Merge branch 'main' into rayhpeng/persistence-scaffold

# Conflicts:
#	backend/Dockerfile
#	backend/uv.lock

backend/packages/harness/deerflow/agents/memory/queue.py

@@ -21,6 +21,7 @@ class ConversationContext:
     timestamp: datetime = field(default_factory=datetime.utcnow)
     agent_name: str | None = None
     correction_detected: bool = False
+    reinforcement_detected: bool = False
 
 
 class MemoryUpdateQueue:
@@ -44,6 +45,7 @@ class MemoryUpdateQueue:
         messages: list[Any],
         agent_name: str | None = None,
         correction_detected: bool = False,
+        reinforcement_detected: bool = False,
     ) -> None:
         """Add a conversation to the update queue.
 
@@ -52,6 +54,7 @@ class MemoryUpdateQueue:
             messages: The conversation messages.
             agent_name: If provided, memory is stored per-agent. If None, uses global memory.
             correction_detected: Whether recent turns include an explicit correction signal.
+            reinforcement_detected: Whether recent turns include a positive reinforcement signal.
         """
         config = get_memory_config()
         if not config.enabled:
@@ -63,11 +66,13 @@ class MemoryUpdateQueue:
                 None,
             )
             merged_correction_detected = correction_detected or (existing_context.correction_detected if existing_context is not None else False)
+            merged_reinforcement_detected = reinforcement_detected or (existing_context.reinforcement_detected if existing_context is not None else False)
             context = ConversationContext(
                 thread_id=thread_id,
                 messages=messages,
                 agent_name=agent_name,
                 correction_detected=merged_correction_detected,
+                reinforcement_detected=merged_reinforcement_detected,
             )
 
             # Check if this thread already has a pending update
@@ -130,6 +135,7 @@ class MemoryUpdateQueue:
                         thread_id=context.thread_id,
                         agent_name=context.agent_name,
                         correction_detected=context.correction_detected,
+                        reinforcement_detected=context.reinforcement_detected,
                     )
                     if success:
                         logger.info("Memory updated successfully for thread %s", context.thread_id)

backend/packages/harness/deerflow/agents/memory/updater.py

@@ -246,7 +246,7 @@ def _fact_content_key(content: Any) -> str | None:
     stripped = content.strip()
     if not stripped:
         return None
-    return stripped
+    return stripped.casefold()
 
 
 class MemoryUpdater:
@@ -272,6 +272,7 @@ class MemoryUpdater:
         thread_id: str | None = None,
         agent_name: str | None = None,
         correction_detected: bool = False,
+        reinforcement_detected: bool = False,
     ) -> bool:
         """Update memory based on conversation messages.
 
@@ -280,6 +281,7 @@ class MemoryUpdater:
             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.
+            reinforcement_detected: Whether recent turns include a positive reinforcement signal.
 
         Returns:
             True if update was successful, False otherwise.
@@ -310,6 +312,14 @@ class MemoryUpdater:
                     "and record the correct approach as a fact with category "
                     '"correction" and confidence >= 0.95 when appropriate.'
                 )
+            if reinforcement_detected:
+                reinforcement_hint = (
+                    "IMPORTANT: Positive reinforcement signals were detected in this conversation. "
+                    "The user explicitly confirmed the agent's approach was correct or helpful. "
+                    "Record the confirmed approach, style, or preference as a fact with category "
+                    '"preference" or "behavior" and confidence >= 0.9 when appropriate.'
+                )
+                correction_hint = (correction_hint + "\n" + reinforcement_hint).strip() if correction_hint else reinforcement_hint
 
             prompt = MEMORY_UPDATE_PROMPT.format(
                 current_memory=json.dumps(current_memory, indent=2),
@@ -441,6 +451,7 @@ def update_memory_from_conversation(
     thread_id: str | None = None,
     agent_name: str | None = None,
     correction_detected: bool = False,
+    reinforcement_detected: bool = False,
 ) -> bool:
     """Convenience function to update memory from a conversation.
 
@@ -449,9 +460,10 @@ def update_memory_from_conversation(
         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.
+        reinforcement_detected: Whether recent turns include a positive reinforcement signal.
 
     Returns:
         True if successful, False otherwise.
     """
     updater = MemoryUpdater()
-    return updater.update_memory(messages, thread_id, agent_name, correction_detected)
+    return updater.update_memory(messages, thread_id, agent_name, correction_detected, reinforcement_detected)

backend/packages/harness/deerflow/agents/middlewares/memory_middleware.py

@@ -29,6 +29,22 @@ _CORRECTION_PATTERNS = (
     re.compile(r"改用"),
 )
 
+_REINFORCEMENT_PATTERNS = (
+    re.compile(r"\byes[,.]?\s+(?:exactly|perfect|that(?:'s| is) (?:right|correct|it))\b", re.IGNORECASE),
+    re.compile(r"\bperfect(?:[.!?]|$)", re.IGNORECASE),
+    re.compile(r"\bexactly\s+(?:right|correct)\b", re.IGNORECASE),
+    re.compile(r"\bthat(?:'s| is)\s+(?:exactly\s+)?(?:right|correct|what i (?:wanted|needed|meant))\b", re.IGNORECASE),
+    re.compile(r"\bkeep\s+(?:doing\s+)?that\b", re.IGNORECASE),
+    re.compile(r"\bjust\s+(?:like\s+)?(?:that|this)\b", re.IGNORECASE),
+    re.compile(r"\bthis is (?:great|helpful)\b(?:[.!?]|$)", re.IGNORECASE),
+    re.compile(r"\bthis is what i wanted\b(?:[.!?]|$)", re.IGNORECASE),
+    re.compile(r"对[，,]?\s*就是这样(?:[。！？!?.]|$)"),
+    re.compile(r"完全正确(?:[。！？!?.]|$)"),
+    re.compile(r"(?:对[，,]?\s*)?就是这个意思(?:[。！？!?.]|$)"),
+    re.compile(r"正是我想要的(?:[。！？!?.]|$)"),
+    re.compile(r"继续保持(?:[。！？!?.]|$)"),
+)
+
 
 class MemoryMiddlewareState(AgentState):
     """Compatible with the `ThreadState` schema."""
@@ -132,6 +148,29 @@ def detect_correction(messages: list[Any]) -> bool:
     return False
 
 
+def detect_reinforcement(messages: list[Any]) -> bool:
+    """Detect explicit positive reinforcement signals in recent conversation turns.
+
+    Complements detect_correction() by identifying when the user confirms the
+    agent's approach was correct. This allows the memory system to record what
+    worked well, not just what went wrong.
+
+    The queue keeps only one pending context per thread, so callers pass the
+    latest filtered message list. Checking only recent user turns keeps signal
+    detection conservative while avoiding stale signals from long histories.
+    """
+    recent_user_msgs = [msg for msg in messages[-6:] if getattr(msg, "type", None) == "human"]
+
+    for msg in recent_user_msgs:
+        content = _extract_message_text(msg).strip()
+        if not content:
+            continue
+        if any(pattern.search(content) for pattern in _REINFORCEMENT_PATTERNS):
+            return True
+
+    return False
+
+
 class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
     """Middleware that queues conversation for memory update after agent execution.
 
@@ -196,12 +235,14 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
 
         # Queue the filtered conversation for memory update
         correction_detected = detect_correction(filtered_messages)
+        reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
         queue = get_memory_queue()
         queue.add(
             thread_id=thread_id,
             messages=filtered_messages,
             agent_name=self._agent_name,
             correction_detected=correction_detected,
+            reinforcement_detected=reinforcement_detected,
         )
 
         return None