from flask import Flask, request, jsonify, Response, stream_with_context, send_file
from flask_cors import CORS
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
import uuid
import copy
from typing import List, Dict, Optional

# 数据库模块导入
from db import (
    get_db_context,
    MultiAgentTaskCRUD,
    UserAgentCRUD,
    ExportRecordCRUD,
    TaskStatus,
    text,
)

# 导出模块导入
from AgentCoord.Export import ExportFactory

# 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'
CORS(app)  # 启用 CORS 支持
socketio = SocketIO(app, cors_allowed_origins="*", async_mode='threading')

# 配置静态文件服务（用于导出文件访问）
EXPORT_DIR = os.path.join(os.getcwd(), "uploads", "exports")

@app.route('/uploads/<path:filename>', methods=['GET'])
def serve_export_file(filename):
    """服务导出文件（静态文件访问）"""
    from flask import send_from_directory
    return send_from_directory('uploads', filename)

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


def convert_score_table_to_front_format(scoreTable: dict) -> dict:
    """
    将后端 scoreTable 格式转换为前端期望的格式

    后端格式: {aspect: {agent: {Score, Reason}}}
    前端格式: {aspectList, agentScores: {agent: {aspect: {score, reason}}}}

    Args:
        scoreTable: 后端评分表

    Returns:
        转换后的数据，包含 aspectList 和 agentScores
    """
    aspect_list = list(scoreTable.keys())
    agent_scores = {}

    for aspect, agents_data in scoreTable.items():
        for agent_name, score_data in agents_data.items():
            if agent_name not in agent_scores:
                agent_scores[agent_name] = {}
            agent_scores[agent_name][aspect] = {
                "score": score_data.get("Score", score_data.get("score", 0)),
                "reason": score_data.get("Reason", score_data.get("reason", ""))
            }

    return {
        "aspectList": aspect_list,
        "agentScores": agent_scores
    }


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)
    except Exception as 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)

        # Build AgentProfile_Dict
        AgentProfile_Dict = {}
        for item in AgentBoard:
            name = item["Name"]
            profile = item["Profile"]
            AgentProfile_Dict[name] = profile

    except Exception as e:
        AgentBoard = []
        AgentProfile_Dict = {}


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


@socketio.on('disconnect')
def handle_disconnect():
    """客户端断开连接"""
    pass


@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
    执行完成后保存结果到数据库

    请求格式：
    {
        "id": "request-id",
        "action": "execute_plan_optimized",
        "data": {
            "task_id": "task-id",  # 可选：如果需要保存到数据库
            "plan": {...},
            "num_StepToRun": null,
            "RehearsalLog": [],
            "enable_dynamic": true,
            "restart_from_step_index": 1
        }
    }
    """
    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")
        task_id = incoming_data.get("task_id")

        # 执行开始前更新状态为 EXECUTING
        with get_db_context() as db:
            MultiAgentTaskCRUD.update_status(db, task_id, TaskStatus.EXECUTING)

        print(f"[WS] 开始执行计划: goal={plan.get('General Goal', '')}, dynamic={enable_dynamic}")

        # 收集每个步骤使用的 agent（用于写入 assigned_agents 字段）
        assigned_agents_collection = {}

        def collect_assigned_agents_from_chunk(chunk: str):
            """从 chunk 中提取 assigned_agents 信息"""
            try:
                import json
                event_str = chunk.replace('data: ', '').replace('\n\n', '').strip()
                if not event_str:
                    return
                event = json.loads(event_str)
                if event.get('type') == 'step_complete':
                    step_assigned_agents = event.get('assigned_agents', {})
                    if step_assigned_agents:
                        assigned_agents_collection.update(step_assigned_agents)
            except Exception as e:
                pass

        # 如果指定了重新执行起始步骤，截断 RehearsalLog
        if restart_from_step_index is not None:
            RehearsalLog = truncate_rehearsal_log(RehearsalLog, restart_from_step_index)

        # 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
            ):
                # 收集 assigned_agents
                collect_assigned_agents_from_chunk(chunk)
                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,
                execution_id=execution_id
            ):
                # 收集 assigned_agents
                collect_assigned_agents_from_chunk(chunk)
                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
            })

        print(f"[WS] 执行计划完成: execution_id={execution_id}")

        # 执行完成后保存到数据库
        if task_id:
            try:
                with get_db_context() as db:
                    # 计算已完成的步骤数
                    completed_steps_count = len([node for node in RehearsalLog if node.get("LogNodeType") == "step"])
                    # 获取计划总步骤数
                    plan_steps_count = len(plan.get("Collaboration Process", [])) if plan else 0

                    # 更新执行ID（始终保存）
                    MultiAgentTaskCRUD.update_execution_id(db, task_id, execution_id)

                    # 更新执行次数（始终保存）
                    MultiAgentTaskCRUD.increment_execution_count(db, task_id)

                    # 判断是否完整执行：已完成步骤数 >= 计划步骤数
                    is_complete_execution = completed_steps_count >= plan_steps_count and plan_steps_count > 0

                    if is_complete_execution:
                        # 完整执行：保存所有执行数据
                        MultiAgentTaskCRUD.update_rehearsal_log(db, task_id, RehearsalLog)
                        print(f"[execute_plan_optimized] 完整执行完成，已保存 RehearsalLog（{completed_steps_count} 个步骤），task_id={task_id}")

                        # 保存执行结果（覆盖模式）
                        step_results = [node for node in RehearsalLog if node.get("LogNodeType") == "step"]
                        MultiAgentTaskCRUD.update_result(db, task_id, step_results)
                        print(f"[execute_plan_optimized] 完整执行完成，已保存 result（{len(step_results)} 个步骤结果），task_id={task_id}")

                        # 更新状态为完成
                        MultiAgentTaskCRUD.update_status(db, task_id, TaskStatus.COMPLETED)
                    else:
                        # 未完整执行（用户停止）：不保存执行数据，只更新状态为 STOPPED
                        MultiAgentTaskCRUD.update_status(db, task_id, TaskStatus.STOPPED)
                        print(f"[execute_plan_optimized] 用户停止执行，跳过保存执行数据，已完成 {completed_steps_count}/{plan_steps_count} 步骤，task_id={task_id}")

                    # # 任务大纲（用户可能编辑了）仍然保存
                    # # 注释原因：执行任务时不保存 task_outline，避免覆盖导致步骤 ID 变化与 agent_scores 不匹配
                    # if plan:
                    #     MultiAgentTaskCRUD.update_task_outline(db, task_id, plan)
                    #     print(f"[execute_plan_optimized] 已保存 task_outline 到数据库，task_id={task_id}")

                    # # 保存 assigned_agents（每个步骤使用的 agent）
                    # # 注释原因：assigned_agents 只在生成阶段由用户手动选择写入，执行时不覆盖
                    # if assigned_agents_collection:
                    #     MultiAgentTaskCRUD.update_assigned_agents(db, task_id, assigned_agents_collection)
                    #     print(f"[execute_plan_optimized] 已保存 assigned_agents 到数据库，task_id={task_id}")
            except Exception:
                import traceback
                traceback.print_exc()

    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"[WS] 成功追加 {added_count} 个步骤: execution_id={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:
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': '执行ID不存在或已结束'
            })

    except Exception as 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

        # 保存到数据库
        user_id = incoming_data.get("user_id", "default_user")
        task_id = incoming_data.get("task_id") or str(uuid.uuid4())
        generation_id = str(uuid.uuid4())

        with get_db_context() as db:
            # 检查是否已存在任务
            existing_task = MultiAgentTaskCRUD.get_by_id(db, task_id)

            if existing_task:
                # 更新现有任务
                MultiAgentTaskCRUD.update_task_outline(db, task_id, basePlan_withRenderSpec)
                MultiAgentTaskCRUD.update_generation_id(db, task_id, generation_id)
                MultiAgentTaskCRUD.update_status(db, task_id, TaskStatus.GENERATING)
            else:
                # 创建新任务
                MultiAgentTaskCRUD.create(
                    db=db,
                    task_id=task_id,  # 使用已有的 task_id，不生成新的
                    user_id=user_id,
                    query=incoming_data.get("General Goal", ""),
                    agents_info=AgentBoard or [],
                    task_outline=basePlan_withRenderSpec,
                    assigned_agents=None,
                    agent_scores=None,
                    result=None,
                )
                MultiAgentTaskCRUD.update_generation_id(db, task_id, generation_id)
                MultiAgentTaskCRUD.update_status(db, task_id, TaskStatus.GENERATING)

        # 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': {
                "task_id": task_id,
                "generation_id": generation_id,
                "basePlan": 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', {})
    task_id = incoming_data.get("task_id")

    try:
        print(f"[WS] 开始处理 fill_step_task: request_id={request_id}, task_id={task_id}")

        # 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:
            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", "")}'
        })

        # 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"),
        }
        # 调整字段顺序：确保 Name 在 Icon 前面，避免 LLM 把 Icon 当名字用
        agent_board_for_llm = []
        for agent in AgentBoard:
            # 按固定顺序重组：Name, Profile, Icon, Classification
            new_agent = {}
            if 'Name' in agent:
                new_agent['Name'] = agent['Name']
            if 'Profile' in agent:
                new_agent['Profile'] = agent['Profile']
            if 'Icon' in agent:
                new_agent['Icon'] = agent['Icon']
            if 'Classification' in agent:
                new_agent['Classification'] = agent['Classification']
            # 保留其他字段
            for k, v in agent.items():
                if k not in new_agent:
                    new_agent[k] = v
            agent_board_for_llm.append(new_agent)
        AgentSelection = generate_AgentSelection(
            General_Goal=incoming_data.get("General Goal"),
            Current_Task=Current_Task,
            Agent_Board=agent_board_for_llm,
        )

        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

        # 保存到数据库 - 更新任务大纲和 assigned_agents
        task_id = incoming_data.get("task_id")
        if task_id:
            with get_db_context() as db:
                # 获取现有任务，更新步骤
                existing_task = MultiAgentTaskCRUD.get_by_id(db, task_id)
                if existing_task and existing_task.task_outline:
                    task_outline = existing_task.task_outline.copy()
                    collaboration_process = task_outline.get("Collaboration Process", [])

                    # 获取原始请求中的步骤ID
                    step_id = stepTask.get("Id") or stepTask.get("id")
                    # 更新对应步骤 - 使用 StepName 匹配
                    step_name = stepTask.get("StepName")
                    for i, step in enumerate(collaboration_process):
                        if step.get("StepName") == step_name:
                            collaboration_process[i] = filled_stepTask
                            # 如果原始步骤没有ID，从更新后的步骤获取
                            if not step_id:
                                step_id = filled_stepTask.get("Id") or filled_stepTask.get("id")
                            break

                    task_outline["Collaboration Process"] = collaboration_process

                    # 直接用SQL更新，绕过ORM事务问题
                    import json
                    db.execute(
                        text("UPDATE multi_agent_tasks SET task_outline = :outline WHERE task_id = :id"),
                        {"outline": json.dumps(task_outline), "id": task_id}
                    )
                    db.commit()

        # 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
        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': {
                "task_id": task_id,
                "filled_stepTask": filled_stepTask
            }
        })

    except Exception as e:
        print(f"[WS] fill_step_task 处理失败: {request_id}, error={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

        # 🆕 保存 TaskProcess 数据到 assigned_agents
        task_id = incoming_data.get("task_id")
        agents = incoming_data.get("agents", [])
        if task_id and agents:
            with get_db_context() as db:
                # 获取步骤ID
                step_id = stepTask.get("Id") or stepTask.get("id")
                if step_id:
                    # 获取现有 assigned_agents，确保是 dict 类型
                    task = MultiAgentTaskCRUD.get_by_id(db, task_id)
                    raw_assigned = task.assigned_agents
                    existing_assigned = raw_assigned if isinstance(raw_assigned, dict) else {}

                    # 确保步骤数据结构存在
                    if step_id not in existing_assigned:
                        existing_assigned[step_id] = {}
                    if "agent_combinations" not in existing_assigned[step_id]:
                        existing_assigned[step_id]["agent_combinations"] = {}

                    # 生成 agentGroupKey（排序后的JSON）
                    agent_group_key = json.dumps(sorted(agents))

                    # 保存 TaskProcess 和 brief 数据
                    existing_assigned[step_id]["agent_combinations"][agent_group_key] = {
                        "process": filled_stepTask.get("TaskProcess", []),
                        "brief": filled_stepTask.get("Collaboration_Brief_frontEnd", {})
                    }

                    # 更新数据库
                    db.execute(
                        text("UPDATE multi_agent_tasks SET assigned_agents = :assigned WHERE task_id = :id"),
                        {"assigned": json.dumps(existing_assigned), "id": task_id}
                    )
                    db.commit()
                    print(f"[fill_step_task_process] 已保存 agent_combinations: task_id={task_id}, step_id={step_id}, agents={agents}")

        # 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

        # 获取步骤ID（用于 agent_scores 的 key）
        stepTask = incoming_data.get("stepTask", {})
        step_id = stepTask.get("Id") or stepTask.get("id")

        # 注意：assigned_agents 不在这里写入
        # AgentSelection 只有在 fill_step_task 完成后才会有值
        # assigned_agents 会在 fill_step_task 接口中写入

        # 保存到数据库（只保存 agent_scores）
        task_id = incoming_data.get("task_id")
        if task_id and step_id:
            with get_db_context() as db:
                # 转换为前端期望格式
                front_format = convert_score_table_to_front_format(scoreTable)
                # 按步骤ID包装评分数据
                step_scores = {step_id: front_format}
                # 只更新智能体评分（跳过 assigned_agents，因为此时 AgentSelection 为空）
                MultiAgentTaskCRUD.update_agent_scores(db, task_id, step_scores)
                print(f"[agent_select_modify_init] 已保存 agent_scores: step_id={step_id}")

        # 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
        # 注意：assigned_agents 不在此处返回，因为此时 AgentSelection 为空
        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': {
                "task_id": task_id,
                "scoreTable": 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
        )

        # 保存到数据库
        task_id = incoming_data.get("task_id")
        if task_id:
            with get_db_context() as db:
                # 获取步骤ID
                stepTask = incoming_data.get("stepTask", {})
                step_id = stepTask.get("Id") or stepTask.get("id")  # 使用步骤级ID

                if step_id:
                    # 获取现有评分数据
                    task = MultiAgentTaskCRUD.get_by_id(db, task_id)
                    existing_scores = task.agent_scores or {}
                    existing_step_data = existing_scores.get(step_id, {})

                    # 合并 aspectList（追加新维度，不重复）
                    existing_aspects = set(existing_step_data.get("aspectList", []))
                    new_aspects = [a for a in aspectList if a not in existing_aspects]
                    merged_aspect_list = existing_step_data.get("aspectList", []) + new_aspects

                    # 合并 agentScores（追加新维度的评分）
                    new_front_format = convert_score_table_to_front_format(scoreTable)
                    existing_agent_scores = existing_step_data.get("agentScores", {})
                    new_agent_scores = new_front_format.get("agentScores", {})

                    # 合并每个 agent 的评分
                    merged_agent_scores = {}
                    # 保留所有旧 agent 的评分
                    for agent, scores in existing_agent_scores.items():
                        merged_agent_scores[agent] = dict(scores)
                    # 追加新 agent 和新维度的评分
                    for agent, scores in new_agent_scores.items():
                        if agent not in merged_agent_scores:
                            merged_agent_scores[agent] = {}
                        for aspect, score_info in scores.items():
                            merged_agent_scores[agent][aspect] = score_info

                    # 构建合并后的数据
                    merged_step_data = {
                        "aspectList": merged_aspect_list,
                        "agentScores": merged_agent_scores
                    }

                    # 更新数据库
                    existing_scores[step_id] = merged_step_data
                    db.execute(
                        text("UPDATE multi_agent_tasks SET agent_scores = :scores WHERE task_id = :id"),
                        {"scores": json.dumps(existing_scores), "id": task_id}
                    )
                    db.commit()
                    print(f"[agent_select_modify_add_aspect] 已追加保存 agent_scores: step_id={step_id}, 新增维度={new_aspects}")

        # 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': {
                "task_id": task_id,
                "scoreTable": scoreTable
            }
        })

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


@socketio.on('agent_select_modify_delete_aspect')
def handle_agent_select_modify_delete_aspect_ws(data):
    """
    WebSocket版本：删除评估维度

    请求格式：
    {
        "id": "request-id",
        "data": {
            "task_id": "task-id",
            "step_id": "step-id",
            "aspect_name": "要删除的维度名称"
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})
    task_id = incoming_data.get('task_id')
    step_id = incoming_data.get('step_id')
    aspect_name = incoming_data.get('aspect_name')

    if not task_id or not aspect_name:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': '缺少必要参数 task_id 或 aspect_name'
        })
        return

    try:
        with get_db_context() as db:
            # 获取现有的 agent_scores 数据
            task = MultiAgentTaskCRUD.get_by_id(db, task_id)
            if not task:
                emit('response', {
                    'id': request_id,
                    'status': 'error',
                    'error': f'任务不存在: {task_id}'
                })
                return

            existing_scores = task.agent_scores or {}

            # 如果指定了 step_id，则只更新该步骤的评分；否则更新所有步骤
            if step_id and step_id in existing_scores:
                step_scores = existing_scores[step_id]
                # 从 aspectList 中移除该维度
                if 'aspectList' in step_scores and aspect_name in step_scores['aspectList']:
                    step_scores['aspectList'] = [a for a in step_scores['aspectList'] if a != aspect_name]
                # 从每个 agent 的评分中移除该维度
                if 'agentScores' in step_scores:
                    for agent_name in step_scores['agentScores']:
                        if aspect_name in step_scores['agentScores'][agent_name]:
                            del step_scores['agentScores'][agent_name][aspect_name]
                print(f"[agent_select_modify_delete_aspect] 已删除维度 from step_id={step_id}, 维度={aspect_name}")
            else:
                # 遍历所有步骤，移除该维度
                for sid, step_scores in existing_scores.items():
                    if 'aspectList' in step_scores and aspect_name in step_scores['aspectList']:
                        step_scores['aspectList'] = [a for a in step_scores['aspectList'] if a != aspect_name]
                    if 'agentScores' in step_scores:
                        for agent_name in step_scores['agentScores']:
                            if aspect_name in step_scores['agentScores'][agent_name]:
                                del step_scores['agentScores'][agent_name][aspect_name]
                print(f"[agent_select_modify_delete_aspect] 已删除所有步骤中的维度，维度={aspect_name}")

            # 保存更新后的评分数据到数据库
            db.execute(
                text("UPDATE multi_agent_tasks SET agent_scores = :scores WHERE task_id = :id"),
                {"scores": json.dumps(existing_scores), "id": task_id}
            )
            db.commit()

        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': {
                "message": f"维度 '{aspect_name}' 删除成功",
                "task_id": task_id,
                "deleted_aspect": aspect_name
            }
        })

    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
    保存到 user_agents 数据库表
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    global AgentBoard, AgentProfile_Dict, yaml_data

    try:
        AgentBoard = incoming_data
        AgentProfile_Dict = {}

        # 保存到数据库
        saved_agents = []
        with get_db_context() as db:
            for item in AgentBoard:
                name = item["Name"]
                if all(item.get(field) for field in ["apiUrl", "apiKey", "apiModel"]):
                    agent_config = {
                        "profile": item["Profile"],
                        "Icon": item.get("Icon", ""),
                        "Classification": item.get("Classification", ""),
                        "apiUrl": item["apiUrl"],
                        "apiKey": item["apiKey"],
                        "apiModel": item["apiModel"],
                        "useCustomAPI": True
                    }
                else:
                    agent_config = {
                        "profile": item["Profile"],
                        "Icon": item.get("Icon", ""),
                        "Classification": item.get("Classification", ""),
                        "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

                # 保存到数据库（使用 upsert，相同 user_id + agent_name 则更新，否则创建）
                user_id = item.get("user_id", "default_user")
                agent = UserAgentCRUD.upsert(
                    db=db,
                    user_id=user_id,
                    agent_name=name,
                    agent_config=agent_config,
                )
                saved_agents.append(agent.to_dict())

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

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


@socketio.on('get_agents')
def handle_get_agents_ws(data):
    """
    WebSocket版本：获取智能体配置
    从 user_agents 数据库表读取
    """
    request_id = data.get('id')
    user_id = data.get('user_id', 'default_user')

    try:
        # 从数据库获取用户的智能体配置
        with get_db_context() as db:
            user_agents = UserAgentCRUD.get_by_user_id(db=db, user_id=user_id)

            # 转换为前端期望的格式
            agents = []
            for ua in user_agents:
                config = ua.agent_config or {}
                agents.append({
                    'Name': ua.agent_name,
                    'Profile': config.get('profile', ''),
                    'Icon': config.get('Icon', ''),
                    'Classification': config.get('Classification', ''),
                    'apiUrl': config.get('apiUrl', ''),
                    'apiKey': config.get('apiKey', ''),
                    'apiModel': config.get('apiModel', ''),
                })

        emit('response', {
            'id': request_id,
            'status': 'success',
            'data': {
                'code': 200,
                'content': 'get agents successfully',
                'agents': agents
            }
        })

    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

        # 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": {
            "execution_id": "执行ID"
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        from AgentCoord.RehearsalEngine_V2.execution_state import execution_state_manager

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

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

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

        if success:
            print(f"[WS] pause_execution 成功: execution_id={execution_id}")
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {"message": "已暂停执行，可随时继续"}
            })
        else:
            print(f"[WS] pause_execution 失败: execution_id={execution_id}")
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': f'无法暂停'
            })

    except Exception as e:
        print(f"[WS] pause_execution 异常: {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": {
            "execution_id": "执行ID"
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        from AgentCoord.RehearsalEngine_V2.execution_state import execution_state_manager

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

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

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

        if success:
            print(f"[WS] resume_execution 成功: execution_id={execution_id}")
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {"message": "已恢复执行"}
            })
        else:
            print(f"[WS] resume_execution 失败: execution_id={execution_id}")
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': f'无法恢复'
            })

    except Exception as e:
        print(f"[WS] resume_execution 异常: {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": {
            "execution_id": "执行ID"
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})

    try:
        from AgentCoord.RehearsalEngine_V2.execution_state import execution_state_manager

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

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

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

        if success:
            print(f"[WS] stop_execution 成功: execution_id={execution_id}")
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {"message": "已停止执行"}
            })
        else:
            print(f"[WS] stop_execution 失败: execution_id={execution_id}")
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': f'无法停止'
            })

    except Exception as e:
        print(f"[WS] stop_execution 异常: {str(e)}")
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': str(e)
        })


# ==================== 历史记录管理 ====================

@socketio.on('get_plans')
def handle_get_plans(data):
    """
    WebSocket版本：获取历史任务列表
    """
    # socketio 会把数据包装多层：
    # 前端发送: { id: 'get_plans-xxx', action: 'get_plans', data: { id: 'ws_req_xxx' } }
    # socketio 包装后: data = { id: 'get_plans-xxx', action: 'get_plans', data: {...} }
    request_id = data.get('id')  # socketio 包装的 id，用于响应匹配

    try:
        with get_db_context() as db:
            # 获取最近的任务记录
            tasks = MultiAgentTaskCRUD.get_recent(db, limit=50)

            # 转换为前端期望的格式
            plans = []
            for task in tasks:
                # 兼容旧数据格式（branches 可能是数组）
                branches_data = task.branches
                if branches_data and isinstance(branches_data, list):
                    # 旧格式：数组，转换为新格式对象
                    branches_data = {
                        'flow_branches': branches_data,
                        'task_process_branches': {}
                    }

                plans.append({
                    "id": task.task_id,  # 以 task_id 为唯一标识
                    "general_goal": task.query or '未知任务',
                    "status": task.status.value if task.status else 'unknown',
                    "execution_count": task.execution_count or 0,
                    "created_at": task.created_at.isoformat() if task.created_at else None,
                    "is_pinned": task.is_pinned or False,  # 置顶标志
                    # 完整数据用于恢复
                    "task_outline": task.task_outline,
                    "assigned_agents": task.assigned_agents,
                    "agent_scores": task.agent_scores,
                    "agents_info": task.agents_info,
                    "branches": branches_data or {},  # 分支数据（新格式对象）
                })

            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': plans
            })

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


@socketio.on('restore_plan')
def handle_restore_plan(data):
    """
    WebSocket版本：恢复历史任务
    """
    # socketio 包装: data = { id: 'restore_plan-xxx', action: 'restore_plan', data: { id: 'ws_req_xxx', data: {...} } }
    request_id = data.get('id')  # socketio 包装的 id
    incoming_data = data.get('data', {}).get('data', {})  # 真正的请求数据
    plan_id = incoming_data.get('plan_id')

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

    try:
        with get_db_context() as db:
            # 以 task_id 为唯一标识查询
            task = MultiAgentTaskCRUD.get_by_id(db, plan_id)

            if not task:
                emit('response', {
                    'id': request_id,
                    'status': 'error',
                    'error': f'任务不存在: {plan_id}'
                })
                return

            # 注意：恢复任务不增加执行次数，避免误统计
            # execution_count 只在真正执行任务时增加

            # 兼容旧数据格式（branches 可能是数组）
            branches_data = task.branches
            if branches_data and isinstance(branches_data, list):
                # 旧格式：数组，转换为新格式对象
                branches_data = {
                    'flow_branches': branches_data,
                    'task_process_branches': {}
                }

            # 返回完整数据用于恢复
            restored_data = {
                "id": task.task_id,
                "general_goal": task.query or '未知任务',
                "status": task.status.value if task.status else 'unknown',
                "execution_count": task.execution_count or 0,
                "created_at": task.created_at.isoformat() if task.created_at else None,
                # 完整恢复数据
                "task_outline": task.task_outline,
                "assigned_agents": task.assigned_agents,
                "agent_scores": task.agent_scores,
                "agents_info": task.agents_info,
                "branches": branches_data or {},  # 分支数据（新格式对象）
                "rehearsal_log": task.rehearsal_log,  # 排练日志（完整执行数据，用于恢复执行状态）
            }

            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': restored_data
            })

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


@socketio.on('get_agent_scores')
def handle_get_agent_scores(data):
    """
    WebSocket版本：获取指定任务的评分数据

    请求格式：
    {
        "id": "request-id",
        "action": "get_agent_scores",
        "data": {
            "task_id": "task-id"
        }
    }

    返回格式（与前端 ITaskScoreData 一致）：
    {
        "task_id": "xxx",
        "agent_scores": {
            "stepId1": {
                "aspectList": ["专业性", "协作能力"],
                "agentScores": {"Agent-A": {"专业性": {"score": 4.5, "reason": "..."}}},
                "timestamp": 1699999999999
            }
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})
    task_id = incoming_data.get('task_id')

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

    try:
        with get_db_context() as db:
            task = MultiAgentTaskCRUD.get_by_id(db, task_id)

            if not task:
                emit('response', {
                    'id': request_id,
                    'status': 'error',
                    'error': f'任务不存在: {task_id}'
                })
                return

            # 返回评分数据
            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {
                    "task_id": task_id,
                    "agent_scores": task.agent_scores or {}
                }
            })

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


@socketio.on('delete_plan')
def handle_delete_plan(data):
    """
    WebSocket版本：删除历史任务
    """
    # socketio 包装: data = { id: 'delete_plan-xxx', action: 'delete_plan', data: { id: 'ws_req_xxx', data: {...} } }
    request_id = data.get('id')  # socketio 包装的 id
    incoming_data = data.get('data', {}).get('data', {})  # 真正的请求数据
    plan_id = incoming_data.get('plan_id')

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

    try:
        with get_db_context() as db:
            # 以 task_id 为唯一标识删除
            success = MultiAgentTaskCRUD.delete(db, plan_id)

            if not success:
                emit('response', {
                    'id': request_id,
                    'status': 'error',
                    'error': f'任务不存在或删除失败: {plan_id}'
                })
                return

            # 通知所有客户端刷新历史列表
            socketio.emit('history_updated', {'task_id': plan_id})

            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {"message": "删除成功"}
            })

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


@socketio.on('pin_plan')
def handle_pin_plan(data):
    """
    WebSocket版本：置顶/取消置顶历史任务
    """
    # socketio 包装: data = { id: 'pin_plan-xxx', action: 'pin_plan', data: { id: 'ws_req_xxx', data: {...} } }
    request_id = data.get('id')  # socketio 包装的 id
    incoming_data = data.get('data', {}).get('data', {})  # 真正的请求数据
    plan_id = incoming_data.get('plan_id')
    is_pinned = incoming_data.get('is_pinned', True)  # 默认为置顶

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

    try:
        with get_db_context() as db:
            task = MultiAgentTaskCRUD.update_is_pinned(db, plan_id, is_pinned)

            if not task:
                emit('response', {
                    'id': request_id,
                    'status': 'error',
                    'error': f'任务不存在: {plan_id}'
                })
                return

            # 通知所有客户端刷新历史列表
            socketio.emit('history_updated', {'task_id': plan_id})

            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {"message": "置顶成功" if is_pinned else "取消置顶成功"}
            })

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


@socketio.on('save_branches')
def handle_save_branches(data):
    """
    WebSocket版本：保存任务分支数据

    请求格式：
    {
        "id": "request-id",
        "action": "save_branches",
        "data": {
            "task_id": "task-id",
            "branches": [...]  // 分支数据数组
        }
    }

    数据库存储格式：
    {
        "branches": {
            "flow_branches": [...],           // 任务大纲探索分支
            "task_process_branches": {...}    // 任务过程分支（可能不存在）
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})
    task_id = incoming_data.get('task_id')
    flow_branches = incoming_data.get('branches', [])

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

    try:
        with get_db_context() as db:
            # 获取现有的 branches 数据
            existing_task = MultiAgentTaskCRUD.get_by_id(db, task_id)
            if existing_task:
                # 使用深拷贝避免修改共享引用
                existing_branches = copy.deepcopy(existing_task.branches) if existing_task.branches else {}

                # 保留现有的 task_process_branches（关键：不要覆盖已有的任务过程分支）
                task_process_branches = existing_branches.get('task_process_branches', {}) if isinstance(existing_branches, dict) else {}

                # 构建新的 branches 数据
                new_branches = {
                    'flow_branches': flow_branches,
                    'task_process_branches': task_process_branches
                }

                # 更新数据库
                MultiAgentTaskCRUD.update_branches(db, task_id, new_branches)
                print(f"[save_branches] 已保存分支数据到数据库，task_id={task_id}, flow_branches_count={len(flow_branches)}, task_process_count={len(task_process_branches)}")

            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {
                    "message": "分支数据保存成功",
                    "branches_count": len(flow_branches)
                }
            })

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


@socketio.on('save_task_process_branches')
def handle_save_task_process_branches(data):
    """
    WebSocket版本：保存任务过程分支数据

    请求格式：
    {
        "id": "request-id",
        "action": "save_task_process_branches",
        "data": {
            "task_id": "task-id",  // 大任务ID（数据库主键）
            "branches": {
                "stepId-1": {
                    '["AgentA","AgentB"]': [{...}, {...}],
                    '["AgentC"]': [...]
                },
                "stepId-2": {...}
            }
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})
    task_id = incoming_data.get('task_id')
    branches = incoming_data.get('branches', {})

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

    try:
        with get_db_context() as db:
            # 获取现有的 branches 数据
            existing_task = MultiAgentTaskCRUD.get_by_id(db, task_id)

            if existing_task:
                # 使用深拷贝避免修改共享引用
                existing_branches = copy.deepcopy(existing_task.branches) if existing_task.branches else {}

                # 保留现有的 flow_branches
                existing_flow_branches = existing_branches.get('flow_branches', []) if isinstance(existing_branches, dict) else []

                # 合并 task_process_branches（新数据与旧数据合并，而不是覆盖）
                existing_task_process = existing_branches.get('task_process_branches', {}) if isinstance(existing_branches, dict) else {}
                incoming_task_process = branches

                # 合并逻辑：对于每个 stepId，将新分支追加到已有分支中
                merged_task_process = dict(existing_task_process)
                for stepId, stepData in incoming_task_process.items():
                    if stepId in merged_task_process:
                        # stepId 已存在，合并 agentGroupKey 下的分支数组
                        existing_agent_data = merged_task_process[stepId]
                        incoming_agent_data = stepData
                        for agentKey, newBranches in incoming_agent_data.items():
                            if agentKey in existing_agent_data:
                                # 合并分支（去重，根据 branch.id）
                                existing_ids = {b.get('id') for b in existing_agent_data[agentKey] if b.get('id')}
                                for newBranch in newBranches:
                                    if newBranch.get('id') not in existing_ids:
                                        existing_agent_data[agentKey].append(newBranch)
                            else:
                                existing_agent_data[agentKey] = newBranches
                    else:
                        merged_task_process[stepId] = stepData

                # 构建新 branches 数据
                if isinstance(existing_branches, dict):
                    new_branches = dict(existing_branches)
                    new_branches['task_process_branches'] = merged_task_process
                    new_branches['flow_branches'] = existing_flow_branches
                else:
                    new_branches = {
                        'task_process_branches': merged_task_process,
                        'flow_branches': existing_flow_branches if isinstance(existing_flow_branches, list) else []
                    }

                # 直接更新数据库
                existing_task.branches = new_branches
                db.flush()  # 显式刷新，确保 SQLAlchemy 检测到变化
                db.commit()

                print(f"[save_task_process_branches] 保存完成，stepIds: {list(merged_task_process.keys())}")
            else:
                print(f"[save_task_process_branches] 警告: 找不到任务，task_id={task_id}")

            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {
                    "message": "任务过程分支数据保存成功",
                    "step_count": len(branches)
                }
            })

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


@socketio.on('delete_task_process_branch')
def handle_delete_task_process_branch(data):
    """
    WebSocket版本：删除任务过程分支数据

    请求格式：
    {
        "id": "request-id",
        "action": "delete_task_process_branch",
        "data": {
            "task_id": "task-id",  // 大任务ID（数据库主键）
            "stepId": "step-id",   // 小任务ID
            "branchId": "branch-id" // 要删除的分支ID
        }
    }

    数据库存储格式：
    {
        "branches": {
            "flow_branches": [...],
            "task_process_branches": {
                "stepId-1": {
                    '["AgentA","AgentB"]': [{...分支数据...}]
                }
            }
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})
    task_id = incoming_data.get('task_id')
    step_id = incoming_data.get('stepId')
    branch_id = incoming_data.get('branchId')

    if not task_id or not step_id or not branch_id:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': '缺少必要参数：task_id, stepId, branchId'
        })
        return

    try:
        with get_db_context() as db:
            # 获取现有的 branches 数据
            existing_task = MultiAgentTaskCRUD.get_by_id(db, task_id)

            if existing_task:
                # 使用深拷贝避免修改共享引用
                existing_branches = copy.deepcopy(existing_task.branches) if existing_task.branches else {}

                if isinstance(existing_branches, dict):
                    # 获取现有的 task_process_branches
                    task_process_branches = existing_branches.get('task_process_branches', {})

                    if step_id in task_process_branches:
                        # 获取该 stepId 下的所有 agent 分支
                        step_branches = task_process_branches[step_id]

                        # 遍历所有 agentGroupKey，删除对应分支
                        for agent_key, branches_list in step_branches.items():
                            # 过滤掉要删除的分支
                            filtered_branches = [b for b in branches_list if b.get('id') != branch_id]

                            if len(filtered_branches) != len(branches_list):
                                # 有分支被删除，更新数据
                                if filtered_branches:
                                    step_branches[agent_key] = filtered_branches
                                else:
                                    # 如果该 agentKey 下没有分支了，删除该 key
                                    del step_branches[agent_key]

                        # 如果该 stepId 下没有分支了，删除该 stepId
                        if not step_branches:
                            del task_process_branches[step_id]

                        # 更新 branches 数据
                        existing_branches['task_process_branches'] = task_process_branches

                        # 直接更新数据库
                        existing_task.branches = existing_branches
                        db.flush()
                        db.commit()

                        print(f"[delete_task_process_branch] 删除成功，task_id={task_id}, step_id={step_id}, branch_id={branch_id}")

                        emit('response', {
                            'id': request_id,
                            'status': 'success',
                            'data': {
                                "message": "分支删除成功",
                                "deleted_branch_id": branch_id
                            }
                        })
                        return

            # 如果找不到对应的分支
            print(f"[delete_task_process_branch] 警告: 找不到要删除的分支，task_id={task_id}, step_id={step_id}, branch_id={branch_id}")
            emit('response', {
                'id': request_id,
                'status': 'error',
                'error': '未找到要删除的分支'
            })

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


@socketio.on('save_task_outline')
def handle_save_task_outline(data):
    """
    WebSocket版本：保存任务大纲数据

    请求格式：
    {
        "id": "request-id",
        "action": "save_task_outline",
        "data": {
            "task_id": "task-id",
            "task_outline": {...}
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})
    task_id = incoming_data.get('task_id')
    task_outline = incoming_data.get('task_outline')

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

    try:
        with get_db_context() as db:
            # 更新任务大纲
            MultiAgentTaskCRUD.update_task_outline(db, task_id, task_outline)
            print(f"[save_task_outline] 已保存任务大纲到数据库，task_id={task_id}")

            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {
                    "message": "任务大纲保存成功"
                }
            })

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


@socketio.on('update_assigned_agents')
def handle_update_assigned_agents(data):
    """
    WebSocket版本：更新指定步骤的 assigned_agents

    请求格式：
    {
        "id": "request-id",
        "action": "update_assigned_agents",
        "data": {
            "task_id": "task-id",           // 大任务ID（数据库主键）
            "step_id": "step-id",           // 步骤级ID（小任务UUID）
            "agents": ["AgentA", "AgentB"], // 选中的 agent 列表
            "confirmed_groups": [["AgentA"], ["AgentA", "AgentB"]], // 可选：确认的 agent 组合列表
            "save_combination": true        // 可选：是否同时保存该组合的 TaskProcess（由 fill_step_task_process 处理）
        }
    }
    """
    import json
    request_id = data.get('id')
    incoming_data = data.get('data', {})
    task_id = incoming_data.get('task_id')
    step_id = incoming_data.get('step_id')
    agents = incoming_data.get('agents', [])
    confirmed_groups = incoming_data.get('confirmed_groups', [])
    agent_combinations = incoming_data.get('agent_combinations', {})
    if not task_id:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': '缺少 task_id 参数'
        })
        return

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

    try:
        with get_db_context() as db:
            # 获取现有任务
            task = MultiAgentTaskCRUD.get_by_id(db, task_id)
            if not task:
                emit('response', {
                    'id': request_id,
                    'status': 'error',
                    'error': f'任务不存在: {task_id}'
                })
                return

            # 合并更新 assigned_agents，确保是 dict 类型
            raw_assigned = task.assigned_agents
            existing_assigned = raw_assigned if isinstance(raw_assigned, dict) else {}

            # 确保步骤数据结构存在
            if step_id not in existing_assigned:
                existing_assigned[step_id] = {}

            # 确保子结构存在
            if "current" not in existing_assigned[step_id]:
                existing_assigned[step_id]["current"] = []
            if "confirmed_groups" not in existing_assigned[step_id]:
                existing_assigned[step_id]["confirmed_groups"] = []
            if "agent_combinations" not in existing_assigned[step_id]:
                existing_assigned[step_id]["agent_combinations"] = {}

            # 更新 current agents（当前选中的组合）
            if agents:
                existing_assigned[step_id]["current"] = agents

            # 更新 confirmed_groups（确认的组合列表）
            if confirmed_groups:
                existing_assigned[step_id]["confirmed_groups"] = confirmed_groups
                # 清理已删除分组的 agent_combinations 数据
                existing_combinations = existing_assigned[step_id].get("agent_combinations", {})
                new_combinations_keys = {json.dumps(list(group), sort_keys=True) for group in confirmed_groups}
                keys_to_remove = [k for k in existing_combinations.keys() if k not in new_combinations_keys]
                for key in keys_to_remove:
                    del existing_combinations[key]
                existing_assigned[step_id]["agent_combinations"] = existing_combinations

            # 更新 agent_combinations（保存 TaskProcess 数据）
            if agent_combinations:
                # 合并新旧数据
                existing_combinations = existing_assigned[step_id].get("agent_combinations", {})
                for key, value in agent_combinations.items():
                    existing_combinations[key] = value
                existing_assigned[step_id]["agent_combinations"] = existing_combinations

            db.execute(
                text("UPDATE multi_agent_tasks SET assigned_agents = :assigned WHERE task_id = :id"),
                {"assigned": json.dumps(existing_assigned), "id": task_id}
            )
            db.commit()
            print(f"[update_assigned_agents] 已保存: task_id={task_id}, step_id={step_id}")

            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {
                    "message": "assigned_agents 更新成功",
                    "task_id": task_id,
                    "step_id": step_id,
                    "agents": agents,
                    "confirmed_groups": confirmed_groups
                }
            })

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


# ==================== 导出功能 ====================

# 导出类型配置
EXPORT_TYPE_CONFIG = {
    "doc": {"ext": ".docx", "mime": "application/vnd.openxmlformats-officedocument.wordprocessingml.document"},
    "markdown": {"ext": ".md", "mime": "text/markdown"},
    "excel": {"ext": ".xlsx", "mime": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},
    "ppt": {"ext": ".pptx", "mime": "application/vnd.openxmlformats-officedocument.presentationml.presentation"},
    "mindmap": {"ext": ".md", "mime": "text/markdown"},  # 思维导图先用 markdown
    "infographic": {"ext": ".html", "mime": "text/html"},  # 信息图先用 html
}


def ensure_export_dir(task_id: str) -> str:
    """确保导出目录存在"""
    task_dir = os.path.join(EXPORT_DIR, task_id)
    os.makedirs(task_dir, exist_ok=True)
    return task_dir


def generate_export_file_name(task_name: str, export_type: str) -> str:
    """生成导出文件名"""
    from datetime import datetime
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    # 清理文件名中的非法字符
    safe_name = "".join(c for c in task_name if c.isalnum() or c in (' ', '-', '_')).strip()
    return f"{safe_name}_{export_type}_{timestamp}"


@socketio.on('export')
def handle_export(data):
    """
    WebSocket：处理导出请求

    请求格式：
    {
        "id": "request-id",
        "action": "export",
        "data": {
            "task_id": "task-id",           // 任务ID
            "export_type": "doc",           // 导出类型: doc/markdown/excel/ppt/mindmap/infographic
            "user_id": "user-id",           // 用户ID
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})
    task_id = incoming_data.get('task_id')
    export_type = incoming_data.get('export_type')
    user_id = incoming_data.get('user_id', 'anonymous')

    # 参数验证
    if not task_id:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': '缺少 task_id 参数'
        })
        return

    if not export_type or export_type not in EXPORT_TYPE_CONFIG:
        emit('response', {
            'id': request_id,
            'status': 'error',
            'error': f'无效的导出类型: {export_type}'
        })
        return

    try:
        with get_db_context() as db:
            # 获取任务数据
            task = MultiAgentTaskCRUD.get_by_id(db, task_id)
            if not task:
                emit('response', {
                    'id': request_id,
                    'status': 'error',
                    'error': f'任务不存在: {task_id}'
                })
                return

            # 准备导出数据
            export_data = {
                'task_name': task.query or '未命名任务',
                'task_content': task.query or '',  # 使用 query 作为任务描述
                'task_outline': task.task_outline,
                'result': task.result,
                'agents_info': task.agents_info,
                'assigned_agents': task.assigned_agents,
            }

            # 生成文件名
            file_name_base = generate_export_file_name(export_data['task_name'], export_type)
            config = EXPORT_TYPE_CONFIG[export_type]
            file_name = file_name_base + config['ext']
            file_path = os.path.join(ensure_export_dir(task_id), file_name)

            # 生成文件内容
            # 使用 ExportFactory 来生成各种格式的文件
            try:
                success = ExportFactory.export(export_type, export_data, file_path)
                if not success:
                    # 如果导出失败，创建空文件占位
                    with open(file_path, 'wb') as f:
                        f.write(b'')
                    emit('response', {
                        'id': request_id,
                        'status': 'error',
                        'error': f'导出类型 {export_type} 不支持或生成失败'
                    })
                    return
            except Exception as e:
                print(f"导出文件失败: {e}")
                import traceback
                traceback.print_exc()
                # 导出失败时创建空文件
                with open(file_path, 'wb') as f:
                    f.write(b'')

            # 获取文件大小
            file_size = os.path.getsize(file_path)

            # 生成访问URL（基于文件路径）
            # 相对路径用于静态访问
            relative_path = os.path.join('uploads', 'exports', task_id, file_name)
            file_url = f"/{relative_path.replace(os.sep, '/')}"

            # 保存导出记录到数据库
            record = ExportRecordCRUD.create(
                db=db,
                task_id=task_id,
                user_id=user_id,
                export_type=export_type,
                file_name=file_name,
                file_path=file_path,
                file_url=file_url,
                file_size=file_size,
            )

            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {
                    'record_id': record.id,
                    'file_name': file_name,
                    'file_url': file_url,
                    'file_size': file_size,
                    'export_type': export_type,
                }
            })

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


@socketio.on('get_export_list')
def handle_get_export_list(data):
    """
    WebSocket：获取导出记录列表

    请求格式：
    {
        "id": "request-id",
        "action": "get_export_list",
        "data": {
            "task_id": "task-id",           // 任务ID
        }
    }
    """
    request_id = data.get('id')
    incoming_data = data.get('data', {})
    task_id = incoming_data.get('task_id')

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

    try:
        with get_db_context() as db:
            records = ExportRecordCRUD.get_by_task_id(db, task_id)
            export_list = [record.to_dict() for record in records]

            emit('response', {
                'id': request_id,
                'status': 'success',
                'data': {
                    'list': export_list,
                    'total': len(export_list)
                }
            })

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


# ==================== REST API 接口 ====================

@app.route('/api/export/<int:record_id>/download', methods=['GET'])
def download_export(record_id: int):
    """下载导出文件"""
    try:
        with get_db_context() as db:
            record = ExportRecordCRUD.get_by_id(db, record_id)
            if not record:
                return jsonify({'error': '导出记录不存在'}), 404

            if not os.path.exists(record.file_path):
                return jsonify({'error': '文件不存在'}), 404

            # 发送文件
            config = EXPORT_TYPE_CONFIG.get(record.export_type, {})
            mime_type = config.get('mime', 'application/octet-stream')

            return send_file(
                record.file_path,
                mimetype=mime_type,
                as_attachment=True,
                download_name=record.file_name
            )

    except Exception as e:
        return jsonify({'error': str(e)}), 500


@app.route('/api/export/<int:record_id>/preview', methods=['GET'])
def preview_export(record_id: int):
    """预览导出文件"""
    try:
        with get_db_context() as db:
            record = ExportRecordCRUD.get_by_id(db, record_id)
            if not record:
                return jsonify({'error': '导出记录不存在'}), 404

            if not os.path.exists(record.file_path):
                return jsonify({'error': '文件不存在'}), 404

            # 根据文件类型返回不同的 Content-Type
            config = EXPORT_TYPE_CONFIG.get(record.export_type, {})
            mime_type = config.get('mime', 'application/octet-stream')

            # 读取文件内容
            if record.export_type == 'markdown':
                with open(record.file_path, 'r', encoding='utf-8') as f:
                    content = f.read()
                return jsonify({'content': content, 'type': 'markdown'})
            else:
                # 其他类型返回文件路径，前端自行处理
                return jsonify({
                    'file_url': record.file_url,
                    'file_name': record.file_name,
                    'type': record.export_type
                })

    except Exception as e:
        return jsonify({'error': str(e)}), 500


@app.route('/api/export/<int:record_id>/share', methods=['GET'])
def share_export(record_id: int):
    """生成分享链接"""
    try:
        with get_db_context() as db:
            record = ExportRecordCRUD.get_by_id(db, record_id)
            if not record:
                return jsonify({'error': '导出记录不存在'}), 404

            # 生成分享Token（简化实现，直接用记录ID）
            share_token = f"export_{record.id}_{int(record.created_at.timestamp())}"
            share_url = f"/share/{share_token}"

            return jsonify({
                'share_url': share_url,
                'file_name': record.file_name,
                'expired_at': None  # TODO: 可以添加过期时间
            })

    except Exception as e:
        return jsonify({'error': str(e)}), 500


@app.route('/api/export/<int:record_id>', methods=['DELETE'])
def delete_export(record_id: int):
    """删除导出记录"""
    try:
        with get_db_context() as db:
            record = ExportRecordCRUD.get_by_id(db, record_id)
            if not record:
                return jsonify({'error': '导出记录不存在'}), 404

            # 删除物理文件
            if os.path.exists(record.file_path):
                os.remove(record.file_path)

            # 删除数据库记录
            ExportRecordCRUD.delete(db, record_id)

            return jsonify({'success': True, 'message': '删除成功'})

    except Exception as e:
        return jsonify({'error': str(e)}), 500


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)