🐈nanobot: hello world!
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nanobot/agent/loop.py
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213
nanobot/agent/loop.py
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"""Agent loop: the core processing engine."""
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import asyncio
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import json
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from pathlib import Path
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from typing import Any
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from loguru import logger
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from nanobot.bus.events import InboundMessage, OutboundMessage
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from nanobot.bus.queue import MessageBus
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from nanobot.providers.base import LLMProvider
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from nanobot.agent.context import ContextBuilder
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from nanobot.agent.tools.registry import ToolRegistry
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from nanobot.agent.tools.filesystem import ReadFileTool, WriteFileTool, EditFileTool, ListDirTool
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from nanobot.agent.tools.shell import ExecTool
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from nanobot.agent.tools.web import WebSearchTool, WebFetchTool
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from nanobot.agent.tools.message import MessageTool
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from nanobot.session.manager import SessionManager
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class AgentLoop:
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"""
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The agent loop is the core processing engine.
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It:
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1. Receives messages from the bus
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2. Builds context with history, memory, skills
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3. Calls the LLM
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4. Executes tool calls
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5. Sends responses back
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"""
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def __init__(
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self,
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bus: MessageBus,
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provider: LLMProvider,
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workspace: Path,
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model: str | None = None,
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max_iterations: int = 20,
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brave_api_key: str | None = None
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):
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self.bus = bus
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self.provider = provider
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self.workspace = workspace
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self.model = model or provider.get_default_model()
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self.max_iterations = max_iterations
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self.brave_api_key = brave_api_key
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self.context = ContextBuilder(workspace)
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self.sessions = SessionManager(workspace)
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self.tools = ToolRegistry()
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self._running = False
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self._register_default_tools()
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def _register_default_tools(self) -> None:
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"""Register the default set of tools."""
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# File tools
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self.tools.register(ReadFileTool())
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self.tools.register(WriteFileTool())
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self.tools.register(EditFileTool())
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self.tools.register(ListDirTool())
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# Shell tool
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self.tools.register(ExecTool(working_dir=str(self.workspace)))
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# Web tools
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self.tools.register(WebSearchTool(api_key=self.brave_api_key))
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self.tools.register(WebFetchTool())
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# Message tool
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message_tool = MessageTool(send_callback=self.bus.publish_outbound)
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self.tools.register(message_tool)
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async def run(self) -> None:
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"""Run the agent loop, processing messages from the bus."""
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self._running = True
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logger.info("Agent loop started")
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while self._running:
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try:
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# Wait for next message
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msg = await asyncio.wait_for(
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self.bus.consume_inbound(),
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timeout=1.0
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)
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# Process it
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try:
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response = await self._process_message(msg)
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if response:
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await self.bus.publish_outbound(response)
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except Exception as e:
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logger.error(f"Error processing message: {e}")
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# Send error response
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await self.bus.publish_outbound(OutboundMessage(
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channel=msg.channel,
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chat_id=msg.chat_id,
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content=f"Sorry, I encountered an error: {str(e)}"
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))
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except asyncio.TimeoutError:
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continue
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def stop(self) -> None:
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"""Stop the agent loop."""
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self._running = False
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logger.info("Agent loop stopping")
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async def _process_message(self, msg: InboundMessage) -> OutboundMessage | None:
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"""
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Process a single inbound message.
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Args:
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msg: The inbound message to process.
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Returns:
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The response message, or None if no response needed.
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"""
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logger.info(f"Processing message from {msg.channel}:{msg.sender_id}")
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# Get or create session
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session = self.sessions.get_or_create(msg.session_key)
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# Update message tool context
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message_tool = self.tools.get("message")
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if isinstance(message_tool, MessageTool):
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message_tool.set_context(msg.channel, msg.chat_id)
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# Build initial messages (use get_history for LLM-formatted messages)
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messages = self.context.build_messages(
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history=session.get_history(),
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current_message=msg.content
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)
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# Agent loop
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iteration = 0
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final_content = None
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while iteration < self.max_iterations:
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iteration += 1
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# Call LLM
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response = await self.provider.chat(
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messages=messages,
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tools=self.tools.get_definitions(),
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model=self.model
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)
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# Handle tool calls
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if response.has_tool_calls:
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# Add assistant message with tool calls
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tool_call_dicts = [
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{
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"id": tc.id,
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"type": "function",
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"function": {
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"name": tc.name,
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"arguments": json.dumps(tc.arguments) # Must be JSON string
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}
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}
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for tc in response.tool_calls
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]
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messages = self.context.add_assistant_message(
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messages, response.content, tool_call_dicts
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)
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# Execute tools
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for tool_call in response.tool_calls:
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logger.debug(f"Executing tool: {tool_call.name}")
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result = await self.tools.execute(tool_call.name, tool_call.arguments)
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messages = self.context.add_tool_result(
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messages, tool_call.id, tool_call.name, result
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)
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else:
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# No tool calls, we're done
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final_content = response.content
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break
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if final_content is None:
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final_content = "I've completed processing but have no response to give."
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# Save to session
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session.add_message("user", msg.content)
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session.add_message("assistant", final_content)
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self.sessions.save(session)
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return OutboundMessage(
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channel=msg.channel,
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chat_id=msg.chat_id,
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content=final_content
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)
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async def process_direct(self, content: str, session_key: str = "cli:direct") -> str:
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"""
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Process a message directly (for CLI usage).
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Args:
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content: The message content.
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session_key: Session identifier.
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Returns:
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The agent's response.
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"""
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msg = InboundMessage(
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channel="cli",
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sender_id="user",
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chat_id="direct",
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content=content
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)
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response = await self._process_message(msg)
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return response.content if response else ""
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