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feat(memory): structured reflection + correction detection in MemoryMiddleware (#1620) (#1668)

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* feat(memory): add structured reflection and correction detection

* fix(memory): align sourceError schema and prompt guidance

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
This commit is contained in:
AochenShen99
2026-04-01 16:45:29 +08:00
committed by GitHub
parent 3e461d9d08
commit 0cdecf7b30
10 changed files with 436 additions and 21 deletions
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backend/packages/harness/deerflow/agents/memory/updater.py
+33 -3
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@@ -266,13 +266,20 @@ class MemoryUpdater:
model_name = self._model_name or config.model_name
return create_chat_model(name=model_name, thinking_enabled=False)
def update_memory(self, messages: list[Any], thread_id: str | None = None, agent_name: str | None = None) -> bool:
def update_memory(
self,
messages: list[Any],
thread_id: str | None = None,
agent_name: str | None = None,
correction_detected: bool = False,
) -> bool:
"""Update memory based on conversation messages.
Args:
messages: List of conversation messages.
thread_id: Optional thread ID for tracking source.
agent_name: If provided, updates per-agent memory. If None, updates global memory.
correction_detected: Whether recent turns include an explicit correction signal.
Returns:
True if update was successful, False otherwise.
@@ -295,9 +302,19 @@ class MemoryUpdater:
return False
# Build prompt
correction_hint = ""
if correction_detected:
correction_hint = (
"IMPORTANT: Explicit correction signals were detected in this conversation. "
"Pay special attention to what the agent got wrong, what the user corrected, "
"and record the correct approach as a fact with category "
'"correction" and confidence >= 0.95 when appropriate.'
)
prompt = MEMORY_UPDATE_PROMPT.format(
current_memory=json.dumps(current_memory, indent=2),
conversation=conversation_text,
correction_hint=correction_hint,
)
# Call LLM
@@ -383,6 +400,8 @@ class MemoryUpdater:
confidence = fact.get("confidence", 0.5)
if confidence >= config.fact_confidence_threshold:
raw_content = fact.get("content", "")
if not isinstance(raw_content, str):
continue
normalized_content = raw_content.strip()
fact_key = _fact_content_key(normalized_content)
if fact_key is not None and fact_key in existing_fact_keys:
@@ -396,6 +415,11 @@ class MemoryUpdater:
"createdAt": now,
"source": thread_id or "unknown",
}
source_error = fact.get("sourceError")
if isinstance(source_error, str):
normalized_source_error = source_error.strip()
if normalized_source_error:
fact_entry["sourceError"] = normalized_source_error
current_memory["facts"].append(fact_entry)
if fact_key is not None:
existing_fact_keys.add(fact_key)
@@ -412,16 +436,22 @@ class MemoryUpdater:
return current_memory
def update_memory_from_conversation(messages: list[Any], thread_id: str | None = None, agent_name: str | None = None) -> bool:
def update_memory_from_conversation(
messages: list[Any],
thread_id: str | None = None,
agent_name: str | None = None,
correction_detected: bool = False,
) -> bool:
"""Convenience function to update memory from a conversation.
Args:
messages: List of conversation messages.
thread_id: Optional thread ID.
agent_name: If provided, updates per-agent memory. If None, updates global memory.
correction_detected: Whether recent turns include an explicit correction signal.
Returns:
True if successful, False otherwise.
"""
updater = MemoryUpdater()
return updater.update_memory(messages, thread_id, agent_name)
return updater.update_memory(messages, thread_id, agent_name, correction_detected)