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* ♻️ refactor(ai): 重构 AI 服务模块并完善文档注释 - 【重构】统一清理并优化所有 AI 服务模块文件的导入语句,将其移至文件顶部 - 【重构】重构 `hooks.py` 中的 `Hooks` 派发逻辑,使用通用管道函数消除重复代码,并引入 `HookPoint` 描述符 - 【重构】重构工具装饰器实现,新增 `toolkit` 类装饰器,优化 `BaseToolkit` 配置合并与前缀处理 - 【功能】Docker 沙箱容器创建时支持自动注入系统代理环境变量并配置 `ExtraHosts` - 【功能】Jupyter 服务启动前自动清理旧进程并初始化临时目录权限 - 【修复】优化 Pydantic 结构化输出校验失败时的错误信息提取,提供更详细的字段级错误反馈 - 【修复】在 `api.py` 中避免将 `ModelRetry` 和 `ControlFlowExit` 异常错误地包装为 `LLMException` - 【文档】为 AI 服务、沙箱、工具链、工作流等核心模块补充完整的 Docstring 和类型注释 * 📝 docs(ai): 补全核心模块文档注释并清理冗余代码 - 补全 `run/context`、`run/hooks` 和 `tools/engine/registry` 中类与方法的中文文档注释 - 清理 `tools/providers/builtin/sandbox` 中未使用的 `PythonPluginProtocol` 协议及相关导入 - 规范化部分代码的格式与尾随逗号 * ♻️ refactor!(flow): 重构 Task 为 AgentTask 并优化工作流元数据定义 - 【Breaking Change】将 `Task` 重命名为 `AgentTask` 以避免命名冲突 - 更新 Agent、Team、Workflow 等模块中的类型声明与相关逻辑 - 引入 `AutoNodeMeta` 强类型元数据,替换工作流装饰器中的裸字典定义 - 将 `StepMeta`、`ConditionMeta` 和 `RouterMeta` 统一移动至 `types.py` - 优化 `RunnableNode` 对上游 `AgentTask` 的处理与拼接逻辑 - 调整团队协作策略中 `FinishAction` 的返回值为完整结果对象 * ♻️ refactor(workflow): 移除人工确认机制并重构错误策略 - 移除工作流节点的人工确认(HITL)与挂起继续机制 - 删除 `auto` 自动化工作流及相关装饰器文件 - 将错误处理策略类从 `types.py` 拆分并移动到新文件 `policies.py` - 优化节点执行失败时的异常信息格式化输出 - 移除 `WorkflowRunResult` 和 `StepOutput` 中与挂起相关的状态字段 * 🚨 auto fix by pre-commit hooks --------- Co-authored-by: webjoin111 <455457521@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
239 lines
8.9 KiB
Python
239 lines
8.9 KiB
Python
import asyncio
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from collections.abc import AsyncGenerator
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from typing import Any
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from zhenxun.services.ai.core.exceptions import (
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AbortException,
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ControlFlowExit,
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LLMException,
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)
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from zhenxun.services.ai.core.messages import UsageInfo
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from zhenxun.services.ai.flow.team.capabilities import TeamRoutingCapability
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from zhenxun.services.ai.flow.team.models import (
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CallAction,
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ConcurrentCallAction,
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FinishAction,
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)
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from zhenxun.services.ai.flow.team.strategy import BaseTeamStrategy
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from zhenxun.services.ai.run import AgentRunResult, RunContext
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from zhenxun.services.ai.run.models import AgentRunEnd
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from zhenxun.services.log import logger
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from zhenxun.utils.pydantic_compat import model_construct
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class TeamRunner:
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"""
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多智能体团队核心执行引擎。
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"""
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def __init__(self, team: Any, strategy: BaseTeamStrategy):
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self.team = team
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self.strategy = strategy
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async def _execute_call_action_to_queue(
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self,
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index: int,
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action: CallAction,
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context: RunContext,
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session_id: str,
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queue: asyncio.Queue,
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):
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"""
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辅助方法:执行单一 Agent 任务,
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并将内部产生的 UI 事件与最终结果通过队列透传回主线程
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"""
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if isinstance(action.agent, str):
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target_agent = next(
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(m for m in self.team.members if m.name == action.agent), None
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)
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if not target_agent:
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logger.error(f"❌ [TeamRunner] 找不到团队成员: {action.agent}")
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await queue.put(
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(
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"result",
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(
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action.agent,
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AgentRunResult(
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output=f"Error: {action.agent} not found",
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usage=UsageInfo(),
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),
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),
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)
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)
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return
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else:
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target_agent = action.agent
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sub_context = context.clone_for_member(target_agent.name)
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sub_context.capabilities = list(sub_context.capabilities)
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from zhenxun.services.ai.flow.team.strategy import RouteStrategy
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if isinstance(self.strategy, RouteStrategy):
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routing_cap = TeamRoutingCapability(
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team_name=self.team.name,
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members=self.team.members,
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state_flow=getattr(self.strategy, "state_flow", None),
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)
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sub_context.capabilities.append(routing_cap)
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logger.debug(f"🚀 **专员 👨💼`{target_agent.name}`** 开始执行子任务...")
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agent_res = None
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try:
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from zhenxun.services.ai.flow.agent.models import AgentConfig
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async with target_agent.run_stream(
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prompt=action.task,
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context=sub_context,
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config=AgentConfig(message_history=action.history),
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**(action.kwargs or {}),
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) as stream_result:
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async for event in stream_result.stream_events():
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if isinstance(event, AgentRunEnd):
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agent_res = event.result
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else:
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await queue.put(("yield_event", event))
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except ControlFlowExit as cfe:
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if isinstance(cfe, AbortException):
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await queue.put(("control_flow_error", cfe))
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return
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else:
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logger.debug(
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f"Agent {target_agent.name} 触发局部控制流: "
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f"{type(cfe).__name__} - {cfe}"
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)
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agent_res = AgentRunResult(output=str(cfe), usage=UsageInfo())
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except Exception as e:
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logger.error(f"Agent {target_agent.name} 执行崩溃: {e}")
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if isinstance(e, LLMException):
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abort_msg = getattr(e, "user_friendly_message", str(e))
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display_msg = (
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f"❌ 智能体 {target_agent.name} 执行发生致命故障: {abort_msg}"
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)
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abort_err = AbortException(
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reason=str(e),
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display=display_msg,
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)
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await queue.put(("control_flow_error", abort_err))
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return
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agent_res = AgentRunResult(output=f"Error: {e}", usage=UsageInfo())
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if agent_res and agent_res.handoff:
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target_name = agent_res.handoff.target
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reason = agent_res.handoff.reason
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logger.info(
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f"🛣️ **路由决策**: 委派给专员 👨💼`{target_name}` (理由: {reason})"
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)
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if not agent_res:
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agent_res = AgentRunResult(
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output="Error: No result returned", usage=UsageInfo()
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)
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logger.debug(f"✅ **专员 👨💼`{target_agent.name}`** 完成任务!")
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await queue.put(("result", index, target_agent.name, agent_res))
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async def run_stream(
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self, prompt: Any, context: RunContext, **kwargs: Any
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) -> AsyncGenerator[Any, None]:
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session_id = context.session_id or "default_team_session"
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task_desc = getattr(prompt, "description", str(prompt))
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logger.info(f"🤝 **团队 [{self.team.name}] 开始协作**: `{task_desc}`")
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plan_gen = self.strategy.generate_plan(self.team, prompt, context, **kwargs)
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send_value = None
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final_result = None
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cumulative_usage = UsageInfo()
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try:
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while True:
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try:
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action = await plan_gen.asend(send_value)
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except StopAsyncIteration:
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break
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if isinstance(action, CallAction):
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queue = asyncio.Queue()
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task = asyncio.create_task(
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self._execute_call_action_to_queue(
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0, action, context, session_id, queue
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)
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)
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try:
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while True:
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msg_type, *payload = await queue.get()
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if msg_type == "yield_event":
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yield payload[0]
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elif msg_type == "control_flow_error":
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raise payload[0]
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elif msg_type == "result":
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idx, agent_name, agent_res = payload
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send_value = agent_res
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cumulative_usage += agent_res.usage
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break
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finally:
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if not task.done():
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task.cancel()
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elif isinstance(action, ConcurrentCallAction):
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queue = asyncio.Queue()
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tasks = []
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for i, act in enumerate(action.actions):
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tasks.append(
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asyncio.create_task(
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self._execute_call_action_to_queue(
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i, act, context, session_id, queue
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)
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)
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)
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results_dict = {}
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try:
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while len(results_dict) < len(action.actions):
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msg_type, *payload = await queue.get()
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if msg_type == "yield_event":
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yield payload[0]
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elif msg_type == "control_flow_error":
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for t in tasks:
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t.cancel()
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raise payload[0]
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elif msg_type == "result":
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idx, agent_name, agent_res = payload
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results_dict[idx] = (agent_name, agent_res)
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cumulative_usage += agent_res.usage
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send_value = [
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results_dict[i] for i in range(len(action.actions))
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]
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finally:
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for task in tasks:
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if not task.done():
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task.cancel()
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elif isinstance(action, FinishAction):
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final_result = action.result
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break
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else:
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raise ValueError(f"TeamRunner 遇到了未知的动作类型: {type(action)}")
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except Exception as e:
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raise e
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logger.info(f"🏁 **团队 [{self.team.name}]** 协作圆满结束!")
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if not isinstance(final_result, AgentRunResult):
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final_result = model_construct(
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AgentRunResult, output=final_result, usage=cumulative_usage
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)
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else:
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final_result.usage += cumulative_usage
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yield AgentRunEnd(result=final_result)
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