JOJO
692748d567
- 子智能体消息渲染为左侧对话气泡 - 多智能体通知细分为进度/完成/提问三类 - 修复多智能体会话存储到 mutiagents 目录并迁移数据 - 修复 token-statistics API 支持多智能体会话 - 修复 ma_debug 未定义报错 - 移除 .message-block content-visibility 修复滚动抖动
451 lines
17 KiB
Python
451 lines
17 KiB
Python
"""多智能体会话状态机。
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一个 MultiAgentState 绑定到一个多智能体对话的 conversation_id，维护：
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- 已创建的子智能体实例（agent_id ↔ role_id ↔ display_name ↔ task_id ↔ status）
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- 待插入到主对话的待发 user 消息队列（pending_master_messages）
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- 主智能体工具调用 answer_sub_agent_question / answer_other_agent 写回答案的 futomap
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- 子智能体调用 ask_master / ask_other_agent 时挂起的 futomap
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关键约定（来自 .astrion/memory/multi_agent_mode_design.md）：
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- 消息格式：`来自 {显示名} 的{类型}\\nid: {消息id}\\n\\n<{显示名}>\\n<{标签}>\\n{内容}\\n</{标签}>\\n</{显示名}>`
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- 接收方决定插入方式：
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- 子智能体 ask 阻塞等待 → main 调 answer_* 返回到工具结果
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- 子智能体 idle 状态 → 主对话的 pending_master_messages 直接插入新轮 user 消息
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- 子智能体 running 中 → inline 插入到当前末尾（在下一轮 model 调用前合并 messages）
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- 通信是「工具调用提问」+「回答返回到工具结果」；其他场景（输出/进度/完成/任务发布/消息/回答）
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才以 user 消息格式插入对话。
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"""
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from __future__ import annotations
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import asyncio
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import json
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import re
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import uuid
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from asyncio import AbstractEventLoop
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from dataclasses import dataclass, field
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional, TYPE_CHECKING
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from modules.multi_agent.debug_logger import ma_debug
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if TYPE_CHECKING:
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from modules.sub_agent.task import SubAgentTask
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# ---------- 消息类型常量 ----------
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TYPE_TASK = "Task" # 主→子 任务发布
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TYPE_OUTPUT = "Output" # 子→主 进度/完成输出（统一）
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TYPE_ASK = "Ask" # 子→主 / 子→子 提问
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TYPE_ANSWER = "Answer" # 主→子 / 子→子 回答（不插入对话，仅做工具结果）
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TYPE_MESSAGE = "Message" # 任意方向 消息
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# 内部枚举到此
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QUESTION_PREFIX_ASK_MASTER = "ask_master"
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QUESTION_PREFIX_ASK_OTHER = "ask_other"
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def format_multi_agent_message(
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*,
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display_name: str,
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msg_type: str,
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content: str,
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msg_id: Optional[str] = None,
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target: Optional[str] = None,
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extra_attrs: Optional[Dict[str, str]] = None,
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msg_type_text: Optional[str] = None,
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subtype: Optional[str] = None,
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) -> str:
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"""按统一格式构造 user 消息字符串。
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Args:
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display_name: 发出方显示名（如 UI Operator_1 / Team Leader）
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msg_type: 消息类型，对应上方 TYPE_* 常量
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content: 消息正文
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msg_id: 消息 id；不传则自动生成
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target: 接收方显示名（用于子→子 提问时标明对谁提问）
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extra_attrs: 额外标签属性（如 question_id="ask_xxx"）
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msg_type_text: 覆盖默认的中文消息类型文案（如"任务结束汇报"）
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subtype: 渲染/分类使用的子类型（如 progress_output / completion_report / ask_master）
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"""
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if not msg_id:
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msg_id = f"msg_{uuid.uuid4().hex[:10]}"
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type_label = msg_type_text or msg_type_to_text(msg_type)
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# 第一行：自然语言前缀（含 target 标识）
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if target:
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prefix = f"来自 {display_name} 向 {target} 的{type_label}"
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else:
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prefix = f"来自 {display_name} 的{type_label}"
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# 第二行：id
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id_line = f"id: {msg_id}"
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# 属性 attr 字符串
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attrs = ""
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if target:
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attrs += f' target="{target}"'
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if subtype:
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attrs += f' subtype="{subtype}"'
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if extra_attrs:
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for k, v in extra_attrs.items():
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attrs += f' {k}="{v}"'
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# XML 包裹
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tag = msg_type
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xml = (
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f"<{display_name}>\n"
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f"<{tag}{attrs}>\n"
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f"{content}\n"
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f"</{tag}>\n"
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f"</{display_name}>"
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)
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return f"{prefix}\n{id_line}\n\n{xml}"
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def msg_type_to_text(msg_type: str) -> str:
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"""把 TYPE_* 转为中文短语，用于 prompt 前缀。"""
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mapping = {
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TYPE_TASK: "任务发布",
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TYPE_OUTPUT: "任务进度输出",
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TYPE_ASK: "提问",
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TYPE_ANSWER: "回答",
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TYPE_MESSAGE: "消息",
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}
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return mapping.get(msg_type, msg_type)
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def build_master_dispatch_text(task: str, msg_id: Optional[str] = None) -> str:
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"""主智能体发布任务时插入到子智能体对话的 user 消息文本。"""
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return format_multi_agent_message(
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display_name="Team Leader",
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msg_type=TYPE_TASK,
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content=task,
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msg_id=msg_id,
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)
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# 子类型常量（用于前端渲染与后端分类）
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SUBTYPE_PROGRESS_OUTPUT = "progress_output"
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SUBTYPE_COMPLETION_REPORT = "completion_report"
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SUBTYPE_ASK_MASTER = "ask_master"
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SUBTYPE_ASK_OTHER = "ask_other"
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def build_sub_agent_output_text(display_name: str, content: str, msg_id: Optional[str] = None, *, is_final: bool = False) -> str:
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"""子智能体输出（进度或完成）插入到主对话的 user 消息文本。"""
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return format_multi_agent_message(
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display_name=display_name,
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msg_type=TYPE_OUTPUT,
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content=content,
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msg_id=msg_id,
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msg_type_text="任务结束汇报" if is_final else "任务进度输出",
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subtype=SUBTYPE_COMPLETION_REPORT if is_final else SUBTYPE_PROGRESS_OUTPUT,
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)
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def build_sub_agent_ask_master_text(display_name: str, question: str, question_id: str) -> str:
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"""子智能体向主智能体提问时插入到主对话的 user 消息文本。"""
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return format_multi_agent_message(
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display_name=display_name,
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msg_type=TYPE_ASK,
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content=question,
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msg_id=question_id,
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subtype=SUBTYPE_ASK_MASTER,
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)
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def build_sub_agent_ask_other_text(
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display_name: str,
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target_display: str,
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question: str,
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question_id: str,
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) -> str:
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"""子智能体向另一个子智能体提问时插入到目标子智能体对话的文本。"""
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return format_multi_agent_message(
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display_name=display_name,
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msg_type=TYPE_ASK,
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content=question,
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msg_id=question_id,
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target=target_display,
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subtype=SUBTYPE_ASK_OTHER,
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)
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_MULTI_AGENT_MESSAGE_RE = re.compile(
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r"^来自\s+(?P<display_name>.+?)\s+的(?P<type_text>.+?)\n"
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r"id:\s*(?P<msg_id>\S+)\n\n"
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r"<(?P=display_name)>\n"
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r"<(?P<tag>\w+)(?P<attrs>[^>]*)>\n"
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r"(?P<content>.*?)\n"
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r"</(?P=tag)>\n"
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r"</(?P=display_name)>$",
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re.DOTALL,
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)
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def parse_multi_agent_message(text: str) -> Optional[Dict[str, str]]:
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"""解析标准多智能体消息格式。
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返回字段：display_name, type_text, msg_id, tag, subtype, content。
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若不是标准格式则返回 None。
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"""
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if not text:
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return None
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m = _MULTI_AGENT_MESSAGE_RE.search(text)
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if not m:
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return None
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attrs = m.group("attrs") or ""
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subtype_match = re.search(r'subtype="([^"]+)"', attrs)
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return {
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"display_name": m.group("display_name").strip(),
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"type_text": m.group("type_text").strip(),
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"msg_id": m.group("msg_id").strip(),
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"tag": m.group("tag").strip(),
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"subtype": subtype_match.group(1) if subtype_match else "",
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"content": m.group("content"),
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}
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def build_master_message_to_sub_agent(message: str, msg_id: Optional[str] = None) -> str:
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"""主智能体 send_message_to_sub_agent 时插入子对话的 user 消息文本。"""
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return format_multi_agent_message(
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display_name="Team Leader",
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msg_type=TYPE_MESSAGE,
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content=message,
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msg_id=msg_id,
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)
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def build_master_answer_to_sub_agent(
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display_name: str,
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target_display: str,
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answer: str,
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question_id: str,
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) -> str:
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"""主智能体回答插入到子对话（仅当子智能体 not waiting 或 idle 时走 user 消息路径）。"""
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return format_multi_agent_message(
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display_name=display_name,
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msg_type=TYPE_ANSWER,
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content=answer,
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msg_id=question_id,
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target=target_display,
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extra_attrs={"question_id": question_id},
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)
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# ---------- 运行态状态机 ----------
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@dataclass
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class AgentInstance:
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"""一个多智能体会话中已创建的子智能体实例。"""
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agent_id: int
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role_id: str
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display_name: str
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task_id: str
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status: str = "running" # running / idle / terminated / failed / timeout
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summary: str = ""
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created_at: float = field(default_factory=lambda: datetime.now().timestamp())
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last_output: str = ""
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def to_dict(self) -> Dict[str, Any]:
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return {
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"agent_id": self.agent_id,
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"role_id": self.role_id,
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"display_name": self.display_name,
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"task_id": self.task_id,
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"status": self.status,
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"summary": self.summary,
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"created_at": self.created_at,
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"last_output": self.last_output,
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}
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class MultiAgentState:
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"""绑到一个 conversation_id 的多智能体运行态。
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线程安全：所有 pubic 方法均假设在 SubAgentManager 的事件循环线程中调用，
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或者由 chat task 主线程通过 manager 的 _run_coro 进入此循环。
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跨线程访问通过 manager._run_coro 桥接，避免直接调用。
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"""
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def __init__(self, conversation_id: str):
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self.conversation_id = conversation_id
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# agent_id 映射；同一会话里 agent_id 唯一
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self.agents: Dict[int, AgentInstance] = {}
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# task_id -> agent_id（便于在 SubAgentTask 完成时回写）
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self.task_id_to_agent_id: Dict[str, int] = {}
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# 主智能体待插入消息队列（每条都是字符串，由 chat task 取走）
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self.pending_master_messages: List[str] = []
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# ask_master / ask_other_agent 的等待 future
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# key = question_id, value = asyncio.Future (结果为 answer str 或 Exception)
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self.pending_questions: Dict[str, asyncio.Future] = {}
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# question_id -> 创建 future 时所在的事件循环，用于跨循环安全 set_result
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self.pending_question_loops: Dict[str, AbstractEventLoop] = {}
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# 回答早于 wait_for_answer 注册时先暂存
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self.pending_answers: Dict[str, str] = {}
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# 一个 agent 可能同时只阻塞在一个 ask 工具上（最简实现）
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# key = agent_id, value = question_id（表示当前 agent 正阻塞等待）
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self.agent_blocking_question: Dict[int, str] = {}
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# 角色实例计数：role_id -> 已分配的最大 agent_id（数字）
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# 用于创建新实例时自动递增编号，但允许调用方显式指定
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self.role_counters: Dict[str, int] = {}
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# ----- 创建/查询 -----
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def next_agent_id_for_role(self, role_id: str) -> int:
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"""为指定角色分配下一个 agent_id 编号。"""
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n = self.role_counters.get(role_id, 0) + 1
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self.role_counters[role_id] = n
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return n
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def register_instance(self, instance: AgentInstance) -> None:
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if instance.agent_id in self.agents:
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raise ValueError(f"agent_id {instance.agent_id} 已存在")
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self.agents[instance.agent_id] = instance
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self.task_id_to_agent_id[instance.task_id] = instance.agent_id
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def get_instance(self, agent_id: int) -> Optional[AgentInstance]:
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return self.agents.get(agent_id)
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def get_instance_by_task_id(self, task_id: str) -> Optional[AgentInstance]:
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aid = self.task_id_to_agent_id.get(task_id)
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if aid is None:
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return None
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return self.agents.get(aid)
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def list_active(self) -> List[AgentInstance]:
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return [a for a in self.agents.values() if a.status == "running" or a.status == "idle"]
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def list_all(self) -> List[AgentInstance]:
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return list(self.agents.values())
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def mark_status(self, agent_id: int, status: str, last_output: str = "") -> None:
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a = self.agents.get(agent_id)
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if a:
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a.status = status
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if last_output:
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a.last_output = last_output
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# ----- 主对话注入 -----
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def push_master_message(self, message_text: str) -> None:
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"""把一条 user 消息追加到主对话待插入队列。"""
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self.pending_master_messages.append(message_text)
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def drain_master_messages(self) -> List[str]:
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"""取出（清空）所有待插入主对话的消息。"""
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msgs = self.pending_master_messages
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self.pending_master_messages = []
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return msgs
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def has_pending_master_messages(self) -> bool:
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return len(self.pending_master_messages) > 0
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# ----- 阻塞问答 -----
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async def wait_for_answer(self, question_id: str, agent_id: int, timeout: float = 600.0) -> str:
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"""子智能体 ask_* 工具调用后阻塞等待答案。
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返回 answer 字符串；超时/取消抛 asyncio.TimeoutError 或 CancelledError。
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"""
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# 如果回答已经提前到达，直接返回
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if question_id in self.pending_answers:
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return self.pending_answers.pop(question_id)
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if question_id in self.pending_questions:
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old_fut = self.pending_questions[question_id]
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try:
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old_loop = old_fut.get_loop()
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if old_loop.is_closed():
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self.pending_questions.pop(question_id, None)
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self.pending_question_loops.pop(question_id, None)
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else:
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raise RuntimeError(f"question_id 已存在: {question_id}")
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except Exception:
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self.pending_questions.pop(question_id, None)
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self.pending_question_loops.pop(question_id, None)
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loop = asyncio.get_running_loop()
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fut: asyncio.Future = loop.create_future()
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self.pending_questions[question_id] = fut
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self.pending_question_loops[question_id] = loop
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self.agent_blocking_question[agent_id] = question_id
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try:
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return await asyncio.wait_for(fut, timeout=timeout)
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finally:
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self.pending_questions.pop(question_id, None)
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self.pending_question_loops.pop(question_id, None)
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if self.agent_blocking_question.get(agent_id) == question_id:
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self.agent_blocking_question.pop(agent_id, None)
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async def _do_provide_answer(self, question_id: str, answer: str) -> bool:
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"""在同 future 所属事件循环内设置结果。"""
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fut = self.pending_questions.get(question_id)
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if not fut or fut.done():
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return False
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try:
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fut.set_result(answer)
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except asyncio.InvalidStateError:
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return False
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return True
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def provide_answer(self, question_id: str, answer: str) -> bool:
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"""主/其他子智能体 answer_* 工具调用时回写答案。
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返回 True 表示找到等待中的 future；False 表示无等待方或已超时。
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支持跨事件循环调用（例如主对话循环回答子智能体循环里的提问）。
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"""
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ma_debug(
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"state_provide_answer",
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question_id=question_id,
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has_pending=question_id in self.pending_questions,
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answer_preview=str(answer)[:300],
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)
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# 如果 wait_for_answer 还没注册，先把答案暂存
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if question_id not in self.pending_questions:
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self.pending_answers[question_id] = answer
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return True
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fut = self.pending_questions.get(question_id)
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if not fut or fut.done():
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self.pending_answers[question_id] = answer
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return False
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loop = self.pending_question_loops.get(question_id)
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if loop is None:
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try:
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loop = fut.get_loop()
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except Exception:
|
||
pass
|
||
if loop is not None:
|
||
try:
|
||
asyncio.run_coroutine_threadsafe(self._do_provide_answer(question_id, answer), loop)
|
||
return True
|
||
except Exception:
|
||
pass
|
||
# 同循环回退（future 所属循环可能已关闭，失败时把答案暂存，避免阻塞方永远等不到）
|
||
try:
|
||
return asyncio.run_coroutine_threadsafe(self._do_provide_answer(question_id, answer), asyncio.get_event_loop()).result(timeout=5)
|
||
except Exception:
|
||
self.pending_questions.pop(question_id, None)
|
||
self.pending_question_loops.pop(question_id, None)
|
||
self.pending_answers[question_id] = answer
|
||
return True
|
||
|
||
def is_agent_blocking(self, agent_id: int) -> bool:
|
||
return agent_id in self.agent_blocking_question
|
||
|
||
def get_blocking_question_id(self, agent_id: int) -> Optional[str]:
|
||
return self.agent_blocking_question.get(agent_id)
|
||
|
||
# ----- 持久化（最简版） -----
|
||
def to_snapshot(self) -> Dict[str, Any]:
|
||
return {
|
||
"conversation_id": self.conversation_id,
|
||
"agents": [a.to_dict() for a in self.agents.values()],
|
||
"role_counters": self.role_counters,
|
||
}
|
||
|
||
@classmethod
|
||
def from_snapshot(cls, snapshot: Dict[str, Any]) -> "MultiAgentState":
|
||
state = cls(conversation_id=snapshot.get("conversation_id", ""))
|
||
state.role_counters = dict(snapshot.get("role_counters") or {})
|
||
for a_data in snapshot.get("agents") or []:
|
||
a = AgentInstance(**a_data)
|
||
state.agents[a.agent_id] = a
|
||
if a.task_id:
|
||
state.task_id_to_agent_id[a.task_id] = a.agent_id
|
||
return state |