fix(memory): case-insensitive fact deduplication and positive reinforcement detection (#1804)

* fix(memory): case-insensitive fact deduplication and positive reinforcement detection

Two fixes to the memory system:

1. _fact_content_key() now lowercases content before comparison, preventing
   semantically duplicate facts like "User prefers Python" and "user prefers
   python" from being stored separately.

2. Adds detect_reinforcement() to MemoryMiddleware (closes #1719), mirroring
   detect_correction(). When users signal approval ("yes exactly", "perfect",
   "完全正确", etc.), the memory updater now receives reinforcement_detected=True
   and injects a hint prompting the LLM to record confirmed preferences and
   behaviors with high confidence.

   Changes across the full signal path:
   - memory_middleware.py: _REINFORCEMENT_PATTERNS + detect_reinforcement()
   - queue.py: reinforcement_detected field in ConversationContext and add()
   - updater.py: reinforcement_detected param in update_memory() and
     update_memory_from_conversation(); builds reinforcement_hint alongside
     the existing correction_hint

Tests: 11 new tests covering deduplication, hint injection, and signal
detection (Chinese + English patterns, window boundary, conflict with correction).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(memory): address Copilot review comments on reinforcement detection

- Tighten _REINFORCEMENT_PATTERNS: remove 很好, require punctuation/end-of-string boundaries on remaining patterns, split this-is-good into stricter variants
- Suppress reinforcement_detected when correction_detected is true to avoid mixed-signal noise
- Use casefold() instead of lower() for Unicode-aware fact deduplication
- Add missing test coverage for reinforcement_detected OR merge and forwarding in queue

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

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

backend/tests/test_memory_queue.py

@@ -47,4 +47,45 @@ def test_process_queue_forwards_correction_flag_to_updater() -> None:
         thread_id="thread-1",
         agent_name="lead_agent",
         correction_detected=True,
+        reinforcement_detected=False,
+    )
+
+
+def test_queue_add_preserves_existing_reinforcement_flag_for_same_thread() -> None:
+    queue = MemoryUpdateQueue()
+
+    with (
+        patch("deerflow.agents.memory.queue.get_memory_config", return_value=_memory_config(enabled=True)),
+        patch.object(queue, "_reset_timer"),
+    ):
+        queue.add(thread_id="thread-1", messages=["first"], reinforcement_detected=True)
+        queue.add(thread_id="thread-1", messages=["second"], reinforcement_detected=False)
+
+    assert len(queue._queue) == 1
+    assert queue._queue[0].messages == ["second"]
+    assert queue._queue[0].reinforcement_detected is True
+
+
+def test_process_queue_forwards_reinforcement_flag_to_updater() -> None:
+    queue = MemoryUpdateQueue()
+    queue._queue = [
+        ConversationContext(
+            thread_id="thread-1",
+            messages=["conversation"],
+            agent_name="lead_agent",
+            reinforcement_detected=True,
+        )
+    ]
+    mock_updater = MagicMock()
+    mock_updater.update_memory.return_value = True
+
+    with patch("deerflow.agents.memory.updater.MemoryUpdater", return_value=mock_updater):
+        queue._process_queue()
+
+    mock_updater.update_memory.assert_called_once_with(
+        messages=["conversation"],
+        thread_id="thread-1",
+        agent_name="lead_agent",
+        correction_detected=False,
+        reinforcement_detected=True,
     )

backend/tests/test_memory_updater.py

@@ -619,3 +619,156 @@ class TestUpdateMemoryStructuredResponse:
         assert result is True
         prompt = model.invoke.call_args[0][0]
         assert "Explicit correction signals were detected" not in prompt
+
+
+class TestFactDeduplicationCaseInsensitive:
+    """Tests that fact deduplication is case-insensitive."""
+
+    def test_duplicate_fact_different_case_not_stored(self):
+        updater = MemoryUpdater()
+        current_memory = _make_memory(
+            facts=[
+                {
+                    "id": "fact_1",
+                    "content": "User prefers Python",
+                    "category": "preference",
+                    "confidence": 0.9,
+                    "createdAt": "2026-01-01T00:00:00Z",
+                    "source": "thread-a",
+                },
+            ]
+        )
+        # Same fact with different casing should be treated as duplicate
+        update_data = {
+            "factsToRemove": [],
+            "newFacts": [
+                {"content": "user prefers python", "category": "preference", "confidence": 0.95},
+            ],
+        }
+
+        with patch(
+            "deerflow.agents.memory.updater.get_memory_config",
+            return_value=_memory_config(max_facts=100, fact_confidence_threshold=0.7),
+        ):
+            result = updater._apply_updates(current_memory, update_data, thread_id="thread-b")
+
+        # Should still have only 1 fact (duplicate rejected)
+        assert len(result["facts"]) == 1
+        assert result["facts"][0]["content"] == "User prefers Python"
+
+    def test_unique_fact_different_case_and_content_stored(self):
+        updater = MemoryUpdater()
+        current_memory = _make_memory(
+            facts=[
+                {
+                    "id": "fact_1",
+                    "content": "User prefers Python",
+                    "category": "preference",
+                    "confidence": 0.9,
+                    "createdAt": "2026-01-01T00:00:00Z",
+                    "source": "thread-a",
+                },
+            ]
+        )
+        update_data = {
+            "factsToRemove": [],
+            "newFacts": [
+                {"content": "User prefers Go", "category": "preference", "confidence": 0.85},
+            ],
+        }
+
+        with patch(
+            "deerflow.agents.memory.updater.get_memory_config",
+            return_value=_memory_config(max_facts=100, fact_confidence_threshold=0.7),
+        ):
+            result = updater._apply_updates(current_memory, update_data, thread_id="thread-b")
+
+        assert len(result["facts"]) == 2
+
+
+class TestReinforcementHint:
+    """Tests that reinforcement_detected injects the correct hint into the prompt."""
+
+    @staticmethod
+    def _make_mock_model(json_response: str):
+        model = MagicMock()
+        response = MagicMock()
+        response.content = f"```json\n{json_response}\n```"
+        model.invoke.return_value = response
+        return model
+
+    def test_reinforcement_hint_injected_when_detected(self):
+        updater = MemoryUpdater()
+        valid_json = '{"user": {}, "history": {}, "newFacts": [], "factsToRemove": []}'
+        model = self._make_mock_model(valid_json)
+
+        with (
+            patch.object(updater, "_get_model", return_value=model),
+            patch("deerflow.agents.memory.updater.get_memory_config", return_value=_memory_config(enabled=True)),
+            patch("deerflow.agents.memory.updater.get_memory_data", return_value=_make_memory()),
+            patch("deerflow.agents.memory.updater.get_memory_storage", return_value=MagicMock(save=MagicMock(return_value=True))),
+        ):
+            msg = MagicMock()
+            msg.type = "human"
+            msg.content = "Yes, exactly! That's what I needed."
+            ai_msg = MagicMock()
+            ai_msg.type = "ai"
+            ai_msg.content = "Great to hear!"
+            ai_msg.tool_calls = []
+
+            result = updater.update_memory([msg, ai_msg], reinforcement_detected=True)
+
+        assert result is True
+        prompt = model.invoke.call_args[0][0]
+        assert "Positive reinforcement signals were detected" in prompt
+
+    def test_reinforcement_hint_absent_when_not_detected(self):
+        updater = MemoryUpdater()
+        valid_json = '{"user": {}, "history": {}, "newFacts": [], "factsToRemove": []}'
+        model = self._make_mock_model(valid_json)
+
+        with (
+            patch.object(updater, "_get_model", return_value=model),
+            patch("deerflow.agents.memory.updater.get_memory_config", return_value=_memory_config(enabled=True)),
+            patch("deerflow.agents.memory.updater.get_memory_data", return_value=_make_memory()),
+            patch("deerflow.agents.memory.updater.get_memory_storage", return_value=MagicMock(save=MagicMock(return_value=True))),
+        ):
+            msg = MagicMock()
+            msg.type = "human"
+            msg.content = "Tell me more."
+            ai_msg = MagicMock()
+            ai_msg.type = "ai"
+            ai_msg.content = "Sure."
+            ai_msg.tool_calls = []
+
+            result = updater.update_memory([msg, ai_msg], reinforcement_detected=False)
+
+        assert result is True
+        prompt = model.invoke.call_args[0][0]
+        assert "Positive reinforcement signals were detected" not in prompt
+
+    def test_both_hints_present_when_both_detected(self):
+        updater = MemoryUpdater()
+        valid_json = '{"user": {}, "history": {}, "newFacts": [], "factsToRemove": []}'
+        model = self._make_mock_model(valid_json)
+
+        with (
+            patch.object(updater, "_get_model", return_value=model),
+            patch("deerflow.agents.memory.updater.get_memory_config", return_value=_memory_config(enabled=True)),
+            patch("deerflow.agents.memory.updater.get_memory_data", return_value=_make_memory()),
+            patch("deerflow.agents.memory.updater.get_memory_storage", return_value=MagicMock(save=MagicMock(return_value=True))),
+        ):
+            msg = MagicMock()
+            msg.type = "human"
+            msg.content = "No wait, that's wrong. Actually yes, exactly right."
+            ai_msg = MagicMock()
+            ai_msg.type = "ai"
+            ai_msg.content = "Got it."
+            ai_msg.tool_calls = []
+
+            result = updater.update_memory([msg, ai_msg], correction_detected=True, reinforcement_detected=True)
+
+        assert result is True
+        prompt = model.invoke.call_args[0][0]
+        assert "Explicit correction signals were detected" in prompt
+        assert "Positive reinforcement signals were detected" in prompt

backend/tests/test_memory_upload_filtering.py

@@ -10,7 +10,7 @@ persisting in long-term memory:
 from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
 
 from deerflow.agents.memory.updater import _strip_upload_mentions_from_memory
-from deerflow.agents.middlewares.memory_middleware import _filter_messages_for_memory, detect_correction
+from deerflow.agents.middlewares.memory_middleware import _filter_messages_for_memory, detect_correction, detect_reinforcement
 
 # ---------------------------------------------------------------------------
 # Helpers
@@ -270,3 +270,73 @@ class TestStripUploadMentionsFromMemory:
         mem = {"user": {}, "history": {}, "facts": []}
         result = _strip_upload_mentions_from_memory(mem)
         assert result == {"user": {}, "history": {}, "facts": []}
+
+
+# ===========================================================================
+# detect_reinforcement
+# ===========================================================================
+
+
+class TestDetectReinforcement:
+    def test_detects_english_reinforcement_signal(self):
+        msgs = [
+            _human("Can you summarise it in bullet points?"),
+            _ai("Here are the key points: ..."),
+            _human("Yes, exactly! That's what I needed."),
+            _ai("Glad it helped."),
+        ]
+
+        assert detect_reinforcement(msgs) is True
+
+    def test_detects_perfect_signal(self):
+        msgs = [
+            _human("Write it more concisely."),
+            _ai("Here is the concise version."),
+            _human("Perfect."),
+            _ai("Great!"),
+        ]
+
+        assert detect_reinforcement(msgs) is True
+
+    def test_detects_chinese_reinforcement_signal(self):
+        msgs = [
+            _human("帮我用要点来总结"),
+            _ai("好的，要点如下：..."),
+            _human("完全正确，就是这个意思"),
+            _ai("很高兴能帮到你"),
+        ]
+
+        assert detect_reinforcement(msgs) is True
+
+    def test_returns_false_without_signal(self):
+        msgs = [
+            _human("What does this function do?"),
+            _ai("It processes the input data."),
+            _human("Can you show me an example?"),
+        ]
+
+        assert detect_reinforcement(msgs) is False
+
+    def test_only_checks_recent_messages(self):
+        # Reinforcement signal buried beyond the -6 window should not trigger
+        msgs = [
+            _human("Yes, exactly right."),
+            _ai("Noted."),
+            _human("Let's discuss tests."),
+            _ai("Sure."),
+            _human("What about linting?"),
+            _ai("Use ruff."),
+            _human("And formatting?"),
+            _ai("Use make format."),
+        ]
+
+        assert detect_reinforcement(msgs) is False
+
+    def test_does_not_conflict_with_correction(self):
+        # A message can trigger correction but not reinforcement
+        msgs = [
+            _human("That's wrong, try again."),
+            _ai("Corrected."),
+        ]
+
+        assert detect_reinforcement(msgs) is False