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https://github.com/bytedance/deer-flow.git
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feat: flush memory before summarization (#2176)
* feat: flush memory before summarization * fix: keep agent-scoped memory on summarization flush * fix: harden summarization hook plumbing * fix: address summarization review feedback * style: format memory middleware
This commit is contained in:
DanielWalnut
@@ -1,50 +1,19 @@
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"""Middleware for memory mechanism."""
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import logging
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import re
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from typing import Any, override
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from typing import override
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from langchain.agents import AgentState
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from langchain.agents.middleware import AgentMiddleware
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from langgraph.config import get_config
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from langgraph.runtime import Runtime
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from deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
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from deerflow.agents.memory.queue import get_memory_queue
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from deerflow.config.memory_config import get_memory_config
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logger = logging.getLogger(__name__)
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_UPLOAD_BLOCK_RE = re.compile(r"<uploaded_files>[\s\S]*?</uploaded_files>\n*", re.IGNORECASE)
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_CORRECTION_PATTERNS = (
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re.compile(r"\bthat(?:'s| is) (?:wrong|incorrect)\b", re.IGNORECASE),
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re.compile(r"\byou misunderstood\b", re.IGNORECASE),
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re.compile(r"\btry again\b", re.IGNORECASE),
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re.compile(r"\bredo\b", re.IGNORECASE),
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re.compile(r"不对"),
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re.compile(r"你理解错了"),
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re.compile(r"你理解有误"),
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re.compile(r"重试"),
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re.compile(r"重新来"),
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re.compile(r"换一种"),
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re.compile(r"改用"),
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)
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_REINFORCEMENT_PATTERNS = (
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re.compile(r"\byes[,.]?\s+(?:exactly|perfect|that(?:'s| is) (?:right|correct|it))\b", re.IGNORECASE),
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re.compile(r"\bperfect(?:[.!?]|$)", re.IGNORECASE),
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re.compile(r"\bexactly\s+(?:right|correct)\b", re.IGNORECASE),
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re.compile(r"\bthat(?:'s| is)\s+(?:exactly\s+)?(?:right|correct|what i (?:wanted|needed|meant))\b", re.IGNORECASE),
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re.compile(r"\bkeep\s+(?:doing\s+)?that\b", re.IGNORECASE),
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re.compile(r"\bjust\s+(?:like\s+)?(?:that|this)\b", re.IGNORECASE),
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re.compile(r"\bthis is (?:great|helpful)\b(?:[.!?]|$)", re.IGNORECASE),
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re.compile(r"\bthis is what i wanted\b(?:[.!?]|$)", re.IGNORECASE),
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re.compile(r"对[，,]?\s*就是这样(?:[。！？!?.]|$)"),
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re.compile(r"完全正确(?:[。！？!?.]|$)"),
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re.compile(r"(?:对[，,]?\s*)?就是这个意思(?:[。！？!?.]|$)"),
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re.compile(r"正是我想要的(?:[。！？!?.]|$)"),
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re.compile(r"继续保持(?:[。！？!?.]|$)"),
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)
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class MemoryMiddlewareState(AgentState):
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"""Compatible with the `ThreadState` schema."""
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@@ -52,125 +21,6 @@ class MemoryMiddlewareState(AgentState):
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pass
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def _extract_message_text(message: Any) -> str:
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"""Extract plain text from message content for filtering and signal detection."""
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content = getattr(message, "content", "")
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if isinstance(content, list):
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text_parts: list[str] = []
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for part in content:
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if isinstance(part, str):
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text_parts.append(part)
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elif isinstance(part, dict):
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text_val = part.get("text")
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if isinstance(text_val, str):
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text_parts.append(text_val)
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return " ".join(text_parts)
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return str(content)
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def _filter_messages_for_memory(messages: list[Any]) -> list[Any]:
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"""Filter messages to keep only user inputs and final assistant responses.
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This filters out:
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- Tool messages (intermediate tool call results)
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- AI messages with tool_calls (intermediate steps, not final responses)
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- The <uploaded_files> block injected by UploadsMiddleware into human messages
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(file paths are session-scoped and must not persist in long-term memory).
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The user's actual question is preserved; only turns whose content is entirely
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the upload block (nothing remains after stripping) are dropped along with
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their paired assistant response.
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Only keeps:
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- Human messages (with the ephemeral upload block removed)
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- AI messages without tool_calls (final assistant responses), unless the
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paired human turn was upload-only and had no real user text.
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Args:
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messages: List of all conversation messages.
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Returns:
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Filtered list containing only user inputs and final assistant responses.
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"""
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filtered = []
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skip_next_ai = False
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for msg in messages:
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msg_type = getattr(msg, "type", None)
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if msg_type == "human":
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content_str = _extract_message_text(msg)
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if "<uploaded_files>" in content_str:
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# Strip the ephemeral upload block; keep the user's real question.
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stripped = _UPLOAD_BLOCK_RE.sub("", content_str).strip()
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if not stripped:
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# Nothing left — the entire turn was upload bookkeeping;
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# skip it and the paired assistant response.
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skip_next_ai = True
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continue
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# Rebuild the message with cleaned content so the user's question
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# is still available for memory summarisation.
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from copy import copy
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clean_msg = copy(msg)
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clean_msg.content = stripped
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filtered.append(clean_msg)
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skip_next_ai = False
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else:
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filtered.append(msg)
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skip_next_ai = False
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elif msg_type == "ai":
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tool_calls = getattr(msg, "tool_calls", None)
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if not tool_calls:
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if skip_next_ai:
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skip_next_ai = False
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continue
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filtered.append(msg)
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# Skip tool messages and AI messages with tool_calls
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return filtered
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def detect_correction(messages: list[Any]) -> bool:
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"""Detect explicit user corrections in recent conversation turns.
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The queue keeps only one pending context per thread, so callers pass the
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latest filtered message list. Checking only recent user turns keeps signal
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detection conservative while avoiding stale corrections from long histories.
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"""
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recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
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for msg in recent_user_msgs:
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content = _extract_message_text(msg).strip()
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if not content:
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continue
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if any(pattern.search(content) for pattern in _CORRECTION_PATTERNS):
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return True
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return False
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def detect_reinforcement(messages: list[Any]) -> bool:
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"""Detect explicit positive reinforcement signals in recent conversation turns.
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Complements detect_correction() by identifying when the user confirms the
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agent's approach was correct. This allows the memory system to record what
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worked well, not just what went wrong.
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The queue keeps only one pending context per thread, so callers pass the
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latest filtered message list. Checking only recent user turns keeps signal
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detection conservative while avoiding stale signals from long histories.
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"""
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recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
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for msg in recent_user_msgs:
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content = _extract_message_text(msg).strip()
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if not content:
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continue
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if any(pattern.search(content) for pattern in _REINFORCEMENT_PATTERNS):
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return True
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return False
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class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
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"""Middleware that queues conversation for memory update after agent execution.
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@@ -223,7 +73,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
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return None
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# Filter to only keep user inputs and final assistant responses
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filtered_messages = _filter_messages_for_memory(messages)
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filtered_messages = filter_messages_for_memory(messages)
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# Only queue if there's meaningful conversation
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# At minimum need one user message and one assistant response
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