diff --git a/backend/AgentCoord/RehearsalEngine_V2/ExecutePlan_Optimized.py b/backend/AgentCoord/RehearsalEngine_V2/ExecutePlan_Optimized.py
new file mode 100644
index 0000000..ea102fa
--- /dev/null
+++ b/backend/AgentCoord/RehearsalEngine_V2/ExecutePlan_Optimized.py
@@ -0,0 +1,401 @@
+import asyncio
+import json
+import time
+from typing import List, Dict, Set, Generator, Any
+import AgentCoord.RehearsalEngine_V2.Action as Action
+import AgentCoord.util as util
+from termcolor import colored
+
+
+# ==================== 配置参数 ====================
+# 最大并发请求数（避免触发 OpenAI API 速率限制）
+MAX_CONCURRENT_REQUESTS = 2
+
+# 批次之间的延迟（秒）
+BATCH_DELAY = 1.0
+
+# 429 错误重试次数和延迟
+MAX_RETRIES = 3
+RETRY_DELAY = 5.0  # 秒
+
+# ==================== 限流器 ====================
+class RateLimiter:
+    """异步限流器，控制并发请求数量"""
+
+    def __init__(self, max_concurrent: int = MAX_CONCURRENT_REQUESTS):
+        self.semaphore = asyncio.Semaphore(max_concurrent)
+        self.max_concurrent = max_concurrent
+
+    async def __aenter__(self):
+        await self.semaphore.acquire()
+        return self
+
+    async def __aexit__(self, *args):
+        self.semaphore.release()
+
+
+# 全局限流器实例
+rate_limiter = RateLimiter()
+
+
+def build_action_dependency_graph(TaskProcess: List[Dict]) -> Dict[int, List[int]]:
+    """
+    构建动作依赖图
+    返回: {action_index: [dependent_action_indices]}
+    """
+    dependency_map = {i: [] for i in range(len(TaskProcess))}
+
+    for i, action in enumerate(TaskProcess):
+        important_inputs = action.get('ImportantInput', [])
+        if not important_inputs:
+            continue
+
+        # 检查是否依赖其他动作的 ActionResult
+        for j, prev_action in enumerate(TaskProcess):
+            if i == j:
+                continue
+
+            # 判断是否依赖前一个动作的结果
+            if any(
+                inp.startswith('ActionResult:') and
+                inp == f'ActionResult:{prev_action["ID"]}'
+                for inp in important_inputs
+            ):
+                dependency_map[i].append(j)
+
+    return dependency_map
+
+
+def get_parallel_batches(TaskProcess: List[Dict], dependency_map: Dict[int, List[int]]) -> List[List[int]]:
+    """
+    将动作分为多个批次，每批内部可以并行执行
+    返回: [[batch1_indices], [batch2_indices], ...]
+    """
+    batches = []
+    completed: Set[int] = set()
+
+    while len(completed) < len(TaskProcess):
+        # 找出所有依赖已满足的动作
+        ready_to_run = [
+            i for i in range(len(TaskProcess))
+            if i not in completed and
+            all(dep in completed for dep in dependency_map[i])
+        ]
+
+        if not ready_to_run:
+            # 避免死循环（循环依赖情况）
+            remaining = [i for i in range(len(TaskProcess)) if i not in completed]
+            if remaining:
+                print(colored(f"警告: 检测到循环依赖，强制串行执行: {remaining}", "yellow"))
+                ready_to_run = remaining[:1]  # 每次只执行一个
+            else:
+                break
+
+        batches.append(ready_to_run)
+        completed.update(ready_to_run)
+
+    return batches
+
+
+async def execute_single_action_async(
+    ActionInfo: Dict,
+    General_Goal: str,
+    TaskDescription: str,
+    OutputName: str,
+    KeyObjects: Dict,
+    ActionHistory: List,
+    agentName: str,
+    AgentProfile_Dict: Dict,
+    InputName_List: List[str]
+) -> Dict:
+    """
+    异步执行单个动作
+    """
+    actionType = ActionInfo["ActionType"]
+
+    # 创建动作实例
+    if actionType in Action.customAction_Dict:
+        currentAction = Action.customAction_Dict[actionType](
+            info=ActionInfo,
+            OutputName=OutputName,
+            KeyObjects=KeyObjects,
+        )
+    else:
+        currentAction = Action.BaseAction(
+            info=ActionInfo,
+            OutputName=OutputName,
+            KeyObjects=KeyObjects,
+        )
+
+    # 执行动作（在线程池中运行，避免阻塞事件循环）
+    loop = asyncio.get_event_loop()
+    ActionInfo_with_Result = await loop.run_in_executor(
+        None,
+        lambda: currentAction.run(
+            General_Goal=General_Goal,
+            TaskDescription=TaskDescription,
+            agentName=agentName,
+            AgentProfile_Dict=AgentProfile_Dict,
+            InputName_List=InputName_List,
+            OutputName=OutputName,
+            KeyObjects=KeyObjects,
+            ActionHistory=ActionHistory,
+        )
+    )
+
+    return ActionInfo_with_Result
+
+
+async def execute_step_async_streaming(
+    stepDescrip: Dict,
+    General_Goal: str,
+    AgentProfile_Dict: Dict,
+    KeyObjects: Dict,
+    step_index: int,
+    total_steps: int
+) -> Generator[Dict, None, None]:
+    """
+    异步执行单个步骤（支持流式返回）
+    返回生成器，每完成一个动作就 yield 一次
+    """
+    # 准备步骤信息
+    StepName = (
+        util.camel_case_to_normal(stepDescrip["StepName"])
+        if util.is_camel_case(stepDescrip["StepName"])
+        else stepDescrip["StepName"]
+    )
+    TaskContent = stepDescrip["TaskContent"]
+    InputName_List = (
+        [
+            (
+                util.camel_case_to_normal(obj)
+                if util.is_camel_case(obj)
+                else obj
+            )
+            for obj in stepDescrip["InputObject_List"]
+        ]
+        if stepDescrip["InputObject_List"] is not None
+        else None
+    )
+    OutputName = (
+        util.camel_case_to_normal(stepDescrip["OutputObject"])
+        if util.is_camel_case(stepDescrip["OutputObject"])
+        else stepDescrip["OutputObject"]
+    )
+    Agent_List = stepDescrip["AgentSelection"]
+    TaskProcess = stepDescrip["TaskProcess"]
+
+    TaskDescription = (
+        util.converter.generate_template_sentence_for_CollaborationBrief(
+            input_object_list=InputName_List,
+            output_object=OutputName,
+            agent_list=Agent_List,
+            step_task=TaskContent,
+        )
+    )
+
+    # 初始化日志节点
+    inputObject_Record = [
+        {InputName: KeyObjects[InputName]} for InputName in InputName_List
+    ]
+    stepLogNode = {
+        "LogNodeType": "step",
+        "NodeId": StepName,
+        "InputName_List": InputName_List,
+        "OutputName": OutputName,
+        "chatLog": [],
+        "inputObject_Record": inputObject_Record,
+    }
+    objectLogNode = {
+        "LogNodeType": "object",
+        "NodeId": OutputName,
+        "content": None,
+    }
+
+    # 先返回步骤开始信息
+    yield {
+        "type": "step_start",
+        "step_index": step_index,
+        "total_steps": total_steps,
+        "step_name": StepName,
+        "task_description": TaskDescription,
+    }
+
+    # 构建动作依赖图
+    dependency_map = build_action_dependency_graph(TaskProcess)
+    batches = get_parallel_batches(TaskProcess, dependency_map)
+
+    ActionHistory = []
+    total_actions = len(TaskProcess)
+    completed_actions = 0
+
+    util.print_colored(
+        f"📋 步骤 {step_index + 1}/{total_steps}: {StepName} ({total_actions} 个动作, 分 {len(batches)} 批并行执行)",
+        text_color="cyan"
+    )
+
+    # 分批执行动作
+    for batch_index, batch_indices in enumerate(batches):
+        batch_size = len(batch_indices)
+
+        if batch_size > 1:
+            util.print_colored(
+                f"🚦 批次 {batch_index + 1}/{len(batches)}: 并行执行 {batch_size} 个动作",
+                text_color="blue"
+            )
+        else:
+            util.print_colored(
+                f"🔄 动作 {completed_actions + 1}/{total_actions}: 串行执行",
+                text_color="yellow"
+            )
+
+        # 并行执行当前批次的所有动作
+        tasks = [
+            execute_single_action_async(
+                TaskProcess[i],
+                General_Goal=General_Goal,
+                TaskDescription=TaskDescription,
+                OutputName=OutputName,
+                KeyObjects=KeyObjects,
+                ActionHistory=ActionHistory,
+                agentName=TaskProcess[i]["AgentName"],
+                AgentProfile_Dict=AgentProfile_Dict,
+                InputName_List=InputName_List
+            )
+            for i in batch_indices
+        ]
+
+        # 等待当前批次完成
+        batch_results = await asyncio.gather(*tasks)
+
+        # 逐个返回结果（流式）
+        for i, result in enumerate(batch_results):
+            action_index_in_batch = batch_indices[i]
+            completed_actions += 1
+
+            util.print_colored(
+                f"✅ 动作 {completed_actions}/{total_actions} 完成: {result['ActionType']} by {result['AgentName']}",
+                text_color="green"
+            )
+
+            ActionHistory.append(result)
+
+            # 立即返回该动作结果（流式）
+            yield {
+                "type": "action_complete",
+                "step_index": step_index,
+                "step_name": StepName,
+                "action_index": action_index_in_batch,
+                "total_actions": total_actions,
+                "completed_actions": completed_actions,
+                "action_result": result,
+                "batch_info": {
+                    "batch_index": batch_index,
+                    "batch_size": batch_size,
+                    "is_parallel": batch_size > 1
+                }
+            }
+
+    # 步骤完成
+    objectLogNode["content"] = KeyObjects[OutputName]
+    stepLogNode["ActionHistory"] = ActionHistory
+
+    yield {
+        "type": "step_complete",
+        "step_index": step_index,
+        "step_name": StepName,
+        "step_log_node": stepLogNode,
+        "object_log_node": objectLogNode,
+    }
+
+
+def executePlan_streaming(
+    plan: Dict,
+    num_StepToRun: int,
+    RehearsalLog: List,
+    AgentProfile_Dict: Dict
+) -> Generator[str, None, None]:
+    """
+    执行计划（流式返回版本）
+    返回生成器，每次返回 SSE 格式的字符串
+
+    使用方式:
+    for event in executePlan_streaming(...):
+        yield event
+    """
+    # 准备执行
+    KeyObjects = {}
+    finishedStep_index = -1
+
+    for logNode in RehearsalLog:
+        if logNode["LogNodeType"] == "step":
+            finishedStep_index += 1
+        if logNode["LogNodeType"] == "object":
+            KeyObjects[logNode["NodeId"]] = logNode["content"]
+
+    # 确定要运行的步骤范围
+    if num_StepToRun is None:
+        run_to = len(plan["Collaboration Process"])
+    else:
+        run_to = (finishedStep_index + 1) + num_StepToRun
+
+    steps_to_run = plan["Collaboration Process"][(finishedStep_index + 1): run_to]
+    total_steps = len(steps_to_run)
+
+    print(colored(f"🚀 开始执行计划（流式推送），共 {total_steps} 个步骤", "cyan", attrs=["bold"]))
+
+    # 使用队列实现流式推送
+    async def produce_events(queue: asyncio.Queue):
+        """异步生产者：每收到一个事件就放入队列"""
+        try:
+            for step_index, stepDescrip in enumerate(steps_to_run):
+                # 执行单个步骤（流式）
+                async for event in execute_step_async_streaming(
+                    stepDescrip,
+                    plan["General Goal"],
+                    AgentProfile_Dict,
+                    KeyObjects,
+                    step_index,
+                    total_steps
+                ):
+                    await queue.put(event)  # ← 立即放入队列
+        finally:
+            await queue.put(None)  # ← 发送完成信号
+
+    # 运行异步任务并实时 yield
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+
+    try:
+        # 创建队列
+        queue = asyncio.Queue(maxsize=10)
+
+        # 启动异步生产者任务
+        producer_task = loop.create_task(produce_events(queue))
+
+        # 同步消费者：从队列读取并 yield（使用 run_until_complete）
+        while True:
+            event = loop.run_until_complete(queue.get())  # ← 从队列获取事件
+            if event is None:  # ← 收到完成信号
+                break
+
+            # 立即转换为 SSE 格式并发送
+            event_str = json.dumps(event, ensure_ascii=False)
+            yield f"data: {event_str}\n\n"  # ← 立即发送给前端
+
+        # 等待生产者任务完成
+        loop.run_until_complete(producer_task)
+
+        # 发送完成信号
+        complete_event = json.dumps({
+            "type": "execution_complete",
+            "total_steps": total_steps
+        }, ensure_ascii=False)
+        yield f"data: {complete_event}\n\n"
+
+    finally:
+        # 清理任务
+        if 'producer_task' in locals():
+            if not producer_task.done():
+                producer_task.cancel()
+        loop.close()