mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
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530 lines
22 KiB
Python
530 lines
22 KiB
Python
"""
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Agent Bridge - Integrates Agent system with existing COW bridge
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"""
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import os
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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 bridge.agent_event_handler import AgentEventHandler
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from bridge.agent_initializer import AgentInitializer
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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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from models.openai_compatible_bot import OpenAICompatibleBot
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def add_openai_compatible_support(bot_instance):
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"""
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Dynamically add OpenAI-compatible tool calling support to a bot instance.
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This allows any bot to gain tool calling capability without modifying its code,
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as long as it uses OpenAI-compatible API format.
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Note: Some bots like ZHIPUAIBot have native tool calling support and don't need enhancement.
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"""
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if hasattr(bot_instance, 'call_with_tools'):
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# Bot already has tool calling support (e.g., ZHIPUAIBot)
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logger.info(f"[AgentBridge] {type(bot_instance).__name__} already has native tool calling support")
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return bot_instance
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# Create a temporary mixin class that combines the bot with OpenAI compatibility
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class EnhancedBot(bot_instance.__class__, OpenAICompatibleBot):
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"""Dynamically enhanced bot with OpenAI-compatible tool calling"""
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def get_api_config(self):
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"""
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Infer API config from common configuration patterns.
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Most OpenAI-compatible bots use similar configuration.
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"""
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from config import conf
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return {
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'api_key': conf().get("open_ai_api_key"),
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'api_base': conf().get("open_ai_api_base"),
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'model': conf().get("model", "gpt-3.5-turbo"),
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'default_temperature': conf().get("temperature", 0.9),
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'default_top_p': conf().get("top_p", 1.0),
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'default_frequency_penalty': conf().get("frequency_penalty", 0.0),
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'default_presence_penalty': conf().get("presence_penalty", 0.0),
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}
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# Change the bot's class to the enhanced version
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bot_instance.__class__ = EnhancedBot
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logger.info(
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f"[AgentBridge] Enhanced {bot_instance.__class__.__bases__[0].__name__} with OpenAI-compatible tool calling")
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return bot_instance
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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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self._use_linkai = conf().get("use_linkai", False) and conf().get("linkai_api_key")
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@property
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def bot(self):
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"""Lazy load the bot and enhance it with tool calling if needed"""
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if self._bot is None:
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# If use_linkai is enabled, use LinkAI bot directly
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if self._use_linkai:
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self._bot = self.bridge.find_chat_bot(const.LINKAI)
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else:
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self._bot = self.bridge.get_bot(self.bot_type)
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# Automatically add tool calling support if not present
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self._bot = add_openai_compatible_support(self._bot)
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# Log bot info
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bot_name = type(self._bot).__name__
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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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'model': self.model # Pass model parameter
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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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# Extract system prompt if present
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system_prompt = getattr(request, 'system', None)
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if system_prompt:
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kwargs['system'] = system_prompt
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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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# 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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'model': self.model # Pass model parameter
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}
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# Only pass max_tokens if explicitly set, let the bot use its default
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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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# 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 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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bot_type = type(self.bot).__name__
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raise NotImplementedError(f"Bot {bot_type} does not support call_with_tools. Please add the method.")
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except Exception as e:
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logger.error(f"AgentLLMModel call_stream error: {e}", exc_info=True)
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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 super Agent with COW
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Manages multiple agent instances per session for conversation isolation
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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.agents = {} # session_id -> Agent instance mapping
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self.default_agent = None # For backward compatibility (no session_id)
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self.agent: Optional[Agent] = None
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self.scheduler_initialized = False
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# Create helper instances
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self.initializer = AgentInitializer(bridge, self)
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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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# Use ToolManager to load all available tools
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from agent.tools import ToolManager
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tool_manager = ToolManager()
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tool_manager.load_tools()
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tools = []
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for tool_name in tool_manager.tool_classes.keys():
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try:
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tool = tool_manager.create_tool(tool_name)
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if tool:
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tools.append(tool)
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except Exception as e:
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logger.warning(f"[AgentBridge] Failed to load tool {tool_name}: {e}")
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# Create agent instance
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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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workspace_dir=kwargs.get("workspace_dir"), # Pass workspace for skills loading
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enable_skills=kwargs.get("enable_skills", True), # Enable skills by default
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memory_manager=kwargs.get("memory_manager"), # Pass memory manager
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max_context_tokens=kwargs.get("max_context_tokens"),
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context_reserve_tokens=kwargs.get("context_reserve_tokens")
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)
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# Log skill loading details
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if agent.skill_manager:
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logger.debug(f"[AgentBridge] SkillManager initialized with {len(agent.skill_manager.skills)} skills")
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return agent
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def get_agent(self, session_id: str = None) -> Optional[Agent]:
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"""
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Get agent instance for the given session
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Args:
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session_id: Session identifier (e.g., user_id). If None, returns default agent.
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Returns:
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Agent instance for this session
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"""
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# If no session_id, use default agent (backward compatibility)
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if session_id is None:
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if self.default_agent is None:
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self._init_default_agent()
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return self.default_agent
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# Check if agent exists for this session
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if session_id not in self.agents:
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self._init_agent_for_session(session_id)
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return self.agents[session_id]
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def _init_default_agent(self):
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"""Initialize default super agent"""
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agent = self.initializer.initialize_agent(session_id=None)
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self.default_agent = agent
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def _init_agent_for_session(self, session_id: str):
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"""Initialize agent for a specific session"""
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agent = self.initializer.initialize_agent(session_id=session_id)
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self.agents[session_id] = agent
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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, contains session_id for user isolation)
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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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# Extract session_id from context for user isolation
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session_id = None
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if context:
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session_id = context.kwargs.get("session_id") or context.get("session_id")
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# Get agent for this session (will auto-initialize if needed)
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agent = self.get_agent(session_id=session_id)
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if not agent:
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return Reply(ReplyType.ERROR, "Failed to initialize super agent")
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# Create event handler for logging and channel communication
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event_handler = AgentEventHandler(context=context, original_callback=on_event)
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# Filter tools based on context
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original_tools = agent.tools
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filtered_tools = original_tools
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# If this is a scheduled task execution, exclude scheduler tool to prevent recursion
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if context and context.get("is_scheduled_task"):
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filtered_tools = [tool for tool in agent.tools if tool.name != "scheduler"]
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agent.tools = filtered_tools
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logger.info(f"[AgentBridge] Scheduled task execution: excluded scheduler tool ({len(filtered_tools)}/{len(original_tools)} tools)")
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else:
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# Attach context to scheduler tool if present
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if context and agent.tools:
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for tool in agent.tools:
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if tool.name == "scheduler":
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try:
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from agent.tools.scheduler.integration import attach_scheduler_to_tool
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attach_scheduler_to_tool(tool, context)
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except Exception as e:
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logger.warning(f"[AgentBridge] Failed to attach context to scheduler: {e}")
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break
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try:
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# Use agent's run_stream method with event handler
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response = agent.run_stream(
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user_message=query,
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on_event=event_handler.handle_event,
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clear_history=clear_history
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)
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finally:
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# Restore original tools
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if context and context.get("is_scheduled_task"):
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agent.tools = original_tools
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# Log execution summary
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event_handler.log_summary()
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# Check if there are files to send (from read tool)
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if hasattr(agent, 'stream_executor') and hasattr(agent.stream_executor, 'files_to_send'):
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files_to_send = agent.stream_executor.files_to_send
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if files_to_send:
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# Send the first file (for now, handle one file at a time)
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file_info = files_to_send[0]
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logger.info(f"[AgentBridge] Sending file: {file_info.get('path')}")
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# Clear files_to_send for next request
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agent.stream_executor.files_to_send = []
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# Return file reply based on file type
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return self._create_file_reply(file_info, response, context)
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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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def _create_file_reply(self, file_info: dict, text_response: str, context: Context = None) -> Reply:
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"""
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Create a reply for sending files
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Args:
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file_info: File metadata from read tool
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text_response: Text response from agent
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context: Context object
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Returns:
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Reply object for file sending
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"""
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file_type = file_info.get("file_type", "file")
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file_path = file_info.get("path")
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# For images, use IMAGE_URL type (channel will handle upload)
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if file_type == "image":
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# Convert local path to file:// URL for channel processing
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file_url = f"file://{file_path}"
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logger.info(f"[AgentBridge] Sending image: {file_url}")
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reply = Reply(ReplyType.IMAGE_URL, file_url)
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# Attach text message if present (for channels that support text+image)
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if text_response:
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reply.text_content = text_response # Store accompanying text
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return reply
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# For all file types (document, video, audio), use FILE type
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if file_type in ["document", "video", "audio"]:
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file_url = f"file://{file_path}"
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logger.info(f"[AgentBridge] Sending {file_type}: {file_url}")
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reply = Reply(ReplyType.FILE, file_url)
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reply.file_name = file_info.get("file_name", os.path.basename(file_path))
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# Attach text message if present
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if text_response:
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reply.text_content = text_response
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return reply
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# For other unknown file types, return text with file info
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message = text_response or file_info.get("message", "文件已准备")
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message += f"\n\n[文件: {file_info.get('file_name', file_path)}]"
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return Reply(ReplyType.TEXT, message)
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def _migrate_config_to_env(self, workspace_root: str):
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"""
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Migrate API keys from config.json to .env file if not already set
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Args:
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workspace_root: Workspace directory path (not used, kept for compatibility)
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"""
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from config import conf
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import os
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# Mapping from config.json keys to environment variable names
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key_mapping = {
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"open_ai_api_key": "OPENAI_API_KEY",
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"open_ai_api_base": "OPENAI_API_BASE",
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"gemini_api_key": "GEMINI_API_KEY",
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"claude_api_key": "CLAUDE_API_KEY",
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"linkai_api_key": "LINKAI_API_KEY",
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}
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# Use fixed secure location for .env file
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env_file = os.path.expanduser("~/.cow/.env")
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# Read existing env vars from .env file
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existing_env_vars = {}
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if os.path.exists(env_file):
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try:
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with open(env_file, 'r', encoding='utf-8') as f:
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for line in f:
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line = line.strip()
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if line and not line.startswith('#') and '=' in line:
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key, _ = line.split('=', 1)
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existing_env_vars[key.strip()] = True
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except Exception as e:
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logger.warning(f"[AgentBridge] Failed to read .env file: {e}")
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# Check which keys need to be migrated
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keys_to_migrate = {}
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for config_key, env_key in key_mapping.items():
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# Skip if already in .env file
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if env_key in existing_env_vars:
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continue
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# Get value from config.json
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value = conf().get(config_key, "")
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if value and value.strip(): # Only migrate non-empty values
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keys_to_migrate[env_key] = value.strip()
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# Log summary if there are keys to skip
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if existing_env_vars:
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logger.debug(f"[AgentBridge] {len(existing_env_vars)} env vars already in .env")
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# Write new keys to .env file
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if keys_to_migrate:
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try:
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# Ensure ~/.cow directory and .env file exist
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env_dir = os.path.dirname(env_file)
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if not os.path.exists(env_dir):
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os.makedirs(env_dir, exist_ok=True)
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if not os.path.exists(env_file):
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open(env_file, 'a').close()
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# Append new keys
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with open(env_file, 'a', encoding='utf-8') as f:
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f.write('\n# Auto-migrated from config.json\n')
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for key, value in keys_to_migrate.items():
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f.write(f'{key}={value}\n')
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# Also set in current process
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os.environ[key] = value
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logger.info(f"[AgentBridge] Migrated {len(keys_to_migrate)} API keys from config.json to .env: {list(keys_to_migrate.keys())}")
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except Exception as e:
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logger.warning(f"[AgentBridge] Failed to migrate API keys: {e}")
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def clear_session(self, session_id: str):
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"""
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Clear a specific session's agent and conversation history
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Args:
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session_id: Session identifier to clear
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"""
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if session_id in self.agents:
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logger.info(f"[AgentBridge] Clearing session: {session_id}")
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del self.agents[session_id]
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def clear_all_sessions(self):
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"""Clear all agent sessions"""
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logger.info(f"[AgentBridge] Clearing all sessions ({len(self.agents)} total)")
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self.agents.clear()
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self.default_agent = None
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def refresh_all_skills(self) -> int:
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"""
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Refresh skills in all agent instances after environment variable changes.
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This allows hot-reload of skills without restarting the agent.
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Returns:
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Number of agent instances refreshed
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"""
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import os
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from dotenv import load_dotenv
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from config import conf
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# Reload environment variables from .env file
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workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
|
|
env_file = os.path.join(workspace_root, '.env')
|
|
|
|
if os.path.exists(env_file):
|
|
load_dotenv(env_file, override=True)
|
|
logger.info(f"[AgentBridge] Reloaded environment variables from {env_file}")
|
|
|
|
refreshed_count = 0
|
|
|
|
# Refresh default agent
|
|
if self.default_agent and hasattr(self.default_agent, 'skill_manager'):
|
|
self.default_agent.skill_manager.refresh_skills()
|
|
refreshed_count += 1
|
|
logger.info("[AgentBridge] Refreshed skills in default agent")
|
|
|
|
# Refresh all session agents
|
|
for session_id, agent in self.agents.items():
|
|
if hasattr(agent, 'skill_manager'):
|
|
agent.skill_manager.refresh_skills()
|
|
refreshed_count += 1
|
|
|
|
if refreshed_count > 0:
|
|
logger.info(f"[AgentBridge] Refreshed skills in {refreshed_count} agent instance(s)")
|
|
|
|
return refreshed_count |