feat:任务执行结果性能优化

backend/AgentCoord/PlanEngine/AgentSelectModify.py

@@ -137,5 +137,6 @@ def AgentSelectModify_init(stepTask, General_Goal, Agent_Board):
 
 
 def AgentSelectModify_addAspect(aspectList, Agent_Board):
-    scoreTable = agentAbilityScoring(Agent_Board, aspectList)
+    newAspect = aspectList[-1]
+    scoreTable = agentAbilityScoring(Agent_Board, [newAspect])
     return scoreTable

backend/AgentCoord/PlanEngine/basePlan_Generator.py

@@ -1,55 +1,53 @@
 from AgentCoord.PlanEngine.planOutline_Generator import generate_PlanOutline
-from AgentCoord.PlanEngine.AgentSelection_Generator import (
-    generate_AgentSelection,
-)
-from AgentCoord.PlanEngine.taskProcess_Generator import generate_TaskProcess
-import AgentCoord.util as util
+# from AgentCoord.PlanEngine.AgentSelection_Generator import (
+#     generate_AgentSelection,
+# )
 
 
 def generate_basePlan(
     General_Goal, Agent_Board, AgentProfile_Dict, InitialObject_List
 ):
-    basePlan = {
-        "Initial Input Object": InitialObject_List,
-        "Collaboration Process": [],
-    }
+    """
+    优化模式：生成大纲 + 智能体选择，但不生成任务流程
+    优化用户体验：
+    1. 快速生成大纲和分配智能体
+    2. 用户可以看到完整的大纲和智能体图标
+    3. TaskProcess由前端通过 fillStepTask API 异步填充
+
+    """
+    # 参数保留以保持接口兼容性
+    _ = AgentProfile_Dict
     PlanOutline = generate_PlanOutline(
-        InitialObject_List=[], General_Goal=General_Goal
+        InitialObject_List=InitialObject_List, General_Goal=General_Goal
     )
+
+    basePlan = {
+        "General Goal": General_Goal,
+        "Initial Input Object": InitialObject_List,
+        "Collaboration Process": []
+    }
+
     for stepItem in PlanOutline:
-        Current_Task = {
-            "TaskName": stepItem["StepName"],
-            "InputObject_List": stepItem["InputObject_List"],
-            "OutputObject": stepItem["OutputObject"],
-            "TaskContent": stepItem["TaskContent"],
+        # # 为每个步骤分配智能体
+        # Current_Task = {
+        #     "TaskName": stepItem["StepName"],
+        #     "InputObject_List": stepItem["InputObject_List"],
+        #     "OutputObject": stepItem["OutputObject"],
+        #     "TaskContent": stepItem["TaskContent"],
+        # }
+        # AgentSelection = generate_AgentSelection(
+        #     General_Goal=General_Goal,
+        #     Current_Task=Current_Task,
+        #     Agent_Board=Agent_Board,
+        # )
+
+        # 添加智能体选择，但不添加任务流程
+        stepItem["AgentSelection"] = []
+        stepItem["TaskProcess"] = []  # 空数组，由前端异步填充
+        stepItem["Collaboration_Brief_frontEnd"] = {
+            "template": "",
+            "data": {}
         }
-        AgentSelection = generate_AgentSelection(
-            General_Goal=General_Goal,
-            Current_Task=Current_Task,
-            Agent_Board=Agent_Board,
-        )
-        Current_Task_Description = {
-            "TaskName": stepItem["StepName"],
-            "AgentInvolved": [
-                {"Name": name, "Profile": AgentProfile_Dict[name]}
-                for name in AgentSelection
-            ],
-            "InputObject_List": stepItem["InputObject_List"],
-            "OutputObject": stepItem["OutputObject"],
-            "CurrentTaskDescription": util.generate_template_sentence_for_CollaborationBrief(
-                stepItem["InputObject_List"],
-                stepItem["OutputObject"],
-                AgentSelection,
-                stepItem["TaskContent"],
-            ),
-        }
-        TaskProcess = generate_TaskProcess(
-            General_Goal=General_Goal,
-            Current_Task_Description=Current_Task_Description,
-        )
-        # add the generated AgentSelection and TaskProcess to the stepItem
-        stepItem["AgentSelection"] = AgentSelection
-        stepItem["TaskProcess"] = TaskProcess
         basePlan["Collaboration Process"].append(stepItem)
-    basePlan["General Goal"] = General_Goal
-    return basePlan
+
+    return basePlan

backend/server.py

@@ -1,7 +1,8 @@
-from flask import Flask, request, jsonify
+from flask import Flask, request, jsonify, Response, stream_with_context
 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 (
@@ -257,6 +258,45 @@ def Handle_executePlan():
     return response
 
 
+@app.route("/executePlanOptimized", methods=["post"])
+def Handle_executePlanOptimized():
+    """
+    优化版流式执行计划（阶段1+2：步骤级流式 + 动作级智能并行）
+
+    返回 SSE 流，每完成一个动作就返回结果
+    - 无依赖关系的动作并行执行
+    - 有依赖关系的动作串行执行
+
+    前端使用 EventSource 接收
+    """
+    incoming_data = request.get_json()
+
+    def generate():
+        try:
+            for chunk in executePlan_streaming(
+                plan=incoming_data["plan"],
+                num_StepToRun=incoming_data.get("num_StepToRun"),
+                RehearsalLog=incoming_data.get("RehearsalLog", []),
+                AgentProfile_Dict=AgentProfile_Dict,
+            ):
+                yield chunk
+        except Exception as e:
+            error_event = json.dumps({
+                "type": "error",
+                "message": str(e)
+            }, ensure_ascii=False)
+            yield f"data: {error_event}\n\n"
+
+    return Response(
+        stream_with_context(generate()),
+        mimetype="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "X-Accel-Buffering": "no",
+        }
+    )
+
+
 @app.route("/_saveRequestCashe", methods=["post"])
 def Handle_saveRequestCashe():
     with open(