feat:通知主题修改

合并远程web分支更新

backend/AgentCoord/LLMAPI/LLMAPI.py

@@ -1,24 +1,35 @@
 import asyncio
-import openai
+
+import httpx
+from openai import OpenAI, AsyncOpenAI, max_retries
 import yaml
 from termcolor import colored
 import os
 
+# Helper function to avoid circular import
+def print_colored(text, text_color="green", background="on_white"):
+    print(colored(text, text_color, background))
+
 # load config (apikey, apibase, model)
 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_file = {}
+    yaml_data = {}
 OPENAI_API_BASE = os.getenv("OPENAI_API_BASE") or yaml_data.get(
     "OPENAI_API_BASE", "https://api.openai.com"
 )
-openai.api_base = OPENAI_API_BASE
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or yaml_data.get(
     "OPENAI_API_KEY", ""
 )
-openai.api_key = OPENAI_API_KEY
+OPENAI_API_MODEL = os.getenv("OPENAI_API_MODEL") or yaml_data.get(
+    "OPENAI_API_MODEL", ""
+)
+
+# Initialize OpenAI clients
+client = OpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_BASE)
+async_client = AsyncOpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_BASE)
 MODEL: str = os.getenv("OPENAI_API_MODEL") or yaml_data.get(
     "OPENAI_API_MODEL", "gpt-4-turbo-preview"
 )
@@ -35,8 +46,11 @@ MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY") or yaml_data.get(
 
 # for LLM completion
 def LLM_Completion(
-    messages: list[dict], stream: bool = True, useGroq: bool = True
+    messages: list[dict], stream: bool = True, useGroq: bool = True,model_config: dict = None
 ) -> str:
+    if model_config:
+        print_colored(f"Using model config: {model_config}", "blue")
+        return _call_with_custom_config(messages,stream,model_config)
     if not useGroq or not FAST_DESIGN_MODE:
         force_gpt4 = True
         useGroq = False
@@ -69,16 +83,107 @@ def LLM_Completion(
         return _chat_completion(messages=messages)
 
 
+def _call_with_custom_config(messages: list[dict], stream: bool, model_config: dict) ->str:
+    "使用自定义配置调用API"
+    api_url = model_config.get("apiUrl", OPENAI_API_BASE)
+    api_key = model_config.get("apiKey", OPENAI_API_KEY)
+    api_model = model_config.get("apiModel", OPENAI_API_MODEL)
+
+    temp_client = OpenAI(api_key=api_key, base_url=api_url)
+    temp_async_client = AsyncOpenAI(api_key=api_key, base_url=api_url)
+
+    try:
+        if stream:
+            try:
+                loop = asyncio.get_event_loop()
+            except RuntimeError as ex:
+                if "There is no current event loop in thread" in str(ex):
+                    loop = asyncio.new_event_loop()
+                    asyncio.set_event_loop(loop)
+            return loop.run_until_complete(
+                _achat_completion_stream_custom(messages=messages, temp_async_client=temp_async_client, api_model=api_model)
+            )
+        else:
+            response = temp_client.chat.completions.create(
+                messages=messages,
+                model=api_model,
+                temperature=0.3,
+                max_tokens=4096,
+                timeout=180
+
+            )
+            # 检查响应是否有效
+            if not response.choices or len(response.choices) == 0:
+                raise Exception(f"API returned empty response for model {api_model}")
+            if not response.choices[0] or not response.choices[0].message:
+                raise Exception(f"API returned invalid response format for model {api_model}")
+
+            full_reply_content = response.choices[0].message.content
+            if full_reply_content is None:
+                raise Exception(f"API returned None content for model {api_model}")
+
+            print(colored(full_reply_content, "blue", "on_white"), end="")
+            return full_reply_content
+    except Exception as e:
+        print_colored(f"Custom API error for model {api_model} :{str(e)}","red")
+        raise
+
+
+async def _achat_completion_stream_custom(messages:list[dict], temp_async_client, api_model: str ) -> str:
+    max_retries=3
+    for attempt in range(max_retries):
+        try:
+            response = await temp_async_client.chat.completions.create(
+                messages=messages,
+                model=api_model,
+                temperature=0.3,
+                max_tokens=4096,
+                stream=True,
+                timeout=180
+            )
+
+            collected_chunks = []
+            collected_messages = []
+            async for chunk in response:
+                collected_chunks.append(chunk)
+                choices = chunk.choices
+                if len(choices) > 0 and choices[0] is not None:
+                    chunk_message = choices[0].delta
+                    if chunk_message is not None:
+                        collected_messages.append(chunk_message)
+                        if chunk_message.content:
+                            print(colored(chunk_message.content, "blue", "on_white"), end="")
+            print()
+            full_reply_content = "".join(
+                [m.content or "" for m in collected_messages if m is not None]
+            )
+
+            # 检查最终结果是否为空
+            if not full_reply_content or full_reply_content.strip() == "":
+                raise Exception(f"Stream API returned empty content for model {api_model}")
+
+            return full_reply_content
+        except httpx.RemoteProtocolError as e:
+            if attempt < max_retries - 1:
+                wait_time = (attempt + 1) *2
+                print_colored(f"⚠️ Stream connection interrupted (attempt {attempt+1}/{max_retries}). Retrying in {wait_time}s...", text_color="yellow")
+                await asyncio.sleep(wait_time)
+                continue
+        except Exception as e:
+            print_colored(f"Custom API stream error for model {api_model} :{str(e)}","red")
+            raise
+
+
 async def _achat_completion_stream_groq(messages: list[dict]) -> str:
     from groq import AsyncGroq
-    client = AsyncGroq(api_key=GROQ_API_KEY)
+    groq_client = AsyncGroq(api_key=GROQ_API_KEY)
 
     max_attempts = 5
 
     for attempt in range(max_attempts):
         print("Attempt to use Groq (Fase Design Mode):")
         try:
-            stream = await client.chat.completions.create(
+            response = await groq_client.chat.completions.create(
                 messages=messages,
                 # model='gemma-7b-it',
                 model="mixtral-8x7b-32768",
@@ -92,9 +197,18 @@ async def _achat_completion_stream_groq(messages: list[dict]) -> str:
             if attempt < max_attempts - 1:  # i is zero indexed
                 continue
             else:
-                raise "failed"
+                raise Exception("failed")
+
+    # 检查响应是否有效
+    if not response.choices or len(response.choices) == 0:
+        raise Exception("Groq API returned empty response")
+    if not response.choices[0] or not response.choices[0].message:
+        raise Exception("Groq API returned invalid response format")
+
+    full_reply_content = response.choices[0].message.content
+    if full_reply_content is None:
+        raise Exception("Groq API returned None content")
 
-    full_reply_content = stream.choices[0].message.content
     print(colored(full_reply_content, "blue", "on_white"), end="")
     print()
     return full_reply_content
@@ -103,14 +217,14 @@ async def _achat_completion_stream_groq(messages: list[dict]) -> str:
 async def _achat_completion_stream_mixtral(messages: list[dict]) -> str:
     from mistralai.client import MistralClient
     from mistralai.models.chat_completion import ChatMessage
-    client = MistralClient(api_key=MISTRAL_API_KEY)
+    mistral_client = MistralClient(api_key=MISTRAL_API_KEY)
     # client=AsyncGroq(api_key=GROQ_API_KEY)
     max_attempts = 5
 
     for attempt in range(max_attempts):
         try:
             messages[len(messages) - 1]["role"] = "user"
-            stream = client.chat(
+            stream = mistral_client.chat(
                 messages=[
                     ChatMessage(
                         role=message["role"], content=message["content"]
@@ -119,31 +233,35 @@ async def _achat_completion_stream_mixtral(messages: list[dict]) -> str:
                 ],
                 # model = "mistral-small-latest",
                 model="open-mixtral-8x7b",
-                # response_format={"type": "json_object"},
             )
             break  # If the operation is successful, break the loop
         except Exception:
             if attempt < max_attempts - 1:  # i is zero indexed
                 continue
             else:
-                raise "failed"
+                raise Exception("failed")
+
+    # 检查响应是否有效
+    if not stream.choices or len(stream.choices) == 0:
+        raise Exception("Mistral API returned empty response")
+    if not stream.choices[0] or not stream.choices[0].message:
+        raise Exception("Mistral API returned invalid response format")
 
     full_reply_content = stream.choices[0].message.content
+    if full_reply_content is None:
+        raise Exception("Mistral API returned None content")
+
     print(colored(full_reply_content, "blue", "on_white"), end="")
     print()
     return full_reply_content
 
 
 async def _achat_completion_stream_gpt35(messages: list[dict]) -> str:
-    openai.api_key = OPENAI_API_KEY
-    openai.api_base = OPENAI_API_BASE
-    response = await openai.ChatCompletion.acreate(
+    response = await async_client.chat.completions.create(
         messages=messages,
         max_tokens=4096,
-        n=1,
-        stop=None,
         temperature=0.3,
-        timeout=3,
+        timeout=600,
         model="gpt-3.5-turbo-16k",
         stream=True,
     )
@@ -154,40 +272,38 @@ async def _achat_completion_stream_gpt35(messages: list[dict]) -> str:
     # iterate through the stream of events
     async for chunk in response:
         collected_chunks.append(chunk)  # save the event response
-        choices = chunk["choices"]
-        if len(choices) > 0:
-            chunk_message = chunk["choices"][0].get(
-                "delta", {}
-            )  # extract the message
+        choices = chunk.choices
+        if len(choices) > 0 and choices[0] is not None:
+            chunk_message = choices[0].delta
+            if chunk_message is not None:
                 collected_messages.append(chunk_message)  # save the message
-            if "content" in chunk_message:
+                if chunk_message.content:
                     print(
-                    colored(chunk_message["content"], "blue", "on_white"),
+                        colored(chunk_message.content, "blue", "on_white"),
                         end="",
                     )
     print()
 
     full_reply_content = "".join(
-        [m.get("content", "") for m in collected_messages]
+        [m.content or "" for m in collected_messages if m is not None]
     )
+
+    # 检查最终结果是否为空
+    if not full_reply_content or full_reply_content.strip() == "":
+        raise Exception("Stream API (gpt-3.5) returned empty content")
+
     return full_reply_content
 
 
-async def _achat_completion_json(messages: list[dict]) -> str:
-    openai.api_key = OPENAI_API_KEY
-    openai.api_base = OPENAI_API_BASE
-
+def _achat_completion_json(messages: list[dict] ) -> str:
     max_attempts = 5
-
     for attempt in range(max_attempts):
         try:
-            stream = await openai.ChatCompletion.acreate(
+            response =  async_client.chat.completions.create(
                 messages=messages,
                 max_tokens=4096,
-                n=1,
-                stop=None,
                 temperature=0.3,
-                timeout=3,
+                timeout=600,
                 model=MODEL,
                 response_format={"type": "json_object"},
             )
@@ -196,18 +312,26 @@ async def _achat_completion_json(messages: list[dict]) -> str:
             if attempt < max_attempts - 1:  # i is zero indexed
                 continue
             else:
-                raise "failed"
+                raise Exception("failed")
+
+    # 检查响应是否有效
+    if not response.choices or len(response.choices) == 0:
+        raise Exception("OpenAI API returned empty response")
+    if not response.choices[0] or not response.choices[0].message:
+        raise Exception("OpenAI API returned invalid response format")
+
+    full_reply_content = response.choices[0].message.content
+    if full_reply_content is None:
+        raise Exception("OpenAI API returned None content")
 
-    full_reply_content = stream.choices[0].message.content
     print(colored(full_reply_content, "blue", "on_white"), end="")
     print()
     return full_reply_content
 
 
 async def _achat_completion_stream(messages: list[dict]) -> str:
-    openai.api_key = OPENAI_API_KEY
-    openai.api_base = OPENAI_API_BASE
-    response = await openai.ChatCompletion.acreate(
+    try:
+        response = await async_client.chat.completions.create(
             **_cons_kwargs(messages), stream=True
         )
 
@@ -217,39 +341,58 @@ async def _achat_completion_stream(messages: list[dict]) -> str:
         # iterate through the stream of events
         async for chunk in response:
             collected_chunks.append(chunk)  # save the event response
-        choices = chunk["choices"]
-        if len(choices) > 0:
-            chunk_message = chunk["choices"][0].get(
-                "delta", {}
-            )  # extract the message
+            choices = chunk.choices
+            if len(choices) > 0 and choices[0] is not None:
+                chunk_message = choices[0].delta
+                if chunk_message is not None:
                     collected_messages.append(chunk_message)  # save the message
-            if "content" in chunk_message:
+                    if chunk_message.content:
                         print(
-                    colored(chunk_message["content"], "blue", "on_white"),
+                            colored(chunk_message.content, "blue", "on_white"),
                             end="",
                         )
         print()
 
         full_reply_content = "".join(
-        [m.get("content", "") for m in collected_messages]
+            [m.content or "" for m in collected_messages if m is not None]
         )
+
+        # 检查最终结果是否为空
+        if not full_reply_content or full_reply_content.strip() == "":
+            raise Exception("Stream API returned empty content")
+
         return full_reply_content
+    except Exception as e:
+        print_colored(f"OpenAI API error in _achat_completion_stream: {str(e)}", "red")
+        raise
 
 
 def _chat_completion(messages: list[dict]) -> str:
-    rsp = openai.ChatCompletion.create(**_cons_kwargs(messages))
-    content = rsp["choices"][0]["message"]["content"]
+    try:
+        rsp = client.chat.completions.create(**_cons_kwargs(messages))
+
+        # 检查响应是否有效
+        if not rsp.choices or len(rsp.choices) == 0:
+            raise Exception("OpenAI API returned empty response")
+        if not rsp.choices[0] or not rsp.choices[0].message:
+            raise Exception("OpenAI API returned invalid response format")
+
+        content = rsp.choices[0].message.content
+        if content is None:
+            raise Exception("OpenAI API returned None content")
+
         return content
+    except Exception as e:
+        print_colored(f"OpenAI API error in _chat_completion: {str(e)}", "red")
+        raise
 
 
 def _cons_kwargs(messages: list[dict]) -> dict:
     kwargs = {
         "messages": messages,
-        "max_tokens": 4096,
-        "n": 1,
-        "stop": None,
-        "temperature": 0.5,
-        "timeout": 3,
+        "max_tokens": 2000,
+        "temperature": 0.3,
+        "timeout": 600,
     }
     kwargs_mode = {"model": MODEL}
     kwargs.update(kwargs_mode)

backend/AgentCoord/PlanEngine/AgentSelectModify.py

@@ -7,6 +7,8 @@ PROMPT_ABILITY_REQUIREMENT_GENERATION = """
 ## Instruction
 Based on "General Goal" and "Current Task", output a formatted "Ability Requirement" which lists at least 3 different ability requirement that is required by the "Current Task". The ability should be summarized concisely within a few words.
 
+**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all ability requirements.**
+
 ## General Goal (The general goal for the collaboration plan, "Current Task" is just one of its substep)
 {General_Goal}
 
@@ -49,6 +51,8 @@ PROMPT_AGENT_ABILITY_SCORING = """
 ## Instruction
 Based on "Agent Board" and "Ability Requirement", output a score for each agent to estimate the possibility that the agent can fulfil the "Ability Requirement". The score should be 1-5. Provide a concise reason before you assign the score.
 
+**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all reasons and explanations.**
+
 ## AgentBoard
 {Agent_Board}
 
@@ -133,5 +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/AgentSelection_Generator.py

@@ -35,6 +35,8 @@ PROMPT_AGENT_SELECTION_GENERATION = """
 ## Instruction
 Based on "General Goal", "Current Task" and "Agent Board", output a formatted "Agent Selection Plan". Your selection should consider the ability needed for "Current Task" and the profile of each agent in "Agent Board".
 
+**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all explanations and reasoning, though agent names should remain in their original form.**
+
 ## General Goal (Specify the general goal for the collaboration plan)
 {General_Goal}
 
@@ -80,6 +82,10 @@ def generate_AbilityRequirement(General_Goal, Current_Task):
 
 
 def generate_AgentSelection(General_Goal, Current_Task, Agent_Board):
+    # Check if Agent_Board is None or empty
+    if Agent_Board is None or len(Agent_Board) == 0:
+        raise ValueError("Agent_Board cannot be None or empty. Please ensure agents are set via /setAgents endpoint before generating a plan.")
+
     messages = [
         {
             "role": "system",

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

backend/AgentCoord/PlanEngine/branch_PlanOutline.py

@@ -9,6 +9,8 @@ PROMPT_PLAN_OUTLINE_BRANCHING = """
 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)
 {General_Goal}
 

backend/AgentCoord/PlanEngine/branch_TaskProcess.py

@@ -29,6 +29,8 @@ PROMPT_TASK_PROCESS_BRANCHING = """
 Based on "Existing Steps", your task is to comeplete the "Remaining Steps" for the "Task for Current Step".
 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 the Description field and all explanations, while keeping ID, ActionType, and AgentName in their original format.**
+
 ## General Goal (The general goal for the collaboration plan, you just design the plan for one of its step (i.e. "Task for Current Step"))
 {General_Goal}
 
@@ -55,27 +57,40 @@ Note: "Modification Requirement" specifies how to modify the "Baseline Completio
       "ID": "Action4",
       "ActionType": "Propose",
       "AgentName": "Mia",
-      "Description": "Propose psychological theories on love and attachment that could be applied to AI's emotional development.",
+      "Description": "提议关于人工智能情感发展的心理学理论，重点关注爱与依恋的概念。",
       "ImportantInput": [
         "InputObject:Story Outline"
       ]
     }},
     {{
       "ID": "Action5",
-      "ActionType": "Propose",
+      "ActionType": "Critique",
       "AgentName": "Noah",
-      "Description": "Propose ethical considerations and philosophical questions regarding AI's capacity for love.",
-      "ImportantInput": []
+      "Description": "对Mia提出的心理学理论进行批判性评估，分析其在AI情感发展场景中的适用性和局限性。",
+      "ImportantInput": [
+        "ActionResult:Action4"
+      ]
     }},
     {{
       "ID": "Action6",
-      "ActionType": "Finalize",
+      "ActionType": "Improve",
       "AgentName": "Liam",
-      "Description": "Combine the poetic elements and ethical considerations into a cohesive set of core love elements for the story.",
+      "Description": "基于Noah的批判性反馈，改进和完善心理学理论框架，使其更贴合AI情感发展的实际需求。",
       "ImportantInput": [
-        "ActionResult:Action1",
+        "ActionResult:Action4",
         "ActionResult:Action5"
       ]
+    }},
+    {{
+      "ID": "Action7",
+      "ActionType": "Finalize",
+      "AgentName": "Mia",
+      "Description": "综合所有提议、批判和改进意见，整合并提交最终的AI情感发展心理学理论框架。",
+      "ImportantInput": [
+        "ActionResult:Action4",
+        "ActionResult:Action5",
+        "ActionResult:Action6"
+      ]
     }}
   ]
 }}
@@ -84,7 +99,12 @@ Note: "Modification Requirement" specifies how to modify the "Baseline Completio
 ImportantInput: Specify if there is any previous result that should be taken special consideration during the execution the action. Should be of format "InputObject:xx" or "ActionResult:xx".
 InputObject_List: List existing objects that should be utilized in current step.
 AgentName: Specify the agent who will perform the action, You CAN ONLY USE THE NAME APPEARS IN "AgentInvolved".
-ActionType: Specify the type of action, note that only the last action can be of type "Finalize", and the last action must be "Finalize".
+ActionType: Specify the type of action. **CRITICAL REQUIREMENTS:**
+  1. The "Remaining Steps" MUST include ALL FOUR action types in the following order: Propose -> Critique -> Improve -> Finalize
+  2. Each action type (Propose, Critique, Improve, Finalize) MUST appear at least once
+  3. The actions must follow the sequence: Propose actions first, then Critique actions, then Improve actions, and Finalize must be the last action
+  4. Even if only one agent is involved in a phase, that phase must still have its corresponding action type
+  5. The last action must ALWAYS be of type "Finalize"
 
 """
 

backend/AgentCoord/PlanEngine/planOutline_Generator.py

@@ -7,6 +7,8 @@ PROMPT_PLAN_OUTLINE_GENERATION = """
 ## Instruction
 Based on "Output Format Example", "General Goal", and "Initial Key Object List", output a formatted "Plan_Outline".
 
+**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all content, including StepName, TaskContent, and OutputObject fields.**
+
 ## Initial Key Object List (Specify the list of initial key objects available, each initial key object should be the input object of at least one Step)
 {InitialObject_List}
 
@@ -83,4 +85,16 @@ def generate_PlanOutline(InitialObject_List, General_Goal):
             ),
         },
     ]
-    return read_LLM_Completion(messages)["Plan_Outline"]
+    result = read_LLM_Completion(messages)
+    if isinstance(result, dict) and "Plan_Outline" in result:
+        return result["Plan_Outline"]
+    else:
+        # 如果格式不正确，返回默认的计划大纲
+        return [
+            {
+                "StepName": "Default Step",
+                "TaskContent": "Generated default plan step due to format error",
+                "InputObject_List": [],
+                "OutputObject": "Default Output"
+            }
+        ]

backend/AgentCoord/PlanEngine/taskProcess_Generator.py

@@ -25,6 +25,8 @@ PROMPT_TASK_PROCESS_GENERATION = """
 ## Instruction
 Based on "General Goal", "Task for Current Step", "Action Set" and "Output Format Example", design a plan for "Task for Current Step", output a formatted "Task_Process_Plan".
 
+**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for the Description field and all explanations, while keeping ID, ActionType, and AgentName in their original format.**
+
 ## General Goal (The general goal for the collaboration plan, you just design the plan for one of its step (i.e. "Task for Current Step"))
 {General_Goal}
 

backend/AgentCoord/RehearsalEngine_V2/Action/baseAction.py

@@ -9,6 +9,8 @@ You are within a multi-agent collaboration for the "Current Task".
 Now it's your turn to take action. Read the "Context Information" and take your action following "Instruction for Your Current Action".
 Note: Important Input for your action are marked with *Important Input*
 
+**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all your answers and outputs.**
+
 ## Context Information
 
 ### General Goal (The "Current Task" is indeed a substep of the general goal)
@@ -80,10 +82,34 @@ class BaseAction():
                 Important_Mark = ""
             action_Record += PROMPT_TEMPLATE_ACTION_RECORD.format(AgentName = actionInfo["AgentName"], Action_Description = actionInfo["AgentName"], Action_Result = actionInfo["Action_Result"], Important_Mark = Important_Mark)
 
-        prompt = PROMPT_TEMPLATE_TAKE_ACTION_BASE.format(agentName = agentName, agentProfile = AgentProfile_Dict[agentName], General_Goal = General_Goal, Current_Task_Description = TaskDescription, Input_Objects = inputObject_Record, History_Action = action_Record, Action_Description = self.info["Description"], Action_Custom_Note = self.Action_Custom_Note)
+        # Handle missing agent profiles gracefully
+        model_config = None
+        if agentName not in AgentProfile_Dict:
+            print_colored(text=f"Warning: Agent '{agentName}' not found in AgentProfile_Dict. Using default profile.", text_color="yellow")
+            agentProfile = f"AI Agent named {agentName}"
+        else:
+            # agentProfile = AgentProfile_Dict[agentName]
+            agent_config = AgentProfile_Dict[agentName]
+            agentProfile = agent_config.get("profile",f"AI Agent named {agentName}")
+            if agent_config.get("useCustomAPI",False):
+                model_config = {
+                    "apiModel":agent_config.get("apiModel"),
+                    "apiUrl":agent_config.get("apiUrl"),
+                    "apiKey":agent_config.get("apiKey"),
+                }
+        prompt = PROMPT_TEMPLATE_TAKE_ACTION_BASE.format(
+            agentName = agentName,
+            agentProfile = agentProfile,
+            General_Goal = General_Goal,
+            Current_Task_Description = TaskDescription,
+            Input_Objects = inputObject_Record,
+            History_Action = action_Record,
+            Action_Description = self.info["Description"],
+            Action_Custom_Note = self.Action_Custom_Note
+        )
         print_colored(text = prompt, text_color="red")
         messages = [{"role":"system", "content": prompt}]
-        ActionResult = LLM_Completion(messages,True,False)
+        ActionResult = LLM_Completion(messages,True,False,model_config=model_config)
         ActionInfo_with_Result = copy.deepcopy(self.info)
         ActionInfo_with_Result["Action_Result"] = ActionResult
 

backend/AgentCoord/RehearsalEngine_V2/Action/customAction_Critique.py

@@ -1,7 +1,7 @@
 from AgentCoord.RehearsalEngine_V2.Action import BaseAction
 
 ACTION_CUSTOM_NOTE = '''
-Note: Since you are in a conversation, your critique must be concise, clear and easy to read, don't overwhelm others. If you want to list some points, list at most 2 points. 
+注意：由于你在对话中，你的批评必须简洁、清晰且易于阅读，不要让人感到压力过大。如果你要列出一些观点，最多列出2点。
 
 '''
 

backend/AgentCoord/RehearsalEngine_V2/Action/customAction_Finalize.py

@@ -2,9 +2,9 @@ from AgentCoord.util.converter import read_outputObject_content
 from AgentCoord.RehearsalEngine_V2.Action import BaseAction
 
 ACTION_CUSTOM_NOTE = '''
-Note: You can say something before you give the final content of {OutputName}. When you decide to give the final content of {OutputName}, it should be enclosed like this:
+注意：你可以在给出{OutputName}的最终内容之前先说一些话。当你决定给出{OutputName}的最终内容时，应该这样包含：
 ```{OutputName}
-(the content of {OutputName})
+（{OutputName}的内容）
 ```
 '''
 

backend/AgentCoord/RehearsalEngine_V2/Action/customAction_Improve.py

@@ -1,12 +1,12 @@
 from AgentCoord.RehearsalEngine_V2.Action import BaseAction
 
 ACTION_CUSTOM_NOTE = '''
-Note: You can say something before you provide the improved version of the content.
-The improved version you provide must be a completed version (e.g. if you provide a improved story, you should give completed story content, rather than just reporting where you have improved).
-When you decide to give the improved version of the content, it should be start like this:
+注意：你可以在提供改进版本的内容之前先说一些话。
+你提供的改进版本必须是完整的版本（例如，如果你提供改进的故事，你应该给出完整的故事内容，而不仅仅是报告你在哪里改进了）。
+当你决定提供内容的改进版本时，应该这样开始：
 
-## Improved version of xxx
-(the improved version of the content)
+## xxx的改进版本
+（改进版本的内容）
 ```
 
 '''

backend/AgentCoord/RehearsalEngine_V2/ExecutePlan.py

@@ -85,9 +85,17 @@ def executePlan(plan, num_StepToRun, RehearsalLog, AgentProfile_Dict):
         # start the group chat
         util.print_colored(TaskDescription, text_color="green")
         ActionHistory = []
+        action_count = 0
+        total_actions = len(TaskProcess)
+
         for ActionInfo in TaskProcess:
+            action_count += 1
             actionType = ActionInfo["ActionType"]
             agentName = ActionInfo["AgentName"]
+
+            # 添加进度日志
+            util.print_colored(f"🔄 Executing action {action_count}/{total_actions}: {actionType} by {agentName}", text_color="yellow")
+
             if actionType in Action.customAction_Dict:
                 currentAction = Action.customAction_Dict[actionType](
                     info=ActionInfo,

backend/AgentCoord/RehearsalEngine_V2/ExecutePlan_Optimized.py

@@ -0,0 +1,608 @@
+"""
+优化版执行计划 - 支持动态追加步骤
+在执行过程中可以接收新的步骤并追加到执行队列
+"""
+
+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
+from AgentCoord.RehearsalEngine_V2.execution_state import execution_state_manager
+from AgentCoord.RehearsalEngine_V2.dynamic_execution_manager import dynamic_execution_manager
+
+
+# ==================== 配置参数 ====================
+# 最大并发请求数
+MAX_CONCURRENT_REQUESTS = 2
+
+# 批次之间的延迟
+BATCH_DELAY = 1.0
+
+# 429错误重试次数和延迟
+MAX_RETRIES = 3
+RETRY_DELAY = 5.0
+
+
+# ==================== 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
+
+    Args:
+        TaskProcess: 任务流程列表
+
+    Returns:
+        依赖映射字典 {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
+
+    Args:
+        TaskProcess: 任务流程列表
+        dependency_map: 依赖图
+
+    Returns:
+        批次列表 [[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
+            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]
+            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
+
+    Args:
+        ActionInfo: 动作信息
+        General_Goal: 总体目标
+        TaskDescription: 任务描述
+        OutputName: 输出对象名称
+        KeyObjects: 关键对象字典
+        ActionHistory: 动作历史
+        agentName: 智能体名称
+        AgentProfile_Dict: 智能体配置字典
+        InputName_List: 输入名称列表
+
+    Returns:
+        动作执行结果
+    """
+    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,
+        )
+
+    # 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
+
+    Args:
+        stepDescrip: 步骤描述
+        General_Goal: 总体目标
+        AgentProfile_Dict: 智能体配置字典
+        KeyObjects: 关键对象字典
+        step_index: 步骤索引
+        total_steps: 总步骤数
+
+    Yields:
+        执行事件字典
+    """
+    # 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,
+    }
+
+    # 返回步骤开始事件
+    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):
+        # 在每个批次执行前检查暂停状态
+        should_continue = await execution_state_manager.async_check_pause()
+        if not should_continue:
+            util.print_colored("🛑 用户请求停止执行", "red")
+            return
+
+        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
+        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
+            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_dynamic(
+    plan: Dict,
+    num_StepToRun: int,
+    RehearsalLog: List,
+    AgentProfile_Dict: Dict,
+    existingKeyObjects: Dict = None,
+    execution_id: str = None
+) -> Generator[str, None, None]:
+    """
+    动态执行计划，支持在执行过程中追加新步骤
+
+    Args:
+        plan: 执行计划
+        num_StepToRun: 要运行的步骤数
+        RehearsalLog: 已执行的历史记录
+        AgentProfile_Dict: 智能体配置
+        existingKeyObjects: 已存在的KeyObjects
+        execution_id: 执行ID（用于动态追加步骤）
+
+    Yields:
+        SSE格式的事件字符串
+    """
+    # 初始化执行状态
+    general_goal = plan.get("General Goal", "")
+    execution_state_manager.start_execution(general_goal)
+
+    print(colored(f"⏸️  执行状态管理器已启动，支持暂停/恢复", "green"))
+
+    # 准备执行
+    KeyObjects = existingKeyObjects.copy() if existingKeyObjects else {}
+    finishedStep_index = -1
+
+    for logNode in RehearsalLog:
+        if logNode["LogNodeType"] == "step":
+            finishedStep_index += 1
+        if logNode["LogNodeType"] == "object":
+            KeyObjects[logNode["NodeId"]] = logNode["content"]
+
+    if existingKeyObjects:
+        print(colored(f"📦 使用已存在的 KeyObjects: {list(existingKeyObjects.keys())}", "cyan"))
+
+    # 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]
+
+    # 使用动态执行管理器
+    if execution_id:
+        # 初始化执行管理器，使用传入的execution_id
+        actual_execution_id = dynamic_execution_manager.start_execution(general_goal, steps_to_run, execution_id)
+        print(colored(f"🚀 开始执行计划（动态模式），共 {len(steps_to_run)} 个步骤，执行ID: {actual_execution_id}", "cyan"))
+    else:
+        print(colored(f"🚀 开始执行计划（流式推送），共 {len(steps_to_run)} 个步骤", "cyan"))
+
+    total_steps = len(steps_to_run)
+
+    # 使用队列实现流式推送
+    async def produce_events(queue: asyncio.Queue):
+        """异步生产者"""
+        try:
+            step_index = 0
+
+            if execution_id:
+                # 动态模式：循环获取下一个步骤
+                # 等待新步骤的最大次数（避免无限等待）
+                max_empty_wait_cycles = 5  # 最多等待60次，每次等待1秒
+                empty_wait_count = 0
+
+                while True:
+                    # 检查暂停状态
+                    should_continue = await execution_state_manager.async_check_pause()
+                    if not should_continue:
+                        print(colored("🛑 用户请求停止执行", "red"))
+                        await queue.put({
+                            "type": "error",
+                            "message": "执行已被用户停止"
+                        })
+                        break
+
+                    # 获取下一个步骤
+                    stepDescrip = dynamic_execution_manager.get_next_step(execution_id)
+
+                    if stepDescrip is None:
+                        # 没有更多步骤了，检查是否应该继续等待
+                        empty_wait_count += 1
+
+                        # 获取执行信息
+                        execution_info = dynamic_execution_manager.get_execution_info(execution_id)
+
+                        if execution_info:
+                            queue_total_steps = execution_info.get("total_steps", 0)
+                            completed_steps = execution_info.get("completed_steps", 0)
+
+                            # 如果没有步骤在队列中（queue_total_steps为0），立即退出
+                            if queue_total_steps == 0:
+                                print(colored(f"⚠️  没有步骤在队列中，退出执行", "yellow"))
+                                break
+
+                            # 如果所有步骤都已完成，等待可能的新步骤
+                            if completed_steps >= queue_total_steps:
+                                if empty_wait_count >= max_empty_wait_cycles:
+                                    # 等待超时，退出执行
+                                    print(colored(f"✅ 所有步骤执行完成，等待超时", "green"))
+                                    break
+                                else:
+                                    # 等待新步骤追加
+                                    print(colored(f"⏳ 等待新步骤追加... ({empty_wait_count}/{max_empty_wait_cycles})", "cyan"))
+                                    await asyncio.sleep(1)
+                                    continue
+                            else:
+                                # 还有步骤未完成，继续尝试获取
+                                print(colored(f"⏳ 等待步骤就绪... ({completed_steps}/{queue_total_steps})", "cyan"))
+                                await asyncio.sleep(0.5)
+                                empty_wait_count = 0  # 重置等待计数
+                                continue
+                        else:
+                            # 执行信息不存在，退出
+                            print(colored(f"⚠️  执行信息不存在，退出执行", "yellow"))
+                            break
+
+                    # 重置等待计数
+                    empty_wait_count = 0
+
+                    # 获取最新的总步骤数（用于显示）
+                    execution_info = dynamic_execution_manager.get_execution_info(execution_id)
+                    current_total_steps = execution_info.get("total_steps", total_steps) if execution_info else total_steps
+
+                    # 执行步骤
+                    async for event in execute_step_async_streaming(
+                        stepDescrip,
+                        plan["General Goal"],
+                        AgentProfile_Dict,
+                        KeyObjects,
+                        step_index,
+                        current_total_steps  # 使用动态更新的总步骤数
+                    ):
+                        if execution_state_manager.is_stopped():
+                            await queue.put({
+                                "type": "error",
+                                "message": "执行已被用户停止"
+                            })
+                            return
+
+                        await queue.put(event)
+
+                    # 标记步骤完成
+                    dynamic_execution_manager.mark_step_completed(execution_id)
+
+                    # 更新KeyObjects
+                    OutputName = stepDescrip.get("OutputObject", "")
+                    if OutputName and OutputName in KeyObjects:
+                        # 对象日志节点会在step_complete中发送
+                        pass
+
+                    step_index += 1
+
+            else:
+                # 非动态模式：按顺序执行所有步骤
+                for step_index, stepDescrip in enumerate(steps_to_run):
+                    should_continue = await execution_state_manager.async_check_pause()
+                    if not should_continue:
+                        print(colored("🛑 用户请求停止执行", "red"))
+                        await queue.put({
+                            "type": "error",
+                            "message": "执行已被用户停止"
+                        })
+                        return
+
+                    async for event in execute_step_async_streaming(
+                        stepDescrip,
+                        plan["General Goal"],
+                        AgentProfile_Dict,
+                        KeyObjects,
+                        step_index,
+                        total_steps
+                    ):
+                        if execution_state_manager.is_stopped():
+                            await queue.put({
+                                "type": "error",
+                                "message": "执行已被用户停止"
+                            })
+                            return
+
+                        await queue.put(event)
+
+        except Exception as e:
+            await queue.put({
+                "type": "error",
+                "message": f"执行出错: {str(e)}"
+            })
+        finally:
+            await queue.put(None)
+
+    # 运行异步任务并实时yield
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+
+    try:
+        queue = asyncio.Queue(maxsize=10)
+        producer_task = loop.create_task(produce_events(queue))
+
+        while True:
+            event = loop.run_until_complete(queue.get())
+            if event is None:
+                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"
+
+        loop.run_until_complete(producer_task)
+
+        if not execution_state_manager.is_stopped():
+            complete_event = json.dumps({
+                "type": "execution_complete",
+                "total_steps": total_steps
+            }, ensure_ascii=False)
+            yield f"data: {complete_event}\n\n"
+
+    finally:
+        # 在关闭事件循环之前先清理执行记录
+        if execution_id:
+            # 清理执行记录
+            dynamic_execution_manager.cleanup(execution_id)
+
+        if 'producer_task' in locals():
+            if not producer_task.done():
+                producer_task.cancel()
+
+        # 确保所有任务都完成后再关闭事件循环
+        try:
+            pending = asyncio.all_tasks(loop)
+            for task in pending:
+                task.cancel()
+            loop.run_until_complete(asyncio.gather(*pending, return_exceptions=True))
+        except Exception:
+            pass  # 忽略清理过程中的错误
+
+        loop.close()
+
+
+# 保留旧版本函数以保持兼容性
+executePlan_streaming = executePlan_streaming_dynamic

backend/AgentCoord/RehearsalEngine_V2/dynamic_execution_manager.py

@@ -0,0 +1,241 @@
+"""
+动态执行管理器
+用于在任务执行过程中动态追加新步骤
+"""
+
+import asyncio
+from typing import Dict, List, Optional, Any
+from threading import Lock
+
+
+class DynamicExecutionManager:
+    """
+    动态执行管理器
+    管理正在执行的任务，支持动态追加新步骤
+    """
+
+    def __init__(self):
+        # 执行状态: goal -> execution_info
+        self._executions: Dict[str, Dict] = {}
+
+        # 线程锁
+        self._lock = Lock()
+
+        # 步骤队列: goal -> List[step]
+        self._step_queues: Dict[str, List] = {}
+
+        # 已执行的步骤索引: goal -> Set[step_index]
+        self._executed_steps: Dict[str, set] = {}
+
+        # 待执行的步骤索引: goal -> List[step_index]
+        self._pending_steps: Dict[str, List[int]] = {}
+
+    def start_execution(self, goal: str, initial_steps: List[Dict], execution_id: str = None) -> str:
+        """
+        开始执行一个新的任务
+
+        Args:
+            goal: 任务目标
+            initial_steps: 初始步骤列表
+            execution_id: 执行ID，如果不提供则自动生成
+
+        Returns:
+            执行ID
+        """
+        with self._lock:
+            # 如果未提供execution_id，则生成一个
+            if execution_id is None:
+                execution_id = f"{goal}_{asyncio.get_event_loop().time()}"
+
+            self._executions[execution_id] = {
+                "goal": goal,
+                "status": "running",
+                "total_steps": len(initial_steps),
+                "completed_steps": 0
+            }
+
+            # 初始化步骤队列
+            self._step_queues[execution_id] = initial_steps.copy()
+
+            # 初始化已执行步骤集合
+            self._executed_steps[execution_id] = set()
+
+            # 初始化待执行步骤索引
+            self._pending_steps[execution_id] = list(range(len(initial_steps)))
+
+            print(f"🚀 启动执行: {execution_id}")
+            print(f"📊 初始步骤数: {len(initial_steps)}")
+            print(f"📋 待执行步骤索引: {self._pending_steps[execution_id]}")
+
+            return execution_id
+
+    def add_steps(self, execution_id: str, new_steps: List[Dict]) -> int:
+        """
+        向执行中追加新步骤
+
+        Args:
+            execution_id: 执行ID
+            new_steps: 新步骤列表
+
+        Returns:
+            追加的步骤数量
+        """
+        with self._lock:
+            if execution_id not in self._step_queues:
+                print(f"⚠️  警告: 执行ID {execution_id} 不存在，无法追加步骤")
+                return 0
+
+            current_count = len(self._step_queues[execution_id])
+
+            # 追加新步骤到队列
+            self._step_queues[execution_id].extend(new_steps)
+
+            # 添加新步骤的索引到待执行列表
+            new_indices = list(range(current_count, current_count + len(new_steps)))
+            self._pending_steps[execution_id].extend(new_indices)
+
+            # 更新总步骤数
+            old_total = self._executions[execution_id]["total_steps"]
+            self._executions[execution_id]["total_steps"] = len(self._step_queues[execution_id])
+            new_total = self._executions[execution_id]["total_steps"]
+
+            print(f"➕ 追加了 {len(new_steps)} 个步骤到 {execution_id}")
+            print(f"📊 步骤总数: {old_total} -> {new_total}")
+            print(f"📋 待执行步骤索引: {self._pending_steps[execution_id]}")
+
+            return len(new_steps)
+
+    def get_next_step(self, execution_id: str) -> Optional[Dict]:
+        """
+        获取下一个待执行的步骤
+
+        Args:
+            execution_id: 执行ID
+
+        Returns:
+            下一个步骤，如果没有则返回None
+        """
+        with self._lock:
+            if execution_id not in self._pending_steps:
+                print(f"⚠️  警告: 执行ID {execution_id} 不存在")
+                return None
+
+            # 获取第一个待执行步骤的索引
+            if not self._pending_steps[execution_id]:
+                return None
+
+            step_index = self._pending_steps[execution_id].pop(0)
+
+            # 从队列中获取步骤
+            if step_index >= len(self._step_queues[execution_id]):
+                print(f"⚠️  警告: 步骤索引 {step_index} 超出范围")
+                return None
+
+            step = self._step_queues[execution_id][step_index]
+
+            # 标记为已执行
+            self._executed_steps[execution_id].add(step_index)
+
+            step_name = step.get("StepName", "未知")
+            print(f"🎯 获取下一个步骤: {step_name} (索引: {step_index})")
+            print(f"📋 剩余待执行步骤: {len(self._pending_steps[execution_id])}")
+
+            return step
+
+    def mark_step_completed(self, execution_id: str):
+        """
+        标记一个步骤完成
+
+        Args:
+            execution_id: 执行ID
+        """
+        with self._lock:
+            if execution_id in self._executions:
+                self._executions[execution_id]["completed_steps"] += 1
+                completed = self._executions[execution_id]["completed_steps"]
+                total = self._executions[execution_id]["total_steps"]
+                print(f"📊 步骤完成进度: {completed}/{total}")
+            else:
+                print(f"⚠️  警告: 执行ID {execution_id} 不存在")
+
+    def get_execution_info(self, execution_id: str) -> Optional[Dict]:
+        """
+        获取执行信息
+
+        Args:
+            execution_id: 执行ID
+
+        Returns:
+            执行信息字典
+        """
+        with self._lock:
+            return self._executions.get(execution_id)
+
+    def get_pending_count(self, execution_id: str) -> int:
+        """
+        获取待执行步骤数量
+
+        Args:
+            execution_id: 执行ID
+
+        Returns:
+            待执行步骤数量
+        """
+        with self._lock:
+            if execution_id not in self._pending_steps:
+                return 0
+            return len(self._pending_steps[execution_id])
+
+    def has_more_steps(self, execution_id: str) -> bool:
+        """
+        检查是否还有更多步骤待执行
+
+        Args:
+            execution_id: 执行ID
+
+        Returns:
+            是否还有待执行步骤
+        """
+        with self._lock:
+            if execution_id not in self._pending_steps:
+                return False
+            return len(self._pending_steps[execution_id]) > 0
+
+    def finish_execution(self, execution_id: str):
+        """
+        完成执行
+
+        Args:
+            execution_id: 执行ID
+        """
+        with self._lock:
+            if execution_id in self._executions:
+                self._executions[execution_id]["status"] = "completed"
+
+    def cancel_execution(self, execution_id: str):
+        """
+        取消执行
+
+        Args:
+            execution_id: 执行ID
+        """
+        with self._lock:
+            if execution_id in self._executions:
+                self._executions[execution_id]["status"] = "cancelled"
+
+    def cleanup(self, execution_id: str):
+        """
+        清理执行记录
+
+        Args:
+            execution_id: 执行ID
+        """
+        with self._lock:
+            self._executions.pop(execution_id, None)
+            self._step_queues.pop(execution_id, None)
+            self._executed_steps.pop(execution_id, None)
+            self._pending_steps.pop(execution_id, None)
+
+
+# 全局单例
+dynamic_execution_manager = DynamicExecutionManager()

backend/AgentCoord/RehearsalEngine_V2/execution_state.py

@@ -0,0 +1,190 @@
+"""
+全局执行状态管理器
+用于支持任务的暂停、恢复和停止功能
+使用轮询检查机制，确保线程安全
+"""
+
+import threading
+import asyncio
+import time
+from typing import Optional
+from enum import Enum
+
+
+class ExecutionStatus(Enum):
+    """执行状态枚举"""
+    RUNNING = "running"      # 正在运行
+    PAUSED = "paused"        # 已暂停
+    STOPPED = "stopped"      # 已停止
+    IDLE = "idle"            # 空闲
+
+
+class ExecutionStateManager:
+    """
+    全局执行状态管理器（单例模式）
+
+    功能：
+    - 管理任务执行状态（运行/暂停/停止）
+    - 使用轮询检查机制，避免异步事件的线程问题
+    - 提供线程安全的状态查询和修改接口
+    """
+
+    _instance: Optional['ExecutionStateManager'] = None
+    _lock = threading.Lock()
+
+    def __new__(cls):
+        """单例模式"""
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    cls._instance = super().__new__(cls)
+                    cls._instance._initialized = False
+        return cls._instance
+
+    def __init__(self):
+        """初始化状态管理器"""
+        if self._initialized:
+            return
+
+        self._initialized = True
+        self._status = ExecutionStatus.IDLE
+        self._current_goal: Optional[str] = None  # 当前执行的任务目标
+        # 使用简单的布尔标志，而不是 asyncio.Event
+        self._should_pause = False
+        self._should_stop = False
+
+    def get_status(self) -> ExecutionStatus:
+        """获取当前执行状态"""
+        with self._lock:
+            return self._status
+
+    def set_goal(self, goal: str):
+        """设置当前执行的任务目标"""
+        with self._lock:
+            self._current_goal = goal
+
+    def get_goal(self) -> Optional[str]:
+        """获取当前执行的任务目标"""
+        with self._lock:
+            return self._current_goal
+
+    def start_execution(self, goal: str):
+        """开始执行"""
+        with self._lock:
+            self._status = ExecutionStatus.RUNNING
+            self._current_goal = goal
+            self._should_pause = False
+            self._should_stop = False
+        print(f"🚀 [DEBUG] start_execution: 状态设置为 RUNNING, goal={goal}")
+
+    def pause_execution(self) -> bool:
+        """
+        暂停执行
+
+        Returns:
+            bool: 是否成功暂停
+        """
+        with self._lock:
+            if self._status != ExecutionStatus.RUNNING:
+                print(f"⚠️  [DEBUG] pause_execution: 当前状态不是RUNNING，而是 {self._status}")
+                return False
+            self._status = ExecutionStatus.PAUSED
+            self._should_pause = True
+            print(f"⏸️  [DEBUG] pause_execution: 状态设置为PAUSED, should_pause=True")
+            return True
+
+    def resume_execution(self) -> bool:
+        """
+        恢复执行
+
+        Returns:
+            bool: 是否成功恢复
+        """
+        with self._lock:
+            if self._status != ExecutionStatus.PAUSED:
+                print(f"⚠️  [DEBUG] resume_execution: 当前状态不是PAUSED，而是 {self._status}")
+                return False
+            self._status = ExecutionStatus.RUNNING
+            self._should_pause = False
+            print(f"▶️  [DEBUG] resume_execution: 状态设置为RUNNING, should_pause=False")
+            return True
+
+    def stop_execution(self) -> bool:
+        """
+        停止执行
+
+        Returns:
+            bool: 是否成功停止
+        """
+        with self._lock:
+            if self._status in [ExecutionStatus.IDLE, ExecutionStatus.STOPPED]:
+                return False
+            self._status = ExecutionStatus.STOPPED
+            self._should_stop = True
+            self._should_pause = False
+            print(f"🛑 [DEBUG] stop_execution: 状态设置为STOPPED")
+            return True
+
+    def reset(self):
+        """重置状态为空闲"""
+        with self._lock:
+            self._status = ExecutionStatus.IDLE
+            self._current_goal = None
+            self._should_pause = False
+            self._should_stop = False
+            print(f"🔄 [DEBUG] reset: 状态重置为IDLE")
+
+    async def async_check_pause(self):
+        """
+        异步检查是否需要暂停（轮询方式）
+
+        如果处于暂停状态，会阻塞当前协程直到恢复或停止
+        应该在执行循环的关键点调用此方法
+
+        Returns:
+            bool: 如果返回True表示应该继续执行，False表示应该停止
+        """
+        # 使用轮询检查，避免异步事件问题
+        while True:
+            # 检查停止标志
+            if self._should_stop:
+                print("🛑 [DEBUG] async_check_pause: 检测到停止信号")
+                return False
+
+            # 检查暂停状态
+            if self._should_pause:
+                # 处于暂停状态，等待恢复
+                print("⏸️  [DEBUG] async_check_pause: 检测到暂停，等待恢复...")
+                await asyncio.sleep(0.1)  # 短暂睡眠，避免占用CPU
+
+                # 如果恢复，继续执行
+                if not self._should_pause:
+                    print("▶️  [DEBUG] async_check_pause: 从暂停中恢复!")
+                    continue
+                # 如果停止了，返回
+                if self._should_stop:
+                    return False
+                # 继续等待
+                continue
+
+            # 既没有停止也没有暂停，可以继续执行
+            return True
+
+    def is_paused(self) -> bool:
+        """检查是否处于暂停状态"""
+        with self._lock:
+            return self._status == ExecutionStatus.PAUSED
+
+    def is_running(self) -> bool:
+        """检查是否正在运行"""
+        with self._lock:
+            return self._status == ExecutionStatus.RUNNING
+
+    def is_stopped(self) -> bool:
+        """检查是否已停止"""
+        with self._lock:
+            return self._status == ExecutionStatus.STOPPED
+
+
+# 全局单例实例
+execution_state_manager = ExecutionStateManager()

backend/AgentCoord/util/converter.py

@@ -20,6 +20,8 @@ def camel_case_to_normal(s):
 def generate_template_sentence_for_CollaborationBrief(
     input_object_list, output_object, agent_list, step_task
 ):
+    # Ensure step_task is not None
+    step_task = step_task if step_task is not None else "perform the task"
     # Check if the names are in camel case (no spaces) and convert them to normal naming convention
     input_object_list = (
         [
@@ -31,29 +33,48 @@ def generate_template_sentence_for_CollaborationBrief(
     )
     output_object = (
         camel_case_to_normal(output_object)
-        if is_camel_case(output_object)
-        else output_object
+        if output_object is not None and is_camel_case(output_object)
+        else (output_object if output_object is not None else "unknown output")
     )
 
     # Format the agents into a string with proper grammar
+    if agent_list is None or len(agent_list) == 0:
+        agent_str = "Unknown agents"
+    elif all(agent is not None for agent in agent_list):
         agent_str = (
             " and ".join([", ".join(agent_list[:-1]), agent_list[-1]])
             if len(agent_list) > 1
             else agent_list[0]
         )
+    else:
+        # Filter out None values
+        filtered_agents = [agent for agent in agent_list if agent is not None]
+        if filtered_agents:
+            agent_str = (
+                " and ".join([", ".join(filtered_agents[:-1]), filtered_agents[-1]])
+                if len(filtered_agents) > 1
+                else filtered_agents[0]
+            )
+        else:
+            agent_str = "Unknown agents"
 
     if input_object_list is None or len(input_object_list) == 0:
         # Combine all the parts into the template sentence
         template_sentence = f"{agent_str} perform the task of {step_task} to obtain {output_object}."
     else:
         # Format the input objects into a string with proper grammar
+        # Filter out None values from input_object_list
+        filtered_input_list = [obj for obj in input_object_list if obj is not None]
+        if filtered_input_list:
             input_str = (
                 " and ".join(
-                [", ".join(input_object_list[:-1]), input_object_list[-1]]
+                    [", ".join(filtered_input_list[:-1]), filtered_input_list[-1]]
                 )
-            if len(input_object_list) > 1
-            else input_object_list[0]
+                if len(filtered_input_list) > 1
+                else filtered_input_list[0]
             )
+        else:
+            input_str = "unknown inputs"
         # Combine all the parts into the template sentence
         template_sentence = f"Based on {input_str}, {agent_str} perform the task of {step_task} to obtain {output_object}."
 
@@ -90,7 +111,7 @@ def read_LLM_Completion(messages, useGroq=True):
                 return json.loads(match.group(0).strip())
             except Exception:
                 pass
-    raise ("bad format!")
+    return {}  # 返回空对象而不是抛出异常
 
 
 def read_json_content(text):
@@ -111,7 +132,7 @@ def read_json_content(text):
     if match:
         return json.loads(match.group(0).strip())
 
-    raise ("bad format!")
+    return {}  # 返回空对象而不是抛出异常
 
 
 def read_outputObject_content(text, keyword):
@@ -127,4 +148,4 @@ def read_outputObject_content(text, keyword):
     if match:
         return match.group(1).strip()
     else:
-        raise ("bad format!")
+        return ""  # 返回空字符串而不是抛出异常

backend/config/config.yaml

@@ -1,10 +1,10 @@
 ## config for default LLM
-OPENAI_API_BASE: ""
-OPENAI_API_KEY: ""
-OPENAI_API_MODEL: "gpt-4-turbo-preview"
+OPENAI_API_BASE: "https://ai.gitee.com/v1"
+OPENAI_API_KEY: "HYCNGM39GGFNSB1F8MBBMI9QYJR3P1CRSYS2PV1A"
+OPENAI_API_MODEL: "DeepSeek-V3"
 
 ## config for fast mode
-FAST_DESIGN_MODE: True
+FAST_DESIGN_MODE: False
 GROQ_API_KEY: ""
 MISTRAL_API_KEY: ""
 

backend/requirements.txt

@@ -1,7 +1,9 @@
 Flask==3.0.2
-openai==0.28.1
+openai==2.8.1
 PyYAML==6.0.1
 termcolor==2.4.0
 groq==0.4.2
 mistralai==0.1.6
-socksio==1.0.0
+flask-socketio==5.3.6
+python-socketio==5.11.0
+simple-websocket==1.0.0

frontend-react/.dockerignore

@@ -0,0 +1,31 @@
+.DS_Store
+
+.pnp
+.pnp.js
+.env.local
+.env.*.local
+.history
+*.log*
+
+node_modules/
+.yarn-integrity
+.pnpm-store/
+*.tsbuildinfo
+.eslintcache
+.changeset/pre.json
+
+dist/
+coverage/
+release/
+output/
+output_resource/
+log/
+
+.vscode/**/*
+!.vscode/settings.json
+!.vscode/extensions.json
+.idea/
+
+**/*/typings/auto-generated
+
+modern.config.local.*

frontend-react/.gitignore

@@ -0,0 +1,32 @@
+.DS_Store
+.env
+
+.pnp
+.pnp.js
+.env.local
+.env.*.local
+.history
+*.log*
+
+node_modules/
+.yarn-integrity
+.pnpm-store/
+*.tsbuildinfo
+.eslintcache
+.changeset/pre.json
+
+dist/
+coverage/
+release/
+output/
+output_resource/
+log/
+
+.vscode/**/*
+!.vscode/settings.json
+!.vscode/extensions.json
+.idea/
+
+**/*/typings/auto-generated
+
+modern.config.local.*

frontend-react/Dockerfile

@@ -0,0 +1,22 @@
+FROM node:20-alpine AS base
+
+FROM base AS installer
+WORKDIR /app
+COPY package.json .npmrc ./
+RUN npm install
+
+FROM base AS builder
+WORKDIR /app
+COPY --from=installer /app/node_modules ./node_modules
+COPY . .
+RUN npm run build
+
+FROM base AS runner
+WORKDIR /app
+EXPOSE 8080/tcp
+COPY .npmrc ./
+RUN npm install @modern-js/app-tools @modern-js/runtime --no-optional --no-shrinkwrap && mkdir src
+COPY modern.config.ts package.json ./
+COPY --from=builder /app/dist ./dist
+ENV API_BASE=
+CMD ["npm", "run", "serve"]

frontend-react/README.md

@@ -0,0 +1,39 @@
+# AgentCoord Frontend
+
+This is the frontend for the AgentCoord project. Root project is located [here](https://github.com/AgentCoord/AgentCoord)
+
+## Installation
+
+You can launch the frontend by simply using `docker-compose` in the root directory of the project.
+
+Or, you can launch the frontend manually by following the steps below.
+
+1. Install the dependencies by running `npm install`.
+
+```bash
+npm install
+```
+
+2. Build the frontend by running `npm run build`.
+
+```bash
+npm run build
+```
+
+3. Start the frontend by running `npm run serve`.
+
+```bash
+npm run serve
+```
+
+Then you can access the frontend by visiting `http://localhost:8080`.
+
+## Development
+
+You can run the frontend in development mode by running `npm run dev`.
+
+```bash
+npm run dev
+```
+
+The frontend website requires the backend server to be running. You can configure the backend server address by copying the `.env.example` file to `.env` and changing the `API_BASE` value to the backend server address.