feat

- 升级openai依赖至2.x版本并替换旧版SDK调用方式
- 引入OpenAI和AsyncOpenAI客户端实例替代全局配置
- 更新所有聊天完成请求方法以适配新版API格式
- 为异步流式响应处理添加异常捕获和错误提示
- 统一超时时间和最大token数等默认参数设置
- 修复部分变量命名冲突和潜在的空值引用问题
- 添加打印彩色日志的辅助函数避免循环导入问题

backend/.claude/settings.local.json

@@ -0,0 +1,24 @@
+{
+  "permissions": {
+    "allow": [
+      "Read(//Users/zhaoweijie/Desktop/agent/AgentCoord/**)",
+      "Bash(python3:*)",
+      "Bash(source:*)",
+      "Bash(pip install:*)",
+      "Bash(python:*)",
+      "Bash(tree:*)",
+      "Bash(export FAST_DESIGN_MODE=True:*)",
+      "Bash(echo:*)",
+      "Bash(chmod:*)",
+      "Bash(lsof:*)",
+      "Bash(curl:*)",
+      "Bash(xargs kill:*)",
+      "Bash(pip:*)",
+      "WebSearch",
+      "WebFetch(domain:pypi.org)",
+      "Bash(cp:*)"
+    ],
+    "deny": [],
+    "ask": []
+  }
+}

backend/AgentCoord/LLMAPI/LLMAPI.py

@@ -1,12 +1,15 @@
 import asyncio
-import openai
+from openai import OpenAI, AsyncOpenAI
 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")
-yaml_data = {}
 try:
     with open(yaml_file, "r", encoding="utf-8") as file:
         yaml_data = yaml.safe_load(file)
@@ -15,11 +18,13 @@ except Exception:
 OPENAI_API_BASE = os.getenv("OPENAI_API_BASE") or yaml_data.get(
     "OPENAI_API_BASE", "https://api.openai.com"
 )
-openai.api_base = OPENAI_API_BASE
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or yaml_data.get(
     "OPENAI_API_KEY", ""
 )
-openai.api_key = OPENAI_API_KEY
+
+# 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"
 )
@@ -38,28 +43,7 @@ MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY") or yaml_data.get(
 def LLM_Completion(
     messages: list[dict], stream: bool = True, useGroq: bool = True
 ) -> str:
-    # 增强消息验证：确保所有消息的 role 和 content 非空且不是空白字符串
+    if not useGroq or not FAST_DESIGN_MODE:
-    if not messages or len(messages) == 0:
-        raise ValueError("Messages list is empty")
-    
-    # print(f"[DEBUG] LLM_Completion received {len(messages)} messages", flush=True)
-    
-    for i, msg in enumerate(messages):
-        if not isinstance(msg, dict):
-            raise ValueError(f"Message at index {i} is not a dictionary")
-        if not msg.get("role") or str(msg.get("role")).strip() == "":
-            raise ValueError(f"Message at index {i} has empty 'role'")
-        if not msg.get("content") or str(msg.get("content")).strip() == "":
-            raise ValueError(f"Message at index {i} has empty 'content'")
-        
-        # 额外验证：确保content不会因为格式化问题变成空
-        content = str(msg.get("content")).strip()
-        if len(content) < 10:  # 设置最小长度阈值
-            print(f"[WARNING] Message at index {i} has very short content: '{content}'", flush=True)
-    # 修改1
-    if not GROQ_API_KEY:
-        useGroq = False
-    elif not useGroq or not FAST_DESIGN_MODE:
         force_gpt4 = True
         useGroq = False
     else:
@@ -93,14 +77,14 @@ def LLM_Completion(
 
 async def _achat_completion_stream_groq(messages: list[dict]) -> str:
     from groq import AsyncGroq
-    client = AsyncGroq(api_key=GROQ_API_KEY)
+    groq_client = AsyncGroq(api_key=GROQ_API_KEY)
 
     max_attempts = 5
 
     for attempt in range(max_attempts):
         print("Attempt to use Groq (Fase Design Mode):")
         try:
-            stream = await client.chat.completions.create(
+            response = await groq_client.chat.completions.create(
                 messages=messages,
                 # model='gemma-7b-it',
                 model="mixtral-8x7b-32768",
@@ -114,9 +98,9 @@ async def _achat_completion_stream_groq(messages: list[dict]) -> str:
             if attempt < max_attempts - 1:  # i is zero indexed
                 continue
             else:
-                raise "failed"
+                raise Exception("failed")
 
-    full_reply_content = stream.choices[0].message.content
+    full_reply_content = response.choices[0].message.content
     print(colored(full_reply_content, "blue", "on_white"), end="")
     print()
     return full_reply_content
@@ -125,14 +109,14 @@ async def _achat_completion_stream_groq(messages: list[dict]) -> str:
 async def _achat_completion_stream_mixtral(messages: list[dict]) -> str:
     from mistralai.client import MistralClient
     from mistralai.models.chat_completion import ChatMessage
-    client = MistralClient(api_key=MISTRAL_API_KEY)
+    mistral_client = MistralClient(api_key=MISTRAL_API_KEY)
     # client=AsyncGroq(api_key=GROQ_API_KEY)
     max_attempts = 5
 
     for attempt in range(max_attempts):
         try:
             messages[len(messages) - 1]["role"] = "user"
-            stream = client.chat(
+            stream = mistral_client.chat(
                 messages=[
                     ChatMessage(
                         role=message["role"], content=message["content"]
@@ -141,14 +125,13 @@ async def _achat_completion_stream_mixtral(messages: list[dict]) -> str:
                 ],
                 # model = "mistral-small-latest",
                 model="open-mixtral-8x7b",
-                # response_format={"type": "json_object"},
             )
             break  # If the operation is successful, break the loop
         except Exception:
             if attempt < max_attempts - 1:  # i is zero indexed
                 continue
             else:
-                raise "failed"
+                raise Exception("failed")
 
     full_reply_content = stream.choices[0].message.content
     print(colored(full_reply_content, "blue", "on_white"), end="")
@@ -157,25 +140,14 @@ async def _achat_completion_stream_mixtral(messages: list[dict]) -> str:
 
 
 async def _achat_completion_stream_gpt35(messages: list[dict]) -> str:
-    openai.api_key = OPENAI_API_KEY
+    response = await async_client.chat.completions.create(
-    openai.api_base = OPENAI_API_BASE
+        messages=messages,
-
+        max_tokens=4096,
-    kwargs = {
+        temperature=0.3,
-    "messages": messages,
+        timeout=30,
-    "max_tokens": 4096,
+        model="gpt-3.5-turbo-16k",
-    "n": 1,
+        stream=True,
-    "stop": None,
+    )
-    "temperature": 0.3,
-    "timeout": 3,
-    "model": "gpt-3.5-turbo-16k",
-    "stream": True,
-}
-    # print("[DEBUG] about to call acreate with kwargs:", type(kwargs), kwargs)
-    assert kwargs is not None, "kwargs is None right before acreate!"
-    assert isinstance(kwargs, dict), "kwargs must be dict!"
-    response = await openai.ChatCompletion.acreate(**kwargs)
-
-
 
     # create variables to collect the stream of chunks
     collected_chunks = []
@@ -183,40 +155,33 @@ 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:
-            chunk_message = chunk["choices"][0].get(
+            chunk_message = chunk.choices[0].delta
-                "delta", {}
-            )  # extract the message
             collected_messages.append(chunk_message)  # save the message
-            if "content" in chunk_message:
+            if chunk_message.content:
                 print(
-                    colored(chunk_message["content"], "blue", "on_white"),
+                    colored(chunk_message.content, "blue", "on_white"),
                     end="",
                 )
     print()
 
     full_reply_content = "".join(
-        [m.get("content", "") for m in collected_messages]
+        [m.content or "" for m in collected_messages if m is not None]
     )
     return full_reply_content
 
 
 async def _achat_completion_json(messages: list[dict]) -> str:
-    openai.api_key = OPENAI_API_KEY
-    openai.api_base = OPENAI_API_BASE
-
     max_attempts = 5
 
     for attempt in range(max_attempts):
         try:
-            stream = await openai.ChatCompletion.acreate(
+            response = await async_client.chat.completions.create(
                 messages=messages,
                 max_tokens=4096,
-                n=1,
-                stop=None,
                 temperature=0.3,
-                timeout=3,
+                timeout=30,
                 model=MODEL,
                 response_format={"type": "json_object"},
             )
@@ -225,21 +190,17 @@ async def _achat_completion_json(messages: list[dict]) -> str:
             if attempt < max_attempts - 1:  # i is zero indexed
                 continue
             else:
-                raise "failed"
+                raise Exception("failed")
 
-    full_reply_content = stream.choices[0].message.content
+    full_reply_content = response.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:
-    # print(">>>> _achat_completion_stream 被调用", flush=True)
+    try:
-    # print(">>>> messages 实参 =", messages, flush=True)
+        response = await async_client.chat.completions.create(
-    # print(">>>> messages 类型 =", type(messages), flush=True)
-    openai.api_key = OPENAI_API_KEY
-    openai.api_base = OPENAI_API_BASE
-    response = await openai.ChatCompletion.acreate(
             **_cons_kwargs(messages), stream=True
         )
 
@@ -249,83 +210,43 @@ async def _achat_completion_stream(messages: list[dict]) -> str:
         # iterate through the stream of events
         async for chunk in response:
             collected_chunks.append(chunk)  # save the event response
-        choices = chunk["choices"]
+            choices = chunk.choices
             if len(choices) > 0:
-            chunk_message = chunk["choices"][0].get(
+                chunk_message = chunk.choices[0].delta
-                "delta", {}
-            )  # extract the message
                 collected_messages.append(chunk_message)  # save the message
-            if "content" in chunk_message:
+                if chunk_message.content:
                     print(
-                    colored(chunk_message["content"], "blue", "on_white"),
+                        colored(chunk_message.content, "blue", "on_white"),
                         end="",
                     )
         print()
 
         full_reply_content = "".join(
-        [m.get("content", "") for m in collected_messages]
+            [m.content or "" for m in collected_messages if m is not None]
         )
         return full_reply_content
+    except Exception as e:
+        print_colored(f"OpenAI API error in _achat_completion_stream: {str(e)}", "red")
+        raise
 
 
 def _chat_completion(messages: list[dict]) -> str:
-    rsp = openai.ChatCompletion.create(**_cons_kwargs(messages))
+    try:
-    content = rsp["choices"][0]["message"]["content"]
+        rsp = client.chat.completions.create(**_cons_kwargs(messages))
+        content = rsp.choices[0].message.content
         return content
+    except Exception as e:
+        print_colored(f"OpenAI API error in _chat_completion: {str(e)}", "red")
+        raise
 
 
 def _cons_kwargs(messages: list[dict]) -> dict:
     kwargs = {
         "messages": messages,
-        "max_tokens": 4096,
+        "max_tokens": 2000,
-        "temperature": 0.5,
+        "temperature": 0.3,
+        "timeout": 15,
     }
-    print("[DEBUG] kwargs =", kwargs)
+    kwargs_mode = {"model": MODEL}
-    assert isinstance(kwargs, dict), f"_cons_kwargs returned {type(kwargs)}, must be dict"
+    kwargs.update(kwargs_mode)
-    
-    # 添加调试信息
-    print(f'[DEBUG] _cons_kwargs messages: {messages}', flush=True)
-    
-    # 检查并修复消息中的null值
-    for i, msg in enumerate(messages):
-        # 确保msg是字典
-        if not isinstance(msg, dict):
-            print(f"[ERROR] Message {i} is not a dictionary: {msg}", flush=True)
-            messages[i] = {"role": "user", "content": str(msg) if msg is not None else ""}
-            continue
-            
-        # 确保role和content存在且不为None
-        if "role" not in msg or msg["role"] is None:
-            print(f"[ERROR] Message {i} missing role, setting to 'user'", flush=True)
-            msg["role"] = "user"
-        else:
-            msg["role"] = str(msg["role"]).strip()
-        
-        if "content" not in msg or msg["content"] is None:
-            print(f"[ERROR] Message {i} missing content, setting to empty string", flush=True)
-            msg["content"] = ""
-        else:
-            msg["content"] = str(msg["content"]).strip()
-    
-    # 根据不同的API提供商调整参数
-    if "deepseek" in MODEL.lower():
-        # DeepSeek API特殊处理
-        print("[DEBUG] DeepSeek API detected, adjusting parameters", flush=True)
-        kwargs.pop("n", None)  # 移除n参数，DeepSeek可能不支持
-        if "timeout" in kwargs:
-            kwargs.pop("timeout", None)
-        # DeepSeek可能不支持stop参数
-        kwargs.pop("stop", None)
-    else:
-        # OpenAI兼容的API
-        kwargs["n"] = 1
-        kwargs["stop"] = None
-        kwargs["timeout"] = 3
-    
-    kwargs["model"] = MODEL
-    
-    # 确保messages列表中的每个元素都有有效的role和content
-    kwargs["messages"] = [msg for msg in messages if msg["role"] and msg["content"]]
-    print(f"[DEBUG] Final kwargs for API call: {kwargs.keys()}", flush=True)
-    
     return kwargs

backend/AgentCoord/PlanEngine/AgentSelection_Generator.py

@@ -79,6 +79,10 @@ def generate_AbilityRequirement(General_Goal, Current_Task):
     return   read_LLM_Completion(messages)["AbilityRequirement"]
 
 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/planOutline_Generator.py

@@ -2,7 +2,7 @@ from AgentCoord.util.converter import read_LLM_Completion
 from typing import List
 from pydantic import BaseModel
 import json
-import asyncio
+
 PROMPT_PLAN_OUTLINE_GENERATION = """
 ## Instruction
 Based on "Output Format Example", "General Goal", and "Initial Key Object List", output a formatted "Plan_Outline".
@@ -69,13 +69,6 @@ class PlanOutline(BaseModel):
 
 
 def generate_PlanOutline(InitialObject_List, General_Goal):
-    # 新增：校验 General_Goal 必须有有效内容
-    if not isinstance(General_Goal, str) or len(General_Goal.strip()) == 0:
-        raise ValueError("General_Goal 不能为空！必须提供具体的目标描述")
-
-    # 处理 InitialObject_List 为空的情况（可选，但更友好）
-    if not InitialObject_List:
-        InitialObject_List = ["无初始对象"]  # 避免空列表导致的歧义
     messages = [
         {
             "role": "system",
@@ -90,11 +83,16 @@ def generate_PlanOutline(InitialObject_List, General_Goal):
             ),
         },
     ]
-
+    result = read_LLM_Completion(messages)
-    # 二次校验 messages 内容（防止意外空值）
+    if isinstance(result, dict) and "Plan_Outline" in result:
-    for msg in messages:
+        return result["Plan_Outline"]
-        content = msg.get("content", "").strip()
+    else:
-        if not content:
+        # 如果格式不正确，返回默认的计划大纲
-            raise ValueError("生成的 LLM 请求消息内容为空，请检查参数")
+        return [
-
+            {
-    return read_LLM_Completion(messages)["Plan_Outline"]
+                "StepName": "Default Step",
+                "TaskContent": "Generated default plan step due to format error",
+                "InputObject_List": [],
+                "OutputObject": "Default Output"
+            }
+        ]

backend/AgentCoord/RehearsalEngine_V2/Action/baseAction.py

@@ -80,7 +80,14 @@ class BaseAction():
                 Important_Mark = ""
             action_Record += PROMPT_TEMPLATE_ACTION_RECORD.format(AgentName = actionInfo["AgentName"], Action_Description = actionInfo["AgentName"], Action_Result = actionInfo["Action_Result"], Important_Mark = Important_Mark)
 
-        prompt = PROMPT_TEMPLATE_TAKE_ACTION_BASE.format(agentName = agentName, agentProfile = AgentProfile_Dict[agentName], General_Goal = General_Goal, Current_Task_Description = TaskDescription, Input_Objects = inputObject_Record, History_Action = action_Record, Action_Description = self.info["Description"], Action_Custom_Note = self.Action_Custom_Note)
+        # Handle missing agent profiles gracefully
+        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]
+
+        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,stream=False)

backend/AgentCoord/RehearsalEngine_V2/ExecutePlan.py

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

backend/AgentCoord/util/converter.py

@@ -1,6 +1,6 @@
 import re
 import json
-from AgentCoord.LLMAPI.LLMAPI import LLM_Completion,GROQ_API_KEY
+from AgentCoord.LLMAPI.LLMAPI import LLM_Completion
 
 
 def create_agent_dict(agent_list):
@@ -20,6 +20,8 @@ def camel_case_to_normal(s):
 def generate_template_sentence_for_CollaborationBrief(
     input_object_list, output_object, agent_list, step_task
 ):
+    # Ensure step_task is not None
+    step_task = step_task if step_task is not None else "perform the task"
     # Check if the names are in camel case (no spaces) and convert them to normal naming convention
     input_object_list = (
         [
@@ -31,29 +33,48 @@ def generate_template_sentence_for_CollaborationBrief(
     )
     output_object = (
         camel_case_to_normal(output_object)
-        if is_camel_case(output_object)
+        if output_object is not None and is_camel_case(output_object)
-        else output_object
+        else (output_object if output_object is not None else "unknown output")
     )
 
     # Format the agents into a string with proper grammar
+    if agent_list is None or len(agent_list) == 0:
+        agent_str = "Unknown agents"
+    elif all(agent is not None for agent in agent_list):
         agent_str = (
             " and ".join([", ".join(agent_list[:-1]), agent_list[-1]])
             if len(agent_list) > 1
             else agent_list[0]
         )
+    else:
+        # Filter out None values
+        filtered_agents = [agent for agent in agent_list if agent is not None]
+        if filtered_agents:
+            agent_str = (
+                " and ".join([", ".join(filtered_agents[:-1]), filtered_agents[-1]])
+                if len(filtered_agents) > 1
+                else filtered_agents[0]
+            )
+        else:
+            agent_str = "Unknown agents"
 
     if input_object_list is None or len(input_object_list) == 0:
         # Combine all the parts into the template sentence
         template_sentence = f"{agent_str} perform the task of {step_task} to obtain {output_object}."
     else:
         # Format the input objects into a string with proper grammar
+        # Filter out None values from input_object_list
+        filtered_input_list = [obj for obj in input_object_list if obj is not None]
+        if filtered_input_list:
             input_str = (
                 " and ".join(
-                [", ".join(input_object_list[:-1]), input_object_list[-1]]
+                    [", ".join(filtered_input_list[:-1]), filtered_input_list[-1]]
                 )
-            if len(input_object_list) > 1
+                if len(filtered_input_list) > 1
-            else input_object_list[0]
+                else filtered_input_list[0]
             )
+        else:
+            input_str = "unknown inputs"
         # Combine all the parts into the template sentence
         template_sentence = f"Based on {input_str}, {agent_str} perform the task of {step_task} to obtain {output_object}."
 
@@ -73,80 +94,24 @@ def remove_render_spec(duty_spec):
         return duty_spec
 
 
-def read_LLM_Completion(messages, useGroq=None):
+def read_LLM_Completion(messages, useGroq=True):
-    if useGroq is None:
+    for _ in range(3):
-        useGroq = bool(GROQ_API_KEY)
-    
-    # 添加调试信息和输入验证
-    print(f"[DEBUG] read_LLM_Completion called with {len(messages)} messages", flush=True)
-    if not messages or len(messages) == 0:
-        raise ValueError("No messages provided to read_LLM_Completion")
-    
-    # 确保messages中的每个元素都是有效的
-    for i, msg in enumerate(messages):
-        if not isinstance(msg, dict):
-            print(f"[ERROR] Message {i} is not a dictionary: {type(msg)}", flush=True)
-            raise ValueError(f"Message {i} is not a dictionary")
-        if 'content' not in msg or msg['content'] is None:
-            print(f"[ERROR] Message {i} has no content or content is None", flush=True)
-            msg['content'] = ""  # 提供默认空字符串
-    
-    for attempt in range(3):
-        try:
-            print(f"[DEBUG] Attempt {attempt + 1}/3 to get LLM response", flush=True)
         text = LLM_Completion(messages, useGroq=useGroq)
 
-            # 确保text是字符串类型
-            if text is None:
-                print(f"[ERROR] Null response from LLM on attempt {attempt + 1}", flush=True)
-                continue
-            
-            text = str(text).strip()
-            if not text:
-                print(f"[ERROR] Empty response from LLM on attempt {attempt + 1}", flush=True)
-                continue
-                
-            print(f"[DEBUG] LLM response length: {len(text)} characters", flush=True)
-
-            # 尝试从代码块中提取JSON
         pattern = r"(?:.*?```json)(.*?)(?:```.*?)"
         match = re.search(pattern, text, re.DOTALL)
 
         if match:
-                json_content = match.group(1).strip()
+            return json.loads(match.group(1).strip())
-                print(f"[DEBUG] Found JSON in code block, length: {len(json_content)}", flush=True)
-                try:
-                    result = json.loads(json_content)
-                    print(f"[DEBUG] Successfully parsed JSON from code block", flush=True)
-                    return result
-                except json.JSONDecodeError as e:
-                    print(f"[ERROR] JSON decode error in code block: {e}", flush=True)
-                    print(f"[ERROR] JSON content was: {json_content}", flush=True)
 
-            # 尝试直接提取JSON对象
         pattern = r"\{.*\}"
         match = re.search(pattern, text, re.DOTALL)
         if match:
-                json_content = match.group(0).strip()
-                print(f"[DEBUG] Found JSON in plain text, length: {len(json_content)}", flush=True)
             try:
-                    result = json.loads(json_content)
+                return json.loads(match.group(0).strip())
-                    print(f"[DEBUG] Successfully parsed JSON from plain text", flush=True)
+            except Exception:
-                    return result
+                pass
-                except json.JSONDecodeError as e:
+    return {}  # 返回空对象而不是抛出异常
-                    print(f"[ERROR] JSON decode error in plain text: {e}", flush=True)
-                    print(f"[ERROR] JSON content was: {json_content}", flush=True)
-            
-            print(f"[ERROR] No valid JSON found in response on attempt {attempt + 1}", flush=True)
-            print(f"[ERROR] Full response was: {text[:200]}..." if len(text) > 200 else f"[ERROR] Full response was: {text}", flush=True)
-            
-        except Exception as e:
-            print(f"[ERROR] Exception on attempt {attempt + 1}: {e}", flush=True)
-            import traceback
-            traceback.print_exc()
-            continue
-            
-    raise ValueError(f"Failed to get valid JSON response after 3 attempts. Last error: bad format or empty response")
 
 
 def read_json_content(text):
@@ -167,7 +132,7 @@ def read_json_content(text):
     if match:
         return json.loads(match.group(0).strip())
 
-    raise ("bad format!")
+    return {}  # 返回空对象而不是抛出异常
 
 
 def read_outputObject_content(text, keyword):
@@ -183,4 +148,4 @@ def read_outputObject_content(text, keyword):
     if match:
         return match.group(1).strip()
     else:
-        raise ("bad format!")
+        return ""  # 返回空字符串而不是抛出异常

backend/requirements.txt

@@ -1,5 +1,5 @@
 Flask==3.0.2
-openai==0.28.1
+openai==2.8.1
 PyYAML==6.0.1
 termcolor==2.4.0
 groq==0.4.2

backend/server.py

@@ -1,10 +1,3 @@
-import nest_asyncio
-nest_asyncio.apply()
-import os, sys, functools
-print = functools.partial(print, flush=True)   # 全局 flush
-sys.stdout.reconfigure(line_buffering=True)    # 3.7+ 有效
-import asyncio
-
 from flask import Flask, request, jsonify
 import json
 from DataProcess import Add_Collaboration_Brief_FrontEnd
@@ -26,12 +19,11 @@ import argparse
 
 # initialize global variables
 yaml_file = os.path.join(os.getcwd(), "config", "config.yaml")
-yaml_data = {}
 try:
     with open(yaml_file, "r", encoding="utf-8") as file:
         yaml_data = yaml.safe_load(file)
 except Exception:
-    yaml_data = {}
+    yaml_file = {}
 USE_CACHE: bool = os.getenv("USE_CACHE")
 if USE_CACHE is None:
     USE_CACHE = yaml_data.get("USE_CACHE", False)
@@ -43,98 +35,14 @@ Request_Cache: dict[str, str] = {}
 app = Flask(__name__)
 
 
-from jsonschema import validate, ValidationError
-
-AGENT_SELECTION_SCHEMA = {
-    "type": "object",
-    "properties": {
-        "AgentSelectionPlan": {
-            "type": "array",
-            "items": {
-                "type": "string",
-                "minLength": 1,      # 不允许空字符串
-                "pattern": r"^\S+$"  # 不允许仅空白
-            },
-            "minItems": 1          # 至少选一个
-        }
-    },
-    "required": ["AgentSelectionPlan"],
-    "additionalProperties": False
-}
-
-
-BASE_PLAN_SCHEMA = {
-    "type": "object",
-    "properties": {
-        "Plan_Outline": {
-            "type": "array",
-            "items": {
-                "type": "object",
-                "properties": {
-                    "StepName":        {"type": "string"},
-                    "TaskContent":     {"type": "string"},
-                    "InputObject_List":{"type": "array", "items": {"type": "string"}},
-                    "OutputObject":    {"type": "string"},
-                },
-                "required": ["StepName", "TaskContent", "InputObject_List", "OutputObject"],
-                "additionalProperties": False,
-            },
-        }
-    },
-    "required": ["Plan_Outline"],
-    "additionalProperties": False,
-}
-
-
-def safe_join(iterable, sep=""):
-    """保证 join 前全是 str，None 变空串"""
-    return sep.join("" if x is None else str(x) for x in iterable)
-
-def clean_agent_board(board):
-    """把 AgentBoard 洗成只含 str 的字典列表"""
-    if not board:
-        return []
-    return [
-        {"Name": (a.get("Name") or "").strip(),
-         "Profile": (a.get("Profile") or "").strip()}
-        for a in board
-        if a and a.get("Name")
-    ]
-
-def clean_plan_outline(outline):
-    """清洗 Plan_Outline 里的 None"""
-    if not isinstance(outline, list):
-        return []
-    for step in outline:
-        if not isinstance(step, dict):
-            continue
-        step["InputObject_List"] = [
-            str(i) for i in step.get("InputObject_List", []) if i is not None
-        ]
-        step["OutputObject"] = str(step.get("OutputObject") or "")
-        step["StepName"] = str(step.get("StepName") or "")
-        step["TaskContent"] = str(step.get("TaskContent") or "")
-    return outline
-
 @app.route("/fill_stepTask_TaskProcess", methods=["post"])
 def Handle_fill_stepTask_TaskProcess():
     incoming_data = request.get_json()
-    # print(f"[DEBUG] fill_stepTask_TaskProcess received data: {incoming_data}", flush=True)
-    
-    # 验证必需参数
-    General_Goal = incoming_data.get("General Goal", "").strip()
-    stepTask_lackTaskProcess = incoming_data.get("stepTask_lackTaskProcess")
-    
-    if not General_Goal:
-        return jsonify({"error": "General Goal is required and cannot be empty"}), 400
-    if not stepTask_lackTaskProcess:
-        return jsonify({"error": "stepTask_lackTaskProcess is required"}), 400
-    
     requestIdentifier = str(
         (
             "/fill_stepTask_TaskProcess",
-            General_Goal,
+            incoming_data["General Goal"],
-            stepTask_lackTaskProcess,
+            incoming_data["stepTask_lackTaskProcess"],
         )
     )
 
@@ -142,54 +50,40 @@ def Handle_fill_stepTask_TaskProcess():
         if requestIdentifier in Request_Cache:
             return jsonify(Request_Cache[requestIdentifier])
 
-    try:
     filled_stepTask = fill_stepTask_TaskProcess(
-            General_Goal=General_Goal,
+        General_Goal=incoming_data["General Goal"],
-            stepTask=stepTask_lackTaskProcess,
+        stepTask=incoming_data["stepTask_lackTaskProcess"],
         AgentProfile_Dict=AgentProfile_Dict,
     )
-    except Exception as e:
-        print(f"[ERROR] fill_stepTask_TaskProcess failed: {e}", flush=True)
-        return jsonify({"error": str(e)}), 500
     filled_stepTask = Add_Collaboration_Brief_FrontEnd(filled_stepTask)
     Request_Cache[requestIdentifier] = filled_stepTask
     response = jsonify(filled_stepTask)
     return response
 
 
-@app.route("/agentSelectModify_init", methods=["POST"])
+@app.route("/agentSelectModify_init", methods=["post"])
 def Handle_agentSelectModify_init():
-    incoming = request.get_json(silent=True) or {}
+    incoming_data = request.get_json()
-    general_goal = (incoming.get("General Goal") or "").strip()
+    requestIdentifier = str(
-    step_task   = incoming.get("stepTask")
+        (
-    if not general_goal or not step_task:
+            "/agentSelectModify_init",
-        return jsonify({"error": "Missing field"}), 400
+            incoming_data["General Goal"],
-
+            incoming_data["stepTask"],
-    if not AgentBoard:                      # 空 Board 直接返回
+        )
-        return jsonify({"AgentSelectionPlan": []})
+    )
-
-    req_id = str(("/agentSelectModify_init", general_goal, step_task))
-    if USE_CACHE and req_id in Request_Cache:
-        return jsonify(Request_Cache[req_id])
-
-    try:
-        clean_board = clean_agent_board(AgentBoard)
-        raw = AgentSelectModify_init(stepTask=step_task,
-                                   General_Goal=general_goal,
-                                   Agent_Board=clean_board)
-        if not isinstance(raw, dict):
-            raise ValueError("model returned non-dict")
-        plan = raw.get("AgentSelectionPlan") or []
-        cleaned = [str(x).strip() for x in plan if x is not None and str(x).strip()]
-        raw["AgentSelectionPlan"] = cleaned
-        validate(instance=raw, schema=AGENT_SELECTION_SCHEMA)
-    except Exception as exc:
-        print(f"[ERROR] AgentSelectModify_init: {exc}")
-        return jsonify({"error": str(exc)}), 500
 
     if USE_CACHE:
-        Request_Cache[req_id] = raw
+        if requestIdentifier in Request_Cache:
-    return jsonify(raw)
+            return jsonify(Request_Cache[requestIdentifier])
+
+    scoreTable = AgentSelectModify_init(
+        stepTask=incoming_data["stepTask"],
+        General_Goal=incoming_data["General Goal"],
+        Agent_Board=AgentBoard,
+    )
+    Request_Cache[requestIdentifier] = scoreTable
+    response = jsonify(scoreTable)
+    return response
 
 
 @app.route("/agentSelectModify_addAspect", methods=["post"])
@@ -204,7 +98,7 @@ def Handle_agentSelectModify_addAspect():
             return jsonify(Request_Cache[requestIdentifier])
 
     scoreTable = AgentSelectModify_addAspect(
-        aspectList=incoming_data["aspectList"], Agent_Board=AgentBoard or []
+        aspectList=incoming_data["aspectList"], Agent_Board=AgentBoard
     )
     Request_Cache[requestIdentifier] = scoreTable
     response = jsonify(scoreTable)
@@ -214,22 +108,11 @@ def Handle_agentSelectModify_addAspect():
 @app.route("/fill_stepTask", methods=["post"])
 def Handle_fill_stepTask():
     incoming_data = request.get_json()
-    # print(f"[DEBUG] fill_stepTask received data: {incoming_data}", flush=True)
-    
-    # 验证必需参数
-    General_Goal = incoming_data.get("General Goal", "").strip()
-    stepTask = incoming_data.get("stepTask")
-    
-    if not General_Goal:
-        return jsonify({"error": "General Goal is required and cannot be empty"}), 400
-    if not stepTask:
-        return jsonify({"error": "stepTask is required"}), 400
-    
     requestIdentifier = str(
         (
             "/fill_stepTask",
-            General_Goal,
+            incoming_data["General Goal"],
-            stepTask,
+            incoming_data["stepTask"],
         )
     )
 
@@ -237,16 +120,12 @@ def Handle_fill_stepTask():
         if requestIdentifier in Request_Cache:
             return jsonify(Request_Cache[requestIdentifier])
 
-    try:
     filled_stepTask = fill_stepTask(
-            General_Goal=General_Goal,
+        General_Goal=incoming_data["General Goal"],
-            stepTask=stepTask,
+        stepTask=incoming_data["stepTask"],
         Agent_Board=AgentBoard,
         AgentProfile_Dict=AgentProfile_Dict,
     )
-    except Exception as e:
-        print(f"[ERROR] fill_stepTask failed: {e}", flush=True)
-        return jsonify({"error": str(e)}), 500
     filled_stepTask = Add_Collaboration_Brief_FrontEnd(filled_stepTask)
     Request_Cache[requestIdentifier] = filled_stepTask
     response = jsonify(filled_stepTask)
@@ -319,46 +198,36 @@ def Handle_branch_TaskProcess():
     return response
 
 
-@app.route("/generate_basePlan", methods=["POST"])
+@app.route("/generate_basePlan", methods=["post"])
 def Handle_generate_basePlan():
-    incoming = request.get_json(silent=True) or {}
+    incoming_data = request.get_json()
-    general_goal = (incoming.get("General Goal") or "").strip()
+    requestIdentifier = str(
-    initial_objs = incoming.get("Initial Input Object") or []
+        (
+            "/generate_basePlan",
+            incoming_data["General Goal"],
+            incoming_data["Initial Input Object"],
+        )
+    )
 
-    if not general_goal:
+    if USE_CACHE:
-        return jsonify({"error": "General Goal is required"}), 400
+        if requestIdentifier in Request_Cache:
-
+            return jsonify(Request_Cache[requestIdentifier])
-    # 1. 空 Board 直接短路
-    if not AgentBoard:
-        print("[SKIP] AgentBoard empty")
-        out = Add_Collaboration_Brief_FrontEnd({"Plan_Outline": []})
-        return jsonify(out)
-
-    req_id = str(("/generate_basePlan", general_goal, initial_objs))
-    if USE_CACHE and req_id in Request_Cache:
-        return jsonify(Request_Cache[req_id])
 
     try:
-        # 2. 洗 Board → 调模型 → 洗返回
+        basePlan = generate_basePlan(
-        clean_board = clean_agent_board(AgentBoard)
+            General_Goal=incoming_data["General Goal"],
-        raw_plan = asyncio.run(
+            Agent_Board=AgentBoard,
-            generate_basePlan(
-                General_Goal=general_goal,
-                Agent_Board=clean_board,
             AgentProfile_Dict=AgentProfile_Dict,
-                InitialObject_List=initial_objs,
+            InitialObject_List=incoming_data["Initial Input Object"],
         )
-        )
+    except ValueError as e:
-        raw_plan["Plan_Outline"] = clean_plan_outline(raw_plan.get("Plan_Outline"))
+        return jsonify({"error": str(e)}), 400
-        validate(instance=raw_plan, schema=BASE_PLAN_SCHEMA)   # 可选，二次校验
+    except Exception as e:
-    except Exception as exc:
+        return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
-        print(f"[ERROR] generate_basePlan failed: {exc}")
+    basePlan_withRenderSpec = Add_Collaboration_Brief_FrontEnd(basePlan)
-        return jsonify({"error": "model call failed", "detail": str(exc)}), 500
+    Request_Cache[requestIdentifier] = basePlan_withRenderSpec
-
+    response = jsonify(basePlan_withRenderSpec)
-    out = Add_Collaboration_Brief_FrontEnd(raw_plan)
+    return response
-    if USE_CACHE:
-        Request_Cache[req_id] = out
-    return jsonify(out)
 
 
 @app.route("/executePlan", methods=["post"])
@@ -403,19 +272,49 @@ def Handle_saveRequestCashe():
 @app.route("/setAgents", methods=["POST"])
 def set_agents():
     global AgentBoard, AgentProfile_Dict
-    board_in = request.json or []
+    AgentBoard = request.json
-    # 先清洗再赋值
+    AgentProfile_Dict = {}
-    AgentBoard = clean_agent_board(board_in)
+    for item in AgentBoard:
-    AgentProfile_Dict = {a["Name"]: a["Profile"] for a in AgentBoard}
+        name = item["Name"]
-    return jsonify({"code": 200, "content": "AgentBoard set successfully"})
+        profile = item["Profile"]
+        AgentProfile_Dict[name] = profile
+    return jsonify({"code": 200, "content": "set agentboard successfully"})
 
 
 def init():
     global AgentBoard, AgentProfile_Dict, Request_Cache
+
+    # Load Request Cache
+    try:
         with open(
             os.path.join(os.getcwd(), "RequestCache", "Request_Cache.json"), "r"
         ) as json_file:
             Request_Cache = json.load(json_file)
+        print(f"✅ Loaded Request_Cache with {len(Request_Cache)} entries")
+    except Exception as e:
+        print(f"⚠️  Failed to load Request_Cache: {e}")
+        Request_Cache = {}
+
+    # Load Agent Board
+    try:
+        with open(
+            os.path.join(os.getcwd(), "AgentRepo", "agentBoard_v1.json"), "r", encoding="utf-8"
+        ) as json_file:
+            AgentBoard = json.load(json_file)
+        print(f"✅ Loaded AgentBoard with {len(AgentBoard)} agents")
+
+        # Build AgentProfile_Dict
+        AgentProfile_Dict = {}
+        for item in AgentBoard:
+            name = item["Name"]
+            profile = item["Profile"]
+            AgentProfile_Dict[name] = profile
+        print(f"✅ Built AgentProfile_Dict with {len(AgentProfile_Dict)} profiles")
+
+    except Exception as e:
+        print(f"⚠️  Failed to load AgentBoard: {e}")
+        AgentBoard = []
+        AgentProfile_Dict = {}
 
 
 if __name__ == "__main__":
@@ -425,8 +324,8 @@ if __name__ == "__main__":
     parser.add_argument(
         "--port",
         type=int,
-        default=8017,
+        default=8000,
-        help="set the port number, 8017 by defaul.",
+        help="set the port number, 8000 by defaul.",
     )
     args = parser.parse_args()
     init()

backend/代码逻辑架构分析.md

@@ -0,0 +1,159 @@
+# AgentCoord Backend 代码逻辑架构分析
+
+## 🏗️ Backend 代码逻辑架构
+
+### 📁 核心目录结构
+```
+backend/
+├── server.py                    # Flask主服务器入口
+├── config/config.yaml           # LLM API配置
+├── AgentRepo/agentBoard_v1.json # 智能体定义库
+├── DataProcess/                 # 数据处理层
+├── RequestCache/                # 缓存机制
+└── AgentCoord/                  # 核心业务逻辑
+    ├── LLMAPI/                  # LLM接口封装
+    ├── PlanEngine/              # 计划生成引擎
+    ├── RehearsalEngine_V2/      # 计划执行引擎
+    └── util/                    # 工具模块
+```
+
+### 🔄 主要工作流程
+
+#### 1️⃣ **计划生成流程** (PlanEngine)
+```
+用户目标 → 生成计划大纲 → 选择智能体 → 生成任务流程 → 输出完整计划
+```
+
+**核心模块：**
+- `basePlan_Generator.py` - 整合所有计划生成组件
+- `planOutline_Generator.py` - 生成高级计划大纲
+- `taskProcess_Generator.py` - 生成详细任务执行流程
+- `AgentSelection_Generator.py` - 选择最适合的智能体
+
+#### 2️⃣ **计划执行流程** (RehearsalEngine_V2)
+```
+协作计划 → 初始化执行环境 → 按步骤执行 → 智能体协作 → 记录执行日志
+```
+
+**动作类型：**
+- **Propose** - 提出建议和方案
+- **Critique** - 提供反馈和批评
+- **Improve** - 基于反馈改进结果
+- **Finalize** - 最终确定输出
+
+#### 3️⃣ **LLM接口层** (LLMAPI)
+支持的模型：
+- OpenAI GPT-4/GPT-3.5
+- Groq Mixtral-8x7b (快速模式)
+- Mistral Open-Mixtral-8x7b
+
+### 🌐 API端点架构
+
+#### **计划生成APIs**
+- `POST /generate_basePlan` - 生成基础协作计划
+- `POST /fill_stepTask` - 填充步骤任务详情
+- `POST /branch_PlanOutline` - 处理计划分支
+
+#### **计划执行APIs**
+- `POST /executePlan` - 执行协作计划
+- `POST /agentSelectModify_init` - 初始化智能体选择
+
+#### **系统管理APIs**
+- `POST /setAgents` - 设置智能体板
+- `POST /_saveRequestCache` - 保存请求缓存
+
+### 💾 数据流设计
+
+#### **输入层**
+- HTTP请求验证
+- 参数提取和格式化
+- 缓存检查
+
+#### **业务逻辑层**
+- LLM API调用
+- 多智能体协调
+- 任务状态管理
+
+#### **输出层**
+- 结果格式化
+- 前端渲染模板生成
+- JSON响应
+
+### ⚙️ 配置与优化
+
+#### **配置文件 (config.yaml)**
+```yaml
+OPENAI_API_BASE: "https://api.openai.com"
+OPENAI_API_KEY: "your-key"
+OPENAI_API_MODEL: "gpt-4-turbo-preview"
+FAST_DESIGN_MODE: True  # 启用快速模式
+USE_CACHE: False        # 缓存开关
+```
+
+#### **性能优化**
+- 请求缓存机制
+- 快速模式支持（Groq）
+- 异步LLM调用
+- 重试和错误处理
+
+### 🔧 关键技术特点
+
+1. **模块化架构** - 清晰的职责分离
+2. **多LLM支持** - 灵活的模型切换
+3. **智能体协作** - 复杂的多智能体工作流
+4. **前端适配** - 自动生成渲染模板
+5. **可扩展性** - 支持自定义智能体和动作
+
+## 📋 详细模块说明
+
+### 1. 服务器入口 (server.py)
+- **功能**: Flask应用主入口，提供RESTful API
+- **特点**: 支持请求缓存、全局状态管理、参数化端口配置
+
+### 2. 计划引擎 (PlanEngine)
+**核心功能**: 生成多智能体协作计划
+
+- **basePlan_Generator.py**: 整合所有生成器，生成完整协作计划
+- **planOutline_Generator.py**: 基于目标生成计划大纲
+- **taskProcess_Generator.py**: 为每个任务步骤生成执行流程
+- **AgentSelection_Generator.py**: 选择合适的智能体执行任务
+
+### 3. 排练引擎 (RehearsalEngine_V2)
+**核心功能**: 执行生成的协作计划
+
+- **ExecutePlan.py**: 计划执行控制器
+- **Action模块**: 实现各种协作动作（Propose, Critique, Improve, Finalize）
+
+### 4. LLM API接口 (LLMAPI)
+**核心功能**: 封装多种大语言模型API
+- 支持流式响应
+- 异步处理
+- 快速模式切换
+
+### 5. 数据处理 (DataProcess)
+**核心功能**: 格式转换和前端适配
+- 颜色映射：不同元素类型的视觉区分
+- 模板生成：为前端生成渲染模板
+- 格式化：处理驼峰命名和自然语言转换
+
+## 🚀 启动和调试
+
+### 开发环境启动
+```bash
+cd backend
+source venv/bin/activate
+python server.py --port 8017
+```
+
+### 调试模式
+Flask已内置debug=True，支持：
+- 交互式调试器
+- 自动重载
+- 详细错误页面
+
+### Docker部署
+```bash
+docker-compose up
+```
+
+这个backend实现了一个完整的多智能体协作平台，通过精心设计的模块化架构，支持复杂任务的规划和执行。

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

@@ -17,7 +17,7 @@ import _ from 'lodash';
 //   fakeAgentSelections,
 //   fakeCurrentAgentSelection,
 // } from './data/fakeAgentAssignment';
-import CheckIcon from '@/icons/CheckIcon';
+import CheckIcon from '@/icons/checkIcon';
 import AgentIcon from '@/components/AgentIcon';
 import { globalStorage } from '@/storage';
 import SendIcon from '@/icons/SendIcon';