mirror of
https://github.com/bytedance/deer-flow.git
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feat: switch memory updater to async LLM calls (#2138)
* docs: mark memory updater async migration as completed - Update TODO.md to mark the replacement of sync model.invoke() with async model.ainvoke() in title_middleware and memory updater as completed using [x] format Addresses #2131 * feat: switch memory updater to async LLM calls - Add async aupdate_memory() method using await model.ainvoke() - Convert sync update_memory() to use async wrapper - Add _run_async_update_sync() for nested loop context handling - Maintain backward compatibility with existing sync API - Add ThreadPoolExecutor for async execution from sync contexts Addresses #2131 * test: add tests for async memory updater - Add test_async_update_memory_uses_ainvoke() to verify async path - Convert existing tests to use AsyncMock and ainvoke assertions - Add test_sync_update_memory_wrapper_works_in_running_loop() - Update all model mocks to use async await patterns Addresses #2131 * fix: apply ruff formatting to memory updater - Format multi-line expressions to single line - Ensure code style consistency with project standards - Fix lint issues caught by GitHub Actions * test: add comprehensive tests for async memory updater - Add test_async_update_memory_uses_ainvoke() to verify async path - Convert existing tests to use AsyncMock and ainvoke assertions - Add test_sync_update_memory_wrapper_works_in_running_loop() - Update all model mocks to use async await patterns - Ensure backward compatibility with sync API * fix: satisfy ruff formatting in memory updater test --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
This commit is contained in:
luo jiyin
@@ -1,10 +1,14 @@
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"""Memory updater for reading, writing, and updating memory data."""
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import asyncio
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import atexit
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import concurrent.futures
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import json
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import logging
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import math
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import re
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import uuid
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from collections.abc import Awaitable
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from typing import Any
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from deerflow.agents.memory.prompt import (
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@@ -21,6 +25,12 @@ from deerflow.models import create_chat_model
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logger = logging.getLogger(__name__)
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_SYNC_MEMORY_UPDATER_EXECUTOR = concurrent.futures.ThreadPoolExecutor(
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max_workers=4,
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thread_name_prefix="memory-updater-sync",
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)
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atexit.register(lambda: _SYNC_MEMORY_UPDATER_EXECUTOR.shutdown(wait=False))
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def _create_empty_memory() -> dict[str, Any]:
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"""Backward-compatible wrapper around the storage-layer empty-memory factory."""
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@@ -206,6 +216,39 @@ def _extract_text(content: Any) -> str:
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return str(content)
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def _run_async_update_sync(coro: Awaitable[bool]) -> bool:
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"""Run an async memory update from sync code, including nested-loop contexts."""
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handed_off = False
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try:
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try:
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loop = asyncio.get_running_loop()
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except RuntimeError:
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loop = None
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if loop is not None and loop.is_running():
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future = _SYNC_MEMORY_UPDATER_EXECUTOR.submit(asyncio.run, coro)
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handed_off = True
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return future.result()
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handed_off = True
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return asyncio.run(coro)
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except Exception:
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if not handed_off:
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close = getattr(coro, "close", None)
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if callable(close):
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try:
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close()
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except Exception:
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logger.debug(
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"Failed to close un-awaited memory update coroutine",
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exc_info=True,
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)
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logger.exception("Failed to run async memory update from sync context")
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return False
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# Matches sentences that describe a file-upload *event* rather than general
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# file-related work. Deliberately narrow to avoid removing legitimate facts
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# such as "User works with CSV files" or "prefers PDF export".
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@@ -269,6 +312,113 @@ class MemoryUpdater:
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model_name = self._model_name or config.model_name
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return create_chat_model(name=model_name, thinking_enabled=False)
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def _build_correction_hint(
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self,
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correction_detected: bool,
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reinforcement_detected: bool,
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) -> str:
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"""Build optional prompt hints for correction and reinforcement signals."""
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correction_hint = ""
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if correction_detected:
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correction_hint = (
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"IMPORTANT: Explicit correction signals were detected in this conversation. "
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"Pay special attention to what the agent got wrong, what the user corrected, "
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"and record the correct approach as a fact with category "
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'"correction" and confidence >= 0.95 when appropriate.'
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)
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if reinforcement_detected:
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reinforcement_hint = (
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"IMPORTANT: Positive reinforcement signals were detected in this conversation. "
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"The user explicitly confirmed the agent's approach was correct or helpful. "
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"Record the confirmed approach, style, or preference as a fact with category "
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'"preference" or "behavior" and confidence >= 0.9 when appropriate.'
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)
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correction_hint = (correction_hint + "\n" + reinforcement_hint).strip() if correction_hint else reinforcement_hint
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return correction_hint
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def _prepare_update_prompt(
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self,
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messages: list[Any],
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agent_name: str | None,
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correction_detected: bool,
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reinforcement_detected: bool,
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) -> tuple[dict[str, Any], str] | None:
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"""Load memory and build the update prompt for a conversation."""
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config = get_memory_config()
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if not config.enabled or not messages:
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return None
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current_memory = get_memory_data(agent_name)
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conversation_text = format_conversation_for_update(messages)
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if not conversation_text.strip():
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return None
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correction_hint = self._build_correction_hint(
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correction_detected=correction_detected,
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reinforcement_detected=reinforcement_detected,
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)
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prompt = MEMORY_UPDATE_PROMPT.format(
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current_memory=json.dumps(current_memory, indent=2),
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conversation=conversation_text,
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correction_hint=correction_hint,
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)
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return current_memory, prompt
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def _finalize_update(
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self,
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current_memory: dict[str, Any],
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response_content: Any,
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thread_id: str | None,
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agent_name: str | None,
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) -> bool:
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"""Parse the model response, apply updates, and persist memory."""
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response_text = _extract_text(response_content).strip()
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if response_text.startswith("```"):
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lines = response_text.split("\n")
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response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
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update_data = json.loads(response_text)
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updated_memory = self._apply_updates(current_memory, update_data, thread_id)
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updated_memory = _strip_upload_mentions_from_memory(updated_memory)
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return get_memory_storage().save(updated_memory, agent_name)
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async def aupdate_memory(
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self,
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messages: list[Any],
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thread_id: str | None = None,
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agent_name: str | None = None,
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correction_detected: bool = False,
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reinforcement_detected: bool = False,
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) -> bool:
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"""Update memory asynchronously based on conversation messages."""
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try:
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prepared = self._prepare_update_prompt(
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messages=messages,
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agent_name=agent_name,
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correction_detected=correction_detected,
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reinforcement_detected=reinforcement_detected,
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)
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if prepared is None:
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return False
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current_memory, prompt = prepared
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model = self._get_model()
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response = await model.ainvoke(prompt)
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return self._finalize_update(
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current_memory=current_memory,
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response_content=response.content,
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thread_id=thread_id,
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agent_name=agent_name,
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)
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except json.JSONDecodeError as e:
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logger.warning("Failed to parse LLM response for memory update: %s", e)
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return False
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except Exception as e:
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logger.exception("Memory update failed: %s", e)
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return False
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def update_memory(
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self,
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messages: list[Any],
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@@ -277,7 +427,7 @@ class MemoryUpdater:
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correction_detected: bool = False,
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reinforcement_detected: bool = False,
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) -> bool:
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"""Update memory based on conversation messages.
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"""Synchronously update memory via the async updater path.
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Args:
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messages: List of conversation messages.
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@@ -289,78 +439,15 @@ class MemoryUpdater:
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Returns:
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True if update was successful, False otherwise.
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"""
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config = get_memory_config()
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if not config.enabled:
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return False
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if not messages:
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return False
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try:
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# Get current memory
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current_memory = get_memory_data(agent_name)
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# Format conversation for prompt
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conversation_text = format_conversation_for_update(messages)
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if not conversation_text.strip():
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return False
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# Build prompt
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correction_hint = ""
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if correction_detected:
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correction_hint = (
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"IMPORTANT: Explicit correction signals were detected in this conversation. "
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"Pay special attention to what the agent got wrong, what the user corrected, "
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"and record the correct approach as a fact with category "
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'"correction" and confidence >= 0.95 when appropriate.'
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)
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if reinforcement_detected:
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reinforcement_hint = (
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"IMPORTANT: Positive reinforcement signals were detected in this conversation. "
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"The user explicitly confirmed the agent's approach was correct or helpful. "
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"Record the confirmed approach, style, or preference as a fact with category "
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'"preference" or "behavior" and confidence >= 0.9 when appropriate.'
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)
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correction_hint = (correction_hint + "\n" + reinforcement_hint).strip() if correction_hint else reinforcement_hint
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prompt = MEMORY_UPDATE_PROMPT.format(
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current_memory=json.dumps(current_memory, indent=2),
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conversation=conversation_text,
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correction_hint=correction_hint,
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return _run_async_update_sync(
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self.aupdate_memory(
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messages=messages,
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thread_id=thread_id,
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agent_name=agent_name,
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correction_detected=correction_detected,
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reinforcement_detected=reinforcement_detected,
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)
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# Call LLM
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model = self._get_model()
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response = model.invoke(prompt)
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response_text = _extract_text(response.content).strip()
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# Parse response
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# Remove markdown code blocks if present
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if response_text.startswith("```"):
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lines = response_text.split("\n")
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response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
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update_data = json.loads(response_text)
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# Apply updates
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updated_memory = self._apply_updates(current_memory, update_data, thread_id)
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# Strip file-upload mentions from all summaries before saving.
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# Uploaded files are session-scoped and won't exist in future sessions,
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# so recording upload events in long-term memory causes the agent to
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# try (and fail) to locate those files in subsequent conversations.
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updated_memory = _strip_upload_mentions_from_memory(updated_memory)
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# Save
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return get_memory_storage().save(updated_memory, agent_name)
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except json.JSONDecodeError as e:
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logger.warning("Failed to parse LLM response for memory update: %s", e)
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return False
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except Exception as e:
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logger.exception("Memory update failed: %s", e)
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return False
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)
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def _apply_updates(
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self,
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