autotel
Version:
Write Once, Observe Anywhere
1 lines • 53.2 kB
Source Map (JSON)
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// 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 ${signal}, flushing telemetry...`,\n );\n }\n\n try {\n await shutdown();\n } catch (error) {\n getLogger().error(\n {\n err: error instanceof Error ? error : undefined,\n },\n '[autotel] Error during shutdown',\n );\n } finally {\n process.exit(0);\n }\n });\n }\n}\n\n// Auto-register shutdown hooks\nregisterShutdownHooks();\n","import type { AttributeValue } from './trace-context';\n\n/**\n * Convert an unknown value to an OTel-compatible AttributeValue.\n * Returns undefined when the value cannot be represented.\n */\nexport function toAttributeValue(value: unknown): AttributeValue | undefined {\n if (\n typeof value === 'string' ||\n typeof value === 'number' ||\n typeof value === 'boolean'\n ) {\n return value;\n }\n if (Array.isArray(value)) {\n if (\n value.every((v) => typeof v === 'string') ||\n value.every((v) => typeof v === 'number') ||\n value.every((v) => typeof v === 'boolean')\n ) {\n return value as AttributeValue;\n }\n try {\n return JSON.stringify(value);\n } catch {\n return '<serialization-failed>';\n }\n }\n if (value instanceof Date) {\n return value.toISOString();\n }\n if (value instanceof Error) {\n return value.message;\n }\n return undefined;\n}\n\n/**\n * Recursively flatten a nested object into dot-notation OTel attributes.\n * Includes circular reference protection via WeakSet.\n */\nexport function flattenToAttributes(\n fields: Record<string, unknown>,\n prefix = '',\n): Record<string, AttributeValue> {\n const out: Record<string, AttributeValue> = {};\n const seen = new WeakSet<object>();\n\n function flatten(obj: Record<string, unknown>, currentPrefix: string): void {\n for (const [key, value] of Object.entries(obj)) {\n if (value == null) continue;\n const nextKey = currentPrefix ? `${currentPrefix}.${key}` : key;\n\n const attr = toAttributeValue(value);\n if (attr !== undefined) {\n out[nextKey] = attr;\n continue;\n }\n\n if (typeof value === 'object' && value.constructor === Object) {\n if (seen.has(value)) {\n out[nextKey] = '<circular-reference>';\n continue;\n }\n seen.add(value);\n flatten(value as Record<string, unknown>, nextKey);\n continue;\n }\n\n try {\n out[nextKey] = JSON.stringify(value);\n } catch {\n out[nextKey] = '<serialization-failed>';\n }\n }\n }\n\n flatten(fields, prefix);\n return out;\n}\n","import { SpanStatusCode } from '@opentelemetry/api';\nimport type { AttributeValue, TraceContext } from './trace-context';\nimport { flattenToAttributes } from './flatten-attributes';\n\nconst internalKey = Symbol.for('autotel.error.internal');\n\nexport interface StructuredErrorInput {\n message: string;\n why?: string;\n fix?: string;\n link?: string;\n code?: string | number;\n status?: number;\n cause?: unknown;\n details?: Record<string, unknown>;\n name?: string;\n /** Backend-only context. Omitted from toJSON() and never serialized to clients. */\n internal?: Record<string, unknown>;\n}\n\nexport interface StructuredError extends Error {\n why?: string;\n fix?: string;\n link?: string;\n code?: string | number;\n status?: number;\n details?: Record<string, unknown>;\n /** Backend-only context. Omitted from toJSON() and never serialized to clients. */\n readonly internal?: Record<string, unknown>;\n}\n\nexport function createStructuredError(\n input: StructuredErrorInput,\n): StructuredError {\n const error = new Error(input.message, {\n cause: input.cause,\n }) as StructuredError;\n\n error.name = input.name ?? 'StructuredError';\n if (input.why !== undefined) error.why = input.why;\n if (input.fix !== undefined) error.fix = input.fix;\n if (input.link !== undefined) error.link = input.link;\n if (input.code !== undefined) error.code = input.code;\n if (input.status !== undefined) error.status = input.status;\n if (input.details !== undefined) error.details = input.details;\n\n if (input.internal !== undefined) {\n Object.defineProperty(error, internalKey, {\n value: input.internal,\n enumerable: false,\n writable: false,\n configurable: true,\n });\n }\n\n Object.defineProperty(error, 'internal', {\n get() {\n return (\n this as StructuredError & { [internalKey]?: Record<string, unknown> }\n )[internalKey];\n },\n enumerable: false,\n configurable: true,\n });\n\n error.toString = () => {\n const lines = [`${error.name}: ${error.message}`];\n if (error.why) lines.push(` Why: ${error.why}`);\n if (error.fix) lines.push(` Fix: ${error.fix}`);\n if (error.link) lines.push(` Link: ${error.link}`);\n if (error.code !== undefined) lines.push(` Code: ${error.code}`);\n if (error.status !== undefined) lines.push(` Status: ${error.status}`);\n if (error.cause) {\n const cause = error.cause as Error;\n lines.push(` Caused by: ${cause.name}: ${cause.message}`);\n }\n return lines.join('\\n');\n };\n\n return error;\n}\n\nexport function structuredErrorToJSON(\n error: StructuredError,\n): Record<string, unknown> {\n const result: Record<string, unknown> = {\n name: error.name,\n message: error.message,\n };\n\n if (error.status !== undefined) result.status = error.status;\n if (error.why || error.fix || error.link) {\n result.data = {\n ...(error.why && { why: error.why }),\n ...(error.fix && { fix: error.fix }),\n ...(error.link && { link: error.link }),\n };\n }\n if (error.code !== undefined) result.code = error.code;\n if (error.details) result.details = error.details;\n if (error.cause instanceof Error) {\n result.cause = { name: error.cause.name, message: error.cause.message };\n }\n\n return result;\n}\n\nexport function getStructuredErrorAttributes(\n error: Error,\n): Record<string, AttributeValue> {\n const structured = error as StructuredError;\n const attributes: Record<string, AttributeValue> = {\n 'error.type': error.name || 'Error',\n 'error.message': error.message,\n };\n\n if (error.stack) attributes['error.stack'] = error.stack;\n if (structured.why) attributes['error.why'] = structured.why;\n if (structured.fix) attributes['error.fix'] = structured.fix;\n if (structured.link) attributes['error.link'] = structured.link;\n if (structured.code !== undefined) {\n attributes['error.code'] =\n typeof structured.code === 'string'\n ? structured.code\n : String(structured.code);\n }\n if (structured.status !== undefined) {\n attributes['error.status'] = structured.status;\n }\n if (structured.details) {\n Object.assign(\n attributes,\n flattenToAttributes(structured.details, 'error.details'),\n );\n }\n\n return attributes;\n}\n\nexport function recordStructuredError(\n ctx: Pick<TraceContext, 'recordException' | 'setAttributes' | 'setStatus'>,\n error: Error,\n): void {\n ctx.recordException(error);\n ctx.setStatus({\n code: SpanStatusCode.ERROR,\n message: error.message,\n });\n ctx.setAttributes(getStructuredErrorAttributes(error));\n}\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';\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<string, unknown>,\n source: Record<string, unknown>,\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<string, unknown>,\n sourceVal as Record<string, unknown>,\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<TraceContext>();\n\nexport function runWithRequestContext<T>(ctx: TraceContext, fn: () => T): T {\n return requestContextStore.run(ctx, fn);\n}\n\nexport interface RequestLogger {\n set(fields: Record<string, unknown>): void;\n info(message: string, fields?: Record<string, unknown>): void;\n warn(message: string, fields?: Record<string, unknown>): void;\n error(error: Error | string, fields?: Record<string, unknown>): void;\n getContext(): Record<string, unknown>;\n emitNow(overrides?: Record<string, unknown>): RequestLogSnapshot;\n fork(label: string, fn: () => void | Promise<void>): void;\n}\n\nexport interface RequestLogSnapshot {\n timestamp: string;\n traceId: string;\n spanId: string;\n correlationId: string;\n context: Record<string, unknown>;\n}\n\nexport interface RequestLoggerOptions {\n /** Callback invoked by emitNow() for manual fan-out. */\n onEmit?: (snapshot: RequestLogSnapshot) => void | Promise<void>;\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<string, unknown> = {};\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<string, unknown>,\n ) => {\n const attrs = fields ? flattenToAttributes(fields) : undefined;\n activeContext.addEvent(`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<string, unknown>) {\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<string, unknown>) {\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<string, unknown>) {\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<string, unknown>) {\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<string, unknown>): 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 activeContext.addEvent('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(label: string, fn: () => void | Promise<void>): 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 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 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 childSpan.end();\n });\n });\n });\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';\n\ntype EventAttrs = Record<string, string | number | boolean>;\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 ctx.addEvent('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 ctx.addEvent('gen_ai.response.received', buildResponseAttrs(event));\n}\n\n/**\n * Record a retry attempt on an LLM call. Call *before* sleeping for\n * `delayMs` so