fix(memory): exclude scheduler-injected pairs from daily memory flush

agent/memory/summarizer.py

@@ -175,6 +175,15 @@ class MemoryFlushManager:
         injection.
         """
         try:
+            # Strip scheduler-injected pairs before any further processing.
+            # These messages already serve as short-term context inside the
+            # receiver session; promoting them into long-term daily memory
+            # produces low-value flat logs (e.g. "11:28 price=1013, normal /
+            # 11:58 price=1013, normal / ...") and wastes summarisation tokens.
+            messages = self._strip_scheduler_pairs(messages)
+            if not messages:
+                return False
+
             import hashlib
             deduped = []
             for m in messages:
@@ -647,6 +656,40 @@ class MemoryFlushManager:
             return "\n".join(parts)
         return ""
 
+    @classmethod
+    def _strip_scheduler_pairs(cls, messages: List[Dict]) -> List[Dict]:
+        """Drop scheduler-injected user/assistant pairs from a flush batch.
+
+        A scheduler user message starts with the ``[SCHEDULED]`` marker
+        (written by ``AgentBridge.remember_scheduled_output``); the message
+        immediately following it (if it is an assistant turn) is its paired
+        output and is dropped together. Regular user/assistant turns and
+        any tool_use / tool_result blocks are preserved as-is.
+        """
+        if not messages:
+            return messages
+
+        SCHEDULED_PREFIX = "[SCHEDULED]"
+        result = []
+        skip_next_assistant = False
+        for msg in messages:
+            if not isinstance(msg, dict):
+                result.append(msg)
+                skip_next_assistant = False
+                continue
+            role = msg.get("role")
+            if skip_next_assistant and role == "assistant":
+                skip_next_assistant = False
+                continue
+            skip_next_assistant = False
+            if role == "user":
+                text = cls._extract_text_from_content(msg.get("content", ""))
+                if text.lstrip().startswith(SCHEDULED_PREFIX):
+                    skip_next_assistant = True
+                    continue
+            result.append(msg)
+        return result
+
 
 def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] = None):
     """