OpenTelemetry GenAI Semantic Conventions

> OpenTelemetry's GenAI SIG (launched April 2024) defines the standard schema for agent telemetry. Span names, attributes, and content-capture rules converge across vendors so agent traces mean the same thing in Datadog, Grafana, Jaeger, and Honeycomb.

Type: Learn + Build

Languages: Python (stdlib)

Prerequisites: Phase 14 · 13 (LangGraph), Phase 14 · 24 (Observability Platforms)

Time: ~60 minutes

Learning Objectives

• Name the GenAI span categories: model/client, agent, tool.
• Distinguish invoke_agent CLIENT vs INTERNAL spans and when each applies.
• List the top-level GenAI attributes: provider name, request model, data-source ID.
• Explain the content-capture contract: opt-in, OTEL_SEMCONV_STABILITY_OPT_IN, external-reference recommendation.

The Problem

Every vendor invents their own span names. Ops teams end up building per-framework dashboards. OpenTelemetry's GenAI SIG fixes this by defining one standard the whole ecosystem targets.

The Concept

Span categories

1. Model / client spans. Cover raw LLM calls. Emitted by provider SDKs (Anthropic, OpenAI, Bedrock) and framework model adapters.
2. Agent spans. create_agent (when the agent is constructed) and invoke_agent (when it runs).
3. Tool spans. One per tool invocation; connected to the agent span by parent-child relation.

Agent span naming

• Span name: invoke_agent {gen_ai.agent.name} if named; fallback to invoke_agent.
• Span kind:

- CLIENT — for remote agent services (OpenAI Assistants API, Bedrock Agents).

- INTERNAL — for in-process agent frameworks (LangChain, CrewAI, local ReAct).

Key attributes

• gen_ai.provider.name — anthropic, openai, aws.bedrock, google.vertex.
• gen_ai.request.model — the model ID.
• gen_ai.response.model — the resolved model (may differ from request due to routing).
• gen_ai.agent.name — agent identifier.
• gen_ai.operation.name — chat, completion, invoke_agent, tool_call.
• gen_ai.data_source.id — for RAG: which corpus or store was consulted.

Technology-specific conventions exist for Anthropic, Azure AI Inference, AWS Bedrock, OpenAI.

Content capture

The default rule: instrumentations SHOULD NOT capture inputs/outputs by default. Capture is opt-in via:

• gen_ai.system_instructions
• gen_ai.input.messages
• gen_ai.output.messages

Recommended production pattern: store content externally (S3, your log store), record references on spans (pointer IDs, not prose). This is the Lesson 27 content-poisoning defense wired into observability.

Stability

Most conventions are experimental as of March 2026. Opt in to the stable preview with:

OTEL_SEMCONV_STABILITY_OPT_IN=gen_ai_latest_experimental

Datadog v1.37+ maps GenAI attributes natively into its LLM Observability schema. Other backends (Grafana, Honeycomb, Jaeger) support the raw attributes.

Where this pattern goes wrong

• Capturing full prompts in spans. PII, secrets, customer data in traces that ops can read. Store externally.
• No gen_ai.provider.name. Multi-provider dashboards break when attribution is missing.
• Spans without parent links. Orphaned tool spans. Always propagate context.
• Not setting stability opt-in. Your attributes may get renamed on backend upgrade.

Build It

code/main.py implements a stdlib span emitter matching GenAI conventions:

• Span with GenAI attribute schema.
• Tracer with start_span, nested contexts.
• A scripted agent run that emits: create_agent, invoke_agent (INTERNAL), per-tool spans, chat spans for LLM calls.
• A content-capture mode that stores prompts externally and records IDs on spans.

Run it:

python3 code/main.py

Output: a span tree with all required GenAI attributes, and an "external store" showing the opt-in content references.

Use It

• Datadog LLM Observability (v1.37+) maps attributes natively.
• Langfuse / Phoenix / Opik (Lesson 24) — auto-instrument the ecosystem.
• Jaeger / Honeycomb / Grafana Tempo — raw OTel traces; build dashboards from GenAI attributes.
• Self-hosted — run the OTel Collector with a GenAI processor.

Ship It

outputs/skill-otel-genai.md wires OTel GenAI spans into an existing agent with content-capture defaults and external-reference storage.

Exercises

1. Instrument your Lesson 01 ReAct loop with invoke_agent (INTERNAL) + per-tool spans. Send to a Jaeger instance.
2. Add content capture in "references only" mode: prompts to SQLite, span attributes carry only row IDs.
3. Read the spec for gen_ai.data_source.id. Wire it into your Lesson 09 Mem0 search.
4. Set OTEL_SEMCONV_STABILITY_OPT_IN=gen_ai_latest_experimental and verify your attributes don't get renamed by the collector.
5. Build a dashboard: "which tool errors correlate with which models" from GenAI attributes alone.

Key Terms

Further Reading

• OpenTelemetry GenAI semantic conventions — the spec
• OpenAI Agents SDK — GenAI spans by default
• AutoGen v0.4 (Microsoft Research) — OTel spans built in
• Claude Agent SDK — W3C trace context propagation