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9 Commits

Author SHA1 Message Date
liailing1026
c00c0072b8 feat 2025-12-11 14:05:08 +08:00
zhaoweijie
6392301833 refactor(LLMAPI): 重构LLM接口以支持新版本OpenAI SDK
- 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
2025-11-22 17:01:25 +08:00
zhaoweijie
ab8c9e294d feat:rename subtree from frontend-vue to frontend 2025-11-20 09:56:51 +08:00
zhaoweijie
1aa9e280b0 Add 'frontend-vue/' from commit '041986f5cd6e67f5367fd047c71b8107865fa5af'
git-subtree-dir: frontend-vue
git-subtree-mainline: 4fa5504697
git-subtree-split: 041986f5cd
2025-11-20 09:51:44 +08:00
zhaoweijie
041986f5cd feat(config): 添加配置文件支持动态标题和提示词
- 新增 public/config.json 配置文件,包含网站标题、副标题及任务提示词
- 在 Header 组件中读取并应用配置中的标题信息-为 Task 组件的搜索建议框引入配置中的提示词列表
- 创建 useConfigStore 管理全局配置状态,并在应用初始化时加载配置
- 更新 main.ts 在应用启动时设置文档标题
- 移除了 Task.vue 中硬编码的提示词数组,改由配置驱动-修复了 agents store 中版本标识监听逻辑,实现存储清理功能
- 添加 MultiLineTooltip 组件用于文本溢出时显示完整内容
-重构 TaskSyllabus 页面布局与样式,提升视觉效果与交互体验
- 引入 Bg
2025-11-04 15:26:52 +08:00
zhaoweijie
00ef22505e feat(agent):重构智能体仓库并优化任务模板交互
-为 public/agent.json 中的每个智能体添加 Classification 字段以支持分类展示
- 新增 AgentRepoList 组件用于渲染智能体列表,提升代码复用性
- 在 src/layout/components/Main/TaskTemplate/AgentRepo/index.vue 中实现基于 Classification 的智能体分组展示逻辑- 移除旧版 popover 方式展示智能体详情,改用新的列表组件统一处理
- 修改任务搜索输入框为 textarea 类型,并优化其聚焦与失焦状态下的样式表现
- 调整任务模板页面布局高度计算方式,确保适配新 UI 结构
-修复任务结果流程图连线方向及透明度判断逻辑,增强可视化准确性- 引入流动动画效果至 jsPlumb 连线,区分 input/output 类型并美化视觉呈现
- 更新配置文件中部分动作类型的配色值,提高界面美观度
- 升级本地存储键名 agents 至 agents-v1,避免
2025-11-03 09:44:14 +08:00
zhaoweijie
b73419b7a0 feat(agent): 初始化代理仓库并优化UI显示
- 添加默认agent.json配置文件,包含19个预定义代理角色及其简介
- 实现AgentRepo组件挂载时自动读取默认配置逻辑
- 优化任务模板中代理操作项的渲染样式和分割线显示
- 调整卡片悬停样式,去除阴影并添加背景色过渡动画
2025-11-02 12:55:13 +08:00
zhaoweijie
974af053ca feat(task):优化任务执行与智能体展示功能
- 更新action.svg图标样式- 重构AgentRepo组件,优化智能体列表展示逻辑
- 改进ExecutePlan组件,支持object类型节点渲染
- 完善TaskResult组件,增加执行计划存储与清理机制
- 调整TaskSyllabus组件,优化卡片激活状态样式
- 在Task组件中添加搜索建议功能
- 更新主题配色变量和全局样式- 替换ElInput为ElAutocomplete组件
- 清理无用的jsplumb连接代码- 优化组件间通信与状态管理
2025-10-31 18:42:31 +08:00
zhaoweijie
0c571dec21 Initial commit: Multi-Agent Coordination Platform
- Vue 3 + TypeScript + Vite project structure
- Element Plus UI components with dark theme
- Pinia state management for agents and tasks
- JSPlumb integration for visual workflow editing
- SVG icon system for agent roles
- Axios request layer with API proxy configuration
- Tailwind CSS for styling
- Docker deployment with Caddy web server
- Complete development toolchain (ESLint, Prettier, Vitest)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-29 10:22:14 +08:00
144 changed files with 30251 additions and 16127 deletions

View File

@@ -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": []
}
}

View File

@@ -1,24 +1,30 @@
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")
try:
with open(yaml_file, "r", encoding="utf-8") as file:
yaml_data = yaml.safe_load(file)
except Exception:
yaml_file = {}
yaml_data = {}
OPENAI_API_BASE = os.getenv("OPENAI_API_BASE") or yaml_data.get(
"OPENAI_API_BASE", "https://api.openai.com"
)
openai.api_base = OPENAI_API_BASE
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or yaml_data.get(
"OPENAI_API_KEY", ""
)
openai.api_key = OPENAI_API_KEY
# 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"
)
@@ -71,14 +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",
@@ -92,9 +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
@@ -103,14 +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"]
@@ -119,14 +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="")
@@ -135,15 +140,11 @@ 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
openai.api_base = OPENAI_API_BASE
response = await openai.ChatCompletion.acreate(
response = await async_client.chat.completions.create(
messages=messages,
max_tokens=4096,
n=1,
stop=None,
temperature=0.3,
timeout=3,
timeout=30,
model="gpt-3.5-turbo-16k",
stream=True,
)
@@ -154,40 +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(
"delta", {}
) # extract the message
chunk_message = chunk.choices[0].delta
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"},
)
@@ -196,62 +190,62 @@ 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:
openai.api_key = OPENAI_API_KEY
openai.api_base = OPENAI_API_BASE
response = await openai.ChatCompletion.acreate(
**_cons_kwargs(messages), stream=True
)
try:
response = await async_client.chat.completions.create(
**_cons_kwargs(messages), stream=True
)
# create variables to collect the stream of chunks
collected_chunks = []
collected_messages = []
# iterate through the stream of events
async for chunk in response:
collected_chunks.append(chunk) # save the event response
choices = chunk["choices"]
if len(choices) > 0:
chunk_message = chunk["choices"][0].get(
"delta", {}
) # extract the message
collected_messages.append(chunk_message) # save the message
if "content" in chunk_message:
print(
colored(chunk_message["content"], "blue", "on_white"),
end="",
)
print()
# create variables to collect the stream of chunks
collected_chunks = []
collected_messages = []
# iterate through the stream of events
async for chunk in response:
collected_chunks.append(chunk) # save the event response
choices = chunk.choices
if len(choices) > 0:
chunk_message = chunk.choices[0].delta
collected_messages.append(chunk_message) # save the message
if chunk_message.content:
print(
colored(chunk_message.content, "blue", "on_white"),
end="",
)
print()
full_reply_content = "".join(
[m.get("content", "") for m in collected_messages]
)
return full_reply_content
full_reply_content = "".join(
[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:
print(messages, flush=True)
rsp = openai.ChatCompletion.create(**_cons_kwargs(messages))
content = rsp["choices"][0]["message"]["content"]
print(content, flush=True)
return content
try:
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,
"n": 1,
"stop": None,
"temperature": 0.5,
"timeout": 3,
"max_tokens": 2000,
"temperature": 0.3,
"timeout": 15,
}
kwargs_mode = {"model": MODEL}
kwargs.update(kwargs_mode)

View File

@@ -42,7 +42,7 @@ def generate_AbilityRequirement(General_Goal, Current_Task):
},
]
print(messages[1]["content"])
return read_LLM_Completion(messages)["AbilityRequirement"]
return read_LLM_Completion(messages)["AbilityRequirement"]
PROMPT_AGENT_ABILITY_SCORING = """
@@ -83,7 +83,6 @@ class JSON_Agent(BaseModel):
class JSON_AGENT_ABILITY_SCORING(RootModel):
root: Dict[str, JSON_Agent]
def agentAbilityScoring(Agent_Board, Ability_Requirement_List):
scoreTable = {}
for Ability_Requirement in Ability_Requirement_List:
@@ -105,7 +104,7 @@ def agentAbilityScoring(Agent_Board, Ability_Requirement_List):
return scoreTable
def AgentSelectModify_init(stepTask, General_Goal, Agent_Board):
async def AgentSelectModify_init(stepTask, General_Goal, Agent_Board):
Current_Task = {
"TaskName": stepTask["StepName"],
"InputObject_List": stepTask["InputObject_List"],
@@ -125,10 +124,10 @@ def AgentSelectModify_init(stepTask, General_Goal, Agent_Board):
),
},
]
Ability_Requirement_List = read_LLM_Completion(messages)[
Ability_Requirement_List = await read_LLM_Completion(messages)[
"AbilityRequirement"
]
scoreTable = agentAbilityScoring(Agent_Board, Ability_Requirement_List)
scoreTable = await agentAbilityScoring(Agent_Board, Ability_Requirement_List)
return scoreTable

View File

@@ -33,7 +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". Agent Selection Plan ranks from high to low according to ability.
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".
## General Goal (Specify the general goal for the collaboration plan)
{General_Goal}
@@ -76,10 +76,13 @@ def generate_AbilityRequirement(General_Goal, Current_Task):
},
]
print(messages[1]["content"])
return read_LLM_Completion(messages)["AbilityRequirement"]
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",
@@ -99,7 +102,7 @@ def generate_AgentSelection(General_Goal, Current_Task, Agent_Board):
agentboard_set = {agent["Name"] for agent in Agent_Board}
while True:
candidate = read_LLM_Completion(messages)["AgentSelectionPlan"]
candidate = read_LLM_Completion(messages)["AgentSelectionPlan"]
if len(candidate) > MAX_TEAM_SIZE:
teamSize = random.randint(2, MAX_TEAM_SIZE)
candidate = candidate[0:teamSize]

View File

@@ -7,14 +7,22 @@ import AgentCoord.util as util
def generate_basePlan(
General_Goal, Agent_Board, AgentProfile_Dict, InitialObject_List, context
General_Goal, Agent_Board, AgentProfile_Dict, InitialObject_List
):
Agent_Board = [
{"Name": (a.get("Name") or "").strip(),"Profile": (a.get("Profile") or "").strip()}
for a in Agent_Board
if a and a.get("Name") is not None
]
if not Agent_Board: # 洗完后还是空 → 直接返回空计划
return {"Plan_Outline": []}
basePlan = {
"Initial Input Object": InitialObject_List,
"Collaboration Process": [],
}
PlanOutline = generate_PlanOutline(
InitialObject_List=[], General_Goal=General_Goal + context
InitialObject_List=[], General_Goal=General_Goal
)
for stepItem in PlanOutline:
Current_Task = {
@@ -44,7 +52,7 @@ def generate_basePlan(
),
}
TaskProcess = generate_TaskProcess(
General_Goal=General_Goal + context,
General_Goal=General_Goal,
Current_Task_Description=Current_Task_Description,
)
# add the generated AgentSelection and TaskProcess to the stepItem

View File

@@ -95,7 +95,7 @@ def branch_PlanOutline(
},
{"role": "system", "content": prompt},
]
Remaining_Steps = read_LLM_Completion(messages, useGroq=False)[
Remaining_Steps = read_LLM_Completion(messages)[
"Remaining Steps"
]
branch_List.append(Remaining_Steps)

View File

@@ -145,7 +145,7 @@ def branch_TaskProcess(
},
{"role": "system", "content": prompt},
]
Remaining_Steps = read_LLM_Completion(messages, useGroq=False)[
Remaining_Steps = read_LLM_Completion(messages)[
"Remaining Steps"
]

View File

@@ -5,7 +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". The number of steps in the Plan Outline cannot exceed 5.
Based on "Output Format Example", "General Goal", and "Initial Key Object List", output a formatted "Plan_Outline".
## 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}
@@ -51,7 +51,6 @@ 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
"""
@@ -84,4 +83,16 @@ def generate_PlanOutline(InitialObject_List, General_Goal):
),
},
]
return read_LLM_Completion(messages)["Plan_Outline"]
result = read_LLM_Completion(messages)
if isinstance(result, dict) and "Plan_Outline" in result:
return result["Plan_Outline"]
else:
# 如果格式不正确,返回默认的计划大纲
return [
{
"StepName": "Default Step",
"TaskContent": "Generated default plan step due to format error",
"InputObject_List": [],
"OutputObject": "Default Output"
}
]

View File

@@ -90,7 +90,6 @@ 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
"""
@@ -107,6 +106,9 @@ class TaskProcessPlan(BaseModel):
def generate_TaskProcess(General_Goal, Current_Task_Description):
# 新增参数验证
if not General_Goal or str(General_Goal).strip() == "":
raise ValueError("General_Goal cannot be empty")
messages = [
{
"role": "system",

View File

@@ -80,10 +80,17 @@ 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,True,False)
ActionResult = LLM_Completion(messages,stream=False)
ActionInfo_with_Result = copy.deepcopy(self.info)
ActionInfo_with_Result["Action_Result"] = ActionResult

View File

@@ -5,7 +5,7 @@ Note: You can say something before you provide the improved version of the conte
The improved version you provide must be a completed version (e.g. if you provide a improved story, you should give completed story content, rather than just reporting where you have improved).
When you decide to give the improved version of the content, it should be start like this:
## xxx的改进版本
## Improved version of xxx
(the improved version of the content)
```

View File

@@ -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, context):
def executePlan(plan, num_StepToRun, RehearsalLog, AgentProfile_Dict):
# Prepare for execution
KeyObjects = {}
finishedStep_index = -1
@@ -85,9 +85,17 @@ def executePlan(plan, num_StepToRun, RehearsalLog, AgentProfile_Dict, context):
# 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,
@@ -101,7 +109,7 @@ def executePlan(plan, num_StepToRun, RehearsalLog, AgentProfile_Dict, context):
KeyObjects=KeyObjects,
)
ActionInfo_with_Result = currentAction.run(
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,
General_Goal=plan["General Goal"],
TaskDescription=TaskDescription,
agentName=agentName,
AgentProfile_Dict=AgentProfile_Dict,
@@ -113,8 +121,6 @@ def executePlan(plan, num_StepToRun, RehearsalLog, AgentProfile_Dict, context):
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

View File

@@ -1,2 +1,23 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 导出常用模块和函数,根据实际需求调整
from .PlanEngine.planOutline_Generator import generate_PlanOutline
from .PlanEngine.basePlan_Generator import generate_basePlan
from .PlanEngine.AgentSelection_Generator import generate_AgentSelection
from .LLMAPI.LLMAPI import LLM_Completion
from .util.converter import read_LLM_Completion
# 定义包元数据
__version__ = "0.1.0"
__all__ = [
"generate_PlanOutline",
"generate_basePlan",
"generate_AgentSelection",
"LLM_Completion",
"read_LLM_Completion"
]

View File

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

View File

@@ -1,97 +0,0 @@
[
{
"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。"
}
]

File diff suppressed because one or more lines are too long

View File

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

View File

@@ -1,7 +1,7 @@
Flask==3.0.2
openai==0.28.1
openai==2.8.1
PyYAML==6.0.1
termcolor==2.4.0
groq==0.4.2
mistralai==0.1.6
mistralai==1.5.2
socksio==1.0.0

13
backend/restart.ps1 Normal file
View File

@@ -0,0 +1,13 @@
# restart.ps1
$port=8000
$env:PYTHONUNBUFFERED="1"
python server.py --port 8000
Write-Host "Killing PID on port $port..." -ForegroundColor Red
Get-NetTCPConnection -LocalPort $port -ErrorAction SilentlyContinue | ForEach-Object {
Stop-Process -Id $_.OwningProcess -Force
}
Write-Host "Starting Flask..." -ForegroundColor Green
python server.py --port $port

View File

@@ -1,6 +1,5 @@
from flask import Flask, request, jsonify
import json
import time
from DataProcess import Add_Collaboration_Brief_FrontEnd
from AgentCoord.RehearsalEngine_V2.ExecutePlan import executePlan
from AgentCoord.PlanEngine.basePlan_Generator import generate_basePlan
@@ -32,56 +31,6 @@ else:
USE_CACHE = USE_CACHE.lower() in ["true", "1", "yes"]
AgentBoard = None
AgentProfile_Dict = {}
c1 = '''
=====数联网搜索结果=====
1. 论文
- 《Surface Defect Detection and Evaluation for Marine Vessels using Multi-Stage Deep Learning》提出一个面向船舶制造与检修的多阶段深度学习流水线用于船体表面缺陷检测与评估重点识别和量化与船舶制造材料腐蚀相关的现象包括腐蚀rust、附着物fouling、涂层剥离delamination等。研究特别关注船舶材料腐蚀在不同涂层颜色、光照条件和相机角度下的鲁棒性有助于提高船舶制造过程中对材料腐蚀的检测与管理能力。论文强调其方法在船舶材料表面腐蚀监测和维修决策中的应用价值。链接https://arxiv.org/abs/2203.09580
论文复现链接http://8.130.138.52:21001/#/view/reproduction?doid=newID/5a4fef68-a138-4adb-ba88-43aa4474c08c
- 《Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer (MCD-Net)》提出了一种高效的金属腐蚀区域检测模型。该模型创新性地结合了卷积编码器与视觉Transformer序列编码器利用注意力融合模块增强边界识别并采用基于得分的多尺度增强机制来突出腐蚀区域。该方法在阴影、遮挡和复杂纹理条件下依然能保持高精度在公开数据集上取得了优异的F1性能特别适用于船舶制造与检修中对船体钢板和涂层腐蚀的自动化识别与量化分析。链接https://www.mdpi.com/2076-3417/14/21/9900
论文复现链接http://8.130.138.52:21001/#/view/reproduction?doid=newID/b200a489-e2c8-4dbd-8767-682d15dd4c04
- 《Mechanical Failure and Metal Degradation of Ships and Marine Structures》系统探讨了船舶与海洋结构中金属材料包括高强钢、不锈钢、铜合金、钛合金等在海洋环境下的机械损伤与电化学腐蚀协同作用问题。重点分析了"机械载荷+腐蚀"耦合导致的疲劳、裂纹及腐蚀裂纹等失效机制并对相应的检测、预警与保护措施进行了深入讨论。链接https://doi.org/10.3390/met13020272
- 《Research Progress of Marine Anti-Fouling Coatings》2024全面综述了海洋防污涂层的研究进展涵盖自我抛光涂料、生物可降解涂料、低表面能涂层、仿生涂层和纳米涂层等新型涂层技术。虽然重点在于防污但文中指出许多防污机制与防腐蚀机制相互关联通过阻碍生物附着可有效减少微生物引起的腐蚀和表面破坏。链接https://doi.org/10.3390/coatings14091227
- 《Corrosion-Wear Behavior of Shipbuilding Steels (EH40, FH40, 921A) in Seawater》研究了三种常用船用钢EH40、FH40、921A在海水环境中磨损与腐蚀共同作用下的复合失效行为。通过机械-电化学方法系统测定了腐蚀磨损损失、表面形貌变化和耐腐蚀性能差异发现在此类工况中921A钢表现最佳。《Journal of Chinese Society for Corrosion and Protection》, Vol 45(4): 894-904, 2025
- 《Sulfur-Selenium Alloy Coatings for Universal Corrosion Resistance》设计了一种硫-硒合金涂层能够在包括模拟海水和硫酸盐还原菌生物环境在内的各种条件下为钢材提供高效的防腐蚀保护约99.9%效率腐蚀速率比裸钢低6-7个数量级。该涂层具有良好的机械性能、非多孔结构能有效阻碍多种扩散性腐蚀因子的渗透。arXiv:2009.02451
2. 代码
- CoaST - Anti-corrosive coatings research (DTU, Denmark)丹麦技术大学的CoaST项目专注于抗腐蚀涂层的性能表征与失效机制分析。该项目采用电化学测试与表面/界面无损检测技术相结合的方法系统评估涂层的保护效果与失效途径为船舶腐蚀防护提供了重要的研究平台。链接https://www.kt.dtu.dk/research/coast/research-areas/anti-corrosive-coatings
- Surface Defect Detection 仓库该仓库集成了多种表面缺陷检测算法为实现船舶腐蚀检测提供了可靠的代码基础和参考架构。链接https://github.com/Charmve/Surface-Defect-Detection
- Hugging Face 预训练模型平台提供了轻量级的腐蚀检测模型可直接下载并微调用于快速验证和部署。链接https://huggingface.co/mahdavian/corrosion-model
3. 数据集
用于训练和验证MCD-Net模型的相关数据集包括
- iamcloud/Corrosion_Dataset一个包含约2000多张标注图像的数据集ImageFolder格式Apache-2.0协议专门收录材料腐蚀图像适用于船舶腐蚀监测模型的训练。链接https://huggingface.co/datasets/iamcloud/Corrosion_Dataset
- Francesco/corrosion-bi3q3此数据集提供了多种腐蚀实例可用于支持船舶涂层损坏与基体腐蚀的自动化检测任务。链接https://huggingface.co/datasets/Francesco/corrosion-bi3q3。
4. 研究团队
- 海洋腐蚀与防护全国重点实验室是我国在该领域最具权威性的国家级研究机构依托于中国船舶集团旗下725所。实验室提供从材料、设计到运维的全生命周期腐蚀控制解决方案其核心优势在于能够进行多技术如“阴极保护+涂层”)的综合集成研发与验证。其研究成果广泛应用于船舶、海洋平台等国家重大工程,为保障我国海洋装备的安全与耐久性提供了关键支撑。
- 水性船舶防腐涂层新材料项目团队是一支源自东北石油大学的创新创业团队专注于推动船舶涂料行业的环保化转型。团队致力于攻克水性涂料在船舶高湿环境下干燥慢、早期耐水性差的技术瓶颈旨在开发出兼具优异防腐性能和施工便利性的水性体系以替代传统溶剂型涂料其目标是实现进口产品替代并大幅减少船舶制造与维修过程中的VOCs排放。
- 微纳一体化防腐耐磨技术研发团队是以广州航海学院青年技术骨干为核心的新锐力量。团队针对铝合金在极端海洋环境中的腐蚀-磨损难题,创新性地提出了“微米级氧化陶瓷层+纳米自润滑固化层+改性硅烷钝化层”的三层复合防护体系。该技术使防护层的结合强度、耐磨性提升2000倍和耐腐蚀性能自腐蚀电流密度降低6个数量级实现了质的飞跃。
- 北京大学南京创新研究院BXCFD团队专注于自主可控的高性能计算流体力学软件研发。团队在船舶与海洋工程水动力学领域取得关键突破其自主研发的软件在船舶兴波阻力预报精度上达到国际先进水平并成功实现了对复杂的船-桨-舵6自由度运动的耦合高精度模拟为船舶水动力性能研究与优化提供了强大的国产化工具。
- 清华大学多相流与生物流动力学实验室由罗先武教授带领,长期专注于流体机械及工程领域。团队重点研究高速船舶喷水推进装置的基础理论与空化流动机理,其研究成果为攻克喷水推进系统中的空化、振动与噪声问题提供了坚实的理论依据和技术支持,对我国高性能船舶推进技术的发展具有重要意义。
'''
context = {"材料腐蚀":c1}
Request_Cache: dict[str, str] = {}
app = Flask(__name__)
@@ -99,7 +48,6 @@ def Handle_fill_stepTask_TaskProcess():
if USE_CACHE:
if requestIdentifier in Request_Cache:
time.sleep(1)
return jsonify(Request_Cache[requestIdentifier])
filled_stepTask = fill_stepTask_TaskProcess(
@@ -170,7 +118,6 @@ def Handle_fill_stepTask():
if USE_CACHE:
if requestIdentifier in Request_Cache:
time.sleep(1)
return jsonify(Request_Cache[requestIdentifier])
filled_stepTask = fill_stepTask(
@@ -264,16 +211,19 @@ def Handle_generate_basePlan():
if USE_CACHE:
if requestIdentifier in Request_Cache:
time.sleep(2)
return jsonify(Request_Cache[requestIdentifier])
basePlan = generate_basePlan(
General_Goal=incoming_data["General Goal"],
Agent_Board=AgentBoard,
AgentProfile_Dict=AgentProfile_Dict,
InitialObject_List=incoming_data["Initial Input Object"],
context="\n请注意,目标是给出解决方案,而不是工程实现,不需要实验验证,也不需要推广计划。"
)
try:
basePlan = generate_basePlan(
General_Goal=incoming_data["General Goal"],
Agent_Board=AgentBoard,
AgentProfile_Dict=AgentProfile_Dict,
InitialObject_List=incoming_data["Initial Input Object"],
)
except ValueError as e:
return jsonify({"error": str(e)}), 400
except Exception as e:
return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
basePlan_withRenderSpec = Add_Collaboration_Brief_FrontEnd(basePlan)
Request_Cache[requestIdentifier] = basePlan_withRenderSpec
response = jsonify(basePlan_withRenderSpec)
@@ -283,10 +233,6 @@ def Handle_generate_basePlan():
@app.route("/executePlan", methods=["post"])
def Handle_executePlan():
incoming_data = request.get_json()
cur_context = ""
if "材料腐蚀" in incoming_data["plan"]["General Goal"]:
cur_context = context["材料腐蚀"]
requestIdentifier = str(
(
"/executePlan",
@@ -298,7 +244,6 @@ def Handle_executePlan():
if USE_CACHE:
if requestIdentifier in Request_Cache:
time.sleep(3)
return jsonify(Request_Cache[requestIdentifier])
RehearsalLog = executePlan(
@@ -306,21 +251,20 @@ def Handle_executePlan():
incoming_data["num_StepToRun"],
incoming_data["RehearsalLog"],
AgentProfile_Dict,
context=cur_context
)
Request_Cache[requestIdentifier] = RehearsalLog
response = jsonify(RehearsalLog)
return response
@app.route("/_saveRequestCashe", methods=["GET"])
@app.route("/_saveRequestCashe", methods=["post"])
def Handle_saveRequestCashe():
with open(
os.path.join(os.getcwd(), "RequestCache", "Request_Cache.json"), "w"
) as json_file:
json.dump(Request_Cache, json_file, indent=4)
response = jsonify(
{"code": 200, "content": "request cashe sucessfully saved: " + os.path.join(os.getcwd(), "RequestCache", "Request_Cache.json")}
{"code": 200, "content": "request cashe sucessfully saved"}
)
return response
@@ -339,10 +283,38 @@ def set_agents():
def init():
global AgentBoard, AgentProfile_Dict, Request_Cache
with open(
os.path.join(os.getcwd(), "RequestCache", "Request_Cache.json"), "r"
) as json_file:
Request_Cache = json.load(json_file)
# 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__":
@@ -352,8 +324,8 @@ if __name__ == "__main__":
parser.add_argument(
"--port",
type=int,
default=8017,
help="set the port number, 8017 by defaul.",
default=8000,
help="set the port number, 8000 by defaul.",
)
args = parser.parse_args()
init()

View File

@@ -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
#### 1⃣ **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
#### 2⃣ **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
#### 3⃣ **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。

View File

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

View File

@@ -15,9 +15,8 @@ FROM base AS runner
WORKDIR /app
EXPOSE 8080/tcp
COPY .npmrc ./
COPY src ./src
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
COPY --from=builder /app/node_modules ./node_modules
ENV API_BASE=
CMD ["npm", "run", "serve"]

View File

@@ -351,7 +351,7 @@ export default observer(({ style = {} }: IPlanModification) => {
overflowY: 'auto',
}}
>
<Box sx={{ marginBottom: '6px', fontWeight: '600' }}></Box>
<Box sx={{ marginBottom: '6px', fontWeight: '600' }}>Assignment</Box>
<Box>
{Object.keys(agentSelections).map(selectionId => (
<Box
@@ -405,7 +405,7 @@ export default observer(({ style = {} }: IPlanModification) => {
overflowY: 'auto',
}}
>
<Box sx={{ marginBottom: '4px', fontWeight: '600' }}></Box>
<Box sx={{ marginBottom: '4px', fontWeight: '600' }}>Comparison</Box>
<Box
sx={{

View File

@@ -65,6 +65,7 @@ export default observer(({ agent, style = {} }: IAgentCardProps) => (
userSelect: 'none',
}}
>
{agent.profile}
</Box>
</Box>
<Box
@@ -104,7 +105,7 @@ export default observer(({ agent, style = {} }: IAgentCardProps) => (
marginLeft: '2px',
}}
>
:
Current Duty:
</Box>
<Box>
{agent.actions.map((action, index) => (

View File

@@ -4,7 +4,6 @@ import Box from '@mui/material/Box';
import MoreHorizIcon from '@mui/icons-material/MoreHoriz';
import UnfoldLessIcon from '@mui/icons-material/UnfoldLess';
import { IAgentAction, globalStorage } from '@/storage';
import { getActionTypeDisplayText } from '@/storage/plan/action'; // 导入映射函数
export interface IDutyItem {
action: IAgentAction;
@@ -39,7 +38,7 @@ export default React.memo<IDutyItem>(({ action, style = {} }) => {
..._style,
}}
>
{getActionTypeDisplayText(action.type)}
{action.type}
<Box
onClick={() => setExpand(true)}
sx={{
@@ -84,7 +83,7 @@ export default React.memo<IDutyItem>(({ action, style = {} }) => {
marginLeft: '2px',
}}
>
{getActionTypeDisplayText(action.type)}
{action.type}
</Box>
<Divider
sx={{

View File

@@ -88,7 +88,7 @@ export default observer(({ style = {} }: IAgentBoardProps) => {
'& > input': { display: 'none' },
}}
>
<Title title="智能体库" />
<Title title="Agent Board" />
<Box
sx={{
flexGrow: 1,

View File

@@ -1,5 +1,4 @@
import AbigailChen from '@/static/AgentIcons/Abigail_Chen.png';
import AbigailChenMilitary from '@/static/AgentIcons/Abigail_Chen_military.png';
import AdamSmith from '@/static/AgentIcons/Adam_Smith.png';
import ArthurBurton from '@/static/AgentIcons/Arthur_Burton.png';
import AyeshaKhan from '@/static/AgentIcons/Ayesha_Khan.png';
@@ -9,11 +8,9 @@ import EddyLin from '@/static/AgentIcons/Eddy_Lin.png';
import FranciscoLopez from '@/static/AgentIcons/Francisco_Lopez.png';
import GiorgioRossi from '@/static/AgentIcons/Giorgio_Rossi.png';
import HaileyJohnson from '@/static/AgentIcons/Hailey_Johnson.png';
import HaileyJohnsonMilitary from '@/static/AgentIcons/Hailey_Johnson_military.png';
import IsabellaRodriguez from '@/static/AgentIcons/Isabella_Rodriguez.png';
import JaneMoreno from '@/static/AgentIcons/Jane_Moreno.png';
import JenniferMoore from '@/static/AgentIcons/Jennifer_Moore.png';
import JenniferMooreMilitary from '@/static/AgentIcons/Jennifer_Moore_military.png';
import JohnLin from '@/static/AgentIcons/John_Lin.png';
import KlausMueller from '@/static/AgentIcons/Klaus_Mueller.png';
import LatoyaWilliams from '@/static/AgentIcons/Latoya_Williams.png';
@@ -30,7 +27,6 @@ import Unknown from '@/static/AgentIcons/Unknow.png';
export enum IconName {
AbigailChen = 'Abigail_Chen',
AbigailChenMilitary = 'Abigail_Chen_military',
AdamSmith = 'Adam_Smith',
ArthurBurton = 'Arthur_Burton',
AyeshaKhan = 'Ayesha_Khan',
@@ -49,11 +45,9 @@ export enum IconName {
GabeSmith = 'Gabe_Smith',
GiorgioRossi = 'Giorgio_Rossi',
HaileyJohnson = 'Hailey_Johnson',
HaileyJohnsonMilitary = 'Hailey_Johnson_military',
IsabellaRodriguez = 'Isabella_Rodriguez',
JaneMoreno = 'Jane_Moreno',
JenniferMoore = 'Jennifer_Moore',
JenniferMooreMilitary = 'Jennifer_Moore_military',
JohnLin = 'John_Lin',
KlausMueller = 'Klaus_Mueller',
LatoyaWilliams = 'Latoya_Williams',
@@ -89,7 +83,6 @@ export const IconMap = new Proxy<{ [key: string]: IconName }>(
export const IconUrl: { [key in IconName]: string } = {
[IconName.Unknown]: Unknown,
[IconName.AbigailChen]: AbigailChen,
[IconName.AbigailChenMilitary]: AbigailChenMilitary,
[IconName.AdamSmith]: AdamSmith,
[IconName.ArthurBurton]: ArthurBurton,
[IconName.AyeshaKhan]: AyeshaKhan,
@@ -108,11 +101,9 @@ export const IconUrl: { [key in IconName]: string } = {
[IconName.GabeSmith]: AbigailChen,
[IconName.GiorgioRossi]: GiorgioRossi,
[IconName.HaileyJohnson]: HaileyJohnson,
[IconName.HaileyJohnsonMilitary]: HaileyJohnsonMilitary,
[IconName.IsabellaRodriguez]: IsabellaRodriguez,
[IconName.JaneMoreno]: JaneMoreno,
[IconName.JenniferMoore]: JenniferMoore,
[IconName.JenniferMooreMilitary]: JenniferMooreMilitary,
[IconName.JohnLin]: JohnLin,
[IconName.KlausMueller]: KlausMueller,
[IconName.LatoyaWilliams]: LatoyaWilliams,

View File

@@ -151,7 +151,7 @@ export default observer(({ style = {} }: { style?: SxProps }) => {
>
<LogoIcon />
<Box sx={{ marginLeft: '6px', fontWeight: 800, fontSize: '20px' }}>
AGENTCOORD
</Box>
</Box>
) : (

View File

@@ -8,7 +8,6 @@ import RemoveIcon from '@mui/icons-material/Remove';
import AdjustIcon from '@mui/icons-material/Adjust';
import { ObjectProps, ProcessProps } from './interface';
import AgentIcon from '@/components/AgentIcon';
import DescriptionCard from '@/components/ProcessDiscription/DescriptionCard';
import { globalStorage } from '@/storage';
export interface IEditObjectProps {
@@ -330,13 +329,6 @@ export const ProcessCard: React.FC<IProcessCardProps> = React.memo(
// handleEditStep(step.name, { ...step, task: text });
}}
/>
{/* 这里直接渲染对应的 DescriptionCard */}
{/* {(() => {
const step = globalStorage.planManager.currentPlan.find(
s => s.id === process.id
);
return step ? <DescriptionCard step={step} /> : null;
})()} */}
</Box>
)}
</Box>

View File

@@ -118,10 +118,10 @@ const D3Graph: React.FC<D3GraphProps> = ({
stepName: stepCardName,
objectName: objectCardName,
type,
x1: stepRect.left + stepRect.width,
y1: stepRect.top + 0.5 * stepRect.height,
x2: objectRect.left,
y2: objectRect.top + 0.5 * objectRect.height,
x1: objectRect.left + objectRect.width,
y1: objectRect.top + 0.5 * objectRect.height,
x2: stepRect.left,
y2: stepRect.top + 0.5 * stepRect.height,
};
});
const objectMetrics = calculateLineMetrics(cardRect, 'object');
@@ -152,17 +152,17 @@ const D3Graph: React.FC<D3GraphProps> = ({
userSelect: 'none',
}}
>
<marker
id="arrowhead"
markerWidth="2"
markerHeight="2"
refX="1"
refY="1"
orient="auto"
markerUnits="strokeWidth"
>
<path d="M0,0 L2,1 L0,2 z" fill="#E5E5E5" />
</marker>
<marker
id="arrowhead"
markerWidth="4"
markerHeight="4"
refX="2"
refY="2"
orient="auto"
markerUnits="strokeWidth"
>
<path d="M0,0 L4,2 L0,4 z" fill="#E5E5E5" />
</marker>
<marker
id="starter"
markerWidth="4"
@@ -184,7 +184,7 @@ const D3Graph: React.FC<D3GraphProps> = ({
fill="#898989"
fontWeight="800"
>
Key Object
</text>
<line
x1={objectLine.x}
@@ -204,7 +204,7 @@ const D3Graph: React.FC<D3GraphProps> = ({
fill="#898989"
fontWeight="800"
>
Process
</text>
<line
x1={processLine.x}

View File

@@ -53,7 +53,7 @@ export default observer(() => {
const handleEditContent = (stepTaskId: string, newContent: string) => {
globalStorage.setStepTaskContent(stepTaskId, newContent);
};
const WidthRatio = ['35%', '10%', '50%'];
const WidthRatio = ['30%', '15%', '52.5%'];
const [cardRefMapReady, setCardRefMapReady] = React.useState(false);
@@ -175,34 +175,6 @@ export default observer(() => {
sx={{ display: 'flex' }}
ref={ref}
>
<Box
sx={{
// display: 'flex',
alignItems: 'center',
// width: WidthRatio[2],
justifyContent: 'center',
flex: `0 0 ${WidthRatio[2]}`,
}}
>
{name && (
<ProcessCard
process={{
id,
name,
icons: agentIcons,
agents,
content,
cardRef: getCardRef(`process.${name}`),
}}
handleProcessClick={handleProcessClick}
isFocusing={focusingStepTaskId === id}
isAddActive={id === activeProcessIdAdd}
handleAddActive={handleProcessAdd}
handleEditContent={handleEditContent}
/>
)}
</Box>
<Box sx={{ flex: `0 0 ${WidthRatio[1]}` }} />
<Box
sx={{
display: 'flex',
@@ -237,6 +209,34 @@ export default observer(() => {
/>
)}
</Box>
<Box sx={{ flex: `0 0 ${WidthRatio[1]}` }} />
<Box
sx={{
// display: 'flex',
alignItems: 'center',
// width: WidthRatio[2],
justifyContent: 'center',
flex: `0 0 ${WidthRatio[2]}`,
}}
>
{name && (
<ProcessCard
process={{
id,
name,
icons: agentIcons,
agents,
content,
cardRef: getCardRef(`process.${name}`),
}}
handleProcessClick={handleProcessClick}
isFocusing={focusingStepTaskId === id}
isAddActive={id === activeProcessIdAdd}
handleAddActive={handleProcessAdd}
handleEditContent={handleEditContent}
/>
)}
</Box>
</Box>
),
)}

View File

@@ -25,7 +25,7 @@ export default observer(({ style = {} }: { style?: SxProps }) => {
...style,
}}
>
<Title title="任务大纲" />
<Title title="Plan Outline" />
<Box
sx={{
position: 'relative',

View File

@@ -19,7 +19,7 @@ export default observer(({ style = {} }: { style?: SxProps }) => {
...style,
}}
>
<Title title="任务流程" />
<Title title="Task Process" />
<Stack
spacing={1}
sx={{

View File

@@ -1,6 +1,6 @@
import React, { useState } from 'react';
import React from 'react';
import { observer } from 'mobx-react-lite';
import { Divider, SxProps, Tooltip, Typography} from '@mui/material';
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';
@@ -9,7 +9,6 @@ import { globalStorage } from '@/storage';
import type { IExecuteStepHistoryItem } from '@/apis/execute-plan';
import AgentIcon from '@/components/AgentIcon';
import { getAgentActionStyle } from '@/storage/plan';
import { getActionTypeDisplayText } from '@/storage/plan/action';
export interface IStepHistoryItemProps {
item: IExecuteStepHistoryItem;
@@ -47,170 +46,6 @@ export default observer(
}, 10);
}
}, [expand]);
interface DataSpaceCard {
name: string;
data_space: string;
doId: string;
fromRepo: string;
}
interface DataSpaceIndicatorProps {
cards?: DataSpaceCard[];
}
const DataSpaceIndicator = ({ cards = [] }: DataSpaceIndicatorProps) => {
const [currentCard, setCurrentCard] = useState(0);
if (!cards || cards.length === 0) {
return null;
}
const tooltipContent = (
<>
{/* 添加标题 */}
<Box
sx={{
display: 'flex',
justifyContent: 'space-between',
alignItems: 'center',
padding: '8px 12px',
backgroundColor: '#1565c0',
color: 'white',
borderRadius: '4px 4px 0 0',
fontWeight: 'bold',
fontSize: '14px',
}}
>
<span></span>
<Box sx={{ display: 'flex', alignItems: 'center', gap: '8px' }}>
<span style={{ fontSize: '12px', color: 'rgba(255,255,255,0.8)' }}>
{currentCard + 1}/{cards.length}
</span>
<button
onClick={(e) => {
e.stopPropagation();
setCurrentCard((prev) => (prev + 1) % cards.length);
}}
style={{
fontSize: '12px',
padding: '2px 8px',
backgroundColor: 'white',
color: '#1565c0',
border: 'none',
borderRadius: '3px',
cursor: 'pointer',
fontWeight: 'bold'
}}
>
</button>
</Box>
</Box>
<Box
sx={{
padding: '12px',
borderRadius: '4px',
backgroundColor: '#e3f2fd',
border: '1px solid #1565c0',
minWidth: '450px', // 设置卡片最小宽度
}}
>
<Box sx={{ display: 'flex', alignItems: 'flex-start', marginBottom: '4px' }}>
<Typography variant="caption" sx={{ fontWeight: 'bold', color: '#666', minWidth: '60px' }}>
:
</Typography>
<Typography variant="body2" sx={{ color: 'black' }}>
{cards[currentCard]?.name}
</Typography>
</Box>
<Box sx={{ display: 'flex', alignItems: 'flex-start', marginBottom: '4px' }}>
<Typography variant="caption" sx={{ fontWeight: 'bold', color: '#666', minWidth: '60px' }}>
:
</Typography>
<Typography variant="body2" sx={{ color: 'black' }}>
{cards[currentCard]?.data_space}
</Typography>
</Box>
<Box sx={{ display: 'flex', alignItems: 'flex-start', marginBottom: '4px' }}>
<Typography variant="caption" sx={{ fontWeight: 'bold', color: '#666', minWidth: '60px' }}>
DOID:
</Typography>
<Typography variant="body2" sx={{ color: 'black' }}>
{cards[currentCard]?.doId}
</Typography>
</Box>
<Box sx={{ display: 'flex', alignItems: 'flex-start', marginBottom: '4px' }}>
<Typography variant="caption" sx={{ fontWeight: 'bold', color: '#666', minWidth: '60px' }}>
:
</Typography>
<Typography variant="body2" sx={{ color: 'black' }}>
{cards[currentCard]?.fromRepo}
</Typography>
</Box>
</Box>
</>
);
return (
<Tooltip
title={tooltipContent}
arrow
slotProps={{
tooltip: {
sx: {
maxWidth: 500, // 只需要这一行来增加宽度
},
},
}}
>
<Box
sx={{
display: 'inline-flex',
alignItems: 'center',
justifyContent: 'center',
width: '20px',
height: '20px',
borderRadius: '50%',
backgroundColor: '#9e9e9e',
color: 'white',
fontSize: '12px',
fontWeight: '600',
cursor: 'pointer',
'&:hover': {
backgroundColor: '#1565c0',
}
}}
>
{cards.length}
</Box>
</Tooltip>
);
};
const cardData = [
{
name: "3D Semantic-Geometric Corrosion Mapping Implementations",
data_space: "江苏省产研院",
doId: "bdware.scenario/d8f3ff8c-3fb3-4573-88a6-5dd823627c37",
fromRepo: "https://arxiv.org/abs/2404.13691"
},
{
name: "RustSEG -- Automated segmentation of corrosion using deep learning",
data_space: "江苏省产研院",
doId: "bdware.scenario/67445299-110a-4a4e-9fda-42e4b5a493c2",
fromRepo: "https://arxiv.org/abs/2205.05426"
},
{
name: "Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation",
data_space: "江苏省产研院",
doId: "bdware.scenario/115d5135-85d3-4123-8b81-9eb9f07b6153",
fromRepo: "https://arxiv.org/abs/2008.05204"
},
];
const s = { ...getAgentActionStyle(item.type), ...style } as SxProps;
React.useEffect(() => {
console.log(item);
@@ -242,7 +77,7 @@ const cardData = [
{item.agent}
</Box>
<Box component="span" sx={{ fontWeight: 400 }}>
: {getActionTypeDisplayText(item.type)}
: {item.type}
</Box>
</Box>
{item.result ? (
@@ -268,14 +103,10 @@ const cardData = [
marginBottom: '4px',
color: '#0009',
fontWeight: 400,
display: 'inline-flex',
alignItems: 'center',
gap: '8px',
}}
>
{item.description}
</Box>
<DataSpaceIndicator cards={cardData} />
<MarkdownBlock
text={item.result}
style={{

View File

@@ -79,7 +79,7 @@ export default observer(({ style = {} }: { style?: SxProps }) => {
alignItems: 'center',
}}
>
<Title title="执行结果" />
<Title title="Execution Result" />
<Box
sx={{
flexGrow: 1,

View File

@@ -4,8 +4,6 @@ import { SxProps } from '@mui/material';
import Box from '@mui/material/Box';
import IconButton from '@mui/material/IconButton';
import FormControl from '@mui/material/FormControl';
import Autocomplete from '@mui/material/Autocomplete';
import TextField from '@mui/material/TextField';
import FilledInput from '@mui/material/FilledInput';
import TelegramIcon from '@mui/icons-material/Telegram';
import CircularProgress from '@mui/material/CircularProgress';
@@ -18,18 +16,6 @@ export interface UserGoalInputProps {
export default observer(({ style = {} }: UserGoalInputProps) => {
const inputRef = React.useRef<string>('');
const inputElementRef = React.useRef<HTMLInputElement>(null);
const goalOptions = [
'如何快速筛选慢性肾脏病药物潜在受试者?',
'如何补充“丹芍活血胶囊”不良反应数据?',
'如何快速研发用于战场失血性休克的药物?',
'二维材料的光电性质受哪些关键因素影响?',
'如何通过AI模拟的方法分析材料的微观结构?',
'如何分析获取液态金属热力学参数?',
'如何解决固态电池的成本和寿命难题?',
'如何解决船舶制造中的材料腐蚀难题?',
'如何解决船舶制造中流体模拟和建模优化难题?',
];
React.useEffect(() => {
if (inputElementRef.current) {
if (globalStorage.planManager) {
@@ -46,70 +32,42 @@ export default observer(({ style = {} }: UserGoalInputProps) => {
...style,
}}
>
<Autocomplete
freeSolo
disableClearable
<FilledInput
disabled={
globalStorage.api.busy ||
!globalStorage.api.agentsReady ||
globalStorage.api.planReady
}
options={goalOptions}
value={inputRef.current || (globalStorage.planManager ? globalStorage.planManager.goal : globalStorage.briefGoal)}
onChange={(event, newValue) => {
if (newValue) {
inputRef.current = newValue;
}
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',
},
}}
onInputChange={(event, newInputValue) => {
inputRef.current = newInputValue;
}}
renderInput={(params) => (
<TextField
{...params}
variant="filled"
placeholder="请输入你的任务"
fullWidth
size="small"
inputRef={inputElementRef}
startAdornment={
<Box
sx={{
borderRadius: '10px',
background: '#E1E1E1',
borderBottom: 'none !important',
'& .MuiFilledInput-root': {
height: '45px',
borderRadius: '10px',
background: '#E1E1E1',
paddingTop: '5px',
borderBottom: 'none !important',
'&::before': {
borderBottom: 'none !important',
},
'&::after': {
borderBottom: 'none !important',
},
},
color: '#4A9C9E',
fontWeight: 800,
fontSize: '18px',
textWrap: 'nowrap',
userSelect: 'none',
}}
InputProps={{
...params.InputProps,
startAdornment: (
<Box
sx={{
color: '#4A9C9E',
fontWeight: 800,
fontSize: '18px',
textWrap: 'nowrap',
userSelect: 'none',
}}
>
</Box>
),
}}
/>
)}
>
\General Goal:
</Box>
}
/>
{globalStorage.api.planGenerating ? (
<CircularProgress
sx={{

View File

@@ -88,24 +88,24 @@ export default observer(() => {
setConnectors(<></>);
}
const outlinePosition =
ElementToLink[1]?.current?.getBoundingClientRect?.();
ElementToLink[0]?.current?.getBoundingClientRect?.();
if (!outlinePosition) {
return;
}
const agentRects = ElementToLink[0]
const agentRects = ElementToLink[1]
.map(ele => ele.current?.getBoundingClientRect?.() as DOMRect)
.filter(rect => rect);
if (agentRects.length === 0) {
return;
}
const agentsPosition = mergeRects(...agentRects);
// const descriptionPosition =
// ElementToLink[2]?.current?.getBoundingClientRect?.();
// if (!descriptionPosition) {
// return;
// }
const descriptionPosition =
ElementToLink[2]?.current?.getBoundingClientRect?.();
if (!descriptionPosition) {
return;
}
const LogRects = ElementToLink[2]
const LogRects = ElementToLink[3]
.map(ele => ele?.current?.getBoundingClientRect?.() as DOMRect)
.filter(rect => rect);
if (LogRects.length > 0) {
@@ -115,14 +115,15 @@ export default observer(() => {
setConnectors(
drawConnectors(
agentsPosition,
outlinePosition,
agentsPosition,
descriptionPosition,
logPosition,
),
);
} else {
setConnectors(
drawConnectors(agentsPosition,outlinePosition),
drawConnectors(outlinePosition, agentsPosition, descriptionPosition),
);
}
} catch (e) {

View File

@@ -44,7 +44,7 @@ export default observer(() => {
)}
{globalStorage.agentAssigmentWindow ? (
<FloatWindow
title="智能体选择"
title="Assignment Exploration"
onClose={() => (globalStorage.agentAssigmentWindow = false)}
>
<AgentAssignment

View File

@@ -99,7 +99,16 @@ export default React.memo(() => {
}}
/>
<Box sx={{ height: 0, flexGrow: 1, display: 'flex' }}>
<ResizeableColumn columnWidth="19%">
<ResizeableColumn columnWidth="25%">
<Outline
style={{
height: '100%',
width: '100%',
marginRight: '6px',
}}
/>
</ResizeableColumn>
<ResizeableColumn columnWidth="25%">
<AgentBoard
style={{
height: '100%',
@@ -109,16 +118,7 @@ export default React.memo(() => {
onAddAgent={() => setShowAgentHiring(true)}
/>
</ResizeableColumn>
<ResizeableColumn columnWidth="26%">
<Outline
style={{
height: '100%',
width: '100%',
marginRight: '6px',
}}
/>
</ResizeableColumn>
{/* <ResizeableColumn columnWidth="25%">
<ResizeableColumn columnWidth="25%">
<ProcessDiscrption
style={{
height: '100%',
@@ -126,7 +126,7 @@ export default React.memo(() => {
marginRight: '6px',
}}
/>
</ResizeableColumn> */}
</ResizeableColumn>
<ProcessRehearsal
style={{
height: '100%',

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@@ -285,15 +285,17 @@ export class GlobalStorage {
}
public get ElementToLink(): [
React.RefObject<HTMLElement> | undefined,
React.RefObject<HTMLElement>[],
React.RefObject<HTMLElement> | undefined,
(React.RefObject<HTMLElement> | undefined)[],
] {
return [
this.refMap.OutlineCard.get(this.focusingStepTaskId ?? ''),
this.focusingStepTask?.agentSelection?.agents?.map(
agent => this.refMap.AgentCard.get(agent) ?? [],
) ?? ([] as any),
this.refMap.OutlineCard.get(this.focusingStepTaskId ?? ''),
this.refMap.DiscriptionCard.get(this.focusingStepTaskId ?? ''),
this.logManager.outdate
? []
: [

View File

@@ -11,17 +11,6 @@ export enum ActionType {
Finalize = 'Finalize',
}
export const getActionTypeDisplayText = (actionType: ActionType | string): string => {
const displayMap: Record<string, string> = {
[ActionType.Propose]: '提议',
[ActionType.Critique]: '评审',
[ActionType.Improve]: '改进',
[ActionType.Finalize]: '总结',
};
return displayMap[actionType] || actionType;
};
const AgentActionStyles = new Map<ActionType | '', SxProps>([
[ActionType.Propose, { backgroundColor: '#B9EBF9', borderColor: '#94c2dc' }],
[ActionType.Critique, { backgroundColor: '#EFF9B9', borderColor: '#c0dc94' }],

View File

@@ -20,7 +20,7 @@ const nameJoin = (names: string[]) => {
const last = tmp.pop()!;
let t = tmp.join(', ');
if (t.length > 0) {
t = `${t} ${last}`;
t = `${t} and ${last}`;
} else {
t = last;
}
@@ -102,14 +102,14 @@ export class StepTaskNode implements INodeBase {
text: this.output,
style: { background: '#FFCA8C' },
};
outputSentence = `得到 !<${indexOffset}>!`;
outputSentence = `to obtain !<${indexOffset}>!`;
}
// Join them togeter
let content = inputSentence;
if (content) {
content = `基于${content}, ${nameSentence} 执行任务 !<${actionIndex}>!`;
content = `Based on ${content}, ${nameSentence} perform the task of !<${actionIndex}>!`;
} else {
content = `${nameSentence} 执行任务 !<${actionIndex}>!`;
content = `${nameSentence} perform the task of !<${actionIndex}>!`;
}
if (outputSentence) {
content = `${content}, ${outputSentence}.`;

View File

@@ -0,0 +1,9 @@
{
"permissions": {
"allow": [
"Bash(npm run type-check:*)"
],
"deny": [],
"ask": []
}
}

6
frontend/.dockerignore Normal file
View File

@@ -0,0 +1,6 @@
node_modules
dist
.idea
.vscode
.git
.gitignore

8
frontend/.editorconfig Normal file
View File

@@ -0,0 +1,8 @@
[*.{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

1
frontend/.env Normal file
View File

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

View File

@@ -0,0 +1,79 @@
{
"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
}
}

1
frontend/.gitattributes vendored Normal file
View File

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

36
frontend/.gitignore vendored Normal file
View File

@@ -0,0 +1,36 @@
# Logs
logs
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
pnpm-debug.log*
lerna-debug.log*
node_modules
.DS_Store
dist
dist-ssr
coverage
*.local
# Editor directories and files
.vscode/*
!.vscode/extensions.json
.idea
*.suo
*.ntvs*
*.njsproj
*.sln
*.sw?
*.tsbuildinfo
.eslintcache
# Cypress
/cypress/videos/
/cypress/screenshots/
# Vitest
__screenshots__/

View File

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

9
frontend/.vscode/extensions.json vendored Normal file
View File

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

95
frontend/CLAUDE.md Normal file
View File

@@ -0,0 +1,95 @@
# 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

27
frontend/Dockerfile Normal file
View File

@@ -0,0 +1,27 @@
ARG CADDY_VERSION=2.6
ARG BUILD_ENV=prod
FROM node:20.19.0 as base
WORKDIR /app
COPY . .
RUN npm install -g pnpm
RUN pnpm install
RUN pnpm build
# The base for mode ENVIRONMENT=prod
FROM caddy:${CADDY_VERSION}-alpine as prod
# Workaround for https://github.com/alpinelinux/docker-alpine/issues/98#issuecomment-679278499
RUN sed -i 's/https/http/' /etc/apk/repositories \
&& apk add --no-cache bash
COPY docker/Caddyfile /etc/caddy/
COPY --from=base /app/dist /frontend
# Run stage
FROM ${BUILD_ENV}
EXPOSE 80 443
VOLUME ["/data", "/etc/caddy"]
CMD ["caddy", "run", "--config", "/etc/caddy/Caddyfile", "--adapter", "caddyfile"]

106
frontend/README.md Normal file
View File

@@ -0,0 +1,106 @@
# 多智能体协同平台 (Agent Coordination Platform)
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。
## ✨ 功能特性
- **多智能体系统**创建具有专门角色和专业知识的AI智能体
- **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
### 开发命令
```bash
# 安装依赖
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
- Node.js ^20.19.0 或 >=22.12.0
- pnpm必需的包管理器
## 🏗️ 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
### IDE设置
[VS Code](https://code.visualstudio.com/) + [Vue (Official)](https://marketplace.visualstudio.com/items?itemName=Vue.volar)禁用Vetur
### 浏览器开发工具
- 基于Chromium的浏览器
- [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
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。
## 📄 许可证
MIT许可证 - 详见LICENSE文件

75
frontend/auto-imports.d.ts vendored Normal file
View File

@@ -0,0 +1,75 @@
/* 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')
}

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#!/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 $SCRIPT_NAME"
check_nodejs
install_claude_code
configure_claude_json
configure_claude
echo ""
log_success "🎉 Installation completed successfully!"
echo ""
echo "🚀 You can now start using Claude Code with:"
echo " claude"
}
main "$@"

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/* 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']
ElDialog: typeof import('element-plus/es')['ElDialog']
ElDivider: typeof import('element-plus/es')['ElDivider']
ElDrawer: typeof import('element-plus/es')['ElDrawer']
ElEmpty: typeof import('element-plus/es')['ElEmpty']
ElInput: typeof import('element-plus/es')['ElInput']
ElPopover: typeof import('element-plus/es')['ElPopover']
ElScrollbar: typeof import('element-plus/es')['ElScrollbar']
ElTooltip: typeof import('element-plus/es')['ElTooltip']
MultiLineTooltip: typeof import('./src/components/MultiLineTooltip/index.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']
}
}

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:80
# Proxy `/api` to backends
handle_path /api/* {
reverse_proxy {$API_HOST}
}
# Frontend
handle {
root * /frontend
encode gzip
try_files {path} /index.html
file_server
}

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version: '3'
services:
agent-coord-font:
image: agent-coord:0.0.1
build:
context: ..
dockerfile: docker/Dockerfile
ports:
- "8080:80"
volumes:
- ./Caddyfile:/etc/caddy/Caddyfile
environment:
- API_HOST="http://host.docker.internal:8000"
- BUILD_ENV=prod

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/// <reference types="vite/client" />
declare global {
const testGlobal: any; // 声明 testGlobal 为全局变量
}

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import { globalIgnores } from 'eslint/config'
import { defineConfigWithVueTs, vueTsConfigs } from '@vue/eslint-config-typescript'
import pluginVue from 'eslint-plugin-vue'
import pluginVitest from '@vitest/eslint-plugin'
import skipFormatting from '@vue/eslint-config-prettier/skip-formatting'
import autoImportConfig from './.eslintrc-auto-import.json' with { type: 'json' }
// To allow more languages other than `ts` in `.vue` files, uncomment the following lines:
// import { configureVueProject } from '@vue/eslint-config-typescript'
// configureVueProject({ scriptLangs: ['ts', 'tsx'] })
// More info at https://github.com/vuejs/eslint-config-typescript/#advanced-setup
export default defineConfigWithVueTs(
{
name: 'app/files-to-lint',
files: ['**/*.{ts,mts,tsx,vue}'],
},
globalIgnores(['**/dist/**', '**/dist-ssr/**', '**/coverage/**']),
pluginVue.configs['flat/essential'],
vueTsConfigs.recommended,
{
...pluginVitest.configs.recommended,
files: ['src/**/__tests__/*'],
},
skipFormatting,
{
name: 'app/custom-rules',
files: ['**/*.{ts,mts,tsx,vue}'],
rules: {
'vue/multi-word-component-names': 'off',
}
},
{
name: 'auto-import-globals',
files: ['**/*.{ts,mts,tsx,vue}'],
languageOptions: {
globals: {
...(autoImportConfig.globals || {}),
testGlobal: 'readonly' // 手动添加一个测试变量
}
},
rules: {
'no-undef': 'off', // 确保关闭 no-undef 规则
'@typescript-eslint/no-undef': 'off'
}
}
)

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frontend/index.html Normal file
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<!DOCTYPE html>
<html lang="">
<head>
<meta charset="UTF-8">
<link rel="icon" href="/logo.png">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>多智能体协同平台</title>
</head>
<body>
<div id="app"></div>
<script type="module" src="/src/main.ts"></script>
</body>
</html>

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frontend/package.json Normal file
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{
"name": "agent-coord",
"version": "0.0.0",
"private": true,
"type": "module",
"engines": {
"node": "^20.19.0 || >=22.12.0"
},
"scripts": {
"dev": "vite",
"build": "vite build",
"preview": "vite preview",
"test:unit": "vitest",
"build-only": "vite build",
"type-check": "vue-tsc --build",
"lint": "eslint . --fix --cache",
"format": "prettier --write src/"
},
"dependencies": {
"@jsplumb/browser-ui": "^6.2.10",
"@types/markdown-it": "^14.1.2",
"@vueuse/core": "^14.0.0",
"axios": "^1.12.2",
"dompurify": "^3.3.0",
"element-plus": "^2.11.5",
"lodash": "^4.17.21",
"markdown-it": "^14.1.0",
"pinia": "^3.0.3",
"qs": "^6.14.0",
"uuid": "^13.0.0",
"vue": "^3.5.22",
"vue-router": "^4.6.3"
},
"devDependencies": {
"@tailwindcss/vite": "^4.1.15",
"@tsconfig/node22": "^22.0.2",
"@types/jsdom": "^27.0.0",
"@types/lodash": "^4.17.20",
"@types/node": "^22.18.11",
"@types/qs": "^6.14.0",
"@vitejs/plugin-vue": "^6.0.1",
"@vitest/eslint-plugin": "^1.3.23",
"@vue/eslint-config-prettier": "^10.2.0",
"@vue/eslint-config-typescript": "^14.6.0",
"@vue/test-utils": "^2.4.6",
"@vue/tsconfig": "^0.8.1",
"eslint": "^9.37.0",
"eslint-plugin-import": "^2.32.0",
"eslint-plugin-vue": "~10.5.0",
"jiti": "^2.6.1",
"jsdom": "^27.0.1",
"npm-run-all2": "^8.0.4",
"prettier": "3.6.2",
"sass": "^1.93.2",
"sass-loader": "^16.0.5",
"tailwindcss": "^4.1.15",
"typescript": "~5.9.0",
"unplugin-auto-import": "^20.2.0",
"unplugin-vue-components": "^30.0.0",
"vite": "^7.1.11",
"vite-plugin-svg-icons": "^2.0.1",
"vite-plugin-vue-devtools": "^8.0.3",
"vitest": "^3.2.4",
"vue-tsc": "^3.1.1"
}
}

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[
{
"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。",
"Classification": "船舶制造数据空间"
},
{
"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。",
"Classification": "船舶制造数据空间"
},
{
"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。",
"Classification": "医药数据空间"
},
{
"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。",
"Classification": "医药数据空间"
},
{
"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。",
"Classification": "医药数据空间"
},
{
"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。",
"Classification": "医药数据空间"
},
{
"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。",
"Classification": "科学数据空间"
},
{
"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)。",
"Classification": "科学数据空间"
},
{
"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。",
"Classification": "科学数据空间"
},
{
"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。",
"Classification": "船舶制造数据空间"
},
{
"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。",
"Classification": "科学数据空间"
},
{
"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。",
"Classification": "医药数据空间"
},
{
"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。",
"Classification": "医药数据空间"
},
{
"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。",
"Classification": "医药数据空间"
},
{
"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。",
"Classification": "医药数据空间"
},
{
"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。",
"Classification": "科学数据空间"
},
{
"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。",
"Classification": "科学数据空间"
},
{
"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。",
"Classification": "科学数据空间"
},
{
"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。",
"Classification": "科学数据空间"
}
]

View File

@@ -0,0 +1,19 @@
{
"title": "数联网",
"subTitle": "众创智能体",
"centerTitle": "多智能体协同平台",
"taskPromptWords": [
"如何快速筛选慢性肾脏病药物潜在受试者?",
"如何补充“丹芍活血胶囊”不良反应数据?",
"如何快速研发用于战场失血性休克的药物?",
"二维材料的光电性质受哪些关键因素影响?",
"如何通过AI模拟的方法分析材料的微观结构?",
"如何分析获取液态金属热力学参数?",
"如何解决固态电池的成本和寿命难题?",
"如何解决船舶制造中的材料腐蚀难题?",
"如何解决船舶制造中流体模拟和建模优化难题?"
],
"agentRepository": {
"storageVersionIdentifier": "1"
}
}

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{
"data": [
{
"name": "3D Semantic-Geometric Corrosion Mapping Implementations",
"data_space": "江苏省产研院",
"doId": "bdware.scenario/d8f3ff8c-3fb3-4573-88a6-5dd823627c37",
"fromRepo": "https://arxiv.org/abs/2404.13691"
},
{
"name": "RustSEG -- Automated segmentation of corrosion using deep learning",
"data_space": "江苏省产研院",
"doId": "bdware.scenario/67445299-110a-4a4e-9fda-42e4b5a493c2",
"fromRepo": "https://arxiv.org/abs/2205.05426"
},
{
"name": "Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation",
"data_space": "江苏省产研院",
"doId": "bdware.scenario/115d5135-85d3-4123-8b81-9eb9f07b6153",
"fromRepo": "https://arxiv.org/abs/2008.05204"
}
]
}

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<script setup lang="ts">
import Layout from './layout/index.vue'
</script>
<template>
<layout />
</template>
<style lang="scss">
#app {
background-color: var(--color-bg);
color: var(--color-text);
}
.jtk-endpoint {
z-index: 100;
}
.card-item + .card-item {
margin-top: 35px;
}
.el-card {
border-radius: 8px;
background: var(--color-bg-tertiary);
border: 2px solid var(--color-card-border);
box-shadow: var(--color-card-border-hover);
&:hover {
background: var(--color-bg-content-hover);
box-shadow: none;
transition: background-color 0.3s ease-in-out;
}
.el-card__body {
padding: 10px;
}
}
:root {
--gradient: linear-gradient(to right, #0093eb, #00d2d1);
}
#task-template {
.active-card {
border: 2px solid transparent;
$bg: var(--el-input-bg-color, var(--el-fill-color-blank));
background: linear-gradient(
var(--color-agent-list-selected-bg),
var(--color-agent-list-selected-bg)
)
padding-box,
linear-gradient(to right, #00c8d2, #315ab4) border-box;
color: var(--color-text);
}
}
/* 1. 定义流动动画:让虚线沿路径移动 */
@keyframes flowAnimation {
to {
stroke-dashoffset: 8; /* 与stroke-dasharray总和一致 */
}
}
@keyframes flowAnimationReverse {
to {
stroke-dashoffset: -8; /* 与stroke-dasharray总和一致 */
}
}
/* 2. 为jsPlumb连线绑定动画作用于SVG的path元素 */
/* jtk-connector是jsPlumb连线的默认SVG类path是实际的线条元素 */
.jtk-connector-output path {
/* 定义虚线规则线段长度5px + 间隙3px总长度8px与动画偏移量匹配 */
stroke-dasharray: 5 3;
/* 应用动画:名称+时长+线性速度+无限循环 */
animation: flowAnimationReverse 0.5s linear infinite;
/* 可选:设置线条基础样式(颜色、宽度) */
stroke-width: 2;
}
/* 2. 为jsPlumb连线绑定动画作用于SVG的path元素 */
/* jtk-connector是jsPlumb连线的默认SVG类path是实际的线条元素 */
.jtk-connector-input path {
/* 定义虚线规则线段长度5px + 间隙3px总长度8px与动画偏移量匹配 */
stroke-dasharray: 5 3;
/* 应用动画:名称+时长+线性速度+无限循环 */
animation: flowAnimationReverse 0.5s linear infinite;
/* 可选:设置线条基础样式(颜色、宽度) */
stroke-width: 2;
}
/* 可选: hover时增强动画效果如加速、变色 */
.jtk-connector path:hover {
animation-duration: 0.5s; /* hover时流动加速 */
}
</style>

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import { describe, it, expect } from 'vitest'
import { mount } from '@vue/test-utils'
import App from '../App.vue'
describe('App', () => {
it('mounts renders properly', () => {
const wrapper = mount(App)
expect(wrapper.text()).toContain('You did it!')
})
})

74
frontend/src/api/index.ts Normal file
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import request from '@/utils/request'
import type { Agent, IRawPlanResponse } from '@/stores'
export interface ActionHistory {
ID: string
ActionType: string
AgentName: string
Description: string
ImportantInput: string[]
Action_Result: string
}
export type IExecuteRawResponse = {
LogNodeType: string
NodeId: string
InputName_List?: string[] | null
OutputName?: string
content?: string
ActionHistory: ActionHistory[]
}
class Api {
// 智能体信息
setAgents = (data: Pick<Agent, 'Name' | 'Profile'>[]) => {
return request({
url: '/setAgents',
data,
method: 'POST',
})
}
generateBasePlan = (data: { goal: string; inputs: string[] }) => {
return request<unknown, IRawPlanResponse>({
url: '/generate_basePlan',
method: 'POST',
data: {
'General Goal': data.goal,
'Initial Input Object': data.inputs,
},
})
}
executePlan = (plan: IRawPlanResponse) => {
return request<unknown, IExecuteRawResponse[]>({
url: '/executePlan',
method: 'POST',
data: {
RehearsalLog: [],
num_StepToRun: null,
plan: {
'Initial Input Object': plan['Initial Input Object'],
'General Goal': plan['General Goal'],
'Collaboration Process': plan['Collaboration Process']?.map(step => ({
StepName: step.StepName,
TaskContent: step.TaskContent,
InputObject_List: step.InputObject_List,
OutputObject: step.OutputObject,
AgentSelection: step.AgentSelection,
Collaboration_Brief_frontEnd: step.Collaboration_Brief_FrontEnd,
TaskProcess: step.TaskProcess.map(action => ({
ActionType: action.ActionType,
AgentName: action.AgentName,
Description: action.Description,
ID: action.ID,
ImportantInput: action.ImportantInput,
})),
})),
},
},
})
}
}
export default new Api()

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