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https://github.com/zhayujie/chatgpt-on-wechat.git
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feat: personal ai agent framework
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saboteur7
288
bridge/agent_bridge.py
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288
bridge/agent_bridge.py
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"""
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Agent Bridge - Integrates Agent system with existing COW bridge
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"""
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from typing import Optional, List
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from agent.protocol import Agent, LLMModel, LLMRequest
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from agent.tools import Calculator, CurrentTime, Read, Write, Edit, Bash, Grep, Find, Ls
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from bridge.bridge import Bridge
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from bridge.context import Context
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from bridge.reply import Reply, ReplyType
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from common import const
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from common.log import logger
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class AgentLLMModel(LLMModel):
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"""
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LLM Model adapter that uses COW's existing bot infrastructure
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"""
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def __init__(self, bridge: Bridge, bot_type: str = "chat"):
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# Get model name directly from config
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from config import conf
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model_name = conf().get("model", const.GPT_41)
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super().__init__(model=model_name)
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self.bridge = bridge
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self.bot_type = bot_type
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self._bot = None
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@property
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def bot(self):
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"""Lazy load the bot"""
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if self._bot is None:
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self._bot = self.bridge.get_bot(self.bot_type)
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return self._bot
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def call(self, request: LLMRequest):
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"""
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Call the model using COW's bot infrastructure
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"""
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try:
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# For non-streaming calls, we'll use the existing reply method
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# This is a simplified implementation
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if hasattr(self.bot, 'call_with_tools'):
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# Use tool-enabled call if available
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kwargs = {
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'messages': request.messages,
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'tools': getattr(request, 'tools', None),
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'stream': False
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}
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# Only pass max_tokens if it's explicitly set
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if request.max_tokens is not None:
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kwargs['max_tokens'] = request.max_tokens
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response = self.bot.call_with_tools(**kwargs)
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return self._format_response(response)
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else:
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# Fallback to regular call
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# This would need to be implemented based on your specific needs
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raise NotImplementedError("Regular call not implemented yet")
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except Exception as e:
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logger.error(f"AgentLLMModel call error: {e}")
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raise
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def call_stream(self, request: LLMRequest):
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"""
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Call the model with streaming using COW's bot infrastructure
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"""
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try:
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if hasattr(self.bot, 'call_with_tools'):
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# Use tool-enabled streaming call if available
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# Ensure max_tokens is an integer, use default if None
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max_tokens = request.max_tokens if request.max_tokens is not None else 4096
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# Extract system prompt if present
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system_prompt = getattr(request, 'system', None)
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# Build kwargs for call_with_tools
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kwargs = {
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'messages': request.messages,
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'tools': getattr(request, 'tools', None),
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'stream': True,
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'max_tokens': max_tokens
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}
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# Add system prompt if present
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if system_prompt:
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kwargs['system'] = system_prompt
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stream = self.bot.call_with_tools(**kwargs)
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# Convert Claude stream format to our expected format
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for chunk in stream:
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yield self._format_stream_chunk(chunk)
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else:
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raise NotImplementedError("Streaming call not implemented yet")
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except Exception as e:
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logger.error(f"AgentLLMModel call_stream error: {e}")
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raise
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def _format_response(self, response):
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"""Format Claude response to our expected format"""
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# This would need to be implemented based on Claude's response format
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return response
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def _format_stream_chunk(self, chunk):
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"""Format Claude stream chunk to our expected format"""
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# This would need to be implemented based on Claude's stream format
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return chunk
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class AgentBridge:
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"""
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Bridge class that integrates single super Agent with COW
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"""
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def __init__(self, bridge: Bridge):
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self.bridge = bridge
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self.agent: Optional[Agent] = None
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def create_agent(self, system_prompt: str, tools: List = None, **kwargs) -> Agent:
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"""
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Create the super agent with COW integration
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Args:
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system_prompt: System prompt
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tools: List of tools (optional)
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**kwargs: Additional agent parameters
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Returns:
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Agent instance
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"""
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# Create LLM model that uses COW's bot infrastructure
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model = AgentLLMModel(self.bridge)
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# Default tools if none provided
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if tools is None:
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tools = [
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Calculator(),
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CurrentTime(),
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Read(),
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Write(),
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Edit(),
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Bash(),
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Grep(),
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Find(),
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Ls()
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]
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# Create the single super agent
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self.agent = Agent(
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system_prompt=system_prompt,
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description=kwargs.get("description", "AI Super Agent"),
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model=model,
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tools=tools,
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max_steps=kwargs.get("max_steps", 15),
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output_mode=kwargs.get("output_mode", "logger")
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)
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return self.agent
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def get_agent(self) -> Optional[Agent]:
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"""Get the super agent, create if not exists"""
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if self.agent is None:
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self._init_default_agent()
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return self.agent
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def _init_default_agent(self):
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"""Initialize default super agent with config and memory"""
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from config import conf
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import os
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# Get base system prompt from config
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base_prompt = conf().get("character_desc", "你是一个AI助手")
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# Setup memory if enabled
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memory_manager = None
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memory_tools = []
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try:
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# Try to initialize memory system
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from agent.memory import MemoryManager, MemoryConfig
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from agent.tools import MemorySearchTool, MemoryGetTool
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# Create memory config directly with sensible defaults
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workspace_root = os.path.expanduser("~/cow")
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memory_config = MemoryConfig(
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workspace_root=workspace_root,
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embedding_provider="local", # Use local embedding (no API key needed)
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embedding_model="all-MiniLM-L6-v2"
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)
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# Create memory manager with the config
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memory_manager = MemoryManager(memory_config)
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# Create memory tools
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memory_tools = [
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MemorySearchTool(memory_manager),
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MemoryGetTool(memory_manager)
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]
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# Build memory guidance and add to system prompt
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memory_guidance = memory_manager.build_memory_guidance(
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lang="zh",
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include_context=True
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)
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system_prompt = base_prompt + "\n\n" + memory_guidance
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logger.info(f"[AgentBridge] Memory system initialized")
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logger.info(f"[AgentBridge] Workspace: {memory_config.get_workspace()}")
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except Exception as e:
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logger.warning(f"[AgentBridge] Memory system not available: {e}")
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logger.info("[AgentBridge] Continuing without memory features")
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system_prompt = base_prompt
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import traceback
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traceback.print_exc()
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logger.info("[AgentBridge] Initializing super agent")
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# Configure file tools to work in the correct workspace
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file_config = {"cwd": workspace_root} if memory_manager else {}
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# Create default tools with workspace config
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from agent.tools import Calculator, CurrentTime, Read, Write, Edit, Bash, Grep, Find, Ls
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tools = [
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Calculator(),
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CurrentTime(),
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Read(config=file_config),
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Write(config=file_config),
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Edit(config=file_config),
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Bash(config=file_config),
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Grep(config=file_config),
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Find(config=file_config),
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Ls(config=file_config)
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]
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# Create agent with configured tools
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agent = self.create_agent(
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system_prompt=system_prompt,
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tools=tools,
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max_steps=50,
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output_mode="logger"
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)
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# Attach memory manager to agent if available
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if memory_manager:
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agent.memory_manager = memory_manager
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# Add memory tools if available
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if memory_tools:
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for tool in memory_tools:
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agent.add_tool(tool)
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logger.info(f"[AgentBridge] Added {len(memory_tools)} memory tools")
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def agent_reply(self, query: str, context: Context = None,
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on_event=None, clear_history: bool = False) -> Reply:
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"""
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Use super agent to reply to a query
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Args:
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query: User query
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context: COW context (optional)
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on_event: Event callback (optional)
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clear_history: Whether to clear conversation history
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Returns:
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Reply object
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"""
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try:
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# Get agent (will auto-initialize if needed)
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agent = self.get_agent()
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if not agent:
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return Reply(ReplyType.ERROR, "Failed to initialize super agent")
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# Use agent's run_stream method
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response = agent.run_stream(
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user_message=query,
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on_event=on_event,
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clear_history=clear_history
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)
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return Reply(ReplyType.TEXT, response)
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except Exception as e:
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logger.error(f"Agent reply error: {e}")
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return Reply(ReplyType.ERROR, f"Agent error: {str(e)}")
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@@ -23,7 +23,7 @@ class Bridge(object):
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if bot_type:
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self.btype["chat"] = bot_type
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else:
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model_type = conf().get("model") or const.GPT35
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model_type = conf().get("model") or const.GPT_41_MINI
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if model_type in ["text-davinci-003"]:
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self.btype["chat"] = const.OPEN_AI
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if conf().get("use_azure_chatgpt", False):
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@@ -64,6 +64,7 @@ class Bridge(object):
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self.bots = {}
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self.chat_bots = {}
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self._agent_bridge = None
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# 模型对应的接口
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def get_bot(self, typename):
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@@ -104,3 +105,29 @@ class Bridge(object):
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重置bot路由
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"""
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self.__init__()
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def get_agent_bridge(self):
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"""
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Get agent bridge for agent-based conversations
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"""
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if self._agent_bridge is None:
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from bridge.agent_bridge import AgentBridge
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self._agent_bridge = AgentBridge(self)
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return self._agent_bridge
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def fetch_agent_reply(self, query: str, context: Context = None,
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on_event=None, clear_history: bool = False) -> Reply:
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"""
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Use super agent to handle the query
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Args:
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query: User query
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context: Context object
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on_event: Event callback for streaming
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clear_history: Whether to clear conversation history
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Returns:
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Reply object
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"""
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agent_bridge = self.get_agent_bridge()
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return agent_bridge.agent_reply(query, context, on_event, clear_history)
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