mirror of
https://github.com/bytedance/deer-flow.git
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17eb509dbd
Allow event content to be a dict (for structured OpenAI-format messages) in addition to plain strings. Dict values are JSON-serialized for the DB backend and deserialized on read; memory and JSONL backends handle dicts natively. Trace truncation now serializes dicts to JSON before measuring. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
320 lines
12 KiB
Python
320 lines
12 KiB
Python
"""Run event capture via LangChain callbacks.
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RunJournal sits between LangChain's callback mechanism and the pluggable
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RunEventStore. It standardizes callback data into RunEvent records and
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handles token usage accumulation.
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Key design decisions:
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- on_llm_new_token is NOT implemented -- only complete messages via on_llm_end
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- All LangChain objects serialized via serialize_lc_object (same as worker.py SSE)
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- Token usage accumulated in memory, written to RunRow on run completion
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- Caller identification via tags injection (lead_agent / subagent:{name} / middleware:{name})
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"""
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from __future__ import annotations
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import asyncio
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import logging
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import time
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from datetime import UTC, datetime
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from typing import TYPE_CHECKING, Any
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from uuid import UUID
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from langchain_core.callbacks import BaseCallbackHandler
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if TYPE_CHECKING:
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from deerflow.runtime.events.store.base import RunEventStore
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logger = logging.getLogger(__name__)
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class RunJournal(BaseCallbackHandler):
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"""LangChain callback handler that captures events to RunEventStore."""
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def __init__(
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self,
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run_id: str,
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thread_id: str,
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event_store: RunEventStore,
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*,
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track_token_usage: bool = True,
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flush_threshold: int = 20,
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):
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super().__init__()
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self.run_id = run_id
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self.thread_id = thread_id
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self._store = event_store
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self._track_tokens = track_token_usage
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self._flush_threshold = flush_threshold
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# Write buffer
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self._buffer: list[dict] = []
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# Token accumulators
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self._total_input_tokens = 0
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self._total_output_tokens = 0
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self._total_tokens = 0
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self._llm_call_count = 0
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self._lead_agent_tokens = 0
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self._subagent_tokens = 0
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self._middleware_tokens = 0
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# Convenience fields
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self._last_ai_msg: str | None = None
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self._first_human_msg: str | None = None
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self._msg_count = 0
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# Latency tracking
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self._llm_start_times: dict[str, float] = {} # langchain run_id -> start time
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# -- Lifecycle callbacks --
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def on_chain_start(self, serialized: dict, inputs: Any, *, run_id: UUID, **kwargs: Any) -> None:
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if kwargs.get("parent_run_id") is not None:
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return
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self._put(
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event_type="run_start",
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category="lifecycle",
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metadata={"input_preview": str(inputs)[:500]},
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)
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def on_chain_end(self, outputs: Any, *, run_id: UUID, **kwargs: Any) -> None:
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if kwargs.get("parent_run_id") is not None:
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return
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self._put(event_type="run_end", category="lifecycle", metadata={"status": "success"})
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self._flush_sync()
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def on_chain_error(self, error: BaseException, *, run_id: UUID, **kwargs: Any) -> None:
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if kwargs.get("parent_run_id") is not None:
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return
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self._put(
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event_type="run_error",
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category="lifecycle",
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content=str(error),
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metadata={"error_type": type(error).__name__},
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)
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self._flush_sync()
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# -- LLM callbacks --
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def on_llm_start(self, serialized: dict, prompts: list[str], *, run_id: UUID, **kwargs: Any) -> None:
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self._llm_start_times[str(run_id)] = time.monotonic()
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self._put(
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event_type="llm_start",
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category="trace",
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metadata={"model_name": serialized.get("name", "")},
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)
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def on_llm_end(self, response: Any, *, run_id: UUID, **kwargs: Any) -> None:
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from deerflow.runtime.serialization import serialize_lc_object
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try:
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message = response.generations[0][0].message
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except (IndexError, AttributeError):
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logger.debug("on_llm_end: could not extract message from response")
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return
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caller = self._identify_caller(kwargs)
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# Latency
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start = self._llm_start_times.pop(str(run_id), None)
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latency_ms = int((time.monotonic() - start) * 1000) if start else None
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# Token usage from message
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usage = getattr(message, "usage_metadata", None)
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usage_dict = dict(usage) if usage else {}
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# Trace event: llm_end (every LLM call)
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content = getattr(message, "content", "")
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self._put(
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event_type="llm_end",
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category="trace",
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content=content if isinstance(content, str) else str(content),
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metadata={
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"message": serialize_lc_object(message),
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"caller": caller,
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"usage": usage_dict,
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"latency_ms": latency_ms,
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},
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)
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# Message event: ai_message (only lead_agent final replies — no pending tool_calls)
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tool_calls = getattr(message, "tool_calls", None) or []
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if caller == "lead_agent" and isinstance(content, str) and content and not tool_calls:
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resp_meta = getattr(message, "response_metadata", None) or {}
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model_name = resp_meta.get("model_name") if isinstance(resp_meta, dict) else None
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self._put(
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event_type="ai_message",
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category="message",
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content=content,
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metadata={"model_name": model_name},
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)
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self._last_ai_msg = content[:2000]
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self._msg_count += 1
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# Token accumulation
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if self._track_tokens:
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input_tk = usage_dict.get("input_tokens", 0) or 0
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output_tk = usage_dict.get("output_tokens", 0) or 0
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total_tk = usage_dict.get("total_tokens", 0) or 0
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if total_tk == 0:
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total_tk = input_tk + output_tk
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if total_tk > 0:
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self._total_input_tokens += input_tk
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self._total_output_tokens += output_tk
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self._total_tokens += total_tk
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self._llm_call_count += 1
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if caller.startswith("subagent:"):
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self._subagent_tokens += total_tk
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elif caller.startswith("middleware:"):
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self._middleware_tokens += total_tk
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else:
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self._lead_agent_tokens += total_tk
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def on_llm_error(self, error: BaseException, *, run_id: UUID, **kwargs: Any) -> None:
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self._llm_start_times.pop(str(run_id), None)
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self._put(event_type="llm_error", category="trace", content=str(error))
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# -- Tool callbacks --
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def on_tool_start(self, serialized: dict, input_str: str, *, run_id: UUID, **kwargs: Any) -> None:
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self._put(
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event_type="tool_start",
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category="trace",
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metadata={
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"tool_name": serialized.get("name", ""),
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"tool_call_id": kwargs.get("tool_call_id"),
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"args": str(input_str)[:2000],
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},
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)
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def on_tool_end(self, output: str, *, run_id: UUID, **kwargs: Any) -> None:
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self._put(
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event_type="tool_end",
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category="trace",
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content=str(output),
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metadata={
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"tool_name": kwargs.get("name", ""),
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"tool_call_id": kwargs.get("tool_call_id"),
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"status": "success",
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},
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)
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def on_tool_error(self, error: BaseException, *, run_id: UUID, **kwargs: Any) -> None:
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self._put(
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event_type="tool_error",
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category="trace",
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content=str(error),
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metadata={
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"tool_name": kwargs.get("name", ""),
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"tool_call_id": kwargs.get("tool_call_id"),
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},
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)
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# -- Custom event callback --
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def on_custom_event(self, name: str, data: Any, *, run_id: UUID, **kwargs: Any) -> None:
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from deerflow.runtime.serialization import serialize_lc_object
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if name == "summarization":
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data_dict = data if isinstance(data, dict) else {}
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self._put(
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event_type="summarization",
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category="trace",
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content=data_dict.get("summary", ""),
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metadata={
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"replaced_message_ids": data_dict.get("replaced_message_ids", []),
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"replaced_count": data_dict.get("replaced_count", 0),
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},
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)
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self._put(
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event_type="summary",
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category="message",
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content=data_dict.get("summary", ""),
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metadata={"replaced_count": data_dict.get("replaced_count", 0)},
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)
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else:
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event_data = serialize_lc_object(data) if not isinstance(data, dict) else data
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self._put(
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event_type=name,
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category="trace",
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metadata=event_data if isinstance(event_data, dict) else {"data": event_data},
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)
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# -- Internal methods --
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def _put(self, *, event_type: str, category: str, content: str | dict = "", metadata: dict | None = None) -> None:
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self._buffer.append({
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"thread_id": self.thread_id,
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"run_id": self.run_id,
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"event_type": event_type,
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"category": category,
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"content": content,
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"metadata": metadata or {},
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"created_at": datetime.now(UTC).isoformat(),
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})
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if len(self._buffer) >= self._flush_threshold:
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self._flush_sync()
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def _flush_sync(self) -> None:
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"""Best-effort flush of buffer to RunEventStore.
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BaseCallbackHandler methods are synchronous. If an event loop is
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running we schedule an async ``put_batch``; otherwise the events
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stay in the buffer and are flushed later by the async ``flush()``
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call in the worker's ``finally`` block.
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"""
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if not self._buffer:
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return
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try:
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loop = asyncio.get_running_loop()
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except RuntimeError:
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# No event loop — keep events in buffer for later async flush.
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return
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batch = self._buffer.copy()
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self._buffer.clear()
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loop.create_task(self._flush_async(batch))
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async def _flush_async(self, batch: list[dict]) -> None:
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try:
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await self._store.put_batch(batch)
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except Exception:
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logger.warning("RunJournal: failed to flush %d events", len(batch), exc_info=True)
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def _identify_caller(self, kwargs: dict) -> str:
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for tag in kwargs.get("tags") or []:
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if isinstance(tag, str) and (tag.startswith("subagent:") or tag.startswith("middleware:") or tag == "lead_agent"):
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return tag
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# Default to lead_agent: the main agent graph does not inject
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# callback tags, while subagents and middleware explicitly tag
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# themselves.
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return "lead_agent"
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# -- Public methods (called by worker) --
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def set_first_human_message(self, content: str) -> None:
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"""Record the first human message for convenience fields."""
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self._first_human_msg = content[:2000] if content else None
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async def flush(self) -> None:
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"""Force flush remaining buffer. Called in worker's finally block."""
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if self._buffer:
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batch = self._buffer.copy()
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self._buffer.clear()
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await self._store.put_batch(batch)
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def get_completion_data(self) -> dict:
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"""Return accumulated token and message data for run completion."""
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return {
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"total_input_tokens": self._total_input_tokens,
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"total_output_tokens": self._total_output_tokens,
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"total_tokens": self._total_tokens,
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"llm_call_count": self._llm_call_count,
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"lead_agent_tokens": self._lead_agent_tokens,
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"subagent_tokens": self._subagent_tokens,
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"middleware_tokens": self._middleware_tokens,
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"message_count": self._msg_count,
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"last_ai_message": self._last_ai_msg,
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"first_human_message": self._first_human_msg,
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}
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