrades (Dedicated Effort)","lvl3":""}}, {"objectID":"ddfea8c41319fdf2128b7963a9a59a1cd398147aa38f0773ec1de345eb0d8fb0","title":"Testing Strategy","url":"/docs/dependency-upgrade-report#testing-strategy","content":"| Phase | Test Coverage | Commands |\n| ------- | ---------------------------------------------------- | --------------------------------------------------------------------------------- |\n| Phase 1 | Provider unit tests + full test suite | pnpm test && pnpm run test:providers |\n| Phase 2 | Full test suite + CLI + integration + build | pnpm test && pnpm run test:cli && pnpm run test:integration && pnpm run build |\n| Phase 3 | Full test suite + type check + complete build | pnpm test && pnpm run check && pnpm run build:complete |\n| Phase 4 | Type check (first), then full suite + complete build | pnpm run check && pnpm test && pnpm run build:complete && pnpm run validate:all |","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Testing Strategy","lvl3":""}}, {"objectID":"b75f7eec07c594f765594d1110b82cfefbf4183631d806d0676265611fe110d7","title":"Rollback Plan","url":"/docs/dependency-upgrade-report#rollback-plan","content":"Each phase should be committed separately so rollback is straightforward:\nPhase 1-3 rollback: Revert the commit, run pnpm install. These are all backward-compatible changes, so reverting is clean.\nPhase 4 rollback (TypeScript): Since TypeScript 5.9 upgrade involves source code changes (fixing new type errors), keep the upgrade on a separate branch. 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Research sources: npm registry, GitHub changelogs, CVE databases, and codebase static analysis.","hierarchy":{"lvl0":"Dependency Upgrade Report","lvl1":"NeuroLink Dependency Upgrade Report","lvl2":"Post-Upgrade Validation","lvl3":""}}, {"objectID":"b64b317dc25a8fbe5203df9db09002d7a191a7378ea03dabeeed02e781cc234a","title":"🏗️ Enterprise Configuration Management Guide","url":"/docs/deployment/configuration-management","content":"🏗️ Enterprise Configuration Management Guide\n\nNeuroLink Configuration System v3.0 - Complete guide to enterprise configuration management with automatic backup/restore, validation, and error recovery.\n\n🎯 Overview\n\nNeuroLink's enterprise configuration system provides:\n✅ Automatic Backup System - Timestamped backups before every config change\n✅ Config Validation - Comprehensive validation with suggestions and warnings\n✅ Error Recovery - Auto-restore on config update failures\n✅ Provider Management - Real-time provider availability monitoring\n✅ Hash Verification - SHA-256 integrity checking for all operations\n✅ Cleanup Utilities - Configurable backup retention and cleanup\n\n🚀 Quick Start\n\nBasic Configuration Setup\n\nEnvironment Configuration\n\n📋 Configuration Structure\n\nNeuroLinkConfig Interface\n\nProvider Configuration\n\nPerformance Configuration\n\n🔄 Automatic Backup System\n\nHow It Works\nBefore Update: Config manager creates timestamped backup\nUpdate Attempt: Apply new configuration\nValidation: Validate new configuration\nSuccess/Failure: Keep new config or auto-restore from backup\n\nBackup File Structure\n\nBackup Metadata\n\nManual Backup Operations\n\n✅ Configuration Validation\n\nValidation Process\nSchema Validation: Check against TypeScript interfaces\nProvider Validation: Verify provider configurations\nDependency Validation: Check inter-config dependencies\nPerformance Validation: Validate performance settings\nSecurity Validation: Check for security issues\n\nValidation Examples\n\nCommon Validation Errors\n\n🛠️ Advanced Configuration\n\nUpdate Strategies\n\nCustom Validation Rules\n\nEvent Handlers\n\n🚨 Error Recovery\n\nAuto-Restore Process\nDetection: Config update fails validation or causes errors\nIdentification: Find most recent valid backup\nRestoration: Restore config from backup\nVerification: Validate restored config\nNotification: Log recovery action\n\nManual Recovery\n\nRecovery Scenarios\nCorrupted Config: Auto-restore from last known good backup\nInvalid Provider: Disable problematic provider, restore working config\nPerformance Issues: Restore previous performance settings\nValidation Failures: Rollback to validated configuration\n\n🧹 Cleanup & Maintenance\n\nAutomatic Cleanup\n\nManual Cleanup\n\n🔍 Monitoring & Diagnostics\n\nConfig Status\n\nProvider Health Monitoring\n\nPerformance Metrics\n\n🚀 Best Practices\n\nConfiguration Management\nAlways Validate: Enable validation before updates\nUse Backups: Keep automatic backups enabled\nMonitor Health: Regular provider health checks\nVersion Control: Consider versioning config files\nEnvironment Separation: Different configs for dev/prod\n\nPerformance Optimization\nCache Settings: Enable caching for frequently used configs\nTimeout Tuning: Set appropriate timeouts for your use case\nProvider Selection: Use fastest available providers\nCleanup Schedule: Regular backup cleanup\n\nSecurity Considerations\nAPI Key Management: Store API keys securely\nBackup Encryption: Consider encrypting sensitive backups\nAccess Control: Limit config update permissions\nAudit Logging: Log all config changes\n\n🆘 Troubleshooting\n\nCommon Issues\n\nConfig Update Fails\n\nBackup System Issues\n\nProvider Configuration Issues\n\nSupport & Resources\nDocumentation: See API Reference for interface details\nMigration Guide: See docs/INTERFACE-MIGRATION-GUIDE.md\nTroubleshooting: See docs/TROUBLESHOOTING.md\nGitHub Issues: Report bugs and feature requests\n\n🎯 Enterprise configuration management provides robust, reliable, and maintainable configuration handling for production NeuroLink deployments.","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"","lvl3":""}}, {"objectID":"c069bb7ea6034bc1cd4547c4aca8a8936d29eb90aa13ec1d501458836c3680ab","title":"🏗️ Enterprise Configuration Management Guide","url":"/docs/deployment/configuration-management#-enterprise-configuration-management-guide","content":"NeuroLink Configuration System v3.0 - Complete guide to enterprise configuration management with automatic backup/restore, validation, and error recovery.","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"🏗️ Enterprise Configuration Management Guide","lvl3":""}}, {"objectID":"c209e41bb1083604689699d778864910ce85538e2106cfca06a686a64174a955","title":"🎯 Overview","url":"/docs/deployment/configuration-management#-overview","content":"NeuroLink's enterprise configuration system provides:\n✅ Automatic Backup System - Timestamped backups before every config change\n✅ Config Validation - Comprehensive validation with suggestions and warnings\n✅ Error Recovery - Auto-restore on config update failures\n✅ Provider Management - Real-time provider availability monitoring\n✅ Hash Verification - SHA-256 integrity checking for all operations\n✅ Cleanup Utilities - Configurable backup retention and cleanup","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"🎯 Overview","lvl3":""}}, {"objectID":"37b6bc9eabad4e23af1b57d6c362838c278bb1123742325cb6830964950d1e0d","title":"Environment Configuration","url":"/docs/deployment/configuration-management#environment-configuration","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Environment Configuration","lvl3":""}}, {"objectID":"9d25e2cff1206b49cd39485d6681dcb02de4b1be9e4520d80710480c81782dd8","title":"Enable automatic backups","url":"/docs/deployment/configuration-management#enable-automatic-backups","content":"NEUROLINKBACKUPENABLED=true\nNEUROLINKBACKUPRETENTION=30\nNEUROLINKBACKUPDIRECTORY=.neurolink.backups","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Enable automatic backups","lvl3":""}}, {"objectID":"12c993e8a1548b2bbe1f100f34aa6280d6c9d22e0f739e6325651d9e9b04b069","title":"Validation settings","url":"/docs/deployment/configuration-management#validation-settings","content":"NEUROLINKVALIDATIONSTRICT=false\nNEUROLINKVALIDATIONWARNINGS=true","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Validation settings","lvl3":""}}, {"objectID":"388260fed729cd1114531b45401acb7d457cafb9103a49dffcb48732deace7fc","title":"Provider monitoring","url":"/docs/deployment/configuration-management#provider-monitoring","content":"NEUROLINKPROVIDERSTATUS_CHECK=true\nNEUROLINKPROVIDERTIMEOUT=30000\n`","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Provider monitoring","lvl3":""}}, {"objectID":"1a0d49f41f9494afba33e6fd7d480aae9648be1af16cacfac334db0a32eb2a7f","title":"How It Works","url":"/docs/deployment/configuration-management#how-it-works","content":"Before Update: Config manager creates timestamped backup\nUpdate Attempt: Apply new configuration\nValidation: Validate new configuration\nSuccess/Failure: Keep new config or auto-restore from backup","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"How It Works","lvl3":""}}, {"objectID":"f7d9dde985a9f10595fcb670bc2c2dbb5e2ba7a58de36c5b1d4dfe084dc9c12b","title":"Validation Process","url":"/docs/deployment/configuration-management#validation-process","content":"Schema Validation: 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backups\nAccess Control: Limit config update permissions\nAudit Logging: Log all config changes","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Security Considerations","lvl3":""}}, {"objectID":"e9a415ada65134043a5580ea06952a70e9ad8c30c1eb37735c4cfd4632d2e182","title":"Common Issues","url":"/docs/deployment/configuration-management#common-issues","content":"Config Update Fails\n\n`bash","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Common Issues","lvl3":""}}, {"objectID":"35ace77a590d457f9d0cbc46cb260613ba5816e190b19d7101912dd20064f614","title":"Check config validation","url":"/docs/deployment/configuration-management#check-config-validation","content":"npx @juspay/neurolink config validate","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Check config validation","lvl3":""}}, {"objectID":"e3577f2f9f68a1e748c818930e702d1826dcb5621e409a1eb3909a3ff48c8a38","title":"Check provider status","url":"/docs/deployment/configuration-management#check-provider-status","content":"npx @juspay/neurolink status","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Check provider status","lvl3":""}}, {"objectID":"6f37cf05f9612c4ced2d15c2e6e0894a329f847404d094ade129816a30d11b63","title":"Restore from backup","url":"/docs/deployment/configuration-management#restore-from-backup","content":"npx @juspay/neurolink config restore --backup latest\nbash","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Restore from backup","lvl3":""}}, {"objectID":"13f0843a0209147056c38af673139539e4cdb48d3f5c2db43dbe2db41d05c63f","title":"Verify backup directory","url":"/docs/deployment/configuration-management#verify-backup-directory","content":"ls -la .neurolink.backups/","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Verify backup directory","lvl3":""}}, {"objectID":"a14a3b80120d5d9a08999e294764f93dccf14da7698d1f9e8d2d6daa68057c21","title":"Check backup integrity","url":"/docs/deployment/configuration-management#check-backup-integrity","content":"npx @juspay/neurolink config verify-backups","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Check backup integrity","lvl3":""}}, {"objectID":"2360f3c5bca2380942d36fdeebc139b9d64b5be976d48e362f16dd16765c8707","title":"Manual cleanup","url":"/docs/deployment/configuration-management#manual-cleanup-1","content":"npx @juspay/neurolink config cleanup --older-than 30\nbash","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Manual cleanup","lvl3":""}}, {"objectID":"ae893e790bbfd322a2dd44a51e26d6b9e92a25611ecb96c2d87e52e7d9ed33a2","title":"Test provider connection","url":"/docs/deployment/configuration-management#test-provider-connection","content":"npx @juspay/neurolink test-provider google","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Test provider connection","lvl3":""}}, {"objectID":"0ba5fa64c234745960f4ef6320db9769b0a1639e202cf25833aebc4e0a65ffcd","title":"Reset provider config","url":"/docs/deployment/configuration-management#reset-provider-config","content":"npx @juspay/neurolink config reset-provider google","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Reset provider config","lvl3":""}}, {"objectID":"42ec88af810424f7d8dca820d02d458dc778495eebba8cc3b280242a24735698","title":"Check environment variables","url":"/docs/deployment/configuration-management#check-environment-variables","content":"npx @juspay/neurolink env check\n`","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Check environment variables","lvl3":""}}, {"objectID":"516e640b602eed2c5d81f3a90cf6260fcff12fc78d397d9ad3bd5215568b9e67","title":"Support & Resources","url":"/docs/deployment/configuration-management#support-resources","content":"Documentation: See API Reference for interface details\nMigration Guide: See docs/INTERFACE-MIGRATION-GUIDE.md\nTroubleshooting: See docs/TROUBLESHOOTING.md\nGitHub Issues: Report bugs and feature requests\n\n🎯 Enterprise configuration management provides robust, reliable, and maintainable configuration handling for production NeuroLink deployments.","hierarchy":{"lvl0":"Deployment","lvl1":"🏗️ Enterprise Configuration Management Guide","lvl2":"Support & Resources","lvl3":""}}, {"objectID":"cc93459ad2879898737dd1c30f20e9f15cea76c2895e538aee06286e2920af62","title":"⚙️ NeuroLink Configuration Guide","url":"/docs/deployment/configuration","content":"⚙️ NeuroLink Configuration Guide\n\n📖 Overview\n\nThis guide covers all configuration options for NeuroLink, including AI provider setup, dynamic model configuration, MCP integration, and environment configuration.\n\nBasic Usage Examples\n\n🤖 AI Provider Configuration\n\nEnvironment Variables\n\nNeuroLink supports multiple AI providers. Set up one or more API keys:\n\n.env File Configuration\n\nCreate a .env file in your project root:\n\nProvider Selection Priority\n\nNeuroLink automatically selects the best available provider:\nGoogle AI Studio (if GOOGLE_AI_API_KEY is set)\nOpenAI (if OPENAI_API_KEY is set)\nAnthropic (if ANTHROPIC_API_KEY is set)\nOther providers in order of availability\n\nForce specific provider:\n\n🎯 Dynamic Model Configuration (v1.8.0+)\n\nOverview\n\nThe dynamic model system enables intelligent model selection, cost optimization, and runtime model configuration without code changes.\n\nEnvironment Variables\n\nModel Configuration Server\n\nStart the model configuration server to enable dynamic model features:\n\nModel Configuration File\n\nCreate or modify config/models.json to define available models:\n\nDynamic Model Usage\n\nCLI Usage\n\nSDK Usage\n\nBenefits\n✅ Runtime Updates: Add new models without code deployment\n✅ Smart Selection: Automatic model selection based on capabilities\n✅ Cost Optimization: Choose models based on price constraints\n✅ Easy Aliases: Use friendly names like \"claude-latest\", \"fastest\"\n✅ Provider Agnostic: Unified interface across all AI providers\n\n🛠️ MCP Configuration\n\nBuilt-in Tools Configuration\n\nBuilt-in tools are automatically available:\n\nTest built-in tools:\n\nExternal MCP Server Configuration\n\nExternal servers are auto-discovered from all major AI tools:\n\nAuto-Discovery Locations\n\nmacOS:\n\nLinux:\n\nWindows:\n\nManual MCP Configuration\n\nCreate .mcp-config.json in your project root:\n\nHTTP Transport Configuration\n\nFor remote MCP servers, use HTTP transport with authentication, retry, and rate limiting:\n\nHTTP Transport Options:\n\n| Option | Type | Description |\n| -------------- | -------- | --------------------------------------- |\n| transport | \"http\" | Transport type for remote servers |\n| url | string | URL of the remote MCP endpoint |\n| headers | object | HTTP headers for authentication |\n| httpOptions | object | Connection and timeout settings |\n| retryConfig | object | Retry behavior with exponential backoff |\n| rateLimiting | object | Rate limiting configuration |\n\nSee MCP HTTP Transport Guide for complete documentation.\n\nMCP Discovery Commands\n\n🖥️ CLI Configuration\n\nGlobal CLI Options\n\nCommand-line Options\n\n📊 Development Configuration\n\nTypeScript Configuration\n\nFor TypeScript projects, add to your tsconfig.json:\n\nPackage.json Scripts\n\nAdd useful scripts to your package.json:\n\nEnvironment Setup Script\n\nCreate setup-neurolink.sh:\n\nContext Compaction Configuration\n\nOverview\n\nContext compaction automatically manages conversation history to keep it within a model's context window. When the estimated input tokens exceed a configurable threshold (default: 80% of available input space), a multi-stage reduction pipeline runs before the next LLM call. The four stages, in order, are:\nTool Output Pruning -- Replace old, large tool results with compact placeholders (no LLM call)\nFile Read Deduplication -- Keep only the latest read of each file path (no LLM call)\nLLM Summarization -- Produce a structured summary of older messages (requires LLM call)\nSliding Window Truncation -- Tag the oldest messages as truncated (no LLM call)\n\nEach stage only runs if the previous stage did not bring token usage below the target. The pipeline exits early once the context fits.\n\nSDK Configuration\n\nConfigure context compaction through the contextCompaction field inside conversationMemory:\n\nField Reference:\n\n| Field | Type | Default | Description |\n| ----------------------- | --------- | ----------------------------------- | ----------------------------------------------- |\n| enabled | boolean | true (when summarization enabled) | Master switch for auto-compaction |\n| threshold | number | 0.80 | Usage ratio that triggers compaction (0.0--1.0) |\n| enablePruning | boolean | true | Enable Stage 1: tool output pruning |\n| enableDeduplication | boolean | true | Enable Stage 2: file read deduplication |\n| enableSlidingWindow | boolean | true | Enable Stage 4: sliding window truncation |\n| maxToolOutputBytes | number | 51200 | Tool output byte limit (50 KB) |\n| maxToolOutputLines | number | 2000 | Tool output line limit |\n| fileReadBudgetPercent | numb","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"","lvl3":""}}, {"objectID":"90cfbab239ac3c2ab9a05635a2ea40e37ba74b88b1bd0fbeb25d96baf8850f03","title":"📖 Overview","url":"/docs/deployment/configuration#-overview","content":"This guide covers all configuration options for NeuroLink, including AI provider setup, dynamic model configuration, MCP integration, and environment configuration.","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"📖 Overview","lvl3":""}}, {"objectID":"b37745bbe379147c780ebbf4db0f8aff5f153210084fbf50cfe97719fda1a26c","title":"Environment Variables","url":"/docs/deployment/configuration#environment-variables","content":"NeuroLink supports multiple AI providers. Set up one or more API keys:\n\n`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Environment Variables","lvl3":""}}, {"objectID":"b6f6f9fcc0ff5907cb70ecfa74281216a9708c573be8b70f67675893cc67396e","title":"Mistral AI","url":"/docs/deployment/configuration#mistral-ai","content":"`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Mistral AI","lvl3":""}}, {"objectID":"bcd8275501459a794f25b1c6ab1c6d7877348a8373644d1e5bae5932b53ec5fd","title":".env File Configuration","url":"/docs/deployment/configuration#env-file-configuration","content":"Create a .env file in your project root:\n\n`env","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":".env File Configuration","lvl3":""}}, {"objectID":"6327fb76fa593d1361543ac033b26592d874bdd5f9b73d775134699d87831f4e","title":".env file - automatically loaded by NeuroLink","url":"/docs/deployment/configuration#env-file---automatically-loaded-by-neurolink","content":"GOOGLEAIAPI_KEY=AIza-your-google-ai-api-key\nOPENAIAPIKEY=sk-your-openai-api-key\nANTHROPICAPIKEY=sk-ant-your-anthropic-api-key","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":".env file - automatically loaded by NeuroLink","lvl3":""}}, {"objectID":"eec584edcd0eaf8f52d1f5799bfb6b539e81608771435050f79ab451d9e00511","title":"Optional: Provider preferences","url":"/docs/deployment/configuration#optional-provider-preferences","content":"NEUROLINKPREFERREDPROVIDER=google-ai\nNEUROLINK_DEBUG=false\n`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Optional: Provider preferences","lvl3":""}}, {"objectID":"020831ddd482e6e2160a13d160bfbce7c83b5563d5f1c71dbc16bdd7ebc1a8f7","title":"Provider Selection Priority","url":"/docs/deployment/configuration#provider-selection-priority","content":"NeuroLink automatically selects the best available provider:\nGoogle AI Studio (if GOOGLE_AI_API_KEY is set)\nOpenAI (if OPENAI_API_KEY is set)\nAnthropic (if ANTHROPIC_API_KEY is set)\nOther providers in order of availability\n\nForce specific provider:\n\n`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Provider Selection Priority","lvl3":""}}, {"objectID":"ff347e75bf498c4b05acbf7cde79062fad74de03e4f46c334fb8ec69b76b28df","title":"CLI","url":"/docs/deployment/configuration#cli","content":"npx neurolink generate \"Hello\" --provider openai\ntypescript\n// SDK\n\nconst neurolink = new NeuroLink();\nconst result = await neurolink.generate({\n input: { text: \"Hello\" },\n provider: \"openai\",\n});\n`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"CLI","lvl3":""}}, {"objectID":"c23423410d129a6ab548f1d2d4767a26dc479c914f7681dffbff6dc8137389b7","title":"Overview","url":"/docs/deployment/configuration#overview","content":"The dynamic model system enables intelligent model selection, cost optimization, and runtime model configuration without code changes.","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Overview","lvl3":""}}, {"objectID":"55a9aceacdf47c74f09e673238b7320c71523d9ba4906591129650d32baddec2","title":"Environment Variables","url":"/docs/deployment/configuration#environment-variables-1","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Environment Variables","lvl3":""}}, {"objectID":"b9305d31470cb0dc65d48fab0acafd0f5e40d64993a3122108908078251d77fa","title":"Dynamic Model System Configuration","url":"/docs/deployment/configuration#dynamic-model-system-configuration","content":"`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Dynamic Model System Configuration","lvl3":""}}, {"objectID":"5e07e5ecfb8ba8359d9b408d97c0de96ba87f6849b093521725a215a737f8662","title":"Model Configuration Server","url":"/docs/deployment/configuration#model-configuration-server","content":"Start the model configuration server to enable dynamic model features:\n\n`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Model Configuration Server","lvl3":""}}, {"objectID":"21149dfa4980b5007ed8d736d8cd94e8256f985b4c0c94f5c65c4bfaf4f14641","title":"Start the model server (provides REST API for model configs)","url":"/docs/deployment/configuration#start-the-model-server-provides-rest-api-for-model-configs","content":"npm run start:model-server","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Start the model server (provides REST API for model configs)","lvl3":""}}, {"objectID":"07c3d6056213f7a9d094d5cf10dad42b1289f739eb3a7bc631a468905c5de228","title":"GET /models/resolve/claude-latest - Resolve aliases","url":"/docs/deployment/configuration#get-modelsresolveclaude-latest---resolve-aliases","content":"`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"GET /models/resolve/claude-latest - Resolve aliases","lvl3":""}}, {"objectID":"bb4b5ae7f202cadfebe491f814f2213703d21709a8c564bc7885fe5f8e702deb","title":"Model Configuration File","url":"/docs/deployment/configuration#model-configuration-file","content":"Create or modify config/models.json to define available models:","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Model Configuration File","lvl3":""}}, {"objectID":"ae6030bf41017c00c4bf1fdab216436c46047bcb7907f086c0f7855f9ebe96c0","title":"CLI Usage","url":"/docs/deployment/configuration#cli-usage","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"CLI Usage","lvl3":""}}, {"objectID":"61e4d6507330c197f8b526672198b87a9cfa3b9b3b6a98a3c570fceba9adb9c2","title":"Use model aliases for convenience","url":"/docs/deployment/configuration#use-model-aliases-for-convenience","content":"npx neurolink generate \"Write code\" --model best-coding","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Use model aliases for convenience","lvl3":""}}, {"objectID":"199051adb7c5028ca71f217a12f8a2359df2b01fc96534f83dffa24c489a0561","title":"Capability-based selection","url":"/docs/deployment/configuration#capability-based-selection","content":"npx neurolink generate \"Describe image\" --capability vision --optimize-cost","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Capability-based selection","lvl3":""}}, {"objectID":"04ca57b2052d38d67b4d1b40fa8fb2057304ab4f159f948b2bada82ae3857590","title":"Search and discover models","url":"/docs/deployment/configuration#search-and-discover-models","content":"npx neurolink models search --capability functionCalling --max-price 0.001\nnpx neurolink models list\nnpx neurolink models best --use-case coding\n`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Search and discover models","lvl3":""}}, {"objectID":"122b28f67e2f9ae441615b32c4851aecbd907d5a76959f00923ea75d86a924fd","title":"Benefits","url":"/docs/deployment/configuration#benefits","content":"✅ Runtime Updates: Add new models without code deployment\n✅ Smart Selection: Automatic model selection based on capabilities\n✅ Cost Optimization: Choose models based on price constraints\n✅ Easy Aliases: Use friendly names like \"claude-latest\", \"fastest\"\n✅ Provider Agnostic: Unified interface across all AI providers","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Benefits","lvl3":""}}, {"objectID":"087c77024205f61c94a0a1b41e99b294bab5e09d35cebf7ca16627fa4aef8e42","title":"Built-in Tools Configuration","url":"/docs/deployment/configuration#built-in-tools-configuration","content":"Built-in tools are automatically available:\n\nTest built-in tools:\n\n`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Built-in Tools Configuration","lvl3":""}}, {"objectID":"5db35b4704a5af7f170024860353c84c547b3329f38651621158f85014cd9928","title":"Built-in tools work immediately","url":"/docs/deployment/configuration#built-in-tools-work-immediately","content":"npx neurolink generate \"What time is it?\" --debug\n`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Built-in tools work immediately","lvl3":""}}, {"objectID":"da21f4f3c06e8a8bea102200086ffc6ff163829525522f9dd1a59909fc4f5da5","title":"External MCP Server Configuration","url":"/docs/deployment/configuration#external-mcp-server-configuration","content":"External servers are auto-discovered from all major AI tools:","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"External MCP Server Configuration","lvl3":""}}, {"objectID":"421585b2783af60c4f06503771128e2041a80ed004f94c7010b310e2e33da0f0","title":"Auto-Discovery Locations","url":"/docs/deployment/configuration#auto-discovery-locations","content":"macOS:\n\nLinux:\n\nWindows:","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Auto-Discovery Locations","lvl3":""}}, {"objectID":"4f3054614379dd3e958bab99e580b0bbf5e3f5e47a65e4e90a2026f261010352","title":"Manual MCP Configuration","url":"/docs/deployment/configuration#manual-mcp-configuration","content":"Create .mcp-config.json in your project root:","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Manual MCP Configuration","lvl3":""}}, {"objectID":"566284bd138b1bc09e1024c4edc84df965d34d5dc5e793e8cda281bdc8d77fc8","title":"HTTP Transport Configuration","url":"/docs/deployment/configuration#http-transport-configuration","content":"For remote MCP servers, use HTTP transport with authentication, retry, and rate limiting:\n\nHTTP Transport Options:\n\n| Option | Type | Description |\n| -------------- | -------- | --------------------------------------- |\n| transport | \"http\" | Transport type for remote servers |\n| url | string | URL of the remote MCP endpoint |\n| headers | object | HTTP headers for authentication |\n| httpOptions | object | Connection and timeout settings |\n| retryConfig | object | Retry behavior with exponential backoff |\n| rateLimiting | object | Rate limiting configuration |\n\nSee MCP HTTP Transport Guide for complete documentation.","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"HTTP Transport Configuration","lvl3":""}}, {"objectID":"d45892a46a2ff058618f37f79bc9233458d5d3b6c87f2335abb223331cad30fb","title":"MCP Discovery Commands","url":"/docs/deployment/configuration#mcp-discovery-commands","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"MCP Discovery Commands","lvl3":""}}, {"objectID":"d093abf8af6a259b028ec5e2e9adf8ac30eedfd11ab0c3d1f6ffbb40aa7e136f","title":"Discover all external servers","url":"/docs/deployment/configuration#discover-all-external-servers","content":"npx neurolink mcp discover --format table","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Discover all external servers","lvl3":""}}, {"objectID":"e16101f7b599d1117a9c99a18efd60ea195d6dbb090a3eee04432d41ef0ead78","title":"Export discovery results","url":"/docs/deployment/configuration#export-discovery-results","content":"npx neurolink mcp discover --format json > discovered-servers.json","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Export discovery results","lvl3":""}}, {"objectID":"cbfd0347494aee382d7d56d03fd0bcc0d41a77e3041fd001c38a0ea83fd931bc","title":"Test discovery","url":"/docs/deployment/configuration#test-discovery","content":"npx neurolink mcp discover --format yaml\n`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Test discovery","lvl3":""}}, {"objectID":"93bea1701d5455e737e5db2c631d37c4b084c11ed958455a1b7f80b1b20d135b","title":"Global CLI Options","url":"/docs/deployment/configuration#global-cli-options","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Global CLI Options","lvl3":""}}, {"objectID":"2549dcae2782d90ef15c916cea1739e4871af2c2b4749b93580437e59b99ebfd","title":"Custom timeout","url":"/docs/deployment/configuration#custom-timeout","content":"`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Custom timeout","lvl3":""}}, {"objectID":"a99df626f78a6c53ebe1e9120dfdcd0868340f5a391a40650b6c4c27770ee9a5","title":"Command-line Options","url":"/docs/deployment/configuration#command-line-options","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Command-line Options","lvl3":""}}, {"objectID":"c5a6b9d6cd9cec0917a76437f02b2736ab4cbea6f57be8236a36de51bbb3bce0","title":"Provider selection","url":"/docs/deployment/configuration#provider-selection","content":"npx neurolink generate \"Hello\" --provider openai","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Provider selection","lvl3":""}}, {"objectID":"321623fe48b92d06e5c5e7807172289cb5ccc180efc82664b662b9c16fe882d2","title":"Debug output","url":"/docs/deployment/configuration#debug-output","content":"npx neurolink generate \"Hello\" --debug","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Debug output","lvl3":""}}, {"objectID":"41bf4f1efa0c8db3c77c4ca4a3f45f47410fff8339df279e06c39cbd9119f131","title":"Temperature control","url":"/docs/deployment/configuration#temperature-control","content":"npx neurolink generate \"Hello\" --temperature 0.7","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Temperature control","lvl3":""}}, {"objectID":"05b28272e4c1ca00b15831031310e1e32a63ac7bd93079d18c8c5cdb650a6c6e","title":"Token limits","url":"/docs/deployment/configuration#token-limits","content":"npx neurolink generate \"Hello\" --max-tokens 1000","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Token limits","lvl3":""}}, {"objectID":"d2aaab5af54ffb83cfce7d29ef3c9178f1b52bd82c79ffac163824cb85ca322b","title":"Disable tools","url":"/docs/deployment/configuration#disable-tools","content":"npx neurolink generate \"Hello\" --disable-tools\n`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Disable tools","lvl3":""}}, {"objectID":"d454a5a329ac59629bf43b65316b6654d468afeb80083c410e3dfcb2150a2651","title":"TypeScript Configuration","url":"/docs/deployment/configuration#typescript-configuration","content":"For TypeScript projects, add to your tsconfig.json:","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"TypeScript Configuration","lvl3":""}}, {"objectID":"591f1c4acdc3e07c436075ad635e8599aeabc98b48f11992a71bb92faf3e7dfe","title":"Package.json Scripts","url":"/docs/deployment/configuration#packagejson-scripts","content":"Add useful scripts to your package.json:","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Package.json Scripts","lvl3":""}}, {"objectID":"665f6e30fbcaa43db41949cda51a7972e763a7d3bd741a7785815ae5f0c5d1a1","title":"Environment Setup Script","url":"/docs/deployment/configuration#environment-setup-script","content":"Create setup-neurolink.sh:\n\n`bash\n#!/bin/bash\n\necho \"🧠 NeuroLink Environment Setup\"","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Environment Setup Script","lvl3":""}}, {"objectID":"9e5fbd1150c14b7677ba62b01490fe72e80e963ab289ec16abaa99a9bd544f5a","title":"Check Node.js version","url":"/docs/deployment/configuration#check-nodejs-version","content":"if ! command -v node &> /dev/null; then\n echo \"❌ Node.js not found. Please install Node.js v18+\"\n exit 1\nfi\n\nNODE_VERSION=$(node -v | cut -d'v' -f2 | cut -d'.' -f1)\nif [ \"$NODE_VERSION\" -lt 18 ]; then\n echo \"❌ Node.js v18+ required. Current version: $(node -v)\"\n exit 1\nfi","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Check Node.js version","lvl3":""}}, {"objectID":"2202e3ba6982d23be6c29118d9805a8b213032b713ef678faefb26928ec3d7fe","title":"Install NeuroLink","url":"/docs/deployment/configuration#install-neurolink","content":"echo \"📦 Installing NeuroLink...\"\nnpm install @juspay/neurolink","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Install NeuroLink","lvl3":""}}, {"objectID":"3601f4d6aa799b8b04b12a41df39e41c1d7fd0615218e68cc2e9b5b68138f9e2","title":"Create .env template","url":"/docs/deployment/configuration#create-env-template","content":"if [ ! -f .env ]; then\n echo \"📝 Creating .env template...\"\n cat > .env << EOF","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Create .env template","lvl3":""}}, {"objectID":"229d994a15ea790051ca77c45b75ff44b445c9a9ac821745e97dfa54b1b6539e","title":"Google AI Studio (Free tier available)","url":"/docs/deployment/configuration#google-ai-studio-free-tier-available","content":"GOOGLEAIAPI_KEY=AIza-your-google-ai-api-key","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Google AI Studio (Free tier available)","lvl3":""}}, {"objectID":"a6caf694ebb7e47fd56f0c8781eed1476e2f4295ab8ad5bbe214f82590ccf9e4","title":"Optional settings","url":"/docs/deployment/configuration#optional-settings","content":"NEUROLINK_DEBUG=false\nNEUROLINKPREFERREDPROVIDER=google-ai\nEOF\n echo \"✅ Created .env template. Please add your API keys.\"\nelse\n echo \"ℹ️ .env file already exists\"\nfi","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Optional settings","lvl3":""}}, {"objectID":"a76f905804c7fe9489144977b26621140fc86b60b1a1d25e32f5c3ad6805a1f6","title":"Test installation","url":"/docs/deployment/configuration#test-installation","content":"echo \"🧪 Testing installation...\"\nif npx neurolink status > /dev/null 2>&1; then\n echo \"✅ NeuroLink installed successfully\"\n\n # Test MCP discovery\n echo \"🔍 Testing MCP discovery...\"\n SERVERS=$(npx neurolink mcp discover --format json 2>/dev/null | jq '.servers | length' 2>/dev/null || echo \"0\")\n echo \"✅ Discovered $SERVERS external MCP servers\"\n\n echo \"\"\n echo \"🎉 Setup complete! Next steps:\"\n echo \"1. Add your API key to .env file\"\n echo \"2. Test: npx neurolink generate 'Hello'\"\n echo \"3. Test MCP tools: npx neurolink generate 'What time is it?' --debug\"\nelse\n echo \"❌ Installation test failed\"\n exit 1\nfi\n`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Test installation","lvl3":""}}, {"objectID":"aaff851e647abccf53e942db5fcb9e3235dcb60e96ded91fd1643275e5e0d427","title":"Overview","url":"/docs/deployment/configuration#overview-1","content":"Context compaction automatically manages conversation history to keep it within a model's context window. When the estimated input tokens exceed a configurable threshold (default: 80% of available input space), a multi-stage reduction pipeline runs before the next LLM call. The four stages, in order, are:\nTool Output Pruning -- Replace old, large tool results with compact placeholders (no LLM call)\nFile Read Deduplication -- Keep only the latest read of each file path (no LLM call)\nLLM Summarization -- Produce a structured summary of older messages (requires LLM call)\nSliding Window Truncation -- Tag the oldest messages as truncated (no LLM call)\n\nEach stage only runs if the previous stage did not bring token usage below the target. The pipeline exits early once the context fits.","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Overview","lvl3":""}}, {"objectID":"d95f8ac726731749e2cc8fff7da6d198038409ca7f51d0ee13691de505da0d7c","title":"SDK Configuration","url":"/docs/deployment/configuration#sdk-configuration","content":"Configure context compaction through the contextCompaction field inside conversationMemory:\n\nField Reference:\n\n| Field | Type | Default | Description |\n| ----------------------- | --------- | ----------------------------------- | ----------------------------------------------- |\n| enabled | boolean | true (when summarization enabled) | Master switch for auto-compaction |\n| threshold | number | 0.80 | Usage ratio that triggers compaction (0.0--1.0) |\n| enablePruning | boolean | true | Enable Stage 1: tool output pruning |\n| enableDeduplication | boolean | true | Enable Stage 2: file read deduplication |\n| enableSlidingWindow | boolean | true | Enable Stage 4: sliding window truncation |\n| maxToolOutputBytes | number | 51200 | Tool output byte limit (50 KB) |\n| maxToolOutputLines | number | 2000 | Tool output line limit |\n| fileReadBudgetPercent | number | 0.60 | Fraction of remaining context for file reads |\n\nSummarization provider/model are configured at the conversationMemory level:\n\n| Field | Type | Default | Description |\n| ----------------------- | -------- | -------------------- | -------------------------------------- |\n| summarizationProvider | string | \"vertex\" | Provider for Stage 3 LLM summarization |\n| summarizationModel | string | \"gemini-2.5-flash\" | Model for Stage 3 LLM summarization |","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"SDK Configuration","lvl3":""}}, {"objectID":"b1e0fd68047227e1ca0b8c2d7a30a1794e6f32dd4362b427aba72fb6d405110a","title":"CLI Flags","url":"/docs/deployment/configuration#cli-flags","content":"The loop command accepts two context compaction flags:\n\n`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"CLI Flags","lvl3":""}}, {"objectID":"247e3543df58214a8d82ba9e191f89e74f8d8e426efdcc35013bb03db51cfb38","title":"Set compaction threshold (0.0-1.0, default: 0.8)","url":"/docs/deployment/configuration#set-compaction-threshold-00-10-default-08","content":"npx neurolink loop --compact-threshold 0.70","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Set compaction threshold (0.0-1.0, default: 0.8)","lvl3":""}}, {"objectID":"290e892f5248f827e1300e8188f93bb19032698cb21ad25b473b1cf980b40f23","title":"Disable automatic compaction entirely","url":"/docs/deployment/configuration#disable-automatic-compaction-entirely","content":"npx neurolink loop --disable-compaction\n\n\n| Flag | Type | Default | Description |\n| ---------------------- | --------- | ------- | ----------------------------------------------- |\n| --compact-threshold | number | 0.8 | Context compaction trigger threshold (0.0--1.0) |\n| --disable-compaction | boolean | false | Disable automatic context compaction |\n\nThese flags map to contextCompaction.threshold and contextCompaction.enabled` respectively.","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Disable automatic compaction entirely","lvl3":""}}, {"objectID":"de754ed8dccee9b5c93558d7482fc58bf78e3571e27dbc0c3ac8034eb230f7f7","title":"Per-Provider Context Windows","url":"/docs/deployment/configuration#per-provider-context-windows","content":"The budget checker uses per-provider, per-model context window sizes to calculate available input tokens. The available input space is:\n\nWhere outputReserve defaults to 35% of the context window (capped at 64,000 tokens), or the explicit maxTokens value if provided.\n\n| Provider | Model | Input Token Limit |\n| ---------------- | --------------------------------------------------------------------------------- | ----------------- |\n| Anthropic | claude-opus-4, claude-sonnet-4, claude-3.5-sonnet, claude-3-opus (all variants) | 200,000 |\n| OpenAI | gpt-4o, gpt-4o-mini, gpt-4-turbo, o1-mini | 128,000 |\n| OpenAI | o1, o1-pro, o3, o3-mini, o4-mini | 200,000 |\n| OpenAI | gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-5 | 1,047,576 |\n| OpenAI | gpt-4 | 8,192 |\n| OpenAI | gpt-3.5-turbo | 16,385 |\n| Google AI | gemini-2.5-pro, gemini-2.5-flash, gemini-2.0-flash, gemini-1.5-flash, gemini-3-\\* | 1,048,576 |\n| Google AI | gemini-1.5-pro | 2,097,152 |\n| Vertex | gemini-2.5-pro, gemini-2.5-flash, gemini-2.0-flash, gemini-1.5-flash | 1,048,576 |\n| Vertex | gemini-1.5-pro | 2,097,152 |\n| Bedrock | anthropic.claude-3-\\* (all variants) | 200,000 |\n| Bedrock | amazon.nova-pro-v1:0, amazon.nova-lite-v1:0 | 300,000 |\n| Azure | gpt-4o,","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Per-Provider Context Windows","lvl3":""}}, {"objectID":"4f77900d5db41bd2806fb8ff3bd4482c1073d9458a4fc95203ad72f287f9ecdd","title":"Manual Compaction with compactSession()","url":"/docs/deployment/configuration#manual-compaction-with-compactsession","content":"You can trigger compaction manually on any session using the CompactionConfig interface, which provides per-stage control beyond what the SDK-level contextCompaction field exposes:\n\nCompactionConfig Field Reference:\n\n| Field | Type | Default | Description |\n| ----------------------- | ---------- | -------------------- | ------------------------------------------------------- |\n| enablePrune | boolean | true | Enable Stage 1: tool output pruning |\n| enableDeduplicate | boolean | true | Enable Stage 2: file read deduplication |\n| enableSummarize | boolean | true | Enable Stage 3: LLM summarization |\n| enableTruncate | boolean | true | Enable Stage 4: sliding window truncation |\n| pruneProtectTokens | number | 40000 | Number of recent tokens protected from pruning |\n| pruneMinimumSavings | number | 20000 | Minimum token savings required to apply pruning |\n| pruneProtectedTools | string[] | [\"skill\"] | Tool names whose outputs are never pruned |\n| summarizationProvider | string | \"vertex\" | Provider for LLM summarization |\n| summarizationModel | string | \"gemini-2.5-flash\" | Model for LLM summarization |\n| keepRecentRatio | number | 0.3 | Fraction of messages kept verbatim during summarization |\n| truncationFraction | number | 0.5 | Fraction of oldest messages tagged as truncated |\n| provider | string | \"\" | Provider hint for token estimation multipliers |","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Manual Compaction with compactSession()","lvl3":""}}, {"objectID":"a46047c62f3f6797f31e15ac4d49ca0ef3f66c987f28dc783dfd751f5e07b05e","title":"File Token Budget Constants","url":"/docs/deployment/configuration#file-token-budget-constants","content":"These constants in src/lib/context/fileTokenBudget.ts control how file reads interact with the context budget:\n\n| Constant | Value | Description |\n| -------------------------- | -------- | -------------------------------------------------------------- |\n| FILE_READ_BUDGET_PERCENT | 0.6 | Fraction of remaining context allocated for file reads |\n| FILE_FAST_PATH_SIZE | 100 KB | Files below this size skip budget validation |\n| FILE_PREVIEW_MODE_SIZE | 5 MB | Files above this size get preview-only mode (first 2000 chars) |\n| FILE_PREVIEW_CHARS | 2000 | Number of characters shown in preview mode |","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"File Token Budget Constants","lvl3":""}}, {"objectID":"58776c23a2569d7630a7f17e7227641424e1d94154af0819bbcc68644170e461","title":"Tool Output Limits Constants","url":"/docs/deployment/configuration#tool-output-limits-constants","content":"These constants in src/lib/context/toolOutputLimits.ts control tool output truncation:\n\n| Constant | Value | Description |\n| ----------------------- | --------------- | ------------------------------------------- |\n| MAX_TOOL_OUTPUT_BYTES | 51200 (50 KB) | Maximum tool output size before truncation |\n| MAX_TOOL_OUTPUT_LINES | 2000 | Maximum tool output lines before truncation |","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Tool Output Limits Constants","lvl3":""}}, {"objectID":"94dbd292976f0795315c753ff6c19b32f06826a1934dde198a258497b917bf20","title":"Logging Configuration","url":"/docs/deployment/configuration#logging-configuration","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Logging Configuration","lvl3":""}}, {"objectID":"4b06a56d61e56e731bd5cdda8e822c80f14fa7c744bc4881254259ce9d275e6b","title":"Custom log format","url":"/docs/deployment/configuration#custom-log-format","content":"`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Custom log format","lvl3":""}}, {"objectID":"8d39b109e3b74173f682a536f53f6867f5ccbcdf7db169c84a99eafe57df2ad1","title":"API Key Security","url":"/docs/deployment/configuration#api-key-security","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"API Key Security","lvl3":""}}, {"objectID":"4d795e57bfd5619440f822fcfdb528081009078d084b654854aa2828fc5069dd","title":"Use .env files (add to .gitignore)","url":"/docs/deployment/configuration#use-env-files-add-to-gitignore","content":"echo \".env\" >> .gitignore\n`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Use .env files (add to .gitignore)","lvl3":""}}, {"objectID":"5516ebd82494cb9e08d6102e22d0abbe257e7210b05d9242e412f67424b5ec28","title":"Test Environment Setup","url":"/docs/deployment/configuration#test-environment-setup","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Test Environment Setup","lvl3":""}}, {"objectID":"d02fcfc2011fa9b9631d2e204fb77f989e6ac5c6c5d217de529eeb47e09b6056","title":"Mock providers for testing","url":"/docs/deployment/configuration#mock-providers-for-testing","content":"`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Mock providers for testing","lvl3":""}}, {"objectID":"69566fe52919aa5206cb5feb1235b91c0124fb99e930ed39cdbae88c7c2f6bab","title":"Validation Commands","url":"/docs/deployment/configuration#validation-commands","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Validation Commands","lvl3":""}}, {"objectID":"5f1c2d08bced14e90f5757015fe8a18b4de148daccdc344539bb1f6051c94722","title":"Validate configuration","url":"/docs/deployment/configuration#validate-configuration","content":"npx neurolink status --verbose","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Validate configuration","lvl3":""}}, {"objectID":"4261a4f12e99b3c17df0773503f636e7c11babe060cc5d813c2667283369d2a9","title":"Test built-in tools","url":"/docs/deployment/configuration#test-built-in-tools","content":"npx neurolink generate \"What time is it?\" --debug","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Test built-in tools","lvl3":""}}, {"objectID":"8d049707778a82eb9081235acee2d720c0c1d3996f8a83a091c203e6ea762854","title":"Test external discovery","url":"/docs/deployment/configuration#test-external-discovery","content":"npx neurolink mcp discover --format table","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Test external discovery","lvl3":""}}, {"objectID":"a6887a72da883fa1f75c257685181930e70f9c28bd07d42e6e4eff842a8f31f9","title":"Full system test","url":"/docs/deployment/configuration#full-system-test","content":"npm run build && npm run test:run -- test/mcp-comprehensive.test.ts\n`","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Full system test","lvl3":""}}, {"objectID":"8c4194c033676b414a9cbcb896eb7d8ced6cdd8c282e081d9f65a35935a8d5ae","title":"Development Setup","url":"/docs/deployment/configuration#development-setup","content":"💡 For most users, setting GOOGLE_AI_API_KEY is sufficient to get started with NeuroLink and test all MCP functionality!","hierarchy":{"lvl0":"Deployment","lvl1":"⚙️ NeuroLink Configuration Guide","lvl2":"Development Setup","lvl3":""}}, {"objectID":"941610580ad261eef95ac397ab4379c749dbdb2273196044111181d77204dee6","title":"🏢 Enterprise & Proxy Setup Guide","url":"/docs/deployment/enterprise-proxy","content":"🏢 Enterprise & Proxy Setup Guide\n\nNeuroLink provides comprehensive proxy support for enterprise environments, enabling AI integration behind corporate firewalls and proxy servers.\n\n✨ Zero Configuration Proxy Support\n\nNeuroLink automatically detects and uses proxy settings when environment variables are configured. No code changes required.\n\nQuick Setup\n\n🔧 Environment Variables\n\nRequired Proxy Variables\n\n| Variable | Description | Example |\n| ------------- | ------------------------------- | ------------------------------- |\n| HTTPS_PROXY | Proxy server for HTTPS requests | http://proxy.company.com:8080 |\n| HTTP_PROXY | Proxy server for HTTP requests | http://proxy.company.com:8080 |\n\nOptional Proxy Variables\n\n| Variable | Description | Default |\n| ---------- | ----------------------- | --------------------- |\n| NO_PROXY | Domains to bypass proxy | localhost,127.0.0.1 |\n\n🌐 Provider-Specific Proxy Support\n\n✅ Full Proxy Support\n\nAll NeuroLink providers automatically work through corporate proxies:\n\n| Provider | Proxy Method | Status |\n| -------------------- | ----------------------------------- | -------------------- |\n| Anthropic Claude | Direct fetch calls with proxy | ✅ Verified + Tested |\n| OpenAI | Global fetch handling | ✅ Verified + Tested |\n| Google Vertex AI | Custom fetch with undici ProxyAgent | ✅ Verified + Tested |\n| Google AI Studio | Custom fetch with undici ProxyAgent | ✅ Verified + Tested |\n| Mistral AI | Custom fetch with undici ProxyAgent | ✅ Verified + Tested |\n| Ollama | Custom fetch with undici ProxyAgent | ✅ Verified + Tested |\n| HuggingFace | Custom fetch with undici ProxyAgent | ✅ Implemented |\n| Azure OpenAI | Custom fetch with undici ProxyAgent | ✅ Implemented |\n| Amazon Bedrock | Global fetch handling | ✅ Implemented |\n\n🚀 Quick Validation\n\nTest Proxy Configuration\n\nVerify Proxy Usage\n\nWhen proxy is working correctly, you should see:\n✅ AI responses generated successfully\n✅ Proxy server logs showing intercepted connections\n✅ No direct internet access required\n✅ Enterprise MCP tools work alongside proxy\n\nEnterprise Grade Testing\n\nNeuroLink includes comprehensive proxy validation tests:\n\nTest Coverage:\n✅ Proxy usage validation (negative/positive testing)\n✅ All enterprise providers (Anthropic, OpenAI, Vertex, Mistral, Ollama)\n✅ MCP + Proxy compatibility (enterprise grade)\n✅ Real-world timeout handling\n✅ SDK and CLI interface testing\n\n🔍 Enterprise Configuration Examples\n\nCorporate Firewall Setup\n\nAuthenticated Proxy\n\nMultiple Environment Setup\n\n🛠️ Technical Implementation\n\nArchitecture Overview\n\nNeuroLink uses the undici ProxyAgent for reliable proxy support:\n\nKey Benefits\n🔄 Automatic Detection - Zero configuration for standard setups\n🏢 Enterprise Ready - Works with corporate authentication\n⚡ High Performance - Optimized undici implementation\n🛡️ Security Compliant - Respects corporate security policies\n\n🔧 Troubleshooting\n\nCommon Issues\n\nProxy Not Working\n\nConnection Timeouts\n\nAuthentication Issues\n\nDebug Mode\n\n🚀 AWS & Cloud Deployment\n\nAWS Corporate Environment\n\nDocker Deployment\n\nKubernetes Configuration\n\n📋 Checklist for Enterprise Deployment\n\nPre-deployment\n[ ] Proxy server details obtained from IT team\n[ ] Network connectivity tested with curl/wget\n[ ] Authentication credentials secured\n[ ] Firewall rules configured for AI provider domains\n\nTesting\n[ ] Environment variables set correctly\n[ ] NeuroLink proxy test successful\n[ ] All required providers accessible\n[ ] Production environment validated\n\nSecurity\n[ ] Proxy credentials stored securely\n[ ] NO_PROXY configured for internal services\n[ ] SSL/TLS verification enabled\n[ ] Logging configured appropriately\n\n🔗 Related Documentation\nProvider Configuration - Detailed provider setup\nCLI Guide - Command line proxy usage\nEnvironment Variables - Complete variable reference\nTroubleshooting - Common issues and solutions\n\nEnterprise Support: For enterprise deployment assistance, contact enterprise@juspay.in","hierarchy":{"lvl0":"Deployment","lvl1":"🏢 Enterprise & Proxy Setup Guide","lvl2":"","lvl3":""}}, {"objectID":"43028d624902e4bac6a20a616166ca1fa966694eab7b659e78c59265e5b70bf3","title":"🏢 Enterprise & Proxy Setup Guide","url":"/docs/deployment/enterprise-proxy#-enterprise-proxy-setup-guide","content":"NeuroLink provides comprehensive proxy support for enterprise environments, enabling AI integration behind corporate firewalls and proxy servers.","hierarchy":{"lvl0":"Deployment","lvl1":"🏢 Enterprise & Proxy Setup Guide","lvl2":"🏢 Enterprise & Proxy Setup Guide","lvl3":""}}, {"objectID":"3f48b9293072b5e0ace06f11dcf94b12660057ce943857684029f5e9d2ccb358","title":"✨ Zero Configuration Proxy Support","url":"/docs/deployment/enterprise-proxy#-zero-configuration-proxy-support","content":"NeuroLink automatically detects and uses proxy settings when environment variables are configured. 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Test with any provider","url":"/docs/deployment/enterprise-proxy#2-test-with-any-provider","content":"npx @juspay/neurolink generate \"Test proxy connection\" --provider google-ai","hierarchy":{"lvl0":"Deployment","lvl1":"🏢 Enterprise & Proxy Setup Guide","lvl2":"2. Test with any provider","lvl3":""}}, {"objectID":"0ec443215cce8d89417442ff2713b18af330f2140eed9474232c909666b2e1b7","title":"3. Check proxy logs for connection intercepts","url":"/docs/deployment/enterprise-proxy#3-check-proxy-logs-for-connection-intercepts","content":"`","hierarchy":{"lvl0":"Deployment","lvl1":"🏢 Enterprise & Proxy Setup Guide","lvl2":"3. 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{"objectID":"cecff94d2f131698e53c20afaac483e2f1c0884a2d25cf028181209828c2a878","title":"Architecture Overview","url":"/docs/deployment/enterprise-proxy#architecture-overview","content":"NeuroLink uses the undici ProxyAgent for reliable proxy support:","hierarchy":{"lvl0":"Deployment","lvl1":"🏢 Enterprise & Proxy Setup Guide","lvl2":"Architecture Overview","lvl3":""}}, {"objectID":"027f16f6b3b5ff932a9f120e8fb72d5f1de1072dad2a01d06c09c6db75afb28a","title":"Key Benefits","url":"/docs/deployment/enterprise-proxy#key-benefits","content":"🔄 Automatic Detection - Zero configuration for standard setups\n🏢 Enterprise Ready - Works with corporate authentication\n⚡ High Performance - Optimized undici implementation\n🛡️ Security Compliant - Respects corporate security policies","hierarchy":{"lvl0":"Deployment","lvl1":"🏢 Enterprise & Proxy Setup Guide","lvl2":"Key Benefits","lvl3":""}}, {"objectID":"d2d81f5d3455a08ae14fb1a8f614b5c0d9706247fafa04364222cdea2850e440","title":"Proxy Not 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Startup Optimization\nDomain Configuration Performance\nMemory Usage Optimization\nGeneration Speed Optimization\nStreaming Performance\nProvider Selection Strategy\nContext Data Optimization\nCaching and Configuration\nMonitoring and Profiling\nTroubleshooting\n\nOverview\n\nThe NeuroLink CLI with Phase 1 Factory Infrastructure introduces domain-specific features that enhance functionality while maintaining performance. This guide helps you optimize performance across different use cases and configurations.\n\nPerformance Goals\nCLI Startup: \\<5 seconds for base commands, \\<6 seconds with domain features\nMemory Usage: \\<200MB base, \\<250MB with domain configurations\nGeneration Speed: \\<3 seconds for dry-run, \\<4 seconds with domain features\nStreaming Responsiveness: \\<2 seconds to start, \\<8 seconds to complete\n\nPerformance Benchmarks\n\nBaseline Performance Measurements\n\nDomain Feature Performance Impact\n\nCLI Startup Optimization\n\nFast Startup Strategies\nUse Specific Commands\nOptimize Environment\nConfiguration Caching\n\n \n\nStartup Performance Monitoring\n\nDomain Configuration Performance\n\nEfficient Domain Usage\nChoose Appropriate Domain\nSelective Feature Enablement\nConfiguration Defaults\n \n\nDomain-Specific Optimizations\n\nHealthcare Domain\n\nAnalytics Domain\n\nFinance Domain\n\nMemory Usage Optimization\n\nMemory-Efficient Practices\nContext Size Management\nToken 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Analytics\n\nSystem-Level Monitoring\nCPU Usage Monitoring\nMemory Usage Tracking\nNetwork Performance\n\n \n\nPerformance Profiling Tools\n\nTroubleshooting\n\nCommon Performance Issues\nSlow CLI Startup\nHigh Memory Usage\nSlow Generation Speed\nStreaming Latency Issues\n\n \n\nPerformance Debugging Commands\n\nPerformance Optimization Checklist\n[ ] Configuration optimized: Run neurolink config init with optimal settings\n[ ] Provider selected: Choose appropriate provider for your use case\n[ ] Token limits set: Use appropriate --max-tokens for your needs\n[ ] Context minimized: Keep context data lean and relevant\n[ ] Features selective: Only enable needed evaluation/analytics features\n[ ] Format appropriate: Choose optimal output format for your workflow\n[ ] Monitoring enabled: Use --enable-analytics to track performance\n[ ] Caching configured: Set up appropriate caching strategy\n[ ] Environment optimized: Configure API keys and environment variables\n[ ] System resources: Ensure adequate CPU and memory available\n\nBest Practices Summary\nStart Simple: Begin with basic commands and add features incrementally\nMeasure First: Establish baseline performance before optimization\nRight-size Resources: Use appropriate token limits and context sizes\nChoose Wisely: Select providers and domains that match your performance needs\nMonitor Continuously: Use built-in analytics and system monitoring\nCache Effectively: Configure caching for frequently used operations\nTest Regularly: Perform regular performance testing as you scale usage\nProfile When Needed: Use profiling tools for detailed performance analysis\n\nFor additional performance optimization support, see the CLI Reference and Configuration Guide.","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide for NeuroLink CLI with Domain Features","lvl2":"","lvl3":""}}, {"objectID":"4cbee2a23f9f822b4165885f244efc1acc0af296b52eaffcdcf8f86516dfb342","title":"Performance Optimization Guide for 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{"objectID":"52cca0aafd8fff8015b11542ad08bcd03e3aa597df9a1e59dab44c8c18aef880","title":"Performance Profiling Tools","url":"/docs/deployment/performance-guide#performance-profiling-tools","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide for NeuroLink CLI with Domain Features","lvl2":"Performance Profiling Tools","lvl3":""}}, {"objectID":"462919e5ea2af60d92adbded46ae71bc323364b3047b845e4ea5c29dcc4b2bb3","title":"Node.js profiling for CLI performance","url":"/docs/deployment/performance-guide#nodejs-profiling-for-cli-performance","content":"NODE_OPTIONS=\"--prof\" neurolink generate \"test\" --dryRun\nnode --prof-process isolate-*.log > performance-profile.txt","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide for NeuroLink CLI with Domain Features","lvl2":"Node.js profiling for CLI performance","lvl3":""}}, {"objectID":"52417401ba98bcebd57ec88253cc422a39061e9a3ae2d104509501b0214691d1","title":"Memory 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Caching\n\nSmart Cache Keys\n\nCache Warming\n\n🎯 Production Optimization\n\nEnvironment Configuration\n\nResource Management\n\nAuto-scaling\n\n🔍 Performance Debugging\n\nProfiling Tools\n\nLatency Analysis\n\nBottleneck Detection\n\n🏭 Enterprise Performance\n\nLoad Testing\n\nStress Testing\n\nCapacity Planning\n\n📊 Performance Benchmarks\n\nProvider Comparison\n\n| Provider | Avg Latency | Throughput | Success Rate | Cost/1K tokens |\n| --------- | ----------- | ---------- | ------------ | -------------- |\n| OpenAI | 1.2s | 150 req/s | 99.5% | $0.03 |\n| Anthropic | 1.8s | 120 req/s | 99.8% | $0.015 |\n| Google AI | 0.9s | 200 req/s | 99.2% | $0.025 |\n| Bedrock | 2.1s | 100 req/s | 99.9% | $0.02 |\n\nOptimization Results\n\n🎛️ Monitoring and Alerting\n\nPerformance Alerts\n\nReal-time Dashboard\n\n🔧 Troubleshooting Performance Issues\n\nCommon Issues\nHigh Latency\nCheck provider response times\nVerify network connectivity\nReview request complexity\nConsider request timeouts\nLow Throughput\nIncrease connection pool size\nEnable parallel processing\nOptimize request batching\nCheck rate limits\nMemory Leaks\nMonitor cache size\nReview object retention\nCheck for unclosed streams\nImplement proper cleanup\n\nDiagnostic Commands\n\n🎥 Video Generation Performance Optimization\n\nVideo generation via Veo 3.1 requires special performance considerations due to longer processing times and larger resource requirements.\n\nTimeout Configuration\n\nVideo generation typically takes 1-3 minutes. Configure appropriate timeouts:\n\nPolling Strategy\n\nVideo generation uses long-polling. Optimize the polling strategy:\n\nResource Optimization\n\nResolution vs Speed Trade-off:\n\n| Resolution | Avg Time | File Size | Use Case |\n| ---------- | -------- | --------- | --------------------------- |\n| 720p | 60-90s | ~5-10MB | Social media, previews |\n| 1080p | 90-180s | ~15-30MB | Professional content, demos |\n\nLength vs Speed Trade-off:\n\n| Length | Avg Time | Use Case |\n| ------ | -------- | ------------------------------- |\n| 4s | 60-90s | Quick animations, teasers |\n| 6s | 75-120s | Social media posts |\n| 8s | 90-180s | Product showcases, storytelling |\n\nBatch Processing Strategy\n\nProcess multiple videos efficiently:\n\nCaching Strategy\n\nVideo generation is expensive. Implement aggressive caching:\n\nCost Optimization\n\nBest Practices:\nUse 720p by default - 30-50% faster, 60% lower cost\nPrefer 4-6 second videos - Faster generation, lower cost\nImplement aggressive caching - Avoid regenerating identical videos\nBatch similar requests - Group by resolution/length for efficiency\nMonitor Vertex AI quotas - Set up alerts before hitting limits\n\nCost Comparison:\n\n| Configuration | Avg Time | Relative Cost | Best For |\n| ------------------ | -------- | ------------- | -------------------- |\n| 720p, 4s, no audio | 60s | 1x | Quick previews |\n| 720p, 6s, audio | 90s | 1.5x | Social media |\n| 1080p, 8s, audio | 180s | 3x | Professional content |\n\nError Handling for Long Operations\n\nMonitoring Video Generation Performance\n\nThis comprehensive performance optimization guide provides the tools and strategies needed to maximize NeuroLink's performance in any environment, from development to large-scale production deployments.\n\n📚 Related Documentation\nAdvanced Analytics - Performance tracking and analysis\nSystem Architecture - Understanding system design\nTroubleshooting - Common performance issues\nEnterprise Setup - Production configuration\nVideo Generation Guide - Complete video generation documentation\nPPT Generation Guide - PowerPoint presentation generation","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"","lvl3":""}}, {"objectID":"e12e5bbfcffba792dde568c97a2244aa36726ae1aefda4a3fd76dfd23530bf31","title":"Performance Optimization Guide","url":"/docs/deployment/performance#performance-optimization-guide","content":"Comprehensive guide for optimizing NeuroLink performance, reducing latency, and maximizing throughput in production environments.","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Performance Optimization Guide","lvl3":""}}, {"objectID":"85666602d9c1f5b19b5ca1caac6872f6cb8680d470fd183974c762b11fecbbb0","title":"Immediate Optimizations","url":"/docs/deployment/performance#immediate-optimizations","content":"Enable Response Caching\nUse Streaming for Long Responses\nImplement Request Batching","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Immediate Optimizations","lvl3":""}}, {"objectID":"c89a611058ee72f29a316243944b028630a805c867e67bc675b88eb0de7ca23b","title":"Batch Operations","url":"/docs/deployment/performance#batch-operations","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Batch Operations","lvl3":""}}, {"objectID":"33744973dd16128c6a78f18cb88ad18d2641f095d152a2325011b9bf93c599ab","title":"High-performance batch processing","url":"/docs/deployment/performance#high-performance-batch-processing","content":"npx @juspay/neurolink batch process \\\n --input large_dataset.jsonl \\\n --output results.jsonl \\\n --parallel 10 \\\n --chunk-size 100 \\\n --enable-caching \\\n --provider-strategy fastest\n`","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"High-performance batch processing","lvl3":""}}, {"objectID":"2f5dc0822c05cdcfd8b0aed6760b2de31a085111b49a4f218f87d57771bb22a4","title":"Parallel Provider Testing","url":"/docs/deployment/performance#parallel-provider-testing","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Parallel Provider Testing","lvl3":""}}, {"objectID":"d728e96a0a8c92689ae2cf1be880d01d89f93150dd4bfcb332dccfbbe652b215","title":"Test multiple providers simultaneously","url":"/docs/deployment/performance#test-multiple-providers-simultaneously","content":"npx @juspay/neurolink benchmark \\\n --providers openai,anthropic,google-ai \\\n --concurrent 3 \\\n --iterations 10 \\\n --output benchmark_results.json\n`","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Test multiple providers simultaneously","lvl3":""}}, {"objectID":"0183dd3e2cdc27929c323057d9ca640be74ebf86a39f7aa1db99798ac107ff04","title":"Streaming Mode","url":"/docs/deployment/performance#streaming-mode","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Streaming Mode","lvl3":""}}, {"objectID":"0becddb99928ce1db6f4578cf8deda4e375e9c8801c869bdfc541097891b4da9","title":"Enable streaming for immediate output","url":"/docs/deployment/performance#enable-streaming-for-immediate-output","content":"npx @juspay/neurolink gen \"Write a long article\" \\\n --stream \\\n --provider anthropic \\\n --no-buffer\n`","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Enable streaming for immediate output","lvl3":""}}, {"objectID":"1bdd0caec51b6e89c17385bc917f6ff87aaf016f1d4096c67c78293c57b19d90","title":"Cache Warming","url":"/docs/deployment/performance#cache-warming","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Cache Warming","lvl3":""}}, {"objectID":"858aeae3d1c8a7c278f4d65d3675774c5f4dd5c0eafe28c5bdf359144553fbd9","title":"Pre-populate cache with common queries","url":"/docs/deployment/performance#pre-populate-cache-with-common-queries","content":"npx @juspay/neurolink cache warm \\\n --patterns common_prompts.txt \\\n --providers openai,anthropic \\\n --temperature-range 0.1,0.5,0.9\n`","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Pre-populate cache with common queries","lvl3":""}}, {"objectID":"69f039be35be4e156ac228a4c00ecbde1cd44af7ea0d96108fa405fbc9f12f3a","title":"Environment Configuration","url":"/docs/deployment/performance#environment-configuration","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Environment Configuration","lvl3":""}}, {"objectID":"b7704df3bf3690c712e801da6bc732d554c039a624f9f5d66a608211d8fab576","title":"Production environment variables","url":"/docs/deployment/performance#production-environment-variables","content":"`","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Production environment variables","lvl3":""}}, {"objectID":"9d6798840ee0352c4503e3fe956b6b77a36193c4495ac325479ea8171c43c81b","title":"Latency Analysis","url":"/docs/deployment/performance#latency-analysis","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Latency Analysis","lvl3":""}}, {"objectID":"b59b66245d6480af7e6217df5bafbe07326313775ed02913ac97807a767e6675","title":"Analyze response time patterns","url":"/docs/deployment/performance#analyze-response-time-patterns","content":"npx @juspay/neurolink analyze latency \\\n --log-file performance.log \\\n --time-range \"last 24h\" \\\n --group-by provider,model \\\n --percentiles 50,90,95,99\n`","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Analyze response time patterns","lvl3":""}}, {"objectID":"3ecc240d9710aa66c8eb244e95dc5da4c8c1a81fb37467b8e95195f0525dd59f","title":"Load Testing","url":"/docs/deployment/performance#load-testing","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Load Testing","lvl3":""}}, {"objectID":"fd173123d25cc76cfc1f45a6ae5048287238de6a28d53fe60ec96e634b134611","title":"Comprehensive load testing","url":"/docs/deployment/performance#comprehensive-load-testing","content":"npx @juspay/neurolink load-test \\\n --target-rps 100 \\\n --duration 10m \\\n --providers openai,anthropic \\\n --scenarios scenarios.json \\\n --report performance_report.html\n`","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Comprehensive load testing","lvl3":""}}, {"objectID":"27d853237230ee2409f5349308aad7d30083d7b9e02cae0a474933714a42bfc8","title":"Provider Comparison","url":"/docs/deployment/performance#provider-comparison","content":"| Provider | Avg Latency | Throughput | Success Rate | Cost/1K tokens |\n| --------- | ----------- | ---------- | ------------ | -------------- |\n| OpenAI | 1.2s | 150 req/s | 99.5% | $0.03 |\n| Anthropic | 1.8s | 120 req/s | 99.8% | $0.015 |\n| Google AI | 0.9s | 200 req/s | 99.2% | $0.025 |\n| Bedrock | 2.1s | 100 req/s | 99.9% | $0.02 |","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Provider Comparison","lvl3":""}}, {"objectID":"9d8d393ee857c3a268d852ccdeae6465606b82ec3d18492077c7e4c8a01bd3e8","title":"Common Issues","url":"/docs/deployment/performance#common-issues","content":"High Latency\nCheck provider response times\nVerify network connectivity\nReview request complexity\nConsider request timeouts\nLow Throughput\nIncrease connection pool size\nEnable parallel processing\nOptimize request batching\nCheck rate limits\nMemory Leaks\nMonitor cache size\nReview object retention\nCheck for unclosed streams\nImplement proper cleanup","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Common Issues","lvl3":""}}, {"objectID":"9adab1d61ebf3785e8e7e544b60b05c235c2d33424ded571c7f797c314617f74","title":"Diagnostic Commands","url":"/docs/deployment/performance#diagnostic-commands","content":"`bash","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Diagnostic Commands","lvl3":""}}, {"objectID":"d9c2c236f171795b76d838f8674a90ab0fc0bab0120f125ad58e221c842b53ba","title":"Performance diagnostics","url":"/docs/deployment/performance#performance-diagnostics","content":"npx @juspay/neurolink diagnose performance \\\n --verbose \\\n --include-providers \\\n --include-cache \\\n --include-memory \\\n --output diagnosis.json\n`","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Performance diagnostics","lvl3":""}}, {"objectID":"1367faf1c502acc7a1a578062be4931c4df891adb3d31f2ef42f02cb75ae267f","title":"🎥 Video Generation Performance Optimization","url":"/docs/deployment/performance#-video-generation-performance-optimization","content":"Video generation via Veo 3.1 requires special performance considerations due to longer processing times and larger resource requirements.","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"🎥 Video Generation Performance Optimization","lvl3":""}}, {"objectID":"6c9e5a2e51cd0c60de2b702a1e4b345298b04d4ea12fc47a40c7e06a50c69f02","title":"Timeout Configuration","url":"/docs/deployment/performance#timeout-configuration","content":"Video generation typically takes 1-3 minutes. Configure appropriate timeouts:","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Timeout Configuration","lvl3":""}}, {"objectID":"ded1bf81d64d93d3baabaf58894828e0453a95bcfddb637a662d8029eeed4066","title":"Polling Strategy","url":"/docs/deployment/performance#polling-strategy","content":"Video generation uses long-polling. Optimize the polling strategy:","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Polling Strategy","lvl3":""}}, {"objectID":"2f44560c9fcf438b50f4540d1d6c5a59974feb37f848d8dec809af405fcde6e1","title":"Resource Optimization","url":"/docs/deployment/performance#resource-optimization","content":"Resolution vs Speed Trade-off:\n\n| Resolution | Avg Time | File Size | Use Case |\n| ---------- | -------- | --------- | --------------------------- |\n| 720p | 60-90s | ~5-10MB | Social media, previews |\n| 1080p | 90-180s | ~15-30MB | Professional content, demos |\n\nLength vs Speed Trade-off:\n\n| Length | Avg Time | Use Case |\n| ------ | -------- | ------------------------------- |\n| 4s | 60-90s | Quick animations, teasers |\n| 6s | 75-120s | Social media posts |\n| 8s | 90-180s | Product showcases, storytelling |","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Resource Optimization","lvl3":""}}, {"objectID":"932b3b24e30e6abd22570e6c61f2da895f6f606251125b961bfd64d60cb05931","title":"Batch Processing Strategy","url":"/docs/deployment/performance#batch-processing-strategy","content":"Process multiple videos efficiently:","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Batch Processing Strategy","lvl3":""}}, {"objectID":"9c33085a712f6c2c4276eb08cd8b491e1921e68bb8ed8025fea97b1846a8079d","title":"Caching Strategy","url":"/docs/deployment/performance#caching-strategy","content":"Video generation is expensive. Implement aggressive caching:","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Caching Strategy","lvl3":""}}, {"objectID":"3a05ebe66b0d86011ede93f5ee0e349e6f1141ad2c02a26c2db769b7d1f0b80c","title":"Cost Optimization","url":"/docs/deployment/performance#cost-optimization","content":"Best Practices:\nUse 720p by default - 30-50% faster, 60% lower cost\nPrefer 4-6 second videos - Faster generation, lower cost\nImplement aggressive caching - Avoid regenerating identical videos\nBatch similar requests - Group by resolution/length for efficiency\nMonitor Vertex AI quotas - Set up alerts before hitting limits\n\nCost Comparison:\n\n| Configuration | Avg Time | Relative Cost | Best For |\n| ------------------ | -------- | ------------- | -------------------- |\n| 720p, 4s, no audio | 60s | 1x | Quick previews |\n| 720p, 6s, audio | 90s | 1.5x | Social media |\n| 1080p, 8s, audio | 180s | 3x | Professional content |","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Cost Optimization","lvl3":""}}, {"objectID":"bc113c4ecdf384a661e135fb87d4d88d76337d1c16cbee6cfdbc2c3b5edc18da","title":"Monitoring Video Generation Performance","url":"/docs/deployment/performance#monitoring-video-generation-performance","content":"This comprehensive performance optimization guide provides the tools and strategies needed to maximize NeuroLink's performance in any environment, from development to large-scale production deployments.","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"Monitoring Video Generation Performance","lvl3":""}}, {"objectID":"aa9575381cda5b2cbb9fa93c3993699893d847058ec7c8d30b6ac98eea55aafd","title":"📚 Related Documentation","url":"/docs/deployment/performance#-related-documentation","content":"Advanced Analytics - Performance tracking and analysis\nSystem Architecture - Understanding system design\nTroubleshooting - Common performance issues\nEnterprise Setup - Production configuration\nVideo Generation Guide - Complete video generation documentation\nPPT Generation Guide - PowerPoint presentation generation","hierarchy":{"lvl0":"Deployment","lvl1":"Performance Optimization Guide","lvl2":"📚 Related Documentation","lvl3":""}}, {"objectID":"855ff4a2701475b61ce35ffe1d9f854cdb9232efca2c52c7f038c8d0ea7c190c","title":"ClientConfigurator Registry Implementation Plan","url":"/docs/development/2026-08-20-client-configurator-registry","content":"ClientConfigurator Registry Implementation Plan\n\nFor agentic workers: REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (- [ ]) syntax for tracking.\n\nGoal: Replace the three hand-written proxy client config writers and their four duplicated call-site blocks with one registry, so onboarding a new AI coding CLI is one new file plus one registry line instead of eleven edits.\n\nArchitecture: Introduce a ClientConfigurator type — { id, displayName, detect(), apply(baseUrl), restore(baseUrl) } — with one module per CLI under src/cli/proxy-clients/. A registry module exports the ordered list. The four call sites in proxy.ts collapse into two loops (applyAll, restoreAll). Behaviour is preserved exactly: same files written, same snapshot keys, same messages, same ordering (Claude → OpenCode → Codex).\n\nTech Stack: TypeScript (strict, type only — no interface), tsx test suites via defineSuite, yargs CLI, Prettier + ESLint with custom neurolink/* rules.\n\nSpec: docs/features/proxy-cli-onboarding.md (§3 touch points, §5 \"where it stops\")\n\nGlobal Constraints\nNo interface. Use type X = { … }; intersection (&) not extends. (CLAUDE.md rule 7, ESLint neurolink/no-interface.)\nAll types live in src/lib/types/. No local type aliases in feature dirs. (rule 2, neurolink/no-local-type-alias.) Filenames must not contain \"Type\"/\"Types\" (rule 8).\nType names globally unique across src/lib/types/; CLI types take the Cli prefix (rule 9).\nBarrel-only type imports: import from ../types/index.js, never ../types/proxy.js (rule 13).\nNo double assertions (x as unknown as T) (rule 14).\nsrc/lib/types/index.ts uses export * only (rule 10).\nTests are end-to-end unless the determinism exception applies, and then the file header must state what determinism buys (rule 15). test/continuous-test-suite-proxy.ts is already on the allow list in eslint.config.js.\nAssertion messages must never quote a payload — defineSuite downgrades a failure to SKIP when the message matches isExpectedProviderError(). Describe the discrepancy instead.\nNever commit to release. Branch refactor/; conventional commits; no ticket prefix in this repo.\nBehaviour must not change. This is a pure refactor. Every file written, every snapshot key, every console string stays byte-identical.\n\nWhy this refactor, in one paragraph\n\nThe three writers share no abstraction and disagree with each other in ways that have already shipped bugs. OpenCode targeted a directory OpenCode never reads and printed ✓ unconditionally (#1366, #1367 — both fixed); Claude still has no installed-check at all and creates ~/.claude/settings.json even when Claude Code is absent; and the same operation reports failure at two different log levels depending on which command ran it (logger.debug at proxy.ts:3499's block versus a visible yellow ⚠ at :5416's). Four duplicated call-site blocks is what let those diverge. The registry removes the duplication that manufactures this class of bug.\n\nCurrent-state map (verified at commit 845c3692)\n\nWriters, all in src/cli/commands/proxy.ts:\n\n| CLI | Constants | Apply | Restore | Snapshot mechanism |\n| -------- | ------------------------------------------------------------------------------------ | --------------------------------------------------- | ----------------------------------------------------- | ------------------------------------------------------- |\n| Claude | CLAUDE_SETTINGS_PATH:248, PROXY_MANAGED_KEYS:623 | setClaudeProxySettings:625 → Promise | clearClaudeProxySettings:654 → Promise | __proxy_original_env key inside settings.json |\n| OpenCode | getOpenCodeConfigDir:711, getOpenCodeConfigPath:724, OPENCODE_ORIGINAL_KEY:736 | setOpenCodeProxySettings:738 → Promise | clearOpenCodeProxySettings:793 → Promise | __proxy_original_neurolink key inside opencode.json |\n| Codex | CODEX_CONFIG_PATH:878, CODEX_SNAPSHOT_PATH:879 | setCodexProxySettings:943 → Promise | clearCodexProxySettings:1017 → Promise | sidecar ~/.neurolink/codex-proxy-snapshot.json |\n\nCall sites (12 calls across 4 blocks):\n\n| Block | Lines | Context |\n| --------- | ---------------------- | ---------------------------------- |\n| Apply A | 3499, 3512, 3526 | daemon start, inside if (!isDev) |\n| Apply B | 5416, 5427, 5438 | proxy setup wizard |\n| Restore A | 3202, 3210, 3215 | shutdown handler |\n| Restore B | 5269, 5271, 5276 | proxy guard cleanup |\n\nThree asymmetries the refactor MUST preserve:\nBase URL differs per client. Claud","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"","lvl3":""}}, {"objectID":"fe51400abcfc1512d1db25764a166a0a2fd215f724eea3a7f9ff920f807f0aaf","title":"ClientConfigurator Registry Implementation Plan","url":"/docs/development/2026-08-20-client-configurator-registry#clientconfigurator-registry-implementation-plan","content":"For agentic workers: REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (- [ ]) syntax for tracking.\n\nGoal: Replace the three hand-written proxy client config writers and their four duplicated call-site blocks with one registry, so onboarding a new AI coding CLI is one new file plus one registry line instead of eleven edits.\n\nArchitecture: Introduce a ClientConfigurator type — { id, displayName, detect(), apply(baseUrl), restore(baseUrl) } — with one module per CLI under src/cli/proxy-clients/. A registry module exports the ordered list. The four call sites in proxy.ts collapse into two loops (applyAll, restoreAll). Behaviour is preserved exactly: same files written, same snapshot keys, same messages, same ordering (Claude → OpenCode → Codex).\n\nTech Stack: TypeScript (strict, type only — no interface), tsx test suites via defineSuite, yargs CLI, Prettier + ESLint with custom neurolink/* rules.\n\nSpec: docs/features/proxy-cli-onboarding.md (§3 touch points, §5 \"where it stops\")","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"ClientConfigurator Registry Implementation Plan","lvl3":""}}, {"objectID":"e2690c9cf3b8aa61641c36c40675511a2fa8a8724490eacf97ba7300f8c1cbb5","title":"Global Constraints","url":"/docs/development/2026-08-20-client-configurator-registry#global-constraints","content":"No interface. Use type X = { … }; intersection (&) not extends. (CLAUDE.md rule 7, ESLint neurolink/no-interface.)\nAll types live in src/lib/types/. No local type aliases in feature dirs. (rule 2, neurolink/no-local-type-alias.) Filenames must not contain \"Type\"/\"Types\" (rule 8).\nType names globally unique across src/lib/types/; CLI types take the Cli prefix (rule 9).\nBarrel-only type imports: import from ../types/index.js, never ../types/proxy.js (rule 13).\nNo double assertions (x as unknown as T) (rule 14).\nsrc/lib/types/index.ts uses export * only (rule 10).\nTests are end-to-end unless the determinism exception applies, and then the file header must state what determinism buys (rule 15). test/continuous-test-suite-proxy.ts is already on the allow list in eslint.config.js.\nAssertion messages must never quote a payload — defineSuite downgrades a failure to SKIP when the message matches isExpectedProviderError(). Describe the discrepancy instead.\nNever commit to release. Branch refactor/; conventional commits; no ticket prefix in this repo.\nBehaviour must not change. This is a pure refactor. Every file written, every snapshot key, every console string stays byte-identical.","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"Global Constraints","lvl3":""}}, {"objectID":"8be352b194f298e1e2661a010f51f432b496760c28d9a8d9f0a5f36ad0e5fc45","title":"Why this refactor, in one paragraph","url":"/docs/development/2026-08-20-client-configurator-registry#why-this-refactor-in-one-paragraph","content":"The three writers share no abstraction and disagree with each other in ways that have already shipped bugs. OpenCode targeted a directory OpenCode never reads and printed ✓ unconditionally (#1366, #1367 — both fixed); Claude still has no installed-check at all and creates ~/.claude/settings.json even when Claude Code is absent; and the same operation reports failure at two different log levels depending on which command ran it (logger.debug at proxy.ts:3499's block versus a visible yellow ⚠ at :5416's). Four duplicated call-site blocks is what let those diverge. The registry removes the duplication that manufactures this class of bug.","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"Why this refactor, in one paragraph","lvl3":""}}, {"objectID":"531c06ab5603f859506aee5d1f4ac3028e53df81856b28ccd7fd0de9da21cbb4","title":"Current-state map (verified at commit 845c3692)","url":"/docs/development/2026-08-20-client-configurator-registry#current-state-map-verified-at-commit-845c3692","content":"Writers, all in src/cli/commands/proxy.ts:\n\n| CLI | Constants | Apply | Restore | Snapshot mechanism |\n| -------- | ------------------------------------------------------------------------------------ | --------------------------------------------------- | ----------------------------------------------------- | ------------------------------------------------------- |\n| Claude | CLAUDE_SETTINGS_PATH:248, PROXY_MANAGED_KEYS:623 | setClaudeProxySettings:625 → Promise | clearClaudeProxySettings:654 → Promise | __proxy_original_env key inside settings.json |\n| OpenCode | getOpenCodeConfigDir:711, getOpenCodeConfigPath:724, OPENCODE_ORIGINAL_KEY:736 | setOpenCodeProxySettings:738 → Promise | clearOpenCodeProxySettings:793 → Promise | __proxy_original_neurolink key inside opencode.json |\n| Codex | CODEX_CONFIG_PATH:878, CODEX_SNAPSHOT_PATH:879 | setCodexProxySettings:943 → Promise | clearCodexProxySettings:1017 → Promise | sidecar ~/.neurolink/codex-proxy-snapshot.json |\n\nCall sites (12 calls across 4 blocks):\n\n| Block | Lines | Context |\n| --------- | ---------------------- | ---------------------------------- |\n| Apply A | 3499, 3512, 3526 | daemon start, inside if (!isDev) |\n| Apply B | 5416, 5427, 5438 | proxy setup wizard |\n| Restore A | 3202, 3210, 3215 | shutdown handler |\n| Restore B | 5269, 5271, 5276 | proxy guard cleanup |\n\nThree asymmetries the refactor MUST preserve:\nBase URL differs per client. Claude and Codex receive url; OpenCode receives ${url}/v1 .","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"Current-state map (verified at commit 845c3692)","lvl3":""}}, {"objectID":"a3e0d20b37cc9df1c17bd3438838479517219339c177d104a76b7eec60181ee1","title":"File Structure","url":"/docs/development/2026-08-20-client-configurator-registry#file-structure","content":"Create:\nsrc/lib/types/proxyClient.ts — the CliProxyClientConfigurator type and its result types. Types-folder rules forbid a Types suffix; proxyClient.ts is the canonical home.\nsrc/cli/proxy-clients/claudeCode.ts — Claude Code configurator.\nsrc/cli/proxy-clients/openCode.ts — OpenCode configurator.\nsrc/cli/proxy-clients/codex.ts — Codex configurator.\nsrc/cli/proxy-clients/registry.ts — ordered list + applyAllClients / restoreAllClients.\n\nModify:\nsrc/lib/types/index.ts — add export * from \"./proxyClient.js\";\nsrc/cli/commands/proxy.ts — delete the six writer functions and their constants; replace the four call-site blocks with loop calls.\ntest/continuous-test-suite-proxy.ts — retarget the three OpenCode tests at the new module; add registry-level tests.\neslint.config.js — no change expected; the proxy suite is already allow-listed.\n\nWhy one file per client rather than one configurators.ts: each client's snapshot mechanism is genuinely different (inline JSON key vs sidecar file vs TOML markers). Splitting by client keeps each file small enough to hold in context and means adding a fourth CLI touches no existing file except registry.ts.","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"File Structure","lvl3":""}}, {"objectID":"a94caeb26d5e445db614a2eb1a24fc37ddd10e2af51419418b26ada030f8f413","title":"Task 1: Define the configurator type","url":"/docs/development/2026-08-20-client-configurator-registry#task-1-define-the-configurator-type","content":"Files:\nCreate: src/lib/types/proxyClient.ts\nModify: src/lib/types/index.ts\nTest: test/continuous-test-suite-proxy.ts\n\nInterfaces:\nConsumes: nothing.\nProduces: CliProxyClientConfigurator, CliProxyClientApplyResult, CliProxyClientRestoreResult — used by every later task.\n[ ] Step 1: Write the failing test\n\nAdd near the other OpenCode cases in test/continuous-test-suite-proxy.ts:\n\nAssert the contract, not the final roster — the roster is only complete after\nTask 4, and every task must end with a green suite.\n\nRegister it alongside the existing OpenCode entries:\n[ ] Step 2: Run test to verify it fails\n\nRun: npx tsx test/continuous-test-suite-proxy.ts 2>&1 | grep -i \"registry\"\nExpected: FAIL — the import throws ERR_MODULE_NOT_FOUND for registry.js.\n[ ] Step 3: Write the type\n\nCreate src/lib/types/proxyClient.ts:\n\nAdd to src/lib/types/index.ts (keep the file's alphabetical grouping, export * only):\n[ ] Step 4: Create a stub registry so the test can reach the contract\n\nCreate src/cli/proxy-clients/registry.ts:\n[ ] Step 5: Run test to verify it now passes\n\nRun: npx tsx test/continuous-test-suite-proxy.ts 2>&1 | grep -i \"registry\"\nExpected: PASS — the contract holds trivially over an empty registry. Each later\ntask adds a configurator and the same test keeps guarding the contract, so the\nsuite is green at every commit.\n[ ] Step 6: Typecheck and commit the contract","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"Task 1: Define the configurator type","lvl3":""}}, {"objectID":"a79f10fd275549f485b7fc49928a8977bca8f44173c8ab1b1e2809c9e0029733","title":"Task 2: Move the OpenCode writer behind the contract","url":"/docs/development/2026-08-20-client-configurator-registry#task-2-move-the-opencode-writer-behind-the-contract","content":"Do OpenCode first: it is the only writer with existing regression tests, so it proves the contract against a covered client before the untested ones move.\n\nFiles:\nCreate: src/cli/proxy-clients/openCode.ts\nModify: src/cli/proxy-clients/registry.ts, src/cli/commands/proxy.ts:711-843, test/continuous-test-suite-proxy.ts\nTest: test/continuous-test-suite-proxy.ts\n\nInterfaces:\nConsumes: CliProxyClientConfigurator from Task 1.\nProduces: openCodeConfigurator, and __openCodeTestHooks re-exported from the new module so existing tests keep a seam.\n[ ] Step 1: Retarget the existing OpenCode tests at the new module\n\nIn test/continuous-test-suite-proxy.ts, change all three OpenCode tests' import from\n\nto\n[ ] Step 2: Run tests to verify they fail\n\nRun: npx tsx test/continuous-test-suite-proxy.ts 2>&1 | grep -i \"OpenCode\"\nExpected: all three FAIL — ERR_MODULE_NOT_FOUND for openCode.js.\n[ ] Step 3: Move the code\n\nCreate src/cli/proxy-clients/openCode.ts containing, moved verbatim from proxy.ts:711-843: getOpenCodeConfigDir, getOpenCodeConfigPath, OPENCODE_ORIGINAL_KEY, setOpenCodeProxySettings, clearOpenCodeProxySettings, plus the __openCodeTestHooks export. Keep every comment — particularly the systemManagedConfigDir() note in getOpenCodeConfigDir, which exists to stop the darwin branch being re-added.\n\nAdd the configurator at the end of that file:\n\nDelete lines 711-843 from proxy.ts and its __openCodeTestHooks export. Import the two functions back into proxy.ts for now so the existing call sites still compile:\n\nRegister it:\n[ ] Step 4: Run tests to verify they pass\n\nRun: npx tsx test/continuous-test-suite-proxy.ts 2>&1 | grep -iE \"OpenCode|registry\"\nExpected: the three OpenCode tests PASS, and the registry-shape test still PASSES (it guards the contract, not the roster).\n[ ] Step 5: Commit","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"Task 2: Move the OpenCode writer behind the contract","lvl3":""}}, {"objectID":"d8a3d8cbd402b1f854d18f35bfa0e71748fe3283900b3b522762aafb452fe031","title":"Task 3: Move the Claude Code and Codex writers","url":"/docs/development/2026-08-20-client-configurator-registry#task-3-move-the-claude-code-and-codex-writers","content":"Files:\nCreate: src/cli/proxy-clients/claudeCode.ts, src/cli/proxy-clients/codex.ts\nModify: src/cli/proxy-clients/registry.ts, src/cli/commands/proxy.ts (delete :248, :623-709, :878-1087)\nTest: test/continuous-test-suite-proxy.ts\n\nInterfaces:\nConsumes: CliProxyClientConfigurator.\nProduces: claudeCodeConfigurator, codexConfigurator, and __claudeCodeTestHooks / __codexClientTestHooks seams mirroring __openCodeTestHooks.\n[ ] Step 1: Write the failing test for the Claude installed-check\n\nThis closes the remaining half of #1368 and fixes the one real behaviour gap: Claude is the only writer with no detect().\n\nRegister it:\n[ ] Step 2: Run test to verify it fails\n\nRun: npx tsx test/continuous-test-suite-proxy.ts 2>&1 | grep -i \"Claude configurator\"\nExpected: FAIL — ERR_MODULE_NOT_FOUND for claudeCode.js.\n[ ] Step 3: Move Claude\n\nCreate src/cli/proxy-clients/claudeCode.ts with CLAUDE_SETTINGS_PATH, PROXY_MANAGED_KEYS, setClaudeProxySettings, clearClaudeProxySettings moved verbatim, then:\n\nConvert CLAUDE_SETTINGS_PATH from a module-level const into getClaudeSettingsPath() returning join(getClaudeSettingsDir(), \"settings.json\"), and update its three uses inside the moved functions — same change already made for OpenCode, and required for the test above to work.\n[ ] Step 4: Move Codex\n\nCreate src/cli/proxy-clients/codex.ts with CODEX_CONFIG_PATH, CODEX_SNAPSHOT_PATH, CODEX_BLOCK_BEGIN/CODEX_BLOCK_END, CODEX_PROVIDER_LINE_RE, CODEX_MODEL_LINE_RE, stripCodexManagedConfig, editTomlPreamble, buildCodexProviderBlock, setCodexProxySettings, clearCodexProxySettings moved verbatim, plus:\n\nConvert CODEX_CONFIG_PATH and CODEX_SNAPSHOT_PATH to getCodexConfigPath() / getCodexSnapshotPath() for the same HOME-resolution reason.\n\nDelete all six functions and their constants from proxy.ts; import the ones the call sites still reference.","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"Task 3: Move the Claude Code and Codex writers","lvl3":""}}, {"objectID":"db5561b1b8b42a5e50df6a1db6436d9ec660cb796b6d24cb492fe31443bd7627","title":"Task 4: Collapse the four call sites into two loops","url":"/docs/development/2026-08-20-client-configurator-registry#task-4-collapse-the-four-call-sites-into-two-loops","content":"Files:\nModify: src/cli/proxy-clients/registry.ts, src/cli/commands/proxy.ts (blocks at 3499-3538, 5416-5450, 3202-3218, 5269-5279)\nTest: test/continuous-test-suite-proxy.ts\n\nInterfaces:\nConsumes: all three configurators.\nProduces: applyAllClients(proxyBaseUrl) → Promise, restoreAllClients(proxyBaseUrl) → Promise.\n[ ] Step 1: Write the failing test\n\nRegister as \"Proxy clients: applyAll reports each client independently\", category proxy-config.\n\nAdd the roster assertion here too — this is the first task at which the full\nroster exists, so this is where pinning it is meaningful:\n\nRegister as \"Proxy clients: roster and apply order are pinned\", category proxy-config.\n[ ] Step 2: Run test to verify it fails\n\nRun: npx tsx test/continuous-test-suite-proxy.ts 2>&1 | grep -i \"applyAll\"\nExpected: FAIL — applyAllClients is not a function.\n[ ] Step 3: Implement the loops\n\nAppend to src/cli/proxy-clients/registry.ts:\n[ ] Step 4: Replace Apply block A (proxy.ts, the if (!isDev) block containing lines 3499-3538)\n[ ] Step 5: Replace Apply block B (proxy.ts, lines 5416-5450, the setup wizard)\n\nNote: the wizard previously printed Set manually: ANTHROPIC_BASE_URL= only on a Claude failure. Preserve it by checking result.id === \"claude-code\" inside the error branch.\n[ ] Step 6: Replace Restore blocks A and B (proxy.ts lines 3202-3218 and 5269-5279)\n\nFor block B, clearClaudeProxySettings's return value was consumed as cleared. Preserve it:\n[ ] Step 7: Run the full proxy suite\n\nRun: npx tsx test/continuous-test-suite-proxy.ts\nExpected: PASS, including the new roster test. Skips are acceptable only for the credential-gated cases.\n[ ] Step 8: Verify the shipped CLI still configures clients\n\nExpected: claude-code=false opencode=true codex=false — proving detect() gates writes in the built artifact.\n[ ] Step 9: Commit","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"Task 4: Collapse the four call sites into two loops","lvl3":""}}, {"objectID":"39ad745501223b2f03e460cbaec1a4d12829705bfd7bfc6b68960955fd0caad0","title":"Task 5: Prove the refactor pays off — add Qwen Code","url":"/docs/development/2026-08-20-client-configurator-registry#task-5-prove-the-refactor-pays-off-add-qwen-code","content":"The registry is only worth having if a fourth client is cheap. This task is the proof, and it delivers real coverage (docs/features/proxy-cli-onboarding.md §1 lists Qwen as installed and OpenAI-compatible).\n\nFiles:\nCreate: src/cli/proxy-clients/qwenCode.ts\nModify: src/cli/proxy-clients/registry.ts (one line), docs/features/proxy-cli-onboarding.md\nTest: test/continuous-test-suite-proxy.ts\n\nInterfaces:\nConsumes: CliProxyClientConfigurator.\nProduces: qwenCodeConfigurator.\n[ ] Step 1: Write the failing test\n[ ] Step 2: Run test to verify it fails\n\nRun: npx tsx test/continuous-test-suite-proxy.ts 2>&1 | grep -i \"qwen\"\nExpected: FAIL with \"qwen-code configurator is not registered\".\n[ ] Step 3: Implement\n\nQwen Code reads OPENAI_BASE_URL and OPENAI_API_KEY from its settings file — verified: @qwen-code/qwen-code@0.17.0's cli.js contains 7 OPENAI_BASE_URL occurrences read via process.env[\"OPENAI_BASE_URL\"]. Before implementing, confirm the on-disk settings shape by reading ~/.qwen/settings.json on a machine with Qwen installed; if the file does not exist, implement the env-var path only and mark the configurator's doc comment @unverified, matching how codexUsage.ts handles an unconfirmed wire shape.\n\nImplement writeQwenSettings / restoreQwenSettings mirroring openCode.ts's snapshot pattern (__proxy_original_* key inside the same file, written only on first touch).\n\nRegister with one line in registry.ts.\n\nThis invalidates the roster test added in Task 4 — update it to\n\"claude-code,opencode,codex,qwen-code\". That the roster test is the only\nexisting test needing a change is the measurable payoff this task is proving.\n[ ] Step 4: Run test to verify it passes\n\nRun: npx tsx test/continuous-test-suite-proxy.ts 2>&1 | grep -i \"qwen\"\nExpected: PASS.","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"Task 5: Prove the refactor pays off — add Qwen Code","lvl3":""}}, {"objectID":"b0d419c0f6f0f9c442f7905f7231a0e0e888ffa73a076f166db2340f3fb252b5","title":"Final verification","url":"/docs/development/2026-08-20-client-configurator-registry#final-verification","content":"[ ] npx tsc --noEmit --strict — clean\n[ ] NODE_OPTIONS='--max-old-space-size=8192' npx eslint . — 0 errors\n[ ] npx tsx test/continuous-test-suite-proxy.ts — PASS, no unexpected skips\n[ ] npx tsx test/continuous-test-suite-codex.ts — PASS\n[ ] pnpm run test:providers-mocked && pnpm run test:provider-structure && pnpm run test:error-classifier-contract — the three CI gates\n[ ] pnpm run build — clean, publint \"All good!\"\n[ ] pnpm run proxy:performance — the proxy-performance CI gate; this refactor touches proxy.ts, which it benchmarks\n[ ] Break one assertion on purpose and confirm the suite reports ✗ and exits non-zero rather than ⊘ — the isExpectedProviderError() skip-masking hazard\n[ ] Manually confirm proxy start then Ctrl+C leaves ~/.claude/settings.json, ~/.codex/config.toml and ~/.config/opencode/opencode.json byte-identical to before","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"Final verification","lvl3":""}}, {"objectID":"086b0c29e80b5d2600cf209db4b9eb0e455e5263735dfefae738da25b87288f4","title":"Risks","url":"/docs/development/2026-08-20-client-configurator-registry#risks","content":"Behaviour drift in messages. The two apply blocks print different strings. The loop parameterises them; a careless merge collapses them into one wording and changes user-visible output. The manual check above catches it.\nHOME resolution timing. Three module-level path constants become functions. If any moved function still closes over a stale constant, tests pass under the suite's isolated HOME but the real writer targets the wrong path — the exact shape of #1366. Grep for remaining const .*_PATH = join(homedir() after Task 3.\nproxy-performance gate. proxy.ts shrinks by roughly 400 lines; the benchmark job imports proxyLifecycle.ts and proxyActivity.ts, not the writers, so impact is unlikely — but it is an always-on CI gate, so run it before pushing.","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"Risks","lvl3":""}}, {"objectID":"4288c7dcdb47b46b9f74920941a0fdff90eea62d3a113294abab6d307f97b37c","title":"Post-review addenda","url":"/docs/development/2026-08-20-client-configurator-registry#post-review-addenda","content":"Two defects surfaced in review after the registry landed. Both are recorded here\nbecause they are properties of the lifecycle the registry now owns, not of any\none configurator.","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"Post-review addenda","lvl3":""}}, {"objectID":"ed4f4f6b06978db4212f25839d2c15def22c247233962ac88ed3184a650d40c7","title":"The snapshot must not outlive the value it describes","url":"/docs/development/2026-08-20-client-configurator-registry#the-snapshot-must-not-outlive-the-value-it-describes","content":"Each JSON writer persists the user's pre-existing value under a\n__proxy_original_* key inside the user's own config file, so a restore\nstill works after a crash or from another process. Snapshotting only on first\ntouch stops a second apply() from recording the proxy's own block as the\n\"original\".\n\nThat guard is presence-only, and the sentinel survives an unclean kill where no\nrestore ever ran. A user who then edits the block by hand — reasonably, since\nthe proxy is gone — hits this sequence:\napply() writes the proxy block; snapshot records \"user had nothing\".\nkill -9. No restore. Sentinel stays in the file.\nUser replaces the block with their own provider config and API key.\nProxy restarts. apply() sees the sentinel, keeps the stale snapshot, and\n overwrites the user's block.\nClean shutdown. restore() reads \"user had nothing\" and deletes it.\n\nFor Qwen that final step destroys a live credential. Each writer therefore also\nrecords what it wrote, under __proxy_written_*; shouldCaptureSnapshot() in\nsnapshot.ts re-snapshots whenever the value in the file is not the value we\nput there. A file written before this change carries no __proxy_written_* key,\nso the old behaviour is preserved for exactly one apply, then self-heals.","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"The snapshot must not outlive the value it describes","lvl3":""}}, {"objectID":"cd0871cc395744936467b1567a07d023c0ea8172ee049b44a89a1de16abd3ba6","title":"uninstall is the only restore point a service ever reaches","url":"/docs/development/2026-08-20-client-configurator-registry#uninstall-is-the-only-restore-point-a-service-ever-reaches","content":"restoreAllClients() runs from the shutdown path only under\nsignal === \"SIGINT\". A launchd-managed service never receives one: launchctl\nunload, launchctl stop and proxy uninstall all send SIGTERM, and the\nsupervisor's own shutdown closure never touched client configs at all. The\nfail-open guard that would otherwise cover this is not spawned when\nmanagedByLaunchd.\n\nSo the documented one-command install — neurolink proxy setup — left all five\nCLIs pointing at a dead socket after uninstall, silently. uninstall now calls\nrestoreClientsOnUninstall() before clearProxyState(), deriving the URL from\nthe recorded host/port (0.0.0.0 normalised to localhost, matching what the\nclients were actually handed).\n\nWidening the signal gate was considered and rejected: a service also receives\nSIGTERM on reboot and on rolling restart, where restoring would be wrong.\nuninstall is the one point where \"going away for good\" is unambiguous.\n\nThe regression test drives the built CLI rather than the helper, because the\ndefect was the missing wiring — a test calling the helper directly would have\npassed for as long as the bug existed.","hierarchy":{"lvl0":"Development","lvl1":"ClientConfigurator Registry Implementation Plan","lvl2":"uninstall is the only restore point a service ever reaches","lvl3":""}}, {"objectID":"a59d844735e590b6ffe570b8ca66561413bbc656aaa74eb4bc53bfae119f02dc","title":"System Architecture","url":"/docs/development/architecture","content":"System Architecture\n\nTechnical architecture overview of NeuroLink's enterprise AI platform, including design patterns, scalability considerations, and integration approaches.\n\n🏗️ High-Level Architecture\n\nCore Components\n\nArchitecture Principles\nProvider Agnostic: Universal interface to multiple AI providers\nFactory Pattern: Consistent creation and management of provider instances\nFail-Safe Design: Automatic fallback and error recovery\nHorizontal Scaling: Stateless design for cloud deployment\nObservability: Comprehensive monitoring and analytics\nExtensibility: Plugin architecture for custom functionality\n\n🔧 Core Platform Design\n\nProvider Router\n\nResponsibility: Intelligent request routing and load balancing\n\nFactory Pattern Engine\n\nResponsibility: Consistent provider instance creation and lifecycle management\n\nAnalytics Engine\n\nResponsibility: Usage tracking, performance monitoring, and insights generation\n\n🔀 Provider Integration Architecture\n\nUniversal Provider Interface\n\nProvider-Specific Implementations\n\n🔧 MCP (Model Context Protocol) Integration\n\nMCP Architecture\n\n📊 Data Flow Architecture\n\nRequest Processing Pipeline\n\nAnalytics Data Pipeline\n\n🚀 Scalability & Performance\n\nHorizontal Scaling Design\n\nCaching Strategy\n\n🔐 Security Architecture\n\nAuthentication & Authorization\n\nAPI Key Management\n\n📈 Monitoring & Observability\n\nMetrics Collection\n\nHealth Monitoring\n\nThis architecture provides a robust, scalable foundation for NeuroLink's enterprise AI platform, ensuring reliability, performance, and security at scale.\n\n📚 Related Documentation\nFactory Patterns - Implementation patterns\nDevelopment Guide - Development setup\nTesting Strategy - Quality assurance\nPerformance Optimization - Monitoring and optimization","hierarchy":{"lvl0":"Development","lvl1":"System Architecture","lvl2":"","lvl3":""}}, {"objectID":"03589988fd761b99630d22eb4c5e021c1353bb68decb65d478aa5135f313dd4a","title":"System Architecture","url":"/docs/development/architecture#system-architecture","content":"Technical architecture overview of NeuroLink's enterprise AI platform, including design patterns, scalability considerations, and integration approaches.","hierarchy":{"lvl0":"Development","lvl1":"System Architecture","lvl2":"System Architecture","lvl3":""}}, {"objectID":"2495bef0195c6dc769468068d1bff7203b1e4501b568bffd8b8e2a6fea3140bf","title":"Architecture Principles","url":"/docs/development/architecture#architecture-principles","content":"Provider Agnostic: Universal interface to multiple AI providers\nFactory Pattern: Consistent creation and management of provider instances\nFail-Safe Design: Automatic fallback and error recovery\nHorizontal Scaling: Stateless design for cloud deployment\nObservability: Comprehensive monitoring and analytics\nExtensibility: Plugin architecture for custom functionality","hierarchy":{"lvl0":"Development","lvl1":"System Architecture","lvl2":"Architecture Principles","lvl3":""}}, {"objectID":"ac5223b3743d50ce7fc222d179bb7406f545fb1146aec137dd43c4a0752049b4","title":"Provider Router","url":"/docs/development/architecture#provider-router","content":"Responsibility: Intelligent request routing and load balancing","hierarchy":{"lvl0":"Development","lvl1":"System Architecture","lvl2":"Provider Router","lvl3":""}}, {"objectID":"2ac6854c7b6a7f2ecf107080181f8fa0cbc4947839ceadb32bf9428f65b29e92","title":"Factory Pattern Engine","url":"/docs/development/architecture#factory-pattern-engine","content":"Responsibility: Consistent provider instance creation and lifecycle management","hierarchy":{"lvl0":"Development","lvl1":"System Architecture","lvl2":"Factory Pattern Engine","lvl3":""}}, {"objectID":"85559d82b037c183c89a1087298f46b7cfe8f4fee3714f3ab75b586368eddddc","title":"Analytics Engine","url":"/docs/development/architecture#analytics-engine","content":"Responsibility: Usage tracking, performance monitoring, and insights generation","hierarchy":{"lvl0":"Development","lvl1":"System Architecture","lvl2":"Analytics Engine","lvl3":""}}, {"objectID":"27ad42ffd67798efe5f183e29c1ed2a29ff73c8e89de21fa0b2df370526c340f","title":"Health Monitoring","url":"/docs/development/architecture#health-monitoring","content":"This architecture provides a robust, scalable foundation for NeuroLink's enterprise AI platform, ensuring reliability, performance, and security at scale.","hierarchy":{"lvl0":"Development","lvl1":"System Architecture","lvl2":"Health Monitoring","lvl3":""}}, {"objectID":"4ce4f1a4ed1e77cd0f011ab3403c5e55e8d0e09c104fe7bc7969eddb6f6ab472","title":"📚 Related Documentation","url":"/docs/development/architecture#-related-documentation","content":"Factory Patterns - Implementation patterns\nDevelopment Guide - Development setup\nTesting Strategy - Quality assurance\nPerformance Optimization - Monitoring and optimization","hierarchy":{"lvl0":"Development","lvl1":"System Architecture","lvl2":"📚 Related Documentation","lvl3":""}}, {"objectID":"0b999063a4269d327621b7f7b34ebe4ce5a9bf01fee1651c6beb0f932f99c2bd","title":"Changelog Automation & Formatting","url":"/docs/development/changelog-automation","content":"Changelog Automation & Formatting\n\nNeuroLink automatically formats the CHANGELOG.md file after generation during the release process to ensure consistent formatting and readability.\n\nOverview\n\nThe project uses semantic-release to automatically generate changelogs based on commit messages. 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release","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Release Process Flow","lvl3":""}}, {"objectID":"7481d6b93980832f303bb391a5a07c8c48b579d58989f56d661eed9f05e52cad","title":"Configuration","url":"/docs/development/changelog-automation#configuration","content":"The formatting is configured in .releaserc.json:","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Configuration","lvl3":""}}, {"objectID":"0f2a5d6dbabcaeeaadb2d65a228be0414430e526415b46dad044efeb674d1894","title":"Format Changelog Script","url":"/docs/development/changelog-automation#format-changelog-script","content":"Location: scripts/format-changelog.ts\n\nStandalone script that formats CHANGELOG.md using Prettier:\n\n`bash","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Format Changelog Script","lvl3":""}}, {"objectID":"45f05871a2c234887443d9f94eebfe38ccdd7350ab1c66ce3ea3966d04bcab76","title":"Run manually","url":"/docs/development/changelog-automation#run-manually","content":"pnpm run format:changelog","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Run manually","lvl3":""}}, {"objectID":"a44fe01f31654d25274d769013373ccdd103a553bcb0c588388183bb15ac5981","title":"Or directly","url":"/docs/development/changelog-automation#or-directly","content":"tsx scripts/format-changelog.ts\n`\n\nFeatures:\n✅ Checks if CHANGELOG.md exists before formatting\n✅ Uses project's Prettier configuration\n✅ Provides clear success/error feedback\n✅ Exits with error code on failure","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Or directly","lvl3":""}}, {"objectID":"d11ebee1f841953dcbf58ed0e62208d21ad85c44d3e4bd1f23f8dee92ceca233","title":"Semantic Release Plugin","url":"/docs/development/changelog-automation#semantic-release-plugin","content":"Location: scripts/semantic-release-format-plugin.cjs\n\nCustom semantic-release plugin that integrates formatting into the release workflow:\n\nFeatures:\n✅ Runs during the prepare step after changelog generation\n✅ Uses semantic-release's logger for consistent output\n✅ Automatically skips if CHANGELOG.md doesn't exist\n✅ Integrates seamlessly with existing release pipeline","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Semantic Release Plugin","lvl3":""}}, {"objectID":"bc1998e84e7215d840efe032c24f9807faa4b394b56b4273e74ce1eedbd68784","title":"Consistent Formatting","url":"/docs/development/changelog-automation#consistent-formatting","content":"All changelog entries follow the same formatting rules\nMarkdown is properly structured and readable\nCode blocks, links, and lists are consistently formatted","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Consistent Formatting","lvl3":""}}, {"objectID":"b7fc26cce5a6c3a7680b56cdbfc5046e4cc72458f757e33d775cb35a996ea05b","title":"Automated 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{"objectID":"40c8d406681511789192973b940f3d467e75652e68d1cae9a476e25aa4ba40c1","title":"Plugin Not Running","url":"/docs/development/changelog-automation#plugin-not-running","content":"If the formatting plugin doesn't run during releases:\nCheck Plugin Order: Ensure the format plugin comes after @semantic-release/changelog\nCheck Plugin Path: Ensure ./scripts/semantic-release-format-plugin.cjs exists and is executable\nCheck Semantic Release Config: Ensure .releaserc.json is valid JSON","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Plugin Not Running","lvl3":""}}, {"objectID":"ba09f40bc0f9ecaf35d9f1a5185243ea26a0daded25480c67cd66452f57ba4cd","title":"Integration with Build Rules","url":"/docs/development/changelog-automation#integration-with-build-rules","content":"The changelog formatting integrates with NeuroLink's comprehensive build rule enforcement:\nPre-commit Hooks: Lint-staged ensures files are formatted before commits\nCI Validation: GitHub Actions verify formatting in pull requests\nRelease Automation: Semantic-release handles the entire release pipeline\nQuality Gates: All formatting must pass before merge","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Integration with Build Rules","lvl3":""}}, {"objectID":"02f5770a1cac6d2c4497ca4f1ad2807330692349239726606411cfc517020f41","title":"Commit Messages","url":"/docs/development/changelog-automation#commit-messages","content":"Use semantic commit messages to generate meaningful changelog entries:\n\n`bash","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Commit Messages","lvl3":""}}, {"objectID":"3c9a08f0cd06f0a47f624dff6bca7610bb6a3ef658e5ae47e4dd280df2987f09","title":"Good - generates clear changelog entry","url":"/docs/development/changelog-automation#good---generates-clear-changelog-entry","content":"feat(auth): add OAuth2 authentication system","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Good - generates clear changelog entry","lvl3":""}}, {"objectID":"e1e3bd62f98ec3ff1d3f5e763eacafa94c126085f976fb5d7c2ff380409efa93","title":"Good - generates clear changelog entry","url":"/docs/development/changelog-automation#good---generates-clear-changelog-entry-1","content":"fix(api): resolve timeout issues in user service","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Good - generates clear changelog entry","lvl3":""}}, {"objectID":"aea439d8b2f7e0fce825017cdc67ef88819daf67ff49f1610992633983d91dc0","title":"Bad - creates unclear changelog entry","url":"/docs/development/changelog-automation#bad---creates-unclear-changelog-entry","content":"Update stuff\n`","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Bad - creates unclear changelog entry","lvl3":""}}, {"objectID":"64b17024a2da584929358b7b07c4038cc131b438ee9bcdaf981b59b04a184f97","title":"Release Workflow","url":"/docs/development/changelog-automation#release-workflow","content":"Development: Make commits with semantic commit messages\nPull Request: CI validates formatting and build rules\nMerge: Squash merge to release branch\nAutomatic Release: semantic-release generates and formats changelog\nDistribution: Formatted changelog is published to npm and GitHub\n\nThis automation ensures that NeuroLink's changelog remains consistently formatted and professional, supporting our commitment to high-quality documentation and developer experience.","hierarchy":{"lvl0":"Development","lvl1":"Changelog Automation & Formatting","lvl2":"Release Workflow","lvl3":""}}, {"objectID":"091eee98b9ff203b741de97760b0c718e7e5e5285e622fe9a38252731dc1f19b","title":"CLI Factory Integration Impact Assessment","url":"/docs/development/cli-factory-impact-assessment","content":"CLI Factory Integration Impact Assessment\n\nOverview\n\nThis document assesses the impact of the Phase 1 Factory Infrastructure implementation on the NeuroLink CLI, demonstrating zero breaking changes while adding powerful enhancement capabilities.\n\nExecutive Summary\n\n✅ Zero Breaking Changes Confirmed \n✅ All Existing CLI Commands Maintained \n✅ Enhanced Capabilities Added Seamlessly \n✅ Performance Impact: Negligible \n✅ Backward Compatibility: 100%\n\nCLI Architecture Analysis\n\nCurrent CLI Structure\n\nThe NeuroLink CLI is built with a robust command factory pattern (CLICommandFactory) that provides:\nGenerate Command: Primary text generation with full options\nStream Command: Real-time streaming generation\nBatch Command: Multiple prompt processing\nProvider Commands: Provider status and management\nModels Commands: Model listing and management\nMCP Commands: MCP server integration\nConfig Commands: Configuration management\n\nFactory Pattern Integration Points\n\nThe factory patterns integrate seamlessly at these levels:\nSDK Level: CLI uses NeuroLink SDK which now includes factory enhancements\nOptions Processing: CLI option processing preserved, enhanced options passed through\nOutput Formatting: Existing output formats maintained, analytics display enhanced\nContext Handling: New context support added without breaking existing functionality\n\nCompatibility Assessment\nCommand Interface Compatibility\n\n| Command | Status | Changes | Notes |\n| ----------------- | ------------- | ------- | ----------------------------------- |\n| generate | ✅ Maintained | None | All existing flags work identically |\n| stream | ✅ Maintained | None | Streaming behavior unchanged |\n| batch | ✅ Maintained | None | Batch processing preserved |\n| provider status | ✅ Maintained | None | Status checking unchanged |\n| models list | ✅ Maintained | None | Model listing preserved |\n| mcp discover | ✅ Maintained | None | MCP discovery unchanged |\n| config | ✅ Maintained | None | Configuration commands preserved |\nFlag Compatibility\n\n| Flag Category | Status | Enhancement |\n| -------------------- | ------------ | --------------------------------------------------------------- |\n| Core Flags | ✅ Preserved | --provider, --model, --temperature, etc. work identically |\n| Analytics Flags | ✅ Enhanced | --enable-analytics now includes factory metadata |\n| Evaluation Flags | ✅ Enhanced | --enable-evaluation supports domain-aware evaluation |\n| Context Flags | ✅ Enhanced | --context now supports factory context processing |\n| Output Flags | ✅ Preserved | --format, --output work identically |\n| Debug Flags | ✅ Enhanced | --debug includes factory enhancement information |\nEnvironment Variables\n\n| Variable | Status | Notes |\n| ----------------------- | ------------ | -------------------------------------- |\n| Provider API Keys | ✅ Unchanged | All provider authentication preserved |\n| NEUROLINK_DEBUG | ✅ Enhanced | Now includes factory debug information |\n| NEUROLINK_CONFIG_FILE | ✅ Unchanged | Configuration file handling preserved |\n| NO_COLOR | ✅ Unchanged | Color control maintained |\n\nPerformance Impact Analysis\n\nCLI Startup Time\nBefore Factory Patterns: ~2-3 seconds\nAfter Factory Patterns: ~2-3 seconds\nImpact: Negligible (factory initialization is lazy)\n\nCommand Execution Time\nEnhancement Processing: \\<10ms per command\nMemory Overhead: \\<5MB additional\nNetwork Performance: No impact (factory patterns are local)\n\nReal-World Performance Tests\n\nNew Capabilities Added\nEnhanced Analytics Integration\n\nOutput Enhancement:\nDomain-Aware Evaluation\n\nEnhanced Evaluation:\nDomain-specific scoring thresholds\nContext-aware relevance assessment\nFactory pattern metadata included\nAdvanced Context Processing\n\nContext Enhancements:\nType-safe context validation\nContext integration modes\nAnalytics context tracking\nFactory pattern context processing\n\nMigration Path for Existing Users\n\nNo Migration Required\n\nExisting CLI usage patterns work identically:\n\nOptional Enhancement Adoption\n\nUsers can gradually adopt new features:\n\nTesting Strategy\n\nComprehensive CLI Test Suite\n\nCreated test/cli/factoryCliIntegration.test.ts with:\n14 test suites covering all CLI functionality\n50+ individual tests validating zero breaking changes\nReal CLI execution using child processes\nPerformance benchmarking for factory overhead\nError handling validation for edge cases\nOutput format compatibility testing\n\nTest Coverage Areas\nCommand Compatibility (5 tests)\nAll existing commands work identically\nFlag compatibility maintained\nOutput formats preserved\nAnalytics Integration (3 tests)\nAnalytics flags work w","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"","lvl3":""}}, {"objectID":"b72d7ea155457aac615cadf5585e8da6736e23965df086dc518944ce311c25fe","title":"Overview","url":"/docs/development/cli-factory-impact-assessment#overview","content":"This document assesses the impact 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Flag Compatibility","url":"/docs/development/cli-factory-impact-assessment#2-flag-compatibility","content":"| Flag Category | Status | Enhancement |\n| -------------------- | ------------ | --------------------------------------------------------------- |\n| Core Flags | ✅ Preserved | --provider, --model, --temperature, etc. work identically |\n| Analytics Flags | ✅ Enhanced | --enable-analytics now includes factory metadata |\n| Evaluation Flags | ✅ Enhanced | --enable-evaluation supports domain-aware evaluation |\n| Context Flags | ✅ Enhanced | --context now supports factory context processing |\n| Output Flags | ✅ Preserved | --format, --output work identically |\n| Debug Flags | ✅ Enhanced | --debug includes factory enhancement information |","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"2. Flag Compatibility","lvl3":""}}, {"objectID":"8b5b8d6ad4f793422f97ff8b95f759e04aab25274e982a517a974366e7cb03a8","title":"3. Environment Variables","url":"/docs/development/cli-factory-impact-assessment#3-environment-variables","content":"| Variable | Status | Notes |\n| ----------------------- | ------------ | -------------------------------------- |\n| Provider API Keys | ✅ Unchanged | All provider authentication preserved |\n| NEUROLINK_DEBUG | ✅ Enhanced | Now includes factory debug information |\n| NEUROLINK_CONFIG_FILE | ✅ Unchanged | Configuration file handling preserved |\n| NO_COLOR | ✅ Unchanged | Color control maintained |","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"3. Environment Variables","lvl3":""}}, {"objectID":"a4924d49af2894c188610245087afaaeb5b8fd3c86ccf3f7e972cc83438d95e0","title":"CLI Startup Time","url":"/docs/development/cli-factory-impact-assessment#cli-startup-time","content":"Before Factory Patterns: ~2-3 seconds\nAfter Factory Patterns: ~2-3 seconds\nImpact: Negligible (factory initialization is lazy)","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"CLI Startup Time","lvl3":""}}, {"objectID":"34782be6f8f42f05cfbf9e22f316368d47a2996242114d3952e4ff6d4c69bf09","title":"Command Execution Time","url":"/docs/development/cli-factory-impact-assessment#command-execution-time","content":"Enhancement Processing: \\<10ms per command\nMemory Overhead: \\<5MB additional\nNetwork Performance: No impact (factory patterns are local)","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Command Execution Time","lvl3":""}}, {"objectID":"a31d8fafcaa055fe61b81bcef0589e9e5e2a4cfa2c4f05ad13f3932a46e7dcf6","title":"Real-World Performance Tests","url":"/docs/development/cli-factory-impact-assessment#real-world-performance-tests","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Real-World Performance Tests","lvl3":""}}, {"objectID":"01b7263e445bc4326ab9e00e5ba0ce9580b8d16f8e75a3032d109c525fba8024","title":"Generate command performance","url":"/docs/development/cli-factory-impact-assessment#generate-command-performance","content":"time neurolink generate \"test\" --provider google-ai","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Generate command performance","lvl3":""}}, {"objectID":"275f0f87ff8f1cdf2fdddbb2f94fd7b183d17d9bc2ce6e7250b35d3e28227244","title":"Stream command performance","url":"/docs/development/cli-factory-impact-assessment#stream-command-performance","content":"time neurolink stream \"test\" --provider google-ai","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Stream command performance","lvl3":""}}, {"objectID":"104b8dc03202437dc355f5dd0ad47f464318bd6fc221f670aecebdfa49efe171","title":"Batch command performance","url":"/docs/development/cli-factory-impact-assessment#batch-command-performance","content":"time neurolink batch test-file.txt --provider google-ai","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Batch command performance","lvl3":""}}, {"objectID":"630facb00351a9327ebe854701b62b62bef8bb6957e3a1ff98b3e5ac4ec66c3d","title":"After: ~15s for 5 prompts (factory overhead amortized)","url":"/docs/development/cli-factory-impact-assessment#after-15s-for-5-prompts-factory-overhead-amortized","content":"`","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"After: ~15s for 5 prompts (factory overhead amortized)","lvl3":""}}, {"objectID":"31bcb0ba58fa6c9a9d5049c63e7bdca3d3de6b4ed46e9903b5ba308eede962d2","title":"1. Enhanced Analytics Integration","url":"/docs/development/cli-factory-impact-assessment#1-enhanced-analytics-integration","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"1. Enhanced Analytics Integration","lvl3":""}}, {"objectID":"d26e40cdcf355f818fdbcf5e7dbf57123353eb5a837ac503115386511f440601","title":"Enhanced analytics with factory metadata","url":"/docs/development/cli-factory-impact-assessment#enhanced-analytics-with-factory-metadata","content":"neurolink generate \"test\" --enable-analytics --provider google-ai\n\n📊 Analytics:\n Provider: google-ai (gemini-2.5-flash)\n Tokens: 8 input + 12 output = 20 total\n Cost: $0.00002\n Time: 1.2s\n Factory Enhancement: domain-configuration (if applicable)\n Enhancement Processing: 3ms\n`","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Enhanced analytics with factory metadata","lvl3":""}}, {"objectID":"5985d5214c5776ddac9977cccb0a4ad28ee7aa4b31fcf1b474e5f335915485f4","title":"2. Domain-Aware Evaluation","url":"/docs/development/cli-factory-impact-assessment#2-domain-aware-evaluation","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"2. Domain-Aware Evaluation","lvl3":""}}, {"objectID":"45659b7ae8bf16702f320d01eaa477b538e5c9725cab9aacb14785078bdc2988","title":"Domain-specific evaluation","url":"/docs/development/cli-factory-impact-assessment#domain-specific-evaluation","content":"neurolink generate \"analyze patient data\" --enable-evaluation --evaluation-domain healthcare\n`\n\nEnhanced Evaluation:\nDomain-specific scoring thresholds\nContext-aware relevance assessment\nFactory pattern metadata included","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Domain-specific evaluation","lvl3":""}}, {"objectID":"19e5fcb6d807897cd54f6c6d45f49c36b26e3a75bc34138fcb4bcb03b4206261","title":"3. Advanced Context Processing","url":"/docs/development/cli-factory-impact-assessment#3-advanced-context-processing","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"3. Advanced Context Processing","lvl3":""}}, {"objectID":"3aa90b7e43c46f54581322c43d44057d8ea02540fc2786a020d74b576f21d73a","title":"Enhanced context processing","url":"/docs/development/cli-factory-impact-assessment#enhanced-context-processing","content":"neurolink generate \"test\" --context '{\"domain\":\"healthcare\",\"userId\":\"doc123\"}'\n`\n\nContext Enhancements:\nType-safe context validation\nContext integration modes\nAnalytics context tracking\nFactory pattern context processing","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Enhanced context processing","lvl3":""}}, {"objectID":"c73ad60273d351ef183be9bed1ca8db139db7696095145ea26093bc7192aa3e2","title":"No Migration Required","url":"/docs/development/cli-factory-impact-assessment#no-migration-required","content":"Existing CLI usage patterns work identically:\n\n`bash","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"No Migration Required","lvl3":""}}, {"objectID":"caea40c0ae2aa74c9215a02545aefa1141eb1f5e54e14c773c0e3e377626237e","title":"All these commands work exactly as before","url":"/docs/development/cli-factory-impact-assessment#all-these-commands-work-exactly-as-before","content":"neurolink generate \"hello world\"\nneurolink stream \"tell me a story\" --provider openai\nneurolink batch prompts.txt --format json\nneurolink provider status\n`","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"All these commands work exactly as before","lvl3":""}}, {"objectID":"e23f044cf986f00a91d7b867be36077abeab6bc0f053e4527e1b8e6c381a8bb3","title":"Optional Enhancement Adoption","url":"/docs/development/cli-factory-impact-assessment#optional-enhancement-adoption","content":"Users can gradually adopt new features:\n\n`bash","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Optional Enhancement Adoption","lvl3":""}}, {"objectID":"10864209704afeb1d443dac9c7a51529afab987e71e4553d21365788a8933e7a","title":"Step 1: Add analytics (optional)","url":"/docs/development/cli-factory-impact-assessment#step-1-add-analytics-optional","content":"neurolink generate \"test\" --enable-analytics","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Step 1: Add analytics (optional)","lvl3":""}}, {"objectID":"0de1cf84d2870ebc99d89a21e547b1e072e5d9fbbe19b343fff7926fcd3bfda1","title":"Step 2: Add evaluation (optional)","url":"/docs/development/cli-factory-impact-assessment#step-2-add-evaluation-optional","content":"neurolink generate \"test\" --enable-evaluation","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Step 2: Add evaluation (optional)","lvl3":""}}, {"objectID":"caea1be5872f1708b53e88b18fa046cb83ed8cfae219435b8336ddb26e1a0f57","title":"Step 3: Add domain awareness (optional)","url":"/docs/development/cli-factory-impact-assessment#step-3-add-domain-awareness-optional","content":"neurolink generate \"test\" --enable-evaluation --evaluation-domain analytics\n`","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Step 3: Add domain awareness (optional)","lvl3":""}}, {"objectID":"e67f91805ce4c9bd0dbce6a3a34af5733df5375806905c30d8cd1638d7daec90","title":"Comprehensive CLI Test Suite","url":"/docs/development/cli-factory-impact-assessment#comprehensive-cli-test-suite","content":"Created test/cli/factoryCliIntegration.test.ts with:\n14 test suites covering all CLI functionality\n50+ individual tests validating zero breaking changes\nReal CLI execution using child processes\nPerformance benchmarking for factory overhead\nError handling validation for edge cases\nOutput format compatibility testing","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Comprehensive CLI Test 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Quiet Modes (2 tests)\nDebug mode enhanced with factory info\nQuiet mode behavior preserved\nBackward Compatibility (2 tests)\nLegacy command formats work\nEnvironment variable compatibility","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Test Coverage Areas","lvl3":""}}, {"objectID":"b111cbf2f1946766e544e5cb6662d3716c6f4688abea0612d01ef7ed077c40e5","title":"Low Risk Areas ✅","url":"/docs/development/cli-factory-impact-assessment#low-risk-areas-","content":"Command Interface: No changes to public API\nFlag Processing: Enhanced but backward compatible\nOutput Formats: Preserved with optional enhancements\nEnvironment Variables: No changes required","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Low Risk Areas ✅","lvl3":""}}, {"objectID":"325649f40626e5e29cf5a419d057d842e0c8b9e314c0f339dcd76b099ac4ff07","title":"Medium Risk Areas ⚠️","url":"/docs/development/cli-factory-impact-assessment#medium-risk-areas-","content":"Performance: Minimal overhead added (\\<10ms per command)\nMemory Usage: Small increase (\\<5MB)\nDebug Output: Enhanced with factory information","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Medium Risk Areas ⚠️","lvl3":""}}, {"objectID":"d627133582c3b4a11524534bc9b676e824c8a517cf4ab65047cc0f20cd2f8010","title":"Mitigation Strategies","url":"/docs/development/cli-factory-impact-assessment#mitigation-strategies","content":"Performance Monitoring: Factory processing time logged in debug mode\nGraceful Degradation: Factory failures don't break core CLI functionality\nOptional Enhancement: New features are opt-in only","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Mitigation Strategies","lvl3":""}}, {"objectID":"582ee1bd599219619162ccd9cf6f4db22a99f027bd589d459167b77f2eaeb070","title":"Code Quality Metrics","url":"/docs/development/cli-factory-impact-assessment#code-quality-metrics","content":"TypeScript Strict Mode: ✅ Full compliance\nESLint + Prettier: ✅ Zero linting errors\nBuild Validation: ✅ All builds successful\nTest Coverage: ✅ 95%+ CLI functionality covered","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Code Quality Metrics","lvl3":""}}, {"objectID":"2a5a6df49332c28703a367428983450215d18f098e27797fd65fcfd414598fe5","title":"Integration Testing","url":"/docs/development/cli-factory-impact-assessment#integration-testing","content":"Real Provider Testing: ✅ Google AI, OpenAI, Anthropic\nCross-Platform: ✅ macOS, Linux, Windows\nNode.js Versions: ✅ 18, 20, 22 compatibility","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Integration Testing","lvl3":""}}, {"objectID":"ff808fad8375e0135df7cbee84f68502f11d393a03d79d5ed2bfdc0e203d5ba9","title":"Rollout Strategy","url":"/docs/development/cli-factory-impact-assessment#rollout-strategy","content":"Phase 1: Deploy with factory patterns enabled (current state)\nPhase 2: Monitor CLI usage patterns and performance\nPhase 3: Gradually promote enhanced features to users","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Rollout Strategy","lvl3":""}}, {"objectID":"c8e658d17697617c92898d1b963f174845d21410bdb46ddbb7359ecb60992ed6","title":"Monitoring Points","url":"/docs/development/cli-factory-impact-assessment#monitoring-points","content":"CLI command execution times\nError rates and types\nFeature adoption metrics (analytics, evaluation usage)\nUser feedback on new capabilities","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Monitoring Points","lvl3":""}}, {"objectID":"166a9ad65e1515e9db6e67931e2910308aa192b2153625dfafbb3b9c1587ec54","title":"Conclusion","url":"/docs/development/cli-factory-impact-assessment#conclusion","content":"The Phase 1 Factory Infrastructure implementation successfully integrates with the NeuroLink CLI while maintaining 100% backward compatibility and zero breaking changes.","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact Assessment","lvl2":"Conclusion","lvl3":""}}, {"objectID":"33820fd465ea242b750f15ef7f8a6fb6f83190494901ec7149fa28bc900f3c16","title":"Key Achievements:","url":"/docs/development/cli-factory-impact-assessment#key-achievements","content":"✅ All existing CLI commands work identically \n✅ New enhancement capabilities added seamlessly \n✅ Performance impact is negligible (\\<10ms per command) \n✅ Comprehensive test coverage validates compatibility \n✅ Optional enhancement adoption path provided","hierarchy":{"lvl0":"Development","lvl1":"CLI Factory Integration Impact 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architecture with BaseProvider inheritance and automatic tool support.\n\n📋 Overview\n\nNeuroLink uses a Factory Pattern architecture with BaseProvider inheritance to provide consistent functionality across supported AI providers. This design eliminates code duplication and ensures each provider has the same core capabilities, including built-in tool support.\n\nKey Benefits\n✅ Zero Code Duplication: Shared logic in BaseProvider\n✅ Automatic Tool Support: Providers inherit 6 built-in tools\n✅ Consistent Interface: Same methods across supported providers\n✅ Easy Provider Addition: Minimal code for new providers\n✅ Centralized Updates: Fix once, apply everywhere\n\n🏗️ Architecture Components\nBaseProvider (Core Foundation)\n\nThe BaseProvider class is the foundation of all AI providers:\nProvider-Specific Implementation\n\nEach provider extends BaseProvider with minimal code:\nFactory Pattern Implementation\n\nThe factory creates providers with consistent configuration:\n\n🔧 Built-in Tool System\n\nTool Registration in BaseProvider\n\nProviders automatically get these tools:\n\nTool Conversion for AI Models\n\nBaseProvider converts tools to provider-specific format:\n\n🌟 Factory Pattern Benefits\nConsistent Provider Creation\nEasy Provider Addition\n\nAdding a new provider requires minimal code:\nCentralized Feature Addition\n\nAdd features once in BaseProvider, and providers get them:\n\n📊 Architecture Diagram\n\n🎯 Design Principles\nSingle Responsibility\n\nEach component has one clear purpose:\nBaseProvider: Core functionality and tool management\nProvider Classes: Provider-specific API integration\nFactory: Provider instantiation\nRegistry: Provider registration and lookup\nOpen/Closed Principle\nOpen for extension: Easy to add new providers\nClosed for modification: Core logic doesn't change\nDependency Inversion\nProviders depend on BaseProvider abstraction\nHigh-level modules don't depend on low-level details\nInterface Segregation\nClean, minimal interface for each provider\nOnly implement what's needed\n\n🔄 Request Flow\n\nHere's how a request flows through the architecture:\n\n💡 Real-World Benefits\n\nBefore Factory Pattern (Old Architecture)\n\nAfter Factory Pattern (Current Architecture)\n\n🚀 Future Extensibility\n\nThe factory pattern makes it easy to add new features:\nNew Tool Categories\nProvider Capabilities\nMiddleware System\n\n📚 Code Examples\n\nCreating Providers\n\nUsing Built-in Tools\n\nExtending with Custom Tools\n\n🏆 Summary\n\nThe Factory Pattern architecture provides:\nUnified Experience: Providers work the same way\nAutomatic Tools: 6 built-in tools for supported providers\nEasy Extension: Add providers with minimal code\nClean Code: No duplication, clear separation\nFuture-Proof: Easy to add new features\n\nThis architecture ensures NeuroLink remains maintainable, extensible, and consistent as new AI providers and features are added.\n\n🎥 Video Generation Handler Architecture\n\nVideo generation via Veo 3.1 follows the same factory pattern architecture with specialized handling for long-running operations.\n\nVideo Handler Implementation\n\nIntegration with BaseProvider\n\nFactory Pattern Benefits for Video Generation\nProvider-Specific Features: Video generation is only available on Vertex AI, but the architecture allows graceful handling:\nAutomatic Provider Routing: Factory can route video requests to Vertex AI:\nConsistent Error Handling: Video-specific errors follow the same pattern:\n\nArchitecture Diagram\n\nKey Design Decisions\nSeparation of Concerns: Video handler is separate from core provider logic\nExtensibility: Easy to add image generation, audio generation, etc.\nConsistent Interface: Same generate() method for text and video\nProvider-Specific Features: Only Vertex AI supports video, handled gracefully\nError Handling: Unified error codes across all features\n\nUnderstanding the architecture helps you build better AI applications! 🚀","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"","lvl3":""}}, {"objectID":"4a713e6b0aefc8075df59cb6e9a6c98d6ae2baf102143111fe6474a6a0cfe34a","title":"🏭 Factory Pattern Architecture","url":"/docs/development/factory-architecture#-factory-pattern-architecture","content":"Understanding NeuroLink's unified architecture with BaseProvider inheritance and automatic tool support.","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"🏭 Factory Pattern Architecture","lvl3":""}}, {"objectID":"5dadb668cd312ec1f7eff140aff200a1ec9991b13768fba31f8739c10764e636","title":"📋 Overview","url":"/docs/development/factory-architecture#-overview","content":"NeuroLink uses a Factory Pattern architecture with BaseProvider inheritance to provide consistent functionality across supported AI providers. This design eliminates code duplication and ensures each provider has the same core capabilities, including built-in tool support.","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"📋 Overview","lvl3":""}}, {"objectID":"8e3e82d01fd07dd00dfd27095f09d4749d40b4894183667b1c37d7f96bc25dc7","title":"Key Benefits","url":"/docs/development/factory-architecture#key-benefits","content":"✅ Zero Code Duplication: Shared logic in BaseProvider\n✅ Automatic Tool Support: Providers inherit 6 built-in tools\n✅ Consistent Interface: Same methods across supported providers\n✅ Easy Provider Addition: Minimal code for new providers\n✅ Centralized Updates: Fix once, apply everywhere","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"Key Benefits","lvl3":""}}, {"objectID":"7bf8f7c438d806fa65aab600e3777e1854f2248dcfa701d2acf938313260e4e5","title":"1. BaseProvider (Core Foundation)","url":"/docs/development/factory-architecture#1-baseprovider-core-foundation","content":"The BaseProvider class is the foundation of all AI providers:","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"1. BaseProvider (Core Foundation)","lvl3":""}}, {"objectID":"5b6ed455b31b69ad82a1ce2a4584726b502df700cf612fd32691c8f97acc4912","title":"2. Provider-Specific Implementation","url":"/docs/development/factory-architecture#2-provider-specific-implementation","content":"Each provider extends BaseProvider with minimal code:","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"2. Provider-Specific Implementation","lvl3":""}}, {"objectID":"653fce94fb29f8649ef07355845877b2999bf066593e94c374651a73fcee10e2","title":"3. Factory Pattern Implementation","url":"/docs/development/factory-architecture#3-factory-pattern-implementation","content":"The factory creates providers with consistent configuration:","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"3. Factory Pattern Implementation","lvl3":""}}, {"objectID":"c4a2eb40e163f1fa8b9bcc90ae489c31d284692476f8bc58e8bc5537bed8d108","title":"Tool Registration in BaseProvider","url":"/docs/development/factory-architecture#tool-registration-in-baseprovider","content":"Providers automatically get these tools:","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"Tool Registration in BaseProvider","lvl3":""}}, {"objectID":"5601d2f077e7145f40c145ccc4cd7a37cbc20eb937578d6dfd0f1c94101377c1","title":"Tool Conversion for AI Models","url":"/docs/development/factory-architecture#tool-conversion-for-ai-models","content":"BaseProvider converts tools to provider-specific format:","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"Tool Conversion for AI Models","lvl3":""}}, {"objectID":"c12f4aadd63c3cf9ee33cdc74b372b6147ae4ec33440cf30805dd0dbe0b8b8e5","title":"2. Easy Provider Addition","url":"/docs/development/factory-architecture#2-easy-provider-addition","content":"Adding a new provider requires minimal code:","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"2. Easy Provider Addition","lvl3":""}}, {"objectID":"860657bced1b0d8d3c72047f96649457b7ef41117b0ec0a577ea57f52140ac38","title":"3. Centralized Feature Addition","url":"/docs/development/factory-architecture#3-centralized-feature-addition","content":"Add features once in BaseProvider, and providers get them:","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"3. Centralized Feature Addition","lvl3":""}}, {"objectID":"54c23a3388f8cc3d1523c04f4171297c5dc287d4a8d9d7224e09a08e94178a5c","title":"1. Single Responsibility","url":"/docs/development/factory-architecture#1-single-responsibility","content":"Each component has one clear purpose:\nBaseProvider: Core functionality and tool management\nProvider Classes: Provider-specific API integration\nFactory: Provider instantiation\nRegistry: Provider registration and lookup","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"1. Single Responsibility","lvl3":""}}, {"objectID":"33c912790bc03eafdca577f8405c81b8780d011b814767b43160dff414195512","title":"2. Open/Closed Principle","url":"/docs/development/factory-architecture#2-openclosed-principle","content":"Open for extension: Easy to add new providers\nClosed for modification: Core logic doesn't change","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"2. Open/Closed Principle","lvl3":""}}, {"objectID":"6c25fd1ed1a24ae339cb2e2454b91787f5bef13112dfd7da5e69adbdc0399a5a","title":"3. Dependency Inversion","url":"/docs/development/factory-architecture#3-dependency-inversion","content":"Providers depend on BaseProvider abstraction\nHigh-level modules don't depend on low-level details","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"3. Dependency Inversion","lvl3":""}}, {"objectID":"5df1eee54967e1f5418691ef5a6390976b4c9eb9bf440af9526dbd720a52ec33","title":"4. Interface Segregation","url":"/docs/development/factory-architecture#4-interface-segregation","content":"Clean, minimal interface for each provider\nOnly implement what's needed","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"4. Interface Segregation","lvl3":""}}, {"objectID":"7fdfd517eeeeeab1baf449be579bf4c13ccb1c991c8827253423445c07fead94","title":"🔄 Request Flow","url":"/docs/development/factory-architecture#-request-flow","content":"Here's how a request flows through the architecture:","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"🔄 Request Flow","lvl3":""}}, {"objectID":"6bcbf2f37cc4e2b47f49fea1404c98c68178de1ea02f69ffded79fa087601c8a","title":"🚀 Future Extensibility","url":"/docs/development/factory-architecture#-future-extensibility","content":"The factory pattern makes it easy to add new features:","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"🚀 Future Extensibility","lvl3":""}}, {"objectID":"007cbf07b462a333cece275e1c810875dd83d69ec9fe6657c4cbe6946336d5e9","title":"🏆 Summary","url":"/docs/development/factory-architecture#-summary","content":"The Factory Pattern architecture provides:\nUnified Experience: Providers work the same way\nAutomatic Tools: 6 built-in tools for supported providers\nEasy Extension: Add providers with minimal code\nClean Code: No duplication, clear separation\nFuture-Proof: Easy to add new features\n\nThis architecture ensures NeuroLink remains maintainable, extensible, and consistent as new AI providers and features are added.","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"🏆 Summary","lvl3":""}}, {"objectID":"cf7efb25d3b7a2babf2584abb6a92798e06c9006b03567b643fdb4b282fbe41f","title":"🎥 Video Generation Handler Architecture","url":"/docs/development/factory-architecture#-video-generation-handler-architecture","content":"Video generation via Veo 3.1 follows the same factory pattern architecture with specialized handling for long-running operations.","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"🎥 Video Generation Handler Architecture","lvl3":""}}, {"objectID":"18e3ab07fb9afe2bb09eb6f3e0204902695fc594c24895a123a0bbec044aab5c","title":"Factory Pattern Benefits for Video Generation","url":"/docs/development/factory-architecture#factory-pattern-benefits-for-video-generation","content":"Provider-Specific Features: Video generation is only available on Vertex AI, but the architecture allows graceful handling:\nAutomatic Provider Routing: Factory can route video requests to Vertex AI:\nConsistent Error Handling: Video-specific errors follow the same pattern:","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"Factory Pattern Benefits for Video Generation","lvl3":""}}, {"objectID":"8ffc8d10abcc7bd7ec83d262a7379a2e2ffa95d496588f05961fec9bdeed4f1e","title":"Key Design Decisions","url":"/docs/development/factory-architecture#key-design-decisions","content":"Separation of Concerns: Video handler is separate from core provider logic\nExtensibility: Easy to add image generation, audio generation, etc.\nConsistent Interface: Same generate() method for text and video\nProvider-Specific Features: Only Vertex AI supports video, handled gracefully\nError Handling: Unified error codes across all features\n\nUnderstanding the architecture helps you build better AI applications! 🚀","hierarchy":{"lvl0":"Development","lvl1":"🏭 Factory Pattern Architecture","lvl2":"Key Design Decisions","lvl3":""}}, {"objectID":"c88e3be7a10c50ed72123bdbd5a139b6be2b1056947b4dbdfcc5bd6682059814","title":"Factory Pattern Migration Guide","url":"/docs/development/factory-migration","content":"Factory Pattern Migration Guide\n\nComprehensive guide for migrating to NeuroLink's factory pattern architecture, ensuring consistent provider management and scalable implementation.\n\n🏭 Factory Pattern Overview\n\nWhy Factory Patterns\n\nThe factory pattern in NeuroLink provides:\nConsistent Provider Creation: Standardized instantiation across all AI providers\nCentralized Configuration: Single source of truth for provider settings\nLifecycle Management: Proper initialization, caching, and cleanup\nType Safety: Full TypeScript support with compile-time validation\nExtensibility: Easy addition of new providers without code changes\n\nCore Factory Components\n\n🔄 Migration Steps\n\nStep 1: Assess Current Implementation\n\nPre-Migration Checklist:\n\nStep 2: Install and Configure NeuroLink\n\nInitial Configuration:\n\nStep 3: Refactor Provider Instantiation\n\nBefore (Legacy Pattern):\n\nAfter (Factory Pattern):\n\nStep 4: Migrate Configuration Management\n\nBefore (Environment Variables):\n\nAfter (Centralized Configuration):\n\nStep 5: Update Error Handling\n\nBefore (Manual Error Handling):\n\nAfter (Factory-Managed Error Handling):\n\n🧪 Testing Migration\n\nUnit Tests for Factory Pattern\n\nIntegration Tests\n\n📊 Performance Optimization\n\nCaching Strategy\n\nLoad Balancing\n\n🔍 Monitoring and Observability\n\nMigration Metrics\n\nLogging and Debugging\n\n🚀 Advanced Migration Patterns\n\nGradual Migration Strategy\n\nFeature Flag Integration\n\n📋 Migration Checklist\n\nPre-Migration\n[ ] Audit existing provider usage patterns\n[ ] Identify all provider instantiation points\n[ ] Document current configuration management\n[ ] Assess error handling strategies\n[ ] Measure baseline performance metrics\n[ ] Plan rollback strategy\n\nDuring Migration\n[ ] Install NeuroLink with factory support\n[ ] Configure provider factory settings\n[ ] Refactor provider instantiation code\n[ ] Update configuration management\n[ ] Implement unified error handling\n[ ] Add comprehensive testing\n[ ] Enable monitoring and logging\n\nPost-Migration\n[ ] Verify all provider functionality\n[ ] Confirm performance improvements\n[ ] Validate error handling behavior\n[ ] Test failover scenarios\n[ ] Monitor production metrics\n[ ] Document new patterns for team\n[ ] Clean up legacy code\n\nValidation Tests\n\n🎯 Success Metrics\n\nKey Performance Indicators\n\nThis comprehensive migration guide ensures a smooth transition to NeuroLink's factory pattern architecture, maximizing the benefits of standardized provider management while minimizing migration risks.\n\n📚 Related Documentation\nSystem Architecture - Overall system design\nTesting Strategy - Quality assurance approaches\nContributing Guide - Development workflow\nAdvanced Patterns - Factory implementation details","hierarchy":{"lvl0":"Development","lvl1":"Factory Pattern Migration Guide","lvl2":"","lvl3":""}}, {"objectID":"b77353b8a14000713eaef315f5c67fae4aa8fdc2924e7e1364ebb371c65cbaee","title":"Factory Pattern Migration Guide","url":"/docs/development/factory-migration#factory-pattern-migration-guide","content":"Comprehensive guide for migrating to NeuroLink's factory pattern architecture, ensuring consistent provider management and scalable implementation.","hierarchy":{"lvl0":"Development","lvl1":"Factory Pattern Migration Guide","lvl2":"Factory Pattern Migration Guide","lvl3":""}}, {"objectID":"f49c6c009f0bcdcecc8fb48ca17029ea660152a934848791a61dd4703825663c","title":"Why Factory Patterns","url":"/docs/development/factory-migration#why-factory-patterns","content":"The factory pattern in NeuroLink provides:\nConsistent Provider Creation: Standardized instantiation across all AI providers\nCentralized Configuration: Single source of truth for provider settings\nLifecycle Management: Proper initialization, caching, and cleanup\nType Safety: Full TypeScript support with compile-time validation\nExtensibility: Easy addition of new providers without code changes","hierarchy":{"lvl0":"Development","lvl1":"Factory Pattern Migration Guide","lvl2":"Why Factory Patterns","lvl3":""}}, {"objectID":"e98fa4fbd395967337fec1d58d1c960279534030ed9d6ad02d810a5e0eab3b11","title":"Step 1: Assess Current Implementation","url":"/docs/development/factory-migration#step-1-assess-current-implementation","content":"Pre-Migration Checklist:","hierarchy":{"lvl0":"Development","lvl1":"Factory 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installation","url":"/docs/development/factory-migration#verify-installation","content":"npx @juspay/neurolink --version\nnpx @juspay/neurolink status\ntypescript\n// neurolink.config.ts\n\n factory: {\n enableCaching: true,\n healthCheckInterval: 30000,\n retryConfiguration: {\n maxRetries: 3,\n backoffMultiplier: 2,\n initialDelay: 1000,\n },\n },\n providers: {\n openai: {\n apiKey: process.env.OPENAIAPIKEY,\n defaultModel: \"gpt-4\",\n timeout: 30000,\n },\n anthropic: {\n apiKey: process.env.ANTHROPICAPIKEY,\n defaultModel: \"claude-3-sonnet-20240229\",\n timeout: 30000,\n },\n \"google-ai\": {\n apiKey: process.env.GOOGLEAIAPI_KEY,\n defaultModel: \"gemini-2.5-pro\",\n timeout: 30000,\n },\n },\n analytics: {\n enabled: true,\n trackUsage: true,\n trackPerformance: true,\n },\n};\n`","hierarchy":{"lvl0":"Development","lvl1":"Factory Pattern Migration Guide","lvl2":"Verify installation","lvl3":""}}, 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{"objectID":"70058ed2ee55a4afc9808da9da91ee30ac2e23f2ac1113720e9e2e60b1053f19","title":"Key Performance Indicators","url":"/docs/development/factory-migration#key-performance-indicators","content":"This comprehensive migration guide ensures a smooth transition to NeuroLink's factory pattern architecture, maximizing the benefits of standardized provider management while minimizing migration risks.","hierarchy":{"lvl0":"Development","lvl1":"Factory Pattern Migration Guide","lvl2":"Key Performance Indicators","lvl3":""}}, {"objectID":"01352a99c713cd63d938c086b918f9cfaebdf1246aec71e0bd851c194caf370a","title":"📚 Related Documentation","url":"/docs/development/factory-migration#-related-documentation","content":"System Architecture - Overall system design\nTesting Strategy - Quality assurance approaches\nContributing Guide - Development workflow\nAdvanced Patterns - Factory implementation details","hierarchy":{"lvl0":"Development","lvl1":"Factory Pattern Migration Guide","lvl2":"📚 Related Documentation","lvl3":""}}, {"objectID":"b5593a1a523fdc5889c9f1f0eff5b9644c34bade7dd677fda08dbbc87f023986","title":"Development","url":"/docs/development","content":"Development\n\nContributing to NeuroLink and extending its capabilities for your specific needs.\n\n🎯 Development Hub\n\nThis section covers everything needed for contributing to NeuroLink, understanding its architecture, and extending its functionality.\nContributing — How to contribute to NeuroLink, including setup, coding standards, and submission guidelines.\nTesting — Comprehensive testing strategies, test suite organization, and validation procedures.\nLogging Guidelines — Log levels, structured fields, redaction, and per-instance routing.\nArchitecture — Deep dive into NeuroLink's architecture, design patterns, and system organization.\nFactory Pattern Migration — Guide for upgrading from older architectures to the new unified factory pattern system.\nDocumentation Versioning — Managing documentation versions across releases using mike for version control and deployment.\nAutomated Link Checking — Automated validation of documentation links with CI/CD integration to prevent broken references.\n\n🚀 Quick Development Setup\n\n🏗️ Architecture Overview\n\nNeuroLink uses a Factory Pattern architecture that provides:\n\nCore Components\n\nDesign Principles\nUnified Interface: All providers implement the same AIProvider interface\nType Safety: Full TypeScript support with strict typing\nExtensibility: Easy to add new providers and tools\nPerformance: Optimized for production use\nReliability: Comprehensive error handling and fallbacks\n\n🔧 Development Features\n\nEnterprise Automation (72+ Commands)\n\nNeuroLink includes comprehensive automation for development:\n\nSmart Testing System\nAdaptive test selection based on code changes\nProvider validation across all AI services\nPerformance benchmarking and regression detection\nComprehensive coverage reporting\n\nAutomated Content Generation\nScreenshot automation for documentation\nVideo generation for demonstrations\nDocumentation synchronization across files\nAsset optimization and management\n\n🧪 Testing Philosophy\n\nNeuroLink uses a multi-layered testing approach:\n\nTest Categories\nUnit Tests - Individual component testing\nIntegration Tests - Provider and tool interaction\nEnd-to-End Tests - Complete workflow validation\nPerformance Tests - Speed and resource usage\nRegression Tests - Prevent breaking changes\n\nTest Organization\n\nRunning Tests\n\n🎨 Code Style & Standards\n\nTypeScript Configuration\nStrict mode enabled for maximum type safety\nPath mapping for clean imports\nESLint and Prettier for consistent formatting\nDocumentation comments for all public APIs\n\nNaming Conventions\nPascalCase for classes and interfaces\ncamelCase for functions and variables\nkebab-case for file names\nUPPER_CASE for constants\n\nFile Organization\n\n🔄 Contribution Workflow\nSetup Development Environment\nCreate Feature Branch\nDevelopment Process\nCommit & Submit\n\n📚 Learning Resources\n\nArchitecture Deep Dive\nFactory Pattern Guide - Understanding the core architecture\nMCP Integration - Tool system implementation\nProvider Development - Adding new AI providers\n\nBest Practices\nError handling patterns and strategies\nPerformance optimization techniques\nTesting methodologies and coverage\nDocumentation standards and automation\n\nCommunity\nGitHub Discussions for questions and ideas\nIssue tracking for bugs and feature requests\nCode reviews for learning and improvement\nRelease notes for staying updated\n\n🔗 Related Resources\nCLI Guide - Understanding the command-line interface\nSDK Reference - API implementation details\nAdvanced Features - Enterprise capabilities\nExamples - Practical implementations","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"","lvl3":""}}, 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{"objectID":"895387173df69a9517c57280abe08c06eb680cefa421c6607b4867024631c158","title":"Install docs dependencies","url":"/docs/development#install-docs-dependencies","content":"pip install -r requirements.txt","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Install docs dependencies","lvl3":""}}, {"objectID":"0342f7bb6071dcf26878299efeae0243cb73b3da1cb383427dd830cceaf7c409","title":"Serve documentation locally","url":"/docs/development#serve-documentation-locally","content":"mkdocs serve","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Serve documentation locally","lvl3":""}}, {"objectID":"478576b499d283639f214c9dacab3c0b472015f6ce23e2b9a1ec2f25c5928c2e","title":"Build documentation","url":"/docs/development#build-documentation","content":"mkdocs build\n`","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Build documentation","lvl3":""}}, {"objectID":"fe3ae02a09dddcd2482f3e989fbf82c04696fedabdbb34a536323314228138c3","title":"🏗️ Architecture 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comprehensive automation for development:\n\n`bash","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Enterprise Automation (72+ Commands)","lvl3":""}}, {"objectID":"582d896716592a35c42cdcad2b125f4595ebc5988ca6feaa818e3fefff56eafa","title":"Environment & Setup","url":"/docs/development#environment-setup","content":"pnpm setup:complete # Complete project setup\npnpm env:setup # Environment configuration\npnpm env:validate # Configuration validation","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Environment & Setup","lvl3":""}}, {"objectID":"8fb23f6bbbd614c9f3ae9dcef8228bd41c5273f99cb0b2e28f7695f65a40e650","title":"Testing & Quality","url":"/docs/development#testing-quality","content":"pnpm test:adaptive # Intelligent test selection\npnpm test:providers # AI provider validation\npnpm quality:check # Full quality pipeline","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Testing & Quality","lvl3":""}}, {"objectID":"cdc9777d372b6696cc943c521c6c896bcbc7f5a82ae1af2a45177a3b6ef59559","title":"Content Generation","url":"/docs/development#content-generation","content":"pnpm content:screenshots # Automated screenshot capture\npnpm content:videos # Video generation\npnpm docs:sync # Documentation synchronization","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Content Generation","lvl3":""}}, {"objectID":"d575c8c97c8ef56fb6d4e8643772bcd58f2ebf4e461fca1f1bc775a03ae3f030","title":"Build & Deployment","url":"/docs/development#build-deployment","content":"pnpm build:complete # 7-phase enterprise pipeline\npnpm dev:health # System health monitoring\n`","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Build & Deployment","lvl3":""}}, {"objectID":"7ac17ab56da2811b9023cbbf8b59ec8007b00717b3d911350d54b46d06e92da2","title":"Smart Testing System","url":"/docs/development#smart-testing-system","content":"Adaptive test selection based on code changes\nProvider validation across all AI services\nPerformance benchmarking and regression detection\nComprehensive coverage reporting","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Smart Testing System","lvl3":""}}, {"objectID":"bb950545eb40d23e5c42f9912d36b3b7de17f0671deb398f297d4840d34dfe79","title":"Automated Content Generation","url":"/docs/development#automated-content-generation","content":"Screenshot automation for documentation\nVideo generation for demonstrations\nDocumentation synchronization across files\nAsset optimization and management","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Automated Content Generation","lvl3":""}}, {"objectID":"7421ea658f416f1abc2680155d85e131c9074660e07fbdadf3d9b2333b150dc2","title":"🧪 Testing Philosophy","url":"/docs/development#-testing-philosophy","content":"NeuroLink uses a multi-layered testing approach:","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"🧪 Testing Philosophy","lvl3":""}}, {"objectID":"9eb4e85215c2b1ec0fdf1c170dd648e2cc5ee09d8211810cb9661b547e1816ad","title":"Test Categories","url":"/docs/development#test-categories","content":"Unit Tests - Individual component testing\nIntegration Tests - Provider and tool interaction\nEnd-to-End Tests - Complete workflow validation\nPerformance Tests - Speed and resource usage\nRegression Tests - Prevent breaking changes","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Test Categories","lvl3":""}}, {"objectID":"9a62364a2b59e0729665783cdfdba5d7616569e237c10e1632216e87e4a09d8e","title":"Running Tests","url":"/docs/development#running-tests","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Running Tests","lvl3":""}}, {"objectID":"49917467376503b289690ed504b3da50f11afb5ca95533e169262de4b008d5c9","title":"Main suite (orchestrates the full integration run)","url":"/docs/development#main-suite-orchestrates-the-full-integration-run","content":"pnpm test","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Main suite (orchestrates the full integration run)","lvl3":""}}, {"objectID":"d4818fafc9f6e940afe3da13fb942fb4791ce5f21f3129b893640f326e41d201","title":"CI pipeline (test + test:client + test:hitl)","url":"/docs/development#ci-pipeline-test-testclient-testhitl","content":"pnpm test:ci","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"CI pipeline (test + test:client + test:hitl)","lvl3":""}}, {"objectID":"6f24994893679a83ef72d557b22a8db778dd2f45b98005ffb4525df28d6cc65e","title":"Domain-specific suites","url":"/docs/development#domain-specific-suites","content":"pnpm test:providers # Provider feature tests\npnpm test:matrix # Capability sweep across all providers\npnpm test:rag # RAG pipeline\npnpm test:voice # Voice (TTS/STT)\npnpm test:mcp # MCP HTTP transport\npnpm test:context # Context compaction + file handling","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Domain-specific suites","lvl3":""}}, {"objectID":"c08a4f7201aa2baeb16bb1777fada57358d34ef928ccdf8845b2dee6fbfef3bb","title":"Run a single suite directly","url":"/docs/development#run-a-single-suite-directly","content":"pnpm exec tsx test/continuous-test-suite-.ts\n`","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Run a single suite directly","lvl3":""}}, {"objectID":"006d485b71d14667178d08a0c4e18fe0c6ca7fff96f73003633bacd98a7614da","title":"TypeScript Configuration","url":"/docs/development#typescript-configuration","content":"Strict mode enabled for maximum type safety\nPath mapping for clean imports\nESLint and Prettier for consistent formatting\nDocumentation comments for all public APIs","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"TypeScript Configuration","lvl3":""}}, {"objectID":"eb125155ccf168dbcf18074407f76307c0e6187e94a0dc93f8d0862ccb635566","title":"Naming Conventions","url":"/docs/development#naming-conventions","content":"PascalCase for classes and interfaces\ncamelCase for functions and variables\nkebab-case for file names\nUPPER_CASE for constants","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Naming Conventions","lvl3":""}}, {"objectID":"10085138e62c1511fb4345b00eaa8683c6273a705ce93e565da4e515c375ba1a","title":"1. Setup Development Environment","url":"/docs/development#1-setup-development-environment","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"1. Setup Development Environment","lvl3":""}}, {"objectID":"fa9dc40d0e773f47b57d33ca0999a80d7497dae4a73535971ce164bc2a9236af","title":"Fork and clone","url":"/docs/development#fork-and-clone","content":"git clone https://github.com/YOUR_USERNAME/neurolink\ncd neurolink\npnpm setup:complete\n`","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Fork and clone","lvl3":""}}, {"objectID":"0c7f2465bdefe5e8eb5581cbb6d4c83204d120828c2e776fb149c4ee706f1062","title":"2. Create Feature Branch","url":"/docs/development#2-create-feature-branch","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"2. Create Feature Branch","lvl3":""}}, {"objectID":"77a24ce4880d84c46f6ceeff286ed8032c4951a2a5623895e6c0d3faafa21544","title":"Create semantic branch","url":"/docs/development#create-semantic-branch","content":"git checkout -b feat/your-feature-name\ngit checkout -b fix/issue-description\ngit checkout -b docs/documentation-update\n`","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Create semantic branch","lvl3":""}}, {"objectID":"339bd32f6e94df0f54d102f1813d8e47d3c34a2c3bb61c71023f97c2f88db418","title":"3. Development Process","url":"/docs/development#3-development-process","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"3. Development Process","lvl3":""}}, {"objectID":"fa1062bb056da0abfee4522450ea3376a91b4ae666f5b700a90a9247d350f4ac","title":"Make changes","url":"/docs/development#make-changes","content":"pnpm dev # Start development server\npnpm test:adaptive # Run relevant tests\npnpm quality:check # Validate code quality\n`","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Make changes","lvl3":""}}, {"objectID":"b3a7971a87e5855f5330728c88710c33faf90bde2ea3887b756ae73a232464f5","title":"4. Commit & Submit","url":"/docs/development#4-commit-submit","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"4. Commit & Submit","lvl3":""}}, {"objectID":"c70fcfade3053347b9961f78ac6ba2ea2baba070a65baf297aa1d397ce54bdb2","title":"Commit with semantic messages","url":"/docs/development#commit-with-semantic-messages","content":"git commit -m \"feat: add new provider support\"\ngit commit -m \"fix: resolve streaming timeout issue\"\ngit commit -m \"docs: update API documentation\"","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Commit with semantic messages","lvl3":""}}, {"objectID":"a3648b0a3eb5613c7d6be7b448139d794d004792446446699667cce0e8b85b01","title":"Push and create PR","url":"/docs/development#push-and-create-pr","content":"git push origin feat/your-feature-name\n`","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Push and create PR","lvl3":""}}, {"objectID":"772153a33436e90f0a0176a785391da4de25a28e468d4e28e15d835d36d5de2b","title":"Architecture Deep Dive","url":"/docs/development#architecture-deep-dive","content":"Factory Pattern Guide - Understanding the core architecture\nMCP Integration - Tool system implementation\nProvider Development - Adding new AI providers","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Architecture Deep Dive","lvl3":""}}, {"objectID":"6a88ffdacaeed83b1c8d6938cd41f7e6fe24e216db4ff0329797268dde5ed95d","title":"Best Practices","url":"/docs/development#best-practices","content":"Error handling patterns and strategies\nPerformance optimization techniques\nTesting methodologies and coverage\nDocumentation standards and automation","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Best Practices","lvl3":""}}, {"objectID":"c38b5188f1045054ee015771c1f8a38cbb2324cbd191616151a3a1a717c97448","title":"Community","url":"/docs/development#community","content":"GitHub Discussions for questions and ideas\nIssue tracking for bugs and feature requests\nCode reviews for learning and improvement\nRelease notes for staying updated","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"Community","lvl3":""}}, {"objectID":"7825bf0bf82f23a9d1ccbe4e844d29dcc220879f1884377e775e15ecea5f4b8d","title":"🔗 Related Resources","url":"/docs/development#-related-resources","content":"CLI Guide - Understanding the command-line interface\nSDK Reference - API implementation details\nAdvanced Features - Enterprise capabilities\nExamples - Practical implementations","hierarchy":{"lvl0":"Development","lvl1":"Development","lvl2":"🔗 Related Resources","lvl3":""}}, {"objectID":"0482522eaebc7faa9431fabd8cee7d073b4bdb5fe199d6fd9c79aca69ced276d","title":"Knowledge Grounding: Initialization and Turn Lifecycle","url":"/docs/development/knowledge-grounding-lifecycle","content":"Knowledge Grounding: Initialization and Turn Lifecycle\n\nThis document explains only the knowledge-grounding path in NeuroLink:\nWhat happens when a NeuroLink instance initializes knowledge grounding.\nWhat happens when knowledge grounding runs for a generate or stream turn.\n\nIt follows the current runtime implementation, not an older design proposal.\n\nSource map\n\n| Responsibility | Source |\n| ---------------------------------------------------- | ------------------------------------- |\n| NeuroLink constructor and call integration | src/lib/neurolink.ts |\n| Public and internal knowledge types | src/lib/types/knowledge.ts |\n| Dynamic per-call options | src/lib/types/dynamic.ts |\n| Knowledge configuration on the NeuroLink constructor | src/lib/types/config.ts |\n| Engine lifecycle | src/lib/knowledge/engine.ts |\n| Process-level index build cache | src/lib/knowledge/indexCache.ts |\n| Source normalization and validation | src/lib/knowledge/resolve.ts |\n| Immutable index and BM25 search | src/lib/knowledge/knowledgeIndex.ts |\n| Per-turn selection | src/lib/knowledge/retrieval.ts |\n| Token-bounded context assembly | src/lib/knowledge/context.ts |\n| Text normalization | src/lib/knowledge/normalize.ts |\n| Default limits, weights, and boosts | src/lib/knowledge/defaults.ts |\nHigh-level lifecycle\n\nEach NeuroLink instance requests index construction once from its constructor\nsources. Identical source configurations in the same process can reuse the\nbounded process-level index cache. Turns reuse the same immutable in-memory\nsnapshot.\n\nThere is no per-session index and no index rebuild per turn.\nConfiguration entering NeuroLink\n\nThe host supplies knowledge grounding through NeurolinkConstructorConfig:\n\nThe important inputs are:\n\n| Field | Purpose |\n| ---------------- | ------------------------------------------------------------------------------------------ |\n| enabled | Master switch checked by both NeuroLink and the engine. |\n| sources | Structured knowledge entries used to build the one-time snapshot. |\n| blockedDomains | Optional blocklist applied before candidate ranking. Empty means all domains are eligible. |\n| retrieval | Candidate, result, relation, field-weight, and exact/alias boost settings. |\n| context | Context token limit and citation behavior. |\n| timeoutMs | Hard ceiling for one grounding operation before it fails open. |\nInitialization path\n\nThe index build is asynchronous. The engine stores its buildPromise, and the\nfirst eligible turn waits for it through ready().\n\n3.1 NeuroLink.constructor()\n\nThe knowledge-specific constructor branch is:\n\nIf knowledge grounding is absent or disabled, knowledgeGroundingEngine remains\nundefined and turns skip the feature.\n\nIf knowledge grounding is enabled but sources is missing, not an array, or an\nempty array, NeuroLink logs the warning shown above, does not instantiate\nKnowledgeGroundingEngine, skips grounding on every turn, and\ngetKnowledgeStatus() returns null.\n\nIf sources is a non-empty array, NeuroLink instantiates\nKnowledgeGroundingEngine even when individual source entries are invalid.\nThose entry-level validation issues are handled inside the engine: the engine is\nvisible through getKnowledgeStatus(), records validation issues, leaves its\nsnapshot unavailable, and keeps reporting not-ready status. Because the\nconstructor starts a one-time build and does not automatically retry with changed\nsource data, grounding returns index-not-ready until the engine is recreated\nwith corrected sources or explicitly rebuilt.\n\n3.2 KnowledgeGroundingEngine.constructor()\n\nThe engine constructor:\nstores KnowledgeGroundingConfig;\ncalls resolveRetrieval() to materialize runtime retrieval settings;\nstores the clock function used for duration metadata;\nstarts build() when at least one source exists;\ncatches an unexpected build rejection and records it in lastError.\n\nIt does not wait synchronously for the index to finish.\n\n3.3 resolveRetrieval()\n\nCombines host overrides with SDK defaults:\ncandidate limit;\nprimary result limit;\nrelationship expansion limit;\nper-field BM25 weights;\nexact-phrase boost;\nreviewed-alias boost.\n\nThese resolved values are calculated once and reused by every turn.\n\n3.4 build()\n\nThe private engine build function:\ncalls getOrBuildKnowledgeIndexSnapshot();\nstores all validation issues","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"","lvl3":""}}, {"objectID":"d4a584183e57f275665eba963061d7b88e076f1a87dc7d92ee8ee82720fac5cd","title":"Knowledge Grounding: Initialization and Turn Lifecycle","url":"/docs/development/knowledge-grounding-lifecycle#knowledge-grounding-initialization-and-turn-lifecycle","content":"This document explains only the knowledge-grounding path in NeuroLink:\nWhat happens when a NeuroLink instance initializes knowledge grounding.\nWhat happens when knowledge grounding runs for a generate or stream turn.\n\nIt follows the current runtime implementation, not an older design proposal.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl3":""}}, {"objectID":"5ea765d91e84e2ffb53a629ab8c04b9a5ca75a74c2fc0a06867f507389c41f8b","title":"Source map","url":"/docs/development/knowledge-grounding-lifecycle#source-map","content":"| Responsibility | Source |\n| ---------------------------------------------------- | ------------------------------------- |\n| NeuroLink constructor and call integration | src/lib/neurolink.ts |\n| Public and internal knowledge types | src/lib/types/knowledge.ts |\n| Dynamic per-call options | src/lib/types/dynamic.ts |\n| Knowledge configuration on the NeuroLink constructor | src/lib/types/config.ts |\n| Engine lifecycle | src/lib/knowledge/engine.ts |\n| Process-level index build cache | src/lib/knowledge/indexCache.ts |\n| Source normalization and validation | src/lib/knowledge/resolve.ts |\n| Immutable index and BM25 search | src/lib/knowledge/knowledgeIndex.ts |\n| Per-turn selection | src/lib/knowledge/retrieval.ts |\n| Token-bounded context assembly | src/lib/knowledge/context.ts |\n| Text normalization | src/lib/knowledge/normalize.ts |\n| Default limits, weights, and boosts | src/lib/knowledge/defaults.ts |","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"Source map","lvl3":""}}, {"objectID":"0b7cf1bbc2eb8c4c1544f43bd7571082c22f0531260bf7a9e1c0a3e413bfe008","title":"1. High-level lifecycle","url":"/docs/development/knowledge-grounding-lifecycle#1-high-level-lifecycle","content":"Each NeuroLink instance requests index construction once from its constructor\nsources. Identical source configurations in the same process can reuse the\nbounded process-level index cache. Turns reuse the same immutable in-memory\nsnapshot.\n\nThere is no per-session index and no index rebuild per turn.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"1. High-level lifecycle","lvl3":""}}, {"objectID":"db68f0032b3edd029959924f566140e231acae8a0cba68826e2975fef043ccd0","title":"2. Configuration entering NeuroLink","url":"/docs/development/knowledge-grounding-lifecycle#2-configuration-entering-neurolink","content":"The host supplies knowledge grounding through NeurolinkConstructorConfig:\n\nThe important inputs are:\n\n| Field | Purpose |\n| ---------------- | ------------------------------------------------------------------------------------------ |\n| enabled | Master switch checked by both NeuroLink and the engine. |\n| sources | Structured knowledge entries used to build the one-time snapshot. |\n| blockedDomains | Optional blocklist applied before candidate ranking. Empty means all domains are eligible. |\n| retrieval | Candidate, result, relation, field-weight, and exact/alias boost settings. |\n| context | Context token limit and citation behavior. |\n| timeoutMs | Hard ceiling for one grounding operation before it fails open. |","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"2. Configuration entering NeuroLink","lvl3":""}}, {"objectID":"07c1bffb3527da6dcbe8d47e695bc2bb578d3743602b879ecce5d6c8c6e3011e","title":"3. Initialization path","url":"/docs/development/knowledge-grounding-lifecycle#3-initialization-path","content":"The index build is asynchronous. The engine stores its buildPromise, and the\nfirst eligible turn waits for it through ready().","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"3. Initialization path","lvl3":""}}, {"objectID":"5a9a1b0fd29ccb1a38c15dc82ccd1cfa1f3aaaca18a4844202f405f592c29ba4","title":"3.1 NeuroLink.constructor()","url":"/docs/development/knowledge-grounding-lifecycle#31-neurolinkconstructor","content":"The knowledge-specific constructor branch is:\n\nIf knowledge grounding is absent or disabled, knowledgeGroundingEngine remains\nundefined and turns skip the feature.\n\nIf knowledge grounding is enabled but sources is missing, not an array, or an\nempty array, NeuroLink logs the warning shown above, does not instantiate\nKnowledgeGroundingEngine, skips grounding on every turn, and\ngetKnowledgeStatus() returns null.\n\nIf sources is a non-empty array, NeuroLink instantiates\nKnowledgeGroundingEngine even when individual source entries are invalid.\nThose entry-level validation issues are handled inside the engine: the engine is\nvisible through getKnowledgeStatus(), records validation issues, leaves its\nsnapshot unavailable, and keeps reporting not-ready status. Because the\nconstructor starts a one-time build and does not automatically retry with changed\nsource data, grounding returns index-not-ready until the engine is recreated\nwith corrected sources or explicitly rebuilt.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"3.1 NeuroLink.constructor()","lvl3":""}}, {"objectID":"89f2c3c409e3b8d9d29d34e7db7e19b249d94d702afb7b2a736ac42049a141b5","title":"3.2 KnowledgeGroundingEngine.constructor()","url":"/docs/development/knowledge-grounding-lifecycle#32-knowledgegroundingengineconstructor","content":"The engine constructor:\nstores KnowledgeGroundingConfig;\ncalls resolveRetrieval() to materialize runtime retrieval settings;\nstores the clock function used for duration metadata;\nstarts build() when at least one source exists;\ncatches an unexpected build rejection and records it in lastError.\n\nIt does not wait synchronously for the index to finish.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"3.2 KnowledgeGroundingEngine.constructor()","lvl3":""}}, {"objectID":"568dacec74eeab7658597267c5bb63b38a28a4e5f053121f4aec5569aeabd612","title":"3.3 resolveRetrieval()","url":"/docs/development/knowledge-grounding-lifecycle#33-resolveretrieval","content":"Combines host overrides with SDK defaults:\ncandidate limit;\nprimary result limit;\nrelationship expansion limit;\nper-field BM25 weights;\nexact-phrase boost;\nreviewed-alias boost.\n\nThese resolved values are calculated once and reused by every turn.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"3.3 resolveRetrieval()","lvl3":""}}, {"objectID":"26d553cb35ea70326e25531fd67157e13972fa6ef3d04ad934bf13825c5b51b1","title":"3.4 build()","url":"/docs/development/knowledge-grounding-lifecycle#34-build","content":"The private engine build function:\ncalls getOrBuildKnowledgeIndexSnapshot();\nstores all validation issues for getStatus();\nleaves snapshot as null when validation contains errors;\notherwise installs the returned snapshot, which may be newly built or reused\n from the process-level cache, and clears lastError.\n\nA partially valid source set is never installed.\n\nThis validation is separate from the constructor's source-shape guard. A\nnon-empty sources array reaches the engine; missing, non-array, or empty\nsources never does.\n\nThe cache key includes the cache schema version, resolved field weights, encoded\nsource IDs and versions, entry IDs, and a deterministic hash of the effective\nsource content. That hash is built from normalized entries, including titles,\nsummaries, bodies, aliases, keywords, integrations, relationships, status, kind,\nand resolved version. The cache is bounded by KNOWLEDGE_INDEX_CACHE_LIMIT and\nevicts the least-recently used entry when the limit is exceeded.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"3.4 build()","lvl3":""}}, {"objectID":"7d8de5e958191621661f29832fffffd0977fe63d54e4853234d7bd5eb48ece6b","title":"3.5 normalizeAndValidate()","url":"/docs/development/knowledge-grounding-lifecycle#35-normalizeandvalidate","content":"This function processes every source and entry.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"3.5 normalizeAndValidate()","lvl3":""}}, {"objectID":"9d81ae76bb105c36ab1481644fce45b343e8200ddde5ab2f26793679ca539e1d","title":"Source loading","url":"/docs/development/knowledge-grounding-lifecycle#source-loading","content":"loadSourceEntries() resolves the source version. The source version is later\nincluded in citations.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"Source loading","lvl3":""}}, {"objectID":"02f0f7cbc0611021593bc2e472c621473ed9db0d9f7a8cb2f98f0a054723f0ce","title":"Entry validation","url":"/docs/development/knowledge-grounding-lifecycle#entry-validation","content":"It reports errors for:\na missing source ID;\na missing entry ID;\na duplicate entry ID;\na missing title;\na missing summary;\na missing domain;\nmissing integrations;\nintegrations that are not an array of strings;\nan unsupported entry kind;\nan unsupported lifecycle status.\n\nIt reports warnings for:\naliases that normalize to an empty string;\nthe same normalized alias belonging to different entries;\nmissing related-entry IDs;\nmissing parent-entry IDs.\n\nErrors block the snapshot. Warnings do not.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"Entry validation","lvl3":""}}, {"objectID":"802f78d0756095e1be57e31925e3a0dbef4b26d6f1d257a2eb1970601913f013","title":"resolveEntry()","url":"/docs/development/knowledge-grounding-lifecycle#resolveentry","content":"Converts KnowledgeEntryInput into NormalizedKnowledgeEntry:\nintegrations is required and cloned after validation;\noptional aliases, keywords, and relationships default to cloned empty\n arrays;\nbody defaults to an empty string;\nkind defaults to text;\nstatus defaults to active;\nthe effective source version is copied to the entry.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"resolveEntry()","lvl3":""}}, {"objectID":"ec1b522eade4aa1e2c058bcffb62e9b826cd164a407e7d48a3effb6b8cf82178","title":"3.6 buildIndexSnapshot()","url":"/docs/development/knowledge-grounding-lifecycle#36-buildindexsnapshot","content":"The snapshot contains:\n\n| Structure | Purpose |\n| --------------- | ------------------------------------------------------ |\n| entriesById | Retrieves the complete normalized entry after scoring. |\n| exactIndex | Maps normalized IDs and titles to entry IDs. |\n| aliasIndex | Maps normalized reviewed aliases to entry IDs. |\n| relationIndex | Maps an entry to related and parent entry IDs. |\n| lexical | Performs weighted field-aware BM25 retrieval. |\n\nFor each entry, buildIndexSnapshot():\ncalls buildDocument();\nstores the normalized entry;\npopulates exact and alias phrase maps;\nrecords relationships;\ncalls KnowledgeLexicalIndex.add().\n\nAfter all entries are added, it calls KnowledgeLexicalIndex.finalize().","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"3.6 buildIndexSnapshot()","lvl3":""}}, {"objectID":"525e34e9757cb80617aec33ccbcb7493425fb107ac96b4691ae6948609510ccb","title":"3.7 buildDocument()","url":"/docs/development/knowledge-grounding-lifecycle#37-builddocument","content":"Creates the internal searchable document:\nnormalizePhrases() builds exact keys from the entry ID and title;\ntokenize() processes title, aliases, keywords, summary, and body;\ndomain, integrations, and status stay on the normalized entry and are used\n later by authorization;\nrelated entry IDs remain available for later expansion.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"3.7 buildDocument()","lvl3":""}}, {"objectID":"2c919bafffd72a92fa92b8fefd1fc17572593ffba5a512f87df802ba985df914","title":"3.8 KnowledgeLexicalIndex","url":"/docs/development/knowledge-grounding-lifecycle#38-knowledgelexicalindex","content":"The index is a field-aware BM25 implementation.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"3.8 KnowledgeLexicalIndex","lvl3":""}}, {"objectID":"4bb3c5d8917dde4bce712fa7424bb486cc259ae1c016c22351d51c4f7537b795","title":"constructor()","url":"/docs/development/knowledge-grounding-lifecycle#constructor","content":"Stores the field weights and initializes postings, document-length maps, and\ntoken totals for title, aliases, keywords, summary, and body.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"constructor()","lvl3":""}}, {"objectID":"28fe60d90cdbd5d5b2ed421292a0e6ceb582e5d84ee803f12ed6118e698c0cc7","title":"add()","url":"/docs/development/knowledge-grounding-lifecycle#add","content":"For each field in one document, records:\nfield length;\ntotal field tokens;\nterm frequency per document;\nterm-to-document postings.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"add()","lvl3":""}}, {"objectID":"e7d65ca92ab7cbf68970470bece76d8594492c82c20f63c5e7fbf00c04ab1082","title":"finalize()","url":"/docs/development/knowledge-grounding-lifecycle#finalize","content":"Calculates average field length across the complete document set. Search does\nnot run until this initialization is complete.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"finalize()","lvl3":""}}, {"objectID":"367190d1b47bf63331d89396a1f89939a01b4b0904528b0174311b466d3df63c","title":"search()","url":"/docs/development/knowledge-grounding-lifecycle#search","content":"At turn time, calculates BM25 contributions for each query term and field,\nmultiplies them by field weights, sums document scores, retains a per-field\nbreakdown, sorts deterministically, and returns the requested top matches.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"search()","lvl3":""}}, {"objectID":"ae6124fc67e7770000b479cffc664353d4df39c2a6fc69ab4cacbca445ce28f8","title":"3.9 ready() and getStatus()","url":"/docs/development/knowledge-grounding-lifecycle#39-ready-and-getstatus","content":"ready() awaits the one-time build promise. It is safe to call on every turn\nbecause a settled promise resolves immediately.\n\ngetStatus() exposes:\nwhether grounding is enabled;\nwhether a snapshot is ready;\nindexed entry count;\nlast build error;\nvalidation issues.\n\nNeuroLink.getKnowledgeStatus() delegates to this method and returns null\nwhen the engine was never configured. For validation failures in a non-empty\nsource array, it returns the engine status with ready: false, entryCount: 0,\nthe validation issues, and the build error; it does not imply a later valid\nsnapshot will appear automatically.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"3.9 ready() and getStatus()","lvl3":""}}, {"objectID":"769f4d598657ab95efdeca8b78b24b8f89048ab619e975268db0f61c3bcab9c7","title":"4. Per-call generate and stream path","url":"/docs/development/knowledge-grounding-lifecycle#4-per-call-generate-and-stream-path","content":"Knowledge grounding is available to both generate() and stream(), but a\ncall uses it only when useKnowledgeGrounding: true is present. Enabling the\nconstructor configuration builds and makes the index available; it does not\nforce every call on the instance to perform retrieval.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"4. Per-call generate and stream path","lvl3":""}}, {"objectID":"a59dfba0a72abe838bf53c921ba926f3a3533adf70fa5d613fb3790be184675e","title":"4.1 NeuroLink.generate() and NeuroLink.stream()","url":"/docs/development/knowledge-grounding-lifecycle#41-neurolinkgenerate-and-neurolinkstream","content":"The knowledge-specific work happens at each outer public call layer:\ncloneOptionsForCallIsolation() protects the caller's original object.\nretrieveKnowledgeGrounding() checks the per-call opt-in and retrieves\n knowledge without mutating options.\nWhen context is returned, the public method creates updated options by\n appending the knowledge block to systemPrompt.\nThe enriched options enter the existing provider/fallback path.\nReturned knowledge metadata is attached to GenerateResult.knowledge or\n StreamResult.knowledge.\n\nThe option replacement is deliberately visible in the public method. The\nretrieval helper only returns data.\n\nCalls that omit useKnowledgeGrounding or set it to false continue through\nthe normal generation or streaming path without knowledge retrieval. The\ngenerate(\"prompt\") shorthand also skips grounding because it has no options\nobject on which to opt in; use generate({ input, useKnowledgeGrounding: true })\nwhen grounding is required.\n\nDynamicOptions exposes useKnowledgeGrounding and knowledgeContext as\nstatic fields because retrieval happens before dynamic arguments are resolved.\nFunction-valued fields such as systemPrompt remain dynamic; NeuroLink wraps a\ndynamic system prompt so the knowledge block is appended after that function\nresolves.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"4.1 NeuroLink.generate() and NeuroLink.stream()","lvl3":""}}, {"objectID":"e596a060c1156dab049b5d5420877aae5aa3fa68e63d03c8f7f8f883130fc94c","title":"4.2 retrieveKnowledgeGrounding()","url":"/docs/development/knowledge-grounding-lifecycle#42-retrieveknowledgegrounding","content":"This NeuroLink helper exits without calling the engine when:\nthe engine was not configured;\ngrounding is disabled;\nuseKnowledgeGrounding is not exactly true for the current call;\noptions.input.text is absent or empty.\n\nOtherwise it:\nuses options.conversationMessages when supplied, otherwise loads prior\n conversation memory through the existing history reader;\nkeeps only user and assistant roles;\nkeeps the latest DEFAULT_RECENT_TURNS messages, currently 4;\nconverts messages into KnowledgeConversationTurn objects, using empty text\n for non-string content;\npasses options.knowledgeContext as the request scope;\ncalls engine.ground();\ncatches an unexpected error and returns undefined so the turn continues.\n\nThis happens before dynamic option resolution, STT transcription, input\nvalidation, PII redaction, and authentication. Grounding sees the current query\nplus inline messages or the existing stored conversation history.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"4.2 retrieveKnowledgeGrounding()","lvl3":""}}, {"objectID":"4e7546113c47e5eee31c8c7b44e1782cdd260a7ddd45af6365f8b3275f436644","title":"5. KnowledgeGroundingEngine.ground()","url":"/docs/development/knowledge-grounding-lifecycle#5-knowledgegroundingengineground","content":"ground() owns the complete per-turn retrieval lifecycle.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"5. KnowledgeGroundingEngine.ground()","lvl3":""}}, {"objectID":"730f18f97809685a4b98ac02086a4cba85f7127da20dc41a69a3794f328e3efe","title":"5.1 Disabled path","url":"/docs/development/knowledge-grounding-lifecycle#51-disabled-path","content":"When config.enabled is false, it returns immediately with:\nephemeralContext: null;\nempty metadata;\nretrieval: null.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"5.1 Disabled path","lvl3":""}}, {"objectID":"db6b23c02584670295f26144a4d9d36c84aaaa03acaa8a9a439bfb91e5dc4415","title":"5.2 Wait for initialization","url":"/docs/development/knowledge-grounding-lifecycle#52-wait-for-initialization","content":"ready() waits for the constructor's build promise. If no snapshot exists\nafterward, the engine returns empty metadata with index-not-ready.\n\nThis covers validation failure or build failure after an engine already exists\nwithout breaking the model turn. Missing, non-array, or empty sources do not\ncreate an engine in NeuroLink.constructor().","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"5.2 Wait for initialization","lvl3":""}}, {"objectID":"31954d983d7545962756ebf8376227b16a3e7e05716175a87706bda70b32e701","title":"5.3 buildRequest()","url":"/docs/development/knowledge-grounding-lifecycle#53-buildrequest","content":"Builds the internal KnowledgeRetrievalRequest:\n\nIt:\nlimits recent turns again to DEFAULT_RECENT_TURNS;\ndefaults enabledIntegrations to [].\n\nThere is no platform, page context, or server field in this request.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"5.3 buildRequest()","lvl3":""}}, {"objectID":"ad725645f5a7317432ea884587888ffefeef3557598018d994d340a39504cdd5","title":"5.4 retrieve()","url":"/docs/development/knowledge-grounding-lifecycle#54-retrieve","content":"The selection pipeline runs in the following order.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"5.4 retrieve()","lvl3":""}}, {"objectID":"7e7dfd5e6c845bf06409627f563e94656f5942d6191e0c5887c036a246db60e8","title":"A. Query normalization","url":"/docs/development/knowledge-grounding-lifecycle#a-query-normalization","content":"tokenize(request.query) creates normalized query tokens.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"A. Query normalization","lvl3":""}}, {"objectID":"aa1d25104bb4df66b0a2ed18858854840eb6b737f1a040562f1575e3eff29e68","title":"B. Exact and alias lookup","url":"/docs/development/knowledge-grounding-lifecycle#b-exact-and-alias-lookup","content":"generateNGrams() produces every contiguous query phrase from one token up to\nthe longest indexed exact or alias phrase in the active snapshot.\nlookupPhrases() checks those phrases against exactIndex and aliasIndex.\n\nAn exact hit means the phrase matched an indexed entry ID or title. An alias\nhit means it matched a reviewed alias.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"B. Exact and alias lookup","lvl3":""}}, {"objectID":"b7d7efd585672ec8d9aa1cccb5ce7f12b82ad9f342a25f3e3700bdc2fafbcc06","title":"C. Lexical query construction","url":"/docs/development/knowledge-grounding-lifecycle#c-lexical-query-construction","content":"buildLexicalTokens() combines:\nthe current query;\neach recent turn, capped to 400 characters.\n\nThe combined text is tokenized for BM25.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"C. Lexical query construction","lvl3":""}}, {"objectID":"bc30fa1c31c5504560b892dd0240f12f52027b83ba006584ad0d547544e3a723","title":"D. BM25 search","url":"/docs/development/knowledge-grounding-lifecycle#d-bm25-search","content":"Before BM25 runs, the retriever builds an eligibleEntryIds set by applying\nstatus, blocked-domain, and enabled-integration checks to every indexed entry.\nsnapshot.lexical.search() receives that set and retrieves up to twice the\nconfigured candidate limit only from eligible entries.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"D. BM25 search","lvl3":""}}, {"objectID":"a3c02e1c3e884b57e658b6f7dfc3ed54daaebf74875585ace6b1d3d51aac9e5d","title":"E. Candidate union","url":"/docs/development/knowledge-grounding-lifecycle#e-candidate-union","content":"Entry IDs from exact hits, alias hits, and BM25 matches are added to one set.\nExact and alias hits are ignored when the entry is not in eligibleEntryIds.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"E. Candidate union","lvl3":""}}, {"objectID":"8221fd3915b786b771b2e2c0191a77d07c734fc6e53690323331607af08b476f","title":"F. Authorization and metadata filtering","url":"/docs/development/knowledge-grounding-lifecycle#f-authorization-and-metadata-filtering","content":"isAuthorized() is the helper used to build eligibleEntryIds before lexical\ntop-K truncation and to re-check relationship-expanded entries. It rejects:\nentries whose status is not active;\nentries inside config.blockedDomains, when a domain blocklist is configured;\nintegration-specific entries that do not match any enabled integration.\n\nIntegration matching is case-insensitive.\n\nAn entry with integrations: [] applies to every request. If the request has\nenabledIntegrations: [], integration-specific entries are excluded because\nnone of their required integrations are enabled.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"F. Authorization and metadata filtering","lvl3":""}}, {"objectID":"5b873f79aa6ffe5fb41836fa03eaafe15f06d95ec4ca30ea26d00d62e1a9cdf1","title":"G. Deterministic scoring","url":"/docs/development/knowledge-grounding-lifecycle#g-deterministic-scoring","content":"For each authorized candidate:\n\ncompareCandidates() sorts by descending score and uses entry ID as a stable\ntiebreaker.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"G. Deterministic scoring","lvl3":""}}, {"objectID":"0397a412905e49585ee6be602c5c2c1170e1538ddc4b3dc50d502080007f693c","title":"H. Primary selection","url":"/docs/development/knowledge-grounding-lifecycle#h-primary-selection","content":"The candidate list is first capped by candidateLimit, then the top\nresultLimit entries become the primary selection.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"H. Primary selection","lvl3":""}}, {"objectID":"77a07dc94f0bad840b8afa7e21ca8e7946ed413563bf2fc378cb35afda0ce9ea","title":"I. Relationship expansion","url":"/docs/development/knowledge-grounding-lifecycle#i-relationship-expansion","content":"For every primary entry, the retriever reads relationIndex and adds related\nentries until relationLimit is reached. Related entries are authorization\nchecked again and duplicates are skipped.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"I. Relationship expansion","lvl3":""}}, {"objectID":"d195bd528418419b532fc665bdd15b92cc6c31445f069af9a0dc2cd5c11b0e04","title":"J. Confidence","url":"/docs/development/knowledge-grounding-lifecycle#j-confidence","content":"classifyConfidence() returns:\n\n| Confidence | Meaning |\n| ---------- | --------------------------------------------------------------------------------------- |\n| none | No candidates survived. |\n| high | The top candidate has an exact or alias hit. |\n| medium | The top candidate matched multiple lexical fields or clearly dominates the next result. |\n| low | A candidate exists but has only a weak lexical signal. |","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"J. Confidence","lvl3":""}}, {"objectID":"9c6d506ebf9aec9d3a851e6f73deaf89d20454857fe5556abb11876fd48acf60","title":"5.5 assembleKnowledgeContext()","url":"/docs/development/knowledge-grounding-lifecycle#55-assembleknowledgecontext","content":"The assembler receives primary and expanded entries.\n\nIt calls:\nbuildOpening() to create trusted reference-data instructions;\nrenderEntry() to render full or summary-only entries;\nestimateTokens() to enforce an approximate four-characters-per-token\n budget.\n\nEntries are added in relevance order:\nprimary entries;\nrelationship-expanded entries.\n\nFor each entry:\ninclude the full entry when it fits;\notherwise include only its title, kind, and summary when that fits;\notherwise stop and drop remaining entries.\n\nThe result is wrapped in and optionally includes stable\ncitations such as:","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"5.5 assembleKnowledgeContext()","lvl3":""}}, {"objectID":"3d6876f5024edaf11a987e86af29e39964ed7742a5f9861a28751817a8d0b796","title":"5.6 Outcome construction","url":"/docs/development/knowledge-grounding-lifecycle#56-outcome-construction","content":"The engine creates KnowledgeRetrievalResult containing:\nselected and expanded entries;\nassembled context;\nconfidence;\ncitations;\nselected and expanded IDs;\ncandidate count;\ncontext token estimate;\ntruncation status;\nretrieval duration.\n\nIt also creates the smaller KnowledgeGroundingMetadata attached to the public\nstream result.\n\nWhen assembled context is empty, the engine returns the retrieval and metadata\nbut no ephemeral context.\n\nWhen context exists, it creates:","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"5.6 Outcome construction","lvl3":""}}, {"objectID":"58ce78428990abee9a3f3ce280d2a3432d7cd5c894bb4fae99204991605407e1","title":"6. Applying knowledge to the model turn","url":"/docs/development/knowledge-grounding-lifecycle#6-applying-knowledge-to-the-model-turn","content":"Back in NeuroLink.generate() or NeuroLink.stream(), the returned block is\napplied explicitly:\n\nThe original caller object is not changed because it was cloned first.\n\nThe knowledge text is part of this turn's system prompt only. It is not written\nto conversation memory as a user or assistant message.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"6. Applying knowledge to the model turn","lvl3":""}}, {"objectID":"fd7ad99cbd2c34b79c26e94c3a5670d659606b8f679c7f166b2303aa5d9c17fe","title":"7. Fallback behavior","url":"/docs/development/knowledge-grounding-lifecycle#7-fallback-behavior","content":"Grounding runs once before the existing provider/fallback orchestration.\n\nAll provider/model attempts reuse the same enriched options:\n\n| Fallback path | Knowledge behavior |\n| ----------------------------------------------------------------- | ------------------------------------ |\n| Stream creation fallback through runWithFallbackOrchestration() | Reuses the existing knowledge block. |\n| Pre-first-chunk fallback through streamWithIterationFallback() | Reuses the existing knowledge block. |\n| ModelPool member fallback | Reuses the existing knowledge block. |\n| Empty-output fallback through handleStreamFallback() | Reuses the existing knowledge block. |\n\nThe engine does not retrieve again for a fallback provider, and the context is\nnot appended a second time.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"7. Fallback behavior","lvl3":""}}, {"objectID":"eb1a9baef7bfed5bf815eb4048a0780d65c2d033651ddece0daab31e7a3d90d6","title":"8. Failure behavior","url":"/docs/development/knowledge-grounding-lifecycle#8-failure-behavior","content":"Knowledge grounding is designed to fail open.\n\n| Failure | Turn behavior |\n| -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |\n| Grounding not configured | Skip grounding. |\n| Grounding disabled | Skip grounding. |\n| SDK-internal utility call | Skip grounding. |\n| Query missing | Skip grounding. |\n| Sources missing, non-array, or empty | No engine is created; getKnowledgeStatus() returns null. |\n| Validation errors in non-empty sources | Engine is created, records issues, keeps ready: false, and returns index-not-ready until recreated or explicitly rebuilt. |\n| No retrieval match | Continue without a knowledge block. |\n| Context budget fits no entry | Continue without a knowledge block. |\n| Unexpected retrieval error | Return failure metadata or undefined; continue ungrounded.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"8. Failure behavior","lvl3":""}}, {"objectID":"8f6fb9a5b2e7603b7ed0219381b17400cde8c0241b24b6c9c74112d32fdabae8","title":"Instance status","url":"/docs/development/knowledge-grounding-lifecycle#instance-status","content":"Call:\n\nThis returns engine readiness, entry count, last build error, and validation\nissues, or null when grounding was not configured.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"Instance status","lvl3":""}}, {"objectID":"978552436e849e927b6c78febc663657d9f744567097b11eec2e2d01c8b6e02a","title":"Turn result","url":"/docs/development/knowledge-grounding-lifecycle#turn-result","content":"After generate() or stream() returns, result.knowledge contains aggregate\ngrounding metadata when the engine ran. It reports selected IDs, expanded IDs,\ncandidate count, context tokens, truncation, duration, confidence, and any\nfailure reason.\n\nThe complete diagnostic retrieval exists inside KnowledgeGroundingOutcome\nbut is not attached wholesale to the public result.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"Turn result","lvl3":""}}, {"objectID":"e9a240b9aba1a7f6a13b78b19ad6c5b5c0e00eac1415e60dc95d4588888d5bf4","title":"10. Current implementation boundaries","url":"/docs/development/knowledge-grounding-lifecycle#10-current-implementation-boundaries","content":"Knowledge grounding runs on both generate() and stream().\nThe index is built once from constructor sources.\nThere is no runtime setKnowledgeSources() implementation.\nThere is no runtime source hot reload or change detection. The process-level\n cache key includes a deterministic source-content hash to avoid stale index\n reuse when source content changes.\nThere is no shadow mode.\nThere is no page-context prior.\nThere is no separate platform field; integrations are the single scope.\nThere is no server field in KnowledgeRetrievalRequest.\nKnowledgeGroundingConfig.timeoutMs bounds one grounding operation; timeout\n returns an ungrounded result with a failure reason.","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"10. Current implementation boundaries","lvl3":""}}, {"objectID":"378765a908209f5b9434fc6287c962a0a6565ada04da742d73314932d08d2ae0","title":"11. Recommended breakpoint order","url":"/docs/development/knowledge-grounding-lifecycle#11-recommended-breakpoint-order","content":"To follow one complete grounding lifecycle in a debugger:","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"11. Recommended breakpoint order","lvl3":""}}, {"objectID":"882f85c43dd9a98ca3e761718ce62078ee393edb66e31aae989da121018eb6e7","title":"Initialization","url":"/docs/development/knowledge-grounding-lifecycle#initialization","content":"NeuroLink.constructor\nKnowledgeGroundingEngine.constructor\nKnowledgeGroundingEngine.build\nnormalizeAndValidate\nresolveEntry\nbuildIndexSnapshot\nbuildDocument\nKnowledgeLexicalIndex.add\nKnowledgeLexicalIndex.finalize","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"Initialization","lvl3":""}}, {"objectID":"dd89a186c3b5bcafe4407f9d8cbb34f93955a54eb571cabad07c1772e686c7e4","title":"One stream turn","url":"/docs/development/knowledge-grounding-lifecycle#one-stream-turn","content":"NeuroLink.stream\nNeuroLink.retrieveKnowledgeGrounding\nKnowledgeGroundingEngine.ground\nKnowledgeGroundingEngine.ready\nKnowledgeGroundingEngine.buildRequest\nretrieve\nKnowledgeLexicalIndex.search\nisAuthorized\nclassifyConfidence\nassembleKnowledgeContext\nReturn to NeuroLink.stream and inspect the enriched options\nInspect StreamResult.knowledge","hierarchy":{"lvl0":"Development","lvl1":"Knowledge Grounding: Initialization and Turn Lifecycle","lvl2":"One stream turn","lvl3":""}}, {"objectID":"203a221e6dbb90e13cb4488d00556574a15f10c09c1603840b7f0ed52a85bca5","title":"Design Doc: Large Context Handling via Map-Reduce Summarization","url":"/docs/development/large-context-design","content":"Design Doc: Large Context Handling via Map-Reduce Summarization\n\nNote: The map-reduce approach described in this design document is a proposed\narchitecture that has not been implemented in the codebase. None of the artifacts\nit specifies (_summarizeLargeText, largeTextHandling option, textUtils.ts)\nexist in production code. The production implementation uses a different approach —\nsee the Context Compaction System and\nsrc/lib/context/contextCompactor.ts.\nOverview\n\nThis document outlines the design and implementation plan for adding large context handling capabilities to the NeuroLink SDK. The core of this proposal is a map-reduce summarization strategy to process text inputs that exceed the context window limits of underlying Large Language Models (LLMs).\nProblem Statement\n\nThe NeuroLink SDK's generate() method currently sends the entire input prompt directly to the AI provider. This design fails when the input text is very large (e.g., a 1MB file), as it surpasses the model's maximum token limit, resulting in an API error and a complete failure of the operation.\n\nThe existing conversation summarization feature is designed for managing the history of a dialogue and does not address the challenge of processing a single, oversized document.\n\nUse Cases\n\nThis feature is critical for enabling new, high-value use cases, such as:\nDocument Summarization: Summarizing large PDF, DOCX, or text files.\nData Analysis: Analyzing long reports, transcripts, or logs to extract key insights.\nQuestion Answering over Documents: Allowing users to ask questions about a large document that is provided as context.\nChallenges and Mitigations\n\n3.1. Latency\nChallenge: Making multiple sequential calls to an LLM will significantly increase the total response time.\nMitigation:\nParallel Processing: The \"Map\" step, where individual chunks are summarized, will be executed in parallel using Promise.all. This reduces the time for this step to the duration of the single longest-running chunk summarization, rather than the sum of all of them.\nModel Flexibility: The system will be designed to allow for the use of faster, more cost-effective models (e.g., gemini-2.5-flash) for the intermediate chunk summarization, while a more powerful model can be used for the final, high-quality summary.\n\n3.2. Context Loss Between Chunks\nChallenge: Splitting the text into independent chunks can cause the loss of context that spans across chunk boundaries.\nMitigation:\nChunk Overlap: The chunking utility will support an overlap parameter. A portion of text from the end of one chunk will be included at the beginning of the next, ensuring a smoother contextual transition.\nIntelligent Splitting: The utility will prioritize splitting text at natural boundaries like sentences (., !, ?) or paragraphs to keep related ideas together within a single chunk.\n\n3.3. Cost\nChallenge: Multiple LLM calls will be more expensive than a single call.\nMitigation: This is an inherent trade-off for gaining this new capability. The ability to use smaller, cheaper models for the initial chunking step will help manage costs effectively. The feature will be opt-in, so users only incur costs when they explicitly need to process large documents.\nProposed Solution & Architecture\n\nWe will implement a Map-Reduce Summarization workflow.\n\nHigh-Level Flow Diagram\nDetailed Design and Implementation\n\n5.1. Sequence Diagram\n\nThis diagram shows the interaction between the different components of the system.\n\n5.2. New Utility: textUtils.ts\n\nA new file will be created at src/lib/utils/textUtils.ts to contain the logic for splitting large texts into manageable pieces.\n\nDetailed Explanation of chunkText\n\nThis function is the foundation of our solution. It intelligently divides a large string into an array of smaller strings (chunks) based on a target size, while trying to maintain the contextual integrity of the original text.\n\n5.3. New Workflow: _summarizeLargeText()\n\nThis new private method orchestrates the entire map-reduce workflow. It will be added to the NeuroLink class in src/lib/neurolink.ts.\n\nDetailed Explanation of _summarizeLargeText\n\nThis function acts as the controller for the large context handling process. It chunks the text, manages the parallel summarization of each chunk, combines the results, and generates the final summary.\n\n5.4. Integration into generate()\n\nThe main generate() method will be modified to delegate to the new workflow when appropriate.\nConfiguration and API Changes\n\nThe GenerateOptions interface in src/lib/types/generateTypes.ts will be updated.\nmode: 'none' (default) or 'summarize'.\nchunkSize: Target size for each text chunk (in characters). Defaults to 4000.\noverlap: Character overlap between chunks. Defaults to 200.\nchunkingProvider / chunkingModel: Optional. Allows specifying a faster/cheaper model for the intermediate \"Map\" step, enhancing performance and cost-effectiveness.\nTesting Strategy\nUnit Tests (test/textUtils.test.ts):\nTest chunkText with empty, short, and lo","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"","lvl3":""}}, {"objectID":"17a3dc09aaf5dad12a8bb44fa17bc4b377fb7b7f1de884ceb2885e8eb572e176","title":"Design Doc: Large Context Handling via Map-Reduce Summarization","url":"/docs/development/large-context-design#design-doc-large-context-handling-via-map-reduce-summarization","content":"Note: The map-reduce approach described in this design document is a proposed\narchitecture that has not been implemented in the codebase. None of the artifacts\nit specifies (_summarizeLargeText, largeTextHandling option, textUtils.ts)\nexist in production code. The production implementation uses a different approach —\nsee the Context Compaction System and\nsrc/lib/context/contextCompactor.ts.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl3":""}}, {"objectID":"7fc8733aa8714e0a61ac66faa5e09d642a41a25d51ce119f3baa628f6969dd62","title":"1. Overview","url":"/docs/development/large-context-design#1-overview","content":"This document outlines the design and implementation plan for adding large context handling capabilities to the NeuroLink SDK. The core of this proposal is a map-reduce summarization strategy to process text inputs that exceed the context window limits of underlying Large Language Models (LLMs).","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"1. Overview","lvl3":""}}, {"objectID":"b33cfeb3f8ea43d4af3e06167458d7efd954fe46e3e48bb499ad51d3aee76e93","title":"2. Problem Statement","url":"/docs/development/large-context-design#2-problem-statement","content":"The NeuroLink SDK's generate() method currently sends the entire input prompt directly to the AI provider. This design fails when the input text is very large (e.g., a 1MB file), as it surpasses the model's maximum token limit, resulting in an API error and a complete failure of the operation.\n\nThe existing conversation summarization feature is designed for managing the history of a dialogue and does not address the challenge of processing a single, oversized document.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"2. Problem Statement","lvl3":""}}, {"objectID":"b0d5aae6fe310f882c8d4b5b6a36d3e4e53c9b40c4c9096b4aa4beae387a2e5f","title":"Use Cases","url":"/docs/development/large-context-design#use-cases","content":"This feature is critical for enabling new, high-value use cases, such as:\nDocument Summarization: Summarizing large PDF, DOCX, or text files.\nData Analysis: Analyzing long reports, transcripts, or logs to extract key insights.\nQuestion Answering over Documents: Allowing users to ask questions about a large document that is provided as context.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"Use Cases","lvl3":""}}, {"objectID":"4b36cf7877d351972936d6a07993dde31a415eb6be9cd1f93605641a3c2cfd98","title":"3.1. Latency","url":"/docs/development/large-context-design#31-latency","content":"Challenge: Making multiple sequential calls to an LLM will significantly increase the total response time.\nMitigation:\nParallel Processing: The \"Map\" step, where individual chunks are summarized, will be executed in parallel using Promise.all. This reduces the time for this step to the duration of the single longest-running chunk summarization, rather than the sum of all of them.\nModel Flexibility: The system will be designed to allow for the use of faster, more cost-effective models (e.g., gemini-2.5-flash) for the intermediate chunk summarization, while a more powerful model can be used for the final, high-quality summary.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"3.1. Latency","lvl3":""}}, {"objectID":"dadabac0b6bc78048a7ed9c1f5502791b6cdd45e7bdcaf4d15463265f1567a5f","title":"3.2. Context Loss Between Chunks","url":"/docs/development/large-context-design#32-context-loss-between-chunks","content":"Challenge: Splitting the text into independent chunks can cause the loss of context that spans across chunk boundaries.\nMitigation:\nChunk Overlap: The chunking utility will support an overlap parameter. A portion of text from the end of one chunk will be included at the beginning of the next, ensuring a smoother contextual transition.\nIntelligent Splitting: The utility will prioritize splitting text at natural boundaries like sentences (., !, ?) or paragraphs to keep related ideas together within a single chunk.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"3.2. Context Loss Between Chunks","lvl3":""}}, {"objectID":"fa8be540808fa68b0e9a42edc5d9ba07cfc0c5553f6966da7958650a54d2c558","title":"3.3. Cost","url":"/docs/development/large-context-design#33-cost","content":"Challenge: Multiple LLM calls will be more expensive than a single call.\nMitigation: This is an inherent trade-off for gaining this new capability. The ability to use smaller, cheaper models for the initial chunking step will help manage costs effectively. The feature will be opt-in, so users only incur costs when they explicitly need to process large documents.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"3.3. Cost","lvl3":""}}, {"objectID":"35df83f67f8e329408db6b1316ae5d765ded276ad12f30173e35c0eccaa888a5","title":"4. Proposed Solution & Architecture","url":"/docs/development/large-context-design#4-proposed-solution-architecture","content":"We will implement a Map-Reduce Summarization workflow.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"4. Proposed Solution & Architecture","lvl3":""}}, {"objectID":"9211aff2b2a4233318b81db3228358c27f0053adba5809144b8f089080f3392e","title":"5.1. Sequence Diagram","url":"/docs/development/large-context-design#51-sequence-diagram","content":"This diagram shows the interaction between the different components of the system.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"5.1. Sequence Diagram","lvl3":""}}, {"objectID":"bab55887c445621b1ffef19f86c22c8726d2ee77894b8d57ef87cca1236d5079","title":"5.2. New Utility: textUtils.ts","url":"/docs/development/large-context-design#52-new-utility-textutilsts","content":"A new file will be created at src/lib/utils/textUtils.ts to contain the logic for splitting large texts into manageable pieces.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"5.2. New Utility: textUtils.ts","lvl3":""}}, {"objectID":"84295911ea30656f2235a20f9404592fba1429a5107b6870847f927b28f7c4e8","title":"Detailed Explanation of chunkText","url":"/docs/development/large-context-design#detailed-explanation-of-chunktext","content":"This function is the foundation of our solution. It intelligently divides a large string into an array of smaller strings (chunks) based on a target size, while trying to maintain the contextual integrity of the original text.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"Detailed Explanation of chunkText","lvl3":""}}, {"objectID":"089d10a389e45a1aac4180cf7eefb0d4b6c9b86a01f6b6acc2eb85a9e239b9cd","title":"5.3. New Workflow: _summarizeLargeText()","url":"/docs/development/large-context-design#53-new-workflow-_summarizelargetext","content":"This new private method orchestrates the entire map-reduce workflow. It will be added to the NeuroLink class in src/lib/neurolink.ts.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"5.3. New Workflow: _summarizeLargeText()","lvl3":""}}, {"objectID":"dadcbd3d4b2b5ed5b45b1231a629aa863ed587cb0f5d7bcbea20748d8a7a6bfd","title":"Detailed Explanation of _summarizeLargeText","url":"/docs/development/large-context-design#detailed-explanation-of-_summarizelargetext","content":"This function acts as the controller for the large context handling process. It chunks the text, manages the parallel summarization of each chunk, combines the results, and generates the final summary.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"Detailed Explanation of _summarizeLargeText","lvl3":""}}, {"objectID":"10109872dea94c494e8ac26e6f13395d64a7b15d0122353443b50717e378190a","title":"5.4. Integration into generate()","url":"/docs/development/large-context-design#54-integration-into-generate","content":"The main generate() method will be modified to delegate to the new workflow when appropriate.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"5.4. Integration into generate()","lvl3":""}}, {"objectID":"7d52c961b0d3172f06fd9b22e02af892d456ec90846270ad2f4502b12ec29646","title":"6. Configuration and API Changes","url":"/docs/development/large-context-design#6-configuration-and-api-changes","content":"The GenerateOptions interface in src/lib/types/generateTypes.ts will be updated.\nmode: 'none' (default) or 'summarize'.\nchunkSize: Target size for each text chunk (in characters). Defaults to 4000.\noverlap: Character overlap between chunks. Defaults to 200.\nchunkingProvider / chunkingModel: Optional. Allows specifying a faster/cheaper model for the intermediate \"Map\" step, enhancing performance and cost-effectiveness.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"6. Configuration and API Changes","lvl3":""}}, {"objectID":"65545a334a9cbe3bfebbab297ec393a8c6e501474878012e244bf793ee7cfb39","title":"7. Testing Strategy","url":"/docs/development/large-context-design#7-testing-strategy","content":"Unit Tests (test/textUtils.test.ts):\nTest chunkText with empty, short, and long strings.\nVerify that overlap is handled correctly.\nEnsure splitting prioritizes sentence boundaries.\nIntegration Tests (test/largeContext.test.ts):\nTest the main generate() method with a string larger than the chunkSize threshold.\nMock the _summarizeLargeText method to confirm it's called when mode is 'summarize'.\nMock the internal generate calls to verify the map-reduce logic is working as expected (i.e., multiple parallel calls followed by one final call).\nConfirm that the normal workflow is used when mode is 'none'.\nEnd-to-End (E2E) Test (examples/summarize-large-file.js):\nCreate a script that reads a large text file from the disk.\nCalls neurolink.generate() with the file content and largeTextHandling: { mode: 'summarize' }.\nPrints the final summary to the console for manual validation of quality.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"7. Testing Strategy","lvl3":""}}, {"objectID":"a35082a04869c29685205794eef5b569d75391fbbcd054bdbfa9d8281a94ff7f","title":"Section 8: Production Implementation","url":"/docs/development/large-context-design#section-8-production-implementation","content":"The map-reduce design described in this document has been complemented by a\nproduction context compaction system. See\nthe Context Compaction Guide for the full\nspecification.\n\nThe production implementation adds:\nContextCompactor (src/lib/context/contextCompactor.ts) -- a multi-stage\n compaction orchestrator with five sequential stages: relevance drop (Stage 0,\n decision-provider gated), tool-output pruning, file-read deduplication, LLM\n summarization (structured 10-section summaries with iterative merging), and\n sliding-window truncation.\nBudgetChecker (src/lib/context/budgetChecker.ts) -- pre-generation validation\n that checks token usage against per-model context windows (maintained in\n src/lib/constants/contextWindows.ts) and triggers auto-compaction at 80 % usage.\nError Detection (src/lib/context/errorDetection.ts) -- cross-provider\n detection of context-overflow errors so compaction can be retried transparently.\ngetContextStats() API -- returns live token estimates, remaining capacity,\n and per-stage reduction metrics for runtime observability.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"Section 8: Production Implementation","lvl3":""}}, {"objectID":"87caa8563f1b6aec126e5f3857eeb75f2182aa25f6d7a5c59aef6026e5054b09","title":"Distinguishing This Design Doc from the Context Compaction System","url":"/docs/development/large-context-design#distinguishing-this-design-doc-from-the-context-compaction-system","content":"These two systems address fundamentally different problems:\n\n| Aspect | This Design Doc (Map-Reduce) | Context Compaction System |\n| ------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Problem | A single input document exceeds the model's context window before generation even begins. | Conversation history grows beyond the context window over the course of a multi-turn session. |\n| Trigger | User opts in via largeTextHandling: { mode: 'summarize' } on a generate() call. | Automatic — BudgetChecker fires before every LLM call when token usage exceeds 80% of the model's context window. |\n| Technique | Map-reduce chunking: split the document into overlapping pieces, summarize each piece in parallel, then reduce the summaries into one final output.","hierarchy":{"lvl0":"Development","lvl1":"Design Doc: Large Context Handling via Map-Reduce Summarization","lvl2":"Distinguishing This Design Doc from the Context Compaction System","lvl3":""}}, {"objectID":"8c5084d18842421a54c0c74d3b7861d2fc5eec8cc9a9abf5ed28c47ab1392571","title":"Automated Link Checking","url":"/docs/development/link-checking","content":"Automated Link Checking\n\nAutomated validation of documentation links to prevent broken references\n\nOverview\n\nAutomated link checking ensures all internal and external links in documentation remain valid, preventing broken links from reaching users. The NeuroLink documentation uses markdown-link-check for automated validation.\n\nBenefits\nPrevent broken links: Catch broken links before deployment\nAutomated validation: Run checks on every commit\nInternal link validation: Verify cross-references between docs\nExternal link monitoring: Check third-party URLs periodically\nCI/CD integration: Fail builds on broken links\n\nQuick Start\n\nLocal Link Checking\n\nOutput:\n\nInstall Dependencies\n\nConfiguration\n\nLink Checker Config\n\nThe script uses /tmp/mlc_config.json with default settings. To customize, create .markdown-link-check.json:\n\nConfiguration Options\n\n| Option | Description | Default |\n| ------------------ | -------------------------- | ----------------- |\n| timeout | HTTP request timeout | 10s |\n| retryOn429 | Retry on rate limit errors | true |\n| retryCount | Number of retries | 3 |\n| aliveStatusCodes | Valid HTTP status codes | [200, 206, ...] |\n| ignorePatterns | URLs to skip checking | [] |\n\nCI/CD Integration\n\nGitHub Actions Workflow\n\nCreate .github/workflows/link-check.yml:\n\nPre-commit Hook\n\nAdd to .husky/pre-commit or .git/hooks/pre-commit:\n\nMake executable:\n\nUsage Patterns\n\nCheck Specific File\n\nCheck All Docs\n\nCheck with Custom Config\n\nQuiet Mode (Only Show Errors)\n\nVerbose Mode (Debug)\n\nCommon Issues\n\nIssue 1: False Positives (Valid Links Marked as Broken)\n\nCause: Some sites block automated requests or have aggressive rate limiting.\n\nSolution: Add to ignore patterns:\n\nOr add to alive status codes:\n\nIssue 2: Slow Checks\n\nCause: External link checking can be slow.\n\nSolution 1: Skip external links for local development:\n\nSolution 2: Use faster internal-only checker:\n\nIssue 3: Relative Path Issues\n\nCause: Relative links may not resolve correctly.\n\nSolution: Use replacement patterns:\n\nIssue 4: Anchor Links Not Validated\n\nCause: markdown-link-check may not validate anchor links (#section).\n\nSolution: Use remark-validate-links:\n\nAdvanced Usage\n\nCustom Link Validation Script\n\nFor complex validation needs, create custom scripts:\n\nRun:\n\nParallel Link Checking\n\nFor faster checking with many files:\n\nBest Practices\nRegular Checks\nOn every commit: Check changed files in pre-commit hook\nOn every PR: Full link check in CI/CD\nWeekly: Scheduled check for external link rot\nSeparate Internal and External\nIgnore Transient Failures\n\nSome external links may fail intermittently. Retry failed checks:\nDocument Known Issues\n\nFor persistent false positives, document in .markdown-link-check.json:\n\nIntegration with MkDocs\n\nBuild-time Link Checking\n\nAdd to mkdocs.yml:\n\nCreate scripts/check-links-hook.py:\n\nRelated Documentation\nVersioning - Documentation version management\nContributing - Contribution guidelines\nTesting - Testing strategies\n\nAdditional Resources\nmarkdown-link-check - Link checker tool\nremark-validate-links - Alternative validator\nGitHub Actions - CI/CD automation","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"","lvl3":""}}, {"objectID":"9de3da6f67cdcdecdc1ecf80f2212783097e13093a24255286979726e2d10ba8","title":"Automated Link Checking","url":"/docs/development/link-checking#automated-link-checking","content":"Automated validation of documentation links to prevent broken references","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Automated Link Checking","lvl3":""}}, {"objectID":"2a4f6ab7a15205af5bc986c5de124a3168e5b43c39d5ba47d1bde5feb0e98b40","title":"Overview","url":"/docs/development/link-checking#overview","content":"Automated link checking ensures all internal and external links in documentation remain valid, preventing broken links from reaching users. The NeuroLink documentation uses markdown-link-check for automated validation.","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Overview","lvl3":""}}, {"objectID":"189f540449da800394a59151a52641ba961e11d3d148b78ed239b54519f45443","title":"Benefits","url":"/docs/development/link-checking#benefits","content":"Prevent broken links: Catch broken links before deployment\nAutomated validation: Run checks on every commit\nInternal link validation: Verify cross-references between docs\nExternal link monitoring: Check third-party URLs periodically\nCI/CD integration: Fail builds on broken links","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Benefits","lvl3":""}}, {"objectID":"de8213b77c54b5d1b0f7bf87404416720b2b2724a4cd7db4a9c4cc68399f6288","title":"Local Link Checking","url":"/docs/development/link-checking#local-link-checking","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Local Link Checking","lvl3":""}}, {"objectID":"21a520e08a0fb7db179be8816a19cec6460706bec0b98f62c05a8d6b4caac0b7","title":"From docs/improve-docs directory","url":"/docs/development/link-checking#from-docsimprove-docs-directory","content":"chmod +x scripts/check-links.sh\n./scripts/check-links.sh docs\n\n🔍 Checking links in docs...\n\n📄 Finding markdown files...\nFound 50 files to check\n\n[1/50] Checking: docs/index.md\n✓ No broken links\n\n[2/50] Checking: docs/getting-started/quick-start.md\n✓ No broken links\n\n...\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📊 Link Check Summary\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\nTotal files checked: 50\nFiles with broken links: 0\n\n✅ All links valid!\n`","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"From docs/improve-docs directory","lvl3":""}}, {"objectID":"989fbfca8040e2c56c54e6ed278ba3447d6b9f31d06ef88775ab71be347c9616","title":"Install Dependencies","url":"/docs/development/link-checking#install-dependencies","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Install Dependencies","lvl3":""}}, {"objectID":"828b20e509914836798ea707ca06e6509eba5c95a97c11f1f3b5e4c5aca35175","title":"Install markdown-link-check globally","url":"/docs/development/link-checking#install-markdown-link-check-globally","content":"npm install -g markdown-link-check","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Install markdown-link-check globally","lvl3":""}}, {"objectID":"01dd3a301b4f30ad864592e7d8b076104baf6af82d9b5360b37725f370c44f98","title":"Or use via npx (no installation)","url":"/docs/development/link-checking#or-use-via-npx-no-installation","content":"npx markdown-link-check docs/index.md\n`","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Or use via npx (no installation)","lvl3":""}}, {"objectID":"285199f3d980fc4a927b69f3700f6547ef31a5babb144e7119ad17345b6a1a70","title":"Link Checker Config","url":"/docs/development/link-checking#link-checker-config","content":"The script uses /tmp/mlc_config.json with default settings. To customize, create .markdown-link-check.json:","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Link Checker Config","lvl3":""}}, {"objectID":"6341d4463842e810a8c476327a844b408cb8c349ffecba45114704f112f3825a","title":"Configuration Options","url":"/docs/development/link-checking#configuration-options","content":"| Option | Description | Default |\n| ------------------ | -------------------------- | ----------------- |\n| timeout | HTTP request timeout | 10s |\n| retryOn429 | Retry on rate limit errors | true |\n| retryCount | Number of retries | 3 |\n| aliveStatusCodes | Valid HTTP status codes | [200, 206, ...] |\n| ignorePatterns | URLs to skip checking | [] |","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Configuration Options","lvl3":""}}, {"objectID":"8801edf88e1cb06a245b7c8c0071c2c770315bffd22215bb0ebba14760fe5fb7","title":"GitHub Actions Workflow","url":"/docs/development/link-checking#github-actions-workflow","content":"Create .github/workflows/link-check.yml:","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"GitHub Actions Workflow","lvl3":""}}, {"objectID":"152f06b9b5892334b235b6efbea99231eff4dccfc12c97cc536e86c2f8f05a00","title":"Pre-commit Hook","url":"/docs/development/link-checking#pre-commit-hook","content":"Add to .husky/pre-commit or .git/hooks/pre-commit:\n\n`bash\n#!/bin/bash","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Pre-commit Hook","lvl3":""}}, {"objectID":"a38104d5f3ef403039b3c5c6215dc50029cba029586cf184d4caecee31409eb1","title":"Check links on changed markdown files","url":"/docs/development/link-checking#check-links-on-changed-markdown-files","content":"CHANGED_MD=$(git diff --cached --name-only --diff-filter=ACMR | grep '\\.md$')\n\nif [ -n \"$CHANGED_MD\" ]; then\n echo \"🔍 Checking links in modified files...\"\n\n for file in $CHANGED_MD; do\n echo \"Checking: $file\"\n npx markdown-link-check \"$file\" || exit 1\n done\n\n echo \"✅ All links valid!\"\nfi\nbash\nchmod +x .git/hooks/pre-commit\n`","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Check links on changed markdown files","lvl3":""}}, {"objectID":"de5462ab2db796ca96362059b4ad6962c85f8ea042fca24afe8ded297f02873d","title":"Issue 1: False Positives (Valid Links Marked as Broken)","url":"/docs/development/link-checking#issue-1-false-positives-valid-links-marked-as-broken","content":"Cause: Some sites block automated requests or have aggressive rate limiting.\n\nSolution: Add to ignore patterns:\n\nOr add to alive status codes:","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Issue 1: False Positives (Valid Links Marked as Broken)","lvl3":""}}, {"objectID":"adb5903e0445623e2eb4ba8a7a7aeff8fd7d33b486b3e589db8a21d69f46f9ac","title":"Issue 2: Slow Checks","url":"/docs/development/link-checking#issue-2-slow-checks","content":"Cause: External link checking can be slow.\n\nSolution 1: Skip external links for local development:\n\nSolution 2: Use faster internal-only checker:\n\n`bash","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Issue 2: Slow Checks","lvl3":""}}, {"objectID":"d7791b2371eb123524ec158bcc2d748e2afe9624e5487af0d32b3e2ee7f3758e","title":"Check only internal links (faster)","url":"/docs/development/link-checking#check-only-internal-links-faster","content":"grep -r \"\\[.*\\](\\./\" docs/ | grep -v \"http\"\n`","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Check only internal links (faster)","lvl3":""}}, {"objectID":"fcede65461e37b84b6320029e9ef01b044115cdc3cedfb5bbd2f7e3ca49d7560","title":"Issue 3: Relative Path Issues","url":"/docs/development/link-checking#issue-3-relative-path-issues","content":"Cause: Relative links may not resolve correctly.\n\nSolution: Use replacement patterns:","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Issue 3: Relative Path Issues","lvl3":""}}, {"objectID":"f278f2005b5ddd86716325416fb480a104f544da7fbf3068ba707069d6a4c835","title":"Issue 4: Anchor Links Not Validated","url":"/docs/development/link-checking#issue-4-anchor-links-not-validated","content":"Cause: markdown-link-check may not validate anchor links (#section).\n\nSolution: Use remark-validate-links:","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Issue 4: Anchor Links Not Validated","lvl3":""}}, {"objectID":"364625b827299c83ec7d4d10c57b8e156388f40bf6758756c245369320e49b2d","title":"Custom Link Validation Script","url":"/docs/development/link-checking#custom-link-validation-script","content":"For complex validation needs, create custom scripts:\n\nRun:","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Custom Link Validation Script","lvl3":""}}, {"objectID":"a250224c5ff80880c098a74231be95306a6d0c7922f8671a3c2403325fa2f928","title":"Parallel Link Checking","url":"/docs/development/link-checking#parallel-link-checking","content":"For faster checking with many files:\n\n`bash","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Parallel Link Checking","lvl3":""}}, {"objectID":"e7898cc75b565b315ff9551f6694179ba8efa1cd4201b696a49e1515c0b66476","title":"Install GNU parallel","url":"/docs/development/link-checking#install-gnu-parallel","content":"brew install parallel # macOS\napt-get install parallel # Linux","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Install GNU parallel","lvl3":""}}, {"objectID":"b9d050de07efa9faf05f2886bbb31f5deb2829d03eaa20c54df793a070e0a72c","title":"Check files in parallel","url":"/docs/development/link-checking#check-files-in-parallel","content":"find docs -name \"*.md\" | parallel -j 4 markdown-link-check {}\n`","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Check files in parallel","lvl3":""}}, {"objectID":"4305dd6a188175db096896296a58ae6c1e230026e8142b70f63c66f9ff291acb","title":"1. Regular Checks","url":"/docs/development/link-checking#1-regular-checks","content":"On every commit: Check changed files in pre-commit hook\nOn every PR: Full link check in CI/CD\nWeekly: Scheduled check for external link rot","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"1. Regular Checks","lvl3":""}}, {"objectID":"e5d5f8a9cf1c5bb407ca7add6475d3b2e0c81974ff052d62c0568075ce28a23e","title":"2. Separate Internal and External","url":"/docs/development/link-checking#2-separate-internal-and-external","content":"`yaml","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"2. Separate Internal and External","lvl3":""}}, {"objectID":"3d176355cda010445ea4b0dc205c17289da7d9494710b0640ccf147c71a2e3b3","title":"Fast check (internal only)","url":"/docs/development/link-checking#fast-check-internal-only","content":"name: Check internal links\n run: ./scripts/check-links.sh docs --internal-only","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Fast check (internal only)","lvl3":""}}, {"objectID":"110f13142585b023d127ab13bc7312c8fb426604e33c13977f8c5097a6d2f977","title":"Slow check (weekly for external)","url":"/docs/development/link-checking#slow-check-weekly-for-external","content":"name: Check external links\n if: github.event.schedule\n run: ./scripts/check-links.sh docs --external-only\n`","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Slow check (weekly for external)","lvl3":""}}, {"objectID":"bae5676f2d5ebd6a49e633b5f62bff294acc7dd7994e0850307a8ee4632fe158","title":"3. Ignore Transient Failures","url":"/docs/development/link-checking#3-ignore-transient-failures","content":"Some external links may fail intermittently. Retry failed checks:\n\n`bash","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"3. Ignore Transient Failures","lvl3":""}}, {"objectID":"61990f76dae9df7ec75d7fee7df5d89ee966a7aceb4b7abda6dd728c1ff03c19","title":"Retry failed checks 3 times","url":"/docs/development/link-checking#retry-failed-checks-3-times","content":"markdown-link-check docs/index.md --retry --retryCount 3\n`","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Retry failed checks 3 times","lvl3":""}}, {"objectID":"2456bc6a00777252509eebc68ba6c08267cb021276c67037ec313582b52606b6","title":"4. Document Known Issues","url":"/docs/development/link-checking#4-document-known-issues","content":"For persistent false positives, document in .markdown-link-check.json:","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"4. Document Known Issues","lvl3":""}}, {"objectID":"8655bbc0db2d8853389936e04247ce18bf5f825c17810ab505c2212ae7ef88d1","title":"Build-time Link Checking","url":"/docs/development/link-checking#build-time-link-checking","content":"Add to mkdocs.yml:\n\nCreate scripts/check-links-hook.py:","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Build-time Link Checking","lvl3":""}}, {"objectID":"4677ec51bc0b0f13c2e326aabf374218f42cf9fd3e2809acda6a932217461adb","title":"Related Documentation","url":"/docs/development/link-checking#related-documentation","content":"Versioning - Documentation version management\nContributing - Contribution guidelines\nTesting - Testing strategies","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Related Documentation","lvl3":""}}, {"objectID":"44c7a422d2affcdb3f335b78d80b0958be5d760990185fb0d4505d45b8a9a325","title":"Additional Resources","url":"/docs/development/link-checking#additional-resources","content":"markdown-link-check - Link checker tool\nremark-validate-links - Alternative validator\nGitHub Actions - CI/CD automation","hierarchy":{"lvl0":"Development","lvl1":"Automated Link Checking","lvl2":"Additional Resources","lvl3":""}}, {"objectID":"e111ff87d03c1f77a7d1932885150cd17c19ce903dce394a0514ed74e4bd0270","title":"Logging Guidelines","url":"/docs/development/logging-guidelines","content":"Logging Guidelines\n\nHow NeuroLink logs, and what a contributor has to get right. This documents the\nconvention the codebase already follows — it is not a proposal to change it.\n\nThe logger\n\nImport the shared logger. Never call console.* in src/: ESLint's\nno-console rule fails the build for console.log, and the exceptions for\nwarn/error/info exist for a handful of legacy call sites, not as an\ninvitation.\n\nThere is one logger for the whole process. logger.setEventEmitter() attaches\nthe process-wide sink; per-instance events reach a worker only through its\nonLog bridge (see per-instance routing below).\n\nLevels\n\n| Level | Use for |\n| ------- | ------------------------------------------------------------------------------------------------------------------------------------------ |\n| debug | Routine operation: request construction, cache hits, resolution steps, internal state. The default for anything that fires per request. |\n| info | Events an operator would want in a quiet log: a server binding, a provider registering, an MCP server connecting. Not per-request chatter. |\n| warn | Something recoverable happened and the code carried on: a retry, a fallback, a deprecated option, a degraded capability. |\n| error | An operation failed, or a failure was swallowed and the caller will not see it. |\n\nlogger.always() bypasses level filtering and writes to the console\nunconditionally. It is CLI output, not logging — user-facing text that must\nappear regardless of --debug. Of ~2,260 call sites, ~2,150 are in src/cli/.\nDo not reach for it in src/lib/.\n\nNothing below error is visible by default\n\nshouldLog() suppresses every level except error unless debug mode is on\n(--debug, or NEUROLINK_DEBUG=true). NEUROLINK_LOG_LEVEL then sets the\nfloor within debug mode. Two consequences worth internalising:\nA debug log is not emitted in a normal run, but its arguments are still\n evaluated. Prefer debug over silence.\nA condition a user must act on cannot be reported at warn alone — they will\n never see it. Either raise it to error or surface it on the result object.\n\nChoosing between warn and debug\n\nThe common mistake is logging an ordinary outcome at warn. If a branch is\nreached on a healthy request, it is debug, however unwelcome it looks locally.\nFileDetector is the worked example: a detection that lands below the\nconfidence threshold is the normal case for most files, so it logs at debug\nand says so in a comment. A detection where no strategy identified a type at\nall is a genuine failure, and logs at error.\n\nFormat\n\nPass a message and a structured data object. The message is a constant; the\nvariables go in the object, where a log consumer can index them.\n\nPrefix the message with the emitting component in brackets — [NeuroLink],\n[OpenAI], [FileDetector], [MCP]. Grepping a debug run is how most of this\ngets read.\n\nTemplate literals are not banned, and a short one carrying a single value is\nfine. What matters is that a value a consumer would want to filter on ends up in\nthe data object rather than baked into the message string.\n\nGuard expensive serialization\n\nlogger.debug(...) evaluates its arguments before shouldLog() ever runs, so a\nJSON.stringify of a large payload costs full price on every request even when\nnothing is logged. Guard it:\n\nThe guard is only needed for work that is expensive to produce. Passing an\nobject you already hold is free — the logger does not serialize it unless it\nemits.\n\nNever log secrets\n\nAPI keys, tokens, credentials, presigned URL query strings, user prompt content\nand absolute host paths must not reach a log or a thrown error message.\nsrc/lib/utils/logSanitize.ts has the helpers, and they are the reason several\npast leaks are closed:\n\n| Helper | Use for |\n| --------------------------- | ------------------------------------------------------------------------------------------ |\n| redactUrlForError(url) | A URL in a log or error — strips query and fragment, so a presigned token cannot survive. |\n| redactUrlCredentials | A URL that may carry user:password@. |\n| redactPathFromMessage | A filesystem path in a message. |\n| sanitizeErrorCause | An error from Node/undici — these embed the full request URL or path in their own message. |\n| sanitizeHeaders | A header bag before logging it. |\n| sanitizeRecord | An arbitrary object, with string truncation. |\n| safeDebugSerialize | A large value in a debug log, length-capped. ","hierarchy":{"lvl0":"Development","lvl1":"Logging Guidelines","lvl2":"","lvl3":""}}, {"objectID":"ec8f0114d488deec97f5a673a32bb2715f189d83292738c48c9fbc75b8344202","title":"Logging Guidelines","url":"/docs/development/logging-guidelines#logging-guidelines","content":"How NeuroLink logs, and what a contributor has to get right. This documents the\nconvention the codebase already follows — it is not a proposal to change it.","hierarchy":{"lvl0":"Development","lvl1":"Logging Guidelines","lvl2":"Logging Guidelines","lvl3":""}}, {"objectID":"5d174f91c3ccb88a59544c066c3cbf3a2116b97ffacd2414a51f4d64f46fda14","title":"The logger","url":"/docs/development/logging-guidelines#the-logger","content":"Import the shared logger. Never call console.* in src/: ESLint's\nno-console rule fails the build for console.log, and the exceptions for\nwarn/error/info exist for a handful of legacy call sites, not as an\ninvitation.\n\nThere is one logger for the whole process. logger.setEventEmitter() attaches\nthe process-wide sink; per-instance events reach a worker only through its\nonLog bridge (see per-instance routing below).","hierarchy":{"lvl0":"Development","lvl1":"Logging Guidelines","lvl2":"The logger","lvl3":""}}, {"objectID":"73c3d51ed27bc5ea62bb86ab9dde6cc2b17cecce7a0452a5aa7593da612cf798","title":"Levels","url":"/docs/development/logging-guidelines#levels","content":"| Level | Use for |\n| ------- | ------------------------------------------------------------------------------------------------------------------------------------------ |\n| debug | Routine operation: request construction, cache hits, resolution steps, internal state. The default for anything that fires per request. |\n| info | Events an operator would want in a quiet log: a server binding, a provider registering, an MCP server connecting. Not per-request chatter. |\n| warn | Something recoverable happened and the code carried on: a retry, a fallback, a deprecated option, a degraded capability. |\n| error | An operation failed, or a failure was swallowed and the caller will not see it. |\n\nlogger.always() bypasses level filtering and writes to the console\nunconditionally. It is CLI output, not logging — user-facing text that must\nappear regardless of --debug. Of ~2,260 call sites, ~2,150 are in src/cli/.\nDo not reach for it in src/lib/.","hierarchy":{"lvl0":"Development","lvl1":"Logging Guidelines","lvl2":"Levels","lvl3":""}}, {"objectID":"4dfd061a8a2bd9527c19e4f651358a6a1f9d1e6ecad7505f47dcc69e88ac5cd2","title":"Nothing below error is visible by default","url":"/docs/development/logging-guidelines#nothing-below-error-is-visible-by-default","content":"shouldLog() suppresses every level except error unless debug mode is on\n(--debug, or NEUROLINK_DEBUG=true). NEUROLINK_LOG_LEVEL then sets the\nfloor within debug mode. Two consequences worth internalising:\nA debug log is not emitted in a normal run, but its arguments are still\n evaluated. Prefer debug over silence.\nA condition a user must act on cannot be reported at warn alone — they will\n never see it. Either raise it to error or surface it on the result object.","hierarchy":{"lvl0":"Development","lvl1":"Logging Guidelines","lvl2":"Nothing below error is visible by default","lvl3":""}}, {"objectID":"19e83441e80eb578fc3e13da08db3aecd6953418f59ea037e40d543ff6f1e812","title":"Choosing between warn and debug","url":"/docs/development/logging-guidelines#choosing-between-warn-and-debug","content":"The common mistake is logging an ordinary outcome at warn. If a branch is\nreached on a healthy request, it is debug, however unwelcome it looks locally.\nFileDetector is the worked example: a detection that lands below the\nconfidence threshold is the normal case for most files, so it logs at debug\nand says so in a comment. A detection where no strategy identified a type at\nall is a genuine failure, and logs at error.","hierarchy":{"lvl0":"Development","lvl1":"Logging Guidelines","lvl2":"Choosing between warn and debug","lvl3":""}}, {"objectID":"5e7a37fe452805fbf06c4864c7c4bedb538788640158dd499a2de613d4c12cae","title":"Format","url":"/docs/development/logging-guidelines#format","content":"Pass a message and a structured data object. The message is a constant; the\nvariables go in the object, where a log consumer can index them.\n\nPrefix the message with the emitting component in brackets — [NeuroLink],\n[OpenAI], [FileDetector], [MCP]. Grepping a debug run is how most of this\ngets read.\n\nTemplate literals are not banned, and a short one carrying a single value is\nfine. What matters is that a value a consumer would want to filter on ends up in\nthe data object rather than baked into the message string.","hierarchy":{"lvl0":"Development","lvl1":"Logging Guidelines","lvl2":"Format","lvl3":""}}, {"objectID":"53c83ffa5114d21fc9998ca5d409a69d0c10feeedb061bc0f757ebd2d3a0d9eb","title":"Guard expensive serialization","url":"/docs/development/logging-guidelines#guard-expensive-serialization","content":"logger.debug(...) evaluates its arguments before shouldLog() ever runs, so a\nJSON.stringify of a large payload costs full price on every request even when\nnothing is logged. Guard it:\n\nThe guard is only needed for work that is expensive to produce. Passing an\nobject you already hold is free — the logger does not serialize it unless it\nemits.","hierarchy":{"lvl0":"Development","lvl1":"Logging Guidelines","lvl2":"Guard expensive serialization","lvl3":""}}, {"objectID":"611da1cea62fd0b5bf313f3659615743913252c6ba487823808c87bee60efd00","title":"Never log secrets","url":"/docs/development/logging-guidelines#never-log-secrets","content":"API keys, tokens, credentials, presigned URL query strings, user prompt content\nand absolute host paths must not reach a log or a thrown error message.\nsrc/lib/utils/logSanitize.ts has the helpers, and they are the reason several\npast leaks are closed:\n\n| Helper | Use for |\n| --------------------------- | ------------------------------------------------------------------------------------------ |\n| redactUrlForError(url) | A URL in a log or error — strips query and fragment, so a presigned token cannot survive. |\n| redactUrlCredentials | A URL that may carry user:password@. |\n| redactPathFromMessage | A filesystem path in a message. |\n| sanitizeErrorCause | An error from Node/undici — these embed the full request URL or path in their own message. |\n| sanitizeHeaders | A header bag before logging it. |\n| sanitizeRecord | An arbitrary object, with string truncation. |\n| safeDebugSerialize | A large value in a debug log, length-capped. |\n| transformParamsForLogging | Tool-call parameters before logging them (this one lives in transformationUtils.ts). |\n\nThe rule for errors is the same as for logs: an error message is read by more\npeople than a log line, not fewer.","hierarchy":{"lvl0":"Development","lvl1":"Logging Guidelines","lvl2":"Never log secrets","lvl3":""}}, {"objectID":"c8bf38e894176c3436e15411303845f458ecf58f7bc7da0d105ecaf057adf7f4","title":"Per-instance routing","url":"/docs/development/logging-guidelines#per-instance-routing","content":"The logger routes per instance, so a worker's onLog bridge\n(NeuroLink.createWorkerInstance({ onLog })) receives only that worker's own\nevents, not everything in the process. The SDK entry points — generate,\nstream, generateText — run their bodies inside an AsyncLocalStorage\nscope carrying the instance's id, so a log call anywhere beneath them is\nattributed without threading an instance through every call site. Two things\nsit outside the scope by construction: logs emitted while a consumer drains a\nreturned stream (iteration runs in the consumer's context), and logs emitted\noutside any call — construction, background MCP reconnects, module init —\nwhich stay unattributed rather than being charged to an arbitrary instance.\n\nWorkerInstanceOptions.onLog's JSDoc (src/lib/types/isolatedAgent.ts) is\nthe authoritative description of this behaviour; check it before relying on\nthe routing in a new integration.\n\nA process-wide sink installed with logger.setEventEmitter() still receives\neverything either way.","hierarchy":{"lvl0":"Development","lvl1":"Logging Guidelines","lvl2":"Per-instance routing","lvl3":""}}, {"objectID":"fcd10d804aaa898a37f6d1303fbf6b07499f6bcd5b7e90e8f727aedec7b3f8e8","title":"Checklist for a new log call","url":"/docs/development/logging-guidelines#checklist-for-a-new-log-call","content":"Level matches the table above, and a routine branch is debug.\nMessage is a constant with a [Component] prefix; variables are in the data\n object.\nAnything expensive to build is behind logger.shouldLog(\"debug\").\nNo key, token, credential, prompt body, presigned URL or absolute host path\n in either the message or the data — run it through logSanitize.ts if in\n doubt.","hierarchy":{"lvl0":"Development","lvl1":"Logging Guidelines","lvl2":"Checklist for a new log call","lvl3":""}}, {"objectID":"b7ea7ebd627e70d0c3debd74687c71a4ad7cb1a63a6b24e2a4818736e0129778","title":"Provider-Agnostic Testing Framework - January 2025 Snapshot","url":"/docs/development/provider-testing","content":"⚠️ HISTORICAL DOCUMENT (January 2025) — This is a snapshot of the provider-agnostic testing milestone when 9 providers were live. The current product ships many provider integrations (incl. voice and the decision-only decide() type). For the up-to-date provider list and capability matrix, see the README and Provider Capabilities Audit.\n\nProvider-Agnostic Testing Framework - January 2025 Snapshot\n\nUpdated: January 20, 2025 \nStatus: COMPLETE SUCCESS — 9/9 providers verified working at the time of writing \nObjective: Complete provider testing after resolving critical configuration bug\n\n🎯 MISSION ACCOMPLISHED\n\nProblem Solved\n\nThe previous testing framework was hardcoded to Google AI, making it impossible to validate other providers during migration. This has been completely fixed.\n\nSolution Implemented\n\n✅ Provider-agnostic test runner \n✅ Configurable environment validation \n✅ Dynamic provider switching \n✅ Hugging Face implementation complete\n✅ Ready for comprehensive testing phase\n\n🔧 IMPLEMENTATION DETAILS\nEnhanced Test Runner (run-parallel-tests.js)\n\nProvider Configuration System\n\nUsage Examples\n\nEnvironment Validation\n✅ Automatic API key detection\n✅ Clear error messages for missing credentials\n✅ Provider-specific configuration validation\n✅ Dynamic environment variable setup\nProvider-Agnostic Test Files\n\nDynamic Provider Detection\n\nUpdated Test Files\n✅ test/basic-functionality.test.ts - Provider-agnostic\n✅ test/analytics-features.test.ts - Provider-agnostic\n🔄 Additional test files can be updated using same pattern\n\n🧪 VALIDATION RESULTS\n\nGoogle AI Provider Testing\n\nOpenAI Provider Testing\n\nKey Observations\n✅ Both providers pass all tests\n✅ OpenAI is slightly faster (6.15s vs 9.08s)\n✅ Same test suite validates both providers\n✅ No code changes needed between providers\n\n🚀 STRATEGIC BENEFITS\nMigration Confidence\nBaseline Established: Google AI provider validated and working\nTarget Confirmed: OpenAI provider already operational\nTest Coverage: Universal test suite applies to all providers\nRegression Prevention: Any breaking changes immediately detected\nDevelopment Velocity\nParallel Testing: Can test multiple providers simultaneously\nQuick Validation: Individual provider testing in \\<10 seconds\nClear Feedback: Provider-specific error messages and success metrics\nAutomated Reports: JSON reports saved per provider\nQuality Assurance\nNo Manual Testing: Automated validation across all providers\nConsistent Coverage: Same test scenarios for all providers\nPerformance Monitoring: Response time tracking per provider\nEnvironment Validation: Automatic credential checking\n\n📋 NEXT STEPS FOR PHASE 3\n\n✅ Migration Complete - All Providers Operational\n\nWith the provider-agnostic testing framework and factory pattern complete:\n\n✅ Factory Pattern Implementation Complete\n✅ BaseProvider: All 9 providers (at the time of writing) extend BaseProvider (verified)\n✅ Custom Vercel AI SDK: Azure, HuggingFace, Ollama use custom implementations\n✅ Official Vercel AI SDK: OpenAI, Anthropic, Bedrock, Google AI, Mistral\n✅ 100% Success Rate: All 9 providers in this snapshot tested and operational\n\n✅ Architecture Achievements\n✅ No External Package Issues: Custom implementations solve compatibility problems\n✅ Universal Analytics: Analytics helper integrated across all providers\n✅ Unified Interface: Single parameter handling system operational\n✅ Enterprise Ready: Complete factory-first MCP architecture\n\nTesting Strategy for Phase 3\n\n🎯 SUCCESS CRITERIA MET\n\nOriginal Requirements\n✅ Fix testing script to be provider agnostic\n✅ Test with OpenAI first (already implemented)\n✅ Validate provider-agnostic functionality working\n\nAdditional Achievements\n✅ Support for 4 providers (Google AI, OpenAI, Anthropic, Bedrock)\n✅ Automatic environment validation\n✅ Clear error messaging\n✅ Performance benchmarking\n✅ JSON report generation\n\n🏆 CONCLUSION\n\nThe provider-agnostic testing framework is now complete and operational.\nProblem Solved: No longer bound to Google AI\nQuality Assured: Both existing providers validated\nFoundation Ready: Perfect infrastructure for Phase 3 migration\nDevelopment Ready: Can proceed with confidence\n\nWe can now begin Phase 3 migration knowing that every step can be validated immediately with comprehensive, provider-agnostic testing.","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"","lvl3":""}}, {"objectID":"40155d0164fbc188b20eb8158f339edd47393bae047ec9d23dc1a22745059fa8","title":"Provider-Agnostic Testing Framework - January 2025 Snapshot","url":"/docs/development/provider-testing#provider-agnostic-testing-framework---january-2025-snapshot","content":"Updated: January 20, 2025 \nStatus: COMPLETE SUCCESS — 9/9 providers verified working at the time of writing \nObjective: Complete provider testing after resolving critical configuration bug","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl3":""}}, {"objectID":"3212634220f7620a7f5dd8d7ffa3a5fb8b359e4c6722504ae1d7881d0020a6da","title":"Problem Solved","url":"/docs/development/provider-testing#problem-solved","content":"The previous testing framework was hardcoded to Google AI, making it impossible to validate other providers during migration. This has been completely fixed.","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Problem Solved","lvl3":""}}, {"objectID":"7f3693bb49886451df629951ddeab92578715dda9bfabbd5d34a0d0c16c747a2","title":"Solution Implemented","url":"/docs/development/provider-testing#solution-implemented","content":"✅ Provider-agnostic test runner \n✅ Configurable environment validation \n✅ Dynamic provider switching \n✅ Hugging Face implementation complete\n✅ Ready for comprehensive testing phase","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Solution Implemented","lvl3":""}}, {"objectID":"4048e20fce8dd11c3a2cd6adb4d91b0d61c52cdab62784e725ce65315331e375","title":"Usage Examples","url":"/docs/development/provider-testing#usage-examples","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Usage Examples","lvl3":""}}, {"objectID":"764422fb8ba98a9514610adada7998cf15937b3e2e61182513306e8a26bb09d5","title":"Test Google AI (default)","url":"/docs/development/provider-testing#test-google-ai-default","content":"node run-parallel-tests.js --provider google-ai","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Test Google AI (default)","lvl3":""}}, {"objectID":"d562ab2e227318dbe5cd72bd98e01c8b97e528dbf4b2ab07798a27783f3af459","title":"Test OpenAI","url":"/docs/development/provider-testing#test-openai","content":"node run-parallel-tests.js --provider openai","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Test OpenAI","lvl3":""}}, {"objectID":"d0ccaeb8abafe4574df38690886446b5afd588ccbab38795a7d054ec9e6b7d87","title":"Test Anthropic","url":"/docs/development/provider-testing#test-anthropic","content":"node run-parallel-tests.js --provider anthropic","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Test Anthropic","lvl3":""}}, {"objectID":"8a8e3b2bec13813946a6a6338c4323e809a2b9ecd18b667e9d7544c2407d6d84","title":"Test Bedrock","url":"/docs/development/provider-testing#test-bedrock","content":"node run-parallel-tests.js --provider bedrock","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Test Bedrock","lvl3":""}}, {"objectID":"907812e9e14d0b68c8b6cb62a54d6696b4ef9b2c844c5485904f611f097f3bed","title":"Show help","url":"/docs/development/provider-testing#show-help","content":"node run-parallel-tests.js --help\n`","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Show help","lvl3":""}}, {"objectID":"bf57f89e0cfff572e863a1cac745698cf8f7081e8d6e31eac9305e23ac9316f9","title":"Environment Validation","url":"/docs/development/provider-testing#environment-validation","content":"✅ Automatic API key detection\n✅ Clear error messages for missing credentials\n✅ Provider-specific configuration validation\n✅ Dynamic environment variable setup","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Environment Validation","lvl3":""}}, {"objectID":"7b658f4ec9e9083cf8084ffef6f91ce75b6f93d53bd4a2dde1704c94a0ac47a3","title":"Updated Test Files","url":"/docs/development/provider-testing#updated-test-files","content":"✅ test/basic-functionality.test.ts - Provider-agnostic\n✅ test/analytics-features.test.ts - Provider-agnostic\n🔄 Additional test files can be updated using same pattern","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Updated Test Files","lvl3":""}}, {"objectID":"21e42e63d4623c9f7653a0f22c431de1342f147780c5cbbc375e75e29bbf977d","title":"Key Observations","url":"/docs/development/provider-testing#key-observations","content":"✅ Both providers pass all tests\n✅ OpenAI is slightly faster (6.15s vs 9.08s)\n✅ Same test suite validates both providers\n✅ No code changes needed between providers","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Key Observations","lvl3":""}}, {"objectID":"d1ba6925bfb354e4b9bb3179b917cff8e335f3ed3a105d5744078e029eefebe1","title":"1. Migration Confidence","url":"/docs/development/provider-testing#1-migration-confidence","content":"Baseline Established: Google AI provider validated and working\nTarget Confirmed: OpenAI provider already operational\nTest Coverage: Universal test suite applies to all providers\nRegression Prevention: Any breaking changes immediately detected","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"1. Migration Confidence","lvl3":""}}, {"objectID":"e50bbb6f88571e13851e0436a3af6cf42ebf0d3fa16226c041167095af0b658e","title":"2. Development Velocity","url":"/docs/development/provider-testing#2-development-velocity","content":"Parallel Testing: Can test multiple providers simultaneously\nQuick Validation: Individual provider testing in \\<10 seconds\nClear Feedback: Provider-specific error messages and success metrics\nAutomated Reports: JSON reports saved per provider","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"2. Development Velocity","lvl3":""}}, {"objectID":"2d3e6923fe38d4e34e38eaf8692581c0ca8c8268ac4721fbbff31aca45092afa","title":"3. Quality Assurance","url":"/docs/development/provider-testing#3-quality-assurance","content":"No Manual Testing: Automated validation across all providers\nConsistent Coverage: Same test scenarios for all providers\nPerformance Monitoring: Response time tracking per provider\nEnvironment Validation: Automatic credential checking","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"3. Quality Assurance","lvl3":""}}, {"objectID":"10b22354fc90dab200a03e3ba1d227f8404c4409bbee53c4e97cc2d163e6f212","title":"✅ Migration Complete - All Providers Operational","url":"/docs/development/provider-testing#-migration-complete---all-providers-operational","content":"With the provider-agnostic testing framework and factory pattern complete:","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"✅ Migration Complete - All Providers Operational","lvl3":""}}, {"objectID":"4713e028b25762337bdddacad34a35feb70846480a2676edea98030c49cb8d45","title":"✅ Factory Pattern Implementation Complete","url":"/docs/development/provider-testing#-factory-pattern-implementation-complete","content":"✅ BaseProvider: All 9 providers (at the time of writing) extend BaseProvider (verified)\n✅ Custom Vercel AI SDK: Azure, HuggingFace, Ollama use custom implementations\n✅ Official Vercel AI SDK: OpenAI, Anthropic, Bedrock, Google AI, Mistral\n✅ 100% Success Rate: All 9 providers in this snapshot tested and operational","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"✅ Factory Pattern Implementation Complete","lvl3":""}}, {"objectID":"f93eef5700ccbfed757369eedc7fc8f61419b4a084d22a35fb658a1d923a5f26","title":"✅ Architecture Achievements","url":"/docs/development/provider-testing#-architecture-achievements","content":"✅ No External Package Issues: Custom implementations solve compatibility problems\n✅ Universal Analytics: Analytics helper integrated across all providers\n✅ Unified Interface: Single parameter handling system operational\n✅ Enterprise Ready: Complete factory-first MCP architecture","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"✅ Architecture Achievements","lvl3":""}}, {"objectID":"f81994dc6bb97137f56dbe75adf8a517ed69b50ce7a989c9d9571ff3a2429ba4","title":"Testing Strategy for Phase 3","url":"/docs/development/provider-testing#testing-strategy-for-phase-3","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Testing Strategy for Phase 3","lvl3":""}}, {"objectID":"e7a119d9234ea37b76016795bfc19e297d85aadb4818660e615dd4d6041d8cbe","title":"Before any migration","url":"/docs/development/provider-testing#before-any-migration","content":"node run-parallel-tests.js --provider","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Before any migration","lvl3":""}}, {"objectID":"5aff87ade6f53339bd20f487cc4f319f95e48191e45bd67cf83cca2e65b0bb4b","title":"After migration","url":"/docs/development/provider-testing#after-migration","content":"node run-parallel-tests.js --provider","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"After migration","lvl3":""}}, {"objectID":"d4b58cd6e712c114c7f8f6a5072942e07e69d8bd211da8873789c5faef3aa386","title":"Compare results to ensure no regression","url":"/docs/development/provider-testing#compare-results-to-ensure-no-regression","content":"`","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Compare results to ensure no regression","lvl3":""}}, {"objectID":"99e31f5f2849754f04c57504d9e8c0d6057b9749848b4d1441725e862587c324","title":"Original Requirements","url":"/docs/development/provider-testing#original-requirements","content":"✅ Fix testing script to be provider agnostic\n✅ Test with OpenAI first (already implemented)\n✅ Validate provider-agnostic functionality working","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Original Requirements","lvl3":""}}, {"objectID":"fa24a0d64576f62c6bdb481b3b94a018831c25037422ddee58434d259762f278","title":"Additional Achievements","url":"/docs/development/provider-testing#additional-achievements","content":"✅ Support for 4 providers (Google AI, OpenAI, Anthropic, Bedrock)\n✅ Automatic environment validation\n✅ Clear error messaging\n✅ Performance benchmarking\n✅ JSON report generation","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"Additional Achievements","lvl3":""}}, {"objectID":"107e3d765f994d9c2c8d8b44019ab74782e3281527deac640b7a1600d1dd3ca2","title":"🏆 CONCLUSION","url":"/docs/development/provider-testing#-conclusion","content":"The provider-agnostic testing framework is now complete and operational.\nProblem Solved: No longer bound to Google AI\nQuality Assured: Both existing providers validated\nFoundation Ready: Perfect infrastructure for Phase 3 migration\nDevelopment Ready: Can proceed with confidence\n\nWe can now begin Phase 3 migration knowing that every step can be validated immediately with comprehensive, provider-agnostic testing.","hierarchy":{"lvl0":"Development","lvl1":"Provider-Agnostic Testing Framework - January 2025 Snapshot","lvl2":"🏆 CONCLUSION","lvl3":""}}, {"objectID":"35410012f1496cb552b2f7f9d029a7f1c953515724e1acbf18688e72b2f81a11","title":"Proxy context protection and audit closure","url":"/docs/development/proxy-context-and-audit","content":"Proxy context protection and audit closure\n\nContext checks run before native Anthropic, native Codex, and translated SDK\nprovider dispatch. Each fallback uses its actual serving model. The proxy records\nestimated input, instruction, tool and output-schema tokens, the output/reasoning\nreserve, discovered/configured model limits, and retained tool counts. It never\nsilently truncates conversation history.\n\nNEUROLINK_PROXY_CONTEXT_POLICY is an optional JSON object:\n\nThese numbers illustrate the schema, not recommended model limits. Unknown\nmodel context windows stay unknown; a guessed catalog default never becomes a\nhard limit. Successful Codex model discovery can register advertised limits\nwithout an additional discovery request. Codex does not support the bridged\nClaude max_tokens transport setting, so its reservation uses the known model\noutput ceiling or configured/default output reserve instead.\n\nAn optional toolAllowlist selects tools by name. Tools referenced by prior\ncalls and an explicit tool choice are retained; unnamed native tools are also\nretained. This is opt-in and can change which new tools the model can choose.\nThe proxy preserves required instructions, history and call/result structure.\nAutomated history summarization has not been introduced: reducing text without\nan application-specific quality evaluation can discard information required to\ncomplete a task.\n\nToken quantities are estimates, including media estimates. Media requests are\nmarked uncertain and are not rejected solely from a guessed combined context\nwindow. A configured estimated input cap is not an exact provider-token cap.\nProvider tokenization, media metering and model quality evaluations remain\nnecessary before claiming exact limits or performance-preserving compression.\nInvalid configured policy and local context/budget refusals are explicit,\nnonretryable errors; automatic fallback cannot bypass them.\n\nShared account/session spending controls are described in\nproxy-updates-and-token-budgets.md.\nCollector durability and body separation are described in\ncollector durability profile.\n\nThe telemetry doctor accepts --admission-lookback-minutes,\n--request-timeout-ms, and --ingestion-grace-ms. The lookback must exceed the\ndefault request deadline plus ingestion grace. If the effective queried window\ncannot cover a longer observed request deadline, admission reconciliation is\nunverified; it cannot establish that missing endings were detected.\n\nAudit coverage\n\nThe table preserves every requirement in the incident audit. “Source” means\nimplemented with deterministic isolated checks; it does not mean deployed.\nA package version on disk, a passing test or a merged PR does not prove the\nserving supervisor and workers adopted it.\n\n| Item | Requirement | Change or evidence boundary |\n| ---- | ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| T01 | OTel-only application logging | Existing behavior retained. Retest file-write inactivity after authorized adoption; collector storage is distinct. |\n| T02 | Native collector/backend | Existing deployment choice retained. Native processes still consume CPU/RAM and backend disk. |\n| T03 | Stored correlation and complete accounting | Source: bounded admission-to-terminal reconciliation, explicit missing/out-of-window evidence, route attribution. |\n| T04 | Retry copies | Stable event identity deduplication retained; collector acknowledgment is not exactly-once storage. |\n| T05 | Capture burst handling | Source: bounded shared body batching, independent metadata transport, per-capture export outcomes. |\n| T06 | Persistent collector queue | Opt-in durable native collector profile; isolated outage/restart replay tested. Requires operator activation. |\n| T07 | Reasoning usage | Existing reasoning accounting preserved. Reasoning remains included in output totals. |\n| T08 | Capture completeness | Source: source/processing truncation and redaction loss distinguished; body limits/rejections remain explicit. Universal losslessness is not promised. |\n| T09 | Translation coverage | Source: Claude/OpenAI/Gemini streaming/buffered attempts and finals preserve requested/served models, usage a","hierarchy":{"lvl0":"Development","lvl1":"Proxy context protection and audit closure","lvl2":"","lvl3":""}}, {"objectID":"c401d02fac80b6798cf630160d196bf8c5c52ca1579a2425117ad665b8cfad66","title":"Proxy context protection and audit closure","url":"/docs/development/proxy-context-and-audit#proxy-context-protection-and-audit-closure","content":"Context checks run before native Anthropic, native Codex, and translated SDK\nprovider dispatch. Each fallback uses its actual serving model. The proxy records\nestimated input, instruction, tool and output-schema tokens, the output/reasoning\nreserve, discovered/configured model limits, and retained tool counts. It never\nsilently truncates conversation history.\n\nNEUROLINK_PROXY_CONTEXT_POLICY is an optional JSON object:\n\nThese numbers illustrate the schema, not recommended model limits. Unknown\nmodel context windows stay unknown; a guessed catalog default never becomes a\nhard limit. Successful Codex model discovery can register advertised limits\nwithout an additional discovery request. Codex does not support the bridged\nClaude max_tokens transport setting, so its reservation uses the known model\noutput ceiling or configured/default output reserve instead.\n\nAn optional toolAllowlist selects tools by name. Tools referenced by prior\ncalls and an explicit tool choice are retained; unnamed native tools are also\nretained. This is opt-in and can change which new tools the model can choose.\nThe proxy preserves required instructions, history and call/result structure.\nAutomated history summarization has not been introduced: reducing text without\nan application-specific quality evaluation can discard information required to\ncomplete a task.\n\nToken quantities are estimates, including media estimates. Media requests are\nmarked uncertain and are not rejected solely from a guessed combined context\nwindow. A configured estimated input cap is not an exact provider-token cap.\nProvider tokenization, media metering and model quality evaluations remain\nnecessary before claiming exact limits or performance-preserving compression.\nInvalid configured policy and local context/budget refusals are explicit,\nnonretryable errors; automatic fallback cannot bypass them.\n\nShared account/session spending controls are described in\nproxy-updates-and-token-budgets.md.","hierarchy":{"lvl0":"Development","lvl1":"Proxy context protection and audit closure","lvl2":"Proxy context protection and audit closure","lvl3":""}}, {"objectID":"d02e066dca7e96ca4dec9f24c093dca01aeee93fec65bdecc2600b8980d5f704","title":"Audit coverage","url":"/docs/development/proxy-context-and-audit#audit-coverage","content":"The table preserves every requirement in the incident audit. “Source” means\nimplemented with deterministic isolated checks; it does not mean deployed.\nA package version on disk, a passing test or a merged PR does not prove the\nserving supervisor and workers adopted it.\n\n| Item | Requirement | Change or evidence boundary |\n| ---- | ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| T01 | OTel-only application logging | Existing behavior retained. Retest file-write inactivity after authorized adoption; collector storage is distinct. |\n| T02 | Native collector/backend | Existing deployment choice retained. Native processes still consume CPU/RAM and backend disk. |\n| T03 | Stored correlation and complete accounting | Source: bounded admission-to-terminal reconciliation, explicit missing/out-of-window evidence, route attribution. |\n| T04 | Retry copies | Stable event identity deduplication retained; collector acknowledgment is not exactly-once storage. |\n| T05 | Capture burst handling | Source: bounded shared body batching, independent metadata transport, per-capture export outcomes. |\n| T06 | Persistent collector queue | Opt-in durable native collector profile; isolated outage/restart replay tested. Requires operator activation.","hierarchy":{"lvl0":"Development","lvl1":"Proxy context protection and audit closure","lvl2":"Audit coverage","lvl3":""}}, {"objectID":"edfbf0f68ba12b915834b087670188daee1a88a0d5402dc8795418dc0fdca337","title":"Isolated verification","url":"/docs/development/proxy-context-and-audit#isolated-verification","content":"The suites use temporary HOME/config/credentials, fake providers and test-owned\nloopback sockets. They do not use a live proxy as a development target.\n\nFor actual collector replay, set NEUROLINK_TEST_OTELCOL_BIN as documented in the\ncollector guide. Without it that case is explicitly skipped. Production proof\nrequires a separately authorized rollout and observation of actual serving\nversions, route coverage, stored metadata/body outcomes, latency, resource use,\nand loss/retry counters during representative traffic.","hierarchy":{"lvl0":"Development","lvl1":"Proxy context protection and audit closure","lvl2":"Isolated verification","lvl3":""}}, {"objectID":"73da35acfb7ae479348e6cf4e06e32453d3b73bff3d8bc8754b66280b70f28c1","title":"Proxy incident reliability verification","url":"/docs/development/proxy-incident-verification","content":"Proxy incident reliability verification\n\nThis change addresses worker termination after delayed IPC commits, missing\nadmission evidence after worker death, misleading analysis windows, bulk capture\nwork on the serving event loop, and lost transport-error attribution.\n\nAcceptance evidence\n\nLocal verification on macOS, Node 24.14.1, September 7, 2026. All network fixtures\nuse ephemeral loopback listeners and isolated storage. No installed proxy,\nprovider account, launchd service, or production request was used.\n\n| Requirement | Evidence |\n| --------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Delayed acceptance gets its own full commit deadline | Actual child-process IPC fixture; no failed transfer or worker replacement |\n| A failed commit preserves unrelated streams | Actual shipped worker runtime; affected connection closes, other stream delivers every byte, replacement serves requests; commit timeouts and IPC write failures retain distinct reasons |\n| Accepted work remains identifiable after worker death | Confirmed admission append, fixture SIGKILL, independent parent exit journal, built analyzer joins by process instance |\n| Unknown outcomes remain unknown | Missing terminal plus worker exit is reported as unconfirmedAtWorkerExit; conflicting exit evidence is excluded |\n| Storage failures cannot dispatch unrecorded upstream work | HTTP fixture returns classified 503 after admission write failure; upstream invocation count stays zero |\n| Time-window analysis does not fabricate sequence gaps | Excluded requests between selected events still participate in sequence auditing; auxiliary requests have separate HTTP accounting |\n| Capture work stays bounded and reconstructable | Real worker-thread capture, gzip/hash validation, secret redaction, UTF-8 truncation, queue saturation, slow publication sink and worker failure accounting |\n| Transport attribution and safe retry | Two-account HTTP fixtures: EPIPE and ambiguous socket loss stop after one attempt; connect timeout can rotate; final cause/account match the last attempt |\n| Burst admission is exact | 150 concurrent connections, 150 completed bodies, 150 unique persisted admissions, no rejected or failed handoffs |\n| Status rows and fallback routing remain correct | Built status CLI emits each qualified account once; recorded fallback requests retain gpt-6-astra and xhigh |\n\nCommands and results:\npnpm run test:proxy-telemetry: 41 passed.\npnpm run test:browser-bundle: 6 passed, including callback and cancellation behavior without Node globals.\npnpm run check and pnpm run check:tools-tests: passed, including strict types for the test fixtures.\npnpm run test:codex: 69 passed.\npnpm run test:proxy-connect-retry: 5 passed.\nNEUROLINK_PROXY_TEST_ALLOW_LIVE=0 pnpm run test:proxy: 97 passed, 7 live-provider cases skipped.\npnpm run build: package, CLI, browser bundle and publint passed. Library changes require the full package build; build:cli alone can retain an older library artifact.\n\nPerformance observations\n\nThe existing lifecycle gate wrote 100,000 events for 25,000 requests with zero\ndrops or uncertain writes. Enqueue p95 was 12 microseconds; added response-tracking\np95 was 49 microseconds. The statistics gate reconciled 50,000 requests exactly.\nThe loopback transport gate added 1.69 ms p95 against its 5 ms budget.\n\nThe first rolling gate preserved all traffic but failed the sustained latency\nbudget: 51.28 ms added p95 against 25 ms. We then ran three alternating comparisons\nagainst unchanged release 382217975c39433df058e2b2c2fb91b283230725, using the\nsame fixture, machine and background scheduling priority. No budgets were changed.\n\n| Round | Release added p95 under sustained load | Patched added p95 under sustained load |\n| ----- | -------------------------------------: | -------------------------------------: |\n| 1 | ","hierarchy":{"lvl0":"Development","lvl1":"Proxy incident reliability verification","lvl2":"","lvl3":""}}, {"objectID":"76e63d72c9a7cc150d0aee79c3cbaaf27043a03eff9991f3d5169c7411a17959","title":"Proxy incident reliability verification","url":"/docs/development/proxy-incident-verification#proxy-incident-reliability-verification","content":"This change addresses worker termination after delayed IPC commits, missing\nadmission evidence after worker death, misleading analysis windows, bulk capture\nwork on the serving event loop, and lost transport-error attribution.","hierarchy":{"lvl0":"Development","lvl1":"Proxy incident reliability verification","lvl2":"Proxy incident reliability verification","lvl3":""}}, {"objectID":"9d37e7d66f60fa6af16eaee381757f1d39b03c29d805748d72980fc766a80afc","title":"Acceptance evidence","url":"/docs/development/proxy-incident-verification#acceptance-evidence","content":"Local verification on macOS, Node 24.14.1, September 7, 2026. All network fixtures\nuse ephemeral loopback listeners and isolated storage. No installed proxy,\nprovider account, launchd service, or production request was used.\n\n| Requirement | Evidence |\n| --------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Delayed acceptance gets its own full commit deadline | Actual child-process IPC fixture; no failed transfer or worker replacement |\n| A failed commit preserves unrelated streams | Actual shipped worker runtime; affected connection closes, other stream delivers every byte, replacement serves requests; commit timeouts and IPC write failures retain distinct reasons |\n| Accepted work remains identifiable after worker death | Confirmed admission append, fixture SIGKILL, independent parent exit journal, built analyzer joins by process instance |\n| Unknown outcomes remain unknown | Missing terminal plus worker exit is reported as unconfirmedAtWorkerExit; conflicting exit evidence is excluded |\n| Storage failures cannot dispatch unrecorded upstream work | HTTP fixture returns classified 503 after admission write failure; upstream invocation count stays zero |\n| Time-window analysis does not","hierarchy":{"lvl0":"Development","lvl1":"Proxy incident reliability verification","lvl2":"Acceptance evidence","lvl3":""}}, {"objectID":"e445d51bde52184ea947c95c6b1f2105e1c1dd67b2ed01509cbfa320d3a396db","title":"Performance observations","url":"/docs/development/proxy-incident-verification#performance-observations","content":"The existing lifecycle gate wrote 100,000 events for 25,000 requests with zero\ndrops or uncertain writes. Enqueue p95 was 12 microseconds; added response-tracking\np95 was 49 microseconds. The statistics gate reconciled 50,000 requests exactly.\nThe loopback transport gate added 1.69 ms p95 against its 5 ms budget.\n\nThe first rolling gate preserved all traffic but failed the sustained latency\nbudget: 51.28 ms added p95 against 25 ms. We then ran three alternating comparisons\nagainst unchanged release 382217975c39433df058e2b2c2fb91b283230725, using the\nsame fixture, machine and background scheduling priority. No budgets were changed.\n\n| Round | Release added p95 under sustained load | Patched added p95 under sustained load |\n| ----- | -------------------------------------: | -------------------------------------: |\n| 1 | 6.042 ms | 24.840 ms |\n| 2 | 6.826 ms | 5.355 ms |\n| 3 | 9.827 ms | 4.811 ms |\n\nEvery comparison passed the existing budgets, preserved the active stream, and\nreported zero dropped requests. The fixture includes 100 concurrent handoff\nrequests and 64-way sustained traffic. These observations expose variability;\nthey do not certify a latency ceiling under arbitrary host contention.","hierarchy":{"lvl0":"Development","lvl1":"Proxy incident reliability verification","lvl2":"Performance observations","lvl3":""}}, {"objectID":"5cb2fe62f38e8943b94facdd1ddd6a4a968fbbb161744152fe47c3c269d575cf","title":"Interpretation limits","url":"/docs/development/proxy-incident-verification#interpretation-limits","content":"The CLI has one signal owner. Proxy workers and supervisors own their drain and\nexit; the ordinary CLI telemetry cleanup cannot exit alongside them. Repeated\nSIGINT/SIGTERM signals reuse that shutdown. Ordinary command cleanup catches\nflush failures and uses a five-second deadline. Worker and supervisor telemetry\ncleanup also has a deadline after request draining. Supervisor SIGHUP forwards a\nreload to the ready serving worker, while a candidate loads configuration during\nstartup. Isolated subprocess fixtures in test:proxy-restart exercise reload,\nduplicate signals, failed flushes, stalled flushes and drain failures. If startup\nregisters a shutdown owner during ordinary cleanup, the original signal is\ndelegated to that owner after cleanup settles, including rejection or timeout.\nSignal-triggered ordinary cleanup preserves an existing command failure code.\nSupervisor SIGHUP handling is installed before startup awaits, ignores reloads\nuntil a serving worker is ready, and forwards them during control-plane setup.\nSupervisor shutdown ownership is also registered before startup awaits. A stop\nprevents later setup stages, waits for already-started resource creation, and\ncloses the resulting resources before exiting. Cleanup continues after an\nindividual close failure and exits unsuccessfully when a resource cannot close.\nIsolated child-process regressions signal bootstrap, pending worker startup,\npending restart-control setup, and updater startup, including duplicate signals\nand a rejected control close.\nResource cleanup runs concurrently: worker creation and drain have a 35-second\ndeadline, and control cleanup has a 5-second deadline. Telemetry then has its\n5-second budget, leaving headroom inside the launchd service's 45-second stop\ntimeout. A stalled resource is reported and causes an unsuccessful exit; a late\nresource returned while telemetry is flushing is still closed without extending\nthe exit deadline.","hierarchy":{"lvl0":"Development","lvl1":"Proxy incident reliability verification","lvl2":"Interpretation limits","lvl3":""}}, {"objectID":"e8a7f76ea9249969c4e7f7f8767fd2cac18478baca29ee921e7a7ca73825db69","title":"Proxy package updates and token budgets","url":"/docs/development/proxy-updates-and-token-budgets","content":"Proxy package updates and token budgets\n\nAutomatic updates prepare a separate package before asking the supervisor to\nactivate it. They do not run a global package replacement over files used by\nserving workers. These changes take effect after the release is installed and\nits supervisor is activated; merging a PR does not change a running service.\n\nUpdate and restart behavior\n\nThe updater installs the current running version, when needed for rollback, and\nthe candidate under ~/.neurolink/proxy-packages/. Installation uses a\nprivate temporary directory and publishes the version directory only after the\npackage name, version, executable location, and JavaScript syntax validate.\nA candidate with missing dependencies can still fail startup: actual worker\nreadiness remains the activation gate, and the serving generation is retained\nwhen replacement fails.\n\nThe package-manager process runs asynchronously. Output progress renews a\ntwo-minute inactivity deadline; a fifteen-minute maximum still bounds an\ninstaller that emits output forever. A timed-out installer and its process group\nare stopped, its unpublished directory is removed, and transient errors receive\nthe updater's bounded retry schedule. Progress telemetry contains byte counts and\ntiming, not raw package-manager output that may contain authenticated URLs.\n\nThe stable launcher selects one validated package. Its --version check reads\npackage metadata in a minimal Node process; it does not initialize provider,\ntelemetry, or proxy code. Each worker is spawned from its expected package, so a\nnew candidate cannot silently change the recovery executable of an older worker.\nRollback restores the previous selection rather than reinstalling over live\nfiles. Proxy auto-update does not change the global CLI package; invoking an older\nglobal CLI to reinstall the service retains the selected proxy package instead\nof downgrading it or rejecting its version. Version directories are intentionally retained: they can still be needed\nby active/draining generations or rollback. This increases package disk usage;\nthere is currently no automatic garbage collection of retained versions.\n\nBefore refreshing a supervisor, all of the following must be settled:\nActive requests in the current worker.\nEvery draining worker generation.\nQueued sockets and pending socket transfers.\nAny candidate worker.\n\nIncomplete rolling status is not evidence of idleness. A drain attempt lasts at\nmost thirty seconds before deferral and admission recovery; it never kills an\nolder stream to make the update proceed. The worker also has a ninety-second\nadmission recovery lease if the updater disappears. Package installation and\nvalidation finish before a legacy service closes admission.\n\nUse neurolink proxy restart --check for a read-only restart preflight and\nneurolink proxy restart for a supervisor-owned rolling replacement. A failed\ncheck does not fall back to a forced process restart. The result distinguishes a\nverified replacement from an activated worker whose final verification failed.\nA worker restart does not, by itself, upgrade the supervisor.\n\nStatus separates diskPackageVersion (metadata at the executable used by the\nreporting process), selectedPackageVersion (next launcher selection),\nvalidatedVersion (last updater-validated package), candidateVersion, the\nserving worker version, and the supervisor version. A changed disk manifest or a\nvalidated package does not prove live adoption.\n\nReinstall first refuses any running worker, draining generation, or loaded launchd\njob before writing files or changing processes. Unknown/permission-denied process\nor launchd state also refuses. Use the rolling restart command for a live service;\nexplicitly stop and unload it before an intended reinstall.\n\nFor a stopped service, reinstall reads the saved definition before writing it. Existing\nhost/port, environment-file path, routing-config path, and operator OTel/proxy\nvariables become defaults; explicit arguments and current environment values\nwin. An unreadable service definition aborts reinstall instead of silently\nremoving its settings. Worker IPC identity is not persisted. This includes\nNEUROLINK_PROXY_OTLP_BODIES_ENDPOINT, NEUROLINK_PROXY_CONTEXT_POLICY, and\nNEUROLINK_PROXY_TOKEN_BUDGET.\n\nLocal compatibility polyfills\n\nAn automatic package upgrade leaves the routing config, environment file, and\nlaunchd definition intact. It also preserves the exact previous package path,\nincluding when a local build and an official build share a version number.\nManaged service starts and worker replacements do not reapply CLI client\nconfiguration. User changes to Codex, Claude Code, and other client settings\nremain in place. Use explicit proxy setup or foreground proxy start to\nconfigure client routing.\n\nEach freshly staged package records its original proxy runtime files. When a\nlater upgrade is available, the updater compares the serving package with that\nbaseline and applies only the remaining local diff hunk","hierarchy":{"lvl0":"Development","lvl1":"Proxy package updates and token budgets","lvl2":"","lvl3":""}}, {"objectID":"d8b758e463be06d23598c7f1722e82d46f3d5f2df3c3c98058f400923f83ac8f","title":"Proxy package updates and token budgets","url":"/docs/development/proxy-updates-and-token-budgets#proxy-package-updates-and-token-budgets","content":"Automatic updates prepare a separate package before asking the supervisor to\nactivate it. They do not run a global package replacement over files used by\nserving workers. These changes take effect after the release is installed and\nits supervisor is activated; merging a PR does not change a running service.","hierarchy":{"lvl0":"Development","lvl1":"Proxy package updates and token budgets","lvl2":"Proxy package updates and token budgets","lvl3":""}}, {"objectID":"b31737c456bf0dd066337629bccccd40c4572b96ad3b51fdf48de2c44d45e82a","title":"Update and restart behavior","url":"/docs/development/proxy-updates-and-token-budgets#update-and-restart-behavior","content":"The updater installs the current running version, when needed for rollback, and\nthe candidate under ~/.neurolink/proxy-packages/. Installation uses a\nprivate temporary directory and publishes the version directory only after the\npackage name, version, executable location, and JavaScript syntax validate.\nA candidate with missing dependencies can still fail startup: actual worker\nreadiness remains the activation gate, and the serving generation is retained\nwhen replacement fails.\n\nThe package-manager process runs asynchronously. Output progress renews a\ntwo-minute inactivity deadline; a fifteen-minute maximum still bounds an\ninstaller that emits output forever. A timed-out installer and its process group\nare stopped, its unpublished directory is removed, and transient errors receive\nthe updater's bounded retry schedule. Progress telemetry contains byte counts and\ntiming, not raw package-manager output that may contain authenticated URLs.\n\nThe stable launcher selects one validated package. Its --version check reads\npackage metadata in a minimal Node process; it does not initialize provider,\ntelemetry, or proxy code. Each worker is spawned from its expected package, so a\nnew candidate cannot silently change the recovery executable of an older worker.\nRollback restores the previous selection rather than reinstalling over live\nfiles. Proxy auto-update does not change the global CLI package; invoking an older\nglobal CLI to reinstall the service retains the selected proxy package instead\nof downgrading it or rejecting its version. Version directories are intentionally retained: they can still be needed\nby active/draining generations or rollback. This increases package disk usage;\nthere is currently no automatic garbage collection of retained versions.\n\nBefore refreshing a supervisor, all of the following must be settled:\nActive requests in the current worker.\nEvery draining worker generation.\nQueued sockets and pending socket transfers.\nAny candidate worker.","hierarchy":{"lvl0":"Development","lvl1":"Proxy package updates and token budgets","lvl2":"Update and restart behavior","lvl3":""}}, {"objectID":"69e1f9556ad4141f30fa78e44edc0297f70c1dcc1e775c5955e44c912f79a0b6","title":"Local compatibility polyfills","url":"/docs/development/proxy-updates-and-token-budgets#local-compatibility-polyfills","content":"An automatic package upgrade leaves the routing config, environment file, and\nlaunchd definition intact. It also preserves the exact previous package path,\nincluding when a local build and an official build share a version number.\nManaged service starts and worker replacements do not reapply CLI client\nconfiguration. User changes to Codex, Claude Code, and other client settings\nremain in place. Use explicit proxy setup or foreground proxy start to\nconfigure client routing.\n\nEach freshly staged package records its original proxy runtime files. When a\nlater upgrade is available, the updater compares the serving package with that\nbaseline and applies only the remaining local diff hunks to a new candidate.\nIndependent release changes remain intact. A fix already present in the release\nis skipped and disappears from the local diff on subsequent upgrades. The\nserving package is never edited during this reconciliation.\n\nAn overlapping change, ambiguous patch context, unsupported dependency change,\nor invalid JavaScript aborts candidate preparation. The current worker,\nlauncher, config, and rollback selection remain in place. Automatic staging\nlogs the number of files applied or already included without logging their\ncontents. Runtime JavaScript and JSON are supported; dependency trees and\nbrowser bundles are not local proxy polyfills.\n\nClean legacy installations bootstrap their baseline by comparing with a\nseparately staged registry package of the same version. An edited legacy build\ncannot be safely compared without its actual original base, so its upgrade is\nretained until that base is recorded explicitly:\n\nThis command records the original files; it does not apply edits, select a\npackage, or restart a service. Supply the pristine package from which the local\nedit was made. A different existing baseline is refused rather than overwritten.","hierarchy":{"lvl0":"Development","lvl1":"Proxy package updates and token budgets","lvl2":"Local compatibility polyfills","lvl3":""}}, {"objectID":"82a142fcb5a4a408bcc1ef4a742c15da227f7efe0ca8039f32786bd317776acc","title":"Optional token reservations","url":"/docs/development/proxy-updates-and-token-budgets#optional-token-reservations","content":"NEUROLINK_PROXY_TOKEN_BUDGET accepts a JSON object. No token cap is enabled by\ndefault. For example, an operator could configure:\n\nThese numbers are illustrative, not recommended account allowances. Supported\nfields must be positive safe integers; an invalid configuration fails closed.\nmaxInFlightTokens is per provider/account. accountWindowTokens caps charged\ntokens in that account's fixed window. sessionWindowTokens applies across\naccounts and providers for the same client session. The default window is one\nhour when a cap is set without windowMs.\n\nBefore each native upstream attempt or translated SDK invocation, the proxy\nreserves estimated input plus the output allowance. An SDK invocation can contain\nprovider-internal retries that the proxy cannot observe individually; its single\nreservation does not prove or cap the combined tokens of those hidden retries. Tool definitions and instructions contribute to the input\nestimate. This is an estimate with recorded provenance, not an exact provider\ntokenization or a guarantee of monetary cost. A configured estimated input maximum\nis not a hard maximum on actual provider input tokens, especially for images,\naudio, and files. Model/context policy is configured\nseparately with NEUROLINK_PROXY_CONTEXT_POLICY; it must use the actual serving\nmodel. Provider-reported total input plus output replaces the estimate once when\ncomplete usage is known. Reasoning is already part of output and is not added a\nsecond time. Missing or uncertain usage retains the estimate. Cancellation\nbefore dispatch refunds the unused reservation; cancellation after dispatch does\nnot presume that the provider billed zero.\n\nActive and draining workers reserve atomically against one in-memory supervisor\ncoordinator. Outstanding reservations remain charged across a window boundary;\na reset cannot create new capacity for requests that are still running. Worker\nexit retains estimated spending for its unresolved calls while releasing its\nin-flight occupancy.","hierarchy":{"lvl0":"Development","lvl1":"Proxy package updates and token budgets","lvl2":"Optional token reservations","lvl3":""}}, {"objectID":"183568ccdb77340136a3026e2df12e9e82c5722ba3ddbf3b5749ba6c62de231b","title":"OTLP acknowledgement boundaries","url":"/docs/development/proxy-updates-and-token-budgets#otlp-acknowledgement-boundaries","content":"Both metadata and body queues serialize through OpenTelemetry's official JSON\nserializer. A response-aware HTTP sender validates the complete, bounded response\nbefore reporting transport acknowledgement. HTTP 200 with {} or a zero-rejection\npartialSuccess warning is acknowledged. A known partial rejection leaves the\nwhole batch unconfirmed because the response does not identify the rejected\nrecords; that batch is not replayed. Empty, malformed, interrupted, oversized,\nunexpected-shape or ambiguous-alias success responses are also unconfirmed and\nare not retried. Empty JSON is not {}; the zero-byte protobuf convention does\nnot apply to this JSON sender. Canonical and proto snake_case response names are\naccepted, but duplicate aliases are conservatively rejected.\n\nTransient HTTP 429/502/503/504 and transport failures before a response may retry\nwithin the existing 30-second total export deadline, with at most five attempts\nand exponential backoff with jitter even when Retry-After is zero. Retries retain the same\nserialized event IDs and bytes, enabling query-side deduplication. Retry-After\ncannot extend the deadline. Response reads are capped at 64 KiB. The exporter\npreserves merged generic/log-specific OTLP headers, gzip request compression,\nand configured CA/client certificate/key files; invalid configuration fails\nexplicitly. It requests uncompressed JSON responses and treats unsupported\nresponse encodings as unconfirmed. Diagnostics omit collector response bodies\nand credentials. Transport acknowledgement remains distinct from backend\npersistence; query reconciliation is still needed to confirm ingestion.\n\nA staged update may finish after its original supervisor/updater has been\nreplaced. Before publishing, validation rollback, or activation, the updater\nrechecks that its recorded supervisor and updater PIDs still own the service\nand that the original parent is running. Unknown ownership defers mutation.","hierarchy":{"lvl0":"Development","lvl1":"Proxy package updates and token budgets","lvl2":"OTLP acknowledgement boundaries","lvl3":""}}, {"objectID":"3d5db69fb501ec2b11df1700773d0e7648ef30c85f643c3b54975e4f45a3a05b","title":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","url":"/docs/development/testing-plan","content":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN\nTest Results Documentation:\nUpdated Documentation:\n\nLighthouse Integration Testing Strategy\nDate: 2025-07-06 02:55 AM\nEstimated Duration: 3 hours total\n\n📋 TESTING OVERVIEW\n\nWhat We're Testing:\n✅ Real-time WebSocket Infrastructure - New streaming services, WebSocket server, enhanced chat\n✅ Advanced Telemetry Integration - OpenTelemetry stack (15+ dependencies, optional by default)\n✅ Voice AI Removal - Complete cleanup of voice dependencies and code\n✅ Backward Compatibility - All existing functionality preserved\n✅ New API Surface - Factory methods, exports, TypeScript interfaces\n\nCritical Success Criteria:\n✅ Zero Breaking Changes: All existing code works unchanged\n✅ Build Success: TypeScript compilation with 0 errors\n✅ Performance: \\<5% overhead when new features disabled\n✅ Optional Features: Telemetry disabled by default, WebSocket services optional\n✅ Complete Integration: New features work with existing AI providers and MCP tools\n\n🔄 PHASE A: IMMEDIATE VERIFICATION (30 minutes)\n\nPriority: CRITICAL | Blocking: Must pass before proceeding\n\nA.1 File System Verification (10 minutes)\n\nSuccess Criteria:\n✅ WebSocket infrastructure files exist\n✅ Streaming services files exist\n✅ Telemetry files exist\n✅ NO voice-related files remain\n✅ Enhanced chat files exist\n\nA.2 Build Validation (15 minutes)\n\nSuccess Criteria:\n✅ TypeScript compilation: 0 errors\n✅ Vite build: successful\n✅ CLI build: successful\n✅ publint: \"All good!\"\n✅ Package integrity: pnpm pack succeeds\n\nA.3 Dependency Verification (5 minutes)\n\nSuccess Criteria:\n✅ Voice AI dependencies: 0 found\n✅ OpenTelemetry dependencies: 15+ installed\n✅ No dependency conflicts\n✅ Package.json reflects changes\n\n🔧 PHASE B: CORE TESTING (1 hour)\n\nPriority: HIGH | Focus: New feature functionality\n\nB.1 WebSocket Infrastructure Testing (20 minutes)\n\nTests to Create:\ntest/websocket-server.test.ts\ntest/streaming-manager.test.ts\ntest/websocket-chat-handler.test.ts\n\nSuccess Criteria:\n✅ WebSocket server starts on specified port\n✅ Connection management works\n✅ Room creation/joining functional\n✅ Streaming channels operational\n✅ Error handling graceful\n\nB.2 Telemetry Integration Testing (20 minutes)\n\nTests to Create:\ntest/telemetryService.test.ts\ntest/ai-instrumentation.test.ts\ntest/mcp-instrumentation.test.ts\n\nSuccess Criteria:\n✅ Telemetry disabled by default\n✅ Telemetry enables when configured\n✅ AI operation tracking works\n✅ MCP tool instrumentation functional\n✅ Zero overhead when disabled\n\nB.3 Enhanced Chat Testing (20 minutes)\n\nTests to Create:\ntest/enhanced-chat.test.ts\ntest/chat-integration.test.ts\n\nSuccess Criteria:\n✅ Enhanced chat service creates successfully\n✅ SSE mode works\n✅ WebSocket mode works\n✅ Dual mode integration functional\n✅ Backward compatibility with existing chat\n\n🚀 PHASE C: COMPREHENSIVE VALIDATION (1 hour)\n\nPriority: HIGH | Focus: Integration and performance\n\nC.1 Existing Functionality Regression Testing (20 minutes)\n\nSuccess Criteria:\n✅ All existing tests pass\n✅ CLI commands work unchanged\n✅ SDK methods work unchanged\n✅ AI providers function correctly\n✅ MCP tools continue working\n\nC.2 Performance Impact Testing (20 minutes)\n\nSuccess Criteria:\n✅ Default performance unchanged\n✅ Performance overhead \\<5% when features enabled\n✅ Memory usage remains stable\n✅ No performance regressions\n\nC.3 Real-World Scenario Testing (20 minutes)\n\nSuccess Criteria:\n✅ WebSocket chat works end-to-end\n✅ Telemetry collects accurate data\n✅ Multi-provider scenarios 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{"objectID":"932ab0fc3c5319bb706a147597448f628218a05114984cd13b3e62c06a66fd5c","title":"Check telemetry dependencies added","url":"/docs/development/testing-plan#check-telemetry-dependencies-added","content":"npm list | grep -E \"(@opentelemetry)\"","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Check telemetry dependencies added","lvl3":""}}, {"objectID":"b988f938d8d7addf94a01c9d1cab99699639330b70b415dd2a7489d8ed0bbc50","title":"Should show 15+ OpenTelemetry packages","url":"/docs/development/testing-plan#should-show-15-opentelemetry-packages","content":"`\n\nSuccess Criteria:\n✅ Voice AI dependencies: 0 found\n✅ OpenTelemetry dependencies: 15+ installed\n✅ No dependency conflicts\n✅ Package.json reflects changes","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Should show 15+ OpenTelemetry packages","lvl3":""}}, 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minutes)","url":"/docs/development/testing-plan#b3-enhanced-chat-testing-20-minutes","content":"Tests to Create:\ntest/enhanced-chat.test.ts\ntest/chat-integration.test.ts\n\nSuccess Criteria:\n✅ Enhanced chat service creates successfully\n✅ SSE mode works\n✅ WebSocket mode works\n✅ Dual mode integration functional\n✅ Backward compatibility with existing chat","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"B.3 Enhanced Chat Testing (20 minutes)","lvl3":""}}, {"objectID":"3fcc2ac1721cf72a74afc65c32f9af52dcf25a41bbd7c8e1aef66098c5196b01","title":"🚀 PHASE C: COMPREHENSIVE VALIDATION (1 hour)","url":"/docs/development/testing-plan#-phase-c-comprehensive-validation-1-hour","content":"Priority: HIGH | Focus: Integration and performance","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"🚀 PHASE C: COMPREHENSIVE VALIDATION (1 hour)","lvl3":""}}, 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generate \"Hello world\" --provider google-ai\nnode dist/cli/index.js provider status","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Test CLI functionality unchanged","lvl3":""}}, {"objectID":"93e0ed8093e7d282b7ae4ca4d9ee9daee1ced0d3943c4d109b58c8abaaa93960","title":"Test SDK functionality unchanged","url":"/docs/development/testing-plan#test-sdk-functionality-unchanged","content":"node -e \"import('@juspay/neurolink').then(sdk => sdk.createBestAIProvider().then(p => p.generate({input: {text: 'test'}})))\"\n`\n\nSuccess Criteria:\n✅ All existing tests pass\n✅ CLI commands work unchanged\n✅ SDK methods work unchanged\n✅ AI providers function correctly\n✅ MCP tools continue working","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Test SDK functionality unchanged","lvl3":""}}, {"objectID":"7aaf80cf42cdbbd6a45289718218dc556867863b4f26a3092eccf391cf6ceb32","title":"C.2 Performance Impact Testing 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{"objectID":"8c7d51a360d8ff62c2878eb0248d41927d423d3f7368340757c5daa6eb3bbaa2","title":"D.2 Documentation Synchronization (10 minutes)","url":"/docs/development/testing-plan#d2-documentation-synchronization-10-minutes","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"D.2 Documentation Synchronization (10 minutes)","lvl3":""}}, {"objectID":"698f30cf53ca30f58cc0e8ee75ae23140c7ff286ca2e4a5d5f61d76e10648e1c","title":"Check documentation reflects implementation","url":"/docs/development/testing-plan#check-documentation-reflects-implementation","content":"grep -r \"WebSocket\" docs/ | wc -l # Should find references\ngrep -r \"voice\" docs/ | wc -l # Should be minimal/removed\ngrep -r \"telemetry\" docs/ | wc -l # Should find references\n`\n\nSuccess Criteria:\n✅ Documentation reflects actual implementation\n✅ Voice references removed/minimal\n✅ New features documented\n✅ Examples are 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{"objectID":"bb819e7112200b834139f3ffd697db3b2a1691f6580a1e5456f65263f3f6a3cb","title":"Test installation simulation","url":"/docs/development/testing-plan#test-installation-simulation","content":"mkdir /tmp/test-install\ncd /tmp/test-install\nnpm init -y\nnpm install $WORKSPACE/neurolink/juspay-neurolink-*.tgz\nnode -e \"console.log(require('@juspay/neurolink'))\"\n`\n\nSuccess Criteria:\n✅ Package builds correctly\n✅ Installation works\n✅ Imports work after installation\n✅ No missing files\n✅ Ready for npm publish","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Test installation simulation","lvl3":""}}, {"objectID":"7afbffa75ac1e32586c3848f2696491688db7dc01dea2c77ff2c20c8c071c137","title":"Critical (Must Pass):","url":"/docs/development/testing-plan#critical-must-pass","content":"✅ Build Success: 0 TypeScript errors, successful compilation\n✅ Backward Compatibility: All existing functionality works unchanged\n✅ Performance: \\<5% overhead when new features disabled\n✅ Voice AI Removal: No voice dependencies or code remaining","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Critical (Must Pass):","lvl3":""}}, {"objectID":"0733df68703c885062aaa1795d288612e446973a392d9fcbdcbfa60db552f658","title":"Important (Should Pass):","url":"/docs/development/testing-plan#important-should-pass","content":"✅ WebSocket Infrastructure: Real-time services operational\n✅ Telemetry Integration: Optional monitoring works when enabled\n✅ Enhanced Chat: Dual-mode chat capabilities functional\n✅ API Consistency: New exports and types work correctly","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Important (Should Pass):","lvl3":""}}, {"objectID":"d9ca7bd0dc945f5571324e74a12d8a476841752069e149c302a3211773308e23","title":"Nice to Have (Can Be Fixed):","url":"/docs/development/testing-plan#nice-to-have-can-be-fixed","content":"✅ Documentation Completeness: All features documented\n✅ Example Applications: Working demos available\n✅ Performance Optimization: Further optimization opportunities","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Nice to Have (Can Be Fixed):","lvl3":""}}, {"objectID":"2e94ea3e593b8fcb7cd590a41f9cca2aeaac414bbfd5c57f7d75a63bf3555846","title":"Sequential Execution Required:","url":"/docs/development/testing-plan#sequential-execution-required","content":"Phase A → Must pass completely before proceeding\nPhase B → Core functionality validation\nPhase C → Integration and performance validation\nPhase D → Final production readiness","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Sequential Execution Required:","lvl3":""}}, {"objectID":"f0a4f9e4ee8e5535aac35dbdd29f65ef5bc00afe589ceafc9d0ef2b9b199850c","title":"Parallel Execution Possible:","url":"/docs/development/testing-plan#parallel-execution-possible","content":"Within each phase, tests can run in parallel\nDocumentation verification can happen alongside testing\nPerformance testing can run concurrently with functionality testing","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Parallel Execution Possible:","lvl3":""}}, {"objectID":"a046852873410059f08d68f535a4e13ebc229dc8150832a0764265da965b2058","title":"Failure Handling:","url":"/docs/development/testing-plan#failure-handling","content":"Phase A Failure: STOP - Fix build/dependency issues first\nPhase B Failure: Address core functionality before integration\nPhase C Failure: Performance/integration issues - may proceed with fixes\nPhase D Failure: Polish issues - fix before production deployment","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Failure Handling:","lvl3":""}}, {"objectID":"18b6cc8f68581e30dd8752ba2e830b1ffb0917a4a748bead42e49973eb52f8dc","title":"Test Environment Preparation:","url":"/docs/development/testing-plan#test-environment-preparation","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Test Environment Preparation:","lvl3":""}}, {"objectID":"5df56bfab1b5609049c474f5eb774ff0b1713d83e4063d8f1fec7483b027d15e","title":"Clean environment","url":"/docs/development/testing-plan#clean-environment","content":"rm -rf node_modules/ dist/ .svelte-kit/\npnpm install","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Clean environment","lvl3":""}}, {"objectID":"c32fc7f1b40bcfd34f0c0164c7f5beddf9d0393137c215e3443e93d61df0d8de","title":"Environment variables for testing","url":"/docs/development/testing-plan#environment-variables-for-testing","content":"`","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION 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recommendations\n\nReady for Execution: This plan provides comprehensive validation of all Lighthouse integration work while ensuring zero breaking changes and optimal performance.\n\nEstimated Total Time: 3 hours for complete validation\nCritical Path: Phase A must pass before proceeding to subsequent phases\nSuccess Rate Target: 100% pass rate for Critical criteria, 90%+ for Important criteria","hierarchy":{"lvl0":"Development","lvl1":"🧪 COMPREHENSIVE TESTING & VERIFICATION PLAN","lvl2":"Updated Documentation:","lvl3":""}}, {"objectID":"51b6b861923c4ecf8410549bd73ebac7e465ba84aef90c9a499f86d45bd8225b","title":"🧪 NeuroLink Testing Guide — Providers Validated in CI","url":"/docs/development/testing","content":"🧪 NeuroLink Testing Guide — Providers Validated in CI\n\n🎉 Provider Testing Status\n\nProviders supported — validated in CI where credentials are configured (unconfigured providers are skipped): OpenAI, Anthropic, Google AI, Google Vertex, AWS Bedrock, Azure OpenAI, Mistral, Hugging Face, Ollama, LiteLLM, AWS SageMaker, OpenAI-compatible, OpenRouter, DeepSeek, NVIDIA NIM, LM Studio, llama.cpp, TypeSafe Jev — plus voice (OpenAI TTS, ElevenLabs, Deepgram, Azure Speech, Google TTS/STT, Whisper, OpenAI Realtime, Gemini Live).\n\nQuick Provider Validation\n\nComprehensive Testing\n\nExpected Results\n\nCLI Enhancement Output:\n\nSDK Enhancement Output:\n\nProvider Testing\n\nGoogle AI Provider Validation\n\nOpenAI Provider Validation\n\nMulti-Provider Testing\n\nBackward Compatibility Testing\n\nEnsure No Breaking Changes\n\nTest Existing SDK Integration\n\nError Handling Testing\n\nInvalid Model Names\n\nMissing API Keys\n\nNetwork Issues\n\nPerformance Testing\n\nResponse Time Validation\n\nToken Counting Accuracy\n\nEnhancement Feature Validation\n\nAnalytics Data Completeness\n\nEvaluation Data Validation\n\nContext Flow Testing\n\nTroubleshooting Guide\n\nCommon Issues\nEmpty Responses from Google AI\nCheck model name in .env file\nUse gemini-2.5-pro 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instructions","url":"/docs/development/testing#expected-helpful-setup-instructions","content":"`","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"Expected: Helpful setup instructions","lvl3":""}}, {"objectID":"6f84c9acd43c65eed6e126ecb28707eac9dbf9d2251aa495da657c84519144ac","title":"Network Issues","url":"/docs/development/testing#network-issues","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"Network Issues","lvl3":""}}, {"objectID":"535fb979e4072fd5943718b60f942cfe2581e59d94f02bf0ab3f9c5296aed133","title":"Test with invalid API endpoint (simulated)","url":"/docs/development/testing#test-with-invalid-api-endpoint-simulated","content":"node ./dist/cli/index.js generate \"test\" --timeout 5s --debug","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"Test with invalid API endpoint (simulated)","lvl3":""}}, {"objectID":"4a4ddc7e1ba7da05e8ac247d462c74e1d4796c47ca815b27edd6210911545a66","title":"Expected: Fallback to other providers if available","url":"/docs/development/testing#expected-fallback-to-other-providers-if-available","content":"`","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"Expected: Fallback to other providers if available","lvl3":""}}, {"objectID":"732e97053310081527b3956475ec8f9543528f27634cb9a9437be97491fc022e","title":"Response Time Validation","url":"/docs/development/testing#response-time-validation","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"Response Time Validation","lvl3":""}}, {"objectID":"83436dd951e6c29193b97a7214bf39e68bba4321e298f87657bd15499ff55728","title":"Test response times with analytics","url":"/docs/development/testing#test-response-times-with-analytics","content":"node ./dist/cli/index.js 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{"objectID":"ccd90219360ecc36f15b0643eba076beb6ece97d47df2e726c02d5551923af2f","title":"Test accurate token counting","url":"/docs/development/testing#test-accurate-token-counting","content":"node ./dist/cli/index.js generate \"This is a test prompt for token counting\" --enable-analytics --debug","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"Test accurate token counting","lvl3":""}}, {"objectID":"33c361903e5d2155aee10120ed991aaf5c4ebc49b12169c7ffeb49e51b659acf","title":"Expected: Token counts match actual usage","url":"/docs/development/testing#expected-token-counts-match-actual-usage","content":"`","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"Expected: Token counts match actual usage","lvl3":""}}, {"objectID":"b879714c080fe2a85b50539fb224809866bec0b5ae9df829069f821f3356d017","title":"Analytics Data 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timestamp: ISO string","lvl3":""}}, {"objectID":"83e222546aabbdc6c7bc9f23c5d11b80d63b2b7fef71fcbe3ea2093f44cfb7d8","title":"Evaluation Data Validation","url":"/docs/development/testing#evaluation-data-validation","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"Evaluation Data Validation","lvl3":""}}, {"objectID":"cb4404c5157f480b12911f00292e0578971e88becf2998f836a5f86f2e82139c","title":"Test evaluation scoring","url":"/docs/development/testing#test-evaluation-scoring","content":"node ./dist/cli/index.js generate \"Explain quantum physics\" --enable-evaluation --debug","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"Test evaluation scoring","lvl3":""}}, {"objectID":"ab73a02c6fd432b8740d1624770287217547ab7d9cea1f53080f9611db83937b","title":"- evaluationTime: number","url":"/docs/development/testing#--evaluationtime-number","content":"`","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"- evaluationTime: number","lvl3":""}}, {"objectID":"a762e4c091d9b3e54bd2a51a4cdf02851c6636cae857996467d2a9437ec085a2","title":"Context Flow Testing","url":"/docs/development/testing#context-flow-testing","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"Context Flow Testing","lvl3":""}}, {"objectID":"d7eb0273fcdf438807e6ad66b71d1b1511436d60f50712f7246d76e3a4441865","title":"Test context preservation","url":"/docs/development/testing#test-context-preservation","content":"node ./dist/cli/index.js generate \"Help with task\" --context '{\"userId\":\"123\",\"department\":\"sales\"}' --enable-analytics --debug","hierarchy":{"lvl0":"Development","lvl1":"🧪 NeuroLink Testing Guide — Providers Validated in CI","lvl2":"Test context 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This allows users to view documentation for the specific version they're using.\n\nBenefits\nVersion-specific docs: Users can view docs matching their installed version\nPreserved history: Old versions remain accessible\nEasy switching: Version selector in navigation\nAutomated deployment: Integrate with CI/CD for automatic publishing\n\nSetup\nInstall Dependencies\nVerify Configuration\n\nThe mkdocs.yml already includes mike configuration:\n\nLocal Usage\n\nCreate First Version\n\nDeploy New Version\n\nList All Versions\n\nOutput:\n\nServe Versioned Docs Locally\n\nVisit http://localhost:8000 to test version switching.\n\nDelete a Version\n\nVersion Management Workflow\n\nFor Minor Releases (1.0 → 1.1)\n\nFor Major Releases (1.x → 2.0)\n\nFor Patch Releases (1.0.0 → 1.0.1)\n\nCI/CD Integration\n\nGitHub Actions Workflow\n\nCreate .github/workflows/docs.yml:\n\nAutomatic Version Detection\n\nBest Practices\nVersion Naming\nStable releases: 1.0, 1.1, 2.0 (match npm version)\nPre-releases: 1.0-beta, 2.0-rc1\nDevelopment: dev (always latest from main branch)\nAlias Strategy\nVersion Cleanup\nDocumentation Updates\n\nFor bug fixes to old versions:\n\nAdvanced Configuration\n\nCustom Version Selector\n\nAdd to mkdocs.yml:\n\nVersion Warnings\n\nAdd version-specific warnings in docs/index.md:\n\nTroubleshooting\n\nIssue: \"gh-pages branch not found\"\n\nIssue: Version selector not appearing\n\nVerify mike is installed:\n\nCheck mkdocs.yml configuration:\n\nIssue: Wrong default version\n\nVersion History\n\n| Version | Release Date | Status | Notes |\n| ------- | ---------------- | -------------- | --------------------- |\n| 7.47.x | Current | ✅ Active | Latest features |\n| 7.46.x | 2024-12 | ✅ Active | Previous stable |\n| 7.45.x | 2024-11 | ⚠️ Old | Security updates only |\n| < 7.45 | 2024 and earlier | ❌ Unsupported | Upgrade recommended |\n\nRelated Documentation\nContributing - How to contribute documentation\nDevelopment Setup - Local development environment\nArchitecture - Documentation structure\n\nAdditional Resources\nmike Documentation - Official mike guide\nMkDocs Material Versioning - Material theme versioning\nGitHub Pages - Hosting documentation","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"","lvl3":""}}, {"objectID":"e93b7f831e0fae0433eba697498889c8d09b69aecf3fbf3eda30c0965c348afa","title":"Documentation Versioning","url":"/docs/development/versioning#documentation-versioning","content":"Managing documentation versions across releases using mike","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Documentation Versioning","lvl3":""}}, {"objectID":"9cd60208b8c84605e89660a919d9094bb8345ba244f95c376d65305ad8e80ea3","title":"Overview","url":"/docs/development/versioning#overview","content":"NeuroLink documentation uses mike to maintain multiple versions of documentation for different releases. This allows users to view documentation for the specific version they're using.","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Overview","lvl3":""}}, {"objectID":"67067b0be7766ca4b11879c48a5e6c9b158f4e3e612e5faeb25ad015746a0366","title":"Benefits","url":"/docs/development/versioning#benefits","content":"Version-specific docs: Users can view docs matching their installed version\nPreserved history: Old versions remain accessible\nEasy switching: Version selector in navigation\nAutomated deployment: Integrate with CI/CD for automatic publishing","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Benefits","lvl3":""}}, {"objectID":"205f6ca7c68a10e6e240d1562c0692ae26d20fb1a0fddc93d9140f65354d3562","title":"1. Install Dependencies","url":"/docs/development/versioning#1-install-dependencies","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"1. 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Verify Configuration","lvl3":""}}, {"objectID":"2707c3148cd253f7a7a09d6cc759294ad508430ad2e1746db3f91e5dbbf6a832","title":"Create First Version","url":"/docs/development/versioning#create-first-version","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Create First Version","lvl3":""}}, {"objectID":"dfa97898b4e4e0cab210571c9933ad4ba7f692c1432d5fcf69d765fcbd362289","title":"Deploy current docs as version 1.0","url":"/docs/development/versioning#deploy-current-docs-as-version-10","content":"mike deploy 1.0 latest --update-aliases","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Deploy current docs as version 1.0","lvl3":""}}, {"objectID":"1ac3d7afd7e90dc537c5152c8777a2c92443fea0597ce76cf43ed79b512333b3","title":"Set 1.0 as the default version","url":"/docs/development/versioning#set-10-as-the-default-version","content":"mike set-default latest\n`","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Set 1.0 as the default version","lvl3":""}}, {"objectID":"37812f756c82384e64bbc4d53fd633df91bec9b802b4d9b4c0caff60fe883e73","title":"Deploy New Version","url":"/docs/development/versioning#deploy-new-version","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Deploy New Version","lvl3":""}}, {"objectID":"88193f0b5dfc913d3a0a913e8fc65944494aa9f7d520cb391f4ee68a9a3de31f","title":"Deploy new version 1.1","url":"/docs/development/versioning#deploy-new-version-11","content":"mike deploy 1.1 latest --update-aliases","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Deploy new version 1.1","lvl3":""}}, {"objectID":"a0a7d2650add667b81a87278dea25551525d704967eb483a1a0e8e6e3df0731b","title":"Deploy specific version without making it latest","url":"/docs/development/versioning#deploy-specific-version-without-making-it-latest","content":"mike deploy 1.0.5\n`","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Deploy specific version without making it latest","lvl3":""}}, {"objectID":"76aa48ee0e6d4eacf863fbc827a387e57abcd06cba4c902671be300e5ef0a541","title":"List All Versions","url":"/docs/development/versioning#list-all-versions","content":"Output:","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"List All Versions","lvl3":""}}, {"objectID":"f645d65c48ee8f4b1edd508ebc908af0e371e9a4c4c2c7b7be98d6a3e247e1b6","title":"Serve Versioned Docs Locally","url":"/docs/development/versioning#serve-versioned-docs-locally","content":"Visit http://localhost:8000 to test version switching.","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Serve Versioned Docs Locally","lvl3":""}}, {"objectID":"68b383e6bd151359fb828c44b1ab50a7388daf4deafa4d9c4bae4b2d6b70e62d","title":"For Minor Releases (1.0 → 1.1)","url":"/docs/development/versioning#for-minor-releases-10-11","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"For Minor Releases (1.0 → 1.1)","lvl3":""}}, {"objectID":"49a0a9026d000d2522a3cf83887668a937bae3d9592f299320f6ca41857615be","title":"2. Deploy new version","url":"/docs/development/versioning#2-deploy-new-version","content":"mike deploy 1.1 latest --update-aliases --push","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"2. Deploy new version","lvl3":""}}, {"objectID":"744bb67efafda6fb45950bcde6704e72805ee2aaa132e17fafcd7f6cc9893c5f","title":"3. Verify","url":"/docs/development/versioning#3-verify","content":"mike list\n`","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"3. Verify","lvl3":""}}, {"objectID":"81999426b13a83937b8ef1c35de6e74b4d069224746f21f6ad48fb3b81e7b711","title":"For Major Releases (1.x → 2.0)","url":"/docs/development/versioning#for-major-releases-1x-20","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"For Major Releases (1.x → 2.0)","lvl3":""}}, {"objectID":"d49ab3e59c1746e2461a159434bb788ae0af61ccc9408d17743c3e6814cfd911","title":"1. 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Keep 1.x docs accessible","lvl3":""}}, {"objectID":"0ddbe2ef267723b9a4b28499b2a4890534153351d450094f077c685ba32ab480","title":"2.0 [latest]","url":"/docs/development/versioning#20-latest","content":"`","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"2.0 [latest]","lvl3":""}}, {"objectID":"6a0c0fc6ad1669bfd7371beca18ce7fc138c9a13a2c99a10613fe139048ca6b2","title":"For Patch Releases (1.0.0 → 1.0.1)","url":"/docs/development/versioning#for-patch-releases-100-101","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"For Patch Releases (1.0.0 → 1.0.1)","lvl3":""}}, {"objectID":"97b71484563411d5f4008a8fa41e400738341d0fdd79c075d67fe6cef8f4b6ba","title":"Update existing version (same alias)","url":"/docs/development/versioning#update-existing-version-same-alias","content":"mike deploy 1.0 latest --update-aliases --push\n`","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Update existing version (same alias)","lvl3":""}}, {"objectID":"412860c2c3c49451315f3c56b18301211eff130cdde30a66bf12d2f7d5940867","title":"GitHub Actions Workflow","url":"/docs/development/versioning#github-actions-workflow","content":"Create .github/workflows/docs.yml:","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"GitHub Actions Workflow","lvl3":""}}, {"objectID":"c526224b127d418d711837ba13429f6b8e678f657ff7e325c041fdcb1aa8f2dc","title":"1. 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Version Cleanup","url":"/docs/development/versioning#3-version-cleanup","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"3. Version Cleanup","lvl3":""}}, {"objectID":"09ed001e42edcce67e7616e7c0a0566bfc0da73edb614c2f7204fea240b205a3","title":"Remove old versions (keep last 3 major versions)","url":"/docs/development/versioning#remove-old-versions-keep-last-3-major-versions","content":"mike delete 0.9\nmike delete 1.0\n`","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Remove old versions (keep last 3 major versions)","lvl3":""}}, {"objectID":"5f3758f5bc1fd5fe5b5ada2df8ef11f1dd8e30a60ad69545ea18d22447cab1fd","title":"4. Documentation Updates","url":"/docs/development/versioning#4-documentation-updates","content":"For bug fixes to old versions:\n\n`bash","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"4. Documentation Updates","lvl3":""}}, {"objectID":"e8cd4a8e98594a599a4fe540728ad351138970220cea39822d3047b09a282459","title":"Checkout old version","url":"/docs/development/versioning#checkout-old-version","content":"git checkout v1.0.0","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Checkout old version","lvl3":""}}, {"objectID":"0942c4e848112a0f706c45f58c23b981a63f027394b9122ac6dd65f31a357c7a","title":"Redeploy specific version","url":"/docs/development/versioning#redeploy-specific-version","content":"mike deploy 1.0 --push\n`","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Redeploy specific version","lvl3":""}}, {"objectID":"69efae34d72db6dd7693a763270c2bef17269e3d83d3bdf7ef117431bcc62209","title":"Custom Version Selector","url":"/docs/development/versioning#custom-version-selector","content":"Add to mkdocs.yml:","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Custom Version Selector","lvl3":""}}, {"objectID":"2514a9954e9fcca7e829906a530eb8cddad689f529f86372187094e214b4eff8","title":"Version Warnings","url":"/docs/development/versioning#version-warnings","content":"Add version-specific warnings in docs/index.md:","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Version Warnings","lvl3":""}}, {"objectID":"c98540fafd1aac8e62012cd7a1e0d83a9325f92c3c4044de3ba2b993f81012fb","title":"Issue: \"gh-pages branch not found\"","url":"/docs/development/versioning#issue-gh-pages-branch-not-found","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Issue: \"gh-pages branch not found\"","lvl3":""}}, {"objectID":"f59cfae0cbab669b358ad0b5ca46932cf20d1c55ba848ca7580bec4df60e6781","title":"Create gh-pages branch","url":"/docs/development/versioning#create-gh-pages-branch","content":"git checkout --orphan gh-pages\ngit rm -rf .\ngit commit --allow-empty -m \"Initialize gh-pages\"\ngit push origin gh-pages\ngit checkout main\n`","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Create gh-pages branch","lvl3":""}}, {"objectID":"7bdf1c303f0338471309ac7e30f2213fc1f1e5cc98248c61fcae5e34ee5dba19","title":"Issue: Version selector not appearing","url":"/docs/development/versioning#issue-version-selector-not-appearing","content":"Verify mike is installed:\n\nCheck mkdocs.yml configuration:","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Issue: Version selector not appearing","lvl3":""}}, {"objectID":"81dd3ac696d4131408eb59953b98d9b7b2cac80ea8110fd034234b746fe39069","title":"Issue: Wrong default version","url":"/docs/development/versioning#issue-wrong-default-version","content":"`bash","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Issue: Wrong default version","lvl3":""}}, {"objectID":"651117b1fc6cdbbe41bcaab827fef84b72e05ddf9bdc20ce83dd5272cc832b40","title":"Set correct default","url":"/docs/development/versioning#set-correct-default","content":"mike set-default latest\nmike serve # Verify locally\n`","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Set correct default","lvl3":""}}, {"objectID":"d3c3d27c677208a71ec7715d961715ac7e11b4f06d75666a21add878dcc8ea6d","title":"Version History","url":"/docs/development/versioning#version-history","content":"| Version | Release Date | Status | Notes |\n| ------- | ---------------- | -------------- | --------------------- |\n| 7.47.x | Current | ✅ Active | Latest features |\n| 7.46.x | 2024-12 | ✅ Active | Previous stable |\n| 7.45.x | 2024-11 | ⚠️ Old | Security updates only |\n| < 7.45 | 2024 and earlier | ❌ Unsupported | Upgrade recommended |","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Version History","lvl3":""}}, {"objectID":"a284bd1abb05234c40552d3f420f133f9c41537f388dc10ea5cae1c4dc6aedb5","title":"Related Documentation","url":"/docs/development/versioning#related-documentation","content":"Contributing - How to contribute documentation\nDevelopment Setup - Local development environment\nArchitecture - Documentation structure","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Related Documentation","lvl3":""}}, {"objectID":"cd92b8f80c52e58a5e488bd41ccd2b6fe4c59a311c7eb5a8bb78d4bd312f293d","title":"Additional Resources","url":"/docs/development/versioning#additional-resources","content":"mike Documentation - Official mike guide\nMkDocs Material Versioning - Material theme versioning\nGitHub Pages - Hosting documentation","hierarchy":{"lvl0":"Development","lvl1":"Documentation Versioning","lvl2":"Additional Resources","lvl3":""}}, {"objectID":"58be6da0ed7cbf566b01ebe9ad6672ab32a2225e97bff803bca77fda4232aeeb","title":"Advanced Examples","url":"/docs/examples/advanced","content":"Advanced Examples\n\nComplex integration patterns, enterprise workflows, and sophisticated use cases for NeuroLink.\n\n🏗️ Enterprise Architecture\n\nMulti-Provider Load Balancing\n\nCaching and Performance Optimization\n\n🔄 Workflow Automation\n\nDocument Processing Pipeline\n\nMulti-Stage Content Creation\n\n🤖 AI Agent Framework\n\nSpecialized AI Agents\n\n📊 Advanced Analytics Integration\n\nCustom Analytics Collection\n\nThis advanced examples documentation provides sophisticated patterns for enterprise usage, workflow automation, AI agent frameworks, and comprehensive analytics integration. These examples demonstrate how NeuroLink can be extended for complex, production-ready applications.\n\n📚 Related Documentation\nBasic Usage - Simple examples to get started\nBusiness Examples - Business-focused use cases\nCLI Advanced Usage - Command-line patterns\nSDK Reference - Complete API documentation","hierarchy":{"lvl0":"Examples","lvl1":"Advanced Examples","lvl2":"","lvl3":""}}, {"objectID":"1c5bcbec3363504a1822408109b5b54db7eb67cdcb0b91f6ff058eaefa0d9835","title":"Advanced Examples","url":"/docs/examples/advanced#advanced-examples","content":"Complex integration patterns, enterprise workflows, and sophisticated use cases for NeuroLink.","hierarchy":{"lvl0":"Examples","lvl1":"Advanced Examples","lvl2":"Advanced Examples","lvl3":""}}, {"objectID":"e9a54b776e15cdd96d5735b0b60ab6f23078a6d3ab11ac1915cce1be34bcd871","title":"Custom Analytics Collection","url":"/docs/examples/advanced#custom-analytics-collection","content":"This advanced examples documentation provides sophisticated patterns for enterprise usage, workflow automation, AI agent frameworks, and comprehensive analytics integration. These examples demonstrate how NeuroLink can be extended for complex, production-ready applications.","hierarchy":{"lvl0":"Examples","lvl1":"Advanced Examples","lvl2":"Custom Analytics Collection","lvl3":""}}, {"objectID":"01df2849be414a2657c7a6a51681f6420ed501d3878a282478c1443b1fa2584e","title":"📚 Related Documentation","url":"/docs/examples/advanced#-related-documentation","content":"Basic Usage - Simple examples to get started\nBusiness Examples - Business-focused use cases\nCLI Advanced Usage - Command-line patterns\nSDK Reference - Complete API documentation","hierarchy":{"lvl0":"Examples","lvl1":"Advanced Examples","lvl2":"📚 Related Documentation","lvl3":""}}, {"objectID":"a3edad2b65ded368d5bf3402fc387f8d956b9a23e3b08cd5e2f328971b435a3a","title":"Basic Usage Examples","url":"/docs/examples/basic-usage","content":"Basic Usage Examples\n\nSimple examples to get started with NeuroLink in different scenarios and programming languages.\n\nPrerequisites: Before running these examples, ensure you have configured at least one AI provider. See Provider Configuration Guide for setup instructions.\n\n🚀 Quick Start Examples\n\nSimple Text Generation\n\nCLI Basic Usage\n\n🔧 SDK Integration Examples\n\nNode.js Application\n\nExpress.js API\n\n⚛️ React Integration\n\nBasic React Component\n\nReact Hook for AI\n\n🎯 Common Use Cases\n\nCode Generation\n\nContent Creation\n\nData Analysis\n\nMulti-Model Access with LiteLLM\n\nCustom Model Access with SageMaker\n\nSageMaker Model Comparison\n\nProduction SageMaker Integration\n\nMulti-Provider Strategy with SageMaker\n\n🔧 Configuration Examples\n\nEnvironment-based Configuration\n\nProvider Fallback\n\n🛠️ Utility Functions\n\nText Processing Helpers\n\nBatch Processing\n\n📚 Related Documentation\nCLI Examples - Command-line usage examples\nAdvanced Examples - Complex integration patterns\nFramework Integration - Specific framework guides\nProvider Setup - API key configuration","hierarchy":{"lvl0":"Examples","lvl1":"Basic Usage Examples","lvl2":"","lvl3":""}}, {"objectID":"b4a95623a5c45f3be333fad1434f8253386c953aafa3c80ae0ff2cc9a36e4504","title":"Basic Usage Examples","url":"/docs/examples/basic-usage#basic-usage-examples","content":"Simple examples to get started with NeuroLink in different scenarios and programming languages.\n\nPrerequisites: Before running these examples, ensure you have configured at least one AI provider. See Provider Configuration Guide for setup instructions.","hierarchy":{"lvl0":"Examples","lvl1":"Basic Usage Examples","lvl2":"Basic Usage Examples","lvl3":""}}, {"objectID":"ea545d1df6f355a0a325247bf56f566dfdf5782f2886dbe5a25490cceaba6a46","title":"CLI Basic Usage","url":"/docs/examples/basic-usage#cli-basic-usage","content":"`bash","hierarchy":{"lvl0":"Examples","lvl1":"Basic Usage Examples","lvl2":"CLI Basic Usage","lvl3":""}}, {"objectID":"20271c3f758aab3ac3978f28b88329dba78ba1414967cbfe25e1e02bfaf2d415","title":"Simple generation","url":"/docs/examples/basic-usage#simple-generation","content":"npx @juspay/neurolink gen \"Write a haiku about programming\"","hierarchy":{"lvl0":"Examples","lvl1":"Basic Usage Examples","lvl2":"Simple generation","lvl3":""}}, {"objectID":"f02633e51a19fc554beb87b1e6ee48507c5001b02d4fbd547d72db8ae7b4ab6a","title":"With specific provider","url":"/docs/examples/basic-usage#with-specific-provider","content":"npx @juspay/neurolink gen \"Explain quantum computing\" --provider google-ai","hierarchy":{"lvl0":"Examples","lvl1":"Basic Usage Examples","lvl2":"With specific provider","lvl3":""}}, {"objectID":"34748f76bae6ab50aa6a5faac2622bfd38b3727d639428e1be648c9f9ecae1b3","title":"Save to file","url":"/docs/examples/basic-usage#save-to-file","content":"npx @juspay/neurolink gen \"Create a README template\" > README.md\n`","hierarchy":{"lvl0":"Examples","lvl1":"Basic Usage Examples","lvl2":"Save to file","lvl3":""}}, {"objectID":"83734e7b3e7410e27e03337502c93f5b06dafd3a97de380ac77c818bbb746341","title":"📚 Related Documentation","url":"/docs/examples/basic-usage#-related-documentation","content":"CLI Examples - Command-line usage examples\nAdvanced Examples - Complex integration patterns\nFramework Integration - Specific framework guides\nProvider Setup - API key configuration","hierarchy":{"lvl0":"Examples","lvl1":"Basic Usage Examples","lvl2":"📚 Related Documentation","lvl3":""}}, {"objectID":"d846dda0d22017815313b44954d11988fb0898a101a8731396c0488dca6c850b","title":"Business Applications","url":"/docs/examples/business","content":"Business Applications\n\nEnterprise-focused examples demonstrating NeuroLink's value in business environments, ROI optimization, and organizational workflows.\n\n💼 Executive Decision Support\n\nStrategic Planning Assistant\n\nScenario: C-level executives need AI-powered insights for strategic decisions.\n\nCLI for Executive Workflows\n\n🏭 Operations & Process Optimization\n\nBusiness Process Analysis\n\n💰 Financial Planning & Analysis\n\nFinancial Decision Support\n\n📈 Sales & Revenue Optimization\n\nSales Intelligence\n\n🎯 Marketing & Customer Success\n\nMarketing Intelligence\n\nCustomer Success Optimization\n\n🏆 Performance Management\n\nExecutive KPI Dashboard\n\n📋 Compliance & Risk Management\n\nRegulatory Compliance\n\nThese business applications demonstrate how NeuroLink can drive value across all organizational functions, from strategic decision-making to operational optimization, providing measurable ROI and competitive advantages.\n\n📚 Related Documentation\nUse Cases - Industry-specific applications\nAdvanced Examples - Complex integration patterns\nAnalytics Features - Business intelligence capabilities\nEnterprise Setup - Enterprise configuration","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"","lvl3":""}}, {"objectID":"7d0487b68ca93d1890aaef842d002f9c7aae9de9dacbcb6439cbdb48f1f0dfbb","title":"Business Applications","url":"/docs/examples/business#business-applications","content":"Enterprise-focused examples demonstrating NeuroLink's value in business environments, ROI optimization, and organizational workflows.","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Business Applications","lvl3":""}}, {"objectID":"86ae6b9c5e09eafd312ac15d891d9fca946bc031afb78c86e5b4f6e3f8933c77","title":"Strategic Planning Assistant","url":"/docs/examples/business#strategic-planning-assistant","content":"Scenario: C-level executives need AI-powered insights for strategic decisions.","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Strategic Planning Assistant","lvl3":""}}, {"objectID":"c243bcce6a058670e830f00b5ab4075a9b68c3bb5070eb7a7f3fc96cd9316035","title":"CLI for Executive Workflows","url":"/docs/examples/business#cli-for-executive-workflows","content":"`bash\n#!/bin/bash","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"CLI for Executive Workflows","lvl3":""}}, {"objectID":"87d8bdf7625d75f2c803f71c5012c49538a1da9474976b42618198349e3628c3","title":"Executive daily briefing automation","url":"/docs/examples/business#executive-daily-briefing-automation","content":"DATE=$(date +\"%Y-%m-%d\")\n\necho \"🏢 Generating Executive Daily Briefing for $DATE\"","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Executive daily briefing automation","lvl3":""}}, {"objectID":"bcb983c976737181b4c0837055152164d73fe27fc5373bc7cbd9d5f3384a8520","title":"Market analysis","url":"/docs/examples/business#market-analysis","content":"npx @juspay/neurolink gen \"\nAnalyze today's key business news and market trends relevant to SaaS companies.\nFocus on: AI/ML industry, enterprise software, regulatory changes, competitive moves.\nProvide 3-5 key insights with business implications.\n\" --enable-analytics \\\n --context '{\"role\":\"executive\",\"type\":\"market_briefing\",\"date\":\"'$DATE'\"}' \\\n > briefing-market-$DATE.md","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Market analysis","lvl3":""}}, {"objectID":"607394f6f61207910dfe170ee8e9d22ce5a19fe815c259accbedf5d3260cefc1","title":"Industry intelligence","url":"/docs/examples/business#industry-intelligence","content":"npx @juspay/neurolink gen \"\nGenerate strategic intelligence for enterprise AI software company:\nEmerging technology trends affecting our market\nNew competitors or competitive threats\nPartnership and acquisition opportunities\nRegulatory developments\nCustomer behavior shifts\n\nFormat as executive summary with action items.\n\" --provider anthropic \\\n --enable-evaluation \\\n --evaluation-domain \"Business Strategy Consultant\" \\\n > briefing-intelligence-$DATE.md","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Industry intelligence","lvl3":""}}, {"objectID":"c2e55b4050702593d8bef7396573c10c7335b19466b3fb1a57cbae2c8e408985","title":"Performance analysis","url":"/docs/examples/business#performance-analysis","content":"npx @juspay/neurolink gen \"\nBased on typical SaaS metrics, create analysis framework for:\nRevenue growth assessment\nCustomer acquisition cost optimization\nChurn reduction strategies\nMarket expansion opportunities\n\nInclude KPIs to track and red flags to monitor.\n\" --context '{\"companystage\":\"growth\",\"sector\":\"b2bsaas\"}' \\\n > performance-framework-$DATE.md\n\necho \"✅ Executive briefing complete\"\necho \"📄 Files generated:\"\necho \" - briefing-market-$DATE.md\"\necho \" - briefing-intelligence-$DATE.md\"\necho \" - performance-framework-$DATE.md\"\n`","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Performance analysis","lvl3":""}}, {"objectID":"e68cb2d7ff5d7545f94122ef45c0fa0b335f15ed50452a2481b4d6a33bece5dc","title":"Financial Decision Support","url":"/docs/examples/business#financial-decision-support","content":"`bash","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Financial Decision Support","lvl3":""}}, {"objectID":"59e51626604c495be237d5c4a48367c1a5bf7c06f29ee8f251c7824f068eb46b","title":"Budget analysis and planning","url":"/docs/examples/business#budget-analysis-and-planning","content":"npx @juspay/neurolink gen \"\nAnalyze our Q4 budget performance and create Q1 planning recommendations:\n\nQ4 Performance:\nRevenue: $2.8M (target: $3M)\nOpEx: $2.1M (budget: $2M)\nCustomer Acquisition Cost: $450\nGross margin: 78%\n\nCreate Q1 budget recommendations focusing on:\nRevenue optimization strategies\nCost structure improvements\nInvestment priorities\nRisk mitigation measures\n\" --provider anthropic \\\n --enable-analytics \\\n --context '{\"department\":\"finance\",\"type\":\"budget_planning\"}' \\\n > q1-budget-analysis.md","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Budget analysis and planning","lvl3":""}}, {"objectID":"53742759ccf0046b307855834f2763edfe480e66dc62dac5bdb320ab09251803","title":"Investment proposal evaluation","url":"/docs/examples/business#investment-proposal-evaluation","content":"npx @juspay/neurolink gen \"\nEvaluate this investment proposal:\nNew AI development team: $500K annual cost\nExpected output: 2x faster feature development\nMarket opportunity: $10M TAM expansion\nTimeline: 18 month payback projected\n\nAnalyze from CFO perspective:\nFinancial viability\nRisk assessment\nAlternative approaches\nInvestment committee recommendation\n\" --enable-evaluation \\\n --evaluation-domain \"Chief Financial Officer\" \\\n > investment-proposal-analysis.md","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Investment proposal evaluation","lvl3":""}}, {"objectID":"4b8bf5c2d1d0ef0649b8e84a63234baa05307870f611e261d1d88ca1a058171b","title":"Cash flow forecasting","url":"/docs/examples/business#cash-flow-forecasting","content":"npx @juspay/neurolink gen \"\nCreate 12-month cash flow forecast model framework for SaaS business:\n\nInclude considerations for:\nSubscription revenue recognition\nSeasonal variations\nCustomer churn impact\nGrowth investment timing\nWorking capital requirements\n\nProvide Excel-ready formulas and scenarios (conservative, base, optimistic).\n\" --max-tokens 1500 \\\n > cashflow-model-framework.md\n`","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Cash flow forecasting","lvl3":""}}, {"objectID":"2d6c47b39d86d3f688b2beeef8bcadc780af5e6b0c1bc5fecf59edb31e3cb953","title":"Marketing Intelligence","url":"/docs/examples/business#marketing-intelligence","content":"`bash","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Marketing Intelligence","lvl3":""}}, {"objectID":"c13267f99329b4fef5dbe91fb5661cfa2fb9438d76fec85e0d42885ac7b9383e","title":"Campaign performance analysis","url":"/docs/examples/business#campaign-performance-analysis","content":"npx @juspay/neurolink gen \"\nAnalyze our Q4 marketing campaign performance:\n\nCampaign Results:\nEmail marketing: 4.2% CTR, 18% open rate, $15 CPA\nPaid search: 3.8% CTR, $22 CPA, 1.2M impressions\nContent marketing: 125K blog views, 850 leads\nSocial media: 15K engagement, 320 qualified leads\nEvents: 3 conferences, 180 leads, $45K spend\n\nProvide:\nPerformance assessment vs industry benchmarks\nChannel effectiveness and ROI analysis\nAttribution modeling insights\nOptimization recommendations for Q1\nBudget reallocation suggestions\n\" --enable-analytics \\\n --context '{\"department\":\"marketing\",\"type\":\"campaign_analysis\"}' \\\n > marketing-performance-q4.md","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Campaign performance analysis","lvl3":""}}, {"objectID":"c381874d73f5f11c13764059fb192ff300671604ee66198400662f23225ab382","title":"Customer segmentation strategy","url":"/docs/examples/business#customer-segmentation-strategy","content":"npx @juspay/neurolink gen \"\nDevelop customer segmentation strategy for B2B SaaS:\n\nCurrent customer base:\n2,500 total customers\nIndustries: Tech (40%), Financial (25%), Healthcare (20%), Other (15%)\nCompany sizes: SMB (5000, 10%)\nUsage patterns: Power users (25%), Regular users (50%), Light users (25%)\n\nCreate segmentation framework for:\nTargeted messaging and positioning\nProduct development priorities\nCustomer success strategies\nUpselling and expansion opportunities\n\" --provider anthropic \\\n --enable-evaluation \\\n --evaluation-domain \"VP of Marketing\" \\\n > customer-segmentation-strategy.md","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Customer segmentation strategy","lvl3":""}}, {"objectID":"95e6bb971689f2d45761a00a1b9e4dfe76f0887e8f6bbb11dd335bccd9a7dfeb","title":"Content marketing strategy","url":"/docs/examples/business#content-marketing-strategy","content":"npx @juspay/neurolink gen \"\nCreate comprehensive content marketing strategy:\n\nTarget audience: IT decision makers at mid-market companies\nKey topics: AI adoption, digital transformation, security, compliance\nContent goals: Brand awareness, lead generation, thought leadership\n\nDevelop:\nContent pillar framework\nEditorial calendar structure\nContent distribution strategy\nPerformance measurement framework\nResource requirements and budget\n90-day implementation plan\n\" --temperature 0.7 \\\n --max-tokens 1500 \\\n > content-marketing-strategy.md\n`","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Content marketing strategy","lvl3":""}}, {"objectID":"407b1e3bda20d38490f986efca2950c335fbae73445f93dff7d63897bbe5426e","title":"Executive KPI Dashboard","url":"/docs/examples/business#executive-kpi-dashboard","content":"`bash\n#!/bin/bash","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Executive KPI Dashboard","lvl3":""}}, {"objectID":"13c0fa53f5ede9b0e69f7c747d8e42fb82c17f5a16f81e23dc7463997418686d","title":"Generate weekly executive summary","url":"/docs/examples/business#generate-weekly-executive-summary","content":"npx @juspay/neurolink gen \"\nCreate executive dashboard summary for SaaS company:\n\nKey Metrics (Week over Week):\nMRR: $850K (+3.2%)\nNew customers: 45 (+12%)\nChurn rate: 2.1% (-0.3%)\nCAC: $420 (-8%)\nNPS: 67 (+2 points)\nTeam productivity: 87% (+5%)\n\nGenerate executive summary including:\nKey performance highlights\nConcerning trends requiring attention\nStrategic recommendations\nResource allocation suggestions\nRisk mitigation priorities\n\nFormat for C-level consumption.\n\" --provider anthropic \\\n --enable-analytics \\\n --context '{\"audience\":\"executives\",\"format\":\"dashboard_summary\"}' \\\n > executive-summary-$(date +%Y%m%d).md","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Generate weekly executive summary","lvl3":""}}, {"objectID":"283a922c0d418abe62745832d2255487a6daeda3144ca39d775ae7ba9a37d0d2","title":"Department performance analysis","url":"/docs/examples/business#department-performance-analysis","content":"npx @juspay/neurolink gen \"\nAnalyze cross-departmental performance alignment:\n\nSales: 108% of target, strong pipeline health\nMarketing: 95% lead target, improved conversion rates\nEngineering: 92% sprint completion, technical debt concerns\nCustomer Success: 98% retention target, expansion opportunities\nFinance: On budget, cash flow positive\n\nIdentify:\nInter-departmental dependencies and bottlenecks\nResource reallocation opportunities\nPerformance improvement initiatives\nCross-functional collaboration needs\n\" --enable-evaluation \\\n --evaluation-domain \"Chief Operating Officer\" \\\n > departmental-performance-$(date +%Y%m%d).md\n\necho \"✅ Executive dashboards generated\"\n`","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Department performance analysis","lvl3":""}}, {"objectID":"14deb220bf4dc018a226ba4f4f584c37ab81ba69fcaebb09232ff7b23fc72ec9","title":"Regulatory Compliance","url":"/docs/examples/business#regulatory-compliance","content":"These business applications demonstrate how NeuroLink can drive value across all organizational functions, from strategic decision-making to operational optimization, providing measurable ROI and competitive advantages.","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"Regulatory Compliance","lvl3":""}}, {"objectID":"3d51e99d2c8b251d7b1da00cb7edd9d6e9774c45704ea81f8a049e61c5dcbec9","title":"📚 Related Documentation","url":"/docs/examples/business#-related-documentation","content":"Use Cases - Industry-specific applications\nAdvanced Examples - Complex integration patterns\nAnalytics Features - Business intelligence capabilities\nEnterprise Setup - Enterprise configuration","hierarchy":{"lvl0":"Examples","lvl1":"Business Applications","lvl2":"📚 Related Documentation","lvl3":""}}, {"objectID":"aa3c2538857750db306737699819fb187e754e554e0c719981339592d4800661","title":"Examples & Tutorials","url":"/docs/examples","content":"Examples & Tutorials\n\nLearn NeuroLink through practical examples and step-by-step tutorials for real-world applications.\n\n🎯 What You'll Find Here\n\nThis section contains practical implementations, use cases, and tutorials to help you integrate NeuroLink into your projects effectively.\nBasic Usage — Fundamental examples for both CLI and SDK usage, covering core functionality and common patterns.\nAdvanced Examples — Complex implementations showcasing advanced features like custom tools, analytics, and streaming.\nUse Cases — Real-world scenarios and applications across different industries and project types.\nBusiness Applications — Enterprise-focused examples for production deployments and business automation.\n\n🚀 Quick Examples\n\n🏗️ Framework Integration Examples\n\n🎨 Common Use Cases\n\nContent Creation\n\nCode Generation\n\nData Analysis\n\n🔄 Batch Processing\n\n🎯 Learning Path\nStart with Basic Usage - Core functionality\nExplore Use Cases - Find relevant scenarios\nTry Advanced Examples - Complex implementations\nStudy Business Applications - Production patterns\n\n🔗 Related Resources\nCLI Guide - Complete command reference\nSDK Reference - API documentation\nAdvanced Features - Enterprise capabilities\nVisual Demos - See examples in action","hierarchy":{"lvl0":"Examples","lvl1":"Examples & Tutorials","lvl2":"","lvl3":""}}, {"objectID":"b50cae7ceca0fed0c9c1e8c0fa017f83128ee5cca8c2b6131bb6e57562c60654","title":"Examples & Tutorials","url":"/docs/examples#examples-tutorials","content":"Learn NeuroLink through practical examples and step-by-step tutorials for real-world applications.","hierarchy":{"lvl0":"Examples","lvl1":"Examples & Tutorials","lvl2":"Examples & Tutorials","lvl3":""}}, {"objectID":"a4c34b4b2b398a96fc9fa44975e879355daa69951cae7c0fea0c7a8654d0337b","title":"🎯 What You'll Find Here","url":"/docs/examples#-what-youll-find-here","content":"This section contains practical implementations, use cases, and tutorials to help you integrate NeuroLink into your projects effectively.\nBasic Usage — Fundamental examples for both CLI and SDK usage, covering core functionality and common patterns.\nAdvanced Examples — Complex implementations showcasing advanced features like custom tools, analytics, and streaming.\nUse Cases — Real-world scenarios and applications across different industries and project 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provider","url":"/docs/examples#with-specific-provider","content":"npx @juspay/neurolink gen \"Explain AI\" --provider google-ai\ntypescript\n// SDK - Basic integration\n\nconst neurolink = new NeuroLink();\nconst result = await neurolink.generate({\n input: { text: \"Create a product description\" },\n});\n\nconsole.log(result.content);\nbash","hierarchy":{"lvl0":"Examples","lvl1":"Examples & Tutorials","lvl2":"With specific provider","lvl3":""}}, {"objectID":"6948b968f6ccf275b473e810f8b6857417765c9a6b0f8f5dc2bd8a01b7242d87","title":"CLI - Track usage and costs","url":"/docs/examples#cli---track-usage-and-costs","content":"npx @juspay/neurolink generate \"Business proposal\" \\\n --enable-analytics \\\n --enable-evaluation \\\n --debug\ntypescript\n// SDK - Monitor performance\nconst result = await neurolink.generate({\n input: { text: \"Market analysis report\" },\n enableAnalytics: true,\n enableEvaluation: true,\n});\n\nconsole.log(Cost: $${result.analytics.cost});\nconsole.log(Quality: ${result.evaluation.overall}/10);\ntypescript\n// Register a custom weather tool\nneurolink.registerTool(\"weather\", {\n description: \"Get weather for a city\",\n parameters: z.object({\n city: z.string(),\n units: z.enum([\"C\", \"F\"]).default(\"C\"),\n }),\n execute: async ({ city, units }) => {\n const data = await fetchWeather(city);\n return {\n city,\n temperature: units === \"F\"\n ? (data.temp * 9/5) + 32\n : data.temp,\n condition: data.condition,\n };\n },\n});\n\n// Use the tool\nconst result = await neurolink.generate({\n input: { text: \"What's the weather in Tokyo?\" },\n});\n`","hierarchy":{"lvl0":"Examples","lvl1":"Examples & Tutorials","lvl2":"CLI - Track usage and costs","lvl3":""}}, {"objectID":"fbabfdad33ccf529b524ee5b9a3918367fe63f3c9ce0b47a8f511603c04db8c9","title":"🔄 Batch Processing","url":"/docs/examples#-batch-processing","content":"`bash","hierarchy":{"lvl0":"Examples","lvl1":"Examples & Tutorials","lvl2":"🔄 Batch Processing","lvl3":""}}, {"objectID":"317e9604797d4b3ad6bb9fc22cb6d45bd8c0452c371c18d958ee459dfb6a9f0d","title":"CLI batch processing","url":"/docs/examples#cli-batch-processing","content":"echo -e \"Product description for laptop\\nProduct description for phone\\nProduct description for tablet\" > products.txt\nnpx @juspay/neurolink batch products.txt --output descriptions.json\ntypescript\n// SDK batch processing\nconst generateMultiple = async (prompts: string[]) => {\n const results = await Promise.all(\n prompts.map((prompt) =>\n neurolink.generate({\n input: { text: prompt },\n enableAnalytics: true,\n }),\n ),\n );\n\n const totalCost = results.reduce(\n (sum, result) => sum + (result.analytics?.cost || 0),\n 0,\n );\n\n return { results, totalCost };\n};\n`","hierarchy":{"lvl0":"Examples","lvl1":"Examples & Tutorials","lvl2":"CLI batch processing","lvl3":""}}, {"objectID":"3f34dd53751c11c304dfaadf638f6ac5bcc10113201fded006fc3e77b4da4909","title":"🎯 Learning Path","url":"/docs/examples#-learning-path","content":"Start with Basic Usage - Core functionality\nExplore Use Cases - Find relevant scenarios\nTry Advanced Examples - Complex implementations\nStudy Business Applications - Production patterns","hierarchy":{"lvl0":"Examples","lvl1":"Examples & Tutorials","lvl2":"🎯 Learning Path","lvl3":""}}, {"objectID":"ef0ae74cfb2df26676612017b1b8ebe26221326ceb4bd02ed6a8caa7b3f50152","title":"🔗 Related Resources","url":"/docs/examples#-related-resources","content":"CLI Guide - Complete command reference\nSDK Reference - API documentation\nAdvanced Features - Enterprise capabilities\nVisual Demos - See examples in action","hierarchy":{"lvl0":"Examples","lvl1":"Examples & Tutorials","lvl2":"🔗 Related Resources","lvl3":""}}, {"objectID":"25e632f61153fbaef0aa76d8e6e1f70aef8fbf021ccae962d5db859f85650069","title":"Tool Blocking Feature Example","url":"/docs/examples/mcp-tool-blocking-example","content":"Tool Blocking Feature Example\n\nThis example demonstrates how to use the blockedTools feature to prevent specific tools from being executed on external MCP servers.\n\nExample Configuration\n\nCreate or update your .mcp-config.json file:\n\nTesting the Feature\nLoad the Configuration\nList Available Tools\nAttempt to Execute a Blocked Tool\nExecute an Allowed Tool\n\nUse Cases\nProduction Safety\n\nBlock destructive operations in production:\nRead-Only GitHub Access\n\nAllow read operations but block writes:\nCompliance and Audit\n\nBlock sensitive operations that require audit trails:\n\nVerification\n\nRun tests to verify the feature works correctly:\n\nNotes\nBlocked tools are filtered during discovery, so they won't appear in the list of available tools\nAttempts to execute blocked tools will throw an error with a clear message\nThe blockedTools array can be empty or omitted if no tools need to be blocked\nTool names are case-sensitive and must match exactly","hierarchy":{"lvl0":"Examples","lvl1":"Tool Blocking Feature Example","lvl2":"","lvl3":""}}, {"objectID":"e090e77f5632de6e425b6d986492e2c6de6c74c6c1c240bc5621fd4586665fca","title":"Tool Blocking Feature Example","url":"/docs/examples/mcp-tool-blocking-example#tool-blocking-feature-example","content":"This example demonstrates how to use the blockedTools feature to prevent specific tools from being executed on external MCP servers.","hierarchy":{"lvl0":"Examples","lvl1":"Tool Blocking Feature Example","lvl2":"Tool Blocking Feature Example","lvl3":""}}, {"objectID":"94b87229cbb966362000fc605463d0bfc60684973a24922dc3ae8d2e34c03f63","title":"Example Configuration","url":"/docs/examples/mcp-tool-blocking-example#example-configuration","content":"Create or update your .mcp-config.json file:","hierarchy":{"lvl0":"Examples","lvl1":"Tool Blocking Feature Example","lvl2":"Example Configuration","lvl3":""}}, {"objectID":"e7425e89662d94b610f806fe4f307164cbb328cf8df190ee52e518f94360796c","title":"1. Production Safety","url":"/docs/examples/mcp-tool-blocking-example#1-production-safety","content":"Block destructive operations in production:","hierarchy":{"lvl0":"Examples","lvl1":"Tool Blocking Feature Example","lvl2":"1. Production Safety","lvl3":""}}, {"objectID":"291ada546a04f1aff55ea603404459ea11d46e6599795aee8284c30502f620e5","title":"2. Read-Only GitHub Access","url":"/docs/examples/mcp-tool-blocking-example#2-read-only-github-access","content":"Allow read operations but block writes:","hierarchy":{"lvl0":"Examples","lvl1":"Tool Blocking Feature Example","lvl2":"2. Read-Only GitHub Access","lvl3":""}}, {"objectID":"bdfbeb262b7151021cf5b6fe53f75efa090018ff7cba8075138ab6b2ae83df3f","title":"3. Compliance and Audit","url":"/docs/examples/mcp-tool-blocking-example#3-compliance-and-audit","content":"Block sensitive operations that require audit trails:","hierarchy":{"lvl0":"Examples","lvl1":"Tool Blocking Feature Example","lvl2":"3. Compliance and Audit","lvl3":""}}, {"objectID":"84fde65275092423ed37e9dea4196454155bd98e8c44211574a8a22daa835420","title":"Verification","url":"/docs/examples/mcp-tool-blocking-example#verification","content":"Run tests to verify the feature works correctly:\n\n`bash","hierarchy":{"lvl0":"Examples","lvl1":"Tool Blocking Feature Example","lvl2":"Verification","lvl3":""}}, {"objectID":"9f7539c4d1bab94a2976c933545c90b2119d043aad9e4e847f8ca4ba1fa12367","title":"Run the blocklist tests","url":"/docs/examples/mcp-tool-blocking-example#run-the-blocklist-tests","content":"pnpm test test/unit/mcp/externalServerBlocklist.test.ts","hierarchy":{"lvl0":"Examples","lvl1":"Tool Blocking Feature Example","lvl2":"Run the blocklist tests","lvl3":""}}, {"objectID":"a6a33dbdaa9f09d86a62e1a043a4583f57c1ced7b2e786830b73ed431d5faa15","title":"Or run all tests","url":"/docs/examples/mcp-tool-blocking-example#or-run-all-tests","content":"pnpm test\n`","hierarchy":{"lvl0":"Examples","lvl1":"Tool Blocking Feature Example","lvl2":"Or run all tests","lvl3":""}}, {"objectID":"4063c82ce53cbddddb37aaa3f2c4e6cda3979f7c42245ab87dd1f9b32d952ebf","title":"Notes","url":"/docs/examples/mcp-tool-blocking-example#notes","content":"Blocked tools are filtered during discovery, so they won't appear in the list of available tools\nAttempts to execute blocked tools will throw an error with a clear message\nThe blockedTools array can be empty or omitted if no tools need to be blocked\nTool names are case-sensitive and must match exactly","hierarchy":{"lvl0":"Examples","lvl1":"Tool Blocking Feature Example","lvl2":"Notes","lvl3":""}}, {"objectID":"5341e63ac43674fa9af2ce7263aeda1cd91585d86e51d5d3a89b1f372e615a1c","title":"Use Cases","url":"/docs/examples/use-cases","content":"This page has moved to Real-World Use Cases.","hierarchy":{"lvl0":"Examples","lvl1":"Use Cases","lvl2":"","lvl3":""}}, {"objectID":"663b4543baa0f126dd73ee25ad1e8111b108f06eec1f22e84f8cc195c99b00e8","title":"Audio Input & Transcription Guide","url":"/docs/features/audio-input","content":"Audio Input & Voice Conversations Guide\n\nNeuroLink provides comprehensive audio input capabilities, enabling real-time voice conversations with AI models. This guide covers currently available features, audio specifications, and upcoming enhancements.\n\nOverview\n\nCurrently Available\n\nNeuroLink supports the following audio capabilities today:\nReal-time voice conversations via Gemini Live (Google AI Studio)\nText-to-Speech (TTS) output via Google Cloud TTS, OpenAI TTS, ElevenLabs, and Azure TTS\nSpeech-to-Text (STT) via generate() and stream() options (Whisper/OpenAI STT, Google STT, Deepgram, Azure STT)\nWebSocket-based voice streaming for web applications\nBidirectional audio - speak and hear AI responses in real-time\n\nPlanned\n\nThe following features are planned for future releases:\nCLI commands: neurolink audio transcribe, neurolink audio analyze, neurolink audio summarize\nCLI commands: neurolink voice chat, neurolink voice demo\nCross-provider audio support (Anthropic, AWS Transcribe still planned)\nFile-based audio input processing\n\nProvider Support Matrix\n\n| Provider | Real-time Voice | TTS Output | Audio Transcription | Status |\n| -------------------- | --------------- | ---------- | ---------------------------- | ---------------- |\n| Google AI Studio | Yes | Yes | Yes (via Google STT) | Production Ready |\n| Google Vertex AI | Planned | Yes | Yes (via Google STT) | Available |\n| OpenAI | Planned | Yes | Yes (via Whisper/OpenAI STT) | Available |\n| Deepgram | Planned | No | Yes | Available |\n| Azure | Planned | Yes | Yes (via Azure STT) | Available |\n| Anthropic | Planned | Planned | Planned | Planned |\n| AWS Bedrock | Planned | Planned | Planned | Planned |\n\nSupported Model for Real-time Voice:\n\n| Model | Provider | Capabilities |\n| ---------------------------------------------- | --------- | -------------------------------- |\n| gemini-2.5-flash-preview-native-audio-dialog | Google AI | Bidirectional audio, low latency |\n\nQuick Start: Real-Time Voice (SDK)\n\nReal-time voice conversations are available through the SDK using Gemini Live's native audio dialog model.\n\nPrerequisites\n\nBasic Real-time Voice Streaming\n\nComplete Voice Session Example\n\nQuick Start: TTS Integration\n\nNeuroLink provides Text-to-Speech output via Google Cloud TTS. TTS can be combined with any text generation.\n\nCLI Usage\n\nSDK Usage\n\nFor comprehensive TTS documentation, see the TTS Integration Guide.\n\nVoice Demo Example\n\nNeuroLink includes a complete voice demo application demonstrating real-time bidirectional audio conversations.\n\nLocation\n\nRunning the Demo\n\nThe demo will:\nStart a WebSocket server on port 5175 (or next available port)\nOpen your browser automatically to the demo interface\nAllow you to speak and receive real-time AI audio responses\n\nDemo Architecture\n\nKey Code from Voice Demo Server\n\nAudio Specifications\n\nInput Audio Format\n\n| Parameter | Value | Notes |\n| --------------- | ------------------- | ------------------------------------ |\n| Encoding | PCM16LE | 16-bit signed integer, little-endian |\n| Sample Rate | 16,000 Hz | 16 kHz mono |\n| Channels | 1 (mono) | Stereo not supported in Phase 1 |\n| Frame Size | 20-60ms recommended | ~320-960 samples per frame |\n| Byte Order | Little-endian | Intel/ARM standard |\n\nOutput Audio Format\n\n| Parameter | Value | Notes |\n| --------------- | ------------- | ------------------------------------ |\n| Encoding | PCM16LE | 16-bit signed integer, little-endian |\n| Sample Rate | 24,000 Hz | 24 kHz mono |\n| Channels | 1 (mono) | Single channel output |\n| Byte Order | Little-endian | Intel/ARM standard |\n\nConverting Audio Formats\n\nFrom Float32 to PCM16LE (for input):\n\nFrom PCM16LE to Float32 (for output playback):\n\nBrowser Audio Context Setup\n\nSDK API Reference\n\nAudioInputSpec\n\nConfiguration for streaming audio input.\n\nAudioChunk\n\nAudio output chunk received from streaming responses.\n\nStreamOptions with Audio\n\nStream Result Events\n\nAudioContent (File-based - Future)\n\nFor file-based audio input (planned feature).\n\nRoadmap\n\nPhase 1 (Current)\nReal-time voice with Gemini Live\nBidirectional audio streaming via SDK\nVoice demo example application\nTTS output integration\n\nPhase 2 (Planned)\nCLI Voice Commands\nAudio Transcription\n\n \n\nPhase 3 (Partially Available)\nSpeech-to-Text via generate() / stream() — Available now via the stt option. There is no standalone neur","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"","lvl3":""}}, {"objectID":"a32f77567943264cd3a10e47531543e59494110d83eacb73e06c777b325a954d","title":"Audio Input & Voice Conversations Guide","url":"/docs/features/audio-input#audio-input-voice-conversations-guide","content":"NeuroLink provides comprehensive audio input capabilities, enabling real-time voice conversations with AI models. This guide covers currently available features, audio specifications, and upcoming enhancements.","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Audio Input & Voice Conversations Guide","lvl3":""}}, {"objectID":"e6130b0676f57a7cf79b67937961a503147a86874d2071c61df0cd0c5983606b","title":"Currently Available","url":"/docs/features/audio-input#currently-available","content":"NeuroLink supports the following audio capabilities today:\nReal-time voice conversations via Gemini Live (Google AI Studio)\nText-to-Speech (TTS) output via Google Cloud TTS, OpenAI TTS, ElevenLabs, and Azure TTS\nSpeech-to-Text (STT) via generate() and stream() options (Whisper/OpenAI STT, Google STT, Deepgram, Azure STT)\nWebSocket-based voice streaming for web applications\nBidirectional audio - speak and hear AI responses in real-time","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Currently Available","lvl3":""}}, {"objectID":"a323022725e083a1bd3cc081f0bb30671115674fd3059d81a59a1e5748bf2e40","title":"Planned","url":"/docs/features/audio-input#planned","content":"The following features are planned for future releases:\nCLI commands: neurolink audio transcribe, neurolink audio analyze, neurolink audio summarize\nCLI commands: neurolink voice chat, neurolink voice demo\nCross-provider audio support (Anthropic, AWS Transcribe still planned)\nFile-based audio input processing","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Planned","lvl3":""}}, {"objectID":"8d491157b4f86d94b665dc67602d242028afbb11311c376e92d5fb72180b9dd2","title":"Provider Support Matrix","url":"/docs/features/audio-input#provider-support-matrix","content":"| Provider | Real-time Voice | TTS Output | Audio Transcription | Status |\n| -------------------- | --------------- | ---------- | ---------------------------- | ---------------- |\n| Google AI Studio | Yes | Yes | Yes (via Google STT) | Production Ready |\n| Google Vertex AI | Planned | Yes | Yes (via Google STT) | Available |\n| OpenAI | Planned | Yes | Yes (via Whisper/OpenAI STT) | Available |\n| Deepgram | Planned | No | Yes | Available |\n| Azure | Planned | Yes | Yes (via Azure STT) | Available |\n| Anthropic | Planned | Planned | Planned | Planned |\n| AWS Bedrock | Planned | Planned | Planned | Planned |\n\nSupported Model for Real-time Voice:\n\n| Model | Provider | Capabilities |\n| ---------------------------------------------- | --------- | -------------------------------- |\n| gemini-2.5-flash-preview-native-audio-dialog | Google AI | Bidirectional audio, low latency |","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Provider Support Matrix","lvl3":""}}, {"objectID":"675ff9de750e2e6e6eabfd9b19d8e2107d5c403305242ddd9e306f98b799635e","title":"Quick Start: Real-Time Voice (SDK)","url":"/docs/features/audio-input#quick-start-real-time-voice-sdk","content":"Real-time voice conversations are available through the SDK using Gemini Live's native audio dialog model.","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Quick Start: Real-Time Voice (SDK)","lvl3":""}}, {"objectID":"19f0a0ab6d49a5c80fc761412dcedc3e85257f38aed0542bd0561f58d95febdf","title":"Prerequisites","url":"/docs/features/audio-input#prerequisites","content":"`bash","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Prerequisites","lvl3":""}}, {"objectID":"73a73af8929aec5bebdfa114ba2a35ba5f7d8b0c78bfbdd21131132020de7368","title":"OR","url":"/docs/features/audio-input#or","content":"`","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"OR","lvl3":""}}, {"objectID":"b8bbe48264f5c2fa2199f5216378d101f1297d6a7496fbf42011f846f8eca010","title":"Quick Start: TTS Integration","url":"/docs/features/audio-input#quick-start-tts-integration","content":"NeuroLink provides Text-to-Speech output via Google Cloud TTS. TTS can be combined with any text generation.","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Quick Start: TTS Integration","lvl3":""}}, {"objectID":"3c5420c5e4868c46fd89b36067629b735a3c3c6f9f0eb1c23a16a33c4c561688","title":"CLI Usage","url":"/docs/features/audio-input#cli-usage","content":"`bash","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"CLI Usage","lvl3":""}}, {"objectID":"0663810a6345a212e41034d9388cdb51f841d1e879093e2245e456c52a3cf122","title":"Generate text and convert to speech","url":"/docs/features/audio-input#generate-text-and-convert-to-speech","content":"neurolink generate \"Hello, world!\" \\\n --provider google-ai \\\n --tts-voice en-US-Neural2-C","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Generate text and convert to speech","lvl3":""}}, {"objectID":"6cf97993895e9da66a07a99e0e777f5fcbcb8b9b66e19e079baaa97d7fdbbe7e","title":"Save audio to file","url":"/docs/features/audio-input#save-audio-to-file","content":"neurolink generate \"Welcome to NeuroLink\" \\\n --provider google-ai \\\n --tts-voice en-US-Neural2-C \\\n --tts-output welcome.mp3","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Save audio to file","lvl3":""}}, {"objectID":"a575a4dfab88dc59df25d34e5a97faf31d5317876eedd39d80a536f4528cf363","title":"Customize voice parameters","url":"/docs/features/audio-input#customize-voice-parameters","content":"neurolink generate \"This is a test\" \\\n --provider google-ai \\\n --tts-voice en-US-Wavenet-D \\\n --tts-speed 1.2 \\\n --tts-pitch 2.0 \\\n --tts-format mp3 \\\n --tts-output test.mp3","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Customize voice parameters","lvl3":""}}, {"objectID":"ab79ff1a9beb884c3660fa5ac54ed1d3be091ef9cbc0dee7d91a38d6f44f4fff","title":"Synthesize AI response (not input text)","url":"/docs/features/audio-input#synthesize-ai-response-not-input-text","content":"neurolink generate \"Tell me a joke\" \\\n --provider google-ai \\\n --tts-voice en-US-Neural2-C \\\n --tts-use-ai-response \\\n --tts-output joke.mp3\n`","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Synthesize AI response (not input text)","lvl3":""}}, {"objectID":"102b6d9eb4d66435e016f9c864c371270b5c758941d0b3160a0f0e4fe31aadae","title":"SDK Usage","url":"/docs/features/audio-input#sdk-usage","content":"For comprehensive TTS documentation, see the TTS Integration Guide.","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"SDK Usage","lvl3":""}}, {"objectID":"fcddc7b401a11446d3a72bd25339216dc36286f7de48b3d4fc93701fdf77d6be","title":"Voice Demo Example","url":"/docs/features/audio-input#voice-demo-example","content":"NeuroLink includes a complete voice demo application demonstrating real-time bidirectional audio conversations.","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Voice Demo Example","lvl3":""}}, {"objectID":"2fdffc78fd2690e9257b08ea4a3ebe3d9ec2eea0e42f8434113099a13f9f3bf9","title":"Running the Demo","url":"/docs/features/audio-input#running-the-demo","content":"`bash","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Running the Demo","lvl3":""}}, {"objectID":"017e78eebc6a69729c893274c9a30036263b3957b48abbaf03171d56490c6636","title":"Navigate to the project root","url":"/docs/features/audio-input#navigate-to-the-project-root","content":"cd /path/to/neurolink","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Navigate to the project root","lvl3":""}}, {"objectID":"75a8cd2dbab534bd6ded4728bbf51307b78a1839f6f4c144049d24dab3ae4b96","title":"Build the SDK first","url":"/docs/features/audio-input#build-the-sdk-first","content":"pnpm run build","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Build the SDK first","lvl3":""}}, {"objectID":"74e42025ad34baabfd6cde5e784309f574b3b13c2cc5d0412552cb87fcec0f58","title":"Run the demo server","url":"/docs/features/audio-input#run-the-demo-server","content":"node examples/voice-demo/server.mjs\n`\n\nThe demo will:\nStart a WebSocket server on port 5175 (or next available port)\nOpen your browser automatically to the demo interface\nAllow you to speak and receive real-time AI audio responses","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Run the demo server","lvl3":""}}, {"objectID":"d38c45c3e64a598935c9183b5a97988f9cbf0bf7662eee96eadbc8e16d8218d8","title":"Input Audio Format","url":"/docs/features/audio-input#input-audio-format","content":"| Parameter | Value | Notes |\n| --------------- | ------------------- | ------------------------------------ |\n| Encoding | PCM16LE | 16-bit signed integer, little-endian |\n| Sample Rate | 16,000 Hz | 16 kHz mono |\n| Channels | 1 (mono) | Stereo not supported in Phase 1 |\n| Frame Size | 20-60ms recommended | ~320-960 samples per frame |\n| Byte Order | Little-endian | Intel/ARM standard |","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Input Audio Format","lvl3":""}}, {"objectID":"971b6dc610698a4f557f37772279796aad8c698ad175ce4e0331dd6843e29e21","title":"Output Audio Format","url":"/docs/features/audio-input#output-audio-format","content":"| Parameter | Value | Notes |\n| --------------- | ------------- | ------------------------------------ |\n| Encoding | PCM16LE | 16-bit signed integer, little-endian |\n| Sample Rate | 24,000 Hz | 24 kHz mono |\n| Channels | 1 (mono) | Single channel output |\n| Byte Order | Little-endian | Intel/ARM standard |","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Output Audio Format","lvl3":""}}, {"objectID":"7d77a11777da38abc477ca451f47b06a9e244c9134b4ae04725f6012f010813c","title":"Converting Audio Formats","url":"/docs/features/audio-input#converting-audio-formats","content":"From Float32 to PCM16LE (for input):\n\nFrom PCM16LE to Float32 (for output playback):","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Converting Audio Formats","lvl3":""}}, {"objectID":"98b435ae1676df230fc545833a511bb7c68b3e150195714166f1f9ea205664b3","title":"AudioInputSpec","url":"/docs/features/audio-input#audioinputspec","content":"Configuration for streaming audio input.","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"AudioInputSpec","lvl3":""}}, {"objectID":"26ca164ec395c5e70697bb8ef3dc5d524707aa5826602af62c722410d0e2aaa5","title":"AudioChunk","url":"/docs/features/audio-input#audiochunk","content":"Audio output chunk received from streaming responses.","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"AudioChunk","lvl3":""}}, {"objectID":"64814f99af3a8c638dc56306ba5fc44bb6e5b272f8d938ab34259be39af63e4e","title":"AudioContent (File-based - Future)","url":"/docs/features/audio-input#audiocontent-file-based---future","content":"For file-based audio input (planned feature).","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"AudioContent (File-based - Future)","lvl3":""}}, {"objectID":"fe70dce325b6ac96a408645173cdd649cc0abc95d6d9db868e0e4c4446c75435","title":"Phase 1 (Current)","url":"/docs/features/audio-input#phase-1-current","content":"Real-time voice with Gemini Live\nBidirectional audio streaming via SDK\nVoice demo example application\nTTS output integration","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Phase 1 (Current)","lvl3":""}}, {"objectID":"ec029d3f48cdb5f19fbcd4e201f677de97cff852a4d01194570def1804083244","title":"Phase 2 (Planned)","url":"/docs/features/audio-input#phase-2-planned","content":"CLI Voice Commands\nAudio Transcription","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Phase 2 (Planned)","lvl3":""}}, {"objectID":"4052f7016112515e4c48c4c7369cea3135af4fa874d935ab0d0382a9c7ffef4f","title":"Phase 3 (Partially Available)","url":"/docs/features/audio-input#phase-3-partially-available","content":"Speech-to-Text via generate() / stream() — Available now via the stt option. There is no standalone neurolink.transcribe() method; STT is integrated directly into generate() and stream():\n\n \n\n CLI equivalent:\n\n \n\n Available STT providers: whisper / openai-stt, google-stt, deepgram, azure-stt\n\n CLI STT flags: --stt, --stt-provider , --input-audio , --stt-language \nCross-provider Audio Support\nAnthropic voice capabilities — Planned\nAWS Transcribe — Planned\nFile-based Audio Input","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Phase 3 (Partially Available)","lvl3":""}}, {"objectID":"c2f07309fbd4e1af66d45bb90451dd8c9f9f62b6fbda59435f2de302a7ba8451","title":"Required Environment Variables","url":"/docs/features/audio-input#required-environment-variables","content":"`bash","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Required Environment Variables","lvl3":""}}, {"objectID":"8c38f46a58844c8f91aa4f8d748b70535368b4328ca702e5a17f2d34cd9b2848","title":"OR use the same GOOGLE_AI_API_KEY with Cloud TTS API enabled","url":"/docs/features/audio-input#or-use-the-same-google_ai_api_key-with-cloud-tts-api-enabled","content":"`","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"OR use the same GOOGLE_AI_API_KEY with Cloud TTS API enabled","lvl3":""}}, {"objectID":"1cca651280e08979aa7530b5865c51a0cc7ca8d763740d7c3b113dade8207585","title":"API Key Configuration","url":"/docs/features/audio-input#api-key-configuration","content":"For Gemini Live and TTS to work with an API key:\nGo to Google Cloud Console > APIs & Services > Credentials\nCreate or select your API key\nUnder \"API restrictions\", enable:\nGenerative Language API (for Gemini)\nCloud Text-to-Speech API (for TTS output)","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"API Key Configuration","lvl3":""}}, {"objectID":"0e647b5e96e94d60a8f1f0277326ee5227887bc96397718549986f1bf85ee86a","title":"Common Issues","url":"/docs/features/audio-input#common-issues","content":"| Issue | Cause | Solution |\n| --------------------------- | ------------------------ | -------------------------------------------------- |\n| No audio output | Missing API key | Set GOOGLE_AI_API_KEY or GEMINI_API_KEY |\n| \"disableTools required\" | Tools enabled with audio | Add disableTools: true to stream options |\n| Choppy audio playback | Buffer underrun | Increase buffer size or frame rate |\n| Wrong sample rate | Mismatched audio context | Use 16kHz input, 24kHz output contexts |\n| WebSocket disconnects | Network timeout | Implement reconnection logic |\n| \"Model not found\" | Invalid model name | Use gemini-2.5-flash-preview-native-audio-dialog |","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Common Issues","lvl3":""}}, {"objectID":"3666c2d8d6b40a6c724ff2038fd852cac1d4499211f0070664568c2a43c58dbc","title":"Audio Quality Issues","url":"/docs/features/audio-input#audio-quality-issues","content":"Clipping/Distortion:\nEnsure input samples are normalized to [-1, 1] range\nCheck gain levels before PCM conversion\n\nEcho/Feedback:\nMute microphone during AI audio playback\nImplement voice activity detection (VAD)\n\nLatency:\nUse smaller frame sizes (20ms)\nProcess audio in real-time, avoid buffering\nUse WebSocket for low-latency transport","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Audio Quality Issues","lvl3":""}}, {"objectID":"c4735ccd8979c95a1a8b2ee8ef9e09cffceb62ecf4e7dbc35e80df6260c67eaf","title":"Debug Mode","url":"/docs/features/audio-input#debug-mode","content":"Enable debug logging to troubleshoot audio issues:","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Debug Mode","lvl3":""}}, {"objectID":"5dd242268990d7407d240c3e419eda43acc8e8f28d62f4fb9f6ae0302e337e98","title":"Related Features","url":"/docs/features/audio-input#related-features","content":"Audio & Voice:\nTTS Integration Guide - Complete Text-to-Speech documentation\nVideo Generation - AI-powered video with audio\nPPT Generation - AI-powered PowerPoint presentations\n\nMultimodal Capabilities:\nMultimodal Guide - Images, PDFs, CSV inputs\nPDF Support - Document processing\n\nAdvanced Features:\nStreaming - Stream AI responses in real-time\nProvider Orchestration - Multi-provider failover\n\nDocumentation:\nCLI Commands - Complete CLI reference\nSDK API Reference - Full API documentation\nTroubleshooting - Extended error catalog","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Related Features","lvl3":""}}, {"objectID":"da325dca7aa585f458336acc2e7671004db8097ef1b4babdabdf6c0dadab102d","title":"Summary","url":"/docs/features/audio-input#summary","content":"NeuroLink's audio input capabilities provide:\n\nCurrently Available:\nReal-time voice conversations via Gemini Live\nBidirectional audio streaming (speak and hear)\nTTS output via Google Cloud, OpenAI TTS, ElevenLabs, and Azure TTS\nSTT via generate({ stt: { ... } }) — Whisper/OpenAI STT, Google STT, Deepgram, Azure STT\nVoice demo example application\nPCM16LE audio format support\n\nPlanned:\nCLI voice commands (voice chat, audio transcribe)\nAnthropic and AWS Transcribe audio support\nFile-based audio processing\n\nNext Steps:\nSet up environment variables\nTry the voice demo application\nIntegrate real-time voice in your SDK code\nExplore TTS output for text-to-speech\nCheck troubleshooting if you encounter issues","hierarchy":{"lvl0":"Features","lvl1":"Audio Input & Transcription Guide","lvl2":"Summary","lvl3":""}}, {"objectID":"ed053e7b833b8a623c856b423196d8d1dde3599d21b1976ce7a5fe867487cce3","title":"Authentication Providers","url":"/docs/features/authentication-providers","content":"Authentication Providers\n\nStatus: Stable | Availability: SDK + CLI + Server\n\nOverview\n\nNeuroLink ships with a pluggable authentication system that validates tokens, manages sessions, and enforces role-based access control (RBAC) across your AI endpoints. Rather than forcing a single auth solution, NeuroLink supports 11 providers through a unified interface so you can use the same identity platform your application already relies on.\n\nKey capabilities:\nToken validation -- verify JWTs and opaque tokens from any supported provider\nSession management -- in-memory or Redis-backed session storage with auto-refresh\nRBAC enforcement -- role and permission checks with hierarchical wildcard support\nPer-call authentication -- validate tokens on every generate() or stream() call\nMiddleware pipeline -- composable auth, RBAC, and rate-limiting middleware for server routes\nAsyncLocalStorage context -- access the authenticated user from anywhere in the request lifecycle without explicit parameter passing\nCLI management -- list providers, validate tokens, and check health from the command line\n\nQuick Start\n\nSDK -- Constructor Auth Config\n\nPass authentication configuration in the NeuroLink constructor. The provider is lazily initialized on first use.\n\nSDK -- Per-Call Token Validation\n\nWhen an auth provider is configured, pass auth: { token } to generate() or stream() to validate the caller's token before the AI request executes. Validated user identity is automatically injected into the request context.\n\nSDK -- Pre-Validated Request Context\n\nIf your server has already validated the user, pass requestContext instead. When both auth.token and requestContext are provided, token-derived identity fields take precedence to prevent privilege escalation.\n\nServer Middleware\n\nProtect HTTP routes with composable middleware.\n\nProvider Support\n\n| Provider | Type | JWT Validation | Session Mgmt | RBAC | Health Check | Aliases |\n| ----------- | ------------- | -------------- | ------------ | ---- | ------------ | --------------------------------- |\n| Auth0 | auth0 | Yes | Yes | Yes | Yes | auth0-jwt, auth0-oauth |\n| Clerk | clerk | Yes | Yes | Yes | Yes | clerk-jwt |\n| Firebase | firebase | Yes | Yes | Yes | Yes | firebase-auth |\n| Supabase | supabase | Yes | Yes | Yes | Yes | supabase-auth |\n| AWS Cognito | cognito | Yes | Yes | Yes | Yes | aws-cognito, amazon-cognito |\n| Keycloak | keycloak | Yes | Yes | Yes | Yes | keycloak-oidc |\n| WorkOS | workos | Yes | Yes | Yes | Yes | workos-sso, work-os |\n| Better Auth | better-auth | Yes | Yes | Yes | Yes | betterauth, better_auth |\n| OAuth2 | oauth2 | Yes | Yes | Yes | Yes | oauth, oidc, openid-connect |\n| JWT | jwt | Yes | Yes | Yes | Yes | jwt-auth, jwt-token |\n| Custom | custom | Yes | Yes | Yes | Yes | custom-auth |\n\nAll providers implement the AuthProvider interface, ensuring a consistent API regardless of which identity platform you choose.\n\nSDK API\n\nConstructor Configuration\n\nThe auth field in NeurolinkConstructorConfig accepts several forms:\n\nThe NeuroLinkAuthConfig union type supports all 11 provider types with their specific config shapes:\n\n| Config Type | Required Fields |\n| ------------------------------------- | ------------------------------------------ |\n| { type: \"auth0\"; config: ... } | domain, clientId |\n| { type: \"clerk\"; config: ... } | secretKey |\n| { type: \"firebase\"; config: ... } | projectId |\n| { type: \"supabase\"; config: ... } | url, anonKey |\n| { type: \"cognito\"; config: ... } | userPoolId, clientId, region |\n| { type: \"keycloak\"; config: ... } | serverUrl, realm, clientId |\n| { type: \"workos\"; config: ... } | apiKey, clientId |\n| { type: \"better-auth\"; config: ...} | secret, baseUrl |\n| { type: \"oauth2\"; config: ... } | clientId, authorizationUrl, tokenUrl |\n| { type: \"jwt\"; config: ... } | secret or publicKey |\n| { type: \"custom\"; config: ... } | validateToken function |\n\nsetAuthProvider(config)\n\nSet or change the authentication provider at runtime.\n\ngetAuthProvider()\n\nGet the currently configured authentication provider, or undefined if none is set.\n\nsetAuthContext(context)\n\nSet the current","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"","lvl3":""}}, {"objectID":"b8e1dc28b6d943c42b083e5a7fa691a81822cb83ed865eba567871bb2da0f7be","title":"Authentication Providers","url":"/docs/features/authentication-providers#authentication-providers","content":"Status: Stable | Availability: SDK + CLI + Server","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Authentication Providers","lvl3":""}}, {"objectID":"176a251158462a4eef0a027e59d3cb1b6060317255bdd3c4fab77bd60c183f72","title":"Overview","url":"/docs/features/authentication-providers#overview","content":"NeuroLink ships with a pluggable authentication system that validates tokens, manages sessions, and enforces role-based access control (RBAC) across your AI endpoints. Rather than forcing a single auth solution, NeuroLink supports 11 providers through a unified interface so you can use the same identity platform your application already relies on.\n\nKey capabilities:\nToken validation -- verify JWTs and opaque tokens from any supported provider\nSession management -- in-memory or Redis-backed session storage with auto-refresh\nRBAC enforcement -- role and permission checks with hierarchical wildcard support\nPer-call authentication -- validate tokens on every generate() or stream() call\nMiddleware pipeline -- composable auth, RBAC, and rate-limiting middleware for server routes\nAsyncLocalStorage context -- access the authenticated user from anywhere in the request lifecycle without explicit parameter passing\nCLI management -- list providers, validate tokens, and check health from the command line","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Overview","lvl3":""}}, {"objectID":"0894e83796f241c8e1d357e705c5b581b918ba27a28c74622340ee274ab32a77","title":"SDK -- Constructor Auth Config","url":"/docs/features/authentication-providers#sdk----constructor-auth-config","content":"Pass authentication configuration in the NeuroLink constructor. The provider is lazily initialized on first use.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"SDK -- Constructor Auth Config","lvl3":""}}, {"objectID":"8080d96ef1f530688f421eca8dba280fdcb7063d946f0e0db8e454b2508268ec","title":"SDK -- Per-Call Token Validation","url":"/docs/features/authentication-providers#sdk----per-call-token-validation","content":"When an auth provider is configured, pass auth: { token } to generate() or stream() to validate the caller's token before the AI request executes. Validated user identity is automatically injected into the request context.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"SDK -- Per-Call Token Validation","lvl3":""}}, {"objectID":"eb389101e7c2c4f4283e3f60f5fe4ba149a2239a0bf6f6d134518ed8a97ff708","title":"SDK -- Pre-Validated Request Context","url":"/docs/features/authentication-providers#sdk----pre-validated-request-context","content":"If your server has already validated the user, pass requestContext instead. When both auth.token and requestContext are provided, token-derived identity fields take precedence to prevent privilege escalation.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"SDK -- Pre-Validated Request Context","lvl3":""}}, {"objectID":"aff4ca6c749d9cba7965164300797f56082fcc55ff6e49d9868e79c9bd82b8c0","title":"Server Middleware","url":"/docs/features/authentication-providers#server-middleware","content":"Protect HTTP routes with composable middleware.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Server Middleware","lvl3":""}}, {"objectID":"dda719f93ac777689f7355278e66d42892046f8155e7da9bca010030443675e1","title":"Provider Support","url":"/docs/features/authentication-providers#provider-support","content":"| Provider | Type | JWT Validation | Session Mgmt | RBAC | Health Check | Aliases |\n| ----------- | ------------- | -------------- | ------------ | ---- | ------------ | --------------------------------- |\n| Auth0 | auth0 | Yes | Yes | Yes | Yes | auth0-jwt, auth0-oauth |\n| Clerk | clerk | Yes | Yes | Yes | Yes | clerk-jwt |\n| Firebase | firebase | Yes | Yes | Yes | Yes | firebase-auth |\n| Supabase | supabase | Yes | Yes | Yes | Yes | supabase-auth |\n| AWS Cognito | cognito | Yes | Yes | Yes | Yes | aws-cognito, amazon-cognito |\n| Keycloak | keycloak | Yes | Yes | Yes | Yes | keycloak-oidc |\n| WorkOS | workos | Yes | Yes | Yes | Yes | workos-sso, work-os |\n| Better Auth | better-auth | Yes | Yes | Yes | Yes | betterauth, better_auth |\n| OAuth2 | oauth2 | Yes | Yes | Yes | Yes | oauth, oidc, openid-connect |\n| JWT | jwt | Yes | Yes | Yes | Yes | jwt-auth, jwt-token |\n| Custom | custom | Yes | Yes | Yes | Yes | custom-auth |\n\nAll providers implement the AuthProvider interface, ensuring a consistent API regardless of which identity platform you choose.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Provider Support","lvl3":""}}, {"objectID":"b21527aabc70fe5b9bcb7c1cd4d692d4275deceeb228f5950b94633caefa623e","title":"Constructor Configuration","url":"/docs/features/authentication-providers#constructor-configuration","content":"The auth field in NeurolinkConstructorConfig accepts several forms:\n\nThe NeuroLinkAuthConfig union type supports all 11 provider types with their specific config shapes:\n\n| Config Type | Required Fields |\n| ------------------------------------- | ------------------------------------------ |\n| { type: \"auth0\"; config: ... } | domain, clientId |\n| { type: \"clerk\"; config: ... } | secretKey |\n| { type: \"firebase\"; config: ... } | projectId |\n| { type: \"supabase\"; config: ... } | url, anonKey |\n| { type: \"cognito\"; config: ... } | userPoolId, clientId, region |\n| { type: \"keycloak\"; config: ... } | serverUrl, realm, clientId |\n| { type: \"workos\"; config: ... } | apiKey, clientId |\n| { type: \"better-auth\"; config: ...} | secret, baseUrl |\n| { type: \"oauth2\"; config: ... } | clientId, authorizationUrl, tokenUrl |\n| { type: \"jwt\"; config: ... } | secret or publicKey |\n| { type: \"custom\"; config: ... } | validateToken function |","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Constructor Configuration","lvl3":""}}, {"objectID":"19369e2e6b36211fcc338d934994280cb10a139aff327f62c3888fef4b6ff18b","title":"setAuthProvider(config)","url":"/docs/features/authentication-providers#setauthproviderconfig","content":"Set or change the authentication provider at runtime.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"setAuthProvider(config)","lvl3":""}}, {"objectID":"f8e55f20947bd16473291a40629d6e412f503e958127806711d3354f2c0f334b","title":"getAuthProvider()","url":"/docs/features/authentication-providers#getauthprovider","content":"Get the currently configured authentication provider, or undefined if none is set.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"getAuthProvider()","lvl3":""}}, {"objectID":"865ae0f614c7c9bd2f2fb91d1a5377ed000d479496aaf1735984d161d1fe7fdb","title":"setAuthContext(context)","url":"/docs/features/authentication-providers#setauthcontextcontext","content":"Set the current authentication context for request handling. Useful when integrating with server frameworks that have already authenticated the user.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"setAuthContext(context)","lvl3":""}}, {"objectID":"a2a993214303bb670fe41889d4c56e1db57da62da309cf0184ccb1a6068f4b0e","title":"generate() / stream() Auth Options","url":"/docs/features/authentication-providers#generate-stream-auth-options","content":"Both generate() and stream() accept auth and requestContext options:\n\n| Option | Type | Description |\n| ---------------- | ------------------------- | ---------------------------------------------------- |\n| auth.token | string | Raw token validated by the configured auth provider |\n| requestContext | Record | Pre-validated user context (userId, userRoles, etc.) |\n\nWhen auth.token is provided:\nNeuroLink calls authProvider.authenticateToken(token) with a 5-second timeout\nIf invalid, an InvalidTokenError is thrown\nIf valid, userId, userEmail, and userRoles are merged into the request context\nToken-derived identity fields take precedence over requestContext to prevent privilege escalation","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"generate() / stream() Auth Options","lvl3":""}}, {"objectID":"89636aa20af131b123bb44fbaedf04b7d3cb508988897663b2158c8c482045e7","title":"CLI Usage","url":"/docs/features/authentication-providers#cli-usage","content":"The auth command provides subcommands for managing authentication:\n\n`bash","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"CLI Usage","lvl3":""}}, {"objectID":"c24509ee17e821574f417fda898ad32d166e349f3515107067a61f4bdc993cdb","title":"List available auth providers","url":"/docs/features/authentication-providers#list-available-auth-providers","content":"neurolink auth providers\nneurolink auth providers --format json\nneurolink auth providers --format table","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"List available auth providers","lvl3":""}}, {"objectID":"0a4e84a81c364b10c03831c2d4b4c643bc35c514ad496aa0b00a5032800b3cb1","title":"Validate a token against a provider","url":"/docs/features/authentication-providers#validate-a-token-against-a-provider","content":"neurolink auth validate --provider auth0 --domain your-tenant.auth0.com --client-id your-id\nneurolink auth validate --provider clerk --secret-key sktestxxx\nneurolink auth validate --provider jwt --secret your-jwt-secret","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Validate a token against a provider","lvl3":""}}, {"objectID":"a8cd1510ab9df084e0cde739f16a69373b27124c103e21f41d1b371165811548","title":"Check provider health","url":"/docs/features/authentication-providers#check-provider-health","content":"neurolink auth health --provider auth0 --domain your-tenant.auth0.com --client-id your-id\nneurolink auth health --provider supabase --url https://xxx.supabase.co --anon-key xxx","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Check provider health","lvl3":""}}, {"objectID":"38b829627c84d6deaf36f390bb8c6e00343255a1064ac746a55987dc319dc11f","title":"Anthropic OAuth management","url":"/docs/features/authentication-providers#anthropic-oauth-management","content":"neurolink auth login anthropic\nneurolink auth logout anthropic\nneurolink auth status anthropic\nneurolink auth refresh anthropic\n`","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Anthropic OAuth management","lvl3":""}}, {"objectID":"46f887cf5fd20cba96d7a7a3788797ea709bac571921e481de7ffc68329fd04a","title":"Environment Variable Configuration","url":"/docs/features/authentication-providers#environment-variable-configuration","content":"Provider configuration can be supplied via environment variables instead of CLI flags:\n\n| Provider | Environment Variables |\n| ----------- | ------------------------------------------------------------------------------------------------------------------------------------ |\n| Auth0 | AUTH0_DOMAIN, AUTH0_CLIENT_ID, AUTH0_AUDIENCE |\n| Clerk | CLERK_SECRET_KEY, CLERK_PUBLISHABLE_KEY |\n| Supabase | SUPABASE_URL, SUPABASE_ANON_KEY, SUPABASE_JWT_SECRET |\n| Firebase | FIREBASE_PROJECT_ID, FIREBASE_API_KEY |\n| WorkOS | WORKOS_API_KEY, WORKOS_CLIENT_ID |\n| Better Auth | BETTER_AUTH_SECRET, BETTER_AUTH_BASE_URL |\n| OAuth2 | OAUTH2_CLIENT_ID, OAUTH2_CLIENT_SECRET, OAUTH2_AUTHORIZATION_URL, OAUTH2_TOKEN_URL, OAUTH2_USERINFO_URL, OAUTH2_JWKS_URL |\n| Cognito | COGNITO_USER_POOL_ID, COGNITO_CLIENT_ID, COGNITO_REGION (or AWS_REGION) |\n| Keycloak | KEYCLOAK_REALM, KEYCLOAK_SERVER_URL, KEYCLOAK_CLIENT_ID |\n| JWT | JWT_SECRET, JWT_PUBLIC_KEY, JWT_ISSUER, JWT_AUDIENCE |","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Environment Variable Configuration","lvl3":""}}, {"objectID":"9398ce19087fed7349394cb1336d5e25bcb14a20f44c2b05b07d35e50e9324a2","title":"Base Provider Config","url":"/docs/features/authentication-providers#base-provider-config","content":"All providers share these base configuration fields:\n\n| Field | Type | Default | Description |\n| ----------------- | ------------------------- | ------- | --------------------------------------- |\n| required | boolean | true | Whether authentication is mandatory |\n| debug | boolean | false | Enable debug logging |\n| tokenValidation | TokenValidationConfig | -- | Token issuer, audience, clock tolerance |\n| tokenExtraction | TokenExtractionStrategy | Bearer | Where to find the token in requests |\n| session | SessionConfig | -- | Session storage and duration |\n| rbac | RBACConfig | -- | Role hierarchy and permissions |\n| cache | AuthCacheConfig | -- | Token validation result caching |","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Base Provider Config","lvl3":""}}, {"objectID":"b23ca07a0a5329b1c8cd3556f27100808d2a268def676e38e1cbd99488db9230","title":"Token Extraction Strategy","url":"/docs/features/authentication-providers#token-extraction-strategy","content":"Configure where and how tokens are extracted from requests:","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Token Extraction Strategy","lvl3":""}}, {"objectID":"254c0381d51d8c4886e702a1678d4bfeb74b74864ca2674a1fe5efcab50a3594","title":"Authentication Middleware","url":"/docs/features/authentication-providers#authentication-middleware","content":"Create middleware that validates tokens and attaches user context to requests.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Authentication Middleware","lvl3":""}}, {"objectID":"74d4d9e8fda2276cc2341f81efbeb93e28b8f2d00688af9fdf7027d23d6ded14","title":"RBAC Middleware","url":"/docs/features/authentication-providers#rbac-middleware","content":"Enforce role and permission requirements after authentication.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"RBAC Middleware","lvl3":""}}, {"objectID":"7b4318fd797aad6bd94f7bdb806cc69c1fd2b26c8bde46ffaa8e0bfbf0dafae9","title":"Rate Limiting by User","url":"/docs/features/authentication-providers#rate-limiting-by-user","content":"Apply per-user rate limits with role-based differentiation and memory or Redis storage.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Rate Limiting by User","lvl3":""}}, {"objectID":"a30fa33a7c726425762944c405bd280bde1494a1b9f7703103f51e72758d7854","title":"Session Management","url":"/docs/features/authentication-providers#session-management","content":"Sessions are managed through the SessionManager class with pluggable storage backends.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Session Management","lvl3":""}}, {"objectID":"9743ba6d189d280234418dba2060c68ae70dd68da4a83f8b964a75c1038a7c34","title":"In-Memory Sessions","url":"/docs/features/authentication-providers#in-memory-sessions","content":"Default for single-instance deployments. Sessions are lost on restart.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"In-Memory Sessions","lvl3":""}}, {"objectID":"d751a957a1ace69df16532d8527089cd6363376437c590d75c371c54c9a19596","title":"Redis Sessions","url":"/docs/features/authentication-providers#redis-sessions","content":"For multi-instance deployments with distributed session state.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Redis Sessions","lvl3":""}}, {"objectID":"63483d1869f8f7513bcc1414b63c694551e1b64af77186a0231e7118fe7ce690","title":"Session Config Reference","url":"/docs/features/authentication-providers#session-config-reference","content":"| Field | Type | Default | Description |\n| ----------------------- | --------------------------------- | ----------------------- | ---------------------------------------- |\n| storage | \"memory\" \\| \"redis\" \\| \"custom\" | \"memory\" | Storage backend |\n| duration | number | 3600 | Session duration in seconds |\n| autoRefresh | boolean | true | Auto-refresh sessions near expiration |\n| refreshThreshold | number | 300 | Seconds before expiry to trigger refresh |\n| allowMultipleSessions | boolean | -- | Allow multiple sessions per user |\n| maxSessionsPerUser | number | -- | Maximum concurrent sessions |\n| redis.url | string | -- | Redis connection URL |\n| redis.prefix | string | \"neurolink:sessions:\" | Key prefix |\n| redis.ttl | number | -- | Redis key TTL in seconds |","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Session Config Reference","lvl3":""}}, {"objectID":"f2769a43502b46a6d9c1e26121e056bb8d788187266077c2fd16c9cb62d79a07","title":"Auth Context (AsyncLocalStorage)","url":"/docs/features/authentication-providers#auth-context-asynclocalstorage","content":"NeuroLink uses Node.js AsyncLocalStorage to propagate authentication context through the request lifecycle. This means any function in the call chain can access the current user without explicit parameter passing.\n\nFor environments where AsyncLocalStorage is not available, use the AuthContextHolder:","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Auth Context (AsyncLocalStorage)","lvl3":""}}, {"objectID":"83467ee0df6da8dd4432afc251e9fa4cf71473859bdced7558182687a5f7cf89","title":"Error Handling","url":"/docs/features/authentication-providers#error-handling","content":"All auth errors extend AuthError with typed subclasses for different failure modes:\n\n| Error Class | Use Case | HTTP Status |\n| ------------------------------ | -------------------------------- | ----------- |\n| AuthenticationFailedError | Invalid credentials or token | 401 |\n| MissingTokenError | No token provided | 401 |\n| InvalidTokenError | Malformed or unverifiable token | 401 |\n| TokenExpiredError | Token has expired | 401 |\n| InsufficientPermissionsError | User lacks required permissions | 403 |\n| SessionNotFoundError | Session ID does not exist | 401 |\n| SessionExpiredError | Session has expired | 401 |\n| UserNotFoundError | User not found in provider | 404 |\n| ProviderInitializationError | Provider setup failed | 500 |\n| InvalidConfigurationError | Missing or invalid config fields | 500 |\n| ProviderAPIError | Provider API returned an error | 502 |\n| AuthRateLimitError | Too many auth attempts | 429 |","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Error Handling","lvl3":""}}, {"objectID":"aa35a54198b7b7221827d8063f9b984e692dda9ff3f78ab860016c74efc6cc39","title":"Error Codes","url":"/docs/features/authentication-providers#error-codes","content":"| Code | Meaning |\n| -------- | ------------------------ |\n| AUTH-001 | Invalid token |\n| AUTH-002 | Expired token |\n| AUTH-003 | Invalid credentials |\n| AUTH-004 | Invalid signature |\n| AUTH-005 | Missing token |\n| AUTH-006 | Token decode failed |\n| AUTH-007 | JWKS fetch failed |\n| AUTH-008 | Session not found |\n| AUTH-009 | Session expired |\n| AUTH-010 | Session revoked |\n| AUTH-011 | Insufficient permissions |\n| AUTH-012 | Insufficient roles |\n| AUTH-013 | Access denied |\n| AUTH-014 | Provider error |\n| AUTH-015 | Configuration error |\n| AUTH-016 | Rate limited |\n| AUTH-017 | User not found |\n| AUTH-018 | User disabled |\n| AUTH-019 | Email not verified |\n| AUTH-020 | MFA required |","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Error Codes","lvl3":""}}, {"objectID":"9a2cb7ef10a7e195ef9005b35916a606bb6aa3a4aa87f5fae3d6927c6b16ffe1","title":"Auth Events","url":"/docs/features/authentication-providers#auth-events","content":"Auth providers emit events you can subscribe to for logging or monitoring:","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Auth Events","lvl3":""}}, {"objectID":"929f49841311ac46b19a1267e09350e18205455019da00c52bdbb48084cde6d7","title":"Best Practices","url":"/docs/features/authentication-providers#best-practices","content":"Always validate tokens server-side. Never trust client-provided identity claims without auth: { token } validation.\nUse RBAC for fine-grained control. Define role hierarchies and permission mappings rather than checking roles directly in application code.\nUse Redis sessions in production. In-memory sessions are suitable for development but are lost on restart and do not work across multiple instances.\nLeverage AsyncLocalStorage context. Use runWithAuthContext and getCurrentUser() instead of passing user objects through every function parameter.\nSet token extraction strategy explicitly. The default (Authorization: Bearer) works for most APIs, but configure cookie or custom extraction for browser-based flows.\nHandle token expiration gracefully. Catch TokenExpiredError and return a response that tells the client to refresh.\nUse rate limiting per user. Apply createRateLimitByUserMiddleware to prevent abuse while giving premium users higher limits.\nKeep secrets in environment variables. Never hard-code client secrets, JWT secrets, or service account keys in source code.","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Best Practices","lvl3":""}}, {"objectID":"d63c328f7c66744701d2f7325de1c7862575cf1bdebadd73b4efd3bab6f30569","title":"Key Files","url":"/docs/features/authentication-providers#key-files","content":"| File | Purpose |\n| -------------------------------------------- | ------------------------------------------------------- |\n| src/lib/auth/AuthProviderFactory.ts | Factory for creating auth provider instances |\n| src/lib/auth/AuthProviderRegistry.ts | Registry for provider metadata and discovery |\n| src/lib/auth/authProvider.ts | BaseAuthProvider abstract class |\n| src/lib/auth/authContext.ts | AsyncLocalStorage context propagation |\n| src/lib/auth/authErrors.ts | Typed error hierarchy |\n| src/lib/auth/sessionManager.ts | Session storage (memory + Redis) |\n| src/lib/auth/middleware/AuthMiddleware.ts | Auth and RBAC middleware factories |\n| src/lib/auth/middleware/rateLimitByUser.ts | Per-user rate limiting middleware |\n| src/lib/auth/providers/auth0.ts | Auth0 provider implementation |\n| src/lib/auth/providers/clerk.ts | Clerk provider implementation |\n| src/lib/auth/providers/firebase.ts | Firebase provider implementation |\n| src/lib/auth/providers/supabase.ts | Supabase provider implementation |\n| src/lib/auth/providers/CognitoProvider.ts | AWS Cognito provider implementation |\n| src/lib/auth/providers/KeycloakProvider.ts | Keycloak provider implementation |\n| src/lib/auth/providers/workos.ts | WorkOS provider implementation |\n| src/lib/auth/providers/betterAuth.ts | Better Auth provider implementation |\n| src/lib/auth/providers/oauth2.ts | OAuth2/OIDC provider implementation |\n|","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"Key Files","lvl3":""}}, {"objectID":"1a5fc2d71645d4d92433557e7f8ec6eafffafb08cc61e080e82c428fbc80120e","title":"See Also","url":"/docs/features/authentication-providers#see-also","content":"Auth Architecture Guide -- factory + registry pattern, request flow, and integration points\nServer Adapters -- deploying NeuroLink as an HTTP API with auth middleware\nObservability Guide -- tracing authenticated requests with Langfuse context\nSDK API Reference -- complete SDK reference","hierarchy":{"lvl0":"Features","lvl1":"Authentication Providers","lvl2":"See Also","lvl3":""}}, {"objectID":"a0d1147947e423c33a6ff4369bc65f8383be0ae72a73d474756c54accd451a32","title":"Auto Evaluation Engine","url":"/docs/features/auto-evaluation","content":"Auto Evaluation Engine\n\nNeuroLink provides an automated quality gate that scores every response using an LLM-as-judge pipeline. Scores, rationales, and severity flags are surfaced in both CLI and SDK workflows so you can monitor drift and enforce minimum quality thresholds.\n\nWhat It Does\nGenerates a structured evaluation payload (result.evaluation) for every call with enableEvaluation: true.\nCalculates relevance, accuracy, completeness, and an overall score (1–10) using a RAGAS-style rubric.\nSupports retry loops: re-ask the provider when the score falls below your threshold.\nEmits analytics-friendly JSON so you can pipe results into dashboards.\n\nQuick Start\n\nEvaluation uses additional AI calls to the judge model (default: gemini-2.5-flash). Each evaluated response incurs extra API costs. For high-volume production workloads, consider sampling (e.g., evaluate 10% of requests) or disabling evaluation after quality stabilizes.\n\nUsage Examples\n\nCLI output (text mode):\n\nStreaming with Evaluation\nEvaluation works in streaming mode\nEvaluation payload arrives in final chunks\nCapture the evaluation object\nAccess overall score (1-10) and sub-scores\n\nConfiguration Options\n\n| Option | Where | Description |\n| ------------------------------------- | -------------------------------- | ------------------------------------------------------------------ |\n| enableEvaluation | CLI flag / request option | Turns the middleware on for this call. |\n| evaluationDomain | CLI flag / request option | Provides context to the judge model (e.g., \"Healthcare\"). |\n| NEUROLINK_EVALUATION_THRESHOLD | Env variable / loop session var | Minimum passing score; failures trigger retries or errors. |\n| NEUROLINK_EVALUATION_MODEL | Env variable / middleware config | Override the default judge model (defaults to gemini-2.5-flash). |\n| NEUROLINK_EVALUATION_PROVIDER | Env variable | Force the judge provider (google-ai by default). |\n| NEUROLINK_EVALUATION_RETRY_ATTEMPTS | Env variable | Number of re-evaluation attempts before surfacing failure. |\n| NEUROLINK_EVALUATION_TIMEOUT | Env variable | Millisecond timeout for judge requests. |\n| offTopicThreshold | Middleware config | Score below which a response is flagged as off-topic. |\n| highSeverityThreshold | Middleware config | Score threshold for triggering high-severity alerts. |\n\nSet global defaults by exporting environment variables in your .env:\n\nLoop sessions respect these values. Inside neurolink loop, use set NEUROLINK_EVALUATION_THRESHOLD 8 or unset NEUROLINK_EVALUATION_THRESHOLD to adjust the gate on the fly.\n\nBest Practices\n\nOnly enable evaluation when needed: during prompt engineering, quality regression testing, or high-stakes production calls. For routine operations, disable evaluation and rely on Analytics for zero-cost observability.\n\nPair evaluation with analytics to track cost vs. quality trends.\nLower the threshold during experimentation, then tighten once prompts stabilise.\nRegister a custom onEvaluationComplete handler to forward scores to BI systems.\nExclude massive prompts from evaluation when latency matters; analytics is zero-cost without evaluation.\n\nTroubleshooting\n\n| Issue | Fix |\n| ------------------------------------ | ---------------------------------------------------------------------------------------------------------------- |\n| Evaluation model not configured | Ensure judge provider API keys are present or set NEUROLINK_EVALUATION_PROVIDER. |\n| CLI exits with failure | Lower NEUROLINK_EVALUATION_THRESHOLD or configure the middleware with blocking: false. |\n| Evaluation takes too long | Reduce NEUROLINK_EVALUATION_RETRY_ATTEMPTS or switch to a smaller judge model (e.g., gemini-2.5-flash-lite). |\n| Off-topic false positives | Increase offTopicThreshold to a lower score (e.g., 3). |\n| JSON output missing evaluation block | Confirm --format json and --enableEvaluation are both set. |\n\nRelated Features\n\nQ4 2025 Features:\nGuardrails Middleware – Combine evaluation with content filtering for comprehensive quality control\n\nQ3 2025 Features:\nMultimodal Chat – Evaluate vision-based responses\nCLI Loop Sessions – Set evaluation threshold in loop mode\n\nDocumentation:\nAnalytics Guide – Track evaluation metrics over time\nSDK","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"","lvl3":""}}, {"objectID":"c59d0b19e9e583584722663ea949eea9bf65cf00aa0f2df76b5e9a4fd9db67ec","title":"Auto Evaluation Engine","url":"/docs/features/auto-evaluation#auto-evaluation-engine","content":"NeuroLink provides an automated quality gate that scores every response using an LLM-as-judge pipeline. Scores, rationales, and severity flags are surfaced in both CLI and SDK workflows so you can monitor drift and enforce minimum quality thresholds.","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Auto Evaluation Engine","lvl3":""}}, {"objectID":"01b6520a465deb5a7b4eeb1aec06749ae04bb7d0b842de5ca2c427437674a1ba","title":"What It Does","url":"/docs/features/auto-evaluation#what-it-does","content":"Generates a structured evaluation payload (result.evaluation) for every call with enableEvaluation: true.\nCalculates relevance, accuracy, completeness, and an overall score (1–10) using a RAGAS-style rubric.\nSupports retry loops: re-ask the provider when the score falls below your threshold.\nEmits analytics-friendly JSON so you can pipe results into dashboards.","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"What It Does","lvl3":""}}, {"objectID":"b9867fef529c2603b0108d39eec8885ea43b7954246495273ed0a341360893c5","title":"Quick Start","url":"/docs/features/auto-evaluation#quick-start","content":"Evaluation uses additional AI calls to the judge model (default: gemini-2.5-flash). Each evaluated response incurs extra API costs. For high-volume production workloads, consider sampling (e.g., evaluate 10% of requests) or disabling evaluation after quality stabilizes.","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Quick Start","lvl3":""}}, {"objectID":"1edc1e841196e2f69ef2e27658be7f3e94c9be94abdcd46c61c05867e81e8cf6","title":"Usage Examples","url":"/docs/features/auto-evaluation#usage-examples","content":"`bash","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Usage Examples","lvl3":""}}, {"objectID":"0a98da685fe25451e093ccf592763f84dafca1eccc0659ff28c0045523b8df12","title":"Baseline quality check","url":"/docs/features/auto-evaluation#baseline-quality-check","content":"npx @juspay/neurolink generate \"Draft onboarding email\" --enableEvaluation","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Baseline quality check","lvl3":""}}, {"objectID":"bfc3dd8f4e6d1705ab2eb24c83fada185e6487d5391019e400dfbcf60bee3f98","title":"Combine with analytics for observability dashboards","url":"/docs/features/auto-evaluation#combine-with-analytics-for-observability-dashboards","content":"npx @juspay/neurolink generate \"Summarise release notes\" \\\n --enableEvaluation --enableAnalytics --format json","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Combine with analytics for observability dashboards","lvl3":""}}, {"objectID":"f916a27458bca7ff3607ef8a72729bd0a59d57f5ca3338030164c7a728a73535","title":"Domain-aware evaluations shape the rubric","url":"/docs/features/auto-evaluation#domain-aware-evaluations-shape-the-rubric","content":"npx @juspay/neurolink generate \"Refactor this API\" \\\n --enableEvaluation --evaluationDomain \"Principal Engineer\"","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Domain-aware evaluations shape the rubric","lvl3":""}}, {"objectID":"29ca50ceb4b14047b9a4dcd818b7da5e6474851e2ae8049d7d79f787d90f1c6c","title":"Fail the command if the score dips below 7 (set env variable first)","url":"/docs/features/auto-evaluation#fail-the-command-if-the-score-dips-below-7-set-env-variable-first","content":"NEUROLINKEVALUATIONTHRESHOLD=7 npx @juspay/neurolink generate \"Write compliance summary\" \\\n --enableEvaluation\n\nEvaluation Summary\nOverall: 8.6/10 (Passing threshold: 7)\nRelevance: 9.0 - Accuracy: 8.5 - Completeness: 8.0\nReasoning: Response covers all requested sections with correct policy references.\n`","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Fail the command if the score dips below 7 (set env variable first)","lvl3":""}}, {"objectID":"567db9b72940faddd735e169aaabbc1fad8b48e62e9c9dd261f4f77e107c25f7","title":"Streaming with Evaluation","url":"/docs/features/auto-evaluation#streaming-with-evaluation","content":"Evaluation works in streaming mode\nEvaluation payload arrives in final chunks\nCapture the evaluation object\nAccess overall score (1-10) and sub-scores","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Streaming with Evaluation","lvl3":""}}, {"objectID":"6ef24869f412b8509e1487ea41adb2b4eeeeb0da6954aa8b4779687e51c60634","title":"Configuration Options","url":"/docs/features/auto-evaluation#configuration-options","content":"| Option | Where | Description |\n| ------------------------------------- | -------------------------------- | ------------------------------------------------------------------ |\n| enableEvaluation | CLI flag / request option | Turns the middleware on for this call. |\n| evaluationDomain | CLI flag / request option | Provides context to the judge model (e.g., \"Healthcare\"). |\n| NEUROLINK_EVALUATION_THRESHOLD | Env variable / loop session var | Minimum passing score; failures trigger retries or errors. |\n| NEUROLINK_EVALUATION_MODEL | Env variable / middleware config | Override the default judge model (defaults to gemini-2.5-flash). |\n| NEUROLINK_EVALUATION_PROVIDER | Env variable | Force the judge provider (google-ai by default). |\n| NEUROLINK_EVALUATION_RETRY_ATTEMPTS | Env variable | Number of re-evaluation attempts before surfacing failure. |\n| NEUROLINK_EVALUATION_TIMEOUT | Env variable | Millisecond timeout for judge requests. |\n| offTopicThreshold | Middleware config | Score below which a response is flagged as off-topic. |\n| highSeverityThreshold | Middleware config | Score threshold for triggering high-severity alerts. |\n\nSet global defaults by exporting environment variables in your .env:\n\nLoop sessions respect these values. Inside neurolink loop, use set NEUROLINK_EVALUATION_THRESHOLD 8 or unset NEUROLINK_EVALUATION_THRESHOLD to adjust the gate on the fly.","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Configuration Options","lvl3":""}}, {"objectID":"85b3015a29347cde7b0febb45259399fc9b0735a868c9aa811260ec636902910","title":"Best Practices","url":"/docs/features/auto-evaluation#best-practices","content":"Only enable evaluation when needed: during prompt engineering, quality regression testing, or high-stakes production calls. For routine operations, disable evaluation and rely on Analytics for zero-cost observability.\n\nPair evaluation with analytics to track cost vs. quality trends.\nLower the threshold during experimentation, then tighten once prompts stabilise.\nRegister a custom onEvaluationComplete handler to forward scores to BI systems.\nExclude massive prompts from evaluation when latency matters; analytics is zero-cost without evaluation.","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Best Practices","lvl3":""}}, {"objectID":"ec5f054ec0d1b01161d85e8ff56ec0f02115b105ce55e62ec7a9898e5a028607","title":"Troubleshooting","url":"/docs/features/auto-evaluation#troubleshooting","content":"| Issue | Fix |\n| ------------------------------------ | ---------------------------------------------------------------------------------------------------------------- |\n| Evaluation model not configured | Ensure judge provider API keys are present or set NEUROLINK_EVALUATION_PROVIDER. |\n| CLI exits with failure | Lower NEUROLINK_EVALUATION_THRESHOLD or configure the middleware with blocking: false. |\n| Evaluation takes too long | Reduce NEUROLINK_EVALUATION_RETRY_ATTEMPTS or switch to a smaller judge model (e.g., gemini-2.5-flash-lite). |\n| Off-topic false positives | Increase offTopicThreshold to a lower score (e.g., 3). |\n| JSON output missing evaluation block | Confirm --format json and --enableEvaluation are both set. |","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Troubleshooting","lvl3":""}}, {"objectID":"e612c757d282ac9f6f8526ca4d82cd51ac6711194b46cb1a3f9d770f7a5d2822","title":"Related Features","url":"/docs/features/auto-evaluation#related-features","content":"Q4 2025 Features:\nGuardrails Middleware – Combine evaluation with content filtering for comprehensive quality control\n\nQ3 2025 Features:\nMultimodal Chat – Evaluate vision-based responses\nCLI Loop Sessions – Set evaluation threshold in loop mode\n\nDocumentation:\nAnalytics Guide – Track evaluation metrics over time\nSDK API Reference – Evaluation options\nTroubleshooting – Common evaluation issues","hierarchy":{"lvl0":"Features","lvl1":"Auto Evaluation Engine","lvl2":"Related Features","lvl3":""}}, {"objectID":"64cc2ddc5d7e2ac5fe27b1dc6cf72e1c8ea4dfd8e929c76dc40a157370dcf7aa","title":"AutoResearch - Autonomous AI Experiment Engine","url":"/docs/features/autoresearch","content":"AutoResearch - Autonomous AI Experiment Engine\n\nOverview\n\nAutoResearch is an autonomous experiment loop that proposes code changes, executes experiments, evaluates results against a deterministic metric, and keeps or discards each change — running unattended for hours. Inspired by Karpathy's autoresearch concept, it lets an AI agent continuously improve a program by iterating on code, measuring outcomes, and git-committing improvements.\n\nThe system is available as both an SDK API and CLI commands, and integrates with NeuroLink's existing TaskManager for scheduled, long-running research sessions.\nPhase-gated tool access — The AI only sees tools relevant to its current phase, preventing premature actions\nGit-backed safety — Every candidate change is committed to a branch; failed experiments are reverted automatically\nDeterministic evaluation — Metrics are parsed from experiment output via regex, not LLM judgment\nTwo execution paths — Run a single cycle interactively (run-once) or schedule continuous research via TaskManager (start)\n10 typed events — Full observability into the experiment lifecycle via NeuroLink's event emitter\n\nQuick Start\n\nGet a research loop running in 5 steps:\nSet up a git repo with a training script and a research program:\nInitialize AutoResearch:\nRun a single experiment cycle:\nCheck results:\n(Optional) Schedule continuous research via TaskManager:\n\nFor SDK usage, see Direct Usage (ResearchWorker) below.\n\nCore Concepts\n\nResearch Program\n\nA Markdown document (program.md by default) that describes the research objective, constraints, and evaluation criteria. The AI reads this to understand what it should optimize and how.\n\nPhases\n\nAutoResearch operates as a state machine with 9 phases. Each phase gates which tools the AI can use:\n\n| Phase | Description | Tools Available | Forced Tool |\n| -------------------- | ------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | --------------------------- |\n| bootstrap | Read the program and understand the codebase | research_get_context, research_read_file, research_checkpoint | research_get_context |\n| baseline | Run the experiment to establish a baseline | research_run_experiment, research_parse_log, research_record, research_accept, research_checkpoint | research_run_experiment |\n| propose | Propose a code change based on context | research_get_context, research_read_file | research_get_context |\n| edit | Write the proposed changes to mutable files | research_read_file, research_write_candidate, research_diff | — |\n| commit | Git-commit the candidate change | research_commit_candidate | research_commit_candidate |\n| run | Execute the experiment | research_run_experiment | research_run_experiment |\n| evaluate | Parse the log and inspect failures | research_parse_log, research_inspect_failure | research_parse_log |\n| record | Record the result to results.tsv | research_record, research_checkpoint | research_record |\n| accept_or_revert | Keep the change or revert to the last good commit | research_accept, research_revert, research_checkpoint | — |\n\nAfter accept_or_revert, the loop returns to propose for the next experiment cycle.\n\nMetric\n\nA quantitative measure extracted from experiment output using a regex pattern. You configure:\nname — Human-readable metric name (e.g., val_bpb, accuracy)\ndirection — Whether lower or higher is better\npattern — Regex with one capture group to extract the numeric value from stdout/stderr\n\nMemory Metric (Optional)\n\nA secondary metric (e.g., peak_vram_mb) tracked for informational purposes but not used for accept/reject decisions.\n\nMutable vs Immutable Paths\nmutablePaths — Files the AI is allowed to modify (e.g., [\"train.py\"])\nimmutablePaths — Files the AI can read but must never modify (e.g., [\"prepare.py\", \"program.md\"])\n\nState\n\nResearch state is persisted to .autoresearch/state.json and includes the current phase, branch name, accepted commit, baseline/best metrics, run count, and keep count. This enables resuming ","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"","lvl3":""}}, {"objectID":"72aa194a4c03a013a45f618ca20abc182a284f7515bb95103e9f2b9ad7be979a","title":"Overview","url":"/docs/features/autoresearch#overview","content":"AutoResearch is an autonomous experiment loop that proposes code changes, executes experiments, evaluates results against a deterministic metric, and keeps or discards each change — running unattended for hours. Inspired by Karpathy's autoresearch concept, it lets an AI agent continuously improve a program by iterating on code, measuring outcomes, and git-committing improvements.\n\nThe system is available as both an SDK API and CLI commands, and integrates with NeuroLink's existing TaskManager for scheduled, long-running research sessions.\nPhase-gated tool access — The AI only sees tools relevant to its current phase, preventing premature actions\nGit-backed safety — Every candidate change is committed to a branch; failed experiments are reverted automatically\nDeterministic evaluation — Metrics are parsed from experiment output via regex, not LLM judgment\nTwo execution paths — Run a single cycle interactively (run-once) or schedule continuous research via TaskManager (start)\n10 typed events — Full observability into the experiment lifecycle via NeuroLink's event emitter","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Overview","lvl3":""}}, {"objectID":"ef4688123212ac86caf84aebb20f20b86a960ce8c8ea0f0a7fb0244e26c771f0","title":"Quick Start","url":"/docs/features/autoresearch#quick-start","content":"Get a research loop running in 5 steps:\nSet up a git repo with a training script and a research program:\n\n`bash\nmkdir /tmp/my-research && cd /tmp/my-research\ngit init","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Quick Start","lvl3":""}}, {"objectID":"3d987e0d6a0d07009bd33640598b5e12c6074ec1627780356699f7b9e9eb983e","title":"Add your training script (e.g., train.py) and a program.md describing your research goal","url":"/docs/features/autoresearch#add-your-training-script-eg-trainpy-and-a-programmd-describing-your-research-goal","content":"git add -A && git commit -m \"initial\"\nbash\nneurolink autoresearch init /tmp/my-research \\\n --tag \"run1\" \\\n --target \"train.py\" \\\n --immutable \"program.md\" \\\n --run-command \"python3 train.py\" \\\n --metric-name val_bpb \\\n --metric-pattern \"^val_bpb:\\\\s+([\\\\d.]+)\" \\\n --metric-direction lower \\\n --timeout 120\nbash\nneurolink autoresearch run-once /tmp/my-research\nbash\nneurolink autoresearch status /tmp/my-research\nneurolink autoresearch results /tmp/my-research\nbash\nneurolink autoresearch start /tmp/my-research --interval 300 --max-runs 50\nneurolink task start # Start the task worker to begin execution\n`\n\nFor SDK usage, see Direct Usage (ResearchWorker) below.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Add your training script (e.g., train.py) and a program.md describing your research goal","lvl3":""}}, {"objectID":"d129c2b2c0a84149293acd648ed8cca86458d790a2ec6a10ca1f3340eafbeac8","title":"Research Program","url":"/docs/features/autoresearch#research-program","content":"A Markdown document (program.md by default) that describes the research objective, constraints, and evaluation criteria. The AI reads this to understand what it should optimize and how.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Research Program","lvl3":""}}, {"objectID":"868aa627099b33db5b1e2945bdba78312402f1c87ca7f7384cd716e66c5876af","title":"Phases","url":"/docs/features/autoresearch#phases","content":"AutoResearch operates as a state machine with 9 phases. Each phase gates which tools the AI can use:\n\n| Phase | Description | Tools Available | Forced Tool |\n| -------------------- | ------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | --------------------------- |\n| bootstrap | Read the program and understand the codebase | research_get_context, research_read_file, research_checkpoint | research_get_context |\n| baseline | Run the experiment to establish a baseline | research_run_experiment, research_parse_log, research_record, research_accept, research_checkpoint | research_run_experiment |\n| propose | Propose a code change based on context | research_get_context, research_read_file | research_get_context |\n| edit | Write the proposed changes to mutable files | research_read_file, research_write_candidate, research_diff | — |\n| commit | Git-commit the candidate change | research_commit_candidate | research_commit_candidate |\n| run | Execute the experiment | research_run_experiment | research_run_experiment |\n| evaluate | Parse the log and inspect failures | research_parse_log, research_inspect_failure | research_parse_log |\n| record","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Phases","lvl3":""}}, {"objectID":"025b1da6f8cc2d2adc95102272d6a3bd56bb1b3b4fbdd65172b8688ee7e30174","title":"Metric","url":"/docs/features/autoresearch#metric","content":"A quantitative measure extracted from experiment output using a regex pattern. You configure:\nname — Human-readable metric name (e.g., val_bpb, accuracy)\ndirection — Whether lower or higher is better\npattern — Regex with one capture group to extract the numeric value from stdout/stderr","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Metric","lvl3":""}}, {"objectID":"00a9fe0695773b67ae2e5183d11653f6612dcd184b1cb9d148840405999e445d","title":"Memory Metric (Optional)","url":"/docs/features/autoresearch#memory-metric-optional","content":"A secondary metric (e.g., peak_vram_mb) tracked for informational purposes but not used for accept/reject decisions.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Memory Metric (Optional)","lvl3":""}}, {"objectID":"b513a82b56f8323afba12c746c37212bc7e2fae19f5fae5df33e1849cf29830b","title":"Mutable vs Immutable Paths","url":"/docs/features/autoresearch#mutable-vs-immutable-paths","content":"mutablePaths — Files the AI is allowed to modify (e.g., [\"train.py\"])\nimmutablePaths — Files the AI can read but must never modify (e.g., [\"prepare.py\", \"program.md\"])","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Mutable vs Immutable Paths","lvl3":""}}, {"objectID":"5949502e8dce0287d97e30918529356b6afe8c2e85eb4662aedf0d38f6624cb5","title":"State","url":"/docs/features/autoresearch#state","content":"Research state is persisted to .autoresearch/state.json and includes the current phase, branch name, accepted commit, baseline/best metrics, run count, and keep count. This enables resuming interrupted sessions.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"State","lvl3":""}}, {"objectID":"5b5cc2aadaac10d34eef38741c53ac76fd3eb2667ecd204bd0883296eb1f56f5","title":"Architecture","url":"/docs/features/autoresearch#architecture","content":"Follows NeuroLink's established Factory + Registry pattern with dedicated subsystems for each concern.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Architecture","lvl3":""}}, {"objectID":"4c702e6a032c928b62e1c03b2c9bc18288c1fee3d59d74e693d98487457f4a16","title":"How It Fits Into NeuroLink","url":"/docs/features/autoresearch#how-it-fits-into-neurolink","content":"AutoResearch integrates at two levels:\nDirect SDK usage — Import ResearchWorker from @juspay/neurolink/autoresearch and call runExperimentCycle() directly\nTaskManager integration — The autoresearchTaskExecutor routes scheduled tasks to ResearchWorker, advancing phases after each generate() call","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"How It Fits Into NeuroLink","lvl3":""}}, {"objectID":"f3110b05bf57c5303f7f94bb4883d1f22073cacd5dcc2cb815e222817647afef","title":"Direct Usage (ResearchWorker)","url":"/docs/features/autoresearch#direct-usage-researchworker","content":"For full control, import ResearchWorker from the @juspay/neurolink/autoresearch subpath export:","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Direct Usage (ResearchWorker)","lvl3":""}}, {"objectID":"0995cb244e4c79e4311e8218b42a20a98206f01097c71af50712b092d794de6b","title":"Scheduled Usage (TaskManager)","url":"/docs/features/autoresearch#scheduled-usage-taskmanager","content":"For continuous, unattended research, use TaskManager integration:","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Scheduled Usage (TaskManager)","lvl3":""}}, {"objectID":"5381ceddc7b1cea7e96091a1e7a5727ecff77b8c6e30be885d9eed3767b6423f","title":"CLI Commands","url":"/docs/features/autoresearch#cli-commands","content":"All CLI commands are under the neurolink autoresearch namespace.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"CLI Commands","lvl3":""}}, {"objectID":"86fb015cb2f4ef9e81caf130f19e3324ee2799073f9ab33f04f68eddebe94a0d","title":"Initialize a Research Session","url":"/docs/features/autoresearch#initialize-a-research-session","content":"This creates .autoresearch/state.json and .autoresearch/config.json in the repo and creates a git branch (autoresearch/).","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Initialize a Research Session","lvl3":""}}, {"objectID":"0c4d18f2edb9b22660fc839b1b91d5cd6d8a1868f0cfc76c556b028ccd33a4cb","title":"Start Scheduled Research (via TaskManager)","url":"/docs/features/autoresearch#start-scheduled-research-via-taskmanager","content":"`bash","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Start Scheduled Research (via TaskManager)","lvl3":""}}, {"objectID":"b7b2b37dc67429ccb90f23f3e37fe71fc8567246ada0dd584e2daaee233c0bc4","title":"300 seconds between ticks, stop after 100 experiments","url":"/docs/features/autoresearch#300-seconds-between-ticks-stop-after-100-experiments","content":"neurolink autoresearch start /path/to/repo --interval 300 --max-runs 100\n\n\n> **Two-step process:** neurolink autoresearch start saves an autoresearch task to the task store but does **not** begin execution by itself. You must separately start the task worker with neurolink task start` to begin processing scheduled tasks. The task worker picks up saved tasks and executes experiment cycles on the configured interval.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"300 seconds between ticks, stop after 100 experiments","lvl3":""}}, {"objectID":"2a5ccec1911fef4c984f3d9b83dbd8011362ea201a91202674dde526e6ad19b8","title":"Manage Scheduled Tasks","url":"/docs/features/autoresearch#manage-scheduled-tasks","content":"Note: These commands update the task's stored status in the task store (e.g., marking it as paused or cancelled). They do not directly signal a running task worker process. The task worker checks stored status before each cycle and will honor the updated state on its next tick.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Manage Scheduled Tasks","lvl3":""}}, {"objectID":"f2aa6c2270d348d4f275a4aac232d3aacbe6af5fcdf4ada5aea2a8259a2601f3","title":"Reset State","url":"/docs/features/autoresearch#reset-state","content":"This deletes the entire .autoresearch/ directory (state, config, and all local artifacts). The git branch and committed experiment code on that branch are preserved.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Reset State","lvl3":""}}, {"objectID":"66a4ee145ab8cade07ba0d02b8755dc140bca20df582122afe51e7358dd79f56","title":"Research Tools","url":"/docs/features/autoresearch#research-tools","content":"AutoResearch registers 12 tools that the AI uses during experiment cycles. Tool availability is gated by the current phase.\n\n| Tool | Description |\n| --------------------------- | -------------------------------------------------------- |\n| research_get_context | Read the research program, current state, and results |\n| research_read_file | Read a file from the repository (respects path policies) |\n| research_write_candidate | Write changes to a mutable file |\n| research_diff | Show the git diff of pending changes |\n| research_commit_candidate | Git-commit the current candidate changes |\n| research_run_experiment | Execute the run command and capture output |\n| research_parse_log | Parse experiment output for metrics |\n| research_inspect_failure | Inspect why an experiment crashed or timed out |\n| research_record | Record an experiment result to results.tsv |\n| research_accept | Accept the current candidate (update accepted commit) |\n| research_revert | Revert to the last accepted commit |\n| research_checkpoint | Save current state to disk |","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Research Tools","lvl3":""}}, {"objectID":"eac617af15363c3df0e6d861fb30e9d676bab479d83e0d59152860df2d254578","title":"Events","url":"/docs/features/autoresearch#events","content":"AutoResearch emits events via NeuroLink's TypedEventEmitter. Subscribe to these for monitoring, alerting, or integration.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Events","lvl3":""}}, {"objectID":"159ddb43953deac106cf46635100155e00b9d96f8391618bde671adb8eb85a94","title":"Artifacts","url":"/docs/features/autoresearch#artifacts","content":"AutoResearch produces the following files in the repository:\n\n| Path | Description |\n| ----------------------------- | -------------------------------------------------- |\n| .autoresearch/state.json | Current research state (phase, metrics, run count) |\n| .autoresearch/config.json | Persisted configuration from init |\n| .autoresearch/runs.jsonl | JSONL audit log of all experiment records |\n| results.tsv | Tab-separated experiment results log |\n| run.log | Stdout/stderr from the last experiment run |\n| autoresearch/ (branch) | Git branch containing all experiment commits |","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Artifacts","lvl3":""}}, {"objectID":"f7c1e4c5a6e5a862688352e79b2668305168cb4f6ad394d9ad82ec491ea9f9c4","title":"results.tsv Format","url":"/docs/features/autoresearch#resultstsv-format","content":"Note: The second column header is the metric name from your config (e.g., val_bpb, accuracy). The header is generated dynamically as commit\\t{metric.name}\\tmemory_gb\\tstatus\\tdescription.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"results.tsv Format","lvl3":""}}, {"objectID":"91b0eada144a704995615affe125baf29705152cd7d16c64e544ec58df46e103","title":"ResearchConfig Fields","url":"/docs/features/autoresearch#researchconfig-fields","content":"| Field | Type | Default | Description |\n| ---------------- | -------------------- | ---------------------------- | -------------------------------------- |\n| repoPath | string | required | Absolute path to the git repository |\n| programPath | string | \"program.md\" | Research program document path |\n| mutablePaths | string[] | required | Files the AI can modify |\n| immutablePaths | string[] | [] | Files the AI can read but not modify |\n| resultsPath | string | \"results.tsv\" | Path for the results log |\n| statePath | string | \".autoresearch/state.json\" | Path for persisted state |\n| runCommand | string | required | Command to run the experiment |\n| logPath | string | \"run.log\" | Path for experiment stdout/stderr |\n| metric | MetricConfig | required | Primary metric configuration |\n| memoryMetric | MemoryMetricConfig | undefined | Optional secondary metric |\n| timeoutMs | number | 600_000 (10 min) | Per-experiment timeout in milliseconds |\n| branchPrefix | string | \"autoresearch/\" | Git branch prefix |\n| provider | string | SDK default | AI provider override |\n| model | string | SDK default | Model override |\n| maxExperiments | number | undefined (unlimited) | Maximum experiment count |\n| thinkingLevel | ThinkingLevel | \"medium\" | Thinking level for LLM calls |","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"ResearchConfig Fields","lvl3":""}}, {"objectID":"212a722ba805ac6d84258d3e6f6fb8f5c367f7fced358ae09f4288e9800f2d19","title":"MetricConfig Fields","url":"/docs/features/autoresearch#metricconfig-fields","content":"| Field | Type | Description |\n| ----------- | --------------------- | -------------------------------------------------- |\n| name | string | Human-readable metric name |\n| direction | \"lower\" \\| \"higher\" | Whether lower or higher values are better |\n| pattern | string | Regex with exactly one capture group for the value |","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"MetricConfig Fields","lvl3":""}}, {"objectID":"4e671d41e0022844c2a9d513bc1a052567e7af6c95dae389788bc122b8d606e8","title":"Experiment stuck in bootstrap phase","url":"/docs/features/autoresearch#experiment-stuck-in-bootstrap-phase","content":"The AI's first action in bootstrap is forced to research_get_context. If the program file doesn't exist or is empty, the AI has no context to work with. Ensure program.md exists in the repo with a clear research objective.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Experiment stuck in bootstrap phase","lvl3":""}}, {"objectID":"07f669c86c8277523e9606fafa1c286ef3783efb3adc13443780195a77b42c49","title":"Metric not being parsed","url":"/docs/features/autoresearch#metric-not-being-parsed","content":"Verify your regex pattern matches the experiment output. Test it:\n\nThe pattern must have exactly one capture group. Common issue: escaping \\d — in CLI flags and JSON, you need \\\\d.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Metric not being parsed","lvl3":""}}, {"objectID":"8e6a838552d30ae62e2bee8544f0b12d87cadae626fbcf91ae27b810bd3304b1","title":"Experiments timing out","url":"/docs/features/autoresearch#experiments-timing-out","content":"Increase timeoutMs (default: 600,000ms / 10 minutes). For long-running training scripts, set appropriately:","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Experiments timing out","lvl3":""}}, {"objectID":"f283ec766223fa981594075b52d5edf537ebfb630e7ba1343782de0003ea2cd2","title":"Git revert failures","url":"/docs/features/autoresearch#git-revert-failures","content":"AutoResearch requires a clean working tree for reverts. If you have uncommitted changes outside mutable paths, commit or stash them first.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"Git revert failures","lvl3":""}}, {"objectID":"6e1d3efcbcd2958a47bd91d71b60a4f1fb9bae5db434a0f80b84c8c207a80af0","title":"State file corruption","url":"/docs/features/autoresearch#state-file-corruption","content":"If .autoresearch/state.json becomes invalid, use reset to clear it:\n\nThen re-initialize with init. Your git branch and committed experiments are preserved.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"State file corruption","lvl3":""}}, {"objectID":"3f9da31ccb4193e87888801724b536c1b3d42e0265faca0faf47b0632b1283a2","title":"FAQ","url":"/docs/features/autoresearch#faq","content":"Q: What providers work with AutoResearch?\nA: Any provider supported by NeuroLink. The AI needs tool-calling capability, so use models that support function calling (GPT-4o, Claude Sonnet, Gemini Flash/Pro, etc.).\n\nQ: Can I use AutoResearch with Python/ML training scripts?\nA: Yes — that's the primary use case. Set runCommand to your training script and configure the metric pattern to parse your output format.\n\nQ: How does the AI decide what to change?\nA: The AI reads the research program (program.md), the current code, and past experiment results. It proposes changes based on this context, using its understanding of the domain. The quality of your program document directly affects the quality of proposals.\n\nQ: What happens if the experiment crashes?\nA: The crash is detected via non-zero exit code or output parsing. The result is recorded as crash status, the candidate is reverted, and the loop continues with a new proposal.\n\nQ: Can I run AutoResearch on a remote server?\nA: Yes. Use the TaskManager integration with BullMQ (Redis) for scheduled runs. The repo must be accessible on the machine running NeuroLink.\n\nQ: How do I stop a running experiment?\nA: For run-once: Ctrl+C. For scheduled tasks: neurolink autoresearch stop or await neurolink.tasks.delete(taskId).\n\nQ: Is there a limit on experiment count?\nA: Set maxExperiments in config or --max-runs in CLI. Without a limit, research continues indefinitely until manually stopped.\n\nQ: Can multiple AutoResearch sessions run on the same repo?\nA: Not recommended. Each session operates on its own git branch, but concurrent filesystem operations on the same repo can conflict. Use separate working directories or git worktrees for parallel sessions.\n\nQ: Does AutoResearch work with structuredOutput?\nA: No. AutoResearch uses tool calling (function calling) which is mutually exclusive with structuredOutput JSON schemas on Gemini providers. This is an API limitation, not a bug.","hierarchy":{"lvl0":"Features","lvl1":"AutoResearch - Autonomous AI Experiment Engine","lvl2":"FAQ","lvl3":""}}, {"objectID":"b62fe03b51882b07b58c3e35542449b69a1cdfa87de89867ee32b32e4a915257","title":"The model catalogue","url":"/docs/features/classifier-router-catalog","content":"The model catalogue\n\nThe classifier router has always routed\nacross a pool you declare — the set of models you are willing to be billed\nfor is not something NeuroLink can invent. The catalogue is an opt-in way to\nwiden that pool: it builds candidates from the model registry, intersected\nwith the credentials this host actually holds, and hands them to the\njev strategy as one choice\nquestion.\n\nThe registry is 64 models across 7 providers (openai 21, anthropic 19,\nazure 7, ollama 6, bedrock 5, mistral 4, google-ai 2), with 132 aliases on top\nof those 64 ids — only a subset of the providers NeuroLink can call. The\ncatalogue is strictly an addition to a declared pool, never a replacement\nfor one: a host routing over LiteLLM, OpenRouter, an OpenAI-compatible\nendpoint, or anything self-hosted still declares those members by hand, and\nenrichCandidate ranks declared and catalogue members together on one scale\nrather than sorting them into separate buckets. Enabling catalog widens the\npool for the 7 providers it knows; it does not make the rest of the\nregistered providers appear.\n\nThe degradation contract. catalog.enabled defaults to unset, and\nbuildModelCatalog() returns [] when it is. Nothing about routing changes\nuntil you turn it on: the declared pool remains the only source of\ncandidates.\n\nA declared pool and an enabled catalog combine: declared members win on a\nduplicate id (or provider/model when no id is set), so a hand-declared\noverride always beats the catalogue's own entry for the same model.\n\nBuilding the routable pool\n\nbuildModelCatalog() starts from every model the registry knows and removes:\ndeprecated models, unless includeDeprecated: true\nproviders this host has no reachable credentials for (see below)\nproviders not in catalog.providers, when that list is set\nmodels whose context window is below catalog.minContextWindow\n\nReachability is deliberately permissive, in two directions. A provider\nwhose credentials resolve through an external chain — Bedrock's AWS default\nchain, Vertex's several auth paths — is kept even though no single env var\nproves it is configured; a local runtime with no credential at all (Ollama, LM\nStudio) is likewise kept. What this actually filters out is the common case: a\ncloud provider with one named API-key env var that is simply unset. The worst\ncase of being too permissive is a candidate that fails at call time and falls\nback, which the router already handles.\n\nWhat is left is capped to maxModels (default 120) by a tier-neutral merit\nscore — tierScore(model, \"moderate\") — so a truncated list, if there is one,\nkeeps the models most likely to be broadly useful rather than whichever the\nregistry happened to list first. The default never truncates today: the\nwhole registry is 64 models, well under the cap. maxModels is a guard\nagainst a future registry that outgrows what a single question can carry, not\na limit anyone is currently hitting.\n\nOne line per model\n\nEach surviving model becomes a ClassifierCandidate and renderCandidate()\nturns it into one terse line for jev's criteria map. The order and the\nprecedence are both deliberate:\ndescription always renders first, when present.\ntiers, when declared, renders next as intended for\ntrivial/simple tasks — the host's most direct statement of where a\n model belongs.\nContext window and capability flags always render. These are facts\n about the model (\"accepts images\", \"128K context\"), not opinions about\n how good it is, so nothing suppresses them.\nDeclared cost/quality replace the registry's own opinion, rather\n than sitting beside it. When either is set, the line adds capability N\n(higher is more capable) / relative cost N (lower is cheaper) and\n suppresses the registry's price, speed bucket, quality bucket, and\n \"strong at …\" use-case scores entirely. When neither is set, the registry\n fills all of that in exactly as it always did.\n\nTwo real renders, from the same model, show the difference:\n\nThis precedence exists because the registry's opinion used to win, and a\nlive measurement showed it silently overriding the host's own routing\nintent. A pool member declared exactly the second line above — an explicit\nstatement that this model is for rote work — but the registry rates the\nsame underlying model highly on general benchmarks, so the old rendering\nappended its own \"high quality\" and \"strong at coding, analysis, reasoning\"\non top. Five registry clauses against one line of host prose, and the host\nlost: a hard concurrency-bug task routed to the cheap model in 5 of 8 runs,\nbecause the model-pick question asks for \"the cheapest one that can still\ncomplete this request correctly\" and had just been told the cheap one was\nhigh quality and strong at reasoning. The declared quality: 2 never\nreached the model at all — only description did. After the fix, the same\n15-prompt suite (trivial/simple/conversational/hard/expert) routed 15/15 to\nthe intended pool member, and the hard prompt went 8/8 to the capable model\n(previously 3/8","hierarchy":{"lvl0":"Features","lvl1":"The model catalogue","lvl2":"","lvl3":""}}, {"objectID":"d64b5f675ca9b47c7f4d4861bb339a5e6948e3d44b307d80ee1ee80cae9dece1","title":"The model catalogue","url":"/docs/features/classifier-router-catalog#the-model-catalogue","content":"The classifier router has always routed\nacross a pool you declare — the set of models you are willing to be billed\nfor is not something NeuroLink can invent. The catalogue is an opt-in way to\nwiden that pool: it builds candidates from the model registry, intersected\nwith the credentials this host actually holds, and hands them to the\njev strategy as one choice\nquestion.\n\nThe registry is 64 models across 7 providers (openai 21, anthropic 19,\nazure 7, ollama 6, bedrock 5, mistral 4, google-ai 2), with 132 aliases on top\nof those 64 ids — only a subset of the providers NeuroLink can call. The\ncatalogue is strictly an addition to a declared pool, never a replacement\nfor one: a host routing over LiteLLM, OpenRouter, an OpenAI-compatible\nendpoint, or anything self-hosted still declares those members by hand, and\nenrichCandidate ranks declared and catalogue members together on one scale\nrather than sorting them into separate buckets. Enabling catalog widens the\npool for the 7 providers it knows; it does not make the rest of the\nregistered providers appear.\n\nThe degradation contract. catalog.enabled defaults to unset, and\nbuildModelCatalog() returns [] when it is. Nothing about routing changes\nuntil you turn it on: the declared pool remains the only source of\ncandidates.\n\nA declared pool and an enabled catalog combine: declared members win on a\nduplicate id (or provider/model when no id is set), so a hand-declared\noverride always beats the catalogue's own entry for the same model.","hierarchy":{"lvl0":"Features","lvl1":"The model catalogue","lvl2":"The model catalogue","lvl3":""}}, {"objectID":"071858e4fe13a1d63ea7f7c9dc52e1d185fcca54e327ec105d54cc20fea25543","title":"Building the routable pool","url":"/docs/features/classifier-router-catalog#building-the-routable-pool","content":"buildModelCatalog() starts from every model the registry knows and removes:\ndeprecated models, unless includeDeprecated: true\nproviders this host has no reachable credentials for (see below)\nproviders not in catalog.providers, when that list is set\nmodels whose context window is below catalog.minContextWindow\n\nReachability is deliberately permissive, in two directions. A provider\nwhose credentials resolve through an external chain — Bedrock's AWS default\nchain, Vertex's several auth paths — is kept even though no single env var\nproves it is configured; a local runtime with no credential at all (Ollama, LM\nStudio) is likewise kept. What this actually filters out is the common case: a\ncloud provider with one named API-key env var that is simply unset. The worst\ncase of being too permissive is a candidate that fails at call time and falls\nback, which the router already handles.\n\nWhat is left is capped to maxModels (default 120) by a tier-neutral merit\nscore — tierScore(model, \"moderate\") — so a truncated list, if there is one,\nkeeps the models most likely to be broadly useful rather than whichever the\nregistry happened to list first. The default never truncates today: the\nwhole registry is 64 models, well under the cap. maxModels is a guard\nagainst a future registry that outgrows what a single question can carry, not\na limit anyone is currently hitting.","hierarchy":{"lvl0":"Features","lvl1":"The model catalogue","lvl2":"Building the routable pool","lvl3":""}}, {"objectID":"2cad48473fb9c200377fe1a4b2f1e1feea861afe5ae7ee9a53caf6cbe313cc4a","title":"One line per model","url":"/docs/features/classifier-router-catalog#one-line-per-model","content":"Each surviving model becomes a ClassifierCandidate and renderCandidate()\nturns it into one terse line for jev's criteria map. The order and the\nprecedence are both deliberate:\ndescription always renders first, when present.\ntiers, when declared, renders next as intended for\ntrivial/simple tasks — the host's most direct statement of where a\n model belongs.\nContext window and capability flags always render. These are facts\n about the model (\"accepts images\", \"128K context\"), not opinions about\n how good it is, so nothing suppresses them.\nDeclared cost/quality replace the registry's own opinion, rather\n than sitting beside it. When either is set, the line adds capability N\n(higher is more capable) / relative cost N (lower is cheaper) and\n suppresses the registry's price, speed bucket, quality bucket, and\n \"strong at …\" use-case scores entirely. When neither is set, the registry\n fills all of that in exactly as it always did.\n\nTwo real renders, from the same model, show the difference:\n\n`","hierarchy":{"lvl0":"Features","lvl1":"The model catalogue","lvl2":"One line per model","lvl3":""}}, {"objectID":"ed0bc85ff16693c3ce8715b098e802cd31abaf513fd4a5e1ec8305d00a5e44a9","title":"No declared cost/quality — the registry fills in:","url":"/docs/features/classifier-router-catalog#no-declared-costquality-the-registry-fills-in","content":"GPT-4 Omni Mini; 128K context; 0.01c per 1K in; fast speed; high quality; supports vision, tools, reasoning, code, multimodal; strong at coding, analysis, conversation, reasoning, translation, summarization","hierarchy":{"lvl0":"Features","lvl1":"The model catalogue","lvl2":"No declared cost/quality — the registry fills in:","lvl3":""}}, {"objectID":"5ade37aef4d86ff4bbe4e3c1ecbe91426ad835a5bb4b854e5cd18bb18fb76b18","title":"Declared quality: 2, cost: 1 — the registry's opinion is suppressed:","url":"/docs/features/classifier-router-catalog#declared-quality-2-cost-1-the-registrys-opinion-is-suppressed","content":"Cheap and fast; rote edits and simple lookups; 128K context; capability 2 (higher is more capable); relative cost 1 (lower is cheaper); supports vision, tools, reasoning, code, multimodal\n\n\n**This precedence exists because the registry's opinion used to win, and a\nlive measurement showed it silently overriding the host's own routing\nintent.** A pool member declared exactly the second line above — an explicit\nstatement that this model is for rote work — but the registry rates the\nsame underlying model highly on general benchmarks, so the old rendering\nappended its own \"high quality\" and \"strong at coding, analysis, reasoning\"\non top. Five registry clauses against one line of host prose, and the host\nlost: a hard concurrency-bug task routed to the cheap model in 5 of 8 runs,\nbecause the model-pick question asks for \"the cheapest one that can still\ncomplete this request correctly\" and had just been told the cheap one was\nhigh quality and strong at reasoning. The declared quality: 2 never\nreached the model at all — only description did. After the fix, the same\n15-prompt suite (trivial/simple/conversational/hard/expert) routed 15/15 to\nthe intended pool member, and the hard prompt went 8/8 to the capable model\n(previously 3/8).\n\n**Every catalogue-built candidate takes the \"declared\" branch, not just\nhand-written ones.** buildModelCatalog() fills in cost and quality for\nevery model it selects (mapped from the registry's own bucket — high\nbecomes 3, for instance), and that happens _before_ the merged pool\nreaches renderCandidate(). So the richer registry-style line above (real\nprice, speed bucket, quality bucket, \"strong at …\") is only ever reached by\na **hand-declared** member that leaves both cost and quality unset. A\nmodel sourced from the catalogue always renders with the terse\ncapability N / relative cost N phrasing — never with the registry's own\nuse-case scores.","hierarchy":{"lvl0":"Features","lvl1":"The model catalogue","lvl2":"Declared quality: 2, cost: 1 — the registry's opinion is suppressed:","lvl3":""}}, {"objectID":"cac106bc67990b1f4b93d0096370fca3aa0e30de9da3db2bb3636813f4dd6b7d","title":"A choice over N models is a ranking of N","url":"/docs/features/classifier-router-catalog#a-choice-over-n-models-is-a-ranking-of-n","content":"Because readDecisionChoice returns every option's probability, one choice\nquestion over the whole catalogue doesn't just name a winner — it ranks all N\ncandidates by how likely each is to be the right pick. This is what makes\npicking from up to maxModels (120 by default; the whole registry today is 64) a single request rather than N binary questions.","hierarchy":{"lvl0":"Features","lvl1":"The model catalogue","lvl2":"A choice over N models is a ranking of N","lvl3":""}}, {"objectID":"a0961baceadf689ad6859272fa26f697f86c61f66d83070a78eae6af11db354e","title":"Deterministic fallback ranking","url":"/docs/features/classifier-router-catalog#deterministic-fallback-ranking","content":"Every other decide consumer in this codebase falls back to _what the code\nalready did_. There was no prior \"pick from the whole registry\" behaviour to\nfall back to, so the catalogue needed its own: rankCatalogue() runs whenever\nno decision provider is configured, the call fails, or the difficulty tier's own\ntop-of-pool choice is what's needed (selectModels() always computes it, even\nwhen jev's pick clears its bar, as the fallback list that ships alongside the\nwinner).\n\nThe ranking is tierScore() — a deterministic formula per difficulty:\n\nsuitability reads the registry's own 1–10 score for the dimension that tier\ncares about (conversation for trivial/simple, coding for moderate,\nreasoning for hard/expert). tierCostWeight is what makes this a real\nranker rather than a sort by price: at trivial it is 1 (the cheapest\nadequate model wins outright), at expert it is 0.05 (price is nearly\nirrelevant). This never consults the network and is the ordering the decision\nmodel's own pick is compared against when deciding whether it clears its bar.","hierarchy":{"lvl0":"Features","lvl1":"The model catalogue","lvl2":"Deterministic fallback ranking","lvl3":""}}, {"objectID":"b0a5b78dc4cfcabcd52b58ea94936ee81e97b59b5907f058ccba74881a7aeacd","title":"Context-window filtering","url":"/docs/features/classifier-router-catalog#context-window-filtering","content":"Nothing in routing read maxContextTokens before this. Two independent checks\nnow do:\nrankCatalogue() filters candidates whose contextWindow is below the\n request's estimated input tokens, applied only when it would leave something\n behind (an empty result falls back to the unfiltered pool rather than\n routing nowhere).\nThe classifier's own pick is separately vetoed. Even when jev picks a\n model directly, fitsRequest() checks whether that specific model's window\n can hold the request. If it can't, the pick is dropped regardless of\n confidence — this is a hard provider error, not a degraded answer, because\n an oversized request against a real model puts that model into ModelPool's\n permanent (10-year) cooldown. A wrong guess here is not \"less accurate,\" it\n is unrecoverable for the life of the process.","hierarchy":{"lvl0":"Features","lvl1":"The model catalogue","lvl2":"Context-window filtering","lvl3":""}}, {"objectID":"e34966644db91aef39b46ef5c2f2fa259e9c828895197c9b2260a07c6e640bce","title":"What this is bad at","url":"/docs/features/classifier-router-catalog#what-this-is-bad-at","content":"Registry quality is coarse, and the catalogue path never shows its own\n work. Auto-enriched quality is a 3-bucket scale (high/medium/low)\n before buildModelCatalog() turns it into 1/2/3; two \"high\" models\n cannot be separated on capability alone. Worse for this page's topic: because\n the catalogue always populates cost/quality, its rendered line never\n shows the registry's own price, speed bucket, or \"strong at …\" scores — only\n the terse capability N / relative cost N form. Declare tiers/quality\n on a hand-declared pool member for finer control over the number itself; there\n is no way to get the richer rendering for a catalogue-sourced model.\nA catalogue candidate's \"relative cost\" is real pricing wearing a relative\n label. buildModelCatalog() seeds it from inputCostPer1K + outputCostPer1K\n — a small decimal like 0.00074 — not a small integer a host would typically\n pick for a hand-declared cost. The number is still correct and still never\n rendered as currency, but it does not compare cleanly against a hand-declared\n member's cost: 1 in the same pool, since one is a real price and the other\n is an arbitrary scale.\nUnknown-to-the-registry models rank on relative numbers, not real prices.\n A model the registry doesn't know (self-hosted, brand-new) keeps whatever\n cost/quality the host declared, compared only against other declared\n values on the same relative scale — never rendered as a dollar figure it\n isn't.\nA wide catalogue is still one choice question, and the cap is hard, not\n smart, when it does bind. Every model adds tokens to that single question;\n maxModels is the safeguard, and a model past it would simply never be\n offered rather than offered with lower priority. Today this is theoretical —\n the registry is 64 models against a default cap of 120, so nothing is\n dropped — but the mechanism has no ranking behavior for the day a registry\n does exceed it.\nPermissive reachability means occasional dead candidates.","hierarchy":{"lvl0":"Features","lvl1":"The model catalogue","lvl2":"What this is bad at","lvl3":""}}, {"objectID":"2d7354001109ce1051977450cf365d1f2a8e13b345ad206ee23b1f5466228454","title":"See also","url":"/docs/features/classifier-router-catalog#see-also","content":"Classifier Router\nModel routing with a decision model\nThe decide inference type","hierarchy":{"lvl0":"Features","lvl1":"The model catalogue","lvl2":"See also","lvl3":""}}, {"objectID":"57703ab4e28ce3ad7f4c83d5d3e43f1b1d5541cbb2ac1d6f10f7a057046f0e1b","title":"Model routing with a decision model","url":"/docs/features/classifier-router-jev-strategy","content":"Model routing with a decision model\n\nThe classifier router has a jev strategy: one\ndecision-model round trip answers difficulty,\nrequired capabilities, risk and the model pick simultaneously, with a\ncalibrated confidence on each. This page is the strategy's own mechanics —\nclassifier-router.md covers the router as a whole.\n\nThe degradation contract. With no decision provider configured,\nresolveStrategy() resolves auto to heuristic, exactly as it always did.\nclassifyJev() itself never throws: any failure, timeout, or malformed answer\nfalls back to classifyHeuristic(). Configuring a decision provider (for example TYPESAFE_API_KEY or\nAI_GATEWAY_API_KEY) upgrades routing; it cannot make routing worse than before\nthe key existed. The same holds for PERPLEXITY_API_KEY, which is also the\nPerplexity text provider's key, and for CLOUDFLARE_API_KEY with\nCLOUDFLARE_ACCOUNT_ID, which are also the Cloudflare Workers AI text provider's\ntoken and account id.\n\nWhat goes into the state\n\nclassifyJev() sends the request truncated to 8000 characters, plus four\nsignals the caller already has on hand:\n\nAlongside it, one batch asks: difficulty (a choice over the five tiers),\nneeds_vision / needs_tools / needs_reasoning (boolean), risky\n(boolean), context (a score — see\nper-request context budget), and, only when the\npool has more than one member, model (a choice over the pool, rendered by\nthe catalogue). All of this rides in\none request. On TypeSafe that takes ~400ms, because latency is flat in question\ncount; on Perplexity each further question adds about 65 ms, up to 128 (see\nits guide); on\nCloudflare Clef a small request took 0.3 to 1.0 s on 2026-10-03 (see\nits guide). See\nthe batching rule.\n\nThe Cloudflare Clef endpoint ignores state text past about 2,048 tokens, and\nNeuroLink refuses a state it estimates at more than 1,500 tokens with\nmax_tokens_exceeded. A long request is therefore refused there, and the\nheuristic classifier's tier stands, as it does on any failure.\n\nThe difficulty rubric\n\nFive tiers, ordered easiest to hardest, worded about the shape of the work —\nthe model is never told a provider or model name:\n\n| Tier | Criterion |\n| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |\n| trivial | Mechanical and local: rename a symbol, fix a typo, add an import, run one named command, or answer something already stated. |\n| simple | A small localised change or a direct factual answer. One file, one obvious approach. |\n| moderate | Ordinary engineering: implement a well-specified change across a few files, write tests, fix a clearly described bug, review a small diff. |\n| hard | Deep reasoning: architecture and design, debugging a failure whose cause is unknown, security analysis, concurrency, cross-system refactors. |\n| expert | Frontier-level work: ambiguous requirements, novel design with no established pattern, or analysis where a wrong answer is costly and hard to detect. |\n\nAsymmetric confidence bars, and why they differ\n\nA verdict harder than the neutral tier (moderate) spends more if wrong; a\nverdict easier than neutral spends less if wrong. Those are not the same\nmistake:\nRouting a simple task to an expensive model wastes money. Cheap to be wrong\n about, so the bar to route up is low: minUpgradeConfidence defaults to\n 0.3.\nRouting a hard task to a weak model produces a wrong answer. Expensive to be\n wrong about, so the bar to route down is high: minDowngradeConfidence\n defaults to 0.6.\n\nBelow the applicable bar, the difficulty verdict is discarded and the heuristic\nclassifier's tier stands instead — not a downgraded jev answer, the ordinary\nzero-cost fallback.\n\nOne case skips both bars: a risky reading above 0.7 forces the tier to at\nleast hard, unconditionally. Risk can only ever raise the tier, never lower\none, and it is not itself gated by confidence.\n\nAsk about the act, not the subject\n\nThe risk question is not \"does this touch production, money, or credentials\":\n\nCarrying out this request would itself change production, move real money,\nexpose credentials, or alter data that cannot be restored. Writing or testing\ncode that deals with such things, without running it against the real system,\ndoes not count.\n\nThe naive phrasing was tried first and scored high on ordinary code that merely\nconcerns those things — \"add a refund endpoint that calls Stripe\" — which\nwould have escalated every such request to the most expensive tier. The second\nsentence is what separates writing the code from running it against something\nreal.\n\nThe model pick faces a bar too — but a different one\n\nWhen the pool has more than one member, jev is also asked to ch","hierarchy":{"lvl0":"Features","lvl1":"Model routing with a decision model","lvl2":"","lvl3":""}}, {"objectID":"bd360e5abf4e93403d1fb23a27c946fed1b86d8d8863a98519338332ca726639","title":"Model routing with a decision model","url":"/docs/features/classifier-router-jev-strategy#model-routing-with-a-decision-model","content":"The classifier router has a jev strategy: one\ndecision-model round trip answers difficulty,\nrequired capabilities, risk and the model pick simultaneously, with a\ncalibrated confidence on each. This page is the strategy's own mechanics —\nclassifier-router.md covers the router as a whole.\n\nThe degradation contract. With no decision provider configured,\nresolveStrategy() resolves auto to heuristic, exactly as it always did.\nclassifyJev() itself never throws: any failure, timeout, or malformed answer\nfalls back to classifyHeuristic(). Configuring a decision provider (for example TYPESAFE_API_KEY or\nAI_GATEWAY_API_KEY) upgrades routing; it cannot make routing worse than before\nthe key existed. The same holds for PERPLEXITY_API_KEY, which is also the\nPerplexity text provider's key, and for CLOUDFLARE_API_KEY with\nCLOUDFLARE_ACCOUNT_ID, which are also the Cloudflare Workers AI text provider's\ntoken and account id.","hierarchy":{"lvl0":"Features","lvl1":"Model routing with a decision model","lvl2":"Model routing with a decision model","lvl3":""}}, {"objectID":"a6b27c365174a477eafb6835b9ab5990c7434acd58cb21534f8419ea21c63dd5","title":"What goes into the state","url":"/docs/features/classifier-router-jev-strategy#what-goes-into-the-state","content":"classifyJev() sends the request truncated to 8000 characters, plus four\nsignals the caller already has on hand:\n\nAlongside it, one batch asks: difficulty (a choice over the five tiers),\nneeds_vision / needs_tools / needs_reasoning (boolean), risky\n(boolean), context (a score — see\nper-request context budget), and, only when the\npool has more than one member, model (a choice over the pool, rendered by\nthe catalogue). All of this rides in\none request. On TypeSafe that takes ~400ms, because latency is flat in question\ncount; on Perplexity each further question adds about 65 ms, up to 128 (see\nits guide); on\nCloudflare Clef a small request took 0.3 to 1.0 s on 2026-10-03 (see\nits guide). See\nthe batching rule.\n\nThe Cloudflare Clef endpoint ignores state text past about 2,048 tokens, and\nNeuroLink refuses a state it estimates at more than 1,500 tokens with\nmax_tokens_exceeded. A long request is therefore refused there, and the\nheuristic classifier's tier stands, as it does on any failure.","hierarchy":{"lvl0":"Features","lvl1":"Model routing with a decision model","lvl2":"What goes into the state","lvl3":""}}, {"objectID":"ed3d90e1e698e522c2fb4cbd27b81d89a769c3d860bbbb85d004ef5cf1d1090d","title":"The difficulty rubric","url":"/docs/features/classifier-router-jev-strategy#the-difficulty-rubric","content":"Five tiers, ordered easiest to hardest, worded about the shape of the work —\nthe model is never told a provider or model name:\n\n| Tier | Criterion |\n| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |\n| trivial | Mechanical and local: rename a symbol, fix a typo, add an import, run one named command, or answer something already stated. |\n| simple | A small localised change or a direct factual answer. One file, one obvious approach. |\n| moderate | Ordinary engineering: implement a well-specified change across a few files, write tests, fix a clearly described bug, review a small diff. |\n| hard | Deep reasoning: architecture and design, debugging a failure whose cause is unknown, security analysis, concurrency, cross-system refactors. |\n| expert | Frontier-level work: ambiguous requirements, novel design with no established pattern, or analysis where a wrong answer is costly and hard to detect. |","hierarchy":{"lvl0":"Features","lvl1":"Model routing with a decision model","lvl2":"The difficulty rubric","lvl3":""}}, {"objectID":"2bcf89adce5486bff7cc01f6916f9bf26080de8996ec2277df6e15eb3eb264e3","title":"Asymmetric confidence bars, and why they differ","url":"/docs/features/classifier-router-jev-strategy#asymmetric-confidence-bars-and-why-they-differ","content":"A verdict harder than the neutral tier (moderate) spends more if wrong; a\nverdict easier than neutral spends less if wrong. Those are not the same\nmistake:\nRouting a simple task to an expensive model wastes money. Cheap to be wrong\n about, so the bar to route up is low: minUpgradeConfidence defaults to\n 0.3.\nRouting a hard task to a weak model produces a wrong answer. Expensive to be\n wrong about, so the bar to route down is high: minDowngradeConfidence\n defaults to 0.6.\n\nBelow the applicable bar, the difficulty verdict is discarded and the heuristic\nclassifier's tier stands instead — not a downgraded jev answer, the ordinary\nzero-cost fallback.\n\nOne case skips both bars: a risky reading above 0.7 forces the tier to at\nleast hard, unconditionally. Risk can only ever raise the tier, never lower\none, and it is not itself gated by confidence.","hierarchy":{"lvl0":"Features","lvl1":"Model routing with a decision model","lvl2":"Asymmetric confidence bars, and why they differ","lvl3":""}}, {"objectID":"516e7c128214404728015c7b49bc089992935102cb4baebbcdc3d8170078873c","title":"Ask about the act, not the subject","url":"/docs/features/classifier-router-jev-strategy#ask-about-the-act-not-the-subject","content":"The risk question is not \"does this touch production, money, or credentials\":\n\nCarrying out this request would itself change production, move real money,\nexpose credentials, or alter data that cannot be restored. Writing or testing\ncode that deals with such things, without running it against the real system,\ndoes not count.\n\nThe naive phrasing was tried first and scored high on ordinary code that merely\nconcerns those things — \"add a refund endpoint that calls Stripe\" — which\nwould have escalated every such request to the most expensive tier. The second\nsentence is what separates writing the code from running it against something\nreal.","hierarchy":{"lvl0":"Features","lvl1":"Model routing with a decision model","lvl2":"Ask about the act, not the subject","lvl3":""}}, {"objectID":"d90c4af28403b7660202646864195bb2ff353a7f7a29ae72f01fd8aa997a39dd","title":"The model pick faces a bar too — but a different one","url":"/docs/features/classifier-router-jev-strategy#the-model-pick-faces-a-bar-too-but-a-different-one","content":"When the pool has more than one member, jev is also asked to choose directly:\n_\"Which of these models is the cheapest one that can still complete this\nrequest correctly?\"_ — over\nthe rendered candidate lines.\nThat pick is reported with its own confidence, separate from the difficulty\nconfidence, and the router decides which bar applies:\n\nPicking something costlier than what the difficulty tier would have picked\non its own risks only spending more than necessary, so it clears the low\nupgrade bar. Picking something cheaper risks handing the task to a model\nthat cannot do it, so it must clear the high downgrade bar. Agreeing with the\ntier needs no bar at all. If the pick fails its bar, it is dropped — logged as\n\"classifier pick dropped — below its bar\" — and the tier's own ranked list is\nused instead.\n\nThis is a genuinely different question from the difficulty asymmetry above:\nthat one asks whether the tier is trustworthy; this one asks whether the\nspecific model choice, once a tier is settled, is trustworthy — and the two\ncan point in opposite directions (a confident-enough \"hard\" verdict whose model\npick still misses its own, stricter bar).\n\nA pick that cannot physically hold the request is not gated at all — it is\ndropped outright, regardless of confidence, because that is a hard provider\nerror rather than a degraded answer: see\nthe catalogue's context-window filtering.\n\nThe llm strategy's picks are exempt from both bars: it reports no confidence\nfor its pick, so a bar would either always pass or always fail. Its picks are\nhonoured exactly as they were before jev existed — imposing a bar here would\nsilently change an unrelated, already-shipped strategy.","hierarchy":{"lvl0":"Features","lvl1":"Model routing with a decision model","lvl2":"The model pick faces a bar too — but a different one","lvl3":""}}, {"objectID":"4553a84789531ca2e1bad075175d684e27e4ab3fcbb009062048cdf11765c2fb","title":"What this is bad at","url":"/docs/features/classifier-router-jev-strategy#what-this-is-bad-at","content":"It cannot tell you why. The reason field is a debug string built from\n the numbers, not an explanation the model gave. If you need an auditable\n rationale for a routing decision, this is the wrong tool.\nA close call still routes somewhere. A 0.29 upgrade verdict and a 0.61\n downgrade verdict are both one hundredth of a point from the bar, and both\n fall all the way back to the heuristic tier rather than to \"the second most\n likely tier.\" There is no partial credit.\nThe risk question is a single boolean. It cannot express \"risky, but\n only mildly\" — anything past 0.7 jumps straight to hard, whatever the\n actual severity.\nIt shares the base model's general limits. Literal reading, no\n arithmetic, and state relevance affecting accuracy all apply here exactly as\n described in what decide is bad at.\nThe pool still has to exist. jev chooses among what you declared (or\n what the catalogue built); it\n cannot invent a model you have no credentials for.","hierarchy":{"lvl0":"Features","lvl1":"Model routing with a decision model","lvl2":"What this is bad at","lvl3":""}}, {"objectID":"ab4d25365369a722077741378e60be6e3ef36f612ed7a85340c93cf9d47223da","title":"See also","url":"/docs/features/classifier-router-jev-strategy#see-also","content":"The decide inference type\nClassifier Router\nThe model catalogue\nPer-request context budget","hierarchy":{"lvl0":"Features","lvl1":"Model routing with a decision model","lvl2":"See also","lvl3":""}}, {"objectID":"3f990fb1489f99d505b19941e00fd05b9368a5a91745c83666639aac85865343","title":"Classifier Router","url":"/docs/features/classifier-router","content":"Classifier Router\n\nStatus: Stable | Availability: SDK + CLI | Opt-in (disabled by default)\n\nOverview\n\nThe Classifier Router lets NeuroLink decide, per request, which model to use (and, optionally, which tools to expose) from a pool you declare — routing hard/complex tasks to more capable models and easy tasks to cheaper, faster ones. You give it a pool of models; it classifies the incoming prompt and switches to the best one transparently before the call runs.\n\nIt is opt-in (classifierRouter.enabled defaults to false), fails open (any classifier/selection error leaves the call exactly as it would have been), and is fully backward compatible — a default new NeuroLink() is unchanged.\n\nTypical use cases:\nCost optimization — send \"hi\" to a cheap model and a multi-step architecture question to a powerful one, automatically.\nLatency optimization — keep simple turns on fast models.\nCustom / self-hosted fleets — route across LiteLLM, OpenAI-compatible, or Ollama models that aren't in any registry.\nPer-difficulty tool scoping — expose fewer tools for trivial tasks.\n\nHow it works\n\nEach request flows through two stages:\nClassify — produce a difficulty bucket (trivial | simple | moderate | hard | expert) plus optional requiredCapabilities and tool hints. Four strategies:\nauto (default): resolves to jev when a decision provider is configured (TYPESAFE_API_KEY or AI_GATEWAY_API_KEY, LAYA_API_KEY with LAYA_BASE_URL, XOR_API_KEY with XOR_BASE_URL, PERPLEXITY_API_KEY, or CLOUDFLARE_API_KEY with CLOUDFLARE_ACCOUNT_ID), and heuristic otherwise. Setting a key therefore upgrades routing with no code change; without one, behaviour is exactly as it was. PERPLEXITY_API_KEY is also the Perplexity text provider's key, so it counts here too, and CLOUDFLARE_API_KEY with CLOUDFLARE_ACCOUNT_ID are also the Cloudflare Workers AI text provider's token and account id, so they count here as well. The gate reads the credentials given to the NeuroLink constructor and the environment, not credentials passed on a single call, so a per-call credentials.perplexityDecider or credentials.cloudflareClef does not influence it.\nheuristic: zero-cost keyword/length scoring of the prompt text. No LLM call, fully deterministic, provider-agnostic.\nllm: a cheap \"classifier model\" reads the prompt and returns a difficulty — and, when given your pool, picks a model directly by id.\njev: a decision model (the first configured decision provider, in the order TypeSafe's Jev, Laya, XOR, Perplexity, Cloudflare Clef) answers difficulty, required capabilities and the model pick in one request, with a confidence for each answer. On TypeSafe's Jev that request takes ~400 ms and the confidence is calibrated; the Perplexity guide gives its latency and the confidence it reports, and the Cloudflare Clef guide gives its latency and its state limit (about 2,048 tokens, so a long prompt is refused and the heuristic stands). Verdicts that miss the applicable confidence bar (minUpgradeConfidence 0.3 to route up, minDowngradeConfidence 0.6 to route down) fall through to the heuristic rather than acting on a guess. See the decide inference type.\nSelect — turn that into a concrete { provider, model, region } from your pool, optionally narrowing tools.\n\nThe router runs before the provider/model is constructed (it reuses the same pre-call seam as requestRouter). It is skipped when the caller pinned both provider and model, or when a modelPool is configured (the pool owns selection).\n\nQuick start (heuristic, SDK)\n\nDefining \"which model for which case\"\n\nThe router resolves a model using the first of these that applies:\n\n| # | Mechanism | How you define it | Best for |\n| --- | ---------------------- | ---------------------------------------------------------------------------------- | ------------------------------------- |\n| 1 | LLM direct pick | classifier: \"llm\" + a description on each pool member | Custom/registry-less models; smartest |\n| 2 | tierMap | Explicit difficulty → members map | Full, deterministic control |\n| 3 | Per-member tiers | tiers: [\"hard\",\"expert\"] on a member | Simple, explicit, generic |\n| 4 | Metadata scoring | cost / quality per member (declared, or auto-enriched from the model registry) | Known models with comparable metadata |\n\nWhen two members can't be separated (e.g. equal declared quality, or the model registry only knows both as \"high\" quality), the router keeps the declared pool order. For reliable hard-vs-easy separation, prefer mechanisms 1–3, or give members distinct quality values.\n\nMetadata scoring rules\ntrivial / simple → cheapest first (cost ascending)\nmoderate → best quality − cost\nhard / expert → most capable first (quality descending)\n\nMembers may declare cost (relative, lower =","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"","lvl3":""}}, {"objectID":"b8bb35b0db438959a0a557c68b22afa16480504b96128260a8947ba3c8a81f9f","title":"Classifier Router","url":"/docs/features/classifier-router#classifier-router","content":"Status: Stable | Availability: SDK + CLI | Opt-in (disabled by default)","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"Classifier Router","lvl3":""}}, {"objectID":"543ddabeefd7754f6047758090a6a0ac6f517c98b9afb146645b9073ad75c5ee","title":"Overview","url":"/docs/features/classifier-router#overview","content":"The Classifier Router lets NeuroLink decide, per request, which model to use (and, optionally, which tools to expose) from a pool you declare — routing hard/complex tasks to more capable models and easy tasks to cheaper, faster ones. You give it a pool of models; it classifies the incoming prompt and switches to the best one transparently before the call runs.\n\nIt is opt-in (classifierRouter.enabled defaults to false), fails open (any classifier/selection error leaves the call exactly as it would have been), and is fully backward compatible — a default new NeuroLink() is unchanged.\n\nTypical use cases:\nCost optimization — send \"hi\" to a cheap model and a multi-step architecture question to a powerful one, automatically.\nLatency optimization — keep simple turns on fast models.\nCustom / self-hosted fleets — route across LiteLLM, OpenAI-compatible, or Ollama models that aren't in any registry.\nPer-difficulty tool scoping — expose fewer tools for trivial tasks.","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"Overview","lvl3":""}}, {"objectID":"abdbdbfcfd51c6b40ef9cdf848fa23aeb87e4e4513c71572f89ee754be41d834","title":"How it works","url":"/docs/features/classifier-router#how-it-works","content":"Each request flows through two stages:\nClassify — produce a difficulty bucket (trivial | simple | moderate | hard | expert) plus optional requiredCapabilities and tool hints. Four strategies:\nauto (default): resolves to jev when a decision provider is configured (TYPESAFE_API_KEY or AI_GATEWAY_API_KEY, LAYA_API_KEY with LAYA_BASE_URL, XOR_API_KEY with XOR_BASE_URL, PERPLEXITY_API_KEY, or CLOUDFLARE_API_KEY with CLOUDFLARE_ACCOUNT_ID), and heuristic otherwise. Setting a key therefore upgrades routing with no code change; without one, behaviour is exactly as it was. PERPLEXITY_API_KEY is also the Perplexity text provider's key, so it counts here too, and CLOUDFLARE_API_KEY with CLOUDFLARE_ACCOUNT_ID are also the Cloudflare Workers AI text provider's token and account id, so they count here as well. The gate reads the credentials given to the NeuroLink constructor and the environment, not credentials passed on a single call, so a per-call credentials.perplexityDecider or credentials.cloudflareClef does not influence it.\nheuristic: zero-cost keyword/length scoring of the prompt text. No LLM call, fully deterministic, provider-agnostic.\nllm: a cheap \"classifier model\" reads the prompt and returns a difficulty — and, when given your pool, picks a model directly by id.\njev: a decision model (the first configured decision provider, in the order TypeSafe's Jev, Laya, XOR, Perplexity, Cloudflare Clef) answers difficulty, required capabilities and the model pick in one request, with a confidence for each answer. On TypeSafe's Jev that request takes ~400 ms and the confidence is calibrated; the Perplexity guide gives its latency and the confidence it reports, and the Cloudflare Clef guide gives its latency and its state limit (about 2,048 tokens, so a long prompt is refused and the heuristic stands).","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"How it works","lvl3":""}}, {"objectID":"08f69ffa1f39bf410a5c236efa5f21f51efc93034747ddab6d761f4b1615d7df","title":"Defining \"which model for which case\"","url":"/docs/features/classifier-router#defining-which-model-for-which-case","content":"The router resolves a model using the first of these that applies:\n\n| # | Mechanism | How you define it | Best for |\n| --- | ---------------------- | ---------------------------------------------------------------------------------- | ------------------------------------- |\n| 1 | LLM direct pick | classifier: \"llm\" + a description on each pool member | Custom/registry-less models; smartest |\n| 2 | tierMap | Explicit difficulty → members map | Full, deterministic control |\n| 3 | Per-member tiers | tiers: [\"hard\",\"expert\"] on a member | Simple, explicit, generic |\n| 4 | Metadata scoring | cost / quality per member (declared, or auto-enriched from the model registry) | Known models with comparable metadata |\n\nWhen two members can't be separated (e.g. equal declared quality, or the model registry only knows both as \"high\" quality), the router keeps the declared pool order. For reliable hard-vs-easy separation, prefer mechanisms 1–3, or give members distinct quality values.","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"Defining \"which model for which case\"","lvl3":""}}, {"objectID":"bd4863bf711f00f17fb26ea1e60a541aef1f396afc1050ee47b9e29731c90f9a","title":"Metadata scoring rules","url":"/docs/features/classifier-router#metadata-scoring-rules","content":"trivial / simple → cheapest first (cost ascending)\nmoderate → best quality − cost\nhard / expert → most capable first (quality descending)\n\nMembers may declare cost (relative, lower = cheaper) and quality (relative, higher = more capable). If omitted, NeuroLink tries to enrich them from its model registry; if the model is unknown (e.g. a custom LiteLLM endpoint), use mechanisms 1–3 instead.","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"Metadata scoring rules","lvl3":""}}, {"objectID":"9e5f93906c1460117b27372537b30d578daec8b1d3fc38ecf1b0fb05c9944801","title":"Custom & self-hosted models (LiteLLM, OpenAI-compatible, Ollama)","url":"/docs/features/classifier-router#custom-self-hosted-models-litellm-openai-compatible-ollama","content":"These models aren't in any registry, so define routing explicitly — both approaches are fully generic:\n\nHeuristic + tiers (deterministic, no LLM cost):\n\nLLM picks per-prompt from plain-English descriptions (most flexible):\n\nThe classifier model is shown each candidate's id (defaults to provider/model) and description, and returns the best id for the prompt. An invalid or absent pick falls back to difficulty-based selection; any classifier failure falls back to the heuristic.","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"Custom & self-hosted models (LiteLLM, OpenAI-compatible, Ollama)","lvl3":""}}, {"objectID":"9649f6b1ff6a2d58f2ee81de7229558754703d21db437215218ca40f752d2cff","title":"Narrowing tools per difficulty","url":"/docs/features/classifier-router#narrowing-tools-per-difficulty","content":"Filter the tool set per difficulty with toolDirectives (and/or let the LLM classifier suggest tools). toolFilter is an allowlist; excludeTools is a denylist — both are enforced by the provider before the model call.","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"Narrowing tools per difficulty","lvl3":""}}, {"objectID":"33be1197833f5be4c1a7ddeab4a4fc85cadd3c6bc9cc735b44253f8640f62883","title":"CLI usage","url":"/docs/features/classifier-router#cli-usage","content":"`bash","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"CLI usage","lvl3":""}}, {"objectID":"cc0754af3d357fb93691ebfaad77343948f167866e856d3e941738c8293fa323","title":"Heuristic routing across a pool (inline JSON or a file path)","url":"/docs/features/classifier-router#heuristic-routing-across-a-pool-inline-json-or-a-file-path","content":"neurolink generate \"hi\" \\\n --classifier-router \\\n --classifier-pool '[{\"provider\":\"vertex\",\"model\":\"gemini-2.5-flash\",\"tiers\":[\"trivial\",\"simple\",\"moderate\"]},{\"provider\":\"vertex\",\"model\":\"gemini-2.5-pro\",\"tiers\":[\"hard\",\"expert\"]}]'","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"Heuristic routing across a pool (inline JSON or a file path)","lvl3":""}}, {"objectID":"c1599b72e48c45d71d427eff367de7a998cb96e25063546d18d1d8a9a520a74d","title":"LLM classifier picks the model per prompt (descriptions drive the choice)","url":"/docs/features/classifier-router#llm-classifier-picks-the-model-per-prompt-descriptions-drive-the-choice","content":"neurolink generate \"Design a multi-region architecture\" \\\n --classifier-router \\\n --classifier-strategy llm \\\n --classifier-model-provider vertex --classifier-model-name gemini-2.5-flash \\\n --classifier-pool ./pool.json\n\n\n| Flag | Description |\n| --------------------------------------- | ------------------------------------------------------- |\n| --classifier-router | Enable the classifier router. |\n| --classifier-strategy | auto (default), heuristic, llm or jev. |\n| --classifier-min-upgrade-confidence | Confidence needed to route UP (jev; 0.3). |\n| --classifier-min-downgrade-confidence | Confidence needed to route DOWN (jev; 0.6). |\n| --classifier-model-provider | Provider for the LLM classifier model (strategy=llm). |\n| --classifier-model-name | Model name for the LLM classifier model. |\n| --classifier-model-region | Region for the LLM classifier model. |\n| --classifier-pool | JSON file path or inline JSON array of pool members. |\n| --classifier-timeout | LLM classifier hard timeout (ms). |\n\n> CLI flags cover the common case (strategy, classifier model, pool). For tierMap and toolDirectives`, use the SDK config.","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"LLM classifier picks the model per prompt (descriptions drive the choice)","lvl3":""}}, {"objectID":"9fe3ff72b1728db15b0ad44eb4108926b4917c45feb4a7f6842cf5c247d300e3","title":"Precedence & interactions","url":"/docs/features/classifier-router#precedence-interactions","content":"Model selection resolves in this order: caller-pinned provider+model > classifierRouter > requestRouter > legacy enableOrchestration. The classifier marks the request so the downstream selectors stand down.\nmodelPool — when a modelPool is configured the classifier stands down (the pool owns selection); use one or the other for model choice.\ntoolRouting — the dedicated tool-routing feature still applies; classifier toolDirectives are additive.","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"Precedence & interactions","lvl3":""}}, {"objectID":"fac896bd6ef5ba1295df2e3b8249209a904d5a4b66780e388934069517ed6014","title":"Caveats","url":"/docs/features/classifier-router#caveats","content":"Registry quality is coarse. Auto-enrichment maps a model to a 3-bucket quality (high/medium/low), so two \"high\" models can't be separated on capability alone — declare quality/tiers/tierMap or use the LLM pick for reliable hard-vs-easy routing.\nLLM classifier latency/cost. The llm strategy adds one cheap call per uncached turn; prefer a small, fast, non-Gemini model and use heuristic where determinism matters.\nJev confidence is a gate, not a score. Unlike the llm strategy — whose self-reported confidence defaults to a hard-coded 0.7 when the model omits it — jev returns the confidence the decision provider reports (calibrated, on TypeSafe's Jev) rather than a constant, which is what makes minConfidence meaningful. The two bars differ because the mistakes cost differently: spending more on a wrong guess wastes money, spending less produces a wrong answer. Set either above 1 to force the heuristic while leaving the strategy configured.\nGemini tools + JSON schema. The classifier call uses a schema with tools disabled, so the Gemini exclusivity rule doesn't apply to it; when routing a tools + structured-output request, prefer a non-Gemini target model.\nPrompt privacy (llm and jev). Both strategies send a truncated copy of the prompt off-machine — to the classifier model, or to the configured decision provider — so the same data-handling and retention considerations as any provider call apply. heuristic keeps classification fully in-process (no prompt leaves your environment); prefer it where that matters, and note that auto selects jev as soon as a decision provider's key is present, including a PERPLEXITY_API_KEY set only for the Perplexity text provider, or a CLOUDFLARE_API_KEY and CLOUDFLARE_ACCOUNT_ID set only for the Cloudflare Workers AI text provider.","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"Caveats","lvl3":""}}, {"objectID":"44f6d5bc4c0e752129323d87508ca78b8f296e06d39a994f0b93706adff75033","title":"See also","url":"/docs/features/classifier-router#see-also","content":"Provider Orchestration & Model Pool\nProvider Fallback\nPer-Request Credentials\nModel routing with a decision model\nThe model catalogue","hierarchy":{"lvl0":"Features","lvl1":"Classifier Router","lvl2":"See also","lvl3":""}}, {"objectID":"2c5c9a8afc6acb37fc955bbc5ed44588962efef98dbf7e9dd26126eb58d08241","title":"Claude Proxy Architecture","url":"/docs/features/claude-proxy-architecture","content":"Claude Proxy Architecture\nSystem Overview\n\nThe Claude proxy is a local HTTP server that sits between Claude Code and the Anthropic API. It provides multi-account rotation, automatic token refresh, rate-limit handling with exponential backoff, and optional model translation to non-Anthropic providers.\n\nTwo operational modes\n\n| Mode | When | What happens |\n| --------------- | ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Passthrough | Target provider is anthropic (or null) | The request body is forwarded byte-for-byte to api.anthropic.com via plain fetch() with client headers forwarded. No parsing, no tool injection, no SDK involvement. |\n| Translation | Target provider is anything else (e.g. vertex, openai) | The Claude-format request is parsed by parseClaudeRequest(), routed through ctx.neurolink.stream() / ctx.neurolink.generate(), and the NeuroLink response is serialized back to Claude SSE format via ClaudeStreamSerializer. |\n\nPassthrough exists because Claude Code sends complex bodies (multi-turn conversations, tool definitions, thinking blocks, context management betas) that would be lossy to parse and re-serialize. The proxy's job for Claude-to-Claude is purely auth and account management.\n\nHow it fits into NeuroLink\n\nThe proxy is started via the CLI (neurolink proxy start) and creates a Hono HTTP server. It registers routes from createClaudeProxyRoutes() and injects a live NeuroLink SDK instance into the request context for translation-mode and fallback paths. MCP initialization is explicitly skipped (NEUROLINK_SKIP_MCP=true) because tools come from Claude Code, not from MCP servers.\nRequest Lifecycle\n\nA complete request through the passthrough path:\nAccount Management\n\nAccount loading priority\n\nAccounts are loaded in the POST /v1/messages handler on every request (not cached across requests), in this order:\nTokenStore compound keys (anthropic: