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https://github.com/zhayujie/chatgpt-on-wechat.git
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283 lines
11 KiB
Python
283 lines
11 KiB
Python
import os
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import yaml
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from typing import Dict, List, Optional
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from agentmesh import AgentTeam, Agent, LLMModel
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from agentmesh.models import ClaudeModel
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from agentmesh.tools import ToolManager
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from config import conf
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import plugins
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from plugins import Plugin, Event, EventContext, EventAction
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from bridge.context import ContextType
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from bridge.reply import Reply, ReplyType
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from common.log import logger
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@plugins.register(
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name="agent",
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desc="Use AgentMesh framework to process tasks with multi-agent teams",
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version="0.1.0",
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author="Saboteur7",
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desire_priority=1,
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)
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class AgentPlugin(Plugin):
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"""Plugin for integrating AgentMesh framework."""
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def __init__(self):
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super().__init__()
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self.handlers[Event.ON_HANDLE_CONTEXT] = self.on_handle_context
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self.name = "agent"
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self.description = "Use AgentMesh framework to process tasks with multi-agent teams"
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self.config = self._load_config()
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self.tool_manager = ToolManager()
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self.tool_manager.load_tools(config_dict=self.config.get("tools"))
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logger.info("[agent] inited")
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def _load_config(self) -> Dict:
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"""Load configuration from config.yaml file."""
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config_path = os.path.join(self.path, "config.yaml")
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if not os.path.exists(config_path):
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logger.warning(f"Config file not found at {config_path}")
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return {}
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with open(config_path, 'r', encoding='utf-8') as f:
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return yaml.safe_load(f)
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def get_help_text(self, verbose=False, **kwargs):
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"""Return help message for the agent plugin."""
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help_text = "通过AgentMesh实现对终端、浏览器、文件系统、搜索引擎等工具的执行，并支持多智能体协作。"
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trigger_prefix = conf().get("plugin_trigger_prefix", "$")
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if not verbose:
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return help_text
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teams = self.get_available_teams()
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teams_str = ", ".join(teams) if teams else "未配置任何团队"
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help_text += "\n\n使用说明：\n"
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help_text += f"{trigger_prefix}agent [task] - 使用默认团队执行任务\n"
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help_text += f"{trigger_prefix}agent teams - 列出可用的团队\n"
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help_text += f"{trigger_prefix}agent use [team_name] [task] - 使用特定团队执行任务\n\n"
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help_text += f"可用团队: \n{teams_str}\n\n"
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help_text += f"示例:\n"
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help_text += f"{trigger_prefix}agent 帮我查看当前文件夹路径\n"
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help_text += f"{trigger_prefix}agent use software_team 帮我写一个产品预约体验的表单页面"
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return help_text
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def get_available_teams(self) -> List[str]:
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"""Get list of available teams from configuration."""
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teams_config = self.config.get("teams", {})
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return list(teams_config.keys())
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def create_team_from_config(self, team_name: str) -> Optional[AgentTeam]:
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"""Create a team from configuration."""
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# Get teams configuration
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teams_config = self.config.get("teams", {})
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# Check if the specified team exists
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if team_name not in teams_config:
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logger.error(f"Team '{team_name}' not found in configuration.")
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available_teams = list(teams_config.keys())
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logger.info(f"Available teams: {', '.join(available_teams)}")
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return None
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# Get team configuration
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team_config = teams_config[team_name]
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# Get team's model
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team_model_name = team_config.get("model", "gpt-4.1-mini")
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team_model = self.create_llm_model(team_model_name)
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# Get team's max_steps (default to 20 if not specified)
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team_max_steps = team_config.get("max_steps", 20)
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# Create team with the model
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team = AgentTeam(
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name=team_name,
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description=team_config.get("description", ""),
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rule=team_config.get("rule", ""),
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model=team_model,
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max_steps=team_max_steps
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)
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# Create and add agents to the team
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agents_config = team_config.get("agents", [])
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for agent_config in agents_config:
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# Check if agent has a specific model
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if agent_config.get("model"):
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agent_model = self.create_llm_model(agent_config.get("model"))
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else:
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agent_model = team_model
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# Get agent's max_steps
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agent_max_steps = agent_config.get("max_steps")
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agent = Agent(
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name=agent_config.get("name", ""),
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system_prompt=agent_config.get("system_prompt", ""),
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model=agent_model, # Use agent's model if specified, otherwise will use team's model
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description=agent_config.get("description", ""),
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max_steps=agent_max_steps
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)
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# Add tools to the agent if specified
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tool_names = agent_config.get("tools", [])
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for tool_name in tool_names:
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tool = self.tool_manager.create_tool(tool_name)
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if tool:
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agent.add_tool(tool)
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else:
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if tool_name == "browser":
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logger.warning(
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"Tool 'Browser' loaded failed, "
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"please install the required dependency with: \n"
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"'pip install browser-use>=0.1.40' or 'pip install agentmesh-sdk[full]'\n"
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)
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else:
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logger.warning(f"Tool '{tool_name}' not found for agent '{agent.name}'\n")
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# Add agent to team
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team.add(agent)
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return team
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def on_handle_context(self, e_context: EventContext):
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"""Handle the message context."""
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if e_context['context'].type != ContextType.TEXT:
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return
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content = e_context['context'].content
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trigger_prefix = conf().get("plugin_trigger_prefix", "$")
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if not content.startswith(f"{trigger_prefix}agent "):
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e_context.action = EventAction.CONTINUE
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return
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if not self.config:
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reply = Reply()
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reply.type = ReplyType.ERROR
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reply.content = "未找到插件配置，请在 plugins/agent 目录下创建 config.yaml 配置文件，可根据 config-template.yml 模板文件复制"
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e_context['reply'] = reply
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e_context.action = EventAction.BREAK_PASS
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return
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# Extract the actual task
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task = content[len(f"{trigger_prefix}agent "):].strip()
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# If task is empty, return help message
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if not task:
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reply = Reply()
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reply.type = ReplyType.TEXT
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reply.content = self.get_help_text(verbose=True)
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e_context['reply'] = reply
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e_context.action = EventAction.BREAK_PASS
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return
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# Check if task is asking for available teams
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if task.lower() in ["teams", "list teams", "show teams"]:
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teams = self.get_available_teams()
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reply = Reply()
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reply.type = ReplyType.TEXT
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if not teams:
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reply.content = "未配置任何团队。请检查 config.yaml 文件。"
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else:
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reply.content = f"可用团队: {', '.join(teams)}"
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e_context['reply'] = reply
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e_context.action = EventAction.BREAK_PASS
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return
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# Check if task specifies a team
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team_name = None
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if task.startswith("use "):
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parts = task[4:].split(" ", 1)
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if len(parts) > 0:
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team_name = parts[0]
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if len(parts) > 1:
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task = parts[1].strip()
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else:
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reply = Reply()
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reply.type = ReplyType.TEXT
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reply.content = f"已选择团队 '{team_name}'。请输入您想执行的任务。"
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e_context['reply'] = reply
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e_context.action = EventAction.BREAK_PASS
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return
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if not team_name:
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team_name = self.config.get("team")
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# If no team specified, use default or first available
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if not team_name:
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teams = self.configself.get_available_teams()
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if not teams:
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reply = Reply()
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reply.type = ReplyType.TEXT
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reply.content = "未配置任何团队。请检查 config.yaml 文件。"
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e_context['reply'] = reply
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e_context.action = EventAction.BREAK_PASS
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return
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team_name = teams[0]
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# Create team
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team = self.create_team_from_config(team_name)
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if not team:
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reply = Reply()
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reply.type = ReplyType.TEXT
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reply.content = f"创建团队 '{team_name}' 失败。请检查配置。"
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e_context['reply'] = reply
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e_context.action = EventAction.BREAK_PASS
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return
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# Run the task
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try:
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logger.info(f"[agent] Running task '{task}' with team '{team_name}', team_model={team.model.model}")
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result = team.run_async(task=task)
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for agent_result in result:
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res_text = f"🤖 {agent_result.get('agent_name')}\n\n{agent_result.get('final_answer')}"
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_send_text(e_context, content=res_text)
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reply = Reply()
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reply.type = ReplyType.TEXT
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reply.content = ""
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e_context['reply'] = reply
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e_context.action = EventAction.BREAK_PASS
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except Exception as e:
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logger.exception(f"Error running task with team '{team_name}'")
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reply = Reply()
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reply.type = ReplyType.ERROR
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reply.content = f"执行任务时出错: {str(e)}"
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e_context['reply'] = reply
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e_context.action = EventAction.BREAK_PASS
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return
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def create_llm_model(self, model_name) -> LLMModel:
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if conf().get("use_linkai"):
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api_base = "https://api.link-ai.tech/v1"
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api_key = conf().get("linkai_api_key")
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elif model_name.startswith(("gpt", "text-davinci", "o1", "o3")):
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api_base = conf().get("open_ai_api_base") or "https://api.openai.com/v1"
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api_key = conf().get("open_ai_api_key")
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elif model_name.startswith("claude"):
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return ClaudeModel(model=model_name, api_key=conf().get("claude_api_key"))
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elif model_name.startswith("moonshot"):
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api_base = "https://api.moonshot.cn/v1"
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api_key = conf().get("moonshot_api_key")
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elif model_name.startswith("qwen"):
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api_base = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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api_key = conf().get("dashscope_api_key")
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else:
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api_base = conf().get("open_ai_api_base") or "https://api.openai.com/v1"
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api_key = conf().get("open_ai_api_key")
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llm_model = LLMModel(model=model_name, api_key=api_key, api_base=api_base)
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return llm_model
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def _send_text(e_context: EventContext, content: str):
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reply = Reply(ReplyType.TEXT, content)
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channel = e_context["channel"]
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channel.send(reply, e_context["context"])
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