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Write the scalar gen_ai.* subset of an LLM Observability span onto the APM span, so model, provider, application, conversation and token usage become indexed, searchable APM tags. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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type: feature
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The openai-java instrumentation keeps tracing when LLM Observability is disabled, and the operation, model, provider and ml_app are all known on that path. Token usage and conversation id are not computed there, so they stay unreported. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The repository does not accept new .groovy files. The module already has a JUnit forked-test base for LLMObs configurations, so the coverage moves there. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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The finish-time mapping reads only scalar LLM Observability tags. It keeps message bodies off APM spans and limits usage metrics to LLM and embedding spans.
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Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Both tests in the class produce an openai.request span, so taking the first one written let a late trace from the sibling test be asserted on. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The attributes are derived from the LLMObs tag set, which the openai decorators do not build with LLM Observability off. Drop the fallback that resolved operation, model and ml_app from APM tags instead, and collapse GenAiApmTags back to a single apply(span). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Sets _dd.llmobs.artificial_gen_ai_tags alongside the gen_ai.* attributes so the backend processor does not treat them as a user annotation and create a duplicate LLM Observability span. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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What Does This Do
Emits the scalar
gen_ai.*attributes onto APM spans, making them indexed APM tags:gen_ai.operation.name,gen_ai.request.model,gen_ai.provider.name,gen_ai.application.name,gen_ai.conversation.idgen_ai.usage.{input,output,total,cache_read_input,cache_write_input,reasoning_output}_tokensGenAiApmTagsreads the values back off the span's_ml_obs_tag.*/_ml_obs_metric.*keys at finish time and is called from both emission sites: manual SDK spans (DDLLMObsSpan.finish) and auto-instrumentation (OpenAiDecorator.doBeforeFinish). SinceLLMObsSpanMapperonly serializes_ml_obs_*keys, the unprefixed tags land on the APM span only.Alongside them it sets the internal
_dd.llmobs.artificial_gen_ai_tagstag, marking the attributes as tracer-written rather than user-annotated. Without it the backend processor treats the APM span as a user-annotated gen_ai span and creates a duplicate LLM Observability span.Message bodies (input, output, tool definitions, retrieval documents) stay off the APM span and keep coming from the LLMObs track. Port of DataDog/dd-trace-py#20083, DataDog/dd-trace-py#20411 and DataDog/dd-trace-js#10261.
Motivation
The APM trace UI merges these keys from the LLMObs track client-side, so the values render but aren't indexed. You can't filter, facet, or monitor on model, provider, or token usage in APM.
Testing
disabled span:
https://app.datadoghq.com/u/d4735e3a/haW-kUa-8V4
enabled span:
https://app.datadoghq.com/u/d4735e3a/rWc-4gR-dGd
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