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observability extension for the Pi Agent SDK — OTEL GenAI semantic-convention spans for chat completions and tool execution","maintainers":[{"name":"yoga-introspection","email":"yoga@introspection.dev"},{"name":"brightsparc","email":"brightsparc@gmail.com"}],"readme":"# introspection-pi\n\nIntrospection observability extension for the\n[Pi Agent SDK](https://github.com/badlogic/pi-mono) — emits OpenTelemetry\n[GenAI semantic-convention](https://opentelemetry.io/docs/specs/semconv/gen-ai/)\nspans for chat completions and tool execution.\n\n## Installation\n\n```bash\nnpm install @introspection-sdk/introspection-pi \\\n  @opentelemetry/api \\\n  @earendil-works/pi-ai \\\n  @earendil-works/pi-agent-core\n```\n\n## Usage\n\n### One-call session attach\n\nHosts holding a live coding-agent session (an object exposing `agent` and\n`sessionManager`) attach everything at once — chat spans through the\nwrapped stream function, `execute_tool` spans per tool call, one\n`invoke_agent` span per run that the others nest under, and structural\ncompaction detection from the session tree:\n\n```ts\nimport { trace } from \"@opentelemetry/api\";\nimport { instrumentSession } from \"@introspection-sdk/introspection-pi\";\n\nconst handle = instrumentSession(session, {\n  tracer: trace.getTracer(\"my-app\"),\n  meta: { conversationId, agentId, agentName },\n});\n\n// Later, on shutdown — restores the stream function, finalizes open spans:\nhandle.detach();\n```\n\nHosts that already create their own turn/run spans keep their topology:\n\n```ts\ninstrumentSession(session, {\n  tracer,\n  meta,\n  runSpans: false,\n  getParentContext: () => currentTurnContext,\n  abortTerminationReason: () => (userRequestedStop ? \"cancelled\" : null),\n  extraAttributes: () => ({ \"tenant.id\": tenantId }),\n});\n```\n\n### Composing the pieces yourself\n\n```ts\nimport { trace } from \"@opentelemetry/api\";\nimport { Agent } from \"@earendil-works/pi-agent-core\";\nimport { streamSimple } from \"@earendil-works/pi-ai/compat\";\nimport { getBuiltinModel } from \"@earendil-works/pi-ai/providers/all\";\nimport {\n  instrumentAgent,\n  instrumentStream,\n  type AgentMeta,\n} from \"@introspection-sdk/introspection-pi\";\n\nconst tracer = trace.getTracer(\"my-app\");\nconst meta: AgentMeta = {\n  conversationId: \"conv_123\",\n  agentId: \"support-agent\",\n  agentName: \"Support\",\n};\n\nconst agent = new Agent({\n  streamFn: streamSimple,\n  initialState: {\n    model: getBuiltinModel(\"anthropic\", \"claude-sonnet-4-6\"),\n    systemPrompt: \"You are a helpful support agent.\",\n  },\n});\n\n// One chat span per LLM call\nagent.streamFunction = instrumentStream(agent.streamFunction, { tracer, meta });\n\n// One execute_tool span per tool call\nconst tools = instrumentAgent(agent, { tracer, meta });\n\nawait agent.prompt(\"Help me understand my latest invoice.\");\n\n// Later, on shutdown:\ntools.stop();\n```\n\n### Adding caller-specific attributes\n\nUse the `extraAttributes` hook to layer non-semconv attributes on every\nchat span (tenant labels, correlation IDs, feature flags):\n\n```ts\nagent.streamFunction = instrumentStream(agent.streamFunction, {\n  tracer,\n  meta,\n  extraAttributes: (model, ctx) => ({\n    \"introspection.byok\": !process.env.PROXY_KEY,\n    \"tenant.id\": meta.conversationId,\n  }),\n});\n```\n\n### Parenting spans under a turn span\n\nIf you wrap an entire user turn in your own span, pass\n`getParentContext` so each chat / tool span lands under it:\n\n```ts\nconst turnSpan = tracer.startSpan(`turn ${meta.agentName}`);\nconst turnContext = trace.setSpan(context.active(), turnSpan);\n\nagent.streamFunction = instrumentStream(agent.streamFunction, {\n  tracer,\n  meta,\n  getParentContext: () => turnContext,\n});\n```\n\n## What gets emitted\n\nFor each LLM call (`chat ${model}` span):\n\n- `gen_ai.conversation.id`, `gen_ai.agent.id`, `gen_ai.agent.name`\n- `gen_ai.operation.name = \"chat\"`\n- `gen_ai.provider.name`, `gen_ai.request.model`, `gen_ai.response.model`\n- `gen_ai.request.stream = true`\n- `gen_ai.system_instructions`, `gen_ai.tool.definitions`\n- `gen_ai.input.messages`, `gen_ai.output.messages`\n- `gen_ai.response.id`, `gen_ai.response.finish_reasons`\n- `gen_ai.response.time_to_first_chunk`\n- `gen_ai.conversation.compacted` when compacted history was sent\n- `gen_ai.usage.input_tokens`, `gen_ai.usage.output_tokens`\n- `gen_ai.usage.reasoning.output_tokens` when reported\n- `gen_ai.usage.cache_read.input_tokens`, `gen_ai.usage.cache_creation.input_tokens` (when > 0)\n- `gen_ai.cost.usd` (when reported)\n- `introspection.termination_reason = \"cancelled\" | \"awaiting_user\"` for requested aborts\n\nRequested aborts are not recorded as errors. A user/runtime cancellation or an\ninterrupt pause ends the span with `gen_ai.response.finish_reasons = [\"aborted\"]`\nand `introspection.termination_reason`, but without `setStatus(ERROR)` or a\nsynthetic exception. Unclaimed aborts and provider/model failures are still\nrecorded as errors with a standard exception span event.\n\nFor each tool call (`execute_tool ${tool_name}` span):\n\n- `gen_ai.conversation.id`, `gen_ai.agent.id`, `gen_ai.agent.name`\n- `gen_ai.operation.name = \"execute_tool\"`\n- `gen_ai.tool.name`, `gen_ai.tool.type`, `gen_ai.tool.call.id`\n- `gen_ai.tool.call.arguments`, `gen_ai.tool.call.result`\n- Tool results are retained for both successful and failed executions so the\n  conversation can be reconstructed losslessly. Tool errors are recorded with\n  `setStatus(ERROR)`. Tool calls cut short by a\n  requested abort are marked with `introspection.termination_reason =\n\"cancelled\"` and are not marked as errors.\n\n## Content scrubbing\n\ngen_ai spans carry full conversation content. Hosts that export one span\nstream to two backends with different data policies — whole spans to a\nconversation store, structure-only to infrastructure observability — wrap\nthe second backend's exporter:\n\n```ts\nimport { GenAiContentScrubbingExporter } from \"@introspection-sdk/introspection-pi\";\nimport { BatchSpanProcessor } from \"@opentelemetry/sdk-trace-base\";\nimport { OTLPTraceExporter } from \"@opentelemetry/exporter-trace-otlp-proto\";\n\nnew BatchSpanProcessor(\n  new GenAiContentScrubbingExporter(new OTLPTraceExporter()),\n);\n```\n\nThe wrapper strips the content-bearing attributes (input/output messages,\nsystem instructions, tool definitions/arguments/results, and the\n`gen_ai_encrypted.*` mirror) while keeping the structural signal\n(operation, provider, model, usage, timing, tool names). Scrubbing is\ndecided per attribute on every span — content keys only exist on gen_ai\nspans, so no scope configuration is needed and none can fail open — and\nthe original span object is never mutated, so a second processor on the\nsame stream still sees it whole. `isGenAiContentAttribute(key)` exposes\nthe predicate for hosts that scrub in their own pipeline.\n\n## License\n\nApache-2.0\n","readmeFilename":"README.md"}