feat: centralize telemetry logging at LLM client level (#8815)
* feat: centralize telemetry logging at LLM client level Moves telemetry logging from individual adapters to LLMClientBase: - Add TelemetryStreamWrapper for streaming telemetry on stream close - Add request_async_with_telemetry() for non-streaming requests - Add stream_async_with_telemetry() for streaming requests - Add set_telemetry_context() to configure agent_id, run_id, step_id Updates adapters and agents to use new pattern: - LettaLLMAdapter now accepts agent_id/run_id in constructor - Adapters call set_telemetry_context() before LLM requests - Removes duplicate telemetry logging from adapters - Enriches traces with agent_id, run_id, call_type metadata 🐙 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: accumulate streaming response content for telemetry TelemetryStreamWrapper now extracts actual response data from chunks: - Content text (concatenated from deltas) - Tool calls (id, name, arguments) - Model name, finish reason, usage stats 🐙 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * refactor: move streaming telemetry to caller (option 3) - Remove TelemetryStreamWrapper class - Add log_provider_trace_async() helper to LLMClientBase - stream_async_with_telemetry() now just returns raw stream - Callers log telemetry after processing with rich interface data Updated callers: - summarizer.py: logs content + usage after stream processing - letta_agent.py: logs tool_call, reasoning, model, usage 🐙 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: pass agent_id and run_id to parent adapter class LettaLLMStreamAdapter was not passing agent_id/run_id to parent, causing "unexpected keyword argument" errors. 🐙 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> --------- Co-authored-by: Letta <noreply@letta.com>
This commit is contained in:
Kian Jones
committed by
Sarah Wooders
Sarah Wooders
parent
9418ab9815
commit
a92e868ee6
@@ -414,7 +414,9 @@ class LettaAgent(BaseAgent):
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provider_trace=ProviderTrace(
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request_json=request_data,
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response_json=response_data,
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step_id=step_id, # Use original step_id for telemetry
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step_id=step_id,
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agent_id=self.agent_id,
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run_id=self.current_run_id,
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),
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)
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step_progression = StepProgression.LOGGED_TRACE
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@@ -759,7 +761,9 @@ class LettaAgent(BaseAgent):
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provider_trace=ProviderTrace(
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request_json=request_data,
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response_json=response_data,
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step_id=step_id, # Use original step_id for telemetry
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step_id=step_id,
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agent_id=self.agent_id,
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run_id=self.current_run_id,
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),
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)
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step_progression = StepProgression.LOGGED_TRACE
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@@ -1117,6 +1121,22 @@ class LettaAgent(BaseAgent):
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stop_reason = LettaStopReason(stop_reason=StopReasonType.invalid_tool_call.value)
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raise e
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reasoning_content = interface.get_reasoning_content()
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# Log provider trace telemetry after stream processing
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await llm_client.log_provider_trace_async(
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request_data=request_data,
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response_json={
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"content": {
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"tool_call": tool_call.model_dump() if tool_call else None,
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"reasoning": [c.model_dump() for c in reasoning_content] if reasoning_content else [],
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},
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"model": getattr(interface, "model", None),
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"usage": {
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"input_tokens": interface.input_tokens,
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"output_tokens": interface.output_tokens,
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},
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},
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)
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persisted_messages, should_continue, stop_reason = await self._handle_ai_response(
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tool_call,
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valid_tool_names,
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@@ -1208,7 +1228,9 @@ class LettaAgent(BaseAgent):
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"output_tokens": usage.completion_tokens,
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},
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},
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step_id=step_id, # Use original step_id for telemetry
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step_id=step_id,
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agent_id=self.agent_id,
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run_id=self.current_run_id,
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),
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)
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step_progression = StepProgression.LOGGED_TRACE
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@@ -1430,8 +1452,14 @@ class LettaAgent(BaseAgent):
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log_event("agent.stream_no_tokens.llm_request.created")
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async with AsyncTimer() as timer:
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# Attempt LLM request
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response = await llm_client.request_async(request_data, agent_state.llm_config)
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# Attempt LLM request with telemetry
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llm_client.set_telemetry_context(
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telemetry_manager=self.telemetry_manager,
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agent_id=self.agent_id,
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run_id=self.current_run_id,
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call_type="agent_step",
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)
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response = await llm_client.request_async_with_telemetry(request_data, agent_state.llm_config)
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# Track LLM request time
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step_metrics.llm_request_ns = int(timer.elapsed_ns)
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@@ -1492,10 +1520,18 @@ class LettaAgent(BaseAgent):
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attributes={"request_start_to_provider_request_start_ns": ns_to_ms(request_start_to_provider_request_start_ns)},
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)
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# Attempt LLM request
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# Set telemetry context before streaming
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llm_client.set_telemetry_context(
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telemetry_manager=self.telemetry_manager,
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agent_id=self.agent_id,
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run_id=self.current_run_id,
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call_type="agent_step",
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)
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# Attempt LLM request with telemetry wrapper
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return (
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request_data,
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await llm_client.stream_async(request_data, agent_state.llm_config),
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await llm_client.stream_async_with_telemetry(request_data, agent_state.llm_config),
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current_in_context_messages,
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new_in_context_messages,
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valid_tool_names,
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