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* ✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架 - 【重构】将原 services/llm 重构并迁移至全新的 services/ai 架构,提供向下兼容垫片 - 【新增】引入 Agent、Team、Workflow 三大智能体与工作流编排范式 - 【新增】引入基于 RAG 的长期向量记忆与中期槽位记忆系统 - 【新增】引入基于 Docker 的安全代码执行沙箱环境 - 【新增】支持 MCP 协议,允许动态管理和调用 MCP 服务 - 【新增】引入输入输出安全合规护栏与自愈反思机制 - 【优化】重构并优化多厂商 API 适配器 (Gemini, OpenAI, DeepSeek, GLM 等) - 【优化】优化日志脱敏与 Token 预估机制 - 【移除】移除旧版 llm default 和 llm reset-key 命令,新增 llm mcp 管理命令 * 🔧 chore(deps): 更新项目依赖与配置 - 添加 mcp、jieba 和 aiodocker 依赖到配置文件及 requirements.txt - 在 pyright 配置中设置 reportMissingImports 为 none - 调整 .gitignore 中 resources 目录的忽略规则 * ♻️ refactor(tools): 重构工具终止机制并清理知识库日志输出 - 统一使用 `context.state["__end_run__"]` 替代 `EndRunResult` 控制任务结束 - 移除文件系统和向量知识库检索工具中 `ToolResult` 的 `.with_log` 调用 - 调整指令处理器(Directive)的返回值为 `tool_res.output` - 修复部分类型检查警告并优化联合类型判断语法 * ♻️ refactor(tools): 重构工具副作用指令与控制流熔断机制 - 引入 `DirectivePayload` 及 `ToolResult` 的子类以结构化表达工具副作用 - 移除通过 `context.state` 传递魔术变量的隐式控制流设计 - 重构 `DirectiveManager` 处理器接口,直接在处理器中修改 `AgentState` 并构建 `AgentRunResult` - 在 `StandardAgentExecutor` 中统一通过 `directive_manager` 调度工具返回的副作用指令 - 补全 `MessageBuilder` 中部分核心方法的文档注释 * 🐛 fix(sandbox): 修复 Docker 沙箱容器状态检测与会话清理逻辑 -【修复】修正 `is_alive` 中直接读取私有属性的问题,改用 `show()` 返回值 -【修复】解决 `execute_code` 中缓存的执行器与当前会话不一致的问题 -【优化】在清理工作区前增加容器存活检测,避免向已死容器发送请求 -【优化】创建容器时增加运行状态校验,若已停止则自动从缓存中移除并重建 -【优化】优化容器销毁和清理逻辑,静默处理容器不存在 (404) 的异常 * 📝 docs(core): 补充核心模块初始化方法的文档注释 * 🚨 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>
236 lines
8.8 KiB
Python
236 lines
8.8 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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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 = AgentRunResult(output=final_result, usage=cumulative_usage)
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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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