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error : new Error(String(error)),\n },\n '[autotel] Flush error',\n );\n throw error;\n }\n}\n\n/**\n * Shutdown telemetry and cleanup resources\n *\n * - Flushes all pending data\n * - Shuts down OpenTelemetry SDK\n * - Cleans up resources\n *\n * Call this before process exit.\n *\n * Always performs cleanup even if flush fails, preventing resource leaks\n * in serverless handlers or tests.\n *\n * @example Express server\n * ```typescript\n * const server = app.listen(3000)\n *\n * process.on('SIGTERM', async () => {\n * await server.close()\n * await shutdown()\n * process.exit(0)\n * })\n * ```\n */\nexport async function shutdown(): Promise {\n const logger = getLogger();\n let shutdownError: Error | null = null;\n\n // Attempt to flush (with queue shutdown so new events are rejected), but continue with cleanup even if it fails\n try {\n await flush({ forShutdown: true });\n } catch (error) {\n const err = error instanceof Error ? error : new Error(String(error));\n shutdownError = err;\n logger.error(\n {\n err,\n },\n '[autotel] Flush failed during shutdown, continuing cleanup',\n );\n }\n\n // Always shutdown SDK and clean up resources\n try {\n // Shutdown OpenTelemetry SDK\n const sdk = getSdk();\n if (sdk) {\n await sdk.shutdown();\n }\n } catch (error) {\n const err = error instanceof Error ? error : new Error(String(error));\n\n // Ignore ECONNREFUSED errors - this happens when no OTLP endpoint was configured\n // The SDK tries to flush exporters that don't exist, which is harmless\n const isConnectionRefused =\n typeof error === 'object' &&\n error !== null &&\n 'code' in error &&\n error.code === 'ECONNREFUSED';\n\n if (!isConnectionRefused) {\n // Only store/log non-connection errors\n if (!shutdownError) {\n shutdownError = err;\n }\n logger.error({ err }, '[autotel] SDK shutdown failed');\n }\n } finally {\n await _closeEmbeddedDevtools();\n\n // Clean up singleton Maps and queues to prevent memory leaks\n // This runs even if SDK shutdown fails\n const eventsQueue = getEventQueue();\n if (eventsQueue && typeof eventsQueue.cleanup === 'function') {\n eventsQueue.cleanup();\n }\n resetEvents();\n resetMetrics();\n resetEventQueue();\n }\n\n // Rethrow first error after cleanup completes\n // This allows tests and CI to detect failures while still ensuring cleanup\n if (shutdownError) {\n throw shutdownError;\n }\n}\n\n/**\n * Register automatic shutdown hooks for common signals\n *\n * Handles:\n * - SIGTERM (Docker/K8s graceful shutdown)\n * - SIGINT (Ctrl+C)\n *\n * @internal Called automatically on module load\n */\nfunction registerShutdownHooks(): void {\n if (typeof process === 'undefined') return; // Not in Node.js\n\n const signals: NodeJS.Signals[] = ['SIGTERM', 'SIGINT'];\n let shuttingDown = false;\n\n for (const signal of signals) {\n process.on(signal, async () => {\n if (shuttingDown) return; // Prevent double shutdown\n shuttingDown = true;\n\n if (process.env.NODE_ENV !== 'test') {\n getLogger().info(\n {},\n `[autotel] Received ${signal}, flushing telemetry...`,\n );\n }\n\n try {\n await shutdown();\n } catch (error) {\n getLogger().error(\n {\n err: error instanceof Error ? error : undefined,\n },\n '[autotel] Error during shutdown',\n );\n } finally {\n process.exit(0);\n }\n });\n }\n}\n\n// Auto-register shutdown hooks\nregisterShutdownHooks();\n","import { AsyncLocalStorage } from 'node:async_hooks';\nimport { trace as otelTrace } from '@opentelemetry/api';\nimport type { TraceContext } from './trace-context';\nimport { createTraceContext } from './trace-context';\nimport { recordStructuredError } from './structured-error';\nimport { flattenToAttributes } from './flatten-attributes';\nimport { emitCorrelatedEvent } from './correlated-events';\n\nconst POST_EMIT_FORK_HINT =\n \"For intentional background work tied to this request, use log.fork('label', fn) when available.\";\n\nfunction warnPostEmit(method: string, detail: string): void {\n console.warn(\n `[autotel] ${method} called after the wide event was emitted — ${detail} This data will not appear in observability. ${POST_EMIT_FORK_HINT}`,\n );\n}\n\nfunction mergeInto(\n target: Record,\n source: Record,\n): void {\n for (const key in source) {\n const sourceVal = source[key];\n if (sourceVal === undefined) continue;\n const targetVal = target[key];\n if (\n sourceVal !== null &&\n typeof sourceVal === 'object' &&\n !Array.isArray(sourceVal) &&\n targetVal !== null &&\n typeof targetVal === 'object' &&\n !Array.isArray(targetVal)\n ) {\n mergeInto(\n targetVal as Record,\n sourceVal as Record,\n );\n } else if (Array.isArray(targetVal) && Array.isArray(sourceVal)) {\n target[key] = [...targetVal, ...sourceVal];\n } else {\n target[key] = sourceVal;\n }\n }\n}\n\nconst requestContextStore = new AsyncLocalStorage();\n\nexport function runWithRequestContext(ctx: TraceContext, fn: () => T): T {\n return requestContextStore.run(ctx, fn);\n}\n\nexport interface RequestLogger {\n set(fields: Record): void;\n info(message: string, fields?: Record): void;\n warn(message: string, fields?: Record): void;\n error(error: Error | string, fields?: Record): void;\n getContext(): Record;\n emitNow(overrides?: Record): RequestLogSnapshot;\n fork(\n label: string,\n fn: () => void | Promise,\n options?: ForkOptions,\n ): void;\n}\n\nexport interface RequestLogSnapshot {\n timestamp: string;\n traceId: string;\n spanId: string;\n correlationId: string;\n context: Record;\n}\n\nexport interface RequestLoggerOptions {\n /** Callback invoked by emitNow() for manual fan-out. */\n onEmit?: (snapshot: RequestLogSnapshot) => void | Promise;\n}\n\n/**\n * Optional lifecycle hooks for adapters that need to track child loggers\n * spawned by `log.fork()` (e.g. active logger maps in framework integrations).\n */\nexport interface ForkLifecycle {\n /** Called after the child logger is created, before `fn` runs. */\n onChildEnter?: (child: RequestLogger) => void;\n /** Called after the child has finished (emit + drain), success or failure. */\n onChildExit?: (child: RequestLogger) => void;\n}\n\nexport interface ForkOptions {\n lifecycle?: ForkLifecycle;\n}\n\nfunction resolveContext(ctx?: TraceContext): TraceContext {\n if (ctx) return ctx;\n\n const stored = requestContextStore.getStore();\n if (stored) return stored;\n\n const span = otelTrace.getActiveSpan();\n if (!span) {\n throw new Error(\n '[autotel] getRequestLogger() requires an active span or runWithRequestContext(). Wrap your handler with trace() or use runWithRequestContext().',\n );\n }\n return createTraceContext(span);\n}\n\nexport function getRequestLogger(\n ctx?: TraceContext,\n options?: RequestLoggerOptions,\n): RequestLogger {\n const activeContext = resolveContext(ctx);\n let contextState: Record = {};\n let emitted = false;\n let lastSnapshot: RequestLogSnapshot | null = null;\n\n const addLogEvent = (\n level: 'info' | 'warn' | 'error',\n message: string,\n fields?: Record,\n ) => {\n const attrs = fields ? flattenToAttributes(fields) : undefined;\n emitCorrelatedEvent(activeContext, `log.${level}`, {\n message,\n ...(attrs ?? {}),\n });\n };\n\n const sealCheck = (method: string, keys: string[]): void => {\n if (emitted) {\n warnPostEmit(\n method,\n `Keys dropped: ${keys.length ? keys.join(', ') : '(empty)'}.`,\n );\n }\n };\n\n return {\n set(fields: Record) {\n sealCheck('log.set()', Object.keys(fields));\n if (emitted) return;\n mergeInto(contextState, fields);\n activeContext.setAttributes(flattenToAttributes(fields));\n },\n\n info(message: string, fields?: Record) {\n const keys = fields\n ? ['message', ...Object.keys(fields).filter((k) => k !== 'requestLogs')]\n : ['message'];\n sealCheck('log.info()', keys);\n if (emitted) return;\n addLogEvent('info', message, fields);\n if (fields) {\n mergeInto(contextState, fields);\n activeContext.setAttributes(flattenToAttributes(fields));\n }\n },\n\n warn(message: string, fields?: Record) {\n const keys = fields\n ? ['message', ...Object.keys(fields).filter((k) => k !== 'requestLogs')]\n : ['message'];\n sealCheck('log.warn()', keys);\n if (emitted) return;\n addLogEvent('warn', message, fields);\n activeContext.setAttribute('autotel.log.level', 'warn');\n if (fields) {\n mergeInto(contextState, fields);\n activeContext.setAttributes(flattenToAttributes(fields));\n }\n },\n\n error(error: Error | string, fields?: Record) {\n const keys = fields ? [...Object.keys(fields), 'error'] : ['error'];\n sealCheck('log.error()', keys);\n if (emitted) return;\n const err = typeof error === 'string' ? new Error(error) : error;\n recordStructuredError(activeContext, err);\n addLogEvent('error', err.message, fields);\n\n if (fields) {\n mergeInto(contextState, fields);\n activeContext.setAttributes(flattenToAttributes(fields));\n }\n activeContext.setAttribute('autotel.log.level', 'error');\n },\n\n getContext() {\n return { ...contextState };\n },\n\n emitNow(overrides?: Record): RequestLogSnapshot {\n if (emitted) {\n warnPostEmit('log.emitNow()', 'Ignoring duplicate emit.');\n return lastSnapshot as RequestLogSnapshot;\n }\n\n const mergedContext = {\n ...contextState,\n ...(overrides ?? {}),\n };\n const flattened = flattenToAttributes(mergedContext);\n activeContext.setAttributes(flattened);\n\n const snapshot: RequestLogSnapshot = {\n timestamp: new Date().toISOString(),\n traceId: activeContext.traceId,\n spanId: activeContext.spanId,\n correlationId: activeContext.correlationId,\n context: mergedContext,\n };\n\n emitCorrelatedEvent(activeContext, 'log.emit.manual', {\n ...flattened,\n });\n\n if (options?.onEmit) {\n Promise.resolve(options.onEmit(snapshot)).catch((error) => {\n console.warn('[autotel] request logger onEmit failed:', error);\n });\n }\n\n emitted = true;\n lastSnapshot = snapshot;\n return snapshot;\n },\n\n fork(\n label: string,\n fn: () => void | Promise,\n forkOptions?: ForkOptions,\n ): void {\n const parentRequestId = activeContext.correlationId;\n if (typeof parentRequestId !== 'string' || parentRequestId.length === 0) {\n throw new Error(\n '[autotel] log.fork() requires the parent logger to have a correlationId. ' +\n 'Ensure the request was created by autotel middleware.',\n );\n }\n\n const tracer = otelTrace.getTracer('autotel.request-logger');\n const lifecycle = forkOptions?.lifecycle;\n void tracer.startActiveSpan(`request.fork:${label}`, (childSpan) => {\n const childContext: TraceContext = {\n ...createTraceContext(childSpan),\n correlationId: crypto.randomUUID(),\n };\n\n requestContextStore.run(childContext, () => {\n const childLog = getRequestLogger(childContext);\n childLog.set({\n operation: label,\n _parentCorrelationId: parentRequestId,\n });\n\n lifecycle?.onChildEnter?.(childLog);\n\n void Promise.resolve()\n .then(() => fn())\n .then(() => {\n childLog.emitNow();\n })\n .catch((err: unknown) => {\n const error = err instanceof Error ? err : new Error(String(err));\n childLog.error(error);\n childLog.emitNow();\n })\n .finally(() => {\n try {\n lifecycle?.onChildExit?.(childLog);\n } catch (hookError) {\n console.warn(\n '[autotel] fork onChildExit hook threw:',\n hookError,\n );\n }\n childSpan.end();\n });\n });\n });\n },\n };\n}\n","export interface DrainOptions {\n /** Stable identifier used in error logs. */\n name: string;\n /** Return null to skip draining (e.g. missing API key in dev). */\n resolve: () => TConfig | null | Promise;\n /** Transform contexts into payloads. Defaults to identity. */\n transform?: (contexts: TContext[]) => TPayload[];\n /** Transport implementation. */\n send: (payloads: TPayload[], config: TConfig) => Promise;\n}\n\nexport interface HttpDrainRequest {\n url: string;\n headers: Record;\n body: string;\n}\n\nexport interface HttpDrainOptions<\n TContext,\n TConfig,\n TPayload = TContext,\n> extends Omit, 'send'> {\n encode: (payloads: TPayload[], config: TConfig) => HttpDrainRequest | null;\n timeoutMs?: number;\n retries?: number;\n resolveTimeoutMs?: (config: TConfig) => number | undefined;\n resolveRetries?: (config: TConfig) => number | undefined;\n}\n\nconst DEFAULT_TIMEOUT_MS = 5000;\nconst DEFAULT_RETRIES = 2;\n\nfunction delay(ms: number): Promise {\n return new Promise((resolve) => {\n const t = setTimeout(resolve, ms);\n t.unref?.();\n });\n}\n\nasync function postWithRetry(options: {\n name: string;\n request: HttpDrainRequest;\n timeoutMs: number;\n retries: number;\n}): Promise {\n const { name, request, timeoutMs, retries } = options;\n const attempts = Math.max(1, retries);\n let lastError: unknown;\n\n for (let attempt = 1; attempt <= attempts; attempt++) {\n const controller = new AbortController();\n const timeout = setTimeout(() => controller.abort(), timeoutMs);\n timeout.unref?.();\n try {\n const response = await fetch(request.url, {\n method: 'POST',\n headers: request.headers,\n body: request.body,\n signal: controller.signal,\n });\n if (!response.ok) {\n throw new Error(\n `[autotel/${name}] HTTP ${response.status} draining ${request.url}`,\n );\n }\n return;\n } catch (error) {\n lastError = error;\n if (attempt < attempts) {\n await delay(100 * attempt);\n }\n } finally {\n clearTimeout(timeout);\n }\n }\n\n throw lastError;\n}\n\nexport function defineDrain(\n options: DrainOptions,\n): (ctx: TContext | TContext[]) => Promise {\n return async (ctx: TContext | TContext[]) => {\n const contexts = Array.isArray(ctx) ? ctx : [ctx];\n if (contexts.length === 0) return;\n\n const config = await options.resolve();\n if (!config) return;\n\n const payloads = options.transform\n ? options.transform(contexts)\n : (contexts as unknown as TPayload[]);\n\n if (payloads.length === 0) return;\n\n try {\n await options.send(payloads, config);\n } catch (error) {\n console.error(`[autotel/${options.name}] drain failed:`, error);\n }\n };\n}\n\nexport function defineHttpDrain(\n options: HttpDrainOptions,\n): (ctx: TContext | TContext[]) => Promise {\n return defineDrain({\n name: options.name,\n resolve: options.resolve,\n transform: options.transform,\n send: async (payloads, config) => {\n const request = options.encode(payloads, config);\n if (!request) return;\n const timeoutMs =\n options.resolveTimeoutMs?.(config) ??\n options.timeoutMs ??\n DEFAULT_TIMEOUT_MS;\n const retries =\n options.resolveRetries?.(config) ?? options.retries ?? DEFAULT_RETRIES;\n\n await postWithRetry({\n name: options.name,\n request,\n timeoutMs,\n retries,\n });\n },\n });\n}\n","export interface EnrichContext> {\n event: TEvent;\n request?: {\n method?: string;\n path?: string;\n requestId?: string;\n };\n response?: {\n status?: number;\n };\n headers?: Record;\n}\n\nexport interface EnricherDefinition<\n TEvent extends Record,\n TValue extends object,\n> {\n /** Stable identifier used in error logs. */\n name: string;\n /** Top-level field to merge computed values into. */\n field: keyof TEvent & string;\n /** Return undefined to skip enrichment. */\n compute: (ctx: EnrichContext) => TValue | undefined;\n}\n\nexport interface EnricherOptions {\n /** Replace existing field value instead of merge. Default false. */\n overwrite?: boolean;\n}\n\nfunction isPlainObject(value: unknown): value is Record {\n return value !== null && typeof value === 'object' && !Array.isArray(value);\n}\n\nfunction mergeInto(\n target: Record,\n source: Record,\n): void {\n for (const key in source) {\n const sourceVal = source[key];\n if (sourceVal === undefined) continue;\n const targetVal = target[key];\n if (isPlainObject(sourceVal) && isPlainObject(targetVal)) {\n mergeInto(targetVal, sourceVal);\n } else {\n target[key] = sourceVal;\n }\n }\n}\n\nexport function defineEnricher<\n TEvent extends Record,\n TValue extends object,\n>(\n def: EnricherDefinition,\n options: EnricherOptions = {},\n): (ctx: EnrichContext) => void {\n return (ctx: EnrichContext) => {\n let computed: TValue | undefined;\n try {\n computed = def.compute(ctx);\n } catch (error) {\n console.error(`[autotel/${def.name}] enrich failed:`, error);\n return;\n }\n\n if (!computed) return;\n\n if (options.overwrite || !isPlainObject(ctx.event[def.field])) {\n (ctx.event as Record)[def.field] = computed;\n return;\n }\n\n mergeInto(\n ctx.event[def.field] as unknown as Record,\n computed as unknown as Record,\n );\n };\n}\n","/**\n * LLM-tuned histogram buckets.\n *\n * Default OpenTelemetry histogram buckets target HTTP latency (0ms–10s)\n * and small counter values. LLM workloads have very different shapes:\n *\n * - **Duration**: single-token prompts can be fast (50ms), long\n * generations and reasoning models can run for minutes. Default buckets\n * crush everything above 10s into one bucket.\n * - **Token usage**: heavily right-skewed. A single request can range\n * from tens of tokens to the million-token context windows.\n * - **Cost (USD)**: per-request values are tiny (fractions of a cent),\n * so linear buckets waste resolution at the low end.\n *\n * This module exposes empirically-chosen bucket arrays and a View helper\n * so users can apply them to their `MeterProvider` without knowing the\n * exact instrument names emitted by OpenAI/Anthropic/Traceloop plugins.\n *\n * @example\n * ```typescript\n * import { NodeSDK } from '@opentelemetry/sdk-node';\n * import { genAiMetricViews } from 'autotel';\n *\n * const sdk = new NodeSDK({\n * serviceName: 'my-agent',\n * views: [...genAiMetricViews()],\n * });\n * sdk.start();\n * ```\n */\n\nimport { AggregationType, type ViewOptions } from '@opentelemetry/sdk-metrics';\n\n/**\n * Duration buckets for LLM operations, in **seconds**. Covers fast\n * completions (50ms) through long-running reasoning jobs (5 min).\n *\n * Aligns with the OTel GenAI semantic conventions' published advice for\n * `gen_ai.client.operation.duration`.\n */\nexport const GEN_AI_DURATION_BUCKETS_SECONDS: readonly number[] = Object.freeze(\n [0.01, 0.05, 0.1, 0.25, 0.5, 1, 2, 5, 10, 20, 30, 60, 120, 300],\n);\n\n/**\n * Token-count buckets for prompt, completion, and total token histograms.\n * Ranges from tiny prompts to million-token context windows.\n *\n * Aligns with the OTel GenAI semantic conventions' published advice for\n * `gen_ai.client.token.usage`.\n */\nexport const GEN_AI_TOKEN_USAGE_BUCKETS: readonly number[] = Object.freeze([\n 1, 4, 16, 64, 256, 1_024, 4_096, 16_384, 65_536, 262_144, 1_048_576,\n 4_194_304,\n]);\n\n/**\n * USD cost buckets. Sub-cent resolution at the low end (fractions of a\n * cent per small call) up to tens of dollars (batch jobs, Opus/o1 runs).\n */\nexport const GEN_AI_COST_USD_BUCKETS: readonly number[] = Object.freeze([\n 0.000_01, 0.000_1, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50,\n]);\n\n/**\n * Instrument-level advice object for `createHistogram(name, advice)`.\n * Use when you control the instrument creation (e.g. custom business\n * LLM metrics); `genAiMetricViews()` is better when the metric comes\n * from a third-party plugin.\n */\nexport function llmHistogramAdvice(kind: 'duration' | 'tokens' | 'cost'): {\n advice: { explicitBucketBoundaries: number[] };\n} {\n const boundaries =\n kind === 'duration'\n ? GEN_AI_DURATION_BUCKETS_SECONDS\n : kind === 'tokens'\n ? GEN_AI_TOKEN_USAGE_BUCKETS\n : GEN_AI_COST_USD_BUCKETS;\n return { advice: { explicitBucketBoundaries: [...boundaries] } };\n}\n\n/**\n * Returns `View`s that re-bucket the standard OTel GenAI histograms. Pass\n * the result to your `MeterProvider`'s `views` option.\n *\n * Matches instrument names emitted by:\n * - OpenTelemetry GenAI autoinstrumentation\n * - OpenInference / OpenLLMetry (traceloop)\n * - Arize Phoenix, LangSmith, etc. that follow the OTel spec\n *\n * Add more instrument patterns via the `extra` argument if you emit\n * custom LLM metrics.\n */\nexport function genAiMetricViews(\n extra: {\n instrumentName: string;\n kind: 'duration' | 'tokens' | 'cost';\n }[] = [],\n): ViewOptions[] {\n const defaults: Array<{\n instrumentName: string;\n kind: 'duration' | 'tokens' | 'cost';\n }> = [\n { instrumentName: 'gen_ai.client.operation.duration', kind: 'duration' },\n { instrumentName: 'gen_ai.client.token.usage', kind: 'tokens' },\n // Autotel-emitted cost metric. No-op if you don't emit it.\n { instrumentName: 'gen_ai.client.cost.usd', kind: 'cost' },\n ];\n\n return [...defaults, ...extra].map(\n ({ instrumentName, kind }) =>\n ({\n instrumentName,\n aggregation: {\n type: AggregationType.EXPLICIT_BUCKET_HISTOGRAM,\n options: {\n boundaries:\n kind === 'duration'\n ? [...GEN_AI_DURATION_BUCKETS_SECONDS]\n : kind === 'tokens'\n ? [...GEN_AI_TOKEN_USAGE_BUCKETS]\n : [...GEN_AI_COST_USD_BUCKETS],\n },\n },\n }) satisfies ViewOptions,\n );\n}\n","/**\n * Span event helpers for LLM lifecycle, aligned with the OpenTelemetry\n * GenAI semantic conventions.\n *\n * Span events are timestamped points within a span — they render as dots\n * on the trace timeline in Jaeger / Tempo / Langfuse / Arize. Use them\n * to mark lifecycle moments the span attributes alone can't express:\n *\n * - When the prompt was sent (vs. when the first token arrived)\n * - When each retry attempt started, and why\n * - When a streaming response produced its first token (TTFT)\n * - When a tool was invoked\n *\n * Every helper pins the event name + attribute keys to the published\n * spec so downstream tooling (autotel-mcp, Langfuse, vendor UIs) can\n * render them consistently.\n *\n * @example\n * ```typescript\n * import { trace, recordPromptSent, recordResponseReceived, recordRetry } from 'autotel';\n *\n * export const chat = trace('chat', ctx => async (prompt: string) => {\n * recordPromptSent(ctx, { model: 'gpt-4o', messageCount: 1 });\n *\n * for (let attempt = 1; attempt <= 3; attempt++) {\n * try {\n * const res = await openai.chat.completions.create({...});\n * recordResponseReceived(ctx, {\n * model: res.model,\n * promptTokens: res.usage?.prompt_tokens,\n * completionTokens: res.usage?.completion_tokens,\n * finishReasons: res.choices.map(c => c.finish_reason),\n * });\n * return res;\n * } catch (err) {\n * recordRetry(ctx, { attempt, reason: 'rate_limit', delayMs: 500 });\n * await sleep(500 * attempt);\n * }\n * }\n * });\n * ```\n */\n\nimport type { TraceContext } from './trace-context';\nimport { emitCorrelatedEvent } from './correlated-events';\n\ntype EventAttrs = Record;\n\n/** Attributes expected on a `gen_ai.prompt.sent` event. */\nexport interface PromptSentEvent {\n /** Model the caller intends to invoke (may differ from response model). */\n model?: string;\n /** Estimated input token count, when known before the call. */\n promptTokens?: number;\n /** Number of messages in a chat request (system + user + assistant). */\n messageCount?: number;\n /** Free-form operation kind — `chat` / `completion` / `embedding`. */\n operation?: string;\n}\n\n/** Attributes expected on a `gen_ai.response.received` event. */\nexport interface ResponseReceivedEvent {\n /** Model the provider actually served (may be more specific than requested). */\n model?: string;\n promptTokens?: number;\n completionTokens?: number;\n totalTokens?: number;\n /** `stop`, `length`, `content_filter`, `tool_calls`, etc. */\n finishReasons?: string[];\n}\n\n/** Attributes expected on a `gen_ai.retry` event. */\nexport interface RetryEvent {\n attempt: number;\n /** `rate_limit` | `timeout` | `provider_error` | custom label. */\n reason?: string;\n /** How long we'll wait before the next attempt. */\n delayMs?: number;\n /** HTTP status that triggered the retry, when applicable. */\n statusCode?: number;\n}\n\n/** Attributes expected on a `gen_ai.tool.call` event. */\nexport interface ToolCallEvent {\n toolName: string;\n /** Call identifier so responses can be correlated back to calls. */\n toolCallId?: string;\n /** Pre-serialised tool arguments; omit if sensitive. */\n arguments?: string;\n}\n\n/** Attributes expected on a `gen_ai.stream.first_token` event. */\nexport interface StreamFirstTokenEvent {\n /** Tokens streamed so far, if the caller tracks that. */\n tokensSoFar?: number;\n}\n\n/**\n * Record that a prompt was dispatched to the provider. Typically called\n * before `await provider.chat.completions.create(...)`.\n */\nexport function recordPromptSent(\n ctx: TraceContext,\n event: PromptSentEvent = {},\n): void {\n emitCorrelatedEvent(ctx, 'gen_ai.prompt.sent', buildPromptSentAttrs(event));\n}\n\n/**\n * Record a successful provider response. Call after the response arrives\n * (for non-streaming) or after the stream completes.\n */\nexport function recordResponseReceived(\n ctx: TraceContext,\n event: ResponseReceivedEvent = {},\n): void {\n emitCorrelatedEvent(\n ctx,\n 'gen_ai.response.received',\n buildResponseAttrs(event),\n );\n}\n\n/**\n * Record a retry attempt on an LLM call. Call *before* sleeping for\n * `delayMs` so the event timestamp accurately marks when the retry\n * decision was made.\n */\nexport function recordRetry(ctx: TraceContext, event: RetryEvent): void {\n emitCorrelatedEvent(ctx, 'gen_ai.retry', buildRetryAttrs(event));\n}\n\n/**\n * Record a tool / function call made in the course of an agent step.\n * Emits an event rather than a child span because many frameworks fire\n * several tool calls within a single provider response.\n */\nexport function recordToolCall(ctx: TraceContext, event: ToolCallEvent): void {\n emitCorrelatedEvent(ctx, 'gen_ai.tool.call', buildToolCallAttrs(event));\n}\n\n/**\n * Record the time-to-first-token for a streaming response. Pair with\n * `recordResponseReceived` at the end so the span carries both the TTFT\n * marker and the final usage numbers.\n */\nexport function recordStreamFirstToken(\n ctx: TraceContext,\n event: StreamFirstTokenEvent = {},\n): void {\n emitCorrelatedEvent(\n ctx,\n 'gen_ai.stream.first_token',\n buildStreamFirstTokenAttrs(event),\n );\n}\n\n// ---- Attribute builders -------------------------------------------------\n\nfunction buildPromptSentAttrs(event: PromptSentEvent): EventAttrs {\n const attrs: EventAttrs = {};\n if (event.model) attrs['gen_ai.request.model'] = event.model;\n if (event.promptTokens !== undefined)\n attrs['gen_ai.usage.input_tokens'] = event.promptTokens;\n if (event.messageCount !== undefined)\n attrs['gen_ai.request.message_count'] = event.messageCount;\n if (event.operation) attrs['gen_ai.operation.name'] = event.operation;\n return attrs;\n}\n\nfunction buildResponseAttrs(event: ResponseReceivedEvent): EventAttrs {\n const attrs: EventAttrs = {};\n if (event.model) attrs['gen_ai.response.model'] = event.model;\n if (event.promptTokens !== undefined)\n attrs['gen_ai.usage.input_tokens'] = event.promptTokens;\n if (event.completionTokens !== undefined)\n attrs['gen_ai.usage.output_tokens'] = event.completionTokens;\n if (event.totalTokens !== undefined)\n attrs['gen_ai.usage.total_tokens'] = event.totalTokens;\n if (event.finishReasons && event.finishReasons.length > 0) {\n // Arrays aren't primitive AttributeValues on this context, so join.\n attrs['gen_ai.response.finish_reasons'] = event.finishReasons.join(',');\n }\n return attrs;\n}\n\nfunction buildRetryAttrs(event: RetryEvent): EventAttrs {\n const attrs: EventAttrs = { 'retry.attempt': event.attempt };\n if (event.reason) attrs['retry.reason'] = event.reason;\n if (event.delayMs !== undefined) attrs['retry.delay_ms'] = event.delayMs;\n if (event.statusCode !== undefined)\n attrs['http.response.status_code'] = event.statusCode;\n return attrs;\n}\n\nfunction buildToolCallAttrs(event: ToolCallEvent): EventAttrs {\n const attrs: EventAttrs = { 'gen_ai.tool.name': event.toolName };\n if (event.toolCallId) attrs['gen_ai.tool.call.id'] = event.toolCallId;\n if (event.arguments) attrs['gen_ai.tool.arguments'] = event.arguments;\n return attrs;\n}\n\nfunction buildStreamFirstTokenAttrs(event: StreamFirstTokenEvent): EventAttrs {\n const attrs: EventAttrs = {};\n if (event.tokensSoFar !== undefined)\n attrs['gen_ai.stream.tokens_so_far'] = event.tokensSoFar;\n return attrs;\n}\n"]}