git clone https://github.com/HKUDS/AutoAgent.gitcd AutoAgent

pip install -e .

cp .env.example .env# 编辑.env文件配置您的设置

docker-compose up -d

from autoagent import MetaChainfrom autoagent.agents import get_system_triage_agent
# 初始化客户端client = MetaChain()
# 获取智能体agent = get_system_triage_agent("gpt-4")
# 运行智能体response = client.run(    agent=agent,    messages=[{"role": "user", "content": "帮我解决这个问题..."}])

from autoagent.agents.meta_agent import agent_editor
# 通过表单创建新智能体new_agent = agent_editor.create_agent(agent_form_xml)

from autoagent.tools import execute_command
# 执行系统命令result = execute_command("ls -la", context_variables)

from autoagent.workflows import create_workflow
# 创建工作流workflow = create_workflow(workflow_form_xml)

async def run_async(self, agent: Agent, messages: List, context_variables: dict = None, debug: bool = False) -> Response:    """    异步运行智能体        参数:        agent: 要运行的智能体实例        messages: 消息历史记录        context_variables: 上下文变量字典        debug: 是否启用调试模式            返回:        Response对象包含运行结果    """    # 初始化上下文    context_variables = context_variables or {}        # 处理函数调用    if self.fn_call:        messages = self._process_fn_call(agent, messages)        # 调用LLM    response = await self._call_llm(agent, messages)        # 处理工具调用    if response.tool_calls:        return await self._handle_tool_calls(agent, response, context_variables)            return response

def register_tool(tool_func):    """    装饰器函数，用于注册新工具        参数:        tool_func: 要注册的工具函数            返回:        注册后的工具函数    """    tool_name = tool_func.__name__    tool_args = inspect.signature(tool_func).parameters        # 构建工具描述    tool_desc = {        "name": tool_name,        "description": tool_func.__doc__,        "parameters": {            "type": "object",            "properties": {                name: {"type": "string"}                 for name in tool_args            }        }    }        # 注册到全局工具库    registry.tools[tool_name] = tool_func    registry.tools_info[tool_name] = tool_desc        return tool_func

class WorkflowEngine:    def __init__(self):        self.events = {}        self.agents = {}            async def run_workflow(self, workflow_xml: str, input_data: dict) -> dict:        """        运行工作流                参数:            workflow_xml: 工作流定义XML            input_data: 输入数据                    返回:            工作流执行结果        """        # 解析工作流        workflow = self._parse_workflow(workflow_xml)                # 初始化上下文        context = {"input": input_data}                # 执行工作流        for event in workflow["events"]:            result = await self._execute_event(event, context)            context.update(result)                    return context["output"]