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Reehal <jag@jagreehal.com> (https://jagreehal.com)","license":"Apache-2.0","homepage":"https://github.com/jagreehal/autotel#readme","keywords":["autotel","opentelemetry","otel","gen-ai","genai","llm","ai-agents","semantic-conventions","observability","token-usage","cost"],"repository":{"url":"https://github.com/jagreehal/autotel","type":"git","directory":"packages/autotel-genai"},"description":"Gold-standard OpenTelemetry GenAI semantic-convention instrumentation for LLM calls, tools, and agents — cost, tokens, metrics, events, and agent governance.","maintainers":[{"name":"jagreehal","email":"jag.reehal@gmail.com"}],"readme":"# autotel-genai\n\n> Gold-standard OpenTelemetry **GenAI** instrumentation for LLM calls, tools, and agents.\n\n`autotel-genai` is the AI layer for [autotel](https://github.com/jagreehal/autotel).\nIt emits the **canonical `gen_ai.*` semantic conventions** (OpenTelemetry semconv\n**`semantic-conventions-genai`, `1.42.0-dev`**) for everything from a single `chat` call to a multi-agent workflow.\nToken usage, cost, latency metrics, content/evaluation events, and an agent\nidentity / delegation / policy / audit governance layer.\n\nCanonical-only by design: no legacy `gen.ai.*`, no `prompt_tokens` /\n`completion_tokens`, no non-registry `total_tokens`. What you record is exactly\nwhat backends (Grafana, Langfuse, Arize, Honeycomb, Jaeger, …) expect.\n\n## Install\n\n```bash\npnpm add autotel autotel-genai\n```\n\n`autotel` is the peer core (trace/span/init). `@opentelemetry/sdk-metrics` is an\noptional peer, needed only for `genAiMetricViews()`.\n\n## Quick start\n\n```ts\nimport {\n  traceGenAI,\n  recordGenAiResponse,\n  recordGenAiUsage,\n} from 'autotel-genai/trace';\nimport OpenAI from 'openai';\n\nconst openai = new OpenAI();\n\n// Span name → `chat gpt-4o`; request attributes set up front.\nexport const chat = traceGenAI({\n  provider: 'openai',\n  model: 'gpt-4o',\n  operation: 'chat',\n  temperature: 0.2,\n})((ctx) => async (prompt: string) => {\n  const res = await openai.chat.completions.create({\n    model: 'gpt-4o',\n    messages: [{ role: 'user', content: prompt }],\n  });\n\n  recordGenAiResponse(ctx, {\n    model: res.model,\n    id: res.id,\n    finishReasons: res.choices.map((c) => c.finish_reason),\n  });\n  // Sets gen_ai.usage.input_tokens / output_tokens + gen_ai.usage.cost.usd\n  recordGenAiUsage(ctx, 'gpt-4o', {\n    inputTokens: res.usage?.prompt_tokens,\n    outputTokens: res.usage?.completion_tokens,\n  });\n\n  return res.choices[0].message.content;\n});\n```\n\n## What you get\n\n| Area                | Import                    | Highlights                                                                                                                                                                                            |\n| ------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| **Semconv**         | `autotel-genai/semconv`   | `GEN_AI.*` keys, `GEN_AI_OPERATION`, `GEN_AI_PROVIDER`, `genAiSpanName()`                                                                                                                             |\n| **Cost**            | `autotel-genai/cost`      | `estimateLLMCost`, `recordLLMCost`, `MODEL_PRICING` (cache-read/write aware)                                                                                                                          |\n| **Metrics**         | `autotel-genai/metrics`   | `genAiMetricViews()` re-buckets the canonical histograms                                                                                                                                              |\n| **Attributes**      | `autotel-genai`           | typed builders → canonical attribute maps                                                                                                                                                             |\n| **Events**          | `autotel-genai/events`    | opt-in content (binary redacted, size-capped) + `inference.operation.details` / `evaluation.result`                                                                                                   |\n| **Trace**           | `autotel-genai/trace`     | `traceGenAI()`, `recordGenAiResponse/Usage`                                                                                                                                                           |\n| **Guard**           | `autotel-genai/guard`     | inline cost/token/loop kill-switch: `createGenAiBudget`, `createGenAiGuard`, `parseGuardRules`                                                                                                        |\n| **Streaming**       | `autotel-genai/streaming` | TTFC, throughput, inter-chunk distribution: `createStreamTimer`, `recordStreamTiming`                                                                                                                 |\n| **AI SDK**          | `autotel-genai/observer`  | `autotelTelemetry()`: `registerTelemetry()` integration: live `gen_ai.*` spans + cost + streaming + nested traces + opt-in content. `subscribeAiTelemetry()`: zero-config `ai:telemetry` channel path |\n| **AI SDK (legacy)** | `autotel-genai/ai-sdk`    | `ai.*` → `gen_ai.*` mapping + cost for `LegacyOpenTelemetry`/older versions; `autotelEnrich()` for `@ai-sdk/otel` `enrichSpan`                                                                        |\n| **Agents**          | `autotel-genai/agent`     | identity, delegation, policy, audit, privacy, non-repudiation                                                                                                                                         |\n\n### Cost\n\n```ts\nimport { estimateLLMCost, recordLLMCost } from 'autotel-genai/cost';\n\nestimateLLMCost('gpt-4o', { inputTokens: 1000, outputTokens: 500 }); // 0.0075\nrecordLLMCost(ctx, 'claude-sonnet-4', {\n  inputTokens: 4000,\n  cacheReadInputTokens: 3500, // priced at the cached rate\n});\n\n// Hosted ids price as the model they name\nestimateLLMCost('eu.anthropic.claude-3-5-haiku-20241022-v1:0', usage);\n\n// Your own rates, merged over the built-in table\nestimateLLMCost('my-finetune', usage, {\n  pricing: { 'my-finetune': { inputPer1M: 0.5, outputPer1M: 1.5 } },\n});\n```\n\n`createGenAiObserver({ pricing })` and `autotelTelemetry({ pricing })` take the\nsame map once, so every span an observer prices uses it.\n\n**Server-side tools are money too.** Web search, code interpreter and file\nsearch are billed per call, outside the token counts. An agent that searches on\nevery step can spend more there than on tokens, and a cost built from tokens\nalone will never show it:\n\n```ts\nrecordLLMCost(ctx, 'claude-sonnet-4', {\n  inputTokens: 4000,\n  serverToolCalls: { web_search: 12 }, // SERVER_TOOL_PRICING_PER_1K\n});\n```\n\nA tool with no price is left out of the figure rather than guessed at zero, and\nnamed on the span as `gen_ai.usage.cost.unpriced_tools` so the gap is visible.\n\n**Cache accounting.** By default cache reads are treated as a subset of\n`inputTokens` (what OpenAI and Anthropic report directly) and cache writes as\nadditive. Gateways and normalised usage objects do not all agree; where yours\nreports cache tokens separately, say so — getting it backwards subtracts a pool\nthat was never in the input, understating exactly the calls that cost most:\n\n```ts\nrecordLLMCost(ctx, model, { ...usage, cacheTokensExclusive: true });\n```\n\n**Where the numbers came from.** `tokenSource: 'observed' | 'estimated'` labels\nthe counts as `autotel.evidence.tokens`. A locally counted token and a\nprovider-reported one are the same number on a span; unlabelled, a guess reads\nas an invoice.\n\n### Metrics\n\n```ts\nimport { NodeSDK } from '@opentelemetry/sdk-node';\nimport { genAiMetricViews } from 'autotel-genai/metrics';\n\nconst sdk = new NodeSDK({\n  serviceName: 'my-agent',\n  views: [...genAiMetricViews()],\n});\n```\n\nRe-buckets `gen_ai.client.operation.duration`, `…time_to_first_chunk`,\n`…time_per_output_chunk`, `gen_ai.client.token.usage`, and the autotel\n`gen_ai.client.cost.usd` extension for LLM-shaped distributions.\n\n### Guard / budget (kill-switch)\n\nMost tracing tells you what an agent _did_, after the bill. A **guard** runs\n_during_ the run: feed it each step, it accumulates cost / tokens / loop state,\nand halts the run when a rule crosses its threshold. Aborting an `AbortSignal`\nand (by default) throwing a `GEN_AI_GUARD_STOP` structured error.\n\n```ts\nimport { createGenAiBudget } from 'autotel-genai/guard';\nimport { estimateLLMCost } from 'autotel-genai/cost';\n\nconst budget = createGenAiBudget({ maxCostUsd: 5, warnAtUsd: 4 });\n\nfor (const task of tasks) {\n  if (budget.stopped) break;\n  const res = await model.chat(task);\n  budget.record(\n    { kind: 'llm', usage: { costUsd: estimateLLMCost('gpt-4o', res.usage) } },\n    ctx, // optional TraceContext → records gen_ai.guard.* + gen_ai.session.* telemetry\n  ); // throws once total cost > $5\n}\n```\n\nRules can also come from a shorthand string. Cost ceilings, token ceilings,\nspin-loop detection (`N identical calls in a window of M`), error loops,\ntool-call / step caps, wall-clock timeouts, and context-window budgets:\n\n```ts\nimport { createGenAiGuard, parseGuardRules } from 'autotel-genai/guard';\n\nconst guard = createGenAiGuard({\n  rules: parseGuardRules('budget:$2,loop:3/10,max-tools:50,timeout:5m'),\n});\n\nguard.record({ kind: 'tool', name: 'search', signature: JSON.stringify(args) });\n```\n\nEach rule fires once; `onStop` chooses `throw` (default), `abort` (signal only),\nor `silent` (record only). All logic is deterministic. No LLM in the loop.\n\n### Streaming performance\n\nStreaming latency is two numbers: **time to first chunk** (the wait before\nanything appears) and **throughput** (how fast tokens then arrive). A single\nduration hides both. `createStreamTimer` captures the full picture and records\nthe headline values as `gen_ai.response.*` attributes.\n\n```ts\nimport { createStreamTimer, recordStreamTiming } from 'autotel-genai/streaming';\n\nconst timer = createStreamTimer();\nlet text = '';\nfor await (const chunk of stream) {\n  timer.chunk(); // first call also marks time-to-first-chunk\n  text += chunk;\n}\nrecordStreamTiming(ctx, timer.finish({ outputTokens: countTokens(text) }));\n// → gen_ai.response.time_to_first_chunk / .time_to_finish /\n//   .output_tokens_per_second / .time_per_output_chunk (seconds)\n```\n\n`computeStreamTiming` is the pure function underneath (TTFC, total time, steady\nthroughput, and an inter-chunk gap distribution `{min,p10,median,avg,p90,max}`).\n\n### Content capture & warnings\n\n`setGenAiContent` gates input and output independently, replaces inline binary\nwith a placeholder, and caps what is left:\n\n```ts\nimport { setGenAiContent, recordModelWarnings } from 'autotel-genai';\n\nsetGenAiContent(\n  ctx,\n  { inputMessages, outputMessages },\n  { recordInputs: false, recordOutputs: true },\n); // keep prompts out of telemetry\n\nrecordModelWarnings(ctx, result.warnings); // surface provider warnings vendors only log\n```\n\n**Binary is redacted, not recorded.** A multimodal prompt carries its images,\naudio and PDFs inline as base64. Serialised verbatim, one such call is a\nmegabyte-scale span attribute that collectors truncate mid-string and nobody\nreads. The bytes were never the interesting part, so they are replaced by a\nplaceholder that says what stood there:\n\n```jsonc\n// gen_ai.input.messages\n[\n  {\n    \"role\": \"user\",\n    \"parts\": [\n      { \"type\": \"text\", \"content\": \"what is in this photo?\" },\n      { \"type\": \"input_image\", \"data\": \"[base64 image/png redacted]\" },\n    ],\n  },\n]\n```\n\nRecognition is contextual: a bare alphanumeric string must be 1KB before it is\nsuspected, but under a key that means binary (`data`, `image_url`,\n`inline_data`) or beside a `mediaType` / `format` hint, 64 bytes is enough. An\nexplicit `text/*` media type settles it the other way and nothing is touched.\n\nWhatever survives is capped at `DEFAULT_MAX_CONTENT_BYTES` (200KB) per\nattribute. Both are declared rather than silent — a cut attribute gains\n`<attribute>.original_size` and the span is labelled `autotel.evidence.input =\n'truncated'` (or `'redacted'`), so a reader can tell a short prompt from a\nprompt you only kept the front of.\n\n```ts\nsetGenAiContent(ctx, content, {\n  redactBinary: false, // only when the payload itself is under investigation\n  maxContentBytes: 0, // 0 or less: no cap\n});\n```\n\n`redactBinaryContent()` and `truncateUtf8()` are exported for payloads that\nnever reach a span — an event body, a log line, your own exporter.\n\n### Agents & tools\n\n```ts\nimport { withScopedTool } from 'autotel-genai/agent';\n\nawait withScopedTool(\n  {\n    action: 'agent.refund.execute',\n    agent: { id: 'refund-specialist' },\n    tool: { name: 'stripe_refund_v3' },\n    requiredScopes: ['refund:write'],\n    delegation: { parentIdentity: 'usr_99824', scope: ['refund:write'] },\n    policy: { decision: 'permit', policyId: 'refund-scope-v2' },\n    ai: { model: 'gpt-4o', operation: 'execute_tool' },\n  },\n  { refundId: 're_123' },\n  async () => stripe.refunds.create(req),\n);\n```\n\nThe agent layer records `agent.*`, `delegation.*`, `tool.*`, `policy.*`\ngovernance attributes **and** canonical `gen_ai.*` when `ai` metadata is present.\nIt honours spec breaking change #242 (`gen_ai.agent.id` is dropped on internal\n`invoke_agent` spans via `genAiAgentAttributes(…, { internal: true })`).\n\n**Eval-sandbox incident replay.** When multiple eval agents share a writable\nregistry (the OpenAI/HuggingFace pattern), use the agent IR helpers below.\nSee the standalone demo at `agent-eval-sandbox-demo` (sibling to ai-sdk-guardrails).\n\n```ts\nimport {\n  CrossAgentMonitor,\n  createHoneyTokenTool,\n  crossAgentDetectionsToSecurityEvents,\n  detectCrossAgentPattern,\n  EVAL_IDENTITY_ATTR,\n  querySpansForEvalIncident,\n  recordEvalRunIdentity,\n} from 'autotel-genai/agent';\n\n// Tag every run so its spans stay separable downstream\n// (eval.run_id / eval.task_id / eval.sandbox_id).\nrecordEvalRunIdentity({ runId, taskId, sandboxId });\n\n// Live: emit agent.shared_channel.detected as tools run\nconst monitor = new CrossAgentMonitor({ minAgents: 2, ctx });\nmonitor.record({ agentId: 'eval-a', resource: 'artifactory:/notes' });\n\n// Offline: the same detection over events you collected yourself\nconst alerts = crossAgentDetectionsToSecurityEvents(\n  detectCrossAgentPattern(collectedEvents, { minAgents: 2 }),\n);\n\n// Batch IR over an exported span array\nconst ir = querySpansForEvalIncident(finishedSpans);\nir.crossAgentEvents; // shared-channel alerts\nir.evalRuns; // every EVAL_IDENTITY_ATTR.runId seen\n\n// Honey-token tool: a decoy credential that reports when an agent touches it\nconst honeyToken = createHoneyTokenTool({\n  name: 'readLeakedCredential',\n  bait: 'AKIA_HONEY_TOKEN_DO_NOT_USE',\n  ctx,\n});\n```\n\n### Vercel AI SDK\n\nRegister `autotelTelemetry()` once and every `generateText` / `streamText` /\n`generateObject` / `streamObject` / `embed` call streams a canonical `gen_ai.*`\nspan tree. Live, as it runs:\n\n```ts\nimport { registerTelemetry } from 'ai';\nimport { autotelTelemetry } from 'autotel-genai/observer';\n\nregisterTelemetry(autotelTelemetry()); // once, at startup\n```\n\nSee `apps/example-ai-sdk-observer` for a runnable AI SDK + Ollama demo\n(generateText, tool loop, streamText timing, embeddings).\n\nIt implements the AI SDK's stable `Telemetry` lifecycle interface (ai v7+), so\nit slots in exactly where `@ai-sdk/otel`'s `OpenTelemetry` does. But it also,\non every `chat` span:\n\n- **prices the call** (`gen_ai.usage.cost.usd`) from `MODEL_PRICING`;\n- records **streaming throughput** (`time_to_first_chunk`, `time_to_finish`,\n  `output_tokens_per_second`);\n- keeps token usage on leaf `chat` spans only, so the `invoke_agent` root never\n  double-counts.\n\nIt is push-based and concurrency-safe (every event carries the SDK `callId`),\nand it pulls in **no** dependency on `ai`. The returned object satisfies the\n`Telemetry` interface structurally, so the snippet above type-checks as-is —\nincluding under `exactOptionalPropertyTypes: true`.\nEmbeddings open on `onEmbedStart` so span duration is the real model call.\n`rerank` has no canonical `gen_ai` operation and is intentionally not mapped.\n\n**`ToolLoopAgent`.** Agents run the same lifecycle, but their telemetry settings\nlive on the **constructor**, not on `.generate()`. Passing `telemetry` to\n`.generate()` is a type error, and spreading it in type-checks while doing\nnothing:\n\n```ts\nconst agent = new ToolLoopAgent({\n  model,\n  id: 'support-agent',\n  telemetry: { functionId: 'support-agent' }, // names the invoke_agent span\n});\n```\n\n**Sessions need opting in.** `runtimeContext.userId` / `sessionId` become\n`user.id` / `gen_ai.conversation.id` and other keys are dropped — but the SDK\nwithholds `runtimeContext` from telemetry entirely unless the call names each\nkey. Without this there is no conversation id on the spans at all:\n\n```ts\ntelemetry: {\n  functionId: 'support-agent',\n  includeRuntimeContext: { sessionId: true },\n}\n```\n\n**Nested traces.** It implements the SDK's `executeTool` / `executeLanguageModelCall`\ncontext runners, so a tool whose `execute` calls `generateText`. And the\nprovider's own auto-instrumented HTTP spans. Nest under the right span\nautomatically.\n\n**Content capture (opt-in).** Off by default for privacy. Turn it on to record\nprompts, responses, system instructions, and tool I/O, mapped to the\n[GenAI SemConv message format](#genai-message-format). The SDK's per-call\n`recordInputs` / `recordOutputs` are honored, and `exportContent` lets you redact\nor drop content per event:\n\n```ts\nregisterTelemetry(\n  autotelTelemetry({\n    captureContent: true,\n    exportContent: (event) => redact(event), // optional: redact before write\n  }),\n);\n```\n\n**Provider attributes.** The lifecycle events carry the AI SDK's unified finish\nreason and nothing from `providerMetadata`. `providerAttributes` receives what\nthe provider returned beside the standard response (its raw finish reason and\n`providerMetadata`, read from the model call's result or its stream's `finish`\npart) and adds attributes to the `chat` span. `autotel-bedrock` supplies one for\nBedrock's stop reason and guardrail trace:\n\n```ts\nimport { bedrockProviderAttributes } from 'autotel-bedrock';\n\nregisterTelemetry(\n  autotelTelemetry({ providerAttributes: bedrockProviderAttributes }),\n);\n```\n\n**Zero-config (no `registerTelemetry`).** Subscribe to the SDK's `ai:telemetry`\nNode tracing channel instead. The SDK publishes operation spans as soon as the\nchannel has a subscriber:\n\n```ts\nimport { subscribeAiTelemetry } from 'autotel-genai/observer';\n\nconst unsubscribe = subscribeAiTelemetry(); // once, at startup\n```\n\nThe channel path gives you the same `invoke_agent › chat › execute_tool` tree\nwith usage and cost, but not the per-call streaming timing (which only the\nlifecycle `onLanguageModelCallEnd` event carries). Prefer\n`registerTelemetry(autotelTelemetry())` when you can.\n\nRegister globally, or pass per-call via `telemetry.integrations` to scope it to\none call. For the **legacy** `LegacyOpenTelemetry`/older-version path, or to\nenrich spans another integration already emitted, the attribute bridge maps\n`ai.*` → `gen_ai.*` and adds cost; for versions before the `Telemetry` interface,\nwalk the finished result with `observeAiSdkResult` (see\n[Observer](#observer-event-stream--spans)):\n\n```ts\nimport { mapAiSdkAttributes, recordAiSdkCost } from 'autotel-genai/ai-sdk';\n\nconst canonical = mapAiSdkAttributes(span.attributes); // ai.* → gen_ai.*\nrecordAiSdkCost(ctx, span.attributes); // sets gen_ai.usage.cost.usd\n```\n\n#### Already using `@ai-sdk/otel`?\n\nDrop `autotelEnrich()` into its `enrichSpan` to stamp autotel provenance and\npromote your `runtimeContext` onto every span:\n\n```ts\nimport { OpenTelemetry } from '@ai-sdk/otel';\nimport { autotelEnrich } from 'autotel-genai/ai-sdk';\n\nregisterTelemetry(new OpenTelemetry({ enrichSpan: autotelEnrich() }));\n```\n\n`enrichSpan` **cannot add cost**. The SDK passes it only\n`{ spanType, operationId, callId, runtimeContext }` (no usage, no model), and its\nown attributes win over custom keys. To get `gen_ai.usage.cost.usd` on the model\nspan, use `autotelTelemetry()` (it owns span creation). Either way,\n[`autotel-devtools`](../autotel-devtools) prices `gen_ai` spans on render, so cost\nshows there regardless of which integration emitted them.\n\n#### Local devtools, one line\n\nPoint an OTLP exporter at a running `autotel-devtools` receiver and you get a\nlive GenAI run view. Cost, token breakdown, tool timeline, and a narrated\n\"Explain run\" walkthrough. That works in production too (unlike\n`@ai-sdk/devtools`, which is dev-only):\n\n```ts\nimport { registerTelemetry } from 'ai';\nimport { autotelTelemetry } from 'autotel-genai/observer';\n\nregisterTelemetry(autotelTelemetry()); // → your OTLP pipeline → autotel-devtools\n// npx autotel-devtools  →  http://localhost:4318\n```\n\n### Observer (event-stream → spans)\n\nWhen you instrument a framework that emits its own lifecycle stream (agent\nruntimes, durable workflows) rather than wrapping calls with `traceGenAI`,\nsubscribe an observer and feed every event through it. It rebuilds the span\ntree, estimates cost, and force-closes any child whose end never arrives:\n\n```ts\nimport { createGenAiObserver } from 'autotel-genai/observer';\n\nconst observe = createGenAiObserver({\n  // Content (messages, tool args/results) is omitted unless you opt in here.\n  exportContent: (event) => redact(event),\n});\n\nobserve({ type: 'agent.start', id: 'a1', agent: { name: 'planner' } });\nobserve({\n  type: 'chat.start',\n  id: 'c1',\n  parentId: 'a1',\n  request: { provider: 'openai', model: 'gpt-4o' },\n});\nobserve({\n  type: 'chat.end',\n  id: 'c1',\n  response: { model: 'gpt-4o' },\n  usage: { inputTokens: 412, outputTokens: 87 },\n});\nobserve({ type: 'agent.end', id: 'a1' }); // closes c1 too if it never ended\n```\n\nToken usage lands on leaf `chat` spans only. Aggregate `agent`/`workflow`\nspans never carry `gen_ai.usage.*`, so summing usage across a trace counts each\ncall exactly once.\n\n**Framework glue** ships with the observer:\n\n```ts\nimport {\n  createGenAiObserver,\n  createLangChainObserver, // LangChain / LangGraph callback handler\n  createMastraObserver, // Mastra ObservabilityExporter\n  observeAiSdkResult, // Vercel AI SDK result walker\n} from 'autotel-genai/observer';\n\nconst observe = createGenAiObserver();\n\n// LangChain / LangGraph — one handler, runId/parentRunId → span tree:\nawait graph.invoke(input, { callbacks: [createLangChainObserver(observe)] });\n\n// Mastra — one exporter on the observability pipeline:\nnew Mastra({\n  agents,\n  observability: new Observability({\n    configs: {\n      autotel: {\n        serviceName: 'support-agent',\n        exporters: [createMastraObserver(observe)],\n      },\n    },\n  }),\n});\n\n// Vercel AI SDK — walk a generateText/streamText result:\nobserveAiSdkResult(observe, await generateText({ model, prompt }), {\n  id: 'gen-1',\n  provider: 'openai',\n  model: 'gpt-4o',\n});\n```\n\nSee `apps/example-langchain-observer` for a runnable LangGraph + Ollama demo.\n\nMastra maps `agent_run` → `invoke_agent`, `model_generation` → `chat`,\n`rag_embedding` → `embeddings`, every tool-call type → `execute_tool`, and\n`workflow_run`/`workflow_step` → `invoke_workflow`. Plumbing (model steps and\nchunks, processors, scorers, mappings) is dropped and its children reparent to\nthe nearest kept ancestor. Pass `skipSpan` to drop additional supported span\ntypes; unsupported plumbing always stays dropped. `model_step` /\n`model_inference` are dropped deliberately: their usage is already summed on the\nenclosing `model_generation`, so keeping both would double-count\n`gen_ai.usage.*`.\n\n`@mastra/otel-exporter` also emits canonical `gen_ai.*`. Prefer this adapter\nwhen you want Mastra's spans to be _autotel's_ spans — Mastra dispatches events\nsynchronously, so they nest under the request span you already opened, reach\nevery `init()` destination, pass through your `spanEnrichers` (including\n`langfuseCompatibility`), and get priced.\n\n### Prompt versions\n\n`gen_ai.prompt.name` alone cannot tell you which edit moved the numbers. Stamp\nthe version too, and cost, latency and evaluation score become splittable by\nprompt change in whatever backend holds the spans:\n\n```ts\ntraceGenAI({\n  provider: 'openai',\n  model: 'gpt-4o',\n  workflow: {\n    promptName: 'initial-router',\n    promptVersion: 7, // or your registry's own id\n    promptLabel: 'production',\n    promptHash: 'sha256:…', // when there is no registry to version it\n  },\n});\n```\n\n### Grouping traces into sessions\n\nA conversation is more than one trace. Two canonical attributes already carry\nthat, and neither is GenAI-specific:\n\n- `gen_ai.conversation.id` — the thread this call belongs to.\n- `session.id` — the visit it happened in, set by autotel core's `setSession()`\n  or stamped on every browser span by `autotel-web`.\n\nSet them and total cost per session, traces per conversation, and\nsession-level latency are ordinary queries. This is the same hierarchy vendor\nLLM-analytics products sell as `$ai_session_id`; here it is spec, so it works\nwherever the spans land.\n\n### Sending the same spans to PostHog\n\nPostHog ingests plain OTLP: `PostHogSpanProcessor` from `@posthog/ai/otel`\nforwards any span carrying `gen_ai.*` attributes and assembles its `$ai_*`\nevent server-side. Because this package emits canonical names, no mapping layer\nis needed — add the processor and the spans you already have become LLM\nanalytics:\n\n```ts\nimport { NodeSDK } from '@opentelemetry/sdk-node';\nimport { PostHogSpanProcessor } from '@posthog/ai/otel';\n\nconst sdk = new NodeSDK({\n  spanProcessors: [new PostHogSpanProcessor({ projectToken: 'phc_…' })],\n});\n```\n\n`src/posthog-contract.test.ts` pins that agreement, so it cannot quietly stop\nbeing true. For the browser half — session, replay and person joined in both\ndirections — see `autotel-posthog`.\n\n## Semantic conventions\n\nAligned to the `semantic-conventions-genai` snapshot. Span names follow the\noperation-specific upstream rules: inference and embeddings use\n`{operation} {request.model}`, retrieval uses `retrieval {data_source.id}`,\n`execute_tool` uses `execute_tool {tool.name}`, agent spans use\n`... {agent.name}` when available, workflow spans use\n`invoke_workflow {workflow.name}`, and memory spans are just the bare\noperation. Usage is `input_tokens` / `output_tokens` with `cache_read` /\n`cache_write` / `reasoning.output_tokens`; providers use the\n`gen_ai.provider.name` enum.\n\n## License\n\nApache-2.0 © Jag Reehal\n","readmeFilename":""}