/**
 * NeuroLink - Unified AI Interface with Real MCP Tool Integration
 *
 * REDESIGNED FALLBACK CHAIN - NO CIRCULAR DEPENDENCIES
 * Enhanced AI provider system with natural MCP tool access.
 * Uses real MCP infrastructure for tool discovery and execution.
 */
import type { CompactionConfig, CompactionResult, SpanData, ObservabilityConfig, MetricsSummary, MCPToolAnnotations, TraceView, AuthenticatedContext, AuthProvider, JsonObject, NeuroLinkEvents, TypedEventEmitter, MCPEnhancementsConfig, NeuroLinkAuthConfig, NeurolinkConstructorConfig, ChatMessage, ExternalMCPOperationResult, ExternalMCPServerInstance, ExternalMCPToolInfo, GenerateOptions, GenerateResult, ProviderStatus, TextGenerationOptions, TextGenerationResult, MCPExecutableTool, MCPServerInfo, MCPStatus, StreamOptions, StreamResult, ToolExecutionContext, ToolExecutionSummary, ToolInfo, ToolRegistrationOptions, BatchOperationResult, StreamGenerationEndContext } from "./types/index.js";
import { ConversationMemoryManager } from "./core/conversationMemoryManager.js";
import type { RedisConversationMemoryManager } from "./core/redisConversationMemoryManager.js";
import { ExternalServerManager } from "./mcp/externalServerManager.js";
import { MCPToolRegistry } from "./mcp/toolRegistry.js";
import type { DynamicOptions } from "./types/index.js";
import { TaskManager } from "./tasks/taskManager.js";
/**
 * Curator P2-4 dedup (concurrency-safe): native providers emit
 * `generation:end` on the shared SDK emitter. We attach a fresh
 * mutable `dedupContext` object directly to the per-call
 * `StreamOptions` (under `_streamDedupContext`) so each stream gets
 * its own instance — concurrent streams have different option objects
 * and therefore different contexts, so they cannot interfere.
 *
 * Native provider emit sites read `options._streamDedupContext` and
 * flip `.providerEmitted = true` before emitting; the orchestration's
 * finally block reads the same closed-over reference and skips its
 * own emit when the flag is set.
 *
 * This avoids the AsyncLocalStorage approach which doesn't reliably
 * propagate through async-generator yield boundaries when iteration
 * happens from outside the original `run()` scope (e.g. when the
 * consumer drives `for await of result.stream` after `sdk.stream(...)`
 * returns).
 */
export declare const STREAM_DEDUP_CONTEXT_KEY: "_streamDedupContext";
/**
 * Native providers call this from their `generation:end` emit sites,
 * passing the same `options` object they received. Safe no-op when
 * the field isn't set.
 */
export declare function markStreamProviderEmittedGenerationEnd(options: {
    _streamDedupContext?: StreamGenerationEndContext;
} | undefined): void;
/**
 * Symbol-based brand for cross-module identification without circular imports.
 *
 * Provider constructors receive `sdk?: unknown` (the factory layer's
 * contract). Rather than duck-typing via `"getInMemoryServers" in sdk`,
 * use `isNeuroLink(value)` from this module to do a brand check —
 * survives minification AND doesn't rely on method-name stability.
 */
export declare const NEUROLINK_BRAND: unique symbol;
/**
 * Type-guard for opaque values that should be a {@link NeuroLink} instance.
 *
 * Designed for the provider-factory boundary where TS can't carry the type
 * through `UnknownRecord` without forcing every caller into a circular
 * dependency. Cheap to call and unaffected by minification.
 */
export declare function isNeuroLink(value: unknown): value is NeuroLink;
export declare class NeuroLink {
    /** @internal Brand for cross-module identification — see {@link isNeuroLink}. */
    readonly [NEUROLINK_BRAND]: true;
    private mcpInitialized;
    private mcpSkipped;
    private mcpInitPromise;
    private emitter;
    private _taskManager?;
    private _taskManagerConfig?;
    private toolRegistry;
    private autoDiscoveredServerInfos;
    private externalServerManager;
    private toolCache;
    private readonly toolCacheDuration;
    private modelAliasConfig?;
    private lastCompactionMessageCount;
    /** Extract sessionId from options context for compaction watermark keying */
    private getCompactionSessionId;
    private mcpToolResultCache?;
    private mcpToolRouter?;
    private mcpToolBatcher?;
    private mcpEnhancedDiscovery?;
    private mcpToolMiddlewares;
    /** Artifact store for externalized MCP tool outputs (set when strategy=externalize). */
    private mcpArtifactStore?;
    private _disableToolCacheForCurrentRequest;
    private mcpEnhancementsConfig?;
    private toolCircuitBreakers;
    private toolExecutionMetrics;
    private currentStreamToolExecutions;
    private toolExecutionHistory;
    private activeToolExecutions;
    /**
     * Helper method to emit tool end event in a consistent way
     * Used by executeTool in both success and error paths
     * @param toolName - Name of the tool
     * @param startTime - Timestamp when tool execution started
     * @param success - Whether the tool execution was successful
     * @param result - The result of the tool execution (optional)
     * @param error - The error if execution failed (optional)
     */
    private emitToolEndEvent;
    conversationMemory?: ConversationMemoryManager | RedisConversationMemoryManager | null;
    private conversationMemoryNeedsInit;
    private conversationMemoryConfig?;
    private enableOrchestration;
    private authProvider?;
    private pendingAuthConfig?;
    private authInitPromise?;
    private credentials?;
    private readonly fallbackConfig;
    /**
     * Merge instance-level credentials with per-call credentials.
     *
     * Semantics: **deep merge at the provider level.** For each provider key
     * present in both `this.credentials` and `callCredentials`, the per-call
     * fields are merged ON TOP of the instance-level fields, so fields not
     * mentioned in the per-call slice are preserved.
     *
     * Example:
     * ```
     * instance:  { openai: { apiKey: "key1", baseURL: "url1" } }
     * per-call:  { openai: { apiKey: "key2" } }
     * merged:    { openai: { apiKey: "key2", baseURL: "url1" } }   // baseURL preserved
     * ```
     *
     * Providers present only in one source are carried through unchanged.
     * Unrelated providers (not overridden in callCredentials) are carried through
     * from instance credentials unchanged.
     */
    private resolveCredentials;
    private hitlManager?;
    private _sessionCostUsd;
    private fileRegistry;
    private cachedFileTools;
    private memoryInstance?;
    private memorySDKConfig?;
    /**
     * Extract and set Langfuse context from options with proper async scoping
     */
    private setLangfuseContextFromOptions;
    private createMetricsTraceContext;
    private enforceSessionBudget;
    private assertInputText;
    private applyAuthenticatedRequestContext;
    private applyGenerateLifecycleMiddleware;
    private applyStreamLifecycleMiddleware;
    private initializeMemoryConfig;
    /**
     * Lazy initialization for memory — called during generate/stream.
     */
    private ensureMemoryReady;
    /**
     * Context storage for tool execution
     * This context will be merged with any runtime context passed by the AI model
     */
    private toolExecutionContext?;
    /**
     * Creates a new NeuroLink instance for AI text generation with MCP tool integration.
     *
     * @param config - Optional configuration object
     * @param config.conversationMemory - Configuration for conversation memory features
     * @param config.conversationMemory.enabled - Whether to enable conversation memory (default: false)
     * @param config.conversationMemory.maxSessions - Maximum number of concurrent sessions (default: 100)
     * @param config.conversationMemory.maxTurnsPerSession - Maximum conversation turns per session (default: 50)
     * @param config.enableOrchestration - Whether to enable smart model orchestration (default: false)
     * @param config.hitl - Configuration for Human-in-the-Loop safety features
     * @param config.hitl.enabled - Whether to enable HITL tool confirmation (default: false)
     * @param config.hitl.dangerousActions - Keywords that trigger confirmation (default: ['delete', 'remove', 'drop'])
     * @param config.hitl.timeout - Confirmation timeout in milliseconds (default: 30000)
     * @param config.hitl.allowArgumentModification - Allow users to modify tool parameters (default: true)
     * @param config.toolRegistry - Optional tool registry instance for advanced use cases (default: new MCPToolRegistry())
     *
     * @example
     * ```typescript
     * // Basic usage
     * const neurolink = new NeuroLink();
     *
     * // With conversation memory
     * const neurolink = new NeuroLink({
     *   conversationMemory: {
     *     enabled: true,
     *     maxSessions: 50,
     *     maxTurnsPerSession: 20
     *   }
     * });
     *
     * // With orchestration enabled
     * const neurolink = new NeuroLink({
     *   enableOrchestration: true
     * });
     *
     * // With HITL safety features
     * const neurolink = new NeuroLink({
     *   hitl: {
     *     enabled: true,
     *     dangerousActions: ['delete', 'remove', 'drop', 'truncate'],
     *     timeout: 30000,
     *     allowArgumentModification: true
     *   }
     * });
     * ```
     *
     * @throws {Error} When provider registry setup fails
     * @throws {Error} When conversation memory initialization fails (if enabled)
     * @throws {Error} When external server manager initialization fails
     * @throws {Error} When HITL configuration is invalid (if enabled)
     */
    private observabilityConfig?;
    private metricsAggregator;
    /**
     * Per-request metrics trace context backed by AsyncLocalStorage.
     * Safe for concurrent requests on the same SDK instance.
     * Context is set via metricsTraceContextStorage.run() in generate/stream.
     */
    private get _metricsTraceContext();
    constructor(config?: NeurolinkConstructorConfig);
    /**
     * TaskManager — scheduled and self-running tasks.
     * Lazy-initialized on first access. Configurable via constructor `tasks` option.
     * The actual async initialization (Redis connect, backend start) happens
     * lazily inside TaskManager on first operation.
     */
    get tasks(): TaskManager;
    /**
     * Initialize provider registry with security settings
     */
    private initializeProviderRegistry;
    /**
     * Initialize conversation memory if enabled
     */
    private initializeConversationMemory;
    /**
     * Initialize HITL (Human-in-the-Loop) if enabled
     */
    private initializeHITL;
    /**
     * Initialize MCP enhancement modules (cache, router, batcher, discovery).
     * Wires standalone MCP modules into the core SDK execution path.
     */
    private initializeMCPEnhancements;
    /**
     * Register file reference tools with the MCP tool registry.
     *
     * Creates file access tools (list_attached_files, read_file_section,
     * search_in_file, get_file_preview) bound to the FileReferenceRegistry
     * and registers them as direct tools so they're available to LLMs.
     */
    private registerFileTools;
    /**
     * Register task management tools bound to a TaskManager instance.
     * Follows the same factory + registry pattern as registerFileTools().
     * Called when TaskManager is created (eagerly or lazily via the `tasks` getter).
     */
    private registerTaskTools;
    /**
     * Register memory retrieval tools that allow the AI to access
     * conversation history, including full tool outputs.
     * Only registered when Redis conversation memory is active.
     */
    private registerMemoryRetrievalTools;
    /** Format memory context for prompt inclusion */
    private formatMemoryContext;
    /**
     * Format memory context from multiple users into a labeled block.
     */
    private formatMultiUserMemoryContext;
    /**
     * Determine whether memory should be read (retrieved) for this call.
     * Respects both the global memory SDK config and per-call overrides.
     */
    private shouldReadMemory;
    /**
     * Determine whether memory should be written (stored) for this call.
     * Respects both the global memory SDK config and per-call overrides.
     */
    private shouldWriteMemory;
    /**
     * Retrieve condensed memory for a user (and optionally additional users).
     * Returns the input text enhanced with memory context, or unchanged if no memory.
     */
    private retrieveMemory;
    /**
     * Store a conversation turn in memory (non-blocking).
     * Calls add(userId, content) which internally condenses old + new via LLM.
     * Supports additional users with per-user prompt and maxWords overrides.
     */
    private storeMemoryInBackground;
    /**
     * Set up HITL event forwarding to main emitter
     */
    private setupHITLEventForwarding;
    /**
     * Initialize external server manager with event handlers
     */
    private initializeExternalServerManager;
    /**
     * Setup event handlers for external server manager
     */
    private setupExternalServerEventHandlers;
    /**
     * Initialize Langfuse observability for AI operations tracking
     */
    private initializeLangfuse;
    /**
     * Log constructor completion with final state summary
     */
    private logConstructorComplete;
    /**
     * Initialize MCP registry with enhanced error handling and resource cleanup
     * Uses isolated async context to prevent hanging
     */
    private initializeMCP;
    /**
     * Actual one-shot MCP initialization logic. Called at most once per
     * NeuroLink instance lifetime (unless cleanup() resets the flag).
     */
    private performMCPInitializationOnce;
    /**
     * Import performance manager with error handling
     */
    private importPerformanceManager;
    /**
     * Perform main MCP initialization logic
     */
    private performMCPInitialization;
    /**
     * Initialize tool registry with timeout protection
     */
    private initializeToolRegistryInternal;
    /**
     * Initialize provider registry
     */
    private initializeProviderRegistryInternal;
    /**
     * Register direct tools server
     */
    private registerDirectToolsServerInternal;
    /**
     * Load MCP configuration from .mcp-config.json with parallel loading for improved performance
     */
    private loadMCPConfigurationInternal;
    /**
     * Log MCP initialization completion
     */
    private logMCPInitComplete;
    /**
     * Apply orchestration to determine optimal provider and model
     * @param options - Original GenerateOptions
     * @returns Modified options with orchestrated provider marked in context, or empty object if validation fails
     */
    private applyOrchestration;
    /**
     * Apply orchestration to determine optimal provider and model for streaming
     * @param options - Original StreamOptions
     * @returns Modified options with orchestrated provider marked in context, or empty object if validation fails
     */
    private applyStreamOrchestration;
    /**
     * MAIN ENTRY POINT: Enhanced generate method with new function signature
     * Replaces both generateText and legacy methods
     */
    /**
     * Extracts the original prompt text from the provided input.
     * If a string is provided, it returns the string directly.
     * If a GenerateOptions object is provided, it returns the input text from the object.
     * @param optionsOrPrompt The prompt input, either as a string or a GenerateOptions object.
     * @returns The original prompt text as a string.
     */
    private _extractOriginalPrompt;
    /**
     * Generate AI content using the best available provider with MCP tool integration.
     * This is the primary method for text generation with full feature support.
     *
     * @param optionsOrPrompt - Either a string prompt or a comprehensive GenerateOptions object
     * @param optionsOrPrompt.input - Input configuration object
     * @param optionsOrPrompt.input.text - The text prompt to send to the AI (required)
     * @param optionsOrPrompt.provider - AI provider to use ('auto', 'openai', 'anthropic', etc.)
     * @param optionsOrPrompt.model - Specific model to use (e.g., 'gpt-4', 'claude-3-opus')
     * @param optionsOrPrompt.temperature - Randomness in response (0.0 = deterministic, 2.0 = very random)
     * @param optionsOrPrompt.maxTokens - Maximum tokens in response
     * @param optionsOrPrompt.systemPrompt - System message to set AI behavior
     * @param optionsOrPrompt.disableTools - Whether to disable MCP tool usage
     * @param optionsOrPrompt.enableAnalytics - Whether to include usage analytics
     * @param optionsOrPrompt.enableEvaluation - Whether to include response quality evaluation
     * @param optionsOrPrompt.context - Additional context for the request
     * @param optionsOrPrompt.evaluationDomain - Domain for specialized evaluation
     * @param optionsOrPrompt.toolUsageContext - Context for tool usage decisions
     *
     * @returns Promise resolving to GenerateResult with content, usage data, and optional analytics
     *
     * @example
     * ```typescript
     * // Simple usage with string prompt
     * const result = await neurolink.generate("What is artificial intelligence?");
     * console.log(result.content);
     *
     * // Advanced usage with options
     * const result = await neurolink.generate({
     *   input: { text: "Explain quantum computing" },
     *   provider: "openai",
     *   model: "gpt-4",
     *   temperature: 0.7,
     *   maxTokens: 500,
     *   enableAnalytics: true,
     *   enableEvaluation: true,
     *   context: { domain: "science", level: "intermediate" }
     * });
     *
     * // Access analytics and evaluation data
     * console.log(result.analytics?.usage);
     * console.log(result.evaluation?.relevance);
     * ```
     *
     * @throws {Error} When input text is missing or invalid
     * @throws {Error} When all providers fail to generate content
     * @throws {Error} When conversation memory operations fail (if enabled)
     */
    /**
     * Get observability configuration
     */
    getObservabilityConfig(): ObservabilityConfig | undefined;
    /**
     * Check if Langfuse telemetry is enabled
     * Centralized utility to avoid duplication across providers
     */
    isTelemetryEnabled(): boolean;
    /**
     * Get comprehensive telemetry status including Langfuse, OTel, and exporter health
     */
    getTelemetryStatus(): {
        enabled: boolean;
        langfuse?: {
            enabled: boolean;
            baseUrl?: string;
            environment?: string;
        };
        openTelemetry?: {
            enabled: boolean;
            endpoint?: string;
            serviceName?: string;
        };
        exporters?: Array<{
            name: string;
            enabled: boolean;
            healthy: boolean;
            pendingSpans: number;
            lastExportTime?: string;
            latencyMs?: number;
            errors?: string[];
        }>;
    };
    /**
     * Get aggregated observability metrics (latency, tokens, cost, success rate)
     */
    getMetrics(): MetricsSummary;
    /**
     * Get all recorded spans
     */
    getSpans(): SpanData[];
    /**
     * Get traces (spans grouped by traceId with parent-child hierarchy)
     */
    getTraces(): TraceView[];
    /**
     * Reset all collected metrics and spans
     */
    resetMetrics(): void;
    /**
     * Record a span for metrics tracking
     */
    recordMetricsSpan(span: SpanData): void;
    /**
     * Record a memory operation span to both instance and global metrics aggregators.
     * This ensures memory spans are visible via sdk.getSpans() and getMetricsAggregator().getSpans().
     */
    private recordMemorySpan;
    /**
     * Public method to initialize Langfuse observability
     * This method can be called externally to ensure Langfuse is properly initialized
     */
    initializeLangfuseObservability(): Promise<void>;
    /**
     * Gracefully shutdown NeuroLink and all MCP connections
     */
    shutdown(): Promise<void>;
    /**
     * Initialize event listeners that feed span data to MetricsAggregator.
     * Listens to generation:end, stream:complete, and tool:end events.
     */
    private initializeMetricsListeners;
    /**
     * Generate AI response with comprehensive feature support.
     *
     * Primary method for AI generation with support for all NeuroLink features:
     * - Multi-provider support (14+ providers)
     * - MCP tool integration
     * - Structured JSON output with Zod schemas
     * - Conversation memory (Redis or in-memory)
     * - HITL security workflows
     * - Middleware execution
     * - Multimodal inputs (images, PDFs, CSV)
     *
     * @category Generation
     *
     * @param optionsOrPrompt - Generation options or simple text prompt
     * @param optionsOrPrompt.input - Input text and optional files
     * @param optionsOrPrompt.provider - AI provider name (e.g., 'vertex', 'openai', 'anthropic')
     * @param optionsOrPrompt.model - Model name to use
     * @param optionsOrPrompt.tools - MCP tools to enable for this generation
     * @param optionsOrPrompt.schema - Zod schema for structured output validation
     * @param optionsOrPrompt.temperature - Sampling temperature (0-2, default: 1.0)
     * @param optionsOrPrompt.maxTokens - Maximum tokens to generate
     * @param optionsOrPrompt.thinkingConfig - Extended thinking configuration (thinkingLevel: 'minimal'|'low'|'medium'|'high')
     * @param optionsOrPrompt.context - Context with conversationId and userId for memory
     * @returns Promise resolving to generation result with content and metadata
     *
     * @example Basic text generation
     * ```typescript
     * const result = await neurolink.generate({
     *   input: { text: 'Explain quantum computing' }
     * });
     * console.log(result.content);
     * ```
     *
     * @example With specific provider
     * ```typescript
     * const result = await neurolink.generate({
     *   input: { text: 'Write a poem' },
     *   provider: 'anthropic',
     *   model: 'claude-3-opus'
     * });
     * ```
     *
     * @example With MCP tools
     * ```typescript
     * const result = await neurolink.generate({
     *   input: { text: 'Read README.md and summarize it' },
     *   tools: ['readFile']
     * });
     * ```
     *
     * @example With structured output
     * ```typescript
     * import { z } from 'zod';
     *
     * const schema = z.object({
     *   name: z.string(),
     *   age: z.number(),
     *   city: z.string()
     * });
     *
     * const result = await neurolink.generate({
     *   input: { text: 'Extract person info: John is 30 years old from NYC' },
     *   schema: schema
     * });
     * // result.structuredData is type-safe!
     * ```
     *
     * @example With conversation memory
     * ```typescript
     * const result = await neurolink.generate({
     *   input: { text: 'What did we discuss earlier?' },
     *   context: {
     *     conversationId: 'conv-123',
     *     userId: 'user-456'
     *   }
     * });
     * ```
     *
     * @example With multimodal input
     * ```typescript
     * const result = await neurolink.generate({
     *   input: {
     *     text: 'Describe this image',
     *     images: ['/path/to/image.jpg']
     *   },
     *   provider: 'vertex'
     * });
     * ```
     *
     * @throws {Error} When input text is missing or invalid
     * @throws {Error} When all providers fail to generate content
     * @throws {Error} When structured output validation fails
     * @throws {Error} When HITL approval is denied
     *
     * @see {@link GenerateOptions} for all available options
     * @see {@link GenerateResult} for result structure
     * @see {@link stream} for streaming generation
     * @since 1.0.0
     */
    generate(optionsOrPrompt: GenerateOptions | DynamicOptions | string): Promise<GenerateResult>;
    private fireConsumerOnErrorIfNotFired;
    /**
     * Curator P2-3: wraps a generate/stream call with the fallback
     * orchestration (`providerFallback` callback + `modelChain` walker).
     *
     * On a model-access-denied error from the inner call:
     *  1. Resolve the effective callback (per-call > instance > synthesised
     *     from modelChain) and the effective chain (per-call > instance).
     *  2. Walk attempts: invoke callback (or pop next chain entry) → emit
     *     `model.fallback` event → re-call inner with the new {provider,
     *     model}.
     *  3. Stop on first success, on a callback returning null, or after
     *     exhausting the chain (throw the most recent error).
     */
    private runWithFallbackOrchestration;
    private attemptInner;
    private executeGenerateWithMetricsContext;
    private executeGenerateRequest;
    private prepareGenerateRequest;
    private maybeHandleEarlyGenerateResult;
    private runStandardGenerateRequest;
    private maybeApplyGenerateOrchestration;
    private prepareGenerateAugmentations;
    private buildGenerateTextOptions;
    private finalizeGenerateRequestResult;
    private emitGenerateErrorEvent;
    /**
     * Schedule non-blocking memory storage after generate completes.
     */
    private scheduleGenerateMemoryStorage;
    /**
     * Handle PPT generation mode
     */
    private generateWithPPT;
    /**
     * Dispatch a music-generation request to the registered music handler
     * for the provider named in `options.output.music.provider`.
     */
    private generateWithMusic;
    /**
     * Dispatch an avatar (lip-sync) request to the registered avatar handler
     * for the provider named in `options.output.avatar.provider`.
     */
    private generateWithAvatar;
    /**
     * Generate with workflow engine integration
     * Returns both original and processed responses for AB testing
     */
    private generateWithWorkflow;
    /**
     * Stream with workflow engine integration
     * Progressive streaming: yields preliminary response (first model) then final synthesis
     */
    private streamWithWorkflow;
    /**
     * BACKWARD COMPATIBILITY: Legacy generateText method
     * Internally calls generate() and converts result format
     */
    generateText(options: TextGenerationOptions): Promise<TextGenerationResult>;
    /**
     * REDESIGNED INTERNAL GENERATION - NO CIRCULAR DEPENDENCIES
     *
     * This method implements a clean fallback chain:
     * 1. Initialize conversation memory if enabled
     * 2. Inject conversation history into prompt
     * 3. Try MCP-enhanced generation if available
     * 4. Fall back to direct provider generation
     * 5. Store conversation turn for future context
     */
    private generateTextInternal;
    private executeGenerateTextInternalWithSpan;
    private initializeGenerateTextInternalContext;
    private runGenerateTextInternalFlow;
    private captureOriginalConversationMessagesForRecovery;
    private finalizeGenerateTextInternalResult;
    private handleGenerateTextInternalFailure;
    private tryRecoverGenerateTextOverflow;
    /**
     * Log generateTextInternal start with comprehensive analysis
     */
    private logGenerateTextInternalStart;
    /**
     * Emit generation start events
     */
    private emitGenerationStartEvents;
    /**
     * Initialize conversation memory for generation
     * Lazily initializes memory if needed from constructor flags
     */
    private initializeConversationMemoryForGeneration;
    /**
     * Attempt MCP generation with retry logic
     */
    private attemptMCPGeneration;
    /**
     * Perform MCP generation with retry logic
     */
    private performMCPGenerationRetries;
    /**
     * Try MCP-enhanced generation (no fallback recursion)
     */
    private tryMCPGeneration;
    private prepareMCPGenerationContext;
    private logMCPConversationSummary;
    private ensureMCPGenerationBudget;
    private compactMCPConversationForBudget;
    private generateWithMCPProvider;
    /**
     * Direct provider generation (no MCP, no recursion)
     */
    private directProviderGeneration;
    /**
     * Create tool-aware system prompt that informs AI about available tools
     */
    /**
     * Apply per-call tool filtering (whitelist/blacklist) to a ToolInfo array.
     * Used to filter the tool list before building the system prompt.
     */
    private applyToolInfoFiltering;
    private createToolAwareSystemPrompt;
    /**
     * Execute tools if available through centralized registry
     * Simplified approach without domain detection - relies on tool registry
     */
    private detectAndExecuteTools;
    /**
     * BACKWARD COMPATIBILITY: Legacy streamText method
     * Internally calls stream() and converts result format
     */
    streamText(prompt: string, options?: Partial<StreamOptions>): Promise<AsyncIterable<string>>;
    /**
     * Stream AI-generated content in real-time using the best available provider.
     * This method provides real-time streaming of AI responses with full MCP tool integration.
     *
     * @param options - Stream configuration options
     * @param options.input - Input configuration object
     * @param options.input.text - The text prompt to send to the AI (required)
     * @param options.provider - AI provider to use ('auto', 'openai', 'anthropic', etc.)
     * @param options.model - Specific model to use (e.g., 'gpt-4', 'claude-3-opus')
     * @param options.temperature - Randomness in response (0.0 = deterministic, 2.0 = very random)
     * @param options.maxTokens - Maximum tokens in response
     * @param options.systemPrompt - System message to set AI behavior
     * @param options.disableTools - Whether to disable MCP tool usage
     * @param options.enableAnalytics - Whether to include usage analytics
     * @param options.enableEvaluation - Whether to include response quality evaluation
     * @param options.context - Additional context for the request
     * @param options.evaluationDomain - Domain for specialized evaluation
     *
     * @returns Promise resolving to StreamResult with an async iterable stream
     *
     * @example
     * ```typescript
     * // Basic streaming usage
     * const result = await neurolink.stream({
     *   input: { text: "Tell me a story about space exploration" }
     * });
     *
     * // Consume the stream
     * for await (const chunk of result.stream) {
     *   process.stdout.write(chunk.content);
     * }
     *
     * // Advanced streaming with options
     * const result = await neurolink.stream({
     *   input: { text: "Explain machine learning" },
     *   provider: "openai",
     *   model: "gpt-4",
     *   temperature: 0.7,
     *   enableAnalytics: true,
     *   context: { domain: "education", audience: "beginners" }
     * });
     *
     * // Access metadata and analytics
     * console.log(result.provider);
     * console.log(result.analytics?.usage);
     * ```
     *
     * @throws {Error} When input text is missing or invalid
     * @throws {Error} When all providers fail to generate content
     * @throws {Error} When conversation memory operations fail (if enabled)
     */
    stream(options: StreamOptions | DynamicOptions): Promise<StreamResult>;
    /**
     * Curator P2-3 / Reviewer Finding #2: stream-fallback that also covers
     * errors thrown during async iteration (e.g. LiteLLM throwing inside
     * `createLiteLLMTransformedStream`). The standard
     * `runWithFallbackOrchestration` only catches errors thrown while the
     * `StreamResult` is being created — once we hand the iterator back to
     * the caller, errors raised during consumption used to bypass
     * `providerFallback` / `modelChain`.
     *
     * This wrapper runs the orchestration to get an initial StreamResult,
     * then wraps `result.stream` so that:
     *   - chunks are forwarded transparently while consumption succeeds
     *   - if iteration throws a model-access-denied error AND no chunks
     *     have been yielded yet, we resolve the next fallback target,
     *     emit `model.fallback`, and recurse
     *   - if chunks were already yielded, the error propagates (mid-stream
     *     recovery isn't safe — the consumer has half a response)
     */
    private streamWithIterationFallback;
    private executeStreamRequest;
    private validateStreamRequestOptions;
    private maybeHandleWorkflowStreamRequest;
    private runStandardStreamRequest;
    /**
     * TTS Mode 2 synthesis helper for the stream() pipeline.
     *
     * m5 — extracted from runStandardStreamRequest so the surrounding generator
     * stays under the max-lines-per-function lint budget. Behaviour preserved
     * exactly:
     * - When Mode 2 is enabled (`tts.enabled && tts.useAiResponse`) AND the
     *   model produced non-empty content: synthesises one final audio buffer
     *   and returns it as an `audioChunk` for the caller to `yield`. Resolves
     *   `ttsResolver` with the `TTSResult`.
     * - When Mode 2 is enabled but synthesis fails: logs a warning and resolves
     *   `ttsResolver` with `undefined`.
     * - When Mode 2 is requested but skipped (empty content / wrong mode):
     *   resolves `ttsResolver` with `undefined` early so callers awaiting
     *   `result.audio` unblock before the surrounding `finally` cleanup
     *   completes (Issue 7 latency micro-opt — the finally block also resolves
     *   defensively, so this is a redundant early signal, not a coverage fix).
     */
    private synthesizeStreamModeTwo;
    /**
     * Prepare stream options: initialize memory, MCP, retrieval, orchestration,
     * Ollama tool auto-disable, factory processing, and tool detection.
     */
    private prepareStreamOptions;
    /**
     * Auto-disable tools for Ollama models that don't support them (stream mode).
     * Prevents overwhelming smaller models with massive tool descriptions in the system message.
     */
    private autoDisableOllamaStreamTools;
    /**
     * Set up event listeners for stream event capture (tool calls, HITL, UI components).
     * Returns the shared event sequence array and a cleanup function to remove all listeners.
     */
    private setupStreamEventListeners;
    /**
     * Handle fallback when the primary stream returns 0 chunks.
     * Yields chunks from a fallback provider and updates metadata accordingly.
     */
    private handleStreamFallback;
    /**
     * Store conversation memory after stream consumption is complete (called from finally block).
     * Handles conversation memory storage in the background.
     */
    private storeStreamConversationMemory;
    /**
     * Validate stream input with comprehensive error reporting
     */
    private validateStreamInput;
    /**
     * Emit stream start events
     */
    private emitStreamStartEvents;
    /**
     * Create MCP stream
     */
    private createMCPStream;
    /**
     * Process stream result
     */
    private processStreamResult;
    /**
     * Emit stream end events
     */
    private emitStreamEndEvents;
    /**
     * Create stream response
     */
    private createStreamResponse;
    /**
     * Handle stream error with fallback
     */
    private handleStreamError;
    /**
     * Get the EventEmitter instance to listen to NeuroLink events for real-time monitoring and debugging.
     * This method provides access to the internal event system that emits events during AI generation,
     * tool execution, streaming, and other operations for comprehensive observability.
     *
     * @returns EventEmitter instance that emits various NeuroLink operation events
     *
     * @example
     * ```typescript
     * // Basic event listening setup
     * const neurolink = new NeuroLink();
     * const emitter = neurolink.getEventEmitter();
     *
     * // Listen to generation events
     * emitter.on('generation:start', (event) => {
     *   console.log(`Generation started with provider: ${event.provider}`);
     *   console.log(`Started at: ${new Date(event.timestamp)}`);
     * });
     *
     * emitter.on('generation:end', (event) => {
     *   console.log(`Generation completed in ${event.responseTime}ms`);
     *   console.log(`Tools used: ${event.toolsUsed?.length || 0}`);
     * });
     *
     * // Listen to streaming events
     * emitter.on('stream:start', (event) => {
     *   console.log(`Streaming started with provider: ${event.provider}`);
     * });
     *
     * emitter.on('stream:end', (event) => {
     *   console.log(`Streaming completed in ${event.responseTime}ms`);
     *   if (event.fallback) console.log('Used fallback streaming');
     * });
     *
     * // Listen to tool execution events
     * emitter.on('tool:start', (event) => {
     *   console.log(`Tool execution started: ${event.toolName}`);
     * });
     *
     * emitter.on('tool:end', (event) => {
     *   console.log(`Tool ${event.toolName} ${event.success ? 'succeeded' : 'failed'}`);
     *   console.log(`Execution time: ${event.responseTime}ms`);
     * });
     *
     * // Listen to tool registration events
     * emitter.on('tools-register:start', (event) => {
     *   console.log(`Registering tool: ${event.toolName}`);
     * });
     *
     * emitter.on('tools-register:end', (event) => {
     *   console.log(`Tool registration ${event.success ? 'succeeded' : 'failed'}: ${event.toolName}`);
     * });
     *
     * // Listen to external MCP server events
     * emitter.on('externalMCP:serverConnected', (event) => {
     *   console.log(`External MCP server connected: ${event.serverId}`);
     *   console.log(`Tools available: ${event.toolCount || 0}`);
     * });
     *
     * emitter.on('externalMCP:serverDisconnected', (event) => {
     *   console.log(`External MCP server disconnected: ${event.serverId}`);
     *   console.log(`Reason: ${event.reason || 'Unknown'}`);
     * });
     *
     * emitter.on('externalMCP:toolDiscovered', (event) => {
     *   console.log(`New tool discovered: ${event.toolName} from ${event.serverId}`);
     * });
     *
     * // Advanced usage with error handling
     * emitter.on('error', (error) => {
     *   console.error('NeuroLink error:', error);
     * });
     *
     * // Clean up event listeners when done
     * function cleanup() {
     *   emitter.removeAllListeners();
     * }
     *
     * process.on('SIGINT', cleanup);
     * process.on('SIGTERM', cleanup);
     * ```
     *
     * @example
     * ```typescript
     * // Advanced monitoring with metrics collection
     * const neurolink = new NeuroLink();
     * const emitter = neurolink.getEventEmitter();
     * const metrics = {
     *   generations: 0,
     *   totalResponseTime: 0,
     *   toolExecutions: 0,
     *   failures: 0
     * };
     *
     * // Collect performance metrics
     * emitter.on('generation:end', (event) => {
     *   metrics.generations++;
     *   metrics.totalResponseTime += event.responseTime;
     *   metrics.toolExecutions += event.toolsUsed?.length || 0;
     * });
     *
     * emitter.on('tool:end', (event) => {
     *   if (!event.success) {
     *     metrics.failures++;
     *   }
     * });
     *
     * // Log metrics every 10 seconds
     * setInterval(() => {
     *   const avgResponseTime = metrics.generations > 0
     *     ? metrics.totalResponseTime / metrics.generations
     *     : 0;
     *
     *   console.log('NeuroLink Metrics:', {
     *     totalGenerations: metrics.generations,
     *     averageResponseTime: `${avgResponseTime.toFixed(2)}ms`,
     *     totalToolExecutions: metrics.toolExecutions,
     *     failureRate: `${((metrics.failures / (metrics.toolExecutions || 1)) * 100).toFixed(2)}%`
     *   });
     * }, 10000);
     * ```
     *
     * **Available Events:**
     *
     * **Generation Events:**
     * - `generation:start` - Fired when text generation begins
     *   - `{ provider: string, timestamp: number }`
     * - `generation:end` - Fired when text generation completes (or fails / is aborted)
     *   - `{ provider: string, responseTime: number, toolsUsed?: string[], timestamp: number, success?: boolean, aborted?: boolean, error?: string }`
     *   - `success` is `false` for both failures and client aborts; `aborted: true`
     *     distinguishes the latter so consumers can route cancellations
     *     differently from real errors. Pipeline B's metrics span maps
     *     `aborted: true` events to `SpanStatus.WARNING` (not ERROR).
     *
     * **Streaming Events:**
     * - `stream:start` - Fired when streaming begins
     *   - `{ provider: string, timestamp: number }`
     * - `stream:end` - Fired when streaming completes
     *   - `{ provider: string, responseTime: number, fallback?: boolean }`
     *
     * **Tool Events:**
     * - `tool:start` - Fired when tool execution begins
     *   - `{ toolName: string, timestamp: number }`
     * - `tool:end` - Fired when tool execution completes
     *   - `{ toolName: string, responseTime: number, success: boolean, timestamp: number }`
     * - `tools-register:start` - Fired when tool registration begins
     *   - `{ toolName: string, timestamp: number }`
     * - `tools-register:end` - Fired when tool registration completes
     *   - `{ toolName: string, success: boolean, timestamp: number }`
     *
     * **External MCP Events:**
     * - `externalMCP:serverConnected` - Fired when external MCP server connects
     *   - `{ serverId: string, toolCount?: number, timestamp: number }`
     * - `externalMCP:serverDisconnected` - Fired when external MCP server disconnects
     *   - `{ serverId: string, reason?: string, timestamp: number }`
     * - `externalMCP:serverFailed` - Fired when external MCP server fails
     *   - `{ serverId: string, error: string, timestamp: number }`
     * - `externalMCP:toolDiscovered` - Fired when external MCP tool is discovered
     *   - `{ toolName: string, serverId: string, timestamp: number }`
     * - `externalMCP:toolRemoved` - Fired when external MCP tool is removed
     *   - `{ toolName: string, serverId: string, timestamp: number }`
     * - `externalMCP:serverAdded` - Fired when external MCP server is added
     *   - `{ serverId: string, config: MCPServerInfo, toolCount: number, timestamp: number }`
     * - `externalMCP:serverRemoved` - Fired when external MCP server is removed
     *   - `{ serverId: string, timestamp: number }`
     *
     * **Error Events:**
     * - `error` - Fired when an error occurs
     *   - `{ error: Error, context?: object }`
     *
     * @throws {Error} This method does not throw errors as it returns the internal EventEmitter
     *
     * @since 1.0.0
     * @see {@link https://nodejs.org/api/events.html} Node.js EventEmitter documentation
     * @see {@link NeuroLink.generate} for events related to text generation
     * @see {@link NeuroLink.stream} for events related to streaming
     * @see {@link NeuroLink.executeTool} for events related to tool execution
     */
    getEventEmitter(): TypedEventEmitter<NeuroLinkEvents>;
    /**
     * Curator P1-1: synchronous credential health check for a single provider.
     *
     * Drives a tiny real call against the provider (1-token completion or
     * `/models` listing depending on provider) to confirm the configured
     * credentials are valid. Useful at startup so a service can refuse to
     * boot if its primary provider's credentials are broken instead of
     * discovering the problem on first user request.
     *
     * @example
     * ```ts
     * const health = await neurolink.checkCredentials({ provider: "litellm" });
     * if (health.status !== "ok") {
     *   throw new Error(`provider not ready: ${health.detail}`);
     * }
     * ```
     *
     * @param input - the provider to check
     * @returns `{ provider, status, detail }`. Possible status values:
     *   - `"ok"` — credentials valid and provider reachable
     *   - `"missing"` — required env / credentials not configured
     *   - `"expired"` — credentials present but rejected (401/403)
     *   - `"denied"` — credentials valid but team not whitelisted for any model
     *   - `"network"` — provider unreachable (timeout, ECONNREFUSED, DNS)
     *   - `"unknown"` — other error; consult `detail`
     */
    checkCredentials(input: {
        provider: string;
        model?: string;
    }): Promise<{
        provider: string;
        status: "ok" | "missing" | "expired" | "denied" | "network" | "unknown";
        detail: string;
    }>;
    /**
     * Emit tool start event with execution tracking
     * @param toolName - Name of the tool being executed
     * @param input - Input parameters for the tool
     * @param startTime - Timestamp when execution started
     * @returns executionId for tracking this specific execution
     */
    emitToolStart(toolName: string, input: unknown, startTime?: number): string;
    /**
     * Emit tool end event with execution summary
     * @param toolName - Name of the tool that finished
     * @param result - Result from the tool execution
     * @param error - Error message if execution failed
     * @param startTime - When execution started
     * @param endTime - When execution finished
     * @param executionId - Optional execution ID for tracking
     */
    emitToolEnd(toolName: string, result?: unknown, error?: string, startTime?: number, endTime?: number, executionId?: string): void;
    /**
     * Get current tool execution contexts for stream metadata
     */
    getCurrentToolExecutions(): ToolExecutionContext[];
    /**
     * Get tool execution history
     */
    getToolExecutionHistory(): ToolExecutionSummary[];
    /**
     * Clear current stream tool executions (called at stream start)
     */
    clearCurrentStreamExecutions(): void;
    /**
     * Register a custom tool that will be available to all AI providers
     * @param name - Unique name for the tool
     * @param tool - Tool in MCPExecutableTool format (unified MCP protocol type)
     */
    registerTool(name: string, tool: MCPExecutableTool, options?: ToolRegistrationOptions): void;
    /**
     * Set the context that will be passed to tools during execution
     * This context will be merged with any runtime context passed by the AI model
     * @param context - Context object containing session info, tokens, shop data, etc.
     */
    setToolContext(context: Record<string, unknown>): void;
    /**
     * Get the current tool execution context
     * @returns Current context or undefined if not set
     */
    getToolContext(): Record<string, unknown> | undefined;
    /**
     * Clear the tool execution context
     */
    clearToolContext(): void;
    /**
     * Register multiple tools at once - Supports both object and array formats
     * @param tools - Object mapping tool names to MCPExecutableTool format OR Array of tools with names
     *
     * Object format (existing): { toolName: MCPExecutableTool, ... }
     * Array format (Lighthouse compatible): [{ name: string, tool: MCPExecutableTool }, ...]
     */
    registerTools(tools: Record<string, MCPExecutableTool> | Array<{
        name: string;
        tool: MCPExecutableTool;
    }>): void;
    /**
     * Unregister a custom tool
     * @param name - Name of the tool to remove
     * @returns true if the tool was removed, false if it didn't exist
     */
    unregisterTool(name: string): boolean;
    /**
     * Register a global tool middleware that runs on every tool execution.
     * Middleware receives the tool, params, context, and a next() function.
     * @param middleware - The middleware function to register
     * @returns this (for chaining)
     */
    useToolMiddleware(middleware: import("./types/index.js").ToolMiddleware): this;
    /**
     * Get all registered tool middlewares
     */
    getToolMiddlewares(): import("./types/index.js").ToolMiddleware[];
    /**
     * Flush any pending batched tool calls immediately
     */
    flushToolBatch(): Promise<void>;
    /**
     * Get the current MCP enhancements configuration
     */
    getMCPEnhancementsConfig(): MCPEnhancementsConfig | undefined;
    /**
     * Update agentic loop report metadata for a conversation session.
     * Upserts a report entry by reportId — updates existing or adds new.
     * Only supported when using Redis conversation memory.
     *
     * @param sessionId The session identifier
     * @param report The agentic loop report metadata to upsert
     * @param userId Optional user identifier
     * @throws Error if conversation memory is not initialized or is not Redis-backed
     *
     * @example
     * ```typescript
     * await neurolink.updateAgenticLoopReport("session-123", {
     *   reportId: "report-abc",
     *   reportType: "META",
     *   reportStatus: "INPROGRESS",
     * });
     * ```
     */
    updateAgenticLoopReport(sessionId: string, report: import("./types/index.js").AgenticLoopReportMetadata, userId?: string): Promise<void>;
    /**
     * Get all registered custom tools
     * @returns Map of tool names to MCPExecutableTool format
     */
    getCustomTools(): Map<string, MCPExecutableTool>;
    /**
     * Add an in-memory MCP server (from git diff)
     * Allows registration of pre-instantiated server objects
     * @param serverId - Unique identifier for the server
     * @param serverInfo - Server configuration
     */
    addInMemoryMCPServer(serverId: string, serverInfo: MCPServerInfo): Promise<void>;
    /**
     * Get all registered in-memory servers as a Map for ID-based lookup.
     *
     * This method is primarily used when you need O(1) lookup by server ID,
     * such as in `testMCPServer()` for checking if a specific server exists.
     *
     * @returns Map of server IDs to MCPServerInfo
     * @see {@link getInMemoryServerInfos} for array-based access (useful for iteration/spreading)
     */
    getInMemoryServers(): Map<string, MCPServerInfo>;
    /**
     * Get in-memory servers as an array of MCPServerInfo.
     *
     * This method is the canonical source for in-memory server filtering.
     * It fetches from the centralized tool registry and filters servers
     * with the "in-memory" category.
     *
     * Use this method when you need to:
     * - Iterate over all in-memory servers
     * - Spread servers into another array (e.g., in `listMCPServers()`)
     * - Get a count of in-memory servers
     *
     * @returns Array of MCPServerInfo for in-memory servers
     * @see {@link getInMemoryServers} for Map-based access (useful for ID lookups)
     */
    getInMemoryServerInfos(): MCPServerInfo[];
    /**
     * Get auto-discovered servers as MCPServerInfo - ZERO conversion needed
     * @returns Array of MCPServerInfo
     */
    getAutoDiscoveredServerInfos(): MCPServerInfo[];
    /**
     * Execute a specific tool by name with robust error handling
     * Supports both custom tools and MCP server tools with timeout, retry, and circuit breaker patterns
     * @param toolName - Name of the tool to execute
     * @param params - Parameters to pass to the tool
     * @param options - Execution options including optional authentication context
     * @returns Tool execution result
     */
    executeTool<T = unknown>(toolName: string, params?: unknown, options?: {
        timeout?: number;
        maxRetries?: number;
        retryDelayMs?: number;
        /** Disable tool result caching for this call */
        disableToolCache?: boolean;
        /** Bypass the request batcher for this call */
        bypassBatcher?: boolean;
        authContext?: {
            userId?: string;
            sessionId?: string;
            user?: Record<string, unknown>;
            [key: string]: unknown;
        };
    }): Promise<T>;
    private createToolExecutionContext;
    private executeToolWithSpan;
    private prepareToolExecutionState;
    private runPreparedToolExecution;
    private handleSuccessfulToolExecution;
    private handleFailedToolExecution;
    /**
     * Internal tool execution method with MCP enhancements wired in:
     * - ToolCache: check/store cached results for non-destructive tools
     * - ToolRouter: route to best server when same tool exists on multiple servers
     * - Annotations: skip cache for destructive tools, retry safe tools on failure
     * - Middleware: apply global middleware chain before execution
     */
    private executeToolInternal;
    /**
     * Get tool annotations for execution decisions (cache, retry).
     * Checks cached tool list first, falls back to inference from tool name.
     */
    private getToolAnnotationsForExecution;
    /**
     * Get all available tools including custom and in-memory ones
     * @returns Array of available tools with metadata
     */
    private invalidateToolCache;
    getAllAvailableTools(): Promise<ToolInfo[]>;
    /**
     * Get comprehensive status of all AI providers
     * Primary method for provider health checking and diagnostics
     */
    getProviderStatus(options?: {
        quiet?: boolean;
    }): Promise<ProviderStatus[]>;
    /**
     * Test a specific AI provider's connectivity and authentication
     * @param providerName - Name of the provider to test
     * @returns Promise resolving to true if provider is working
     */
    testProvider(providerName: string): Promise<boolean>;
    /**
     * Internal method to test provider connection with minimal generation call
     */
    private testProviderConnection;
    /**
     * Get the best available AI provider based on configuration and availability
     * @param requestedProvider - Optional preferred provider name
     * @returns Promise resolving to the best provider name
     */
    getBestProvider(requestedProvider?: string): Promise<string>;
    /**
     * Get list of all available AI provider names
     * @returns Array of supported provider names
     */
    getAvailableProviders(): Promise<string[]>;
    /**
     * Validate if a provider name is supported
     * @param providerName - Provider name to validate
     * @returns True if provider name is valid
     */
    isValidProvider(providerName: string): Promise<boolean>;
    /**
     * Get comprehensive MCP (Model Context Protocol) status information
     * @returns Promise resolving to MCP status details
     */
    getMCPStatus(): Promise<MCPStatus>;
    /**
     * List all configured MCP servers with their status
     * @returns Promise resolving to array of MCP server information
     */
    listMCPServers(): Promise<MCPServerInfo[]>;
    /**
     * Test connectivity to a specific MCP server
     * @param serverId - ID of the MCP server to test
     * @returns Promise resolving to true if server is reachable
     */
    testMCPServer(serverId: string): Promise<boolean>;
    /**
     * Check if a provider has the required environment variables configured
     * @param providerName - Name of the provider to check
     * @returns Promise resolving to true if provider has required env vars
     */
    hasProviderEnvVars(providerName: string): Promise<boolean>;
    /**
     * Perform comprehensive health check on a specific provider
     * @param providerName - Name of the provider to check
     * @param options - Health check options
     * @returns Promise resolving to detailed health status
     */
    checkProviderHealth(providerName: string, options?: {
        timeout?: number;
        includeConnectivityTest?: boolean;
        includeModelValidation?: boolean;
        cacheResults?: boolean;
    }): Promise<{
        provider: string;
        isHealthy: boolean;
        isConfigured: boolean;
        hasApiKey: boolean;
        lastChecked: Date;
        error?: string;
        warning?: string;
        responseTime?: number;
        configurationIssues: string[];
        recommendations: string[];
    }>;
    /**
     * Check health of all supported providers
     * @param options - Health check options
     * @returns Promise resolving to array of health statuses for all providers
     */
    checkAllProvidersHealth(options?: {
        timeout?: number;
        includeConnectivityTest?: boolean;
        includeModelValidation?: boolean;
        cacheResults?: boolean;
    }): Promise<Array<{
        provider: string;
        isHealthy: boolean;
        isConfigured: boolean;
        hasApiKey: boolean;
        lastChecked: Date;
        error?: string;
        warning?: string;
        responseTime?: number;
        configurationIssues: string[];
        recommendations: string[];
    }>>;
    /**
     * Get a summary of provider health across all supported providers
     * @returns Promise resolving to health summary statistics
     */
    getProviderHealthSummary(): Promise<{
        total: number;
        healthy: number;
        configured: number;
        hasIssues: number;
        healthyProviders: string[];
        unhealthyProviders: string[];
        recommendations: string[];
    }>;
    /**
     * Clear provider health cache (useful for re-testing after configuration changes)
     * @param providerName - Optional specific provider to clear cache for
     */
    clearProviderHealthCache(providerName?: string): Promise<void>;
    /**
     * Get execution metrics for all tools
     * @returns Object with execution metrics for each tool
     */
    getToolExecutionMetrics(): Record<string, {
        totalExecutions: number;
        successfulExecutions: number;
        failedExecutions: number;
        successRate: number;
        averageExecutionTime: number;
        lastExecutionTime: number;
        errorCategories: Record<string, number>;
    }>;
    /**
     * NL-004: Set model alias/deprecation configuration.
     * Models in the alias map will be warned, redirected, or blocked based on their action.
     * @param config - Model alias configuration with aliases map
     */
    setModelAliasConfig(config: import("./types/index.js").ModelAliasConfig): void;
    /**
     * Get circuit breaker status for all tools
     * @returns Object with circuit breaker status for each tool
     */
    getToolCircuitBreakerStatus(): Record<string, {
        state: "closed" | "open" | "half-open";
        failureCount: number;
        isHealthy: boolean;
    }>;
    /**
     * Reset circuit breaker for a specific tool
     * @param toolName - Name of the tool to reset circuit breaker for
     */
    resetToolCircuitBreaker(toolName: string): void;
    /**
     * Clear all tool execution metrics
     */
    clearToolExecutionMetrics(): void;
    /**
     * Get comprehensive tool health report
     * @returns Detailed health report for all tools
     */
    getToolHealthReport(): Promise<{
        totalTools: number;
        healthyTools: number;
        unhealthyTools: number;
        tools: Record<string, {
            name: string;
            isHealthy: boolean;
            metrics: {
                totalExecutions: number;
                successRate: number;
                averageExecutionTime: number;
                lastExecutionTime: number;
                errorCategories: Record<string, number>;
            };
            circuitBreaker: {
                state: "closed" | "open" | "half-open";
                failureCount: number;
            };
            issues: string[];
            recommendations: string[];
        }>;
    }>;
    /**
     * Initialize conversation memory if enabled (public method for explicit initialization)
     * This is useful for testing or when you want to ensure conversation memory is ready
     * @returns Promise resolving to true if initialization was successful, false otherwise
     */
    ensureConversationMemoryInitialized(): Promise<boolean>;
    /**
     * Get conversation memory statistics (public API)
     */
    getConversationStats(): Promise<import("./types/conversation.js").ConversationMemoryStats>;
    /**
     * Get complete conversation history for a specific session (public API)
     * @param sessionId - The session ID to retrieve history for
     * @returns Array of ChatMessage objects in chronological order, or empty array if session doesn't exist
     */
    getConversationHistory(sessionId: string): Promise<ChatMessage[]>;
    /**
     * Clear conversation history for a specific session (public API)
     */
    clearConversationSession(sessionId: string): Promise<boolean>;
    /**
     * Clear all conversation history (public API)
     */
    clearAllConversations(): Promise<void>;
    /**
     * Store tool executions in conversation memory if enabled and Redis is configured
     * @param sessionId - Session identifier
     * @param userId - User identifier (optional)
     * @param toolCalls - Array of tool calls
     * @param toolResults - Array of tool results
     * @param currentTime - Date when the tool execution occurred (optional)
     * @returns Promise resolving when storage is complete
     */
    storeToolExecutions(sessionId: string, userId: string | undefined, toolCalls: Array<{
        toolCallId?: string;
        toolName?: string;
        args?: Record<string, unknown>;
        [key: string]: unknown;
    }>, toolResults: Array<{
        toolCallId?: string;
        result?: unknown;
        error?: string;
        [key: string]: unknown;
    }>, currentTime?: Date): Promise<void>;
    /**
     * Check if tool execution storage is available
     * @returns boolean indicating if Redis storage is configured and available
     */
    isToolExecutionStorageAvailable(): boolean;
    /**
     * Get the raw messages array for a session.
     * Returns the full messages list without context filtering or summarization.
     * @param sessionId - The session ID to retrieve messages for
     * @returns Array of ChatMessage objects, or empty array if session doesn't exist
     */
    getSessionMessages(sessionId: string, userId?: string): Promise<ChatMessage[]>;
    /**
     * Replace the entire messages array for a session.
     * @param sessionId - The session ID to update
     * @param messages - The new messages array
     * @param userId - Optional user ID for scoped Redis key lookup
     */
    setSessionMessages(sessionId: string, messages: ChatMessage[], userId?: string): Promise<void>;
    /**
     * Modify the last assistant message in a session using a transformer function.
     * Convenience wrapper around getSessionMessages/setSessionMessages.
     * @param sessionId - The session ID to modify
     * @param transformer - Function that receives the last assistant message content and returns the modified content
     * @param userId - Optional user ID for scoped Redis key lookup
     * @returns true if a message was modified, false if no assistant message was found
     */
    modifyLastAssistantMessage(sessionId: string, transformer: (content: string) => string, userId?: string): Promise<boolean>;
    /**
     * Add an external MCP server
     * Automatically discovers and registers tools from the server
     * @param serverId - Unique identifier for the server
     * @param config - External MCP server configuration
     * @returns Operation result with server instance
     */
    addExternalMCPServer(serverId: string, config: MCPServerInfo): Promise<ExternalMCPOperationResult<ExternalMCPServerInstance>>;
    /**
     * Remove an external MCP server
     * Stops the server and removes all its tools
     * @param serverId - ID of the server to remove
     * @returns Operation result
     */
    removeExternalMCPServer(serverId: string): Promise<ExternalMCPOperationResult<void>>;
    /**
     * List all external MCP servers
     * @returns Array of server health information
     */
    listExternalMCPServers(): Array<{
        serverId: string;
        status: string;
        toolCount: number;
        uptime: number;
        isHealthy: boolean;
        config: MCPServerInfo;
    }>;
    /**
     * Get external MCP server status
     * @param serverId - ID of the server
     * @returns Server instance or undefined if not found
     */
    getExternalMCPServer(serverId: string): ExternalMCPServerInstance | undefined;
    /**
     * Execute a tool from an external MCP server
     * @param serverId - ID of the server
     * @param toolName - Name of the tool
     * @param parameters - Tool parameters
     * @param options - Execution options
     * @returns Tool execution result
     */
    executeExternalMCPTool(serverId: string, toolName: string, parameters: JsonObject, options?: {
        timeout?: number;
    }): Promise<unknown>;
    /**
     * Get all tools from external MCP servers
     * @returns Array of external tool information
     */
    getExternalMCPTools(): ExternalMCPToolInfo[];
    /**
     * Get tools from a specific external MCP server
     * @param serverId - ID of the server
     * @returns Array of tool information for the server
     */
    getExternalMCPServerTools(serverId: string): ExternalMCPToolInfo[];
    /**
     * Test connection to an external MCP server
     * @param config - Server configuration to test
     * @returns Test result with connection status
     */
    testExternalMCPConnection(config: MCPServerInfo): Promise<BatchOperationResult>;
    /**
     * Get external MCP server manager statistics
     * @returns Statistics about external servers and tools
     */
    getExternalMCPStatistics(): {
        totalServers: number;
        connectedServers: number;
        failedServers: number;
        totalTools: number;
        totalConnections: number;
        totalErrors: number;
    };
    /**
     * Shutdown all external MCP servers
     * Called automatically on process exit
     */
    shutdownExternalMCPServers(): Promise<void>;
    /**
     * Get the global elicitation manager for interactive tool input
     * Elicitation allows tools to request additional information from users during execution
     * @returns The global ElicitationManager instance
     * @example
     * ```typescript
     * const elicitationManager = neurolink.getElicitationManager();
     *
     * // Register a handler for confirmations
     * elicitationManager.registerHandler(async (request) => {
     *   if (request.type === 'confirmation') {
     *     const answer = await askUser(request.message);
     *     return { confirmed: answer === 'yes' };
     *   }
     * });
     * ```
     */
    getElicitationManager(): Promise<any>;
    /**
     * Register an elicitation handler for interactive tool input
     * Handlers are called when tools need user input during execution
     * @param handler - Function to handle elicitation requests
     * @example
     * ```typescript
     * neurolink.registerElicitationHandler(async (request) => {
     *   switch (request.type) {
     *     case 'confirmation':
     *       return { confirmed: await confirmWithUser(request.message) };
     *     case 'text':
     *       return { value: await promptUser(request.message) };
     *     case 'select':
     *       return { value: await selectFromOptions(request.options) };
     *   }
     * });
     * ```
     */
    registerElicitationHandler(handler: (request: unknown) => Promise<unknown>): Promise<void>;
    /**
     * Get the multi-server manager for load balancing and coordination
     * Allows managing multiple MCP servers with failover and load balancing
     * @returns The global MultiServerManager instance
     * @example
     * ```typescript
     * const multiServer = neurolink.getMultiServerManager();
     *
     * // Create a server group with load balancing
     * await multiServer.createServerGroup('ai-tools', {
     *   servers: ['openai-server', 'anthropic-server'],
     *   strategy: 'round-robin'
     * });
     * ```
     */
    getMultiServerManager(): Promise<any>;
    /**
     * Get the enhanced tool discovery service
     * Provides advanced search, filtering, and compatibility checking for tools
     * @returns EnhancedToolDiscovery instance
     * @example
     * ```typescript
     * const discovery = neurolink.getEnhancedToolDiscovery();
     *
     * // Search for tools by criteria
     * const results = await discovery.searchTools({
     *   category: 'data-processing',
     *   capabilities: ['streaming', 'batch'],
     *   minReliability: 0.9
     * });
     * ```
     */
    getEnhancedToolDiscovery(): Promise<any>;
    /**
     * Get the MCP registry client for discovering servers from registries
     * Supports multiple registry sources (official, community, custom)
     * @returns The global MCPRegistryClient instance
     * @example
     * ```typescript
     * const registryClient = neurolink.getMCPRegistryClient();
     *
     * // Search for servers
     * const servers = await registryClient.searchServers({
     *   query: 'database',
     *   categories: ['data', 'storage']
     * });
     *
     * // Get a well-known server config
     * const githubServer = registryClient.getWellKnownServer('github');
     * ```
     */
    getMCPRegistryClient(): Promise<any>;
    /**
     * Expose a NeuroLink agent as an MCP tool
     * This allows agents to be called by other systems via MCP
     * @param agent - The agent to expose (must include id, name, description, and execute)
     * @param options - Exposure configuration options (prefix, defaultAnnotations, etc.)
     * @returns The exposed tool definition
     * @example
     * ```typescript
     * const agent = {
     *   id: 'my-agent',
     *   name: 'My Agent',
     *   description: 'An agent that processes data',
     *   execute: async (params) => { ... }
     * };
     * const tool = await neurolink.exposeAgentAsTool(agent, {
     *   prefix: 'agent_'
     * });
     * ```
     */
    exposeAgentAsTool(agent: {
        id: string;
        name: string;
        description: string;
        execute: (params: unknown, context?: unknown) => Promise<unknown>;
    }, options?: {
        prefix?: string;
        includeMetadataInDescription?: boolean;
        wrapWithContext?: boolean;
        executionTimeout?: number;
        enableLogging?: boolean;
    }): Promise<import("./types/mcp.js").ExposureResult>;
    /**
     * Expose a workflow as an MCP tool
     * This allows workflows to be called by other systems via MCP
     * @param workflow - The workflow to expose (must include id, name, description, and execute)
     * @param options - Exposure configuration options (prefix, defaultAnnotations, etc.)
     * @returns The exposed tool definition
     * @example
     * ```typescript
     * const workflow = {
     *   id: 'data-pipeline',
     *   name: 'Data Pipeline',
     *   description: 'Runs the data processing pipeline',
     *   execute: async (params) => { ... }
     * };
     * const tool = await neurolink.exposeWorkflowAsTool(workflow, {
     *   prefix: 'workflow_'
     * });
     * ```
     */
    exposeWorkflowAsTool(workflow: {
        id: string;
        name: string;
        description: string;
        execute: (params: unknown, context?: unknown) => Promise<unknown>;
        steps?: Array<{
            id: string;
            name: string;
            description?: string;
        }>;
    }, options?: {
        prefix?: string;
        includeMetadataInDescription?: boolean;
        wrapWithContext?: boolean;
        executionTimeout?: number;
        enableLogging?: boolean;
    }): Promise<import("./types/mcp.js").ExposureResult>;
    /**
     * Get the tool integration manager for middleware and elicitation
     * Provides advanced tool wrapping with confirmation, timeout, retry, etc.
     * @returns The global ToolIntegrationManager instance
     * @example
     * ```typescript
     * const integration = neurolink.getToolIntegrationManager();
     *
     * // Register a tool with middleware
     * integration.registerTool(myTool, {
     *   timeout: 30000,
     *   retries: 3,
     *   requireConfirmation: true
     * });
     * ```
     */
    getToolIntegrationManager(): Promise<any>;
    /**
     * Convert NeuroLink tools to MCP format
     * Useful for exposing local tools to external MCP clients
     * @param tools - Array of NeuroLink tool definitions
     * @param options - Conversion options
     * @returns Array of MCP-formatted tools
     * @example
     * ```typescript
     * const mcpTools = neurolink.convertToolsToMCPFormat([
     *   { name: 'myTool', description: 'Does something', execute: async () => {} }
     * ]);
     * ```
     */
    convertToolsToMCPFormat(tools: Array<{
        name: string;
        description: string;
        execute?: (params: unknown) => unknown;
    }>, options?: {
        namespacePrefix?: string;
    }): Promise<any>;
    /**
     * Convert MCP tools to NeuroLink format
     * Useful for importing tools from external MCP servers
     * @param tools - Array of MCP tool definitions
     * @param options - Conversion options
     * @returns Array of NeuroLink-formatted tools
     * @example
     * ```typescript
     * const neurolinkTools = neurolink.convertToolsFromMCPFormat(externalTools, {
     *   removeNamespacePrefix: 'external_'
     * });
     * ```
     */
    convertToolsFromMCPFormat(tools: Array<{
        name: string;
        description: string;
        inputSchema?: unknown;
    }>, options?: {
        removeNamespacePrefix?: string;
    }): Promise<any>;
    /**
     * Get tool annotations and safety information
     * Provides insights about tool behavior, safety levels, and retry-ability
     * @param toolName - Name of the tool to analyze
     * @returns Tool annotation summary
     * @example
     * ```typescript
     * const annotations = await neurolink.getToolAnnotations('deleteFile');
     * // Returns: { destructive: true, requiresConfirmation: true, safeToRetry: false }
     * ```
     */
    getToolAnnotations(toolName: string): Promise<{
        annotations: MCPToolAnnotations;
        summary: string;
    } | null>;
    /**
     * Convert external MCP tools to Vercel AI SDK tool format
     * This allows AI providers to use external tools directly
     */
    private convertExternalMCPToolsToAISDKFormat;
    /**
     * Convert JSON Schema to AI SDK compatible format
     * For now, we'll skip schema validation and let the AI SDK handle parameters dynamically
     */
    private convertJSONSchemaToAISDKFormat;
    /**
     * Unregister external MCP tools from a specific server
     */
    private unregisterExternalMCPToolsFromRegistry;
    /**
     * Unregister a specific external MCP tool from the main registry
     */
    private unregisterExternalMCPToolFromRegistry;
    /**
     * Lazily initialize conversation memory when needed
     * This is called the first time a generate or stream operation is performed
     */
    private lazyInitializeConversationMemory;
    /**
     * Unregister all external MCP tools from the main registry
     */
    private unregisterAllExternalMCPToolsFromRegistry;
    /**
     * Create an evaluation pipeline with the specified configuration or preset.
     * Pipelines orchestrate multiple scorers to evaluate AI responses comprehensively.
     *
     * @param configOrPreset - Pipeline configuration object or preset name
     * @returns Initialized evaluation pipeline
     *
     * @example Using a preset
     * ```typescript
     * const neurolink = new NeuroLink();
     * const pipeline = await neurolink.createEvaluationPipeline('rag');
     * const result = await pipeline.execute({
     *   query: 'What is the capital of France?',
     *   response: 'Paris is the capital of France.',
     *   context: ['France is a country in Europe. Paris is its capital.']
     * });
     * console.log(result.overallScore, result.passed);
     * ```
     *
     * @example Using custom configuration
     * ```typescript
     * const pipeline = await neurolink.createEvaluationPipeline({
     *   name: 'custom-quality',
     *   scorers: [
     *     { id: 'toxicity', config: { threshold: 0.9 } },
     *     { id: 'hallucination', config: { weight: 1.5 } },
     *     { id: 'answer-relevancy' }
     *   ],
     *   aggregation: { method: 'weighted' },
     *   passThreshold: 0.8
     * });
     * ```
     */
    createEvaluationPipeline(configOrPreset: import("./types/index.js").PipelineConfig | "safety" | "rag" | "quality" | "comprehensive" | "minimal" | "summarization" | "customerSupport" | "codeGeneration"): Promise<import("./evaluation/pipeline/evaluationPipeline.js").EvaluationPipeline>;
    /**
     * Evaluate an AI response using the specified pipeline or scorers.
     * This is a convenience method that creates a pipeline and executes it in one call.
     *
     * @param input - Scorer input containing query, response, and optional context
     * @param options - Evaluation options including pipeline preset or custom scorers
     * @returns Evaluation pipeline result with scores and pass/fail status
     *
     * @example Using a preset
     * ```typescript
     * const neurolink = new NeuroLink();
     * const result = await neurolink.evaluate(
     *   {
     *     query: 'Explain quantum computing',
     *     response: 'Quantum computing uses qubits...'
     *   },
     *   { pipeline: 'quality' }
     * );
     * console.log(`Score: ${result.overallScore}, Passed: ${result.passed}`);
     * ```
     *
     * @example Using specific scorers
     * ```typescript
     * const result = await neurolink.evaluate(
     *   {
     *     query: 'What causes rain?',
     *     response: 'Rain is caused by water vapor...',
     *     context: ['The water cycle involves evaporation...']
     *   },
     *   { scorers: ['hallucination', 'faithfulness', 'answer-relevancy'] }
     * );
     * ```
     *
     * @example Full RAG evaluation
     * ```typescript
     * const result = await neurolink.evaluate(
     *   {
     *     query: 'Who wrote Hamlet?',
     *     response: 'Shakespeare wrote Hamlet in 1600.',
     *     context: ['William Shakespeare wrote Hamlet around 1600-1601.'],
     *     groundTruth: 'William Shakespeare'
     *   },
     *   { pipeline: 'rag' }
     * );
     * ```
     */
    evaluate(input: import("./types/index.js").ScorerInput, options?: {
        /** Pipeline preset to use */
        pipeline?: "safety" | "rag" | "quality" | "comprehensive" | "minimal" | "summarization" | "customerSupport" | "codeGeneration";
        /** Specific scorers to use (alternative to pipeline) */
        scorers?: string[];
        /** Pass threshold override (0-1) */
        passThreshold?: number;
        /** Execution mode */
        executionMode?: "parallel" | "sequential";
        /** Correlation ID for tracing */
        correlationId?: string;
        /** Overall evaluation timeout in milliseconds */
        timeoutMs?: number;
    }): Promise<import("./types/index.js").PipelineResult>;
    /**
     * Score a response using a single scorer.
     * Useful for quick, targeted evaluations without the overhead of a full pipeline.
     *
     * @param scorerId - The ID of the scorer to use (e.g., 'toxicity', 'hallucination')
     * @param input - Scorer input containing query, response, and optional context
     * @param config - Optional scorer configuration overrides
     * @returns Score result with value, reasoning, and pass/fail status
     *
     * @example Basic scoring
     * ```typescript
     * const neurolink = new NeuroLink();
     * const result = await neurolink.score('toxicity', {
     *   query: '',
     *   response: 'This is a helpful response about cooking recipes.'
     * });
     * console.log(`Toxicity Score: ${result.score}/10, Passed: ${result.passed}`);
     * ```
     *
     * @example Hallucination detection
     * ```typescript
     * const result = await neurolink.score('hallucination', {
     *   query: 'What year was the Eiffel Tower built?',
     *   response: 'The Eiffel Tower was built in 1889.',
     *   context: ['The Eiffel Tower was constructed from 1887-1889.']
     * });
     * console.log(`Score: ${result.score}, Reasoning: ${result.reasoning}`);
     * ```
     *
     * @example With custom threshold
     * ```typescript
     * const result = await neurolink.score(
     *   'faithfulness',
     *   {
     *     query: 'Summarize the article',
     *     response: 'The article discusses...',
     *     context: ['Article content here...']
     *   },
     *   { threshold: 0.85, weight: 1.5 }
     * );
     * ```
     */
    score(scorerId: string, input: import("./types/index.js").ScorerInput, config?: import("./types/index.js").ScorerConfig): Promise<import("./types/index.js").ScoreResult>;
    /**
     * Get a list of all available scorers and their metadata.
     * Useful for discovering what evaluation capabilities are available.
     *
     * @param options - Filter options
     * @returns Array of scorer metadata
     *
     * @example List all scorers
     * ```typescript
     * const neurolink = new NeuroLink();
     * const scorers = await neurolink.getAvailableScorers();
     * for (const scorer of scorers) {
     *   console.log(`${scorer.id}: ${scorer.description} (${scorer.type})`);
     * }
     * ```
     *
     * @example Filter by category
     * ```typescript
     * const safetyScorers = await neurolink.getAvailableScorers({
     *   category: 'safety'
     * });
     * console.log('Safety scorers:', safetyScorers.map(s => s.id));
     * ```
     *
     * @example Filter by type
     * ```typescript
     * const ruleBasedScorers = await neurolink.getAvailableScorers({
     *   type: 'rule'
     * });
     * ```
     */
    getAvailableScorers(options?: {
        /** Filter by category */
        category?: import("./types/index.js").ScorerCategory;
        /** Filter by type */
        type?: import("./types/index.js").ScorerType;
    }): Promise<import("./types/index.js").ScorerMetadata[]>;
    /**
     * Get a list of available evaluation pipeline presets.
     * Presets are pre-configured pipelines for common evaluation scenarios.
     *
     * @returns Array of preset names
     *
     * @example
     * ```typescript
     * const neurolink = new NeuroLink();
     * const presets = await neurolink.getEvaluationPresets();
     * console.log('Available presets:', presets);
     * // Output: ['safety', 'rag', 'quality', 'comprehensive', 'minimal', ...]
     * ```
     */
    getEvaluationPresets(): Promise<string[]>;
    /**
     * Get details of a specific evaluation preset.
     *
     * @param presetName - Name of the preset
     * @returns Pipeline configuration for the preset
     *
     * @example
     * ```typescript
     * const neurolink = new NeuroLink();
     * const ragPreset = await neurolink.getEvaluationPreset('rag');
     * console.log('RAG preset scorers:', ragPreset.scorers.map(s => s.id));
     * console.log('Pass threshold:', ragPreset.passThreshold);
     * ```
     */
    getEvaluationPreset(presetName: "safety" | "rag" | "quality" | "comprehensive" | "minimal" | "summarization" | "customerSupport" | "codeGeneration"): Promise<import("./types/index.js").PipelineConfig>;
    /**
     * Dispose of all resources and cleanup connections
     * Call this method when done using the NeuroLink instance to prevent resource leaks
     * Especially important in test environments where multiple instances are created
     */
    dispose(): Promise<void>;
    /**
     * Get the tool registry instance
     * Used internally by server adapters for tool management
     * @returns The MCPToolRegistry instance
     */
    getToolRegistry(): MCPToolRegistry;
    /**
     * Manually trigger context compaction for a session.
     * Runs the full 4-stage compaction pipeline.
     */
    compactSession(sessionId: string, config?: CompactionConfig): Promise<CompactionResult | null>;
    /**
     * Get context usage statistics for a session.
     * Returns token counts, usage ratio, and breakdown by category.
     */
    getContextStats(sessionId: string, provider?: string, model?: string): Promise<{
        estimatedInputTokens: number;
        availableInputTokens: number;
        usageRatio: number;
        shouldCompact: boolean;
        messageCount: number;
    } | null>;
    /**
     * Check if a session needs compaction.
     */
    needsCompaction(sessionId: string, provider?: string, model?: string): boolean;
    /**
     * Set the authentication provider for the NeuroLink instance
     *
     * @param config - Auth provider or configuration to create one
     */
    setAuthProvider(config: NeuroLinkAuthConfig): Promise<void>;
    private initializeAuthProviderFromConfig;
    /**
     * Get the currently configured authentication provider
     */
    getAuthProvider(): AuthProvider | undefined;
    /**
     * Lazily initialize the auth provider from pendingAuthConfig.
     * Called on first use (generate/stream with auth token) to avoid
     * async work in the synchronous constructor.
     */
    private ensureAuthProvider;
    /**
     * Set the current authentication context for request handling.
     *
     * Delegates to the global AuthContextHolder so that auth state is NOT
     * stored as an instance field (which would leak between concurrent requests
     * sharing the same NeuroLink singleton). Prefer `runWithAuthContext()` from
     * `authContext.ts` for proper request-scoped context via AsyncLocalStorage.
     *
     * @param context - The authenticated user context
     */
    setAuthContext(context: AuthenticatedContext): Promise<void>;
    /**
     * Get the current authentication context.
     *
     * Checks AsyncLocalStorage first, then falls back to the global holder.
     */
    getAuthContext(): Promise<AuthenticatedContext | undefined>;
    /**
     * Clear the current authentication context
     */
    clearAuthContext(): Promise<void>;
    /**
     * Get the external server manager instance
     * Used internally by server adapters for external MCP server management
     * @returns The ExternalServerManager instance
     */
    getExternalServerManager(): ExternalServerManager;
    private buildResolutionContext;
    /**
     * Resolve dynamic arguments in GenerateOptions, mutating the options in place.
     * Only resolves fields that are functions; static values pass through unchanged.
     */
    private resolveDynamicOptions;
    private resolveDynamicFields;
}
export declare const neurolink: NeuroLink;
export default neurolink;