Files
letta-server/letta/services/llm_trace_writer.py
Sarah Wooders 4096b30cd7 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()

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* 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.

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* 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

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* 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

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* 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.

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* test: exercise provider usage parsing live

Make live OpenAI/Anthropic/Gemini requests with credential gating and validate Anthropic cache usage mapping when present.

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* 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

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* 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.

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* 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

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* 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

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* 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)

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* 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)

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* fix: add LLMRawTraceWriter shutdown to FastAPI lifespan

Properly flush pending traces on graceful shutdown via lifespan
instead of relying only on atexit handler.

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* feat: add agent_tags column to ClickHouse traces

Store agent tags as Array(String) for filtering/analytics by tag.

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* 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

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* 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

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* 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

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* 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

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* 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

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* fix: update workflow references to llm_traces

Missed renaming table name in CI workflow files.

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* fix: update clickhouse_direct -> clickhouse_analytics in docstring

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* 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.

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* 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.

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* fix: restore compose.yaml to match main

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* 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

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* fix: correct path to DDL file in CI workflow

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* chore: add provider index to DDL for faster filtering

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* 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.

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* 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.

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* 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

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* 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

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* 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

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---------

Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: Caren Thomas <carenthomas@gmail.com>
2026-02-24 10:52:06 -08:00

206 lines
6.7 KiB
Python

"""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