第一次提交

app-instance/backend/nanobot/agent/memory.py

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+"""Memory system for persistent agent memory."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+from loguru import logger
+
+from nanobot.utils.helpers import ensure_dir
+
+if TYPE_CHECKING:
+    from nanobot.providers.base import LLMProvider
+    from nanobot.session.manager import Session
+
+
+_SAVE_MEMORY_TOOL = [
+    {
+        "type": "function",
+        "function": {
+            "name": "save_memory",
+            "description": "Save the memory consolidation result to persistent storage.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "history_entry": {
+                        "type": "string",
+                        "description": "A paragraph (2-5 sentences) summarizing key events/decisions/topics. "
+                        "Start with [YYYY-MM-DD HH:MM]. Include detail useful for grep search.",
+                    },
+                    "memory_update": {
+                        "type": "string",
+                        "description": "Full updated long-term memory as markdown. Include all existing "
+                        "facts plus new ones. Return unchanged if nothing new.",
+                    },
+                },
+                "required": ["history_entry", "memory_update"],
+            },
+        },
+    }
+]
+
+
+class MemoryStore:
+    """Two-layer memory: MEMORY.md (long-term facts) + HISTORY.md (grep-searchable log)."""
+
+    def __init__(self, workspace: Path):
+        self.memory_dir = ensure_dir(workspace / "memory")
+        self.memory_file = self.memory_dir / "MEMORY.md"
+        self.history_file = self.memory_dir / "HISTORY.md"
+
+    def read_long_term(self) -> str:
+        if self.memory_file.exists():
+            return self.memory_file.read_text(encoding="utf-8")
+        return ""
+
+    def write_long_term(self, content: str) -> None:
+        self.memory_file.write_text(content, encoding="utf-8")
+
+    def append_history(self, entry: str) -> None:
+        with open(self.history_file, "a", encoding="utf-8") as f:
+            f.write(entry.rstrip() + "\n\n")
+
+    def get_memory_context(self) -> str:
+        long_term = self.read_long_term()
+        return f"## Long-term Memory\n{long_term}" if long_term else ""
+
+    async def consolidate(
+        self,
+        session: Session,
+        provider: LLMProvider,
+        model: str,
+        *,
+        archive_all: bool = False,
+        memory_window: int = 50,
+    ) -> bool:
+        """Consolidate old messages into MEMORY.md + HISTORY.md via LLM tool call.
+
+        Returns True on success (including no-op), False on failure.
+        """
+        if archive_all:
+            old_messages = session.messages
+            keep_count = 0
+            logger.info("Memory consolidation (archive_all): {} messages", len(session.messages))
+        else:
+            keep_count = memory_window // 2
+            if len(session.messages) <= keep_count:
+                return True
+            if len(session.messages) - session.last_consolidated <= 0:
+                return True
+            old_messages = session.messages[session.last_consolidated:-keep_count]
+            if not old_messages:
+                return True
+            logger.info("Memory consolidation: {} to consolidate, {} keep", len(old_messages), keep_count)
+
+        lines = []
+        for m in old_messages:
+            if not m.get("content"):
+                continue
+            tools = f" [tools: {', '.join(m['tools_used'])}]" if m.get("tools_used") else ""
+            lines.append(f"[{m.get('timestamp', '?')[:16]}] {m['role'].upper()}{tools}: {m['content']}")
+
+        current_memory = self.read_long_term()
+        prompt = f"""Process this conversation and call the save_memory tool with your consolidation.
+
+## Current Long-term Memory
+{current_memory or "(empty)"}
+
+## Conversation to Process
+{chr(10).join(lines)}"""
+
+        try:
+            response = await provider.chat(
+                messages=[
+                    {"role": "system", "content": "You are a memory consolidation agent. Call the save_memory tool with your consolidation of the conversation."},
+                    {"role": "user", "content": prompt},
+                ],
+                tools=_SAVE_MEMORY_TOOL,
+                model=model,
+            )
+
+            if not response.has_tool_calls:
+                logger.warning("Memory consolidation: LLM did not call save_memory, skipping")
+                return False
+
+            args = response.tool_calls[0].arguments
+            if entry := args.get("history_entry"):
+                if not isinstance(entry, str):
+                    entry = json.dumps(entry, ensure_ascii=False)
+                self.append_history(entry)
+            if update := args.get("memory_update"):
+                if not isinstance(update, str):
+                    update = json.dumps(update, ensure_ascii=False)
+                if update != current_memory:
+                    self.write_long_term(update)
+
+            session.last_consolidated = 0 if archive_all else len(session.messages) - keep_count
+            logger.info("Memory consolidation done: {} messages, last_consolidated={}", len(session.messages), session.last_consolidated)
+            return True
+        except Exception:
+            logger.exception("Memory consolidation failed")
+            return False