feat: log LLM traces to clickhouse (#9111)
* feat: add non-streaming option for conversation messages - Add ConversationMessageRequest with stream=True default (backwards compatible) - stream=true (default): SSE streaming via StreamingService - stream=false: JSON response via AgentLoop.load().step() 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * chore: regenerate API schema for ConversationMessageRequest * feat: add direct ClickHouse storage for raw LLM traces Adds ability to store raw LLM request/response payloads directly in ClickHouse, bypassing OTEL span attribute size limits. This enables debugging and analytics on large LLM payloads (>10MB system prompts, large tool schemas, etc.). New files: - letta/schemas/llm_raw_trace.py: Pydantic schema with ClickHouse row helper - letta/services/llm_raw_trace_writer.py: Async batching writer (fire-and-forget) - letta/services/llm_raw_trace_reader.py: Reader with query methods - scripts/sql/clickhouse/llm_raw_traces.ddl: Production table DDL - scripts/sql/clickhouse/llm_raw_traces_local.ddl: Local dev DDL - apps/core/clickhouse-init.sql: Local dev initialization Modified: - letta/settings.py: Added 4 settings (store_llm_raw_traces, ttl, batch_size, flush_interval) - letta/llm_api/llm_client_base.py: Integration into request_async_with_telemetry - compose.yaml: Added ClickHouse service for local dev - justfile: Added clickhouse, clickhouse-cli, clickhouse-traces commands Feature disabled by default (LETTA_STORE_LLM_RAW_TRACES=false). Uses ZSTD(3) compression for 10-30x reduction on JSON payloads. 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: address code review feedback for LLM raw traces Fixes based on code review feedback: 1. Fix ClickHouse endpoint parsing - default to secure=False for raw host:port inputs (was defaulting to HTTPS which breaks local dev) 2. Make raw trace writes truly fire-and-forget - use asyncio.create_task() instead of awaiting, so JSON serialization doesn't block request path 3. Add bounded queue (maxsize=10000) - prevents unbounded memory growth under load. Drops traces with warning if queue is full. 4. Fix deprecated asyncio usage - get_running_loop() instead of get_event_loop() 5. Add org_id fallback - use _telemetry_org_id if actor doesn't have it 6. Remove unused imports - json import in reader 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: add missing asyncio import and simplify JSON serialization - Add missing 'import asyncio' that was causing 'name asyncio is not defined' error - Remove unnecessary clean_double_escapes() function - the JSON is stored correctly, the clickhouse-client CLI was just adding extra escaping when displaying - Update just clickhouse-trace to use Python client for correct JSON output 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * test: add clickhouse raw trace integration test * test: simplify clickhouse trace assertions * refactor: centralize usage parsing and stream error traces Use per-client usage helpers for raw trace extraction and ensure streaming errors log requests with error metadata. 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * test: exercise provider usage parsing live Make live OpenAI/Anthropic/Gemini requests with credential gating and validate Anthropic cache usage mapping when present. 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * test: fix usage parsing tests to pass - Use GoogleAIClient with GEMINI_API_KEY instead of GoogleVertexClient - Update model to gemini-2.0-flash (1.5-flash deprecated in v1beta) - Add tools=[] for Gemini/Anthropic build_request_data 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * refactor: extract_usage_statistics returns LettaUsageStatistics Standardize on LettaUsageStatistics as the canonical usage format returned by client helpers. Inline UsageStatistics construction for ChatCompletionResponse where needed. 👾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * feat: add is_byok and llm_config_json columns to ClickHouse traces Extend llm_raw_traces table with: - is_byok (UInt8): Track BYOK vs base provider usage for billing analytics - llm_config_json (String, ZSTD): Store full LLM config for debugging and analysis This enables queries like: - BYOK usage breakdown by provider/model - Config parameter analysis (temperature, max_tokens, etc.) - Debugging specific request configurations * feat: add tests for error traces, llm_config_json, and cache tokens - Update llm_raw_trace_reader.py to query new columns (is_byok, cached_input_tokens, cache_write_tokens, reasoning_tokens, llm_config_json) - Add test_error_trace_stored_in_clickhouse to verify error fields - Add test_cache_tokens_stored_for_anthropic to verify cache token storage - Update existing tests to verify llm_config_json is stored correctly - Make llm_config required in log_provider_trace_async() - Simplify provider extraction to use provider_name directly 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * ci: add ClickHouse integration tests to CI pipeline - Add use-clickhouse option to reusable-test-workflow.yml - Add ClickHouse service container with otel database - Add schema initialization step using clickhouse-init.sql - Add ClickHouse env vars (CLICKHOUSE_ENDPOINT, etc.) - Add separate clickhouse-integration-tests job running integration_test_clickhouse_llm_raw_traces.py 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * refactor: simplify provider and org_id extraction in raw trace writer - Use model_endpoint_type.value for provider (not provider_name) - Simplify org_id to just self.actor.organization_id (actor is always pydantic) 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * refactor: simplify LLMRawTraceWriter with _enabled flag - Check ClickHouse env vars once at init, set _enabled flag - Early return in write_async/flush_async if not enabled - Remove ValueError raises (never used) - Simplify _get_client (no validation needed since already checked) 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: add LLMRawTraceWriter shutdown to FastAPI lifespan Properly flush pending traces on graceful shutdown via lifespan instead of relying only on atexit handler. 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * feat: add agent_tags column to ClickHouse traces Store agent tags as Array(String) for filtering/analytics by tag. 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * cleanup * fix(ci): fix ClickHouse schema initialization in CI - Create database separately before loading SQL file - Remove CREATE DATABASE from SQL file (handled in CI step) - Add verification step to confirm table was created - Use -sf flag for curl to fail on HTTP errors 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * refactor: simplify LLM trace writer with ClickHouse async_insert - Use ClickHouse async_insert for server-side batching instead of manual queue/flush loop - Sync cloud DDL schema with clickhouse-init.sql (add missing columns) - Remove redundant llm_raw_traces_local.ddl - Remove unused batch_size/flush_interval settings - Update tests for simplified writer Key changes: - async_insert=1, wait_for_async_insert=1 for reliable server-side batching - Simple per-trace retry with exponential backoff (max 3 retries) - ~150 lines removed from writer 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * refactor: consolidate ClickHouse direct writes into TelemetryManager backend - Add clickhouse_direct backend to provider_trace_backends - Remove duplicate ClickHouse write logic from llm_client_base.py - Configure via LETTA_TELEMETRY_PROVIDER_TRACE_BACKEND=postgres,clickhouse_direct The clickhouse_direct backend: - Converts ProviderTrace to LLMRawTrace - Extracts usage stats from response JSON - Writes via LLMRawTraceWriter with async_insert 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * refactor: address PR review comments and fix llm_config bug Review comment fixes: - Rename clickhouse_direct -> clickhouse_analytics (clearer purpose) - Remove ClickHouse from OSS compose.yaml, create separate compose.clickhouse.yaml - Delete redundant scripts/test_llm_raw_traces.py (use pytest tests) - Remove unused llm_raw_traces_ttl_days setting (TTL handled in DDL) - Fix socket description leak in telemetry_manager docstring - Add cloud-only comment to clickhouse-init.sql - Update justfile to use separate compose file Bug fix: - Fix llm_config not being passed to ProviderTrace in telemetry - Now correctly populates provider, model, is_byok for all LLM calls - Affects both request_async_with_telemetry and log_provider_trace_async DDL optimizations: - Add secondary indexes (bloom_filter for agent_id, model, step_id) - Add minmax indexes for is_byok, is_error - Change model and error_type to LowCardinality for faster GROUP BY 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * refactor: rename llm_raw_traces -> llm_traces Address review feedback that "raw" is misleading since we denormalize fields. Renames: - Table: llm_raw_traces -> llm_traces - Schema: LLMRawTrace -> LLMTrace - Files: llm_raw_trace_{reader,writer}.py -> llm_trace_{reader,writer}.py - Setting: store_llm_raw_traces -> store_llm_traces 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: update workflow references to llm_traces Missed renaming table name in CI workflow files. 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: update clickhouse_direct -> clickhouse_analytics in docstring 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * chore: remove inaccurate OTEL size limit comments The 4MB limit is our own truncation logic, not an OTEL protocol limit. The real benefit is denormalized columns for analytics queries. 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * chore: remove local ClickHouse dev setup (cloud-only feature) - Delete clickhouse-init.sql and compose.clickhouse.yaml - Remove local clickhouse just commands - Update CI to use cloud DDL with MergeTree for testing clickhouse_analytics is a cloud-only feature. For local dev, use postgres backend. 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: restore compose.yaml to match main 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * refactor: merge clickhouse_analytics into clickhouse backend Per review feedback - having two separate backends was confusing. Now the clickhouse backend: - Writes to llm_traces table (denormalized for cost analytics) - Reads from OTEL traces table (will cut over to llm_traces later) Config: LETTA_TELEMETRY_PROVIDER_TRACE_BACKEND=postgres,clickhouse 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: correct path to DDL file in CI workflow 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * chore: add provider index to DDL for faster filtering 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: configure telemetry backend in clickhouse tests Tests need to set telemetry_settings.provider_trace_backends to include 'clickhouse', otherwise traces are routed to default postgres backend. 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: set provider_trace_backend field, not property provider_trace_backends is a computed property, need to set the underlying provider_trace_backend string field instead. 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: error trace test and error_type extraction - Add TelemetryManager to error trace test so traces get written - Fix error_type extraction to check top-level before nested error dict 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: use provider_trace.id for trace correlation across backends - Pass provider_trace.id to LLMTrace instead of auto-generating - Log warning if ID is missing (shouldn't happen, helps debug) - Fallback to new UUID only if not set 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: trace ID correlation and concurrency issues - Strip "provider_trace-" prefix from ID for UUID storage in ClickHouse - Add asyncio.Lock to serialize writes (clickhouse_connect not thread-safe) - Fix Anthropic prompt_tokens to include cached tokens for cost analytics - Log warning if provider_trace.id is missing 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> --------- Co-authored-by: Letta <noreply@letta.com> Co-authored-by: Caren Thomas <carenthomas@gmail.com>
This commit is contained in:
Sarah Wooders
committed by
Caren Thomas
Caren Thomas
parent
24ea7dbaed
commit
4096b30cd7
@@ -88,10 +88,22 @@ class LettaLLMStreamAdapter(LettaLLMAdapter):
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# Extract optional parameters
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# ttft_span = kwargs.get('ttft_span', None)
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request_start_ns = get_utc_timestamp_ns()
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# Start the streaming request (map provider errors to common LLMError types)
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try:
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stream = await self.llm_client.stream_async(request_data, self.llm_config)
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except Exception as e:
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self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
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latency_ms = int((self.llm_request_finish_timestamp_ns - request_start_ns) / 1_000_000)
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await self.llm_client.log_provider_trace_async(
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request_data=request_data,
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response_json=None,
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llm_config=self.llm_config,
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latency_ms=latency_ms,
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error_msg=str(e),
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error_type=type(e).__name__,
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)
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raise self.llm_client.handle_llm_error(e)
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# Process the stream and yield chunks immediately for TTFT
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@@ -101,6 +113,16 @@ class LettaLLMStreamAdapter(LettaLLMAdapter):
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# Yield each chunk immediately as it arrives
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yield chunk
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except Exception as e:
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self.llm_request_finish_timestamp_ns = get_utc_timestamp_ns()
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latency_ms = int((self.llm_request_finish_timestamp_ns - request_start_ns) / 1_000_000)
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await self.llm_client.log_provider_trace_async(
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request_data=request_data,
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response_json=None,
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llm_config=self.llm_config,
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latency_ms=latency_ms,
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error_msg=str(e),
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error_type=type(e).__name__,
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)
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raise self.llm_client.handle_llm_error(e)
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# After streaming completes, extract the accumulated data
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@@ -1152,6 +1152,8 @@ class LettaAgent(BaseAgent):
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"output_tokens": interface.output_tokens,
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},
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},
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llm_config=agent_state.llm_config,
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latency_ms=int(llm_request_ms),
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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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@@ -670,6 +670,7 @@ class GoogleVertexClient(LLMClientBase):
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# "candidatesTokenCount": 27,
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# "totalTokenCount": 36
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# }
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usage = None
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if response.usage_metadata:
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# Extract usage via centralized method
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from letta.schemas.enums import ProviderType
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@@ -82,6 +82,10 @@ class LLMClientBase:
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"""Wrapper around request_async that logs telemetry for all requests including errors.
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Call set_telemetry_context() first to set agent_id, run_id, etc.
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Telemetry is logged via TelemetryManager which supports multiple backends
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(postgres, clickhouse, socket, etc.) configured via
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LETTA_TELEMETRY_PROVIDER_TRACE_BACKEND.
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"""
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from letta.log import get_logger
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@@ -97,6 +101,7 @@ class LLMClientBase:
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error_type = type(e).__name__
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raise
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finally:
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# Log telemetry via configured backends
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if self._telemetry_manager and settings.track_provider_trace:
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if self.actor is None:
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logger.warning(f"Skipping telemetry: actor is None (call_type={self._telemetry_call_type})")
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@@ -116,7 +121,7 @@ class LLMClientBase:
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org_id=self._telemetry_org_id,
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user_id=self._telemetry_user_id,
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compaction_settings=self._telemetry_compaction_settings,
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llm_config=self._telemetry_llm_config,
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llm_config=llm_config.model_dump() if llm_config else self._telemetry_llm_config,
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),
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)
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except Exception as e:
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@@ -130,10 +135,27 @@ class LLMClientBase:
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"""
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return await self.stream_async(request_data, llm_config)
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async def log_provider_trace_async(self, request_data: dict, response_json: dict) -> None:
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async def log_provider_trace_async(
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self,
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request_data: dict,
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response_json: Optional[dict],
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llm_config: Optional[LLMConfig] = None,
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latency_ms: Optional[int] = None,
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error_msg: Optional[str] = None,
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error_type: Optional[str] = None,
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) -> None:
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"""Log provider trace telemetry. Call after processing LLM response.
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Uses telemetry context set via set_telemetry_context().
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Telemetry is logged via TelemetryManager which supports multiple backends.
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Args:
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request_data: The request payload sent to the LLM
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response_json: The response payload from the LLM
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llm_config: LLMConfig for extracting provider/model info
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latency_ms: Latency in milliseconds (not used currently, kept for API compatibility)
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error_msg: Error message if request failed (not used currently)
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error_type: Error type if request failed (not used currently)
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"""
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from letta.log import get_logger
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@@ -146,6 +168,9 @@ class LLMClientBase:
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logger.warning(f"Skipping telemetry: actor is None (call_type={self._telemetry_call_type})")
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return
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if response_json is None:
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return
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try:
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pydantic_actor = self.actor.to_pydantic() if hasattr(self.actor, "to_pydantic") else self.actor
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await self._telemetry_manager.create_provider_trace_async(
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@@ -161,7 +186,7 @@ class LLMClientBase:
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org_id=self._telemetry_org_id,
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user_id=self._telemetry_user_id,
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compaction_settings=self._telemetry_compaction_settings,
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llm_config=self._telemetry_llm_config,
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llm_config=llm_config.model_dump() if llm_config else self._telemetry_llm_config,
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),
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)
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except Exception as e:
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167
letta/schemas/llm_trace.py
Normal file
167
letta/schemas/llm_trace.py
Normal file
@@ -0,0 +1,167 @@
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"""Schema for LLM request/response traces stored in ClickHouse for analytics."""
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from __future__ import annotations
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import uuid
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from datetime import datetime
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from typing import Optional
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from pydantic import Field
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from letta.helpers.datetime_helpers import get_utc_time
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from letta.schemas.letta_base import LettaBase
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class LLMTrace(LettaBase):
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"""
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LLM request/response trace for ClickHouse analytics.
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Stores LLM request/response payloads with denormalized columns for
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fast cost analytics queries (token usage by org/agent/model).
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Attributes:
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id (str): Unique trace identifier (UUID).
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organization_id (str): The organization this trace belongs to.
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project_id (str): The project this trace belongs to.
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agent_id (str): ID of the agent that made the request.
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run_id (str): ID of the run this trace is associated with.
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step_id (str): ID of the step that generated this trace.
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trace_id (str): OTEL trace ID for correlation.
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call_type (str): Type of LLM call ('agent_step', 'summarization', 'embedding').
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provider (str): LLM provider name ('openai', 'anthropic', etc.).
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model (str): Model name/identifier used.
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request_size_bytes (int): Size of request_json in bytes.
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response_size_bytes (int): Size of response_json in bytes.
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prompt_tokens (int): Number of prompt tokens used.
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completion_tokens (int): Number of completion tokens generated.
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total_tokens (int): Total tokens (prompt + completion).
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latency_ms (int): Request latency in milliseconds.
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is_error (bool): Whether the request resulted in an error.
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error_type (str): Exception class name if error occurred.
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error_message (str): Error message if error occurred.
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request_json (str): Full request payload as JSON string.
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response_json (str): Full response payload as JSON string.
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created_at (datetime): Timestamp when the trace was created.
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"""
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__id_prefix__ = "llm_trace"
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# Primary identifier (UUID portion of ProviderTrace.id, prefix stripped for ClickHouse)
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id: str = Field(..., description="Trace UUID (strip 'provider_trace-' prefix to correlate)")
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# Context identifiers
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organization_id: str = Field(..., description="Organization this trace belongs to")
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project_id: Optional[str] = Field(default=None, description="Project this trace belongs to")
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agent_id: Optional[str] = Field(default=None, description="Agent that made the request")
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agent_tags: list[str] = Field(default_factory=list, description="Tags associated with the agent")
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run_id: Optional[str] = Field(default=None, description="Run this trace is associated with")
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step_id: Optional[str] = Field(default=None, description="Step that generated this trace")
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trace_id: Optional[str] = Field(default=None, description="OTEL trace ID for correlation")
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# Request metadata (queryable)
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call_type: str = Field(..., description="Type of LLM call: 'agent_step', 'summarization', 'embedding'")
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provider: str = Field(..., description="LLM provider: 'openai', 'anthropic', 'google_ai', etc.")
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model: str = Field(..., description="Model name/identifier")
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is_byok: bool = Field(default=False, description="Whether this request used BYOK (Bring Your Own Key)")
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# Size metrics
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request_size_bytes: int = Field(default=0, description="Size of request_json in bytes")
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response_size_bytes: int = Field(default=0, description="Size of response_json in bytes")
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# Token usage
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prompt_tokens: int = Field(default=0, description="Number of prompt tokens")
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completion_tokens: int = Field(default=0, description="Number of completion tokens")
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total_tokens: int = Field(default=0, description="Total tokens (prompt + completion)")
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# Cache and reasoning tokens (from LettaUsageStatistics)
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cached_input_tokens: Optional[int] = Field(default=None, description="Number of input tokens served from cache")
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cache_write_tokens: Optional[int] = Field(default=None, description="Number of tokens written to cache (Anthropic)")
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reasoning_tokens: Optional[int] = Field(default=None, description="Number of reasoning/thinking tokens generated")
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# Latency
|
||||
latency_ms: int = Field(default=0, description="Request latency in milliseconds")
|
||||
|
||||
# Error tracking
|
||||
is_error: bool = Field(default=False, description="Whether the request resulted in an error")
|
||||
error_type: Optional[str] = Field(default=None, description="Exception class name if error")
|
||||
error_message: Optional[str] = Field(default=None, description="Error message if error")
|
||||
|
||||
# Raw payloads (JSON strings)
|
||||
request_json: str = Field(..., description="Full request payload as JSON string")
|
||||
response_json: str = Field(..., description="Full response payload as JSON string")
|
||||
llm_config_json: str = Field(default="", description="LLM config as JSON string")
|
||||
|
||||
# Timestamp
|
||||
created_at: datetime = Field(default_factory=get_utc_time, description="When the trace was created")
|
||||
|
||||
def to_clickhouse_row(self) -> tuple:
|
||||
"""Convert to a tuple for ClickHouse insertion."""
|
||||
return (
|
||||
self.id,
|
||||
self.organization_id,
|
||||
self.project_id or "",
|
||||
self.agent_id or "",
|
||||
self.agent_tags,
|
||||
self.run_id or "",
|
||||
self.step_id or "",
|
||||
self.trace_id or "",
|
||||
self.call_type,
|
||||
self.provider,
|
||||
self.model,
|
||||
1 if self.is_byok else 0,
|
||||
self.request_size_bytes,
|
||||
self.response_size_bytes,
|
||||
self.prompt_tokens,
|
||||
self.completion_tokens,
|
||||
self.total_tokens,
|
||||
self.cached_input_tokens,
|
||||
self.cache_write_tokens,
|
||||
self.reasoning_tokens,
|
||||
self.latency_ms,
|
||||
1 if self.is_error else 0,
|
||||
self.error_type or "",
|
||||
self.error_message or "",
|
||||
self.request_json,
|
||||
self.response_json,
|
||||
self.llm_config_json,
|
||||
self.created_at,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def clickhouse_columns(cls) -> list[str]:
|
||||
"""Return column names for ClickHouse insertion."""
|
||||
return [
|
||||
"id",
|
||||
"organization_id",
|
||||
"project_id",
|
||||
"agent_id",
|
||||
"agent_tags",
|
||||
"run_id",
|
||||
"step_id",
|
||||
"trace_id",
|
||||
"call_type",
|
||||
"provider",
|
||||
"model",
|
||||
"is_byok",
|
||||
"request_size_bytes",
|
||||
"response_size_bytes",
|
||||
"prompt_tokens",
|
||||
"completion_tokens",
|
||||
"total_tokens",
|
||||
"cached_input_tokens",
|
||||
"cache_write_tokens",
|
||||
"reasoning_tokens",
|
||||
"latency_ms",
|
||||
"is_error",
|
||||
"error_type",
|
||||
"error_message",
|
||||
"request_json",
|
||||
"response_json",
|
||||
"llm_config_json",
|
||||
"created_at",
|
||||
]
|
||||
@@ -221,6 +221,17 @@ async def lifespan(app_: FastAPI):
|
||||
except Exception as e:
|
||||
logger.warning(f"[Worker {worker_id}] SQLAlchemy instrumentation shutdown failed: {e}")
|
||||
|
||||
# Shutdown LLM raw trace writer (closes ClickHouse connection)
|
||||
if settings.store_llm_traces:
|
||||
try:
|
||||
from letta.services.llm_trace_writer import get_llm_trace_writer
|
||||
|
||||
writer = get_llm_trace_writer()
|
||||
await writer.shutdown_async()
|
||||
logger.info(f"[Worker {worker_id}] LLM raw trace writer shutdown completed")
|
||||
except Exception as e:
|
||||
logger.warning(f"[Worker {worker_id}] LLM raw trace writer shutdown failed: {e}")
|
||||
|
||||
logger.info(f"[Worker {worker_id}] Lifespan shutdown completed")
|
||||
|
||||
|
||||
|
||||
462
letta/services/llm_trace_reader.py
Normal file
462
letta/services/llm_trace_reader.py
Normal file
@@ -0,0 +1,462 @@
|
||||
"""ClickHouse reader for LLM analytics traces.
|
||||
|
||||
Reads LLM traces from ClickHouse for debugging, analytics, and auditing.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from typing import Any, List, Optional
|
||||
from urllib.parse import urlparse
|
||||
|
||||
from letta.helpers.singleton import singleton
|
||||
from letta.log import get_logger
|
||||
from letta.schemas.llm_trace import LLMTrace
|
||||
from letta.settings import settings
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
def _parse_clickhouse_endpoint(endpoint: str) -> tuple[str, int, bool]:
|
||||
"""Return (host, port, secure) for clickhouse_connect.get_client.
|
||||
|
||||
Supports:
|
||||
- http://host:port -> (host, port, False)
|
||||
- https://host:port -> (host, port, True)
|
||||
- host:port -> (host, port, False) # Default to insecure for local dev
|
||||
- host -> (host, 8123, False) # Default HTTP port, insecure
|
||||
"""
|
||||
parsed = urlparse(endpoint)
|
||||
|
||||
if parsed.scheme in ("http", "https"):
|
||||
host = parsed.hostname or ""
|
||||
port = parsed.port or (8443 if parsed.scheme == "https" else 8123)
|
||||
secure = parsed.scheme == "https"
|
||||
return host, port, secure
|
||||
|
||||
# Fallback: accept raw hostname (possibly with :port)
|
||||
# Default to insecure (HTTP) for local development
|
||||
if ":" in endpoint:
|
||||
host, port_str = endpoint.rsplit(":", 1)
|
||||
return host, int(port_str), False
|
||||
|
||||
return endpoint, 8123, False
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class LLMTraceRow:
|
||||
"""Raw row from ClickHouse query."""
|
||||
|
||||
id: str
|
||||
organization_id: str
|
||||
project_id: str
|
||||
agent_id: str
|
||||
agent_tags: List[str]
|
||||
run_id: str
|
||||
step_id: str
|
||||
trace_id: str
|
||||
call_type: str
|
||||
provider: str
|
||||
model: str
|
||||
is_byok: bool
|
||||
request_size_bytes: int
|
||||
response_size_bytes: int
|
||||
prompt_tokens: int
|
||||
completion_tokens: int
|
||||
total_tokens: int
|
||||
cached_input_tokens: Optional[int]
|
||||
cache_write_tokens: Optional[int]
|
||||
reasoning_tokens: Optional[int]
|
||||
latency_ms: int
|
||||
is_error: bool
|
||||
error_type: str
|
||||
error_message: str
|
||||
request_json: str
|
||||
response_json: str
|
||||
llm_config_json: str
|
||||
created_at: datetime
|
||||
|
||||
|
||||
@singleton
|
||||
class LLMTraceReader:
|
||||
"""
|
||||
ClickHouse reader for raw LLM traces.
|
||||
|
||||
Provides query methods for debugging, analytics, and auditing.
|
||||
|
||||
Usage:
|
||||
reader = LLMTraceReader()
|
||||
trace = await reader.get_by_step_id_async(step_id="step-xxx", organization_id="org-xxx")
|
||||
traces = await reader.list_by_agent_async(agent_id="agent-xxx", organization_id="org-xxx")
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self._client = None
|
||||
|
||||
def _get_client(self):
|
||||
"""Initialize ClickHouse client on first use (lazy loading)."""
|
||||
if self._client is not None:
|
||||
return self._client
|
||||
|
||||
import clickhouse_connect
|
||||
|
||||
if not settings.clickhouse_endpoint:
|
||||
raise ValueError("CLICKHOUSE_ENDPOINT is required")
|
||||
|
||||
host, port, secure = _parse_clickhouse_endpoint(settings.clickhouse_endpoint)
|
||||
if not host:
|
||||
raise ValueError("Invalid CLICKHOUSE_ENDPOINT")
|
||||
|
||||
database = settings.clickhouse_database or "otel"
|
||||
username = settings.clickhouse_username or "default"
|
||||
password = settings.clickhouse_password
|
||||
if not password:
|
||||
raise ValueError("CLICKHOUSE_PASSWORD is required")
|
||||
|
||||
self._client = clickhouse_connect.get_client(
|
||||
host=host,
|
||||
port=port,
|
||||
username=username,
|
||||
password=password,
|
||||
database=database,
|
||||
secure=secure,
|
||||
verify=True,
|
||||
)
|
||||
return self._client
|
||||
|
||||
def _row_to_trace(self, row: tuple) -> LLMTrace:
|
||||
"""Convert a ClickHouse row tuple to LLMTrace."""
|
||||
return LLMTrace(
|
||||
id=row[0],
|
||||
organization_id=row[1],
|
||||
project_id=row[2] or None,
|
||||
agent_id=row[3] or None,
|
||||
agent_tags=list(row[4]) if row[4] else [],
|
||||
run_id=row[5] or None,
|
||||
step_id=row[6] or None,
|
||||
trace_id=row[7] or None,
|
||||
call_type=row[8],
|
||||
provider=row[9],
|
||||
model=row[10],
|
||||
is_byok=bool(row[11]),
|
||||
request_size_bytes=row[12],
|
||||
response_size_bytes=row[13],
|
||||
prompt_tokens=row[14],
|
||||
completion_tokens=row[15],
|
||||
total_tokens=row[16],
|
||||
cached_input_tokens=row[17],
|
||||
cache_write_tokens=row[18],
|
||||
reasoning_tokens=row[19],
|
||||
latency_ms=row[20],
|
||||
is_error=bool(row[21]),
|
||||
error_type=row[22] or None,
|
||||
error_message=row[23] or None,
|
||||
request_json=row[24],
|
||||
response_json=row[25],
|
||||
llm_config_json=row[26] or "",
|
||||
created_at=row[27],
|
||||
)
|
||||
|
||||
def _query_sync(self, query: str, parameters: dict[str, Any]) -> List[tuple]:
|
||||
"""Execute a query synchronously."""
|
||||
client = self._get_client()
|
||||
result = client.query(query, parameters=parameters)
|
||||
return result.result_rows if result else []
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Query Methods
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
async def get_by_step_id_async(
|
||||
self,
|
||||
step_id: str,
|
||||
organization_id: str,
|
||||
) -> Optional[LLMTrace]:
|
||||
"""
|
||||
Get the most recent trace for a step.
|
||||
|
||||
Args:
|
||||
step_id: The step ID to look up
|
||||
organization_id: Organization ID for access control
|
||||
|
||||
Returns:
|
||||
LLMTrace if found, None otherwise
|
||||
"""
|
||||
query = """
|
||||
SELECT
|
||||
id, organization_id, project_id, agent_id, agent_tags, run_id, step_id, trace_id,
|
||||
call_type, provider, model, is_byok,
|
||||
request_size_bytes, response_size_bytes,
|
||||
prompt_tokens, completion_tokens, total_tokens,
|
||||
cached_input_tokens, cache_write_tokens, reasoning_tokens,
|
||||
latency_ms,
|
||||
is_error, error_type, error_message,
|
||||
request_json, response_json, llm_config_json,
|
||||
created_at
|
||||
FROM llm_traces
|
||||
WHERE step_id = %(step_id)s
|
||||
AND organization_id = %(organization_id)s
|
||||
ORDER BY created_at DESC
|
||||
LIMIT 1
|
||||
"""
|
||||
|
||||
rows = await asyncio.to_thread(
|
||||
self._query_sync,
|
||||
query,
|
||||
{"step_id": step_id, "organization_id": organization_id},
|
||||
)
|
||||
|
||||
if not rows:
|
||||
return None
|
||||
|
||||
return self._row_to_trace(rows[0])
|
||||
|
||||
async def get_by_id_async(
|
||||
self,
|
||||
trace_id: str,
|
||||
organization_id: str,
|
||||
) -> Optional[LLMTrace]:
|
||||
"""
|
||||
Get a trace by its ID.
|
||||
|
||||
Args:
|
||||
trace_id: The trace ID (UUID)
|
||||
organization_id: Organization ID for access control
|
||||
|
||||
Returns:
|
||||
LLMTrace if found, None otherwise
|
||||
"""
|
||||
query = """
|
||||
SELECT
|
||||
id, organization_id, project_id, agent_id, agent_tags, run_id, step_id, trace_id,
|
||||
call_type, provider, model, is_byok,
|
||||
request_size_bytes, response_size_bytes,
|
||||
prompt_tokens, completion_tokens, total_tokens,
|
||||
cached_input_tokens, cache_write_tokens, reasoning_tokens,
|
||||
latency_ms,
|
||||
is_error, error_type, error_message,
|
||||
request_json, response_json, llm_config_json,
|
||||
created_at
|
||||
FROM llm_traces
|
||||
WHERE id = %(trace_id)s
|
||||
AND organization_id = %(organization_id)s
|
||||
LIMIT 1
|
||||
"""
|
||||
|
||||
rows = await asyncio.to_thread(
|
||||
self._query_sync,
|
||||
query,
|
||||
{"trace_id": trace_id, "organization_id": organization_id},
|
||||
)
|
||||
|
||||
if not rows:
|
||||
return None
|
||||
|
||||
return self._row_to_trace(rows[0])
|
||||
|
||||
async def list_by_agent_async(
|
||||
self,
|
||||
agent_id: str,
|
||||
organization_id: str,
|
||||
limit: int = 100,
|
||||
offset: int = 0,
|
||||
call_type: Optional[str] = None,
|
||||
is_error: Optional[bool] = None,
|
||||
start_date: Optional[datetime] = None,
|
||||
end_date: Optional[datetime] = None,
|
||||
) -> List[LLMTrace]:
|
||||
"""
|
||||
List traces for an agent with optional filters.
|
||||
|
||||
Args:
|
||||
agent_id: Agent ID to filter by
|
||||
organization_id: Organization ID for access control
|
||||
limit: Maximum number of results (default 100)
|
||||
offset: Offset for pagination
|
||||
call_type: Filter by call type ('agent_step', 'summarization')
|
||||
is_error: Filter by error status
|
||||
start_date: Filter by created_at >= start_date
|
||||
end_date: Filter by created_at <= end_date
|
||||
|
||||
Returns:
|
||||
List of LLMTrace objects
|
||||
"""
|
||||
conditions = [
|
||||
"agent_id = %(agent_id)s",
|
||||
"organization_id = %(organization_id)s",
|
||||
]
|
||||
params: dict[str, Any] = {
|
||||
"agent_id": agent_id,
|
||||
"organization_id": organization_id,
|
||||
"limit": limit,
|
||||
"offset": offset,
|
||||
}
|
||||
|
||||
if call_type:
|
||||
conditions.append("call_type = %(call_type)s")
|
||||
params["call_type"] = call_type
|
||||
|
||||
if is_error is not None:
|
||||
conditions.append("is_error = %(is_error)s")
|
||||
params["is_error"] = 1 if is_error else 0
|
||||
|
||||
if start_date:
|
||||
conditions.append("created_at >= %(start_date)s")
|
||||
params["start_date"] = start_date
|
||||
|
||||
if end_date:
|
||||
conditions.append("created_at <= %(end_date)s")
|
||||
params["end_date"] = end_date
|
||||
|
||||
where_clause = " AND ".join(conditions)
|
||||
|
||||
query = f"""
|
||||
SELECT
|
||||
id, organization_id, project_id, agent_id, agent_tags, run_id, step_id, trace_id,
|
||||
call_type, provider, model, is_byok,
|
||||
request_size_bytes, response_size_bytes,
|
||||
prompt_tokens, completion_tokens, total_tokens,
|
||||
cached_input_tokens, cache_write_tokens, reasoning_tokens,
|
||||
latency_ms,
|
||||
is_error, error_type, error_message,
|
||||
request_json, response_json, llm_config_json,
|
||||
created_at
|
||||
FROM llm_traces
|
||||
WHERE {where_clause}
|
||||
ORDER BY created_at DESC
|
||||
LIMIT %(limit)s OFFSET %(offset)s
|
||||
"""
|
||||
|
||||
rows = await asyncio.to_thread(self._query_sync, query, params)
|
||||
return [self._row_to_trace(row) for row in rows]
|
||||
|
||||
async def get_usage_stats_async(
|
||||
self,
|
||||
organization_id: str,
|
||||
start_date: Optional[datetime] = None,
|
||||
end_date: Optional[datetime] = None,
|
||||
group_by: str = "model", # 'model', 'agent_id', 'call_type'
|
||||
) -> List[dict[str, Any]]:
|
||||
"""
|
||||
Get aggregated usage statistics.
|
||||
|
||||
Args:
|
||||
organization_id: Organization ID for access control
|
||||
start_date: Filter by created_at >= start_date
|
||||
end_date: Filter by created_at <= end_date
|
||||
group_by: Field to group by ('model', 'agent_id', 'call_type')
|
||||
|
||||
Returns:
|
||||
List of aggregated stats dicts
|
||||
"""
|
||||
valid_group_by = {"model", "agent_id", "call_type", "provider"}
|
||||
if group_by not in valid_group_by:
|
||||
raise ValueError(f"group_by must be one of {valid_group_by}")
|
||||
|
||||
conditions = ["organization_id = %(organization_id)s"]
|
||||
params: dict[str, Any] = {"organization_id": organization_id}
|
||||
|
||||
if start_date:
|
||||
conditions.append("created_at >= %(start_date)s")
|
||||
params["start_date"] = start_date
|
||||
|
||||
if end_date:
|
||||
conditions.append("created_at <= %(end_date)s")
|
||||
params["end_date"] = end_date
|
||||
|
||||
where_clause = " AND ".join(conditions)
|
||||
|
||||
query = f"""
|
||||
SELECT
|
||||
{group_by},
|
||||
count() as request_count,
|
||||
sum(total_tokens) as total_tokens,
|
||||
sum(prompt_tokens) as prompt_tokens,
|
||||
sum(completion_tokens) as completion_tokens,
|
||||
avg(latency_ms) as avg_latency_ms,
|
||||
sum(request_size_bytes) as total_request_bytes,
|
||||
sum(response_size_bytes) as total_response_bytes,
|
||||
countIf(is_error = 1) as error_count
|
||||
FROM llm_traces
|
||||
WHERE {where_clause}
|
||||
GROUP BY {group_by}
|
||||
ORDER BY total_tokens DESC
|
||||
"""
|
||||
|
||||
rows = await asyncio.to_thread(self._query_sync, query, params)
|
||||
|
||||
return [
|
||||
{
|
||||
group_by: row[0],
|
||||
"request_count": row[1],
|
||||
"total_tokens": row[2],
|
||||
"prompt_tokens": row[3],
|
||||
"completion_tokens": row[4],
|
||||
"avg_latency_ms": row[5],
|
||||
"total_request_bytes": row[6],
|
||||
"total_response_bytes": row[7],
|
||||
"error_count": row[8],
|
||||
}
|
||||
for row in rows
|
||||
]
|
||||
|
||||
async def find_large_requests_async(
|
||||
self,
|
||||
organization_id: str,
|
||||
min_size_bytes: int = 1_000_000, # 1MB default
|
||||
limit: int = 100,
|
||||
) -> List[LLMTrace]:
|
||||
"""
|
||||
Find traces with large request payloads (for debugging).
|
||||
|
||||
Args:
|
||||
organization_id: Organization ID for access control
|
||||
min_size_bytes: Minimum request size in bytes (default 1MB)
|
||||
limit: Maximum number of results
|
||||
|
||||
Returns:
|
||||
List of LLMTrace objects with large requests
|
||||
"""
|
||||
query = """
|
||||
SELECT
|
||||
id, organization_id, project_id, agent_id, agent_tags, run_id, step_id, trace_id,
|
||||
call_type, provider, model, is_byok,
|
||||
request_size_bytes, response_size_bytes,
|
||||
prompt_tokens, completion_tokens, total_tokens,
|
||||
cached_input_tokens, cache_write_tokens, reasoning_tokens,
|
||||
latency_ms,
|
||||
is_error, error_type, error_message,
|
||||
request_json, response_json, llm_config_json,
|
||||
created_at
|
||||
FROM llm_traces
|
||||
WHERE organization_id = %(organization_id)s
|
||||
AND request_size_bytes >= %(min_size_bytes)s
|
||||
ORDER BY request_size_bytes DESC
|
||||
LIMIT %(limit)s
|
||||
"""
|
||||
|
||||
rows = await asyncio.to_thread(
|
||||
self._query_sync,
|
||||
query,
|
||||
{
|
||||
"organization_id": organization_id,
|
||||
"min_size_bytes": min_size_bytes,
|
||||
"limit": limit,
|
||||
},
|
||||
)
|
||||
|
||||
return [self._row_to_trace(row) for row in rows]
|
||||
|
||||
|
||||
# Module-level instance for easy access
|
||||
_reader_instance: Optional[LLMTraceReader] = None
|
||||
|
||||
|
||||
def get_llm_trace_reader() -> LLMTraceReader:
|
||||
"""Get the singleton LLMTraceReader instance."""
|
||||
global _reader_instance
|
||||
if _reader_instance is None:
|
||||
_reader_instance = LLMTraceReader()
|
||||
return _reader_instance
|
||||
205
letta/services/llm_trace_writer.py
Normal file
205
letta/services/llm_trace_writer.py
Normal file
@@ -0,0 +1,205 @@
|
||||
"""ClickHouse writer for LLM analytics traces.
|
||||
|
||||
Writes LLM traces to ClickHouse with denormalized columns for cost analytics.
|
||||
Uses ClickHouse's async_insert feature for server-side batching.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import atexit
|
||||
from typing import TYPE_CHECKING, Optional
|
||||
from urllib.parse import urlparse
|
||||
|
||||
from letta.helpers.singleton import singleton
|
||||
from letta.log import get_logger
|
||||
from letta.settings import settings
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from letta.schemas.llm_trace import LLMTrace
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
# Retry configuration
|
||||
MAX_RETRIES = 3
|
||||
INITIAL_BACKOFF_SECONDS = 1.0
|
||||
|
||||
|
||||
def _parse_clickhouse_endpoint(endpoint: str) -> tuple[str, int, bool]:
|
||||
"""Return (host, port, secure) for clickhouse_connect.get_client.
|
||||
|
||||
Supports:
|
||||
- http://host:port -> (host, port, False)
|
||||
- https://host:port -> (host, port, True)
|
||||
- host:port -> (host, port, False) # Default to insecure for local dev
|
||||
- host -> (host, 8123, False) # Default HTTP port, insecure
|
||||
"""
|
||||
parsed = urlparse(endpoint)
|
||||
|
||||
if parsed.scheme in ("http", "https"):
|
||||
host = parsed.hostname or ""
|
||||
port = parsed.port or (8443 if parsed.scheme == "https" else 8123)
|
||||
secure = parsed.scheme == "https"
|
||||
return host, port, secure
|
||||
|
||||
# Fallback: accept raw hostname (possibly with :port)
|
||||
# Default to insecure (HTTP) for local development
|
||||
if ":" in endpoint:
|
||||
host, port_str = endpoint.rsplit(":", 1)
|
||||
return host, int(port_str), False
|
||||
|
||||
return endpoint, 8123, False
|
||||
|
||||
|
||||
@singleton
|
||||
class LLMTraceWriter:
|
||||
"""
|
||||
Direct ClickHouse writer for raw LLM traces.
|
||||
|
||||
Uses ClickHouse's async_insert feature for server-side batching.
|
||||
Each trace is inserted directly and ClickHouse handles batching
|
||||
for optimal write performance.
|
||||
|
||||
Usage:
|
||||
writer = LLMTraceWriter()
|
||||
await writer.write_async(trace)
|
||||
|
||||
Configuration (via settings):
|
||||
- store_llm_traces: Enable/disable (default: False)
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self._client = None
|
||||
self._shutdown = False
|
||||
self._write_lock = asyncio.Lock() # Serialize writes - clickhouse_connect isn't thread-safe
|
||||
|
||||
# Check if ClickHouse is configured - if not, writing is disabled
|
||||
self._enabled = bool(settings.clickhouse_endpoint and settings.clickhouse_password)
|
||||
|
||||
# Register shutdown handler
|
||||
atexit.register(self._sync_shutdown)
|
||||
|
||||
def _get_client(self):
|
||||
"""Initialize ClickHouse client on first use (lazy loading).
|
||||
|
||||
Configures async_insert with wait_for_async_insert=1 for reliable
|
||||
server-side batching with acknowledgment.
|
||||
"""
|
||||
if self._client is not None:
|
||||
return self._client
|
||||
|
||||
# Import lazily so OSS users who never enable this don't pay import cost
|
||||
import clickhouse_connect
|
||||
|
||||
host, port, secure = _parse_clickhouse_endpoint(settings.clickhouse_endpoint)
|
||||
database = settings.clickhouse_database or "otel"
|
||||
username = settings.clickhouse_username or "default"
|
||||
|
||||
self._client = clickhouse_connect.get_client(
|
||||
host=host,
|
||||
port=port,
|
||||
username=username,
|
||||
password=settings.clickhouse_password,
|
||||
database=database,
|
||||
secure=secure,
|
||||
verify=True,
|
||||
settings={
|
||||
# Enable server-side batching
|
||||
"async_insert": 1,
|
||||
# Wait for acknowledgment (reliable)
|
||||
"wait_for_async_insert": 1,
|
||||
# Flush after 1 second if batch not full
|
||||
"async_insert_busy_timeout_ms": 1000,
|
||||
},
|
||||
)
|
||||
logger.info(f"LLMTraceWriter: Connected to ClickHouse at {host}:{port}/{database} (async_insert enabled)")
|
||||
return self._client
|
||||
|
||||
async def write_async(self, trace: "LLMTrace") -> None:
|
||||
"""
|
||||
Write a trace to ClickHouse (fire-and-forget with retry).
|
||||
|
||||
ClickHouse's async_insert handles batching server-side for optimal
|
||||
write performance. This method retries on failure with exponential
|
||||
backoff.
|
||||
|
||||
Args:
|
||||
trace: The LLMTrace to write
|
||||
"""
|
||||
if not self._enabled or self._shutdown:
|
||||
return
|
||||
|
||||
# Fire-and-forget with create_task to not block the request path
|
||||
try:
|
||||
asyncio.create_task(self._write_with_retry(trace))
|
||||
except RuntimeError:
|
||||
# No running event loop (shouldn't happen in normal async context)
|
||||
pass
|
||||
|
||||
async def _write_with_retry(self, trace: "LLMTrace") -> None:
|
||||
"""Write a single trace with retry on failure."""
|
||||
from letta.schemas.llm_trace import LLMTrace
|
||||
|
||||
for attempt in range(MAX_RETRIES):
|
||||
try:
|
||||
client = self._get_client()
|
||||
row = trace.to_clickhouse_row()
|
||||
columns = LLMTrace.clickhouse_columns()
|
||||
|
||||
# Serialize writes - clickhouse_connect client isn't thread-safe
|
||||
async with self._write_lock:
|
||||
# Run synchronous insert in thread pool
|
||||
await asyncio.to_thread(
|
||||
client.insert,
|
||||
"llm_traces",
|
||||
[row],
|
||||
column_names=columns,
|
||||
)
|
||||
return # Success
|
||||
|
||||
except Exception as e:
|
||||
if attempt < MAX_RETRIES - 1:
|
||||
backoff = INITIAL_BACKOFF_SECONDS * (2**attempt)
|
||||
logger.warning(f"LLMTraceWriter: Retry {attempt + 1}/{MAX_RETRIES}, backoff {backoff}s: {e}")
|
||||
await asyncio.sleep(backoff)
|
||||
else:
|
||||
logger.error(f"LLMTraceWriter: Dropping trace after {MAX_RETRIES} retries: {e}")
|
||||
|
||||
async def shutdown_async(self) -> None:
|
||||
"""Gracefully shutdown the writer."""
|
||||
self._shutdown = True
|
||||
|
||||
# Close client
|
||||
if self._client:
|
||||
try:
|
||||
self._client.close()
|
||||
except Exception as e:
|
||||
logger.warning(f"LLMTraceWriter: Error closing client: {e}")
|
||||
self._client = None
|
||||
|
||||
logger.info("LLMTraceWriter: Shutdown complete")
|
||||
|
||||
def _sync_shutdown(self) -> None:
|
||||
"""Synchronous shutdown handler for atexit."""
|
||||
if not self._enabled or self._shutdown:
|
||||
return
|
||||
|
||||
self._shutdown = True
|
||||
|
||||
if self._client:
|
||||
try:
|
||||
self._client.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
# Module-level instance for easy access
|
||||
_writer_instance: Optional[LLMTraceWriter] = None
|
||||
|
||||
|
||||
def get_llm_trace_writer() -> LLMTraceWriter:
|
||||
"""Get the singleton LLMTraceWriter instance."""
|
||||
global _writer_instance
|
||||
if _writer_instance is None:
|
||||
_writer_instance = LLMTraceWriter()
|
||||
return _writer_instance
|
||||
@@ -1,17 +1,32 @@
|
||||
"""ClickHouse provider trace backend."""
|
||||
"""ClickHouse provider trace backend.
|
||||
|
||||
Writes traces to the llm_traces table with denormalized columns for cost analytics.
|
||||
Reads from the OTEL traces table (will eventually cut over to llm_traces).
|
||||
"""
|
||||
|
||||
import json
|
||||
import uuid
|
||||
from typing import TYPE_CHECKING, Optional
|
||||
|
||||
from letta.log import get_logger
|
||||
from letta.schemas.provider_trace import ProviderTrace
|
||||
from letta.schemas.user import User
|
||||
from letta.services.clickhouse_provider_traces import ClickhouseProviderTraceReader
|
||||
from letta.services.provider_trace_backends.base import ProviderTraceBackendClient
|
||||
from letta.settings import settings
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from letta.schemas.llm_trace import LLMTrace
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
class ClickhouseProviderTraceBackend(ProviderTraceBackendClient):
|
||||
"""
|
||||
Store provider traces in ClickHouse.
|
||||
ClickHouse backend for provider traces.
|
||||
|
||||
Writes flow through OTEL instrumentation, so create_async is a no-op.
|
||||
Only reads are performed directly against ClickHouse.
|
||||
- Writes go to llm_traces table (denormalized for cost analytics)
|
||||
- Reads come from OTEL traces table (will cut over to llm_traces later)
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
@@ -21,9 +36,29 @@ class ClickhouseProviderTraceBackend(ProviderTraceBackendClient):
|
||||
self,
|
||||
actor: User,
|
||||
provider_trace: ProviderTrace,
|
||||
) -> ProviderTrace:
|
||||
# ClickHouse writes flow through OTEL instrumentation, not direct writes.
|
||||
# Return a ProviderTrace with the same ID for consistency across backends.
|
||||
) -> ProviderTrace | None:
|
||||
"""Write provider trace to ClickHouse llm_traces table."""
|
||||
if not settings.store_llm_traces:
|
||||
# Return minimal trace for consistency if writes disabled
|
||||
return ProviderTrace(
|
||||
id=provider_trace.id,
|
||||
step_id=provider_trace.step_id,
|
||||
request_json=provider_trace.request_json or {},
|
||||
response_json=provider_trace.response_json or {},
|
||||
)
|
||||
|
||||
try:
|
||||
from letta.schemas.llm_trace import LLMTrace
|
||||
from letta.services.llm_trace_writer import get_llm_trace_writer
|
||||
|
||||
trace = self._convert_to_trace(actor, provider_trace)
|
||||
if trace:
|
||||
writer = get_llm_trace_writer()
|
||||
await writer.write_async(trace)
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to write trace to ClickHouse: {e}")
|
||||
|
||||
return ProviderTrace(
|
||||
id=provider_trace.id,
|
||||
step_id=provider_trace.step_id,
|
||||
@@ -36,7 +71,125 @@ class ClickhouseProviderTraceBackend(ProviderTraceBackendClient):
|
||||
step_id: str,
|
||||
actor: User,
|
||||
) -> ProviderTrace | None:
|
||||
"""Read from OTEL traces table (will cut over to llm_traces later)."""
|
||||
return await self._reader.get_provider_trace_by_step_id_async(
|
||||
step_id=step_id,
|
||||
organization_id=actor.organization_id,
|
||||
)
|
||||
|
||||
def _convert_to_trace(
|
||||
self,
|
||||
actor: User,
|
||||
provider_trace: ProviderTrace,
|
||||
) -> Optional["LLMTrace"]:
|
||||
"""Convert ProviderTrace to LLMTrace for analytics storage."""
|
||||
from letta.schemas.llm_trace import LLMTrace
|
||||
|
||||
# Serialize JSON fields
|
||||
request_json_str = json.dumps(provider_trace.request_json, default=str)
|
||||
response_json_str = json.dumps(provider_trace.response_json, default=str)
|
||||
llm_config_json_str = json.dumps(provider_trace.llm_config, default=str) if provider_trace.llm_config else "{}"
|
||||
|
||||
# Extract provider and model from llm_config
|
||||
llm_config = provider_trace.llm_config or {}
|
||||
provider = llm_config.get("model_endpoint_type", "unknown")
|
||||
model = llm_config.get("model", "unknown")
|
||||
is_byok = llm_config.get("provider_category") == "byok"
|
||||
|
||||
# Extract usage from response (generic parsing for common formats)
|
||||
usage = self._extract_usage(provider_trace.response_json, provider)
|
||||
|
||||
# Check for error in response
|
||||
is_error = "error" in provider_trace.response_json
|
||||
error_type = None
|
||||
error_message = None
|
||||
if is_error:
|
||||
# error_type may be at top level or inside error dict
|
||||
error_type = provider_trace.response_json.get("error_type")
|
||||
error_data = provider_trace.response_json.get("error", {})
|
||||
if isinstance(error_data, dict):
|
||||
error_type = error_type or error_data.get("type")
|
||||
error_message = error_data.get("message", str(error_data))[:1000]
|
||||
else:
|
||||
error_message = str(error_data)[:1000]
|
||||
|
||||
# Extract UUID from provider_trace.id (strip "provider_trace-" prefix)
|
||||
trace_id = provider_trace.id
|
||||
if not trace_id:
|
||||
logger.warning("ProviderTrace missing id - trace correlation across backends will fail")
|
||||
trace_id = str(uuid.uuid4())
|
||||
elif trace_id.startswith("provider_trace-"):
|
||||
trace_id = trace_id[len("provider_trace-") :]
|
||||
|
||||
return LLMTrace(
|
||||
id=trace_id,
|
||||
organization_id=provider_trace.org_id or actor.organization_id,
|
||||
project_id=None,
|
||||
agent_id=provider_trace.agent_id,
|
||||
agent_tags=provider_trace.agent_tags or [],
|
||||
run_id=provider_trace.run_id,
|
||||
step_id=provider_trace.step_id,
|
||||
trace_id=None,
|
||||
call_type=provider_trace.call_type or "unknown",
|
||||
provider=provider,
|
||||
model=model,
|
||||
is_byok=is_byok,
|
||||
request_size_bytes=len(request_json_str.encode("utf-8")),
|
||||
response_size_bytes=len(response_json_str.encode("utf-8")),
|
||||
prompt_tokens=usage.get("prompt_tokens", 0),
|
||||
completion_tokens=usage.get("completion_tokens", 0),
|
||||
total_tokens=usage.get("total_tokens", 0),
|
||||
cached_input_tokens=usage.get("cached_input_tokens"),
|
||||
cache_write_tokens=usage.get("cache_write_tokens"),
|
||||
reasoning_tokens=usage.get("reasoning_tokens"),
|
||||
latency_ms=0, # Not available in ProviderTrace
|
||||
is_error=is_error,
|
||||
error_type=error_type,
|
||||
error_message=error_message,
|
||||
request_json=request_json_str,
|
||||
response_json=response_json_str,
|
||||
llm_config_json=llm_config_json_str,
|
||||
)
|
||||
|
||||
def _extract_usage(self, response_json: dict, provider: str) -> dict:
|
||||
"""Extract usage statistics from response JSON.
|
||||
|
||||
Handles common formats from OpenAI, Anthropic, and other providers.
|
||||
"""
|
||||
usage = {}
|
||||
|
||||
# OpenAI format: response.usage
|
||||
if "usage" in response_json:
|
||||
u = response_json["usage"]
|
||||
usage["prompt_tokens"] = u.get("prompt_tokens", 0)
|
||||
usage["completion_tokens"] = u.get("completion_tokens", 0)
|
||||
usage["total_tokens"] = u.get("total_tokens", 0)
|
||||
|
||||
# OpenAI reasoning tokens
|
||||
if "completion_tokens_details" in u:
|
||||
details = u["completion_tokens_details"]
|
||||
usage["reasoning_tokens"] = details.get("reasoning_tokens")
|
||||
|
||||
# OpenAI cached tokens
|
||||
if "prompt_tokens_details" in u:
|
||||
details = u["prompt_tokens_details"]
|
||||
usage["cached_input_tokens"] = details.get("cached_tokens")
|
||||
|
||||
# Anthropic format: response.usage with cache fields
|
||||
if provider == "anthropic" and "usage" in response_json:
|
||||
u = response_json["usage"]
|
||||
# input_tokens can be 0 when all tokens come from cache
|
||||
input_tokens = u.get("input_tokens", 0)
|
||||
cache_read = u.get("cache_read_input_tokens", 0)
|
||||
cache_write = u.get("cache_creation_input_tokens", 0)
|
||||
# Total prompt = input + cached (for cost analytics)
|
||||
usage["prompt_tokens"] = input_tokens + cache_read + cache_write
|
||||
usage["completion_tokens"] = u.get("output_tokens", usage.get("completion_tokens", 0))
|
||||
usage["cached_input_tokens"] = cache_read if cache_read else None
|
||||
usage["cache_write_tokens"] = cache_write if cache_write else None
|
||||
|
||||
# Recalculate total if not present
|
||||
if "total_tokens" not in usage or usage["total_tokens"] == 0:
|
||||
usage["total_tokens"] = usage.get("prompt_tokens", 0) + usage.get("completion_tokens", 0)
|
||||
|
||||
return usage
|
||||
|
||||
@@ -561,6 +561,7 @@ async def simple_summary(
|
||||
"output_tokens": getattr(interface, "output_tokens", None),
|
||||
},
|
||||
},
|
||||
llm_config=summarizer_llm_config,
|
||||
)
|
||||
|
||||
if not text:
|
||||
|
||||
@@ -20,10 +20,10 @@ class TelemetryManager:
|
||||
Supports multiple backends for dual-write scenarios (e.g., migration).
|
||||
Configure via LETTA_TELEMETRY_PROVIDER_TRACE_BACKEND (comma-separated):
|
||||
- postgres: Store in PostgreSQL (default)
|
||||
- clickhouse: Store in ClickHouse via OTEL instrumentation
|
||||
- socket: Store via Unix socket to Crouton sidecar (which writes to GCS)
|
||||
- clickhouse: Store in ClickHouse (writes to llm_traces table, reads from OTEL traces)
|
||||
- socket: Store via Unix socket to external sidecar
|
||||
|
||||
Example: LETTA_TELEMETRY_PROVIDER_TRACE_BACKEND=postgres,socket
|
||||
Example: LETTA_TELEMETRY_PROVIDER_TRACE_BACKEND=postgres,clickhouse
|
||||
|
||||
Multi-backend behavior:
|
||||
- Writes: Sent to ALL configured backends concurrently via asyncio.gather.
|
||||
|
||||
@@ -331,6 +331,13 @@ class Settings(BaseSettings):
|
||||
track_agent_run: bool = Field(default=True, description="Enable tracking agent run with cancellation support")
|
||||
track_provider_trace: bool = Field(default=True, description="Enable tracking raw llm request and response at each step")
|
||||
|
||||
# LLM trace storage for analytics (direct ClickHouse, bypasses OTEL for large payloads)
|
||||
# TTL is configured in the ClickHouse DDL (default 90 days)
|
||||
store_llm_traces: bool = Field(
|
||||
default=False,
|
||||
description="Enable storing LLM traces in ClickHouse for cost analytics",
|
||||
)
|
||||
|
||||
# FastAPI Application Settings
|
||||
uvicorn_workers: int = 1
|
||||
uvicorn_reload: bool = False
|
||||
|
||||
350
tests/integration_test_clickhouse_llm_traces.py
Normal file
350
tests/integration_test_clickhouse_llm_traces.py
Normal file
@@ -0,0 +1,350 @@
|
||||
"""
|
||||
Integration tests for ClickHouse-backed LLM raw traces.
|
||||
|
||||
Validates that:
|
||||
1) Agent message requests are stored in ClickHouse (request_json contains the message)
|
||||
2) Summarization traces are stored and retrievable by step_id
|
||||
3) Error traces are stored with is_error, error_type, and error_message
|
||||
4) llm_config_json is properly stored
|
||||
5) Cache and usage statistics are stored (cached_input_tokens, cache_write_tokens, reasoning_tokens)
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
import uuid
|
||||
|
||||
import pytest
|
||||
|
||||
from letta.agents.letta_agent_v3 import LettaAgentV3
|
||||
from letta.config import LettaConfig
|
||||
from letta.schemas.agent import CreateAgent
|
||||
from letta.schemas.embedding_config import EmbeddingConfig
|
||||
from letta.schemas.enums import MessageRole
|
||||
from letta.schemas.letta_message_content import TextContent
|
||||
from letta.schemas.llm_config import LLMConfig
|
||||
from letta.schemas.message import Message, MessageCreate
|
||||
from letta.schemas.run import Run
|
||||
from letta.server.server import SyncServer
|
||||
from letta.services.llm_trace_reader import get_llm_trace_reader
|
||||
from letta.services.provider_trace_backends import get_provider_trace_backends
|
||||
from letta.services.summarizer.summarizer import simple_summary
|
||||
from letta.settings import settings, telemetry_settings
|
||||
|
||||
|
||||
def _require_clickhouse_env() -> dict[str, str]:
|
||||
endpoint = os.getenv("CLICKHOUSE_ENDPOINT")
|
||||
password = os.getenv("CLICKHOUSE_PASSWORD")
|
||||
if not endpoint or not password:
|
||||
pytest.skip("ClickHouse env vars not set (CLICKHOUSE_ENDPOINT, CLICKHOUSE_PASSWORD)")
|
||||
return {
|
||||
"endpoint": endpoint,
|
||||
"password": password,
|
||||
"username": os.getenv("CLICKHOUSE_USERNAME", "default"),
|
||||
"database": os.getenv("CLICKHOUSE_DATABASE", "otel"),
|
||||
}
|
||||
|
||||
|
||||
def _anthropic_llm_config() -> LLMConfig:
|
||||
return LLMConfig(
|
||||
model="claude-3-5-haiku-20241022",
|
||||
model_endpoint_type="anthropic",
|
||||
model_endpoint="https://api.anthropic.com/v1",
|
||||
context_window=200000,
|
||||
max_tokens=2048,
|
||||
put_inner_thoughts_in_kwargs=False,
|
||||
enable_reasoner=False,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
async def server():
|
||||
config = LettaConfig.load()
|
||||
config.save()
|
||||
server = SyncServer(init_with_default_org_and_user=True)
|
||||
await server.init_async()
|
||||
await server.tool_manager.upsert_base_tools_async(actor=server.default_user)
|
||||
yield server
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
async def actor(server: SyncServer):
|
||||
return server.default_user
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def clickhouse_settings():
|
||||
env = _require_clickhouse_env()
|
||||
|
||||
original_values = {
|
||||
"endpoint": settings.clickhouse_endpoint,
|
||||
"username": settings.clickhouse_username,
|
||||
"password": settings.clickhouse_password,
|
||||
"database": settings.clickhouse_database,
|
||||
"store_llm_traces": settings.store_llm_traces,
|
||||
"provider_trace_backend": telemetry_settings.provider_trace_backend,
|
||||
}
|
||||
|
||||
settings.clickhouse_endpoint = env["endpoint"]
|
||||
settings.clickhouse_username = env["username"]
|
||||
settings.clickhouse_password = env["password"]
|
||||
settings.clickhouse_database = env["database"]
|
||||
settings.store_llm_traces = True
|
||||
|
||||
# Configure telemetry to use clickhouse backend (set the underlying field, not the property)
|
||||
telemetry_settings.provider_trace_backend = "clickhouse"
|
||||
# Clear the cached backends so they get recreated with new settings
|
||||
get_provider_trace_backends.cache_clear()
|
||||
|
||||
yield
|
||||
|
||||
settings.clickhouse_endpoint = original_values["endpoint"]
|
||||
settings.clickhouse_username = original_values["username"]
|
||||
settings.clickhouse_password = original_values["password"]
|
||||
settings.clickhouse_database = original_values["database"]
|
||||
settings.store_llm_traces = original_values["store_llm_traces"]
|
||||
telemetry_settings.provider_trace_backend = original_values["provider_trace_backend"]
|
||||
# Clear cache again to restore original backends
|
||||
get_provider_trace_backends.cache_clear()
|
||||
|
||||
|
||||
async def _wait_for_raw_trace(step_id: str, organization_id: str, timeout_seconds: int = 30):
|
||||
"""Wait for a trace to appear in ClickHouse.
|
||||
|
||||
With async_insert + wait_for_async_insert=1, traces should appear quickly,
|
||||
but we poll to handle any propagation delay.
|
||||
"""
|
||||
reader = get_llm_trace_reader()
|
||||
deadline = time.time() + timeout_seconds
|
||||
|
||||
while time.time() < deadline:
|
||||
trace = await reader.get_by_step_id_async(step_id=step_id, organization_id=organization_id)
|
||||
if trace is not None:
|
||||
return trace
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
raise AssertionError(f"Timed out waiting for raw trace with step_id={step_id}")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_agent_message_stored_in_clickhouse(server: SyncServer, actor, clickhouse_settings):
|
||||
"""Test that agent step traces are stored with all fields including llm_config_json."""
|
||||
message_text = f"ClickHouse trace test {uuid.uuid4()}"
|
||||
llm_config = _anthropic_llm_config()
|
||||
|
||||
agent_state = await server.agent_manager.create_agent_async(
|
||||
CreateAgent(
|
||||
name=f"clickhouse_agent_{uuid.uuid4().hex[:8]}",
|
||||
llm_config=llm_config,
|
||||
embedding_config=EmbeddingConfig.default_config(model_name="letta"),
|
||||
),
|
||||
actor=actor,
|
||||
)
|
||||
|
||||
agent = LettaAgentV3(agent_state=agent_state, actor=actor)
|
||||
run = await server.run_manager.create_run(
|
||||
Run(agent_id=agent_state.id),
|
||||
actor=actor,
|
||||
)
|
||||
run_id = run.id
|
||||
response = await agent.step(
|
||||
[MessageCreate(role=MessageRole.user, content=[TextContent(text=message_text)])],
|
||||
run_id=run_id,
|
||||
)
|
||||
|
||||
step_id = next(msg.step_id for msg in reversed(response.messages) if msg.step_id)
|
||||
trace = await _wait_for_raw_trace(step_id=step_id, organization_id=actor.organization_id)
|
||||
|
||||
# Basic trace fields
|
||||
assert trace.step_id == step_id
|
||||
assert message_text in trace.request_json
|
||||
assert trace.is_error is False
|
||||
assert trace.error_type is None
|
||||
assert trace.error_message is None
|
||||
|
||||
# Verify llm_config_json is stored and contains expected fields
|
||||
assert trace.llm_config_json, "llm_config_json should not be empty"
|
||||
config_data = json.loads(trace.llm_config_json)
|
||||
assert config_data.get("model") == llm_config.model
|
||||
assert "context_window" in config_data
|
||||
assert "max_tokens" in config_data
|
||||
|
||||
# Token usage should be populated
|
||||
assert trace.prompt_tokens > 0
|
||||
assert trace.completion_tokens >= 0
|
||||
assert trace.total_tokens > 0
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_summary_stored_with_content_and_usage(server: SyncServer, actor, clickhouse_settings):
|
||||
"""Test that summarization traces are stored with content, usage, and cache info."""
|
||||
step_id = f"step-{uuid.uuid4()}"
|
||||
llm_config = _anthropic_llm_config()
|
||||
summary_source_messages = [
|
||||
Message(role=MessageRole.system, content=[TextContent(text="System prompt")]),
|
||||
Message(role=MessageRole.user, content=[TextContent(text="User message 1")]),
|
||||
Message(role=MessageRole.assistant, content=[TextContent(text="Assistant response 1")]),
|
||||
Message(role=MessageRole.user, content=[TextContent(text="User message 2")]),
|
||||
]
|
||||
|
||||
summary_text = await simple_summary(
|
||||
messages=summary_source_messages,
|
||||
llm_config=llm_config,
|
||||
actor=actor,
|
||||
agent_id=f"agent-{uuid.uuid4()}",
|
||||
agent_tags=["test", "clickhouse"],
|
||||
run_id=f"run-{uuid.uuid4()}",
|
||||
step_id=step_id,
|
||||
compaction_settings={"mode": "partial_evict", "message_buffer_limit": 60},
|
||||
)
|
||||
|
||||
trace = await _wait_for_raw_trace(step_id=step_id, organization_id=actor.organization_id)
|
||||
|
||||
# Basic assertions
|
||||
assert trace.step_id == step_id
|
||||
assert trace.call_type == "summarization"
|
||||
assert trace.is_error is False
|
||||
|
||||
# Verify llm_config_json is stored
|
||||
assert trace.llm_config_json, "llm_config_json should not be empty"
|
||||
config_data = json.loads(trace.llm_config_json)
|
||||
assert config_data.get("model") == llm_config.model
|
||||
|
||||
# Verify summary content in response
|
||||
summary_in_response = False
|
||||
try:
|
||||
response_payload = json.loads(trace.response_json)
|
||||
if isinstance(response_payload, dict):
|
||||
if "choices" in response_payload:
|
||||
content = response_payload.get("choices", [{}])[0].get("message", {}).get("content", "")
|
||||
summary_in_response = summary_text.strip() in (content or "")
|
||||
elif "content" in response_payload:
|
||||
summary_in_response = summary_text.strip() in (response_payload.get("content") or "")
|
||||
except Exception:
|
||||
summary_in_response = False
|
||||
|
||||
assert summary_in_response or summary_text in trace.response_json
|
||||
|
||||
# Token usage should be populated
|
||||
assert trace.prompt_tokens > 0
|
||||
assert trace.total_tokens > 0
|
||||
|
||||
# Cache fields may or may not be populated depending on provider response
|
||||
# Just verify they're accessible (not erroring)
|
||||
_ = trace.cached_input_tokens
|
||||
_ = trace.cache_write_tokens
|
||||
_ = trace.reasoning_tokens
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_error_trace_stored_in_clickhouse(server: SyncServer, actor, clickhouse_settings):
|
||||
"""Test that error traces are stored with is_error=True and error details."""
|
||||
from letta.llm_api.anthropic_client import AnthropicClient
|
||||
|
||||
step_id = f"step-error-{uuid.uuid4()}"
|
||||
|
||||
# Create a client with invalid config to trigger an error
|
||||
invalid_llm_config = LLMConfig(
|
||||
model="invalid-model-that-does-not-exist",
|
||||
model_endpoint_type="anthropic",
|
||||
model_endpoint="https://api.anthropic.com/v1",
|
||||
context_window=200000,
|
||||
max_tokens=2048,
|
||||
)
|
||||
|
||||
from letta.services.telemetry_manager import TelemetryManager
|
||||
|
||||
client = AnthropicClient()
|
||||
client.set_telemetry_context(
|
||||
telemetry_manager=TelemetryManager(),
|
||||
agent_id=f"agent-{uuid.uuid4()}",
|
||||
run_id=f"run-{uuid.uuid4()}",
|
||||
step_id=step_id,
|
||||
call_type="agent_step",
|
||||
org_id=actor.organization_id,
|
||||
)
|
||||
client.actor = actor
|
||||
|
||||
# Make a request that will fail
|
||||
request_data = {
|
||||
"model": invalid_llm_config.model,
|
||||
"messages": [{"role": "user", "content": "test"}],
|
||||
"max_tokens": 100,
|
||||
}
|
||||
|
||||
try:
|
||||
await client.request_async_with_telemetry(request_data, invalid_llm_config)
|
||||
except Exception:
|
||||
pass # Expected to fail
|
||||
|
||||
# Wait for the error trace to be written
|
||||
trace = await _wait_for_raw_trace(step_id=step_id, organization_id=actor.organization_id)
|
||||
|
||||
# Verify error fields
|
||||
assert trace.step_id == step_id
|
||||
assert trace.is_error is True
|
||||
assert trace.error_type is not None, "error_type should be set for error traces"
|
||||
assert trace.error_message is not None, "error_message should be set for error traces"
|
||||
|
||||
# Verify llm_config_json is still stored even for errors
|
||||
assert trace.llm_config_json, "llm_config_json should be stored even for error traces"
|
||||
config_data = json.loads(trace.llm_config_json)
|
||||
assert config_data.get("model") == invalid_llm_config.model
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_cache_tokens_stored_for_anthropic(server: SyncServer, actor, clickhouse_settings):
|
||||
"""Test that Anthropic cache tokens (cached_input_tokens, cache_write_tokens) are stored.
|
||||
|
||||
Note: This test verifies the fields are properly stored when present in the response.
|
||||
Actual cache token values depend on Anthropic's prompt caching behavior.
|
||||
"""
|
||||
message_text = f"Cache test {uuid.uuid4()}"
|
||||
llm_config = _anthropic_llm_config()
|
||||
|
||||
agent_state = await server.agent_manager.create_agent_async(
|
||||
CreateAgent(
|
||||
name=f"cache_test_agent_{uuid.uuid4().hex[:8]}",
|
||||
llm_config=llm_config,
|
||||
embedding_config=EmbeddingConfig.default_config(model_name="letta"),
|
||||
),
|
||||
actor=actor,
|
||||
)
|
||||
|
||||
agent = LettaAgentV3(agent_state=agent_state, actor=actor)
|
||||
run = await server.run_manager.create_run(
|
||||
Run(agent_id=agent_state.id),
|
||||
actor=actor,
|
||||
)
|
||||
|
||||
# Make two requests - second may benefit from caching
|
||||
response1 = await agent.step(
|
||||
[MessageCreate(role=MessageRole.user, content=[TextContent(text=message_text)])],
|
||||
run_id=run.id,
|
||||
)
|
||||
step_id_1 = next(msg.step_id for msg in reversed(response1.messages) if msg.step_id)
|
||||
|
||||
response2 = await agent.step(
|
||||
[MessageCreate(role=MessageRole.user, content=[TextContent(text="Follow up question")])],
|
||||
run_id=run.id,
|
||||
)
|
||||
step_id_2 = next(msg.step_id for msg in reversed(response2.messages) if msg.step_id)
|
||||
|
||||
# Check traces for both requests
|
||||
trace1 = await _wait_for_raw_trace(step_id=step_id_1, organization_id=actor.organization_id)
|
||||
trace2 = await _wait_for_raw_trace(step_id=step_id_2, organization_id=actor.organization_id)
|
||||
|
||||
# Verify cache fields are accessible (may be None if no caching occurred)
|
||||
# The important thing is they're stored correctly when present
|
||||
for trace in [trace1, trace2]:
|
||||
assert trace.prompt_tokens > 0
|
||||
# Cache fields should be stored (may be None or int)
|
||||
assert trace.cached_input_tokens is None or isinstance(trace.cached_input_tokens, int)
|
||||
assert trace.cache_write_tokens is None or isinstance(trace.cache_write_tokens, int)
|
||||
assert trace.reasoning_tokens is None or isinstance(trace.reasoning_tokens, int)
|
||||
|
||||
# Verify llm_config_json
|
||||
assert trace.llm_config_json
|
||||
config_data = json.loads(trace.llm_config_json)
|
||||
assert config_data.get("model") == llm_config.model
|
||||
Reference in New Issue
Block a user