AgentCoord/backend/server.py

from flask import Flask, request, jsonify, Response, stream_with_context
from flask_socketio import SocketIO, emit, join_room, leave_room
import json
from DataProcess import Add_Collaboration_Brief_FrontEnd
from AgentCoord.RehearsalEngine_V2.ExecutePlan import executePlan
from AgentCoord.RehearsalEngine_V2.ExecutePlan_Optimized import executePlan_streaming
from AgentCoord.PlanEngine.basePlan_Generator import generate_basePlan
from AgentCoord.PlanEngine.fill_stepTask import fill_stepTask
from AgentCoord.PlanEngine.fill_stepTask_TaskProcess import (
    fill_stepTask_TaskProcess,
)
from AgentCoord.PlanEngine.branch_PlanOutline import branch_PlanOutline
from AgentCoord.PlanEngine.branch_TaskProcess import branch_TaskProcess
from AgentCoord.PlanEngine.AgentSelectModify import (
    AgentSelectModify_init,
    AgentSelectModify_addAspect,
)
import os
import yaml
import argparse
from typing import List, Dict

# initialize global variables
yaml_file = os.path.join(os.getcwd(), "config", "config.yaml")
try:
    with open(yaml_file, "r", encoding="utf-8") as file:
        yaml_data = yaml.safe_load(file)
except Exception:
    yaml_data = {}
USE_CACHE: bool = os.getenv("USE_CACHE")
if USE_CACHE is None:
    USE_CACHE = yaml_data.get("USE_CACHE", False)
else:
    USE_CACHE = USE_CACHE.lower() in ["true", "1", "yes"]
AgentBoard = None
AgentProfile_Dict = {}
Request_Cache: dict[str, str] = {}
app = Flask(__name__)
app.config['SECRET_KEY'] = 'agentcoord-secret-key'
socketio = SocketIO(app, cors_allowed_origins="*", async_mode='threading')
#socketio = SocketIO(app, cors_allowed_origins="*", async_mode='eventlet')


def truncate_rehearsal_log(RehearsalLog: List, restart_from_step_index: int) -> List:
    """
    截断 RehearsalLog，只保留指定索引之前的步骤结果

    Args:
        RehearsalLog: 原始日志列表
        restart_from_step_index: 重新执行的起始步骤索引（例如：1 表示保留步骤0，从步骤1重新执行）

    Returns:
        截断后的 RehearsalLog

    示例:
        restart_from_step_index = 1
        RehearsalLog = [step0, object0, step1, object1, step2, object2]
        返回 = [step0, object0]  # 只保留步骤0的结果
    """
    truncated_log = []
    step_count = 0

    for logNode in RehearsalLog:
        if logNode.get("LogNodeType") == "step":
            # 只保留 restart_from_step_index 之前的步骤
            if step_count < restart_from_step_index:
                truncated_log.append(logNode)
            step_count += 1
        elif logNode.get("LogNodeType") == "object":
            # object 节点：如果对应的 step 在保留范围内，保留它
            # 策略：保留所有在截断点之前的 object
            if step_count <= restart_from_step_index:
                truncated_log.append(logNode)

    return truncated_log


@app.route("/fill_stepTask_TaskProcess", methods=["post"])
def Handle_fill_stepTask_TaskProcess():
    incoming_data = request.get_json()
    requestIdentifier = str(
        (
            "/fill_stepTask_TaskProcess",
            incoming_data["General Goal"],
            incoming_data["stepTask_lackTaskProcess"],
        )
    )

    if USE_CACHE:
        if requestIdentifier in Request_Cache:
            return jsonify(Request_Cache[requestIdentifier])

    filled_stepTask = fill_stepTask_TaskProcess(
        General_Goal=incoming_data["General Goal"],
        stepTask=incoming_data["stepTask_lackTaskProcess"],
        AgentProfile_Dict=AgentProfile_Dict,
    )
    filled_stepTask = Add_Collaboration_Brief_FrontEnd(filled_stepTask)
    Request_Cache[requestIdentifier] = filled_stepTask
    response = jsonify(filled_stepTask)
    return response


@app.route("/agentSelectModify_init", methods=["post"])
def Handle_agentSelectModify_init():
    incoming_data = request.get_json()
    requestIdentifier = str(
        (
            "/agentSelectModify_init",
            incoming_data["General Goal"],
            incoming_data["stepTask"],
        )
    )

    if USE_CACHE:
        if requestIdentifier in Request_Cache:
            return jsonify(Request_Cache[requestIdentifier])

    scoreTable = AgentSelectModify_init(
        stepTask=incoming_data["stepTask"],
        General_Goal=incoming_data["General Goal"],
        Agent_Board=AgentBoard,
    )
    Request_Cache[requestIdentifier] = scoreTable
    response = jsonify(scoreTable)
    return response


@app.route("/agentSelectModify_addAspect", methods=["post"])
def Handle_agentSelectModify_addAspect():
    incoming_data = request.get_json()
    requestIdentifier = str(
        ("/agentSelectModify_addAspect", incoming_data["aspectList"])
    )

    if USE_CACHE:
        if requestIdentifier in Request_Cache:
            return jsonify(Request_Cache[requestIdentifier])

    scoreTable = AgentSelectModify_addAspect(
        aspectList=incoming_data["aspectList"], Agent_Board=AgentBoard
    )
    Request_Cache[requestIdentifier] = scoreTable
    response = jsonify(scoreTable)
    return response


@app.route("/fill_stepTask", methods=["post"])
def Handle_fill_stepTask():
    incoming_data = request.get_json()
    requestIdentifier = str(
        (
            "/fill_stepTask",
            incoming_data["General Goal"],
            incoming_data["stepTask"],
        )
    )

    if USE_CACHE:
        if requestIdentifier in Request_Cache:
            return jsonify(Request_Cache[requestIdentifier])

    filled_stepTask = fill_stepTask(
        General_Goal=incoming_data["General Goal"],
        stepTask=incoming_data["stepTask"],
        Agent_Board=AgentBoard,
        AgentProfile_Dict=AgentProfile_Dict,
    )
    filled_stepTask = Add_Collaboration_Brief_FrontEnd(filled_stepTask)
    Request_Cache[requestIdentifier] = filled_stepTask
    response = jsonify(filled_stepTask)
    return response


@app.route("/branch_PlanOutline", methods=["post"])
def Handle_branch_PlanOutline():
    incoming_data = request.get_json()
    requestIdentifier = str(
        (
            "/branch_PlanOutline",
            incoming_data["branch_Number"],
            incoming_data["Modification_Requirement"],
            incoming_data["Existing_Steps"],
            incoming_data["Baseline_Completion"],
            incoming_data["Initial Input Object"],
            incoming_data["General Goal"],
        )
    )

    if USE_CACHE:
        if requestIdentifier in Request_Cache:
            return jsonify(Request_Cache[requestIdentifier])

    branchList = branch_PlanOutline(
        branch_Number=incoming_data["branch_Number"],
        Modification_Requirement=incoming_data["Modification_Requirement"],
        Existing_Steps=incoming_data["Existing_Steps"],
        Baseline_Completion=incoming_data["Baseline_Completion"],
        InitialObject_List=incoming_data["Initial Input Object"],
        General_Goal=incoming_data["General Goal"],
    )
    branchList = Add_Collaboration_Brief_FrontEnd(branchList)
    Request_Cache[requestIdentifier] = branchList
    response = jsonify(branchList)
    return response


@app.route("/branch_TaskProcess", methods=["post"])
def Handle_branch_TaskProcess():
    incoming_data = request.get_json()
    requestIdentifier = str(
        (
            "/branch_TaskProcess",
            incoming_data["branch_Number"],
            incoming_data["Modification_Requirement"],
            incoming_data["Existing_Steps"],
            incoming_data["Baseline_Completion"],
            incoming_data["stepTaskExisting"],
            incoming_data["General Goal"],
        )
    )

    if USE_CACHE:
        if requestIdentifier in Request_Cache:
            return jsonify(Request_Cache[requestIdentifier])

    branchList = branch_TaskProcess(
        branch_Number=incoming_data["branch_Number"],
        Modification_Requirement=incoming_data["Modification_Requirement"],
        Existing_Steps=incoming_data["Existing_Steps"],
        Baseline_Completion=incoming_data["Baseline_Completion"],
        stepTaskExisting=incoming_data["stepTaskExisting"],
        General_Goal=incoming_data["General Goal"],
        AgentProfile_Dict=AgentProfile_Dict,
    )
    Request_Cache[requestIdentifier] = branchList
    response = jsonify(branchList)
    return response


@app.route("/generate_basePlan", methods=["post"])
def Handle_generate_basePlan():
    incoming_data = request.get_json()
    requestIdentifier = str(
        (
            "/generate_basePlan",
            incoming_data["General Goal"],
            incoming_data["Initial Input Object"],
        )
    )

    if USE_CACHE:
        if requestIdentifier in Request_Cache:
            return jsonify(Request_Cache[requestIdentifier])

    try:
        basePlan = generate_basePlan(
            General_Goal=incoming_data["General Goal"],
            Agent_Board=AgentBoard,
            AgentProfile_Dict=AgentProfile_Dict,
            InitialObject_List=incoming_data["Initial Input Object"],
        )
    except ValueError as e:
        return jsonify({"error": str(e)}), 400
    except Exception as e:
        return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
    basePlan_withRenderSpec = Add_Collaboration_Brief_FrontEnd(basePlan)
    Request_Cache[requestIdentifier] = basePlan_withRenderSpec
    response = jsonify(basePlan_withRenderSpec)
    return response


@app.route("/executePlan", methods=["post"])
def Handle_executePlan():
    incoming_data = request.get_json()
    requestIdentifier = str(
        (
            "/executePlan",
            incoming_data["num_StepToRun"],
            incoming_data["RehearsalLog"],
            incoming_data["plan"],
        )
    )

    if USE_CACHE:
        if requestIdentifier in Request_Cache:
            return jsonify(Request_Cache[requestIdentifier])

    RehearsalLog = executePlan(
        incoming_data["plan"],
        incoming_data["num_StepToRun"],
        incoming_data["RehearsalLog"],
        AgentProfile_Dict,
    )
    Request_Cache[requestIdentifier] = RehearsalLog
    response = jsonify(RehearsalLog)
    return response


@app.route("/executePlanOptimized", methods=["post"])
def Handle_executePlanOptimized():
    """
    优化版流式执行计划（阶段1+2：步骤级流式 + 动作级智能并行）

    返回 SSE 流，每完成一个动作就返回结果
    - 无依赖关系的动作并行执行
    - 有依赖关系的动作串行执行

    支持参数:
        plan: 执行计划
        num_StepToRun: 要运行的步骤数
        RehearsalLog: 已执行的历史记录
        existingKeyObjects: 已存在的KeyObjects（用于重新执行时传递中间结果）

    前端使用 EventSource 接收
    """
    incoming_data = request.get_json()

    def generate():
        try:
            for chunk in executePlan_streaming(
                plan=incoming_data["plan"],
                num_StepToRun=incoming_data.get("num_StepToRun"),
                RehearsalLog=incoming_data.get("RehearsalLog", []),
                AgentProfile_Dict=AgentProfile_Dict,
                existingKeyObjects=incoming_data.get("existingKeyObjects"),
            ):
                yield chunk
        except Exception as e:
            error_event = json.dumps({
                "type": "error",
                "message": str(e)
            }, ensure_ascii=False)
            yield f"data: {error_event}\n\n"

    return Response(
        stream_with_context(generate()),
        mimetype="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "X-Accel-Buffering": "no",
        }
    )


@app.route("/_saveRequestCashe", methods=["post"])
def Handle_saveRequestCashe():
    with open(
        os.path.join(os.getcwd(), "RequestCache", "Request_Cache.json"), "w"
    ) as json_file:
        json.dump(Request_Cache, json_file, indent=4)
    response = jsonify(
        {"code": 200, "content": "request cashe sucessfully saved"}
    )
    return response


@app.route("/setAgents", methods=["POST"])
def set_agents():
    global AgentBoard, AgentProfile_Dict,yaml_data
    AgentBoard = request.json
    AgentProfile_Dict = {}
    for item in AgentBoard:
        name = item["Name"]
        if all(item.get(field) for field in ["apiUrl","apiKey","apiModel"]):
            agent_config = {
                "profile": item["Profile"],
                "apiUrl": item["apiUrl"],
                "apiKey": item["apiKey"],
                "apiModel": item["apiModel"],
                "useCustomAPI":True
            }
        else:
            agent_config = {
                "profile": item["Profile"],
                "apiUrl": yaml_data.get("OPENAI_API_BASE"),
                "apiKey": yaml_data.get("OPENAI_API_KEY"),
                "apiModel": yaml_data.get("OPENAI_API_MODEL"),
                "useCustomAPI":False
            }
        AgentProfile_Dict[name] = agent_config

    return jsonify({"code": 200, "content": "set agentboard successfully"})


def init():
    global AgentBoard, AgentProfile_Dict, Request_Cache

    # Load Request Cache
    try:
        with open(
            os.path.join(os.getcwd(), "RequestCache", "Request_Cache.json"), "r"
        ) as json_file:
            Request_Cache = json.load(json_file)
        print(f"✅ Loaded Request_Cache with {len(Request_Cache)} entries")
    except Exception as e:
        print(f"⚠️  Failed to load Request_Cache: {e}")
        Request_Cache = {}

    # Load Agent Board
    try:
        with open(
            os.path.join(os.getcwd(), "AgentRepo", "agentBoard_v1.json"), "r", encoding="utf-8"
        ) as json_file:
            AgentBoard = json.load(json_file)
        print(f"✅ Loaded AgentBoard with {len(AgentBoard)} agents")

        # Build AgentProfile_Dict
        AgentProfile_Dict = {}
        for item in AgentBoard:
            name = item["Name"]
            profile = item["Profile"]
            AgentProfile_Dict[name] = profile
        print(f"✅ Built AgentProfile_Dict with {len(AgentProfile_Dict)} profiles")

    except Exception as e:
        print(f"⚠️  Failed to load AgentBoard: {e}")
        AgentBoard = []
        AgentProfile_Dict = {}


# ==================== WebSocket 连接管理 ====================
@socketio.on('connect')
def handle_connect():
    """客户端连接"""
    print(f"✅ WebSocket client connected: {request.sid}")
    emit('connected', {'sid': request.sid, 'message': 'WebSocket连接成功'})


@socketio.on('disconnect')
def handle_disconnect():
    """客户端断开连接"""
    print(f"❌ WebSocket client disconnected: {request.sid}")


@socketio.on('ping')
def handle_ping():
    """心跳检测"""
    emit('pong')


# ==================== WebSocket 事件处理 ====================
# 注：以下为WebSocket版本的接口，与REST API并存
# 逐步迁移核心接口到WebSocket


@socketio.on('execute_plan_optimized')
def handle_execute_plan_optimized_ws(data):
    """
    WebSocket版本：优化版流式执行计划
    支持步骤级流式 + 动作级智能并行 + 动态追加步骤 + 从指定步骤重新执行

    请求格式：
    {
        "id": "request-id",
        "action": "execute_plan_optimized",
        "data": {
            "plan": {...},
            "num_StepToRun": null,
            "RehearsalLog": [],
            "enable_dynamic": true,  # 是否启用动态追加步骤
            "restart_from_step_index": 1  # 可选：从指定步骤重新执行（例如1表示从步骤2重新执行）
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        plan = incoming_data.get("plan")
        num_StepToRun = incoming_data.get("num_StepToRun")
        RehearsalLog = incoming_data.get("RehearsalLog", [])
        enable_dynamic = incoming_data.get("enable_dynamic", False)
        restart_from_step_index = incoming_data.get("restart_from_step_index")  # 新增：支持从指定步骤重新执行

        # 如果指定了重新执行起始步骤，截断 RehearsalLog
        if restart_from_step_index is not None:
            print(f"🔄 从步骤 {restart_from_step_index + 1} 重新执行，正在截断 RehearsalLog...")
            RehearsalLog = truncate_rehearsal_log(RehearsalLog, restart_from_step_index)
            print(f"✅ RehearsalLog 已截断，保留 {sum(1 for node in RehearsalLog if node.get('LogNodeType') == 'step')} 个步骤的结果")

        # 如果前端传入了execution_id，使用前端的；否则生成新的
        execution_id = incoming_data.get("execution_id")
        if not execution_id:
            import time
            execution_id = f"{plan.get('General Goal', '').replace(' ', '_')}_{int(time.time() * 1000)}"

        if enable_dynamic:
            # 动态模式：使用executePlan_streaming_dynamic
            from AgentCoord.RehearsalEngine_V2.ExecutePlan_Optimized import executePlan_streaming_dynamic

            # 发送执行ID（确认使用的ID）
            emit('progress', {
                'id': request_id,
                'status': 'execution_started',
                'execution_id': execution_id,
                'message': '执行已启动，支持动态追加步骤'
            })

            for chunk in executePlan_streaming_dynamic(
                plan=plan,
                num_StepToRun=num_StepToRun,
                RehearsalLog=RehearsalLog,
                AgentProfile_Dict=AgentProfile_Dict,
                execution_id=execution_id
            ):
                emit('progress', {
                    'id': request_id,
                    'status': 'streaming',
                    'data': chunk.replace('data: ', '').replace('\n\n', '')
                })

            # 发送完成信号
            emit('progress', {
                'id': request_id,
                'status': 'complete',
                'data': None
            })

        else:
            # 非动态模式：使用原有方式
            for chunk in executePlan_streaming(
                plan=plan,
                num_StepToRun=num_StepToRun,
                RehearsalLog=RehearsalLog,
                AgentProfile_Dict=AgentProfile_Dict,
            ):
                emit('progress', {
                    'id': request_id,
                    'status': 'streaming',
                    'data': chunk.replace('data: ', '').replace('\n\n', '')
                })

            # 发送完成信号
            emit('progress', {
                'id': request_id,
                'status': 'complete',
                'data': None
            })

    except Exception as e:
        # 发送错误信息
        emit('progress', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('add_steps_to_execution')
def handle_add_steps_to_execution(data):
    """
    WebSocket版本：向正在执行的任务追加新步骤

    请求格式：
    {
        "id": "request-id",
        "action": "add_steps_to_execution",
        "data": {
            "execution_id": "execution_id",
            "new_steps": [...]
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        from AgentCoord.RehearsalEngine_V2.dynamic_execution_manager import dynamic_execution_manager

        execution_id = incoming_data.get('execution_id')
        new_steps = incoming_data.get('new_steps', [])

        if not execution_id:
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': '缺少execution_id参数'
            })
            return

        # 追加新步骤到执行队列
        added_count = dynamic_execution_manager.add_steps(execution_id, new_steps)

        if added_count > 0:
            print(f"✅ 成功追加 {added_count} 个步骤到执行队列: {execution_id}")
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {
                    'message': f'成功追加 {added_count} 个步骤',
                    'added_count': added_count
                }
            })
        else:
            print(f"⚠️  无法追加步骤，执行ID不存在或已结束: {execution_id}")
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': '执行ID不存在或已结束'
            })

    except Exception as e:
        print(f"❌ 追加步骤失败: {str(e)}")
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('generate_base_plan')
def handle_generate_base_plan_ws(data):
    """
    WebSocket版本：生成基础计划（支持流式/分步返回）

    请求格式：
    {
        "id": "request-id",
        "action": "generate_base_plan",
        "data": {
            "General Goal": "...",
            "Initial Input Object": [...]
        }
    }

    流式事件：
    - progress: {"id": request_id, "status": "streaming", "stage": "generating_outline", "message": "正在生成计划大纲..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "processing_steps", "step": 1, "total": 3, "message": "正在处理步骤 1/3..."}
    - response: {"id": request_id, "status": "success", "data": basePlan}
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        # 检查缓存
        requestIdentifier = str((
            "/generate_basePlan",
            incoming_data.get("General Goal"),
            incoming_data.get("Initial Input Object"),
        ))

        if USE_CACHE and requestIdentifier in Request_Cache:
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': Request_Cache[requestIdentifier]
            })
            return

        # 阶段1：生成计划大纲
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'generating_outline',
            'message': '📋 正在生成计划大纲...'
        })

        from AgentCoord.PlanEngine.planOutline_Generator import generate_PlanOutline
        PlanOutline = generate_PlanOutline(
            InitialObject_List=incoming_data.get("Initial Input Object"),
            General_Goal=incoming_data.get("General Goal")
        )

        # 阶段2：构建基础计划（逐步添加步骤）
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'building_plan',
            'total_steps': len(PlanOutline),
            'message': f'🔨 正在构建计划，共 {len(PlanOutline)} 个步骤...'
        })

        basePlan = {
            "General Goal": incoming_data.get("General Goal"),
            "Initial Input Object": incoming_data.get("Initial Input Object"),
            "Collaboration Process": []
        }

        for idx, stepItem in enumerate(PlanOutline, 1):
            # 添加智能体选择和任务流程字段
            stepItem["AgentSelection"] = []
            stepItem["TaskProcess"] = []
            stepItem["Collaboration_Brief_frontEnd"] = {
                "template": "",
                "data": {}
            }
            basePlan["Collaboration Process"].append(stepItem)

            # 发送进度更新
            emit('progress', {
                'id': request_id,
                'status': 'streaming',
                'stage': 'adding_step',
                'step': idx,
                'total': len(PlanOutline),
                'step_name': stepItem.get("StepName", ""),
                'message': f'✅ 已添加步骤 {idx}/{len(PlanOutline)}: {stepItem.get("StepName", "")}'
            })

        # 阶段3：处理渲染规范
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'rendering',
            'message': '🎨 正在处理渲染规范...'
        })

        basePlan_withRenderSpec = Add_Collaboration_Brief_FrontEnd(basePlan)

        # 缓存结果
        if USE_CACHE:
            Request_Cache[requestIdentifier] = basePlan_withRenderSpec

        # 发送完成信号
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'complete',
            'message': '✅ 计划生成完成'
        })

        # 返回最终结果
        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': basePlan_withRenderSpec
        })

    except ValueError as e:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })
    except Exception as e:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': f"An unexpected error occurred: {str(e)}"
        })


@socketio.on('fill_step_task')
def handle_fill_step_task_ws(data):
    """
    WebSocket版本：填充步骤任务（支持流式/分步返回）

    流式事件：
    - progress: {"id": request_id, "status": "streaming", "stage": "starting", "message": "开始填充步骤任务..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "agent_selection", "message": "正在生成智能体选择..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "task_process", "message": "正在生成任务流程..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "complete", "message": "任务填充完成"}
    - response: {"id": request_id, "status": "success", "data": filled_stepTask}
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    print(f"📥 [WS] 收到 fill_step_task 请求: {request_id}")

    try:
        # 检查缓存
        requestIdentifier = str((
            "/fill_stepTask",
            incoming_data.get("General Goal"),
            incoming_data.get("stepTask"),
        ))

        if USE_CACHE and requestIdentifier in Request_Cache:
            print(f"✅ [WS] 使用缓存返回: {request_id}")
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': Request_Cache[requestIdentifier]
            })
            return

        # 开始处理
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'starting',
            'message': f'🚀 开始填充步骤任务: {incoming_data.get("stepTask", {}).get("StepName", "")}'
        })

        print(f"⏳ [WS] 开始处理 fill_step_task...")

        # 阶段1：生成智能体选择
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'agent_selection',
            'message': '👥 正在生成智能体选择...'
        })

        from AgentCoord.PlanEngine.AgentSelection_Generator import generate_AgentSelection
        stepTask = incoming_data.get("stepTask")
        Current_Task = {
            "TaskName": stepTask.get("StepName"),
            "InputObject_List": stepTask.get("InputObject_List"),
            "OutputObject": stepTask.get("OutputObject"),
            "TaskContent": stepTask.get("TaskContent"),
        }
        AgentSelection = generate_AgentSelection(
            General_Goal=incoming_data.get("General Goal"),
            Current_Task=Current_Task,
            Agent_Board=AgentBoard,
        )

        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'agent_selection_done',
            'message': f'✅ 智能体选择完成: {", ".join(AgentSelection)}'
        })

        # 阶段2：生成任务流程
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'task_process',
            'message': '📝 正在生成任务流程...'
        })

        import AgentCoord.util as util
        from AgentCoord.PlanEngine.taskProcess_Generator import generate_TaskProcess
        Current_Task_Description = {
            "TaskName": stepTask.get("StepName"),
            "AgentInvolved": [
                {"Name": name, "Profile": AgentProfile_Dict[name]}
                for name in AgentSelection
            ],
            "InputObject_List": stepTask.get("InputObject_List"),
            "OutputObject": stepTask.get("OutputObject"),
            "CurrentTaskDescription": util.generate_template_sentence_for_CollaborationBrief(
                stepTask.get("InputObject_List"),
                stepTask.get("OutputObject"),
                AgentSelection,
                stepTask.get("TaskContent"),
            ),
        }
        TaskProcess = generate_TaskProcess(
            General_Goal=incoming_data.get("General Goal"),
            Current_Task_Description=Current_Task_Description,
        )

        # 构建结果
        stepTask["AgentSelection"] = AgentSelection
        stepTask["TaskProcess"] = TaskProcess

        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'task_process_done',
            'message': f'✅ 任务流程生成完成，共 {len(TaskProcess)} 个动作'
        })

        # 阶段3：处理渲染规范
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'rendering',
            'message': '🎨 正在处理渲染规范...'
        })

        filled_stepTask = Add_Collaboration_Brief_FrontEnd(stepTask)

        # 缓存结果
        if USE_CACHE:
            Request_Cache[requestIdentifier] = filled_stepTask

        # 发送完成信号
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'complete',
            'message': '✅ 任务填充完成'
        })

        # 返回结果
        print(f"✅ [WS] fill_step_task 处理完成: {request_id}")
        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': filled_stepTask
        })

    except Exception as e:
        print(f"❌ [WS] fill_step_task 处理失败: {request_id}, 错误: {str(e)}")
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('fill_step_task_process')
def handle_fill_step_task_process_ws(data):
    """
    WebSocket版本：填充步骤任务流程（支持流式/分步返回）

    流式事件：
    - progress: {"id": request_id, "status": "streaming", "stage": "starting", "message": "开始生成任务流程..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "generating", "message": "正在生成任务流程..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "complete", "message": "任务流程生成完成"}
    - response: {"id": request_id, "status": "success", "data": filled_stepTask}
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        # 检查缓存
        requestIdentifier = str((
            "/fill_stepTask_TaskProcess",
            incoming_data.get("General Goal"),
            incoming_data.get("stepTask_lackTaskProcess"),
        ))

        if USE_CACHE and requestIdentifier in Request_Cache:
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': Request_Cache[requestIdentifier]
            })
            return

        # 开始处理
        stepTask = incoming_data.get("stepTask_lackTaskProcess")
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'starting',
            'message': f'🚀 开始生成任务流程: {stepTask.get("StepName", "")}'
        })

        # 生成任务流程
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'generating',
            'message': '📝 正在生成任务流程...'
        })

        filled_stepTask = fill_stepTask_TaskProcess(
            General_Goal=incoming_data.get("General Goal"),
            stepTask=stepTask,
            AgentProfile_Dict=AgentProfile_Dict,
        )

        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'generated',
            'message': f'✅ 任务流程生成完成，共 {len(filled_stepTask.get("TaskProcess", []))} 个动作'
        })

        # 处理渲染规范
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'rendering',
            'message': '🎨 正在处理渲染规范...'
        })

        filled_stepTask = Add_Collaboration_Brief_FrontEnd(filled_stepTask)

        # 缓存结果
        if USE_CACHE:
            Request_Cache[requestIdentifier] = filled_stepTask

        # 发送完成信号
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'complete',
            'message': '✅ 任务流程生成完成'
        })

        # 返回结果
        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': filled_stepTask
        })

    except Exception as e:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('branch_plan_outline')
def handle_branch_plan_outline_ws(data):
    """
    WebSocket版本：分支任务大纲（支持流式/分步返回）

    流式事件：
    - progress: {"id": request_id, "status": "streaming", "stage": "starting", "branch": 1, "total": 3, "message": "正在生成分支 1/3..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "complete", "message": "分支大纲生成完成"}
    - response: {"id": request_id, "status": "success", "data": branchList}
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        # 检查缓存
        requestIdentifier = str((
            "/branch_PlanOutline",
            incoming_data.get("branch_Number"),
            incoming_data.get("Modification_Requirement"),
            incoming_data.get("Existing_Steps"),
            incoming_data.get("Baseline_Completion"),
            incoming_data.get("Initial Input Object"),
            incoming_data.get("General Goal"),
        ))

        if USE_CACHE and requestIdentifier in Request_Cache:
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': Request_Cache[requestIdentifier]
            })
            return

        # 开始处理
        branch_Number = incoming_data.get("branch_Number")
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'starting',
            'total_branches': branch_Number,
            'message': f'🚀 开始生成分支大纲，共 {branch_Number} 个分支...'
        })

        # 生成大纲分支（逐步生成）
        from AgentCoord.util.converter import read_LLM_Completion
        from AgentCoord.PlanEngine.branch_PlanOutline import JSON_PLAN_OUTLINE_BRANCHING
        import json

        prompt = f"""
## Instruction
Based on "Existing Steps", your task is to comeplete the "Remaining Steps" for the plan for "General Goal".
Note: "Modification Requirement" specifies how to modify the "Baseline Completion" for a better/alternative solution.

**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all content, including StepName, TaskContent, and OutputObject fields.**

## General Goal (Specify the general goal for the plan)
{incoming_data.get("General Goal")}

## Initial Key Object List (Specify the list of initial key objects available for use as the input object of a Step)
{incoming_data.get("Initial Input Object")}

## Existing Steps
{json.dumps(incoming_data.get("Existing_Steps"), indent=4)}

## Baseline Completion
{json.dumps(incoming_data.get("Baseline_Completion"), indent=4)}

## Modification Requirement
{incoming_data.get("Modification_Requirement")}
"""

        branch_List = []
        for i in range(branch_Number):
            # 发送进度更新
            emit('progress', {
                'id': request_id,
                'status': 'streaming',
                'stage': 'generating_branch',
                'branch': i + 1,
                'total': branch_Number,
                'message': f'🌿 正在生成分支大纲 {i+1}/{branch_Number}...'
            })

            messages = [
                {
                    "role": "system",
                    "content": f" The JSON object must use the schema: {json.dumps(JSON_PLAN_OUTLINE_BRANCHING.model_json_schema(), indent=2)}",
                },
                {"role": "system", "content": prompt},
            ]
            Remaining_Steps = read_LLM_Completion(messages, useGroq=False)["Remaining Steps"]
            branch_List.append(Remaining_Steps)

            emit('progress', {
                'id': request_id,
                'status': 'streaming',
                'stage': 'branch_done',
                'branch': i + 1,
                'total': branch_Number,
                'steps_count': len(Remaining_Steps),
                'message': f'✅ 分支 {i+1}/{branch_Number} 生成完成，包含 {len(Remaining_Steps)} 个步骤'
            })

        # 处理渲染规范
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'rendering',
            'message': '🎨 正在处理渲染规范...'
        })

        branchList = Add_Collaboration_Brief_FrontEnd(branch_List)

        # 缓存结果
        if USE_CACHE:
            Request_Cache[requestIdentifier] = branchList

        # 发送完成信号
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'complete',
            'message': f'✅ 分支大纲生成完成，共 {branch_Number} 个分支'
        })

        # 返回结果
        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': branchList
        })

    except Exception as e:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('branch_task_process')
def handle_branch_task_process_ws(data):
    """
    WebSocket版本：分支任务流程（支持流式/分步返回）

    流式事件：
    - progress: {"id": request_id, "status": "streaming", "stage": "starting", "branch": 1, "total": 3, "message": "正在生成分支任务流程 1/3..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "complete", "message": "分支任务流程生成完成"}
    - response: {"id": request_id, "status": "success", "data": branchList}
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        # 检查缓存
        requestIdentifier = str((
            "/branch_TaskProcess",
            incoming_data.get("branch_Number"),
            incoming_data.get("Modification_Requirement"),
            incoming_data.get("Existing_Steps"),
            incoming_data.get("Baseline_Completion"),
            incoming_data.get("stepTaskExisting"),
            incoming_data.get("General Goal"),
        ))

        if USE_CACHE and requestIdentifier in Request_Cache:
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': Request_Cache[requestIdentifier]
            })
            return

        # 开始处理
        branch_Number = incoming_data.get("branch_Number")
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'starting',
            'total_branches': branch_Number,
            'message': f'🚀 开始生成分支任务流程，共 {branch_Number} 个分支...'
        })

        # 生成任务流程分支（逐步生成）
        from AgentCoord.util.converter import read_LLM_Completion
        from AgentCoord.PlanEngine.branch_TaskProcess import (
            JSON_TASK_PROCESS_BRANCHING,
            ACT_SET,
            PROMPT_TASK_PROCESS_BRANCHING
        )
        import AgentCoord.util as util
        import json

        stepTaskExisting = incoming_data.get("stepTaskExisting")
        Current_Task_Description = {
            "TaskName": stepTaskExisting.get("StepName"),
            "AgentInvolved": [
                {"Name": name, "Profile": AgentProfile_Dict[name]}
                for name in stepTaskExisting.get("AgentSelection", [])
            ],
            "InputObject_List": stepTaskExisting.get("InputObject_List"),
            "OutputObject": stepTaskExisting.get("OutputObject"),
            "CurrentTaskDescription": util.generate_template_sentence_for_CollaborationBrief(
                stepTaskExisting.get("InputObject_List"),
                stepTaskExisting.get("OutputObject"),
                stepTaskExisting.get("AgentSelection"),
                stepTaskExisting.get("TaskContent"),
            ),
        }

        prompt = PROMPT_TASK_PROCESS_BRANCHING.format(
            Modification_Requirement=incoming_data.get("Modification_Requirement"),
            Current_Task_Description=json.dumps(Current_Task_Description, indent=4),
            Existing_Steps=json.dumps(incoming_data.get("Existing_Steps"), indent=4),
            Baseline_Completion=json.dumps(incoming_data.get("Baseline_Completion"), indent=4),
            General_Goal=incoming_data.get("General Goal"),
            Act_Set=ACT_SET,
        )

        branch_List = []
        for i in range(branch_Number):
            # 发送进度更新
            emit('progress', {
                'id': request_id,
                'status': 'streaming',
                'stage': 'generating_branch',
                'branch': i + 1,
                'total': branch_Number,
                'message': f'🌿 正在生成分支任务流程 {i+1}/{branch_Number}...'
            })

            messages = [
                {
                    "role": "system",
                    "content": f" The JSON object must use the schema: {json.dumps(JSON_TASK_PROCESS_BRANCHING.model_json_schema(), indent=2)}",
                },
                {"role": "system", "content": prompt},
            ]
            Remaining_Steps = read_LLM_Completion(messages, useGroq=False)["Remaining Steps"]
            branch_List.append(Remaining_Steps)

            emit('progress', {
                'id': request_id,
                'status': 'streaming',
                'stage': 'branch_done',
                'branch': i + 1,
                'total': branch_Number,
                'actions_count': len(Remaining_Steps),
                'message': f'✅ 分支 {i+1}/{branch_Number} 生成完成，包含 {len(Remaining_Steps)} 个动作'
            })

        # 缓存结果
        if USE_CACHE:
            Request_Cache[requestIdentifier] = branch_List

        # 发送完成信号
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'complete',
            'message': f'✅ 分支任务流程生成完成，共 {branch_Number} 个分支'
        })

        # 返回结果
        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': branch_List
        })

    except Exception as e:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('agent_select_modify_init')
def handle_agent_select_modify_init_ws(data):
    """
    WebSocket版本：智能体选择评分初始化（支持流式/分步返回）

    流式事件：
    - progress: {"id": request_id, "status": "streaming", "stage": "starting", "message": "开始生成能力需求..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "requirements", "message": "能力需求: [xxx, yyy, zzz]"}
    - progress: {"id": request_id, "status": "streaming", "stage": "scoring", "aspect": 1, "total": 3, "message": "正在评分能力 1/3..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "complete", "message": "智能体评分完成"}
    - response: {"id": request_id, "status": "success", "data": scoreTable}
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        # 检查缓存
        requestIdentifier = str((
            "/agentSelectModify_init",
            incoming_data.get("General Goal"),
            incoming_data.get("stepTask"),
        ))

        if USE_CACHE and requestIdentifier in Request_Cache:
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': Request_Cache[requestIdentifier]
            })
            return

        # 开始处理
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'starting',
            'message': '🚀 开始生成智能体能力需求...'
        })

        from AgentCoord.util.converter import read_LLM_Completion
        from AgentCoord.PlanEngine.AgentSelectModify import (
            JSON_ABILITY_REQUIREMENT_GENERATION,
            PROMPT_ABILITY_REQUIREMENT_GENERATION,
            agentAbilityScoring
        )
        import json

        # 阶段1：生成能力需求列表
        stepTask = incoming_data.get("stepTask")
        Current_Task = {
            "TaskName": stepTask.get("StepName"),
            "InputObject_List": stepTask.get("InputObject_List"),
            "OutputObject": stepTask.get("OutputObject"),
            "TaskContent": stepTask.get("TaskContent"),
        }

        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'generating_requirements',
            'message': '📋 正在生成能力需求列表...'
        })

        messages = [
            {
                "role": "system",
                "content": f" The JSON object must use the schema: {json.dumps(JSON_ABILITY_REQUIREMENT_GENERATION.model_json_schema(), indent=2)}",
            },
            {
                "role": "system",
                "content": PROMPT_ABILITY_REQUIREMENT_GENERATION.format(
                    General_Goal=incoming_data.get("General Goal"),
                    Current_Task=json.dumps(Current_Task, indent=4),
                ),
            },
        ]
        Ability_Requirement_List = read_LLM_Completion(messages)["AbilityRequirement"]

        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'requirements_generated',
            'requirements': Ability_Requirement_List,
            'message': f'✅ 能力需求生成完成: {", ".join(Ability_Requirement_List)}'
        })

        # 阶段2：为每个能力需求进行智能体评分
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'scoring',
            'total_aspects': len(Ability_Requirement_List),
            'message': f'📊 开始为 {len(Ability_Requirement_List)} 个能力需求评分...'
        })

        scoreTable = agentAbilityScoring(AgentBoard, Ability_Requirement_List)

        # 逐步报告评分进度
        for idx, (ability, scores) in enumerate(scoreTable.items(), 1):
            emit('progress', {
                'id': request_id,
                'status': 'streaming',
                'stage': 'aspect_scored',
                'aspect': idx,
                'total': len(Ability_Requirement_List),
                'ability': ability,
                'message': f'✅ 能力 "{ability}" 评分完成 ({idx}/{len(Ability_Requirement_List)})'
            })

        # 缓存结果
        if USE_CACHE:
            Request_Cache[requestIdentifier] = scoreTable

        # 发送完成信号
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'complete',
            'message': f'✅ 智能体评分完成，共 {len(Ability_Requirement_List)} 个能力维度'
        })

        # 返回结果
        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': scoreTable
        })

    except Exception as e:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('agent_select_modify_add_aspect')
def handle_agent_select_modify_add_aspect_ws(data):
    """
    WebSocket版本：添加新的评估维度（支持流式/分步返回）

    流式事件：
    - progress: {"id": request_id, "status": "streaming", "stage": "starting", "aspect": "新能力", "message": "开始为新能力评分..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "scoring", "message": "正在评分..."}
    - progress: {"id": request_id, "status": "streaming", "stage": "complete", "message": "评分完成"}
    - response: {"id": request_id, "status": "success", "data": scoreTable}
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        # 检查缓存
        aspectList = incoming_data.get("aspectList")
        newAspect = aspectList[-1] if aspectList else None
        requestIdentifier = str((
            "/agentSelectModify_addAspect",
            aspectList,
        ))

        if USE_CACHE and requestIdentifier in Request_Cache:
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': Request_Cache[requestIdentifier]
            })
            return

        # 开始处理
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'starting',
            'message': f'🚀 开始为新能力维度评分: {newAspect or "Unknown"}'
        })

        # 添加新维度并评分
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'scoring',
            'aspect': newAspect,
            'message': f'📊 正在为能力 "{newAspect}" 评分...'
        })

        scoreTable = AgentSelectModify_addAspect(
            aspectList=aspectList,
            Agent_Board=AgentBoard
        )

        # 发送完成信号
        emit('progress', {
            'id': request_id,
            'status': 'streaming',
            'stage': 'complete',
            'message': f'✅ 能力 "{newAspect}" 评分完成'
        })

        # 缓存结果
        if USE_CACHE:
            Request_Cache[requestIdentifier] = scoreTable

        # 返回结果
        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': scoreTable
        })

    except Exception as e:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('set_agents')
def handle_set_agents_ws(data):
    """
    WebSocket版本：设置智能体
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    global AgentBoard, AgentProfile_Dict, yaml_data

    try:
        AgentBoard = incoming_data
        AgentProfile_Dict = {}

        for item in AgentBoard:
            name = item["Name"]
            if all(item.get(field) for field in ["apiUrl", "apiKey", "apiModel"]):
                agent_config = {
                    "profile": item["Profile"],
                    "apiUrl": item["apiUrl"],
                    "apiKey": item["apiKey"],
                    "apiModel": item["apiModel"],
                    "useCustomAPI": True
                }
            else:
                agent_config = {
                    "profile": item["Profile"],
                    "apiUrl": yaml_data.get("OPENAI_API_BASE"),
                    "apiKey": yaml_data.get("OPENAI_API_KEY"),
                    "apiModel": yaml_data.get("OPENAI_API_MODEL"),
                    "useCustomAPI": False
                }
            AgentProfile_Dict[name] = agent_config

        # 返回结果
        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': {"code": 200, "content": "set agentboard successfully"}
        })

    except Exception as e:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('stop_generation')
def handle_stop_generation(data):
    """
    WebSocket版本：停止生成任务

    请求格式：
    {
        "id": "request-id",
        "action": "stop_generation",
        "data": {
            "goal": "任务描述"
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        goal = incoming_data.get('goal', '')

        # TODO: 这里可以添加实际的停止逻辑
        # 例如：设置全局停止标志，通知所有正在运行的生成任务停止
        print(f"🛑 收到停止生成请求: goal={goal}")

        # 返回成功响应
        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': {"message": "已发送停止信号"}
        })

    except Exception as e:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('pause_execution')
def handle_pause_execution(data):
    """
    WebSocket版本：暂停任务执行

    请求格式：
    {
        "id": "request-id",
        "action": "pause_execution",
        "data": {
            "goal": "任务描述"
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        from AgentCoord.RehearsalEngine_V2.execution_state import execution_state_manager

        goal = incoming_data.get('goal', '')

        # 检查当前执行的任务是否匹配
        current_goal = execution_state_manager.get_goal()
        if current_goal and current_goal != goal:
            print(f"⚠️  任务目标不匹配: 当前={current_goal}, 请求={goal}")
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': '任务目标不匹配'
            })
            return

        # 调用执行状态管理器暂停
        success = execution_state_manager.pause_execution()

        if success:
            print(f"⏸️  [DEBUG] 暂停成功! 当前状态: {execution_state_manager.get_status().value}")
            print(f"⏸️  [DEBUG] should_pause: {execution_state_manager._should_pause}")
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {"message": "已暂停执行，可随时继续"}
            })
        else:
            print(f"⚠️  [DEBUG] 暂停失败，当前状态: {execution_state_manager.get_status().value}")
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': f'无法暂停，当前状态: {execution_state_manager.get_status().value}'
            })

    except Exception as e:
        print(f"❌ 暂停执行失败: {str(e)}")
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('resume_execution')
def handle_resume_execution(data):
    """
    WebSocket版本：恢复任务执行

    请求格式：
    {
        "id": "request-id",
        "action": "resume_execution",
        "data": {
            "goal": "任务描述"
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        from AgentCoord.RehearsalEngine_V2.execution_state import execution_state_manager

        goal = incoming_data.get('goal', '')

        # 检查当前执行的任务是否匹配
        current_goal = execution_state_manager.get_goal()
        if current_goal and current_goal != goal:
            print(f"⚠️  任务目标不匹配: 当前={current_goal}, 请求={goal}")
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': '任务目标不匹配'
            })
            return

        # 调用执行状态管理器恢复
        success = execution_state_manager.resume_execution()

        if success:
            print(f"▶️  已恢复执行: goal={goal}")
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {"message": "已恢复执行"}
            })
        else:
            print(f"⚠️  恢复失败，当前状态: {execution_state_manager.get_status()}")
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': f'无法恢复，当前状态: {execution_state_manager.get_status().value}'
            })

    except Exception as e:
        print(f"❌ 恢复执行失败: {str(e)}")
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


@socketio.on('stop_execution')
def handle_stop_execution(data):
    """
    WebSocket版本：停止任务执行

    请求格式：
    {
        "id": "request-id",
        "action": "stop_execution",
        "data": {
            "goal": "任务描述"
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        from AgentCoord.RehearsalEngine_V2.execution_state import execution_state_manager

        goal = incoming_data.get('goal', '')

        # 检查当前执行的任务是否匹配
        current_goal = execution_state_manager.get_goal()
        if current_goal and current_goal != goal:
            print(f"⚠️  任务目标不匹配: 当前={current_goal}, 请求={goal}")
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': '任务目标不匹配'
            })
            return

        # 调用执行状态管理器停止
        success = execution_state_manager.stop_execution()

        if success:
            print(f"🛑 [DEBUG] 停止成功! 当前状态: {execution_state_manager.get_status().value}")
            print(f"🛑 [DEBUG] should_stop: {execution_state_manager._should_stop}")
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {"message": "已停止执行"}
            })
        else:
            print(f"⚠️  [DEBUG] 停止失败，当前状态: {execution_state_manager.get_status().value}")
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': f'无法停止，当前状态: {execution_state_manager.get_status().value}'
            })

    except Exception as e:
        print(f"❌ 停止执行失败: {str(e)}")
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="start the backend for AgentCoord"
    )
    parser.add_argument(
        "--port",
        type=int,
        default=8000,
        help="set the port number, 8000 by default.",
    )
    args = parser.parse_args()
    init()
    # 使用 socketio.run 替代 app.run，支持WebSocket
    socketio.run(app, host="0.0.0.0", port=args.port, debug=True, allow_unsafe_werkzeug=True)
    #socketio.run(app, host="0.0.0.0", port=args.port, debug=False, allow_unsafe_werkzeug=True)