opentelemetry-genai-spec

name	opentelemetry-genai-spec
description	OpenTelemetry semantic conventions for Generative AI attributes. Use when recording telemetry for GenAI.
license	MIT

opentelemetry-genai-spec

Use this skill when adding or reviewing OpenTelemetry instrumentation for LLM systems.

Goals

• Emit portable gen_ai.* telemetry that works across backends.
• Follow OTel semantic convention MUST/SHOULD guidance.
• Avoid leaking prompt, tool, or retrieval-sensitive data by default.

Minimum baseline attributes

Always try to capture these first:

• gen_ai.provider.name
• gen_ai.operation.name
• gen_ai.request.model
• gen_ai.response.model
• gen_ai.response.id
• gen_ai.response.finish_reasons
• gen_ai.usage.input_tokens
• gen_ai.usage.output_tokens
• gen_ai.conversation.id

Add-by-capability attributes

• Agent metadata: gen_ai.agent.id, gen_ai.agent.name, gen_ai.agent.description, gen_ai.agent.version
• Prompting/runtime controls: gen_ai.request.max_tokens, gen_ai.request.temperature, gen_ai.request.top_p, gen_ai.request.top_k, gen_ai.request.seed, gen_ai.request.stop_sequences, gen_ai.request.choice.count
• Structured content: gen_ai.input.messages, gen_ai.output.messages, gen_ai.system_instructions, gen_ai.output.type
• Tool use: gen_ai.tool.name, gen_ai.tool.type, gen_ai.tool.description, gen_ai.tool.call.id, gen_ai.tool.call.arguments, gen_ai.tool.call.result, gen_ai.tool.definitions
• RAG/retrieval: gen_ai.data_source.id, gen_ai.retrieval.query.text, gen_ai.retrieval.documents
• Evaluation: gen_ai.evaluation.name, gen_ai.evaluation.score.value, gen_ai.evaluation.score.label, gen_ai.evaluation.explanation

Normative rules to enforce

• For gen_ai.input.messages, gen_ai.output.messages, gen_ai.system_instructions, and gen_ai.retrieval.documents, follow the linked OTel JSON schemas.
• On events, record these payloads in structured form.
• On spans, JSON string fallback is allowed only when structured attributes are not supported; prefer structured values.
• Keep input messages in original send order.
• Keep output mapping 1 message to 1 generated choice.
• Use well-known enum values when they apply (provider, operation, output type, token type).
• Keep gen_ai.evaluation.score.label low cardinality.
• Avoid gen_ai.tool.definitions by default; enable only for deep debugging.

Sensitive-data defaults

Treat these as sensitive and opt-in by policy:

• gen_ai.input.messages
• gen_ai.output.messages
• gen_ai.system_instructions
• gen_ai.tool.call.arguments
• gen_ai.tool.call.result
• gen_ai.retrieval.query.text

Implementation guidance:

• Default to metadata-only telemetry in production.
• Add redaction, truncation, or filtering controls before enabling content capture.
• Document who can access raw traces and retention settings.

Deprecation mapping

• gen_ai.system -> gen_ai.provider.name
• gen_ai.usage.prompt_tokens -> gen_ai.usage.input_tokens
• gen_ai.usage.completion_tokens -> gen_ai.usage.output_tokens
• gen_ai.openai.request.response_format -> gen_ai.output.type
• gen_ai.openai.request.seed -> gen_ai.request.seed
• gen_ai.openai.request.service_tier -> openai.request.service_tier
• gen_ai.openai.response.service_tier -> openai.response.service_tier
• gen_ai.openai.response.system_fingerprint -> openai.response.system_fingerprint

Also deprecated with no direct replacement:

• gen_ai.prompt
• gen_ai.completion

Practical rollout checklist

1. Instrument baseline attrs for every LLM call.
2. Add token usage and finish reasons.
3. Add tool and retrieval attrs where those capabilities exist.
4. Gate content attrs behind explicit config.
5. Validate event/span payload encoding rules in tests.
6. Track semconv updates because many GenAI attrs are still Development.