feat: 新增 core/agents 模块和 nanobot
- 新增 agents 模块，包含 agent、api、skills 等子模块 - 新增 nanobot 项目，支持多渠道集成 - 添加启动脚本 start-all.bat 和 start-all.sh Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
463
core/agents/agent/loop.py
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463
core/agents/agent/loop.py
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"""Agent run loop - complete implementation."""
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import asyncio
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import json
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import logging
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import re
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Callable, Awaitable, AsyncGenerator
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from agents.agent.context import ContextBuilder
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from agents.agent.memory import AgentMemory
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from agents.llm import LLMProvider, LLMResponse, ProviderFactory
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from agents.tools import ToolRegistry
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logger = logging.getLogger(__name__)
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class AgentLoop:
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"""Agent loop with message processing, LLM calls, tool execution, and streaming."""
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_TOOL_RESULT_MAX_CHARS = 10000
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def __init__(
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self,
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provider: LLMProvider,
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model: str,
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workspace: Path | None = None,
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max_iterations: int = 10,
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tools: ToolRegistry | None = None,
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):
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"""Initialize the agent loop.
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Args:
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provider: LLM provider (OpenAI, Anthropic, etc.)
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model: Model name to use
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workspace: Workspace directory for memory and configs
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max_iterations: Maximum tool call iterations
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tools: Tool registry (creates default if None)
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"""
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self.provider = provider
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self.model = model
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self.workspace = workspace or Path.cwd()
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self.max_iterations = max_iterations
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self.tools = tools
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self.context = ContextBuilder(self.workspace)
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self.memory = AgentMemory(self.workspace)
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async def chat(
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self,
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message: str,
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history: list[dict[str, Any]] | None = None,
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session_key: str = "default",
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on_progress: Callable[[str], Awaitable[None]] | None = None,
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model_id: str | None = None,
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model_name: str | None = None,
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model_provider: str | None = None,
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api_key: str | None = None,
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base_url: str | None = None,
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use_xbot: bool = False,
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) -> str:
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"""Process a chat message and return the response.
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Args:
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message: User message
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history: Conversation history
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session_key: Session identifier
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on_progress: Optional callback for progress updates
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model_id: Model ID (optional)
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model_name: Model name (optional)
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model_provider: Model provider (optional)
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api_key: API key (optional)
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base_url: Custom API base URL (optional)
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use_xbot: Use xbot mode (optional)
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Returns:
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Agent response content
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"""
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history = history or []
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# Check if dynamic provider parameters are provided
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if api_key or model_provider:
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logger.info(f"Using dynamic provider: model_provider={model_provider}, model_name={model_name}, base_url={base_url}")
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# Create temporary provider with dynamic parameters
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temp_provider = ProviderFactory.create(
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provider=model_provider or "openai",
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api_key=api_key,
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api_base=base_url,
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)
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# Use temporary provider and model
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temp_model = model_name or temp_provider.get_default_model()
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logger.info(f"Created temp provider with model: {temp_model}")
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return await self._chat_with_provider(
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message=message,
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history=history,
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session_key=session_key,
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on_progress=on_progress,
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provider=temp_provider,
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model=temp_model,
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)
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# Build messages for LLM
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messages = self.context.build_messages(
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history=history,
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current_message=message,
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)
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# Log which provider is being used
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logger.info(f"Using static provider: {type(self.provider).__name__}, model={self.model}")
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# Run the agent loop
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final_content, tools_used, all_messages = await self._run_loop(
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messages, on_progress
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)
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# Save to history
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self._save_history(session_key, all_messages, len(history))
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return final_content or "No response generated."
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async def _chat_with_provider(
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self,
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message: str,
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history: list[dict[str, Any]] | None = None,
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session_key: str = "default",
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on_progress: Callable[[str], Awaitable[None]] | None = None,
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provider: LLMProvider | None = None,
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model: str | None = None,
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) -> str:
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"""Chat with a specific provider (used for dynamic provider support).
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Args:
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message: User message
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history: Conversation history
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session_key: Session identifier
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on_progress: Optional callback for progress updates
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provider: LLM provider to use
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model: Model name to use
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Returns:
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Agent response content
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"""
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history = history or []
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provider = provider or self.provider
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model = model or self.model
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# Build messages for LLM
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messages = self.context.build_messages(
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history=history,
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current_message=message,
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)
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# Run the agent loop with custom provider
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final_content, tools_used, all_messages = await self._run_loop(
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messages, on_progress, provider=provider, model=model
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)
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# Save to history
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self._save_history(session_key, all_messages, len(history))
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return final_content or "No response generated."
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async def chat_stream(
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self,
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message: str,
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history: list[dict[str, Any]] | None = None,
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session_key: str = "default",
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model_id: str | None = None,
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model_name: str | None = None,
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model_provider: str | None = None,
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api_key: str | None = None,
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base_url: str | None = None,
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use_xbot: bool = False,
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) -> AsyncGenerator[str, None]:
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"""Process a chat message with streaming response.
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Args:
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message: User message
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history: Conversation history
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session_key: Session identifier
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model_id: Model ID (optional)
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model_name: Model name (optional)
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model_provider: Model provider (optional)
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api_key: API key (optional)
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base_url: Custom API base URL (optional)
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use_xbot: Use xbot mode (optional)
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Yields:
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Response content chunks
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"""
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history = history or []
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# Check if dynamic provider parameters are provided
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if api_key or model_provider:
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logger.info(f"[stream] Using dynamic provider: model_provider={model_provider}, model_name={model_name}, base_url={base_url}")
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# Create temporary provider with dynamic parameters
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temp_provider = ProviderFactory.create(
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provider=model_provider or "openai",
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api_key=api_key,
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api_base=base_url,
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)
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# Use temporary provider and model
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temp_model = model_name or temp_provider.get_default_model()
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logger.info(f"[stream] Created temp provider with model: {temp_model}")
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async for chunk in self._chat_stream_with_provider(
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message=message,
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history=history,
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session_key=session_key,
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provider=temp_provider,
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model=temp_model,
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):
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yield chunk
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return
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# Build messages for LLM
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messages = self.context.build_messages(
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history=history,
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current_message=message,
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)
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# Stream the response
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async for chunk in self._run_loop_stream(messages):
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yield chunk
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async def _chat_stream_with_provider(
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self,
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message: str,
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history: list[dict[str, Any]] | None = None,
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session_key: str = "default",
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provider: LLMProvider | None = None,
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model: str | None = None,
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) -> AsyncGenerator[str, None]:
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"""Stream chat with a specific provider (used for dynamic provider support).
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Args:
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message: User message
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history: Conversation history
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session_key: Session identifier
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provider: LLM provider to use
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model: Model name to use
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Yields:
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Response content chunks
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"""
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history = history or []
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provider = provider or self.provider
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model = model or self.model
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# Build messages for LLM
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messages = self.context.build_messages(
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history=history,
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current_message=message,
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)
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# Stream the response with custom provider
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async for chunk in self._run_loop_stream(messages, provider=provider, model=model):
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yield chunk
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async def _run_loop(
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self,
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initial_messages: list[dict],
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on_progress: Callable[..., Awaitable[None]] | None = None,
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provider: LLMProvider | None = None,
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model: str | None = None,
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) -> tuple[str | None, list[str], list[dict]]:
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"""Run the agent iteration loop.
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Args:
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initial_messages: Initial message list
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on_progress: Progress callback
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provider: Optional LLM provider to use (defaults to self.provider)
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model: Optional model name to use (defaults to self.model)
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Returns:
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Tuple of (final_content, tools_used, all_messages)
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"""
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messages = initial_messages
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iteration = 0
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final_content = None
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tools_used: list[str] = []
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provider = provider or self.provider
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model = model or self.model
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tool_defs = self.tools.get_definitions() if self.tools else []
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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 provider.chat_with_retry(
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messages=messages,
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tools=tool_defs if tool_defs else None,
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model=model,
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)
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if response.has_tool_calls:
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# Progress callback for tool calls
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if on_progress:
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thought = self._strip_think(response.content)
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if thought:
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await on_progress(thought)
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await on_progress(self._tool_hint(response.tool_calls), tool_hint=True)
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# Add assistant message with tool calls
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tool_call_dicts = [tc.to_dict() for tc in response.tool_calls]
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messages = self.context.add_assistant_message(
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messages,
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response.content,
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tool_call_dicts,
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reasoning_content=response.reasoning_content,
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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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tools_used.append(tool_call.name)
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args = tool_call.arguments
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logger.info(f"Tool call: {tool_call.name}({args})")
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# Execute tool
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result = await self._execute_tool(tool_call.name, args)
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# Truncate large results
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if len(result) > self._TOOL_RESULT_MAX_CHARS:
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result = result[:self._TOOL_RESULT_MAX_CHARS] + "\n... (truncated)"
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# Add tool result
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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 - return the response
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clean = self._strip_think(response.content)
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# Handle errors
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if response.finish_reason == "error":
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logger.error(f"LLM error: {clean}")
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final_content = clean or "Sorry, I encountered an error calling the AI model."
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break
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messages = self.context.add_assistant_message(
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messages, clean, reasoning_content=response.reasoning_content
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)
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final_content = clean
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break
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if final_content is None and iteration >= self.max_iterations:
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logger.warning(f"Max iterations ({self.max_iterations}) reached")
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final_content = (
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f"I reached the maximum number of iterations ({self.max_iterations}) "
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"without completing the task."
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)
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return final_content, tools_used, messages
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async def _run_loop_stream(
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self,
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initial_messages: list[dict],
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provider: LLMProvider | None = None,
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model: str | None = None,
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) -> AsyncGenerator[str, None]:
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"""Run the agent loop with streaming response.
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Args:
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initial_messages: Initial message list
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provider: Optional LLM provider to use (defaults to self.provider)
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model: Optional model name to use (defaults to self.model)
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Yields:
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Response content chunks
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"""
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provider = provider or self.provider
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model = model or self.model
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tool_defs = self.tools.get_definitions() if self.tools else []
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# First call to check for tool calls
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response = await provider.chat_with_retry(
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messages=initial_messages,
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tools=tool_defs if tool_defs else None,
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model=model,
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)
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if response.has_tool_calls:
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# Execute tools first
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for tool_call in response.tool_calls:
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logger.info(f"Tool call: {tool_call.name}")
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result = await self._execute_tool(tool_call.name, tool_call.arguments)
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# Add to messages
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initial_messages = self.context.add_tool_result(
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initial_messages, tool_call.id, tool_call.name, result
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)
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# Recursive call after tool execution
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async for chunk in self._run_loop_stream(initial_messages, provider=provider, model=model):
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yield chunk
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else:
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# Stream the content
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content = self._strip_think(response.content)
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if content:
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yield content
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async def _execute_tool(self, tool_name: str, args: dict) -> str:
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"""Execute a tool.
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Args:
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tool_name: Name of the tool to execute
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args: Tool arguments
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Returns:
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Tool execution result
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"""
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if self.tools:
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return await self.tools.execute(tool_name, args)
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return json.dumps({"error": "No tools registered"})
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@staticmethod
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def _strip_think(text: str | None) -> str | None:
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"""Strip think blocks that some models embed in content."""
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if not text:
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return None
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import re
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# Match content between [/INST] or [/CONTINUE] tags commonly used in thinking
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patterns = [
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r"<think>[\s\S]*?</think>",
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r"<\/?think>",
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]
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for pattern in patterns:
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text = re.sub(pattern, "", text)
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return text.strip() or None
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@staticmethod
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def _tool_hint(tool_calls: list) -> str:
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"""Format tool calls as concise hint."""
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def _fmt(tc):
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args = tc.arguments or {}
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val = next(iter(args.values()), None) if isinstance(args, dict) else None
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if not isinstance(val, str):
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return tc.name
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return f'{tc.name}("{val[:40]}...")' if len(val) > 40 else f'{tc.name}("{val}")'
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return ", ".join(_fmt(tc) for tc in tool_calls)
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def _save_history(
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self,
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session_key: str,
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messages: list[dict],
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skip: int = 0,
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) -> None:
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"""Save messages to history.
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Args:
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session_key: Session identifier
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messages: Messages to save
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skip: Number of messages to skip
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"""
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for m in messages[skip:]:
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role = m.get("role")
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content = m.get("content")
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if role == "user" and content:
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self.memory.add_to_history("user", str(content)[:1000], session_key)
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elif role == "assistant" and content:
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self.memory.add_to_history("assistant", str(content)[:1000], session_key)
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