update

backend/AgentCoord/LLMAPI/LLMAPI.py

@@ -1,35 +1,24 @@
 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_data = {}
+    yaml_file = {}
 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_MODEL = os.getenv("OPENAI_API_MODEL") or yaml_data.get(
+openai.api_key = OPENAI_API_KEY
-    "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"
 )
@@ -46,11 +35,8 @@ 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,model_config: dict = None
+    messages: list[dict], stream: bool = True, useGroq: bool = True
 ) -> 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
@@ -83,107 +69,16 @@ 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
-    groq_client = AsyncGroq(api_key=GROQ_API_KEY)
+    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:
-            response = await groq_client.chat.completions.create(
+            stream = await client.chat.completions.create(
                 messages=messages,
                 # model='gemma-7b-it',
                 model="mixtral-8x7b-32768",
@@ -197,18 +92,9 @@ async def _achat_completion_stream_groq(messages: list[dict]) -> str:
             if attempt < max_attempts - 1:  # i is zero indexed
                 continue
             else:
-                raise Exception("failed")
+                raise "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
@@ -217,14 +103,14 @@ 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
-    mistral_client = MistralClient(api_key=MISTRAL_API_KEY)
+    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 = mistral_client.chat(
+            stream = client.chat(
                 messages=[
                     ChatMessage(
                         role=message["role"], content=message["content"]
@@ -233,35 +119,31 @@ 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 Exception("failed")
+                raise "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:
-    response = await async_client.chat.completions.create(
+    openai.api_key = OPENAI_API_KEY
+    openai.api_base = OPENAI_API_BASE
+    response = await openai.ChatCompletion.acreate(
         messages=messages,
         max_tokens=4096,
+        n=1,
+        stop=None,
         temperature=0.3,
-        timeout=600,
+        timeout=3,
         model="gpt-3.5-turbo-16k",
         stream=True,
     )
@@ -272,38 +154,40 @@ 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
+        choices = chunk["choices"]
-        if len(choices) > 0 and choices[0] is not None:
+        if len(choices) > 0:
-            chunk_message = choices[0].delta
+            chunk_message = chunk["choices"][0].get(
-            if chunk_message is not None:
+                "delta", {}
-                collected_messages.append(chunk_message)  # save the message
+            )  # extract the message
-                if chunk_message.content:
+            collected_messages.append(chunk_message)  # save the message
-                    print(
+            if "content" in chunk_message:
-                        colored(chunk_message.content, "blue", "on_white"),
+                print(
-                        end="",
+                    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]
+        [m.get("content", "") for m in collected_messages]
     )
-
-    # 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
 
 
-def _achat_completion_json(messages: list[dict] ) -> str:
+async def _achat_completion_json(messages: list[dict]) -> str:
+    openai.api_key = OPENAI_API_KEY
+    openai.api_base = OPENAI_API_BASE
+
     max_attempts = 5
+
     for attempt in range(max_attempts):
         try:
-            response =  async_client.chat.completions.create(
+            stream = await openai.ChatCompletion.acreate(
                 messages=messages,
                 max_tokens=4096,
+                n=1,
+                stop=None,
                 temperature=0.3,
-                timeout=600,
+                timeout=3,
                 model=MODEL,
                 response_format={"type": "json_object"},
             )
@@ -312,87 +196,62 @@ def _achat_completion_json(messages: list[dict] ) -> str:
             if attempt < max_attempts - 1:  # i is zero indexed
                 continue
             else:
-                raise Exception("failed")
+                raise "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:
-    try:
+    openai.api_key = OPENAI_API_KEY
-        response = await async_client.chat.completions.create(
+    openai.api_base = OPENAI_API_BASE
-            **_cons_kwargs(messages), stream=True
+    response = await openai.ChatCompletion.acreate(
-        )
+        **_cons_kwargs(messages), stream=True
+    )
 
-        # create variables to collect the stream of chunks
+    # create variables to collect the stream of chunks
-        collected_chunks = []
+    collected_chunks = []
-        collected_messages = []
+    collected_messages = []
-        # iterate through the stream of events
+    # iterate through the stream of events
-        async for chunk in response:
+    async for chunk in response:
-            collected_chunks.append(chunk)  # save the event response
+        collected_chunks.append(chunk)  # save the event response
-            choices = chunk.choices
+        choices = chunk["choices"]
-            if len(choices) > 0 and choices[0] is not None:
+        if len(choices) > 0:
-                chunk_message = choices[0].delta
+            chunk_message = chunk["choices"][0].get(
-                if chunk_message is not None:
+                "delta", {}
-                    collected_messages.append(chunk_message)  # save the message
+            )  # extract the message
-                    if chunk_message.content:
+            collected_messages.append(chunk_message)  # save the message
-                        print(
+            if "content" in chunk_message:
-                            colored(chunk_message.content, "blue", "on_white"),
+                print(
-                            end="",
+                    colored(chunk_message["content"], "blue", "on_white"),
-                        )
+                    end="",
-        print()
+                )
+    print()
 
-        full_reply_content = "".join(
+    full_reply_content = "".join(
-            [m.content or "" for m in collected_messages if m is not None]
+        [m.get("content", "") for m in collected_messages]
-        )
+    )
-
+    return full_reply_content
-        # 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:
-    try:
+    print(messages, flush=True)
-        rsp = client.chat.completions.create(**_cons_kwargs(messages))
+    rsp = openai.ChatCompletion.create(**_cons_kwargs(messages))
-
+    content = rsp["choices"][0]["message"]["content"]
-        # 检查响应是否有效
+    print(content, flush=True)
-        if not rsp.choices or len(rsp.choices) == 0:
+    return content
-            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": 2000,
+        "max_tokens": 4096,
-        "temperature": 0.3,
+        "n": 1,
-        "timeout": 600,
+        "stop": None,
+        "temperature": 0.5,
+        "timeout": 3,
     }
     kwargs_mode = {"model": MODEL}
     kwargs.update(kwargs_mode)

backend/AgentCoord/PlanEngine/AgentSelectModify.py

@@ -7,8 +7,6 @@ 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}
 
@@ -51,8 +49,6 @@ 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}
 
@@ -137,6 +133,5 @@ def AgentSelectModify_init(stepTask, General_Goal, Agent_Board):
 
 
 def AgentSelectModify_addAspect(aspectList, Agent_Board):
-    newAspect = aspectList[-1]
+    scoreTable = agentAbilityScoring(Agent_Board, aspectList)
-    scoreTable = agentAbilityScoring(Agent_Board, [newAspect])
     return scoreTable

backend/AgentCoord/PlanEngine/AgentSelection_Generator.py

@@ -33,9 +33,7 @@ class JSON_ABILITY_REQUIREMENT_GENERATION(BaseModel):
 
 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".
+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". Agent Selection Plan ranks from high to low according to ability.
-
-**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}
@@ -82,10 +80,6 @@ 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

@@ -1,53 +1,55 @@
 from AgentCoord.PlanEngine.planOutline_Generator import generate_PlanOutline
-# from AgentCoord.PlanEngine.AgentSelection_Generator import (
+from AgentCoord.PlanEngine.AgentSelection_Generator import (
-#     generate_AgentSelection,
+    generate_AgentSelection,
-# )
+)
+from AgentCoord.PlanEngine.taskProcess_Generator import generate_TaskProcess
+import AgentCoord.util as util
 
 
 def generate_basePlan(
-    General_Goal, Agent_Board, AgentProfile_Dict, InitialObject_List
+    General_Goal, Agent_Board, AgentProfile_Dict, InitialObject_List, context
 ):
-    """
-    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=InitialObject_List, General_Goal=General_Goal
-    )
-
     basePlan = {
-        "General Goal": General_Goal,
         "Initial Input Object": InitialObject_List,
-        "Collaboration Process": []
+        "Collaboration Process": [],
     }
-
+    PlanOutline = generate_PlanOutline(
+        InitialObject_List=[], General_Goal=General_Goal + context
+    )
     for stepItem in PlanOutline:
-        # # 为每个步骤分配智能体
+        Current_Task = {
-        # Current_Task = {
+            "TaskName": stepItem["StepName"],
-        #     "TaskName": stepItem["StepName"],
+            "InputObject_List": stepItem["InputObject_List"],
-        #     "InputObject_List": stepItem["InputObject_List"],
+            "OutputObject": stepItem["OutputObject"],
-        #     "OutputObject": stepItem["OutputObject"],
+            "TaskContent": stepItem["TaskContent"],
-        #     "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 + context,
+            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

@@ -9,8 +9,6 @@ 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

@@ -29,8 +29,6 @@ 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}
 
@@ -57,39 +55,26 @@ Note: "Modification Requirement" specifies how to modify the "Baseline Completio
       "ID": "Action4",
       "ActionType": "Propose",
       "AgentName": "Mia",
-      "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。",
+      "Description": "Propose psychological theories on love and attachment that could be applied to AI's emotional development.",
       "ImportantInput": [
         "InputObject:Story Outline"
       ]
     }},
     {{
       "ID": "Action5",
-      "ActionType": "Critique",
+      "ActionType": "Propose",
       "AgentName": "Noah",
-      "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。",
+      "Description": "Propose ethical considerations and philosophical questions regarding AI's capacity for love.",
-      "ImportantInput": [
+      "ImportantInput": []
-        "ActionResult:Action4"
-      ]
     }},
     {{
       "ID": "Action6",
-      "ActionType": "Improve",
-      "AgentName": "Liam",
-      "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:Action4",
-        "ActionResult:Action5"
-      ]
-    }},
-    {{
-      "ID": "Action7",
       "ActionType": "Finalize",
-      "AgentName": "Mia",
+      "AgentName": "Liam",
-      "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。",
+      "Description": "Combine the poetic elements and ethical considerations into a cohesive set of core love elements for the story.",
       "ImportantInput": [
-        "ActionResult:Action4",
+        "ActionResult:Action1",
-        "ActionResult:Action5",
+        "ActionResult:Action5"
-        "ActionResult:Action6"
       ]
     }}
   ]
@@ -99,12 +84,7 @@ 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. **CRITICAL REQUIREMENTS:**
+ActionType: Specify the type of action, note that only the last action can be of type "Finalize", and the last action must be "Finalize".
-  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

@@ -5,9 +5,7 @@ import json
 
 PROMPT_PLAN_OUTLINE_GENERATION = """
 ## Instruction
-Based on "Output Format Example", "General Goal", and "Initial Key Object List", output a formatted "Plan_Outline".
+Based on "Output Format Example", "General Goal", and "Initial Key Object List", output a formatted "Plan_Outline". The number of steps in the Plan Outline cannot exceed 5.
-
-**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}
@@ -53,6 +51,7 @@ TaskContent: Describe the task of the current step.
 InputObject_List: The list of the input obejects that will be used in current step.
 OutputObject: The name of the final output object of current step.
 
+请用中文回答
 """
 
 
@@ -85,16 +84,4 @@ def generate_PlanOutline(InitialObject_List, General_Goal):
             ),
         },
     ]
-    result = read_LLM_Completion(messages)
+    return read_LLM_Completion(messages)["Plan_Outline"]
-    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

@@ -25,8 +25,6 @@ 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}
 
@@ -92,6 +90,7 @@ 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".
 
+请用中文回答
 """
 
 

backend/AgentCoord/RehearsalEngine_V2/Action/baseAction.py

@@ -9,8 +9,6 @@ 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)
@@ -82,34 +80,10 @@ 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)
 
-        # Handle missing agent profiles gracefully
+        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)
-        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,model_config=model_config)
+        ActionResult = LLM_Completion(messages,True,False)
         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 = '''
-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。
+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. 
 
 '''
 

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 = '''
-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：
+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:
 ```{OutputName}
-（{OutputName}的内容）
+(the content of {OutputName})
 ```
 '''
 

backend/AgentCoord/RehearsalEngine_V2/Action/customAction_Improve.py

@@ -1,12 +1,12 @@
 from AgentCoord.RehearsalEngine_V2.Action import BaseAction
 
 ACTION_CUSTOM_NOTE = '''
-注意：你可以在提供改进版本的内容之前先说一些话。
+Note: You can say something before you provide the improved version of the content.
-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）。
+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).
-当你决定提供内容的改进版本时，应该这样开始：
+When you decide to give the improved version of the content, it should be start like this:
 
 ## xxx的改进版本
-（改进版本的内容）
+(the improved version of the content)
 ```
 
 '''

backend/AgentCoord/RehearsalEngine_V2/ExecutePlan.py

@@ -4,7 +4,7 @@ from termcolor import colored
 
 
 # Accept inputs: num_StepToRun (the number of step to run, if None, run to the end), plan, RehearsalLog, AgentProfile_Dict
-def executePlan(plan, num_StepToRun, RehearsalLog, AgentProfile_Dict):
+def executePlan(plan, num_StepToRun, RehearsalLog, AgentProfile_Dict, context):
     # Prepare for execution
     KeyObjects = {}
     finishedStep_index = -1
@@ -85,17 +85,9 @@ 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,
@@ -109,7 +101,7 @@ def executePlan(plan, num_StepToRun, RehearsalLog, AgentProfile_Dict):
                     KeyObjects=KeyObjects,
                 )
             ActionInfo_with_Result = currentAction.run(
-                General_Goal=plan["General Goal"],
+                General_Goal=plan["General Goal"] + "\n\n### Useful Information (some information can help accomplish the task)\n如果使用该信息，请在回答中显示指出是来自数联网的数据。例如，基于数联网数据搜索结果。" + context,
                 TaskDescription=TaskDescription,
                 agentName=agentName,
                 AgentProfile_Dict=AgentProfile_Dict,
@@ -121,6 +113,8 @@ def executePlan(plan, num_StepToRun, RehearsalLog, AgentProfile_Dict):
             ActionHistory.append(ActionInfo_with_Result)
         # post processing for the group chat (finish)
         objectLogNode["content"] = KeyObjects[OutputName]
+        if StepRun_count == len(plan["Collaboration Process"][(finishedStep_index + 1): run_to]):
+            objectLogNode["content"] += context
         RehearsalLog.append(stepLogNode)
         RehearsalLog.append(objectLogNode)
         stepLogNode["ActionHistory"] = ActionHistory

backend/AgentCoord/RehearsalEngine_V2/ExecutePlan_Optimized.py

@@ -1,608 +0,0 @@
-"""
-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

@@ -1,241 +0,0 @@
-"""
-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

@@ -1,190 +0,0 @@
-"""
-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

@@ -20,8 +20,6 @@ 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 = (
         [
@@ -33,48 +31,29 @@ def generate_template_sentence_for_CollaborationBrief(
     )
     output_object = (
         camel_case_to_normal(output_object)
-        if output_object is not None and is_camel_case(output_object)
+        if is_camel_case(output_object)
-        else (output_object if output_object is not None else "unknown output")
+        else output_object
     )
 
     # Format the agents into a string with proper grammar
-    if agent_list is None or len(agent_list) == 0:
+    agent_str = (
-        agent_str = "Unknown agents"
+        " and ".join([", ".join(agent_list[:-1]), agent_list[-1]])
-    elif all(agent is not None for agent in agent_list):
+        if len(agent_list) > 1
-        agent_str = (
+        else agent_list[0]
-            " 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
+        input_str = (
-        filtered_input_list = [obj for obj in input_object_list if obj is not None]
+            " and ".join(
-        if filtered_input_list:
+                [", ".join(input_object_list[:-1]), input_object_list[-1]]
-            input_str = (
-                " and ".join(
-                    [", ".join(filtered_input_list[:-1]), filtered_input_list[-1]]
-                )
-                if len(filtered_input_list) > 1
-                else filtered_input_list[0]
             )
-        else:
+            if len(input_object_list) > 1
-            input_str = "unknown inputs"
+            else input_object_list[0]
+        )
         # 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}."
 
@@ -111,7 +90,7 @@ def read_LLM_Completion(messages, useGroq=True):
                 return json.loads(match.group(0).strip())
             except Exception:
                 pass
-    return {}  # 返回空对象而不是抛出异常
+    raise ("bad format!")
 
 
 def read_json_content(text):
@@ -132,7 +111,7 @@ def read_json_content(text):
     if match:
         return json.loads(match.group(0).strip())
 
-    return {}  # 返回空对象而不是抛出异常
+    raise ("bad format!")
 
 
 def read_outputObject_content(text, keyword):
@@ -148,4 +127,4 @@ def read_outputObject_content(text, keyword):
     if match:
         return match.group(1).strip()
     else:
-        return ""  # 返回空字符串而不是抛出异常
+        raise ("bad format!")

backend/AgentRepo/科创.json

@@ -0,0 +1,97 @@
+[
+    {
+        "Icon": "Hailey_Johnson.png",
+        "Name": "船舶设计师",
+        "Profile": "提供船舶制造中的实际需求和约束。"
+    },
+    {
+        "Icon": "Jennifer_Moore.png",
+        "Name": "防护工程专家",
+        "Profile": "专注于船舶腐蚀防护技术的设计与应用。在你的总结回答中，必须引用来自数联网的搜索数据，是搜索数据，不是数联网的研究成果。"
+    },
+    {
+        "Icon": "Jane_Moreno.png",
+        "Name": "病理生理学家",
+        "Profile": "专注于失血性休克的疾病机制，为药物研发提供理论靶点。"
+    },
+    {
+        "Icon": "Giorgio_Rossi.png",
+        "Name": "药物化学家",
+        "Profile": "负责将靶点概念转化为实际可合成的分子。"
+    },
+    {
+        "Icon": "Tamara_Taylor.png",
+        "Name": "制剂工程师",
+        "Profile": "负责将活性药物成分（API）变成稳定、可用、符合战场要求的剂型。"
+    },
+    {
+        "Icon": "Maria_Lopez.png",
+        "Name": "监管事务专家",
+        "Profile": "深谙药品审评法规，目标是找到最快的合法上市路径。"
+    },
+    {
+        "Icon": "Sam_Moore.png",
+        "Name": "物理学家",
+        "Profile": "从热力学与统计力学的基本原理出发，研究液态金属的自由能、焓、熵、比热等参数的理论建模。"
+    },
+    {
+        "Icon": "Yuriko_Yamamoto.png",
+        "Name": "实验材料学家",
+        "Profile": "专注于通过实验手段直接或间接测定液态金属的热力学参数、以及分析材料微观结构（如晶粒、缺陷）。"
+    },
+    {
+        "Icon": "Carlos_Gomez.png",
+        "Name": "计算模拟专家",
+        "Profile": "侧重于利用数值计算和模拟技术获取液态金属的热力学参数。"
+    },
+    {
+        "Icon": "John_Lin.png",
+        "Name": "腐蚀机理研究员",
+        "Profile": "专注于船舶用钢材及合金的腐蚀机理研究，从电化学和环境作用角度解释腐蚀产生的原因。在你的总结回答中，必须引用来自数联网的搜索数据，是搜索数据，不是数联网的研究成果。"
+    },
+    {
+        "Icon": "Arthur_Burton.png",
+        "Name": "先进材料研发员",
+        "Profile": "专注于开发和评估新型耐腐蚀材料、复合材料及固态电池材料。"
+    },
+    {
+        "Icon": "Eddy_Lin.png",
+        "Name": "肾脏病学家",
+        "Profile": "专注于慢性肾脏病的诊断、治疗和患者管理，能提供临床洞察。"
+    },
+    {
+        "Icon": "Isabella_Rodriguez.png",
+        "Name": "临床研究协调员",
+        "Profile": "负责受试者招募和临床试验流程优化。"
+    },
+    {
+        "Icon": "Latoya_Williams.png",
+        "Name": "中医药专家",
+        "Profile": "理解药物的中药成分和作用机制。"
+    },
+    {
+        "Icon": "Carmen_Ortiz.png",
+        "Name": "药物安全专家",
+        "Profile": "专注于药物不良反应数据收集、分析和报告。"
+    },    
+    {
+        "Icon": "Rajiv_Patel.png",
+        "Name": "二维材料科学家",
+        "Profile": "专注于二维材料（如石墨烯）的合成、性质和应用。"
+    },  
+    {
+        "Icon": "Tom_Moreno.png",
+        "Name": "光电物理学家",
+        "Profile": "研究材料的光电转换机制和关键影响因素。"
+    },
+    {
+        "Icon": "Ayesha_Khan.png",
+        "Name": "机器学习专家",
+        "Profile": "专注于开发和应用AI模型用于材料模拟。"
+    },
+    {
+        "Icon": "Mei_Lin.png",
+        "Name": "流体动力学专家",
+        "Profile": "专注于流体行为理论和模拟。"
+    }
+]

backend/config/config.yaml

@@ -1,12 +1,12 @@
 ## config for default LLM
-OPENAI_API_BASE: "https://ai.gitee.com/v1"
+OPENAI_API_BASE: ""
-OPENAI_API_KEY: "HYCNGM39GGFNSB1F8MBBMI9QYJR3P1CRSYS2PV1A"
+OPENAI_API_KEY: ""
-OPENAI_API_MODEL: "DeepSeek-V3"
+OPENAI_API_MODEL: "gpt-4-turbo-preview"
 
 ## config for fast mode
-FAST_DESIGN_MODE: False
+FAST_DESIGN_MODE: True
 GROQ_API_KEY: ""
 MISTRAL_API_KEY: ""
 
 ## options under experimentation, leave them as Fasle unless you know what it is for
-USE_CACHE: False
+USE_CACHE: True

backend/requirements.txt

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

docker-compose.yml

@@ -21,11 +21,12 @@ services:
     ports:
       - "8000:8000"
     environment:
-      - OPENAI_API_BASE=htts://api.openai.com
+      - OPENAI_API_BASE=https://api.moleapi.com/v1
-      - OPENAI_API_KEY=
+      - OPENAI_API_KEY=sk-sps7FBCbEvu85DfPoS8SdnPwYLEoW7u5Dd8vCDTXqPLpHuyb
-      - OPENAI_API_MODEL=gpt-4-turbo-preview
+      - OPENAI_API_MODEL=gpt-4.1-mini
-      - FAST_DESIGN_MODE=True
+      - FAST_DESIGN_MODE=False
       - GROQ_API_KEY=
+      - USE_CACHE=True
     networks:
       - agentcoord-network
 

frontend-react/.dockerignore

@@ -1,31 +0,0 @@
-.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

@@ -1,32 +0,0 @@
-.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

@@ -1,22 +0,0 @@
-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

@@ -1,39 +0,0 @@
-# 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.

frontend-react/package.json

@@ -1,66 +0,0 @@
-{
-  "name": "agent-coord-frontend",
-  "version": "0.0.1",
-  "scripts": {
-    "reset": "npx rimraf ./**/node_modules",
-    "dev": "modern dev",
-    "build": "modern build",
-    "start": "modern start",
-    "serve": "modern serve",
-    "new": "modern new",
-    "lint": "modern lint",
-    "prepare": "husky install",
-    "upgrade": "modern upgrade"
-  },
-  "engines": {
-    "node": ">=16.18.1"
-  },
-  "lint-staged": {
-    "*.{js,jsx,ts,tsx,mjs,cjs}": [
-      "node --max_old_space_size=8192 ./node_modules/eslint/bin/eslint.js --fix --color --cache --quiet"
-    ]
-  },
-  "eslintIgnore": [
-    "node_modules/",
-    "dist/"
-  ],
-  "dependencies": {
-    "@emotion/react": "^11.11.3",
-    "@emotion/styled": "^11.11.0",
-    "@modern-js/runtime": "2.46.1",
-    "@mui/icons-material": "^5.15.6",
-    "@mui/material": "^5.15.6",
-    "@sttot/api-hooks": "^1.2.5",
-    "d3": "^7.8.5",
-    "localforage": "^1.10.0",
-    "lodash": "^4.17.21",
-    "mobx": "^6.12.0",
-    "mobx-react-lite": "^4.0.6",
-    "re-resizable": "^6.9.11",
-    "react": "~18.2.0",
-    "react-dom": "~18.2.0",
-    "react-markdown": "^9.0.1",
-    "react-rnd": "^10.4.1",
-    "rehype-highlight": "^7.0.0",
-    "rehype-katex": "^7.0.0",
-    "remark-gfm": "^4.0.0",
-    "remark-math": "^6.0.0"
-  },
-  "devDependencies": {
-    "@modern-js-app/eslint-config": "2.46.1",
-    "@modern-js/app-tools": "2.46.1",
-    "@modern-js/eslint-config": "2.46.1",
-    "@modern-js/tsconfig": "2.46.1",
-    "@types/d3": "^7.4.3",
-    "@types/jest": "~29.2.4",
-    "@types/lodash": "^4.14.202",
-    "@types/node": "~16.11.7",
-    "@types/react": "~18.0.26",
-    "@types/react-dom": "~18.0.10",
-    "husky": "~8.0.1",
-    "lint-staged": "~13.1.0",
-    "prettier": "~2.8.1",
-    "rimraf": "~3.0.2",
-    "typescript": "~5.0.4"
-  }
-}

frontend-react/src/components/ProcessRehearsal/StepHistoryItem.tsx

@@ -1,170 +0,0 @@
-import React from 'react';
-import { observer } from 'mobx-react-lite';
-import { Divider, SxProps } from '@mui/material';
-import Box from '@mui/material/Box';
-import UnfoldLessIcon from '@mui/icons-material/UnfoldLess';
-import MoreHorizIcon from '@mui/icons-material/MoreHoriz';
-import MarkdownBlock from '@/components/MarkdownBlock';
-import { globalStorage } from '@/storage';
-import type { IExecuteStepHistoryItem } from '@/apis/execute-plan';
-import AgentIcon from '@/components/AgentIcon';
-import { getAgentActionStyle } from '@/storage/plan';
-
-export interface IStepHistoryItemProps {
-  item: IExecuteStepHistoryItem;
-  actionRef: React.RefObject<HTMLDivElement | HTMLElement>;
-  style?: SxProps;
-  hoverCallback: (isHovered: boolean) => void;
-  handleExpand?: () => void;
-}
-
-export default observer(
-  ({
-    item,
-    actionRef,
-    hoverCallback,
-    handleExpand,
-    style = {},
-  }: IStepHistoryItemProps) => {
-    const [expand, setExpand] = React.useState(false);
-    const refDetail = React.useRef<HTMLDivElement>(null);
-    // 使用useEffect来更新detail容器的高度
-    React.useEffect(() => {
-      if (refDetail.current) {
-        refDetail.current.style.height = expand
-          ? `${refDetail.current.scrollHeight}px`
-          : '0px';
-      }
-      if (handleExpand) {
-        let count = 0;
-        const intervalId = setInterval(() => {
-          handleExpand();
-          count++;
-          if (count >= 20) {
-            clearInterval(intervalId);
-          }
-        }, 10);
-      }
-    }, [expand]);
-    const s = { ...getAgentActionStyle(item.type), ...style } as SxProps;
-    React.useEffect(() => {
-      console.log(item);
-    }, [item]);
-    return (
-      <Box
-        ref={actionRef}
-        className="step-history-item"
-        sx={{
-          userSelect: 'none',
-          borderRadius: '10px',
-          padding: '4px',
-          fontSize: '14px',
-          position: 'relative',
-          marginTop: '4px',
-          backgroundColor: (s as any).backgroundColor,
-          border: `2px solid ${(s as any).borderColor}`,
-        }}
-        onMouseOver={() => hoverCallback(true)}
-        onMouseOut={() => hoverCallback(false)}
-      >
-        <Box sx={{ display: 'flex', alignItems: 'center' }}>
-          <AgentIcon
-            name={globalStorage.agentIconMap.get(item.agent)}
-            style={{ height: '36px', width: 'auto', margin: '0px' }}
-            tooltipInfo={globalStorage.agentMap.get(item.agent)}
-          />
-          <Box component="span" sx={{ fontWeight: 500, marginLeft: '4px' }}>
-            {item.agent}
-          </Box>
-          <Box component="span" sx={{ fontWeight: 400 }}>
-            : {item.type}
-          </Box>
-        </Box>
-        {item.result ? (
-          <Box
-            ref={refDetail}
-            sx={{
-              overflow: 'hidden',
-              transition: 'height 200ms ease-out', // 添加过渡效果
-            }}
-          >
-            {expand ? (
-              <>
-                <Divider
-                  sx={{
-                    margin: '4px 0px',
-                    borderBottom: '2px dashed', // 设置为虚线
-                    borderColor: '#0003',
-                  }}
-                />
-                <Box
-                  sx={{
-                    marginLeft: '6px',
-                    marginBottom: '4px',
-                    color: '#0009',
-                    fontWeight: 400,
-                  }}
-                >
-                  {item.description}
-                </Box>
-                <MarkdownBlock
-                  text={item.result}
-                  style={{
-                    marginTop: '5px',
-                    borderRadius: '10px',
-                    padding: '6px',
-                    background: '#FFF9',
-                    fontSize: '12px',
-                    maxHeight: '240px',
-                    overflowY: 'auto',
-                    border: '1px solid #0003',
-                    whiteSpace: 'pre-wrap',
-                    wordWrap: 'break-word',
-                    marginBottom: '5px',
-                  }}
-                />
-              </>
-            ) : (
-              <></>
-            )}
-            <Box
-              onClick={e => {
-                setExpand(v => !v);
-                e.stopPropagation();
-              }}
-              sx={{
-                position: 'absolute',
-                right: '8px',
-                // bottom: '12px',
-                top: '24px',
-                cursor: 'pointer',
-                userSelect: 'none',
-                height: '14px',
-                display: 'flex',
-                alignItems: 'center',
-                justifyContent: 'center',
-                background: '#0002',
-                borderRadius: '8px',
-                marginLeft: '4px',
-                padding: '0 4px',
-                '&:hover': {
-                  background: '#0003',
-                },
-              }}
-            >
-              {expand ? (
-                <UnfoldLessIcon
-                  sx={{ fontSize: '16px', transform: 'rotate(90deg)' }}
-                />
-              ) : (
-                <MoreHorizIcon sx={{ fontSize: '16px' }} />
-              )}
-            </Box>
-          </Box>
-        ) : (
-          <></>
-        )}
-      </Box>
-    );
-  },
-);

frontend-react/src/components/UserGoalInput/index.tsx

@@ -1,101 +0,0 @@
-import React from 'react';
-import { observer } from 'mobx-react-lite';
-import { SxProps } from '@mui/material';
-import Box from '@mui/material/Box';
-import IconButton from '@mui/material/IconButton';
-import FormControl from '@mui/material/FormControl';
-import FilledInput from '@mui/material/FilledInput';
-import TelegramIcon from '@mui/icons-material/Telegram';
-import CircularProgress from '@mui/material/CircularProgress';
-import { globalStorage } from '@/storage';
-
-export interface UserGoalInputProps {
-  style?: SxProps;
-}
-
-export default observer(({ style = {} }: UserGoalInputProps) => {
-  const inputRef = React.useRef<string>('');
-  const inputElementRef = React.useRef<HTMLInputElement>(null);
-  React.useEffect(() => {
-    if (inputElementRef.current) {
-      if (globalStorage.planManager) {
-        inputElementRef.current.value = globalStorage.planManager.goal;
-      } else {
-        inputElementRef.current.value = globalStorage.briefGoal;
-      }
-    }
-  }, [globalStorage.planManager]);
-  return (
-    <FormControl
-      sx={{
-        position: 'relative',
-        ...style,
-      }}
-    >
-      <FilledInput
-        disabled={
-          globalStorage.api.busy ||
-          !globalStorage.api.agentsReady ||
-          globalStorage.api.planReady
-        }
-        placeholder="Yout Goal"
-        fullWidth
-        inputRef={inputElementRef}
-        onChange={event => (inputRef.current = event.target.value)}
-        size="small"
-        sx={{
-          borderRadius: '10px',
-          background: '#E1E1E1',
-          borderBottom: 'none !important',
-          '&::before': {
-            borderBottom: 'none !important',
-          },
-          '& > input': {
-            padding: '10px',
-          },
-        }}
-        startAdornment={
-          <Box
-            sx={{
-              color: '#4A9C9E',
-              fontWeight: 800,
-              fontSize: '18px',
-              textWrap: 'nowrap',
-              userSelect: 'none',
-            }}
-          >
-            \General Goal:
-          </Box>
-        }
-      />
-      {globalStorage.api.planGenerating ? (
-        <CircularProgress
-          sx={{
-            position: 'absolute',
-            right: '12px',
-            top: '20px',
-            width: '24px !important',
-            height: '24px !important',
-          }}
-        />
-      ) : (
-        <IconButton
-          disabled={
-            globalStorage.api.busy ||
-            !globalStorage.api.agentsReady ||
-            globalStorage.api.planReady
-          }
-          color="primary"
-          aria-label="提交"
-          sx={{ position: 'absolute', right: '6px', top: '12px' }}
-          onClick={() => {
-            globalStorage.form.goal = inputRef.current;
-            globalStorage.generatePlanBase();
-          }}
-        >
-          <TelegramIcon />
-        </IconButton>
-      )}
-    </FormControl>
-  );
-});

frontend-react/tsconfig.json

@@ -1,14 +0,0 @@
-{
-  "extends": "@modern-js/tsconfig/base",
-  "compilerOptions": {
-    "declaration": false,
-    "jsx": "preserve",
-    "baseUrl": "./",
-    "paths": {
-      "@/*": ["./src/*"],
-      "@shared/*": ["./shared/*"]
-    }
-  },
-  "include": ["src", "shared", "config", "modern.config.ts"],
-  "exclude": ["**/node_modules"]
-}

frontend/.dockerignore

@@ -1,6 +1,31 @@
-node_modules
+.DS_Store
-dist
+
-.idea
+.pnp
-.vscode
+.pnp.js
-.git
+.env.local
-.gitignore
+.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/.editorconfig

@@ -1,8 +0,0 @@
-[*.{js,jsx,mjs,cjs,ts,tsx,mts,cts,vue,css,scss,sass,less,styl}]
-charset = utf-8
-indent_size = 2
-indent_style = space
-insert_final_newline = true
-trim_trailing_whitespace = true
-end_of_line = lf
-max_line_length = 100

frontend/.env

@@ -1 +0,0 @@
-API_BASE=http://127.0.0.1:8000

frontend/.eslintrc-auto-import.json

@@ -1,79 +0,0 @@
-{
-  "globals": {
-    "Component": true,
-    "ComponentPublicInstance": true,
-    "ComputedRef": true,
-    "DirectiveBinding": true,
-    "EffectScope": true,
-    "ExtractDefaultPropTypes": true,
-    "ExtractPropTypes": true,
-    "ExtractPublicPropTypes": true,
-    "InjectionKey": true,
-    "MaybeRef": true,
-    "MaybeRefOrGetter": true,
-    "PropType": true,
-    "Ref": true,
-    "ShallowRef": true,
-    "Slot": true,
-    "Slots": true,
-    "VNode": true,
-    "WritableComputedRef": true,
-    "computed": true,
-    "createApp": true,
-    "customRef": true,
-    "defineAsyncComponent": true,
-    "defineComponent": true,
-    "effectScope": true,
-    "getCurrentInstance": true,
-    "getCurrentScope": true,
-    "getCurrentWatcher": true,
-    "h": true,
-    "inject": true,
-    "isProxy": true,
-    "isReactive": true,
-    "isReadonly": true,
-    "isRef": true,
-    "isShallow": true,
-    "markRaw": true,
-    "nextTick": true,
-    "onActivated": true,
-    "onBeforeMount": true,
-    "onBeforeUnmount": true,
-    "onBeforeUpdate": true,
-    "onDeactivated": true,
-    "onErrorCaptured": true,
-    "onMounted": true,
-    "onRenderTracked": true,
-    "onRenderTriggered": true,
-    "onScopeDispose": true,
-    "onServerPrefetch": true,
-    "onUnmounted": true,
-    "onUpdated": true,
-    "onWatcherCleanup": true,
-    "provide": true,
-    "reactive": true,
-    "readonly": true,
-    "ref": true,
-    "resolveComponent": true,
-    "shallowReactive": true,
-    "shallowReadonly": true,
-    "shallowRef": true,
-    "toRaw": true,
-    "toRef": true,
-    "toRefs": true,
-    "toValue": true,
-    "triggerRef": true,
-    "unref": true,
-    "useAttrs": true,
-    "useCssModule": true,
-    "useCssVars": true,
-    "useId": true,
-    "useModel": true,
-    "useSlots": true,
-    "useTemplateRef": true,
-    "watch": true,
-    "watchEffect": true,
-    "watchPostEffect": true,
-    "watchSyncEffect": true
-  }
-}

frontend/.gitattributes

@@ -1 +0,0 @@
-* text=auto eol=lf

frontend/.gitignore

@@ -1,36 +1,32 @@
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-lerna-debug.log*
-
-node_modules
 .DS_Store
-dist
+.env
-dist-ssr
-coverage
-*.local
 
-# Editor directories and files
+.pnp
-.vscode/*
+.pnp.js
-!.vscode/extensions.json
+.env.local
-.idea
+.env.*.local
-*.suo
+.history
-*.ntvs*
+*.log*
-*.njsproj
-*.sln
-*.sw?
 
+node_modules/
+.yarn-integrity
+.pnpm-store/
 *.tsbuildinfo
-
 .eslintcache
+.changeset/pre.json
 
-# Cypress
+dist/
-/cypress/videos/
+coverage/
-/cypress/screenshots/
+release/
+output/
+output_resource/
+log/
 
-# Vitest
+.vscode/**/*
-__screenshots__/
+!.vscode/settings.json
+!.vscode/extensions.json
+.idea/
+
+**/*/typings/auto-generated
+
+modern.config.local.*

frontend/.prettierrc.json

@@ -1,6 +0,0 @@
-{
-  "$schema": "https://json.schemastore.org/prettierrc",
-  "semi": false,
-  "singleQuote": true,
-  "printWidth": 100
-}

frontend/.vscode/extensions.json

@@ -1,9 +0,0 @@
-{
-  "recommendations": [
-    "Vue.volar",
-    "vitest.explorer",
-    "dbaeumer.vscode-eslint",
-    "EditorConfig.EditorConfig",
-    "esbenp.prettier-vscode"
-  ]
-}

frontend/CLAUDE.md

@@ -1,95 +0,0 @@
-# CLAUDE.md
-
-This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
-
-## Development Commands
-
-```bash
-# Install dependencies
-pnpm install
-
-# Development server with hot reload
-pnpm dev
-
-# Build for production
-pnpm build
-
-# Type checking
-pnpm type-check
-
-# Lint and fix code
-pnpm lint
-
-# Format code
-pnpm format
-
-# Run unit tests
-pnpm test:unit
-```
-
-## Project Architecture
-
-This is a **Multi-Agent Coordination Platform** (多智能体协同平台) built with Vue 3, TypeScript, and Vite. The application enables users to create and manage AI agents with specialized roles and coordinate them to complete complex tasks through visual workflows.
-
-### Tech Stack
-- **Vue 3** with Composition API and TypeScript
-- **Vite** for build tooling and development
-- **Element Plus** for UI components
-- **Pinia** for state management
-- **Tailwind CSS** for styling
-- **Vue Router** for routing (minimal usage)
-- **JSPlumb** for visual workflow connections
-- **Axios** for API requests with custom interceptors
-
-### Key Architecture Components
-
-#### State Management (`src/stores/modules/agents.ts`)
-Central store managing:
-- Agent definitions with profiles and icons
-- Task workflow data structures (`IRawStepTask`, `TaskProcess`)
-- Search functionality and current task state
-- Raw plan responses with UUID generation for tasks
-
-#### Request Layer (`src/utils/request.ts`)
-Custom Axios wrapper with:
-- Proxy configuration for `/api` -> `http://localhost:8000`
-- Response interceptors for error handling
-- `useRequest` hook for reactive data fetching
-- Integrated Element Plus notifications
-
-#### Component Structure
-- **Layout System** (`src/layout/`): Main application layout with Header and Main sections
-- **Task Templates** (`src/layout/components/Main/TaskTemplate/`): Different task types including AgentRepo, TaskSyllabus, and TaskResult
-- **Visual Workflow**: JSPlumb integration for drag-and-drop agent coordination flows
-
-#### Icon System
-- SVG icons stored in `src/assets/icons/`
-- Custom `SvgIcon` component with vite-plugin-svg-icons
-- Icon categories include specialist roles (doctor, engineer, researcher, etc.)
-
-### Build Configuration
-
-#### Vite (`vite.config.ts`)
-- Element Plus auto-import and component resolution
-- SVG icon caching with custom symbol IDs
-- Proxy setup for API requests to backend
-- Path aliases: `@/` maps to `src/`
-
-#### Docker Deployment
-- Multi-stage build: Node.js build + Caddy web server
-- API proxy configured via Caddyfile
-- Environment variable support for different deployment modes
-
-### Data Models
-Key interfaces for the agent coordination system:
-- `Agent`: Name, Profile, Icon
-- `IRawStepTask`: Individual task steps with agent selection and inputs
-- `TaskProcess`: Action descriptions with important inputs
-- `IRichText`: Template-based content formatting with style support
-
-### Development Notes
-- Uses pnpm as package manager (required by package.json)
-- Node version constraint: ^20.19.0 or >=22.12.0
-- Dark theme enabled by default in App.vue
-- Auto-imports configured for Vue APIs
-- No traditional Vue routes - uses component-based navigation

frontend/Dockerfile

@@ -1,27 +1,23 @@
-ARG CADDY_VERSION=2.6
+FROM node:20-alpine AS base
-ARG BUILD_ENV=prod
 
-FROM node:20.19.0 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 install -g pnpm
+RUN npm run build
-RUN pnpm install
-RUN pnpm build
 
-
+FROM base AS runner
-# The base for mode ENVIRONMENT=prod
+WORKDIR /app
-FROM caddy:${CADDY_VERSION}-alpine as prod
+EXPOSE 8080/tcp
-
+COPY .npmrc ./
-# Workaround for https://github.com/alpinelinux/docker-alpine/issues/98#issuecomment-679278499
+COPY src ./src
-RUN sed -i 's/https/http/' /etc/apk/repositories \
+COPY modern.config.ts package.json ./
- && apk add --no-cache bash
+COPY --from=builder /app/dist ./dist
-
+COPY --from=builder /app/node_modules ./node_modules
-COPY docker/Caddyfile /etc/caddy/
+ENV API_BASE=
-COPY --from=base /app/dist /frontend
+CMD ["npm", "run", "serve"]
-
-# Run stage
-FROM ${BUILD_ENV}
-
-EXPOSE 80 443
-VOLUME ["/data", "/etc/caddy"]
-CMD ["caddy", "run", "--config", "/etc/caddy/Caddyfile", "--adapter", "caddyfile"]

frontend/README.md

@@ -1,106 +1,39 @@
-# 多智能体协同平台 (Agent Coordination Platform)
+# AgentCoord Frontend
 
-一个强大的可视化平台，用于创建和管理具有专门角色的AI智能体，通过直观的工作流程协调它们来完成复杂任务。
+This is the frontend for the AgentCoord project. Root project is located [here](https://github.com/AgentCoord/AgentCoord)
 
-## ✨ 功能特性
+## Installation
 
-- **多智能体系统**：创建具有专门角色和专业知识的AI智能体
+You can launch the frontend by simply using `docker-compose` in the root directory of the project.
-- **可视化工作流编辑器**：使用JSPlumb设计智能体协调流程的拖放界面
-- **任务管理**：定义、执行和跟踪复杂的多步骤任务
-- **实时通信**：无缝的智能体交互和协调
-- **丰富的模板系统**：支持样式的灵活内容格式化
-- **TypeScript支持**：整个应用程序的完整类型安全
 
-## 🚀 快速开始
+Or, you can launch the frontend manually by following the steps below.
 
-### 开发命令
+1. Install the dependencies by running `npm install`.
 
 ```bash
-# 安装依赖
+npm install
-pnpm install
-
-# 开发服务器（热重载）
-pnpm dev
-
-# 生产构建
-pnpm build
-
-# 类型检查
-pnpm type-check
-
-# 代码检查和修复
-pnpm lint
-
-# 代码格式化
-pnpm format
-
-# 运行单元测试
-pnpm test:unit
 ```
 
-### 系统要求
+2. Build the frontend by running `npm run build`.
 
-- Node.js ^20.19.0 或 >=22.12.0
+```bash
-- pnpm（必需的包管理器）
+npm run build
-
-## 🏗️ 架构设计
-
-### 技术栈
-
-- **Vue 3**：Composition API 和 TypeScript
-- **Vite**：构建工具和开发环境
-- **Element Plus**：UI组件库
-- **Pinia**：状态管理
-- **Tailwind CSS**：样式框架
-- **JSPlumb**：可视化工作流连接
-- **Axios**：API请求与自定义拦截器
-
-### 核心组件
-
-#### 状态管理
-中央存储管理智能体定义、任务工作流和协调状态
-
-#### 请求层
-自定义Axios包装器，具有代理配置和集成通知
-
-#### 可视化工作流
-JSPlumb集成，用于拖放智能体协调流程
-
-#### 图标系统
-基于SVG的图标，用于不同的智能体专业化和角色
-
-## 📁 项目结构
-
-```
-src/
-├── assets/           # 静态资源，包括智能体图标
-├── components/       # 可复用的Vue组件
-├── layout/          # 应用布局和主要组件
-├── stores/          # Pinia状态管理
-├── utils/           # 工具函数和请求层
-├── views/           # 页面组件
-└── App.vue          # 根组件
 ```
 
-## 🎯 开发指南
+3. Start the frontend by running `npm run serve`.
 
-### IDE设置
+```bash
+npm run serve
+```
 
-[VS Code](https://code.visualstudio.com/) + [Vue (Official)](https://marketplace.visualstudio.com/items?itemName=Vue.volar)（禁用Vetur）。
+Then you can access the frontend by visiting `http://localhost:8080`.
 
-### 浏览器开发工具
+## Development
 
-- 基于Chromium的浏览器：
+You can run the frontend in development mode by running `npm run dev`.
-  - [Vue.js devtools](https://chromewebstore.google.com/detail/vuejs-devtools/nhdogjmejiglipccpnnnanhbledajbpd)
-  - 在DevTools中启用自定义对象格式化程序
-- Firefox：
-  - [Vue.js devtools](https://addons.mozilla.org/en-US/firefox/addon/vue-js-devtools/)
-  - 在DevTools中启用自定义对象格式化程序
 
-## 🚀 部署
+```bash
+npm run dev
+```
 
-应用程序支持Docker部署，使用多阶段构建过程：Node.js用于构建，Caddy作为Web服务器。
+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.
-
-## 📄 许可证
-
-MIT许可证 - 详见LICENSE文件

frontend/auto-imports.d.ts

@@ -1,75 +0,0 @@
-/* eslint-disable */
-/* prettier-ignore */
-// @ts-nocheck
-// noinspection JSUnusedGlobalSymbols
-// Generated by unplugin-auto-import
-// biome-ignore lint: disable
-export {}
-declare global {
-  const EffectScope: typeof import('vue').EffectScope
-  const ElMessage: typeof import('element-plus/es').ElMessage
-  const ElNotification: typeof import('element-plus/es').ElNotification
-  const computed: typeof import('vue').computed
-  const createApp: typeof import('vue').createApp
-  const customRef: typeof import('vue').customRef
-  const defineAsyncComponent: typeof import('vue').defineAsyncComponent
-  const defineComponent: typeof import('vue').defineComponent
-  const effectScope: typeof import('vue').effectScope
-  const getCurrentInstance: typeof import('vue').getCurrentInstance
-  const getCurrentScope: typeof import('vue').getCurrentScope
-  const getCurrentWatcher: typeof import('vue').getCurrentWatcher
-  const h: typeof import('vue').h
-  const inject: typeof import('vue').inject
-  const isProxy: typeof import('vue').isProxy
-  const isReactive: typeof import('vue').isReactive
-  const isReadonly: typeof import('vue').isReadonly
-  const isRef: typeof import('vue').isRef
-  const isShallow: typeof import('vue').isShallow
-  const markRaw: typeof import('vue').markRaw
-  const nextTick: typeof import('vue').nextTick
-  const onActivated: typeof import('vue').onActivated
-  const onBeforeMount: typeof import('vue').onBeforeMount
-  const onBeforeUnmount: typeof import('vue').onBeforeUnmount
-  const onBeforeUpdate: typeof import('vue').onBeforeUpdate
-  const onDeactivated: typeof import('vue').onDeactivated
-  const onErrorCaptured: typeof import('vue').onErrorCaptured
-  const onMounted: typeof import('vue').onMounted
-  const onRenderTracked: typeof import('vue').onRenderTracked
-  const onRenderTriggered: typeof import('vue').onRenderTriggered
-  const onScopeDispose: typeof import('vue').onScopeDispose
-  const onServerPrefetch: typeof import('vue').onServerPrefetch
-  const onUnmounted: typeof import('vue').onUnmounted
-  const onUpdated: typeof import('vue').onUpdated
-  const onWatcherCleanup: typeof import('vue').onWatcherCleanup
-  const provide: typeof import('vue').provide
-  const reactive: typeof import('vue').reactive
-  const readonly: typeof import('vue').readonly
-  const ref: typeof import('vue').ref
-  const resolveComponent: typeof import('vue').resolveComponent
-  const shallowReactive: typeof import('vue').shallowReactive
-  const shallowReadonly: typeof import('vue').shallowReadonly
-  const shallowRef: typeof import('vue').shallowRef
-  const toRaw: typeof import('vue').toRaw
-  const toRef: typeof import('vue').toRef
-  const toRefs: typeof import('vue').toRefs
-  const toValue: typeof import('vue').toValue
-  const triggerRef: typeof import('vue').triggerRef
-  const unref: typeof import('vue').unref
-  const useAttrs: typeof import('vue').useAttrs
-  const useCssModule: typeof import('vue').useCssModule
-  const useCssVars: typeof import('vue').useCssVars
-  const useId: typeof import('vue').useId
-  const useModel: typeof import('vue').useModel
-  const useSlots: typeof import('vue').useSlots
-  const useTemplateRef: typeof import('vue').useTemplateRef
-  const watch: typeof import('vue').watch
-  const watchEffect: typeof import('vue').watchEffect
-  const watchPostEffect: typeof import('vue').watchPostEffect
-  const watchSyncEffect: typeof import('vue').watchSyncEffect
-}
-// for type re-export
-declare global {
-  // @ts-ignore
-  export type { Component, Slot, Slots, ComponentPublicInstance, ComputedRef, DirectiveBinding, ExtractDefaultPropTypes, ExtractPropTypes, ExtractPublicPropTypes, InjectionKey, PropType, Ref, ShallowRef, MaybeRef, MaybeRefOrGetter, VNode, WritableComputedRef } from 'vue'
-  import('vue')
-}

frontend/claude_code_env.sh

@@ -1,205 +0,0 @@
-#!/bin/bash
-
-set -euo pipefail
-
-# ========================
-#       常量定义
-# ========================
-SCRIPT_NAME=$(basename "$0")
-NODE_MIN_VERSION=18
-NODE_INSTALL_VERSION=22
-NVM_VERSION="v0.40.3"
-CLAUDE_PACKAGE="@anthropic-ai/claude-code"
-CONFIG_DIR="$HOME/.claude"
-CONFIG_FILE="$CONFIG_DIR/settings.json"
-API_BASE_URL="https://open.bigmodel.cn/api/anthropic"
-API_KEY_URL="https://open.bigmodel.cn/usercenter/proj-mgmt/apikeys"
-API_TIMEOUT_MS=3000000
-
-# ========================
-#       工具函数
-# ========================
-
-log_info() {
-    echo "🔹 $*"
-}
-
-log_success() {
-    echo "✅ $*"
-}
-
-log_error() {
-    echo "❌ $*" >&2
-}
-
-ensure_dir_exists() {
-    local dir="$1"
-    if [ ! -d "$dir" ]; then
-        mkdir -p "$dir" || {
-            log_error "Failed to create directory: $dir"
-            exit 1
-        }
-    fi
-}
-
-# ========================
-#     Node.js 安装函数
-# ========================
-
-install_nodejs() {
-    local platform=$(uname -s)
-
-    case "$platform" in
-        Linux|Darwin)
-            log_info "Installing Node.js on $platform..."
-
-            # 安装 nvm
-            log_info "Installing nvm ($NVM_VERSION)..."
-            curl -s https://raw.githubusercontent.com/nvm-sh/nvm/"$NVM_VERSION"/install.sh | bash
-
-            # 加载 nvm
-            log_info "Loading nvm environment..."
-            \. "$HOME/.nvm/nvm.sh"
-
-            # 安装 Node.js
-            log_info "Installing Node.js $NODE_INSTALL_VERSION..."
-            nvm install "$NODE_INSTALL_VERSION"
-
-            # 验证安装
-            node -v &>/dev/null || {
-                log_error "Node.js installation failed"
-                exit 1
-            }
-            log_success "Node.js installed: $(node -v)"
-            log_success "npm version: $(npm -v)"
-            ;;
-        *)
-            log_error "Unsupported platform: $platform"
-            exit 1
-            ;;
-    esac
-}
-
-# ========================
-#     Node.js 检查函数
-# ========================
-
-check_nodejs() {
-    if command -v node &>/dev/null; then
-        current_version=$(node -v | sed 's/v//')
-        major_version=$(echo "$current_version" | cut -d. -f1)
-
-        if [ "$major_version" -ge "$NODE_MIN_VERSION" ]; then
-            log_success "Node.js is already installed: v$current_version"
-            return 0
-        else
-            log_info "Node.js v$current_version is installed but version < $NODE_MIN_VERSION. Upgrading..."
-            install_nodejs
-        fi
-    else
-        log_info "Node.js not found. Installing..."
-        install_nodejs
-    fi
-}
-
-# ========================
-#     Claude Code 安装
-# ========================
-
-install_claude_code() {
-    if command -v claude &>/dev/null; then
-        log_success "Claude Code is already installed: $(claude --version)"
-    else
-        log_info "Installing Claude Code..."
-        npm install -g "$CLAUDE_PACKAGE" || {
-            log_error "Failed to install claude-code"
-            exit 1
-        }
-        log_success "Claude Code installed successfully"
-    fi
-}
-
-configure_claude_json(){
-  node --eval '
-      const os = require("os");
-      const fs = require("fs");
-      const path = require("path");
-
-      const homeDir = os.homedir();
-      const filePath = path.join(homeDir, ".claude.json");
-      if (fs.existsSync(filePath)) {
-          const content = JSON.parse(fs.readFileSync(filePath, "utf-8"));
-          fs.writeFileSync(filePath, JSON.stringify({ ...content, hasCompletedOnboarding: true }, null, 2), "utf-8");
-      } else {
-          fs.writeFileSync(filePath, JSON.stringify({ hasCompletedOnboarding: true }, null, 2), "utf-8");
-      }'
-}
-
-# ========================
-#     API Key 配置
-# ========================
-
-configure_claude() {
-    log_info "Configuring Claude Code..."
-    echo "   You can get your API key from: $API_KEY_URL"
-    read -s -p "🔑 Please enter your ZHIPU API key: " api_key
-    echo
-
-    if [ -z "$api_key" ]; then
-        log_error "API key cannot be empty. Please run the script again."
-        exit 1
-    fi
-
-    ensure_dir_exists "$CONFIG_DIR"
-
-    # 写入配置文件
-    node --eval '
-        const os = require("os");
-        const fs = require("fs");
-        const path = require("path");
-
-        const homeDir = os.homedir();
-        const filePath = path.join(homeDir, ".claude", "settings.json");
-        const apiKey = "'"$api_key"'";
-
-        const content = fs.existsSync(filePath)
-            ? JSON.parse(fs.readFileSync(filePath, "utf-8"))
-            : {};
-
-        fs.writeFileSync(filePath, JSON.stringify({
-            ...content,
-            env: {
-                ANTHROPIC_AUTH_TOKEN: apiKey,
-                ANTHROPIC_BASE_URL: "'"$API_BASE_URL"'",
-                API_TIMEOUT_MS: "'"$API_TIMEOUT_MS"'",
-                CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC: 1
-            }
-        }, null, 2), "utf-8");
-    ' || {
-        log_error "Failed to write settings.json"
-        exit 1
-    }
-
-    log_success "Claude Code configured successfully"
-}
-
-# ========================
-#        主流程
-# ========================
-
-main() {
-    echo "🚀 Starting $SCRIPT_NAME"
-
-    check_nodejs
-    install_claude_code
-    configure_claude_json
-    configure_claude
-
-    echo ""
-    log_success "🎉 Installation completed successfully!"
-    echo ""
-    echo "🚀 You can now start using Claude Code with:"
-    echo "   claude"
-}
-
-main "$@"

frontend/components.d.ts

@@ -1,36 +0,0 @@
-/* eslint-disable */
-// @ts-nocheck
-// biome-ignore lint: disable
-// oxlint-disable
-// ------
-// Generated by unplugin-vue-components
-// Read more: https://github.com/vuejs/core/pull/3399
-
-export {}
-
-/* prettier-ignore */
-declare module 'vue' {
-  export interface GlobalComponents {
-    ElAutocomplete: typeof import('element-plus/es')['ElAutocomplete']
-    ElButton: typeof import('element-plus/es')['ElButton']
-    ElCard: typeof import('element-plus/es')['ElCard']
-    ElCollapse: typeof import('element-plus/es')['ElCollapse']
-    ElCollapseItem: typeof import('element-plus/es')['ElCollapseItem']
-    ElDrawer: typeof import('element-plus/es')['ElDrawer']
-    ElEmpty: typeof import('element-plus/es')['ElEmpty']
-    ElIcon: typeof import('element-plus/es')['ElIcon']
-    ElInput: typeof import('element-plus/es')['ElInput']
-    ElPopover: typeof import('element-plus/es')['ElPopover']
-    ElScrollbar: typeof import('element-plus/es')['ElScrollbar']
-    ElTag: typeof import('element-plus/es')['ElTag']
-    ElTooltip: typeof import('element-plus/es')['ElTooltip']
-    MultiLineTooltip: typeof import('./src/components/MultiLineTooltip/index.vue')['default']
-    Notification: typeof import('./src/components/Notification/Notification.vue')['default']
-    RouterLink: typeof import('vue-router')['RouterLink']
-    RouterView: typeof import('vue-router')['RouterView']
-    SvgIcon: typeof import('./src/components/SvgIcon/index.vue')['default']
-  }
-  export interface GlobalDirectives {
-    vLoading: typeof import('element-plus/es')['ElLoadingDirective']
-  }
-}