feat(openai-agents): record response-identification metadata on LLM spans

[hansmire]

Summary

The Responses API Response object carries several fields that downstream trace backends rely on for turn chaining, model-version debugging, and reasoning / service-tier visibility, but that this instrumentor dropped on the floor. This PR plumbs them through to OTel span attributes on every LLM span.

Response field	Span attribute
response.id	gen_ai.response.id
response.model	gen_ai.response.model (kept gen_ai.request.model for back-compat)
response.status	gen_ai.response.status
response.previous_response_id	gen_ai.request.previous_response_id
response.service_tier	gen_ai.openai.request.service_tier
response.reasoning.effort	gen_ai.request.reasoning_effort
response.reasoning.summary	gen_ai.request.reasoning_summary

All additions are defensive: when a field is missing / None on the response, no attribute is emitted (no stringified "None" values — pinned by a regression test).

Before / After

Same 529 eval, same agent, same LLM-span detail panel in the Braintrust trace UI:

Before the LLM span's Metadata panel contains only gen_ai.request.model, temperature, top_p, usage.* — no response identification, no service tier, no reasoning config.
After the panel additionally renders gen_ai.response.id, gen_ai.response.model, gen_ai.response.status, gen_ai.openai.request.service_tier, and gen_ai.request.reasoning_effort — exposing turn chain, served-model version, and reasoning/service-tier settings for debugging.

Why

Braintrust's own native Agents SDK processor surfaces the full response.model_dump(exclude={"input","output","metadata","usage"}) on every LLM span — which is how its UI shows turn-by-turn chains and the exact model version that served each request. Previously openllmetry's equivalent openai.response span carried only temperature, top_p, max_tokens and a conflated gen_ai.request.model. Trace backends couldn't:

• Chain turns (no response.id / previous_response_id) — critical for debugging agents that use auto_previous_response_id=True.
• Tell what actually ran (only request.model was set; the specific served version like gpt-5.4-2026-03-05 was lost).
• See request config (service tier, reasoning effort / summary all absent).

Constants

OTel semconv constants are used where available:

• GenAIAttributes.GEN_AI_RESPONSE_ID
• GenAIAttributes.GEN_AI_RESPONSE_MODEL
• GenAIAttributes.GEN_AI_OPENAI_REQUEST_SERVICE_TIER
• SpanAttributes.LLM_REQUEST_REASONING_EFFORT
• SpanAttributes.LLM_REQUEST_REASONING_SUMMARY

Two fields fall back to string literals because semconv_ai doesn't publish a constant yet:

• gen_ai.response.status
• gen_ai.request.previous_response_id

Happy to add them upstream in semconv_ai in a follow-up if preferred.

Tests

Two new direct unit tests on _extract_response_attributes (no VCR needed):

• test_extract_response_captures_response_identification_fields — feeds a SimpleNamespace with every field set, asserts each maps to the correct span attribute with the expected value.
• test_extract_response_absent_fields_dont_set_attributes — regression guard for the None-passthrough branches.

All 12 tests in tests/test_openai_agents.py pass locally. uv run ruff check clean.

Notes

Part of a small series of openai-agents parity fixes (#4061 cached_tokens + reasoning_tokens, #4062 tool span type + duration, #4063 tool span input + output). Each stands alone off main and can be merged in any order.

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