Method _process_message

nanobot/nanobot/agent/loop.py:161–268

Process a single inbound message. Args: msg: The inbound message to process. Returns: The response message, or None if no response needed.

(self, msg: InboundMessage)

        logger.info("Agent loop stopping")

    async def _process_message(self, msg: InboundMessage) -> OutboundMessage | None:
        """
        Process a single inbound message.

        Args:
            msg: The inbound message to process.

        Returns:
            The response message, or None if no response needed.
        """
        # Handle system messages (subagent announces)
        # The chat_id contains the original "channel:chat_id" to route back to
        if msg.channel == "system":
            return await self._process_system_message(msg)

        preview = msg.content[:80] + "..." if len(msg.content) > 80 else msg.content
        logger.info(f"Processing message from {msg.channel}:{msg.sender_id}: {preview}")

        # Get or create session
        session = self.sessions.get_or_create(msg.session_key)

        # Update tool contexts
        message_tool = self.tools.get("message")
        if isinstance(message_tool, MessageTool):
            message_tool.set_context(msg.channel, msg.chat_id)

        spawn_tool = self.tools.get("spawn")
        if isinstance(spawn_tool, SpawnTool):
            spawn_tool.set_context(msg.channel, msg.chat_id)

        cron_tool = self.tools.get("cron")
        if isinstance(cron_tool, CronTool):
            cron_tool.set_context(msg.channel, msg.chat_id)

        # Build initial messages (use get_history for LLM-formatted messages)
        messages = self.context.build_messages(
            history=session.get_history(),
            current_message=msg.content,
            media=msg.media if msg.media else None,
            channel=msg.channel,
            chat_id=msg.chat_id,
        )

        # Agent loop
        iteration = 0
        final_content = None

        while iteration < self.max_iterations:
            iteration += 1

            # Call LLM
            response = await self.provider.chat(
                messages=messages, tools=self.tools.get_definitions(), model=self.model
            )

            # Handle tool calls
            if response.has_tool_calls:
                # Add assistant message with tool calls