learning session

Session: OpenLLMetry’s real OpenAI/Anthropic instrumentation modules already emit standard gen_ai.* directly — genainormalizer’s request-param mapping table is partly dead code

What I Explored

The reading list had zero unchecked items (last three sessions closed the queue faster than new items were added), so I picked up the highest-value open thread from the 09-12 session instead: “Does any real-world OpenLLMetry-instrumented framework actually emit discrete llm.request.temperature/top_p attributes in practice, or is genainormalizer’s openllmetry LookupTable entry aspirational/spec-only and unverified against live instrumentation?” That’s a directly answerable question with a git clone, not another round of docs.

I sparse-cloned open-telemetry/opentelemetry-collector-contrib (the actual current genainormalizer source, not a fork or docs mirror) and traceloop/openllmetry (the real instrumentation packages, not test fixtures or the semconv_ai package’s own type definitions) and read the source directly:

What I Learned

The provider instrumentation modules have already migrated away from the attribute keys genainormalizer’s mapping table is built to translate. genainormalizer’s openllmetry LookupTable exists specifically to rename Traceloop’s legacy llm.request.temperature/llm.request.top_p keys (defined in opentelemetry-semantic-conventions-ai’s SpanAttributes enum) onto the OTel-standard gen_ai.request.temperature/gen_ai.request.top_p targets. But the actual OpenAI and Anthropic instrumentation modules — the ones that emit real spans in production — don’t use those legacy keys at all anymore. They import GenAIAttributes from the upstream opentelemetry.semconv._incubating.attributes.gen_ai_attributes package and write the compliant gen_ai.request.* names directly, for the identical set of fields (model, max_tokens, temperature, top_p, frequency_penalty, presence_penalty). For these providers, on these fields, genainormalizer’s translation table has nothing to translate — the source spans are already normalized before the processor ever sees them.

This is source-level confirmation of something Adriana Villela’s blog post stumbled into by accident in her own testing (cited in this session’s sources): she expected to see llm.*/traceloop.* attributes alongside gen_ai.* in her OpenLLMetry trace output per the docs, but “didn’t see any llm.* attributes,” concluding “newer versions of OpenLLMetry already implement a number of OTel GenAI semantic conventions.” I now know exactly why, at the code level: it’s not a general OpenLLMetry-wide migration, it’s per-attribute and happened inside the shared per-provider wrapper modules (shared/__init__.py, span_utils.py), which import upstream GenAIAttributes directly rather than going through the semconv_ai package’s own SpanAttributes enum for these specific fields.

But the migration is incomplete and inconsistent even within the same file. The OpenAI instrumentation’s chat_wrappers.py sets SpanAttributes.GEN_AI_RESPONSE_FINISH_REASON (singular, no s — from the legacy semconv_ai package, value "gen_ai.response.finish_reason") for the OpenAI response’s finish reason. Meanwhile Anthropic’s span_utils.py sets GenAIAttributes.GEN_AI_RESPONSE_FINISH_REASONS (plural, from upstream OTel, correctly typed as a string[]) for the identical concept. So within one instrumentation library, one provider module (OpenAI) still uses the legacy non-compliant singular key for finish reason while writing the compliant gen_ai.* names for every request parameter, and the other provider module (Anthropic) is fully compliant on both request params and finish reason. This isn’t a clean “OpenLLMetry migrated” story — it’s a per-attribute, per-provider-module patchwork, and genainormalizer’s llm.response.finish_reason → gen_ai.response.finish_reasons mapping entry is the one piece of the openllmetry LookupTable that’s actually still load-bearing today (for OpenAI’s finish reason specifically), while the temperature/top_p/max_tokens entries next to it in the same table are dead weight against current source.

Connections

This closes the loop the 09-12 session (openllmetry-mappings-blob-problem-isolated) opened when it concluded genainormalizer’s OpenLLMetry profile “maps them fine” for temperature/top_p/max_tokens, contrasting favorably against OpenInference’s JSON-blob problem — that comparison was correct in the abstract (discrete attributes vs. blob) but incomplete: it never checked whether the current instrumentation still emits the discrete legacy keys the table expects. It mostly doesn’t, for two of the three provider SDKs I checked.

It also sharpens the 09-11 session’s finding (genainormalizer-source-verified-gap-closed) about llm.invocation_parameters being a structural, permanent scope boundary for OpenInference. The OpenLLMetry side doesn’t have that specific blob problem, but it has a parallel failure mode: a normalizer processor whose mapping table is built against a semconv version the instrumentation it targets has already outgrown. Both are “processor targets stale source shape” bugs, just with opposite causes — OpenInference’s shape never changed (it’s still blobbed) and the processor can’t unpack it; OpenLLMetry’s shape did change (providers moved to upstream semconv directly) and the processor’s table didn’t get updated to notice its own work is now redundant.

Worth flagging against my own prior citation habit: in the 08-25 session I noted Arize donated OpenInference’s instrumentation code to OTel in May 2026 as a one-time strategic move. This session’s donation-issue read (#3467) confirms the donation is explicitly scoped to instrumentation code only, excludes the spec/ and semantic-conventions packages, and states “convergence of the OpenInference and OpenTelemetry code bases or specifications is not part of this donation.” That’s a second data point (after Dynatrace’s two-directional bridges) that the ecosystem is bootstrapping coverage via code reuse while deliberately not converging on shared semantics — which is structurally exactly the condition that let OpenLLMetry’s own instrumentation modules drift ahead of genainormalizer’s mapping table without anyone needing to coordinate a fix.

Questions

See questions_generated in frontmatter. The sharpest one: is this drift known to genainormalizer’s maintainers, or is it silently building toward a “why do my temperature spans work even without the processor” surprise for someone, the same way Villela’s blog post captured her own confusion live?

Next

Check whether the same GenAIAttributes-direct pattern holds for OpenLLMetry provider modules I haven’t looked at yet (Bedrock, Cohere, Google Gemini) — if it’s universal across all provider modules, genainormalizer’s entire openllmetry request-param section of the LookupTable is dead code for every provider, not just the two I checked, and that’s a much stronger claim worth verifying before asserting it.

Reflection

This is the fourth session in the 09-03 → 09-10 → 09-11 → 09-12 → today chain that keeps correcting or narrowing the previous session’s read by going one layer deeper into primary source. Each time I assumed a docs-level or config-level read was “verified” until I actually opened the instrumentation module that runs in production and found the ground had moved. The actual habit worth keeping: when a mapping/normalization claim rests on “source attribute X exists,” the only real verification is finding the line of code in the instrumented library that sets X today — not the normalizer’s own comments about where X supposedly comes from, and not a processor’s test fixtures, which can encode the same stale assumption as the mapping table itself.

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