feat(anthropic): add Messages.create sync instrumentation #4034
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codefromthecrypt,
karthikscale3,
lmolkova,
lzchen,
nirga and
vasantteja
10 tasks
aabmass
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Dec 22, 2025
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aabmass
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A few suggestions, but looks pretty good, thanks
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| GenAIAttributes.GEN_AI_REQUEST_MODEL: kwargs.get("model"), | ||
| GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS: kwargs.get("max_tokens"), | ||
| GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE: kwargs.get("temperature"), | ||
| GenAIAttributes.GEN_AI_REQUEST_TOP_P: kwargs.get("top_p"), | ||
| GenAIAttributes.GEN_AI_REQUEST_TOP_K: kwargs.get("top_k"), | ||
| GenAIAttributes.GEN_AI_REQUEST_STOP_SEQUENCES: kwargs.get( | ||
| "stop_sequences" | ||
| ), |
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For extracting parameters from the untyped kwargs dict, can you define a function with copied method signature from the Anthropic code being instrumented (and add a link to the code in a comment) and call it with *args, **kwargs? IMO it helps the reader and will let the type checker know the expected types
Example from Vertex:
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| def get_llm_request_attributes( | ||
| kwargs: dict[str, Any], client_instance: Any | ||
| ) -> dict[str, Any]: |
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Can you annotate the return type like this?
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| span_attributes = {**get_llm_request_attributes(kwargs, instance)} | ||
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| span_name = f"{span_attributes[GenAIAttributes.GEN_AI_OPERATION_NAME]} {span_attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]}" | ||
| with tracer.start_as_current_span( | ||
| name=span_name, | ||
| kind=SpanKind.CLIENT, | ||
| attributes=span_attributes, | ||
| end_on_exit=False, | ||
| ) as span: | ||
| try: | ||
| result = wrapped(*args, **kwargs) | ||
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| if span.is_recording(): | ||
| _set_response_attributes(span, result) | ||
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| span.end() | ||
| return result | ||
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| except Exception as error: | ||
| handle_span_exception(span, error) | ||
| raise |
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@keith-decker can TelemetryHandler cover all of the common code in this PR? This seems like a great PR to start integrating GenAI util with
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Description
Implements OpenTelemetry instrumentation for Anthropic's
Messages.createAPI (sync, non-streaming). This adds automatic tracing for Anthropic SDK calls, capturing GenAI semantic convention attributes for observability.P.S: LLM help was used for writing the code.
What's Implemented
patch.py: Wrapper function forMessages.createthat creates spans with request/response attributesutils.py: Helper functions for attribute extraction, error handling, and content capture configuration__init__.py: Wiring for patching/unpatching viawraptSemantic Convention Attributes Captured
Request:
gen_ai.operation.name(chat)gen_ai.system(anthropic)gen_ai.request.modelgen_ai.request.max_tokensgen_ai.request.temperaturegen_ai.request.top_pgen_ai.request.top_kgen_ai.request.stop_sequencesserver.addressResponse:
gen_ai.response.idgen_ai.response.modelgen_ai.response.finish_reasonsgen_ai.usage.input_tokensgen_ai.usage.output_tokensError:
error.type(on exceptions)Fixes #(#3949)
Type of change
How Has This Been Tested?
Ran the full test suite with VCR cassettes for mocked API responses:
Test cases:
Results: 15 tests passed (8 new + 7 existing instrumentor tests)
Does This PR Require a Core Repo Change?
Checklist: