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42 Commits

Author SHA1 Message Date
liailing1026
1749ae4f1e feat:通知主题修改 2026-01-26 17:25:23 +08:00
liailing1026
418b2e5f8f feat:智能体探索窗口预加载完善 2026-01-26 16:46:58 +08:00
liailing1026
641d70033d feat:用户修改步骤后重新执行实现 2026-01-26 15:06:17 +08:00
liailing1026
b287867069 feat:暂停动画以及暂停正确响应 2026-01-24 21:11:49 +08:00
liailing1026
5699635d1a feat:暂停/继续按钮与文字间距增加 2026-01-23 17:07:01 +08:00
liailing1026
ac035d1237 feat:任务大纲停止以及执行结果暂停继续逻辑完善 2026-01-23 15:38:09 +08:00
liailing1026
53add0431e feat:配置文件 2026-01-22 17:25:10 +08:00
liailing1026
786c674d21 feat:RESTful API架构改WebSocket架构-执行结果可以分步显示版本 2026-01-22 17:22:30 +08:00
liailing1026
1c8036adf1 feat:任务执行结果性能优化 2026-01-21 15:36:20 +08:00
liailing1026
45314b7be6 feat:任务执行结果性能优化 2026-01-21 15:18:15 +08:00
liailing1026
c5848410c1 feat:代码优化及格式化注释 2026-01-19 09:12:06 +08:00
liailing1026
571b5101ff feat:任务过程探索分支提示词优化 2026-01-16 11:11:57 +08:00
liailing1026
029df6b5a5 feat:冗余代码清理 2026-01-14 17:54:00 +08:00
liailing1026
edb39d4c1f feat:任务大纲探索窗口分支创建根节点基线完成度计算修改 2026-01-14 10:03:22 +08:00
liailing1026
0e87777ae8 feat:修改任务过程大纲探索后端返回不符合用户要求的bug 2026-01-13 17:58:52 +08:00
liailing1026
244deceb91 Merge branch 'web' of https://gitea.internetapi.cn/iod/AgentCoord into web
合并远程web分支更新
2026-01-13 14:08:52 +08:00
liailing1026
69587c0481 feat:智能体探索窗口与任务过程探索窗口联动修改 2026-01-13 13:57:56 +08:00
zhaoweijie
e0cc11647f feat(plan): 添加中文响应要求并本地化操作提示
- 在多个规划引擎文件中添加中文响应的语言要求说明
- 将操作相关的提示文本从英文翻译为中文
- 确保代理协作中的所有解释和推理使用中文输出
- 保持代理名称等标识符的原始格式不变
2026-01-13 09:49:53 +08:00
liailing1026
59fd94e783 feat:智能体探索窗口与任务大纲探索窗口联动 2026-01-12 17:27:37 +08:00
liailing1026
3ff70463ca feat:任务执行执行状态加载动画添加 2026-01-12 11:17:18 +08:00
liailing1026
82e92f12aa feat:任务大纲卡片超出3行提示框设置最大宽度 2026-01-10 22:07:23 +08:00
liailing1026
920588b063 feat:三个窗口接口联调版本 2026-01-09 13:54:32 +08:00
liailing1026
5847365eee feat:三个浮动窗口功能新增 2025-12-31 19:04:58 +08:00
liailing1026
d42554ce03 feat:智能体库agent左对齐样式问题 2025-12-22 17:15:06 +08:00
zhaoweijie
bcc0c53ba1 feat(config): 支持配置 API 基础路径和开发环境标识
- 在配置存储中新增 dev 和 apiBaseUrl 字段
- 设置默认配置对象,包含基础 URL 构造逻辑
- 更新请求工具以使用动态配置的 baseURL
- 调整应用初始化顺序确保配置先行加载
- 移除指令中不必要的调试日志输出
2025-12-21 20:26:21 +08:00
zhaoweijie
7da5e82d40 feat(ui): 更新任务流程卡片样式和图标
- 引入 Element Plus 图标组件替代原有文字图标
- 移除卡片阴影属性优化视觉效果
- 调整结果区域按钮组布局对齐方式
- 更新文本域样式增加颜色变量支持
- 注释掉编辑卡片背景色定义
- 添加图标依赖包到项目配置
2025-12-21 19:57:33 +08:00
zhaoweijie
cc22655a1e feat(dev): 添加开发模式专用指令和配置支持
- 在 config.json 中新增 dev 配置项用于区分开发与生产环境
- 实现 v-dev-only 指令,仅在开发模式下渲染元素
- 注册全局自定义指令 dev-only,支持通过 binding.value 控制启用状态
- 在 TaskSyllabus/Bg.vue 中使用 v-dev-only 指令隐藏生产环境下的加号区域
- 移除 card 样式中的固定 margin-bottom,改由容器控制间距
- 统一使用 CSS 变量 --color-border-separate 替代硬编码的分割线颜色
- 为 Task.vue 的搜索框添加 clearable 属性并移除弹出项的阴影效果
- Layout 组件名称规范化为首字母大写
- 在主题样式中定义深色与浅色模式下的 --color-border-separate 颜色值
- 覆盖 Element Plus 的 --el-fill-color-blank 以适配暗黑模式背景透明度需求
2025-12-21 18:10:37 +08:00
liailing1026
f0db3c88e4 feat:AdditionalOutputCard.vue未开发完毕版本 2025-12-21 15:56:40 +08:00
liailing1026
b987fe70ad feat:额外产物添加 2025-12-21 15:28:59 +08:00
liailing1026
b42ab5aedd feat:单个agent配置各自的apiurl、apimodel、apikey 2025-12-18 09:51:07 +08:00
zhaoweijie
5ef86c6fa9 fix(LLMAPI): 增加API调用超时时间
- 将LLM API调用的超时时间从15秒延长到60秒
- 防止因网络延迟或模型响应慢导致的超时错误
- 提高长文本生成任务的稳定性
2025-12-17 09:27:00 +08:00
zhaoweijie
907310365a feat(task): 添加任务过程编辑功能
- 新增 ProcessCard 组件用于展示和编辑任务流程
- 实现双击编辑任务描述功能
- 添加编辑状态下的卡片式输入界面
- 支持保存和取消编辑操作
- 实现鼠标悬停高亮效果
- 添加颜色处理函数用于界面美化
- 集成到 TaskResult 组件中展示任务过程
- 支持动态创建和管理任务流程连接线
- 添加额外产物编辑功能
- 实现按钮交互状态管理
- 添加滚动和折叠面板事件处理
- 集成 AgentAllocation 组件用于智能体分配
- 实现椭圆框交互效果展示选中状态
- 添加智能体等级颜色配置
- 支持智能体选中状态切换和排序
2025-12-15 20:47:51 +08:00
zhaoweijie
5dace5f788 feat(assets): 添加 SVG 图标和环境配置文件
- 新增 icons.svg 文件用于定义应用图标
- 添加 .env 配置文件设置 API 基础地址
- 配置图标视图框和路径数据
- 设置图标宽度为 100%,高度为 6
2025-12-15 20:47:28 +08:00
zhaoweijie
77530c49f8 feat(agent): 支持自定义API配置并优化UI交互
- 为agent.json添加apiUrl、apiKey、apiModel字段支持
- 更新API接口类型定义,支持传递自定义API配置
- 优化AgentRepoList组件UI样式和交互效果
- 增强JSON文件上传校验逻辑,支持API配置验证
- 改进任务结果页面布局和视觉呈现
- 添加任务过程查看抽屉功能
- 实现执行按钮动态样式和悬停效果
- 优化节点连接线渲染逻辑和性能
2025-12-15 20:46:54 +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
170 changed files with 32670 additions and 16190 deletions

View File

@@ -1,24 +1,35 @@
import asyncio
import openai
import httpx
from openai import OpenAI, AsyncOpenAI, max_retries
import yaml
from termcolor import colored
import os
# Helper function to avoid circular import
def print_colored(text, text_color="green", background="on_white"):
print(colored(text, text_color, background))
# load config (apikey, apibase, model)
yaml_file = os.path.join(os.getcwd(), "config", "config.yaml")
try:
with open(yaml_file, "r", encoding="utf-8") as file:
yaml_data = yaml.safe_load(file)
except Exception:
yaml_file = {}
yaml_data = {}
OPENAI_API_BASE = os.getenv("OPENAI_API_BASE") or yaml_data.get(
"OPENAI_API_BASE", "https://api.openai.com"
)
openai.api_base = OPENAI_API_BASE
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or yaml_data.get(
"OPENAI_API_KEY", ""
)
openai.api_key = OPENAI_API_KEY
OPENAI_API_MODEL = os.getenv("OPENAI_API_MODEL") or yaml_data.get(
"OPENAI_API_MODEL", ""
)
# Initialize OpenAI clients
client = OpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_BASE)
async_client = AsyncOpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_BASE)
MODEL: str = os.getenv("OPENAI_API_MODEL") or yaml_data.get(
"OPENAI_API_MODEL", "gpt-4-turbo-preview"
)
@@ -35,8 +46,11 @@ MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY") or yaml_data.get(
# for LLM completion
def LLM_Completion(
messages: list[dict], stream: bool = True, useGroq: bool = True
messages: list[dict], stream: bool = True, useGroq: bool = True,model_config: dict = None
) -> str:
if model_config:
print_colored(f"Using model config: {model_config}", "blue")
return _call_with_custom_config(messages,stream,model_config)
if not useGroq or not FAST_DESIGN_MODE:
force_gpt4 = True
useGroq = False
@@ -69,16 +83,107 @@ def LLM_Completion(
return _chat_completion(messages=messages)
def _call_with_custom_config(messages: list[dict], stream: bool, model_config: dict) ->str:
"使用自定义配置调用API"
api_url = model_config.get("apiUrl", OPENAI_API_BASE)
api_key = model_config.get("apiKey", OPENAI_API_KEY)
api_model = model_config.get("apiModel", OPENAI_API_MODEL)
temp_client = OpenAI(api_key=api_key, base_url=api_url)
temp_async_client = AsyncOpenAI(api_key=api_key, base_url=api_url)
try:
if stream:
try:
loop = asyncio.get_event_loop()
except RuntimeError as ex:
if "There is no current event loop in thread" in str(ex):
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
return loop.run_until_complete(
_achat_completion_stream_custom(messages=messages, temp_async_client=temp_async_client, api_model=api_model)
)
else:
response = temp_client.chat.completions.create(
messages=messages,
model=api_model,
temperature=0.3,
max_tokens=4096,
timeout=180
)
# 检查响应是否有效
if not response.choices or len(response.choices) == 0:
raise Exception(f"API returned empty response for model {api_model}")
if not response.choices[0] or not response.choices[0].message:
raise Exception(f"API returned invalid response format for model {api_model}")
full_reply_content = response.choices[0].message.content
if full_reply_content is None:
raise Exception(f"API returned None content for model {api_model}")
print(colored(full_reply_content, "blue", "on_white"), end="")
return full_reply_content
except Exception as e:
print_colored(f"Custom API error for model {api_model} :{str(e)}","red")
raise
async def _achat_completion_stream_custom(messages:list[dict], temp_async_client, api_model: str ) -> str:
max_retries=3
for attempt in range(max_retries):
try:
response = await temp_async_client.chat.completions.create(
messages=messages,
model=api_model,
temperature=0.3,
max_tokens=4096,
stream=True,
timeout=180
)
collected_chunks = []
collected_messages = []
async for chunk in response:
collected_chunks.append(chunk)
choices = chunk.choices
if len(choices) > 0 and choices[0] is not None:
chunk_message = choices[0].delta
if chunk_message is not None:
collected_messages.append(chunk_message)
if chunk_message.content:
print(colored(chunk_message.content, "blue", "on_white"), end="")
print()
full_reply_content = "".join(
[m.content or "" for m in collected_messages if m is not None]
)
# 检查最终结果是否为空
if not full_reply_content or full_reply_content.strip() == "":
raise Exception(f"Stream API returned empty content for model {api_model}")
return full_reply_content
except httpx.RemoteProtocolError as e:
if attempt < max_retries - 1:
wait_time = (attempt + 1) *2
print_colored(f"⚠️ Stream connection interrupted (attempt {attempt+1}/{max_retries}). Retrying in {wait_time}s...", text_color="yellow")
await asyncio.sleep(wait_time)
continue
except Exception as e:
print_colored(f"Custom API stream error for model {api_model} :{str(e)}","red")
raise
async def _achat_completion_stream_groq(messages: list[dict]) -> str:
from groq import AsyncGroq
client = AsyncGroq(api_key=GROQ_API_KEY)
groq_client = AsyncGroq(api_key=GROQ_API_KEY)
max_attempts = 5
for attempt in range(max_attempts):
print("Attempt to use Groq (Fase Design Mode):")
try:
stream = await client.chat.completions.create(
response = await groq_client.chat.completions.create(
messages=messages,
# model='gemma-7b-it',
model="mixtral-8x7b-32768",
@@ -92,9 +197,18 @@ async def _achat_completion_stream_groq(messages: list[dict]) -> str:
if attempt < max_attempts - 1: # i is zero indexed
continue
else:
raise "failed"
raise Exception("failed")
# 检查响应是否有效
if not response.choices or len(response.choices) == 0:
raise Exception("Groq API returned empty response")
if not response.choices[0] or not response.choices[0].message:
raise Exception("Groq API returned invalid response format")
full_reply_content = response.choices[0].message.content
if full_reply_content is None:
raise Exception("Groq API returned None content")
full_reply_content = stream.choices[0].message.content
print(colored(full_reply_content, "blue", "on_white"), end="")
print()
return full_reply_content
@@ -103,14 +217,14 @@ async def _achat_completion_stream_groq(messages: list[dict]) -> str:
async def _achat_completion_stream_mixtral(messages: list[dict]) -> str:
from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
client = MistralClient(api_key=MISTRAL_API_KEY)
mistral_client = MistralClient(api_key=MISTRAL_API_KEY)
# client=AsyncGroq(api_key=GROQ_API_KEY)
max_attempts = 5
for attempt in range(max_attempts):
try:
messages[len(messages) - 1]["role"] = "user"
stream = client.chat(
stream = mistral_client.chat(
messages=[
ChatMessage(
role=message["role"], content=message["content"]
@@ -119,31 +233,35 @@ async def _achat_completion_stream_mixtral(messages: list[dict]) -> str:
],
# model = "mistral-small-latest",
model="open-mixtral-8x7b",
# response_format={"type": "json_object"},
)
break # If the operation is successful, break the loop
except Exception:
if attempt < max_attempts - 1: # i is zero indexed
continue
else:
raise "failed"
raise Exception("failed")
# 检查响应是否有效
if not stream.choices or len(stream.choices) == 0:
raise Exception("Mistral API returned empty response")
if not stream.choices[0] or not stream.choices[0].message:
raise Exception("Mistral API returned invalid response format")
full_reply_content = stream.choices[0].message.content
if full_reply_content is None:
raise Exception("Mistral API returned None content")
print(colored(full_reply_content, "blue", "on_white"), end="")
print()
return full_reply_content
async def _achat_completion_stream_gpt35(messages: list[dict]) -> str:
openai.api_key = OPENAI_API_KEY
openai.api_base = OPENAI_API_BASE
response = await openai.ChatCompletion.acreate(
response = await async_client.chat.completions.create(
messages=messages,
max_tokens=4096,
n=1,
stop=None,
temperature=0.3,
timeout=3,
timeout=600,
model="gpt-3.5-turbo-16k",
stream=True,
)
@@ -154,40 +272,38 @@ async def _achat_completion_stream_gpt35(messages: list[dict]) -> str:
# iterate through the stream of events
async for chunk in response:
collected_chunks.append(chunk) # save the event response
choices = chunk["choices"]
if len(choices) > 0:
chunk_message = chunk["choices"][0].get(
"delta", {}
) # extract the message
collected_messages.append(chunk_message) # save the message
if "content" in chunk_message:
print(
colored(chunk_message["content"], "blue", "on_white"),
end="",
)
choices = chunk.choices
if len(choices) > 0 and choices[0] is not None:
chunk_message = choices[0].delta
if chunk_message is not None:
collected_messages.append(chunk_message) # save the message
if 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]
[m.content or "" for m in collected_messages if m is not None]
)
# 检查最终结果是否为空
if not full_reply_content or full_reply_content.strip() == "":
raise Exception("Stream API (gpt-3.5) returned empty content")
return full_reply_content
async def _achat_completion_json(messages: list[dict]) -> str:
openai.api_key = OPENAI_API_KEY
openai.api_base = OPENAI_API_BASE
def _achat_completion_json(messages: list[dict] ) -> str:
max_attempts = 5
for attempt in range(max_attempts):
try:
stream = await openai.ChatCompletion.acreate(
response = async_client.chat.completions.create(
messages=messages,
max_tokens=4096,
n=1,
stop=None,
temperature=0.3,
timeout=3,
timeout=600,
model=MODEL,
response_format={"type": "json_object"},
)
@@ -196,62 +312,87 @@ async def _achat_completion_json(messages: list[dict]) -> str:
if attempt < max_attempts - 1: # i is zero indexed
continue
else:
raise "failed"
raise Exception("failed")
# 检查响应是否有效
if not response.choices or len(response.choices) == 0:
raise Exception("OpenAI API returned empty response")
if not response.choices[0] or not response.choices[0].message:
raise Exception("OpenAI API returned invalid response format")
full_reply_content = response.choices[0].message.content
if full_reply_content is None:
raise Exception("OpenAI API returned None content")
full_reply_content = stream.choices[0].message.content
print(colored(full_reply_content, "blue", "on_white"), end="")
print()
return full_reply_content
async def _achat_completion_stream(messages: list[dict]) -> str:
openai.api_key = OPENAI_API_KEY
openai.api_base = OPENAI_API_BASE
response = await openai.ChatCompletion.acreate(
**_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 and choices[0] is not None:
chunk_message = choices[0].delta
if chunk_message is not None:
collected_messages.append(chunk_message) # save the message
if 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]
)
# 检查最终结果是否为空
if not full_reply_content or full_reply_content.strip() == "":
raise Exception("Stream API returned empty content")
return full_reply_content
except Exception as e:
print_colored(f"OpenAI API error in _achat_completion_stream: {str(e)}", "red")
raise
def _chat_completion(messages: list[dict]) -> str:
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))
# 检查响应是否有效
if not rsp.choices or len(rsp.choices) == 0:
raise Exception("OpenAI API returned empty response")
if not rsp.choices[0] or not rsp.choices[0].message:
raise Exception("OpenAI API returned invalid response format")
content = rsp.choices[0].message.content
if content is None:
raise Exception("OpenAI API returned None content")
return content
except Exception as e:
print_colored(f"OpenAI API error in _chat_completion: {str(e)}", "red")
raise
def _cons_kwargs(messages: list[dict]) -> dict:
kwargs = {
"messages": messages,
"max_tokens": 4096,
"n": 1,
"stop": None,
"temperature": 0.5,
"timeout": 3,
"max_tokens": 2000,
"temperature": 0.3,
"timeout": 600,
}
kwargs_mode = {"model": MODEL}
kwargs.update(kwargs_mode)

View File

@@ -7,6 +7,8 @@ PROMPT_ABILITY_REQUIREMENT_GENERATION = """
## Instruction
Based on "General Goal" and "Current Task", output a formatted "Ability Requirement" which lists at least 3 different ability requirement that is required by the "Current Task". The ability should be summarized concisely within a few words.
**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all ability requirements.**
## General Goal (The general goal for the collaboration plan, "Current Task" is just one of its substep)
{General_Goal}
@@ -49,6 +51,8 @@ PROMPT_AGENT_ABILITY_SCORING = """
## Instruction
Based on "Agent Board" and "Ability Requirement", output a score for each agent to estimate the possibility that the agent can fulfil the "Ability Requirement". The score should be 1-5. Provide a concise reason before you assign the score.
**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all reasons and explanations.**
## AgentBoard
{Agent_Board}
@@ -133,5 +137,6 @@ def AgentSelectModify_init(stepTask, General_Goal, Agent_Board):
def AgentSelectModify_addAspect(aspectList, Agent_Board):
scoreTable = agentAbilityScoring(Agent_Board, aspectList)
newAspect = aspectList[-1]
scoreTable = agentAbilityScoring(Agent_Board, [newAspect])
return scoreTable

View File

@@ -33,7 +33,9 @@ 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".
**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all explanations and reasoning, though agent names should remain in their original form.**
## General Goal (Specify the general goal for the collaboration plan)
{General_Goal}
@@ -80,6 +82,10 @@ def generate_AbilityRequirement(General_Goal, Current_Task):
def generate_AgentSelection(General_Goal, Current_Task, Agent_Board):
# Check if Agent_Board is None or empty
if Agent_Board is None or len(Agent_Board) == 0:
raise ValueError("Agent_Board cannot be None or empty. Please ensure agents are set via /setAgents endpoint before generating a plan.")
messages = [
{
"role": "system",

View File

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

View File

@@ -9,6 +9,8 @@ PROMPT_PLAN_OUTLINE_BRANCHING = """
Based on "Existing Steps", your task is to comeplete the "Remaining Steps" for the plan for "General Goal".
Note: "Modification Requirement" specifies how to modify the "Baseline Completion" for a better/alternative solution.
**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all content, including StepName, TaskContent, and OutputObject fields.**
## General Goal (Specify the general goal for the plan)
{General_Goal}

View File

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

View File

@@ -5,7 +5,9 @@ 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".
**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all content, including StepName, TaskContent, and OutputObject fields.**
## Initial Key Object List (Specify the list of initial key objects available, each initial key object should be the input object of at least one Step)
{InitialObject_List}
@@ -51,7 +53,6 @@ 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 +85,16 @@ def generate_PlanOutline(InitialObject_List, General_Goal):
),
},
]
return read_LLM_Completion(messages)["Plan_Outline"]
result = read_LLM_Completion(messages)
if isinstance(result, dict) and "Plan_Outline" in result:
return result["Plan_Outline"]
else:
# 如果格式不正确,返回默认的计划大纲
return [
{
"StepName": "Default Step",
"TaskContent": "Generated default plan step due to format error",
"InputObject_List": [],
"OutputObject": "Default Output"
}
]

View File

@@ -25,6 +25,8 @@ PROMPT_TASK_PROCESS_GENERATION = """
## Instruction
Based on "General Goal", "Task for Current Step", "Action Set" and "Output Format Example", design a plan for "Task for Current Step", output a formatted "Task_Process_Plan".
**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for the Description field and all explanations, while keeping ID, ActionType, and AgentName in their original format.**
## General Goal (The general goal for the collaboration plan, you just design the plan for one of its step (i.e. "Task for Current Step"))
{General_Goal}
@@ -90,7 +92,6 @@ 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
"""

View File

@@ -5,10 +5,12 @@ PROMPT_TEMPLATE_TAKE_ACTION_BASE = '''
Your name is {agentName}. You will play the role as the Profile indicates.
Profile: {agentProfile}
You are within a multi-agent collaboration for the "Current Task".
Now it's your turn to take action. Read the "Context Information" and take your action following "Instruction for Your Current Action".
You are within a multi-agent collaboration for the "Current Task".
Now it's your turn to take action. Read the "Context Information" and take your action following "Instruction for Your Current Action".
Note: Important Input for your action are marked with *Important Input*
**IMPORTANT LANGUAGE REQUIREMENT: You must respond in Chinese (中文) for all your answers and outputs.**
## Context Information
### General Goal (The "Current Task" is indeed a substep of the general goal)
@@ -23,8 +25,8 @@ Note: Important Input for your action are marked with *Important Input*
### History Action
{History_Action}
## Instruction for Your Current Action
{Action_Description}
## Instruction for Your Current Action
{Action_Description}
{Action_Custom_Note}
@@ -80,10 +82,34 @@ class BaseAction():
Important_Mark = ""
action_Record += PROMPT_TEMPLATE_ACTION_RECORD.format(AgentName = actionInfo["AgentName"], Action_Description = actionInfo["AgentName"], Action_Result = actionInfo["Action_Result"], Important_Mark = Important_Mark)
prompt = PROMPT_TEMPLATE_TAKE_ACTION_BASE.format(agentName = agentName, agentProfile = AgentProfile_Dict[agentName], General_Goal = General_Goal, Current_Task_Description = TaskDescription, Input_Objects = inputObject_Record, History_Action = action_Record, Action_Description = self.info["Description"], Action_Custom_Note = self.Action_Custom_Note)
# Handle missing agent profiles gracefully
model_config = None
if agentName not in AgentProfile_Dict:
print_colored(text=f"Warning: Agent '{agentName}' not found in AgentProfile_Dict. Using default profile.", text_color="yellow")
agentProfile = f"AI Agent named {agentName}"
else:
# agentProfile = AgentProfile_Dict[agentName]
agent_config = AgentProfile_Dict[agentName]
agentProfile = agent_config.get("profile",f"AI Agent named {agentName}")
if agent_config.get("useCustomAPI",False):
model_config = {
"apiModel":agent_config.get("apiModel"),
"apiUrl":agent_config.get("apiUrl"),
"apiKey":agent_config.get("apiKey"),
}
prompt = PROMPT_TEMPLATE_TAKE_ACTION_BASE.format(
agentName = agentName,
agentProfile = agentProfile,
General_Goal = General_Goal,
Current_Task_Description = TaskDescription,
Input_Objects = inputObject_Record,
History_Action = action_Record,
Action_Description = self.info["Description"],
Action_Custom_Note = self.Action_Custom_Note
)
print_colored(text = prompt, text_color="red")
messages = [{"role":"system", "content": prompt}]
ActionResult = LLM_Completion(messages,True,False)
ActionResult = LLM_Completion(messages,True,False,model_config=model_config)
ActionInfo_with_Result = copy.deepcopy(self.info)
ActionInfo_with_Result["Action_Result"] = ActionResult

View File

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

View File

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

View File

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

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

@@ -0,0 +1,608 @@
"""
优化版执行计划 - 支持动态追加步骤
在执行过程中可以接收新的步骤并追加到执行队列
"""
import asyncio
import json
import time
from typing import List, Dict, Set, Generator, Any
import AgentCoord.RehearsalEngine_V2.Action as Action
import AgentCoord.util as util
from termcolor import colored
from AgentCoord.RehearsalEngine_V2.execution_state import execution_state_manager
from AgentCoord.RehearsalEngine_V2.dynamic_execution_manager import dynamic_execution_manager
# ==================== 配置参数 ====================
# 最大并发请求数
MAX_CONCURRENT_REQUESTS = 2
# 批次之间的延迟
BATCH_DELAY = 1.0
# 429错误重试次数和延迟
MAX_RETRIES = 3
RETRY_DELAY = 5.0
# ==================== 限流器 ====================
class RateLimiter:
"""
异步限流器,控制并发请求数量
"""
def __init__(self, max_concurrent: int = MAX_CONCURRENT_REQUESTS):
self.semaphore = asyncio.Semaphore(max_concurrent)
self.max_concurrent = max_concurrent
async def __aenter__(self):
await self.semaphore.acquire()
return self
async def __aexit__(self, *args):
self.semaphore.release()
# 全局限流器实例
rate_limiter = RateLimiter()
def build_action_dependency_graph(TaskProcess: List[Dict]) -> Dict[int, List[int]]:
"""
构建动作依赖图
Args:
TaskProcess: 任务流程列表
Returns:
依赖映射字典 {action_index: [dependent_action_indices]}
"""
dependency_map = {i: [] for i in range(len(TaskProcess))}
for i, action in enumerate(TaskProcess):
important_inputs = action.get('ImportantInput', [])
if not important_inputs:
continue
# 检查是否依赖其他动作的ActionResult
for j, prev_action in enumerate(TaskProcess):
if i == j:
continue
# 判断是否依赖前一个动作的结果
if any(
inp.startswith('ActionResult:') and
inp == f'ActionResult:{prev_action["ID"]}'
for inp in important_inputs
):
dependency_map[i].append(j)
return dependency_map
def get_parallel_batches(TaskProcess: List[Dict], dependency_map: Dict[int, List[int]]) -> List[List[int]]:
"""
将动作分为多个批次,每批内部可以并行执行
Args:
TaskProcess: 任务流程列表
dependency_map: 依赖图
Returns:
批次列表 [[batch1_indices], [batch2_indices], ...]
"""
batches = []
completed: Set[int] = set()
while len(completed) < len(TaskProcess):
# 找出所有依赖已满足的动作
ready_to_run = [
i for i in range(len(TaskProcess))
if i not in completed and
all(dep in completed for dep in dependency_map[i])
]
if not ready_to_run:
# 避免死循环
remaining = [i for i in range(len(TaskProcess)) if i not in completed]
if remaining:
print(colored(f"警告: 检测到循环依赖,强制串行执行: {remaining}", "yellow"))
ready_to_run = remaining[:1]
else:
break
batches.append(ready_to_run)
completed.update(ready_to_run)
return batches
async def execute_single_action_async(
ActionInfo: Dict,
General_Goal: str,
TaskDescription: str,
OutputName: str,
KeyObjects: Dict,
ActionHistory: List,
agentName: str,
AgentProfile_Dict: Dict,
InputName_List: List[str]
) -> Dict:
"""
异步执行单个动作
Args:
ActionInfo: 动作信息
General_Goal: 总体目标
TaskDescription: 任务描述
OutputName: 输出对象名称
KeyObjects: 关键对象字典
ActionHistory: 动作历史
agentName: 智能体名称
AgentProfile_Dict: 智能体配置字典
InputName_List: 输入名称列表
Returns:
动作执行结果
"""
actionType = ActionInfo["ActionType"]
# 创建动作实例
if actionType in Action.customAction_Dict:
currentAction = Action.customAction_Dict[actionType](
info=ActionInfo,
OutputName=OutputName,
KeyObjects=KeyObjects,
)
else:
currentAction = Action.BaseAction(
info=ActionInfo,
OutputName=OutputName,
KeyObjects=KeyObjects,
)
# 在线程池中运行,避免阻塞事件循环
loop = asyncio.get_event_loop()
ActionInfo_with_Result = await loop.run_in_executor(
None,
lambda: currentAction.run(
General_Goal=General_Goal,
TaskDescription=TaskDescription,
agentName=agentName,
AgentProfile_Dict=AgentProfile_Dict,
InputName_List=InputName_List,
OutputName=OutputName,
KeyObjects=KeyObjects,
ActionHistory=ActionHistory,
)
)
return ActionInfo_with_Result
async def execute_step_async_streaming(
stepDescrip: Dict,
General_Goal: str,
AgentProfile_Dict: Dict,
KeyObjects: Dict,
step_index: int,
total_steps: int
) -> Generator[Dict, None, None]:
"""
异步执行单个步骤,支持流式返回
Args:
stepDescrip: 步骤描述
General_Goal: 总体目标
AgentProfile_Dict: 智能体配置字典
KeyObjects: 关键对象字典
step_index: 步骤索引
total_steps: 总步骤数
Yields:
执行事件字典
"""
# 准备步骤信息
StepName = (
util.camel_case_to_normal(stepDescrip["StepName"])
if util.is_camel_case(stepDescrip["StepName"])
else stepDescrip["StepName"]
)
TaskContent = stepDescrip["TaskContent"]
InputName_List = (
[
(
util.camel_case_to_normal(obj)
if util.is_camel_case(obj)
else obj
)
for obj in stepDescrip["InputObject_List"]
]
if stepDescrip["InputObject_List"] is not None
else None
)
OutputName = (
util.camel_case_to_normal(stepDescrip["OutputObject"])
if util.is_camel_case(stepDescrip["OutputObject"])
else stepDescrip["OutputObject"]
)
Agent_List = stepDescrip["AgentSelection"]
TaskProcess = stepDescrip["TaskProcess"]
TaskDescription = (
util.converter.generate_template_sentence_for_CollaborationBrief(
input_object_list=InputName_List,
output_object=OutputName,
agent_list=Agent_List,
step_task=TaskContent,
)
)
# 初始化日志节点
inputObject_Record = [
{InputName: KeyObjects[InputName]} for InputName in InputName_List
]
stepLogNode = {
"LogNodeType": "step",
"NodeId": StepName,
"InputName_List": InputName_List,
"OutputName": OutputName,
"chatLog": [],
"inputObject_Record": inputObject_Record,
}
objectLogNode = {
"LogNodeType": "object",
"NodeId": OutputName,
"content": None,
}
# 返回步骤开始事件
yield {
"type": "step_start",
"step_index": step_index,
"total_steps": total_steps,
"step_name": StepName,
"task_description": TaskDescription,
}
# 构建动作依赖图
dependency_map = build_action_dependency_graph(TaskProcess)
batches = get_parallel_batches(TaskProcess, dependency_map)
ActionHistory = []
total_actions = len(TaskProcess)
completed_actions = 0
util.print_colored(
f"📋 步骤 {step_index + 1}/{total_steps}: {StepName} ({total_actions} 个动作, 分 {len(batches)} 批并行执行)",
text_color="cyan"
)
# 分批执行动作
for batch_index, batch_indices in enumerate(batches):
# 在每个批次执行前检查暂停状态
should_continue = await execution_state_manager.async_check_pause()
if not should_continue:
util.print_colored("🛑 用户请求停止执行", "red")
return
batch_size = len(batch_indices)
if batch_size > 1:
util.print_colored(
f"🚦 批次 {batch_index + 1}/{len(batches)}: 并行执行 {batch_size} 个动作",
text_color="blue"
)
else:
util.print_colored(
f"🔄 动作 {completed_actions + 1}/{total_actions}: 串行执行",
text_color="yellow"
)
# 并行执行当前批次的所有动作
tasks = [
execute_single_action_async(
TaskProcess[i],
General_Goal=General_Goal,
TaskDescription=TaskDescription,
OutputName=OutputName,
KeyObjects=KeyObjects,
ActionHistory=ActionHistory,
agentName=TaskProcess[i]["AgentName"],
AgentProfile_Dict=AgentProfile_Dict,
InputName_List=InputName_List
)
for i in batch_indices
]
# 等待当前批次完成
batch_results = await asyncio.gather(*tasks)
# 逐个返回结果
for i, result in enumerate(batch_results):
action_index_in_batch = batch_indices[i]
completed_actions += 1
util.print_colored(
f"✅ 动作 {completed_actions}/{total_actions} 完成: {result['ActionType']} by {result['AgentName']}",
text_color="green"
)
ActionHistory.append(result)
# 立即返回该动作结果
yield {
"type": "action_complete",
"step_index": step_index,
"step_name": StepName,
"action_index": action_index_in_batch,
"total_actions": total_actions,
"completed_actions": completed_actions,
"action_result": result,
"batch_info": {
"batch_index": batch_index,
"batch_size": batch_size,
"is_parallel": batch_size > 1
}
}
# 步骤完成
objectLogNode["content"] = KeyObjects[OutputName]
stepLogNode["ActionHistory"] = ActionHistory
yield {
"type": "step_complete",
"step_index": step_index,
"step_name": StepName,
"step_log_node": stepLogNode,
"object_log_node": objectLogNode,
}
def executePlan_streaming_dynamic(
plan: Dict,
num_StepToRun: int,
RehearsalLog: List,
AgentProfile_Dict: Dict,
existingKeyObjects: Dict = None,
execution_id: str = None
) -> Generator[str, None, None]:
"""
动态执行计划,支持在执行过程中追加新步骤
Args:
plan: 执行计划
num_StepToRun: 要运行的步骤数
RehearsalLog: 已执行的历史记录
AgentProfile_Dict: 智能体配置
existingKeyObjects: 已存在的KeyObjects
execution_id: 执行ID用于动态追加步骤
Yields:
SSE格式的事件字符串
"""
# 初始化执行状态
general_goal = plan.get("General Goal", "")
execution_state_manager.start_execution(general_goal)
print(colored(f"⏸️ 执行状态管理器已启动,支持暂停/恢复", "green"))
# 准备执行
KeyObjects = existingKeyObjects.copy() if existingKeyObjects else {}
finishedStep_index = -1
for logNode in RehearsalLog:
if logNode["LogNodeType"] == "step":
finishedStep_index += 1
if logNode["LogNodeType"] == "object":
KeyObjects[logNode["NodeId"]] = logNode["content"]
if existingKeyObjects:
print(colored(f"📦 使用已存在的 KeyObjects: {list(existingKeyObjects.keys())}", "cyan"))
# 确定要运行的步骤范围
if num_StepToRun is None:
run_to = len(plan["Collaboration Process"])
else:
run_to = (finishedStep_index + 1) + num_StepToRun
steps_to_run = plan["Collaboration Process"][(finishedStep_index + 1): run_to]
# 使用动态执行管理器
if execution_id:
# 初始化执行管理器使用传入的execution_id
actual_execution_id = dynamic_execution_manager.start_execution(general_goal, steps_to_run, execution_id)
print(colored(f"🚀 开始执行计划(动态模式),共 {len(steps_to_run)} 个步骤执行ID: {actual_execution_id}", "cyan"))
else:
print(colored(f"🚀 开始执行计划(流式推送),共 {len(steps_to_run)} 个步骤", "cyan"))
total_steps = len(steps_to_run)
# 使用队列实现流式推送
async def produce_events(queue: asyncio.Queue):
"""异步生产者"""
try:
step_index = 0
if execution_id:
# 动态模式:循环获取下一个步骤
# 等待新步骤的最大次数(避免无限等待)
max_empty_wait_cycles = 5 # 最多等待60次每次等待1秒
empty_wait_count = 0
while True:
# 检查暂停状态
should_continue = await execution_state_manager.async_check_pause()
if not should_continue:
print(colored("🛑 用户请求停止执行", "red"))
await queue.put({
"type": "error",
"message": "执行已被用户停止"
})
break
# 获取下一个步骤
stepDescrip = dynamic_execution_manager.get_next_step(execution_id)
if stepDescrip is None:
# 没有更多步骤了,检查是否应该继续等待
empty_wait_count += 1
# 获取执行信息
execution_info = dynamic_execution_manager.get_execution_info(execution_id)
if execution_info:
queue_total_steps = execution_info.get("total_steps", 0)
completed_steps = execution_info.get("completed_steps", 0)
# 如果没有步骤在队列中queue_total_steps为0立即退出
if queue_total_steps == 0:
print(colored(f"⚠️ 没有步骤在队列中,退出执行", "yellow"))
break
# 如果所有步骤都已完成,等待可能的新步骤
if completed_steps >= queue_total_steps:
if empty_wait_count >= max_empty_wait_cycles:
# 等待超时,退出执行
print(colored(f"✅ 所有步骤执行完成,等待超时", "green"))
break
else:
# 等待新步骤追加
print(colored(f"⏳ 等待新步骤追加... ({empty_wait_count}/{max_empty_wait_cycles})", "cyan"))
await asyncio.sleep(1)
continue
else:
# 还有步骤未完成,继续尝试获取
print(colored(f"⏳ 等待步骤就绪... ({completed_steps}/{queue_total_steps})", "cyan"))
await asyncio.sleep(0.5)
empty_wait_count = 0 # 重置等待计数
continue
else:
# 执行信息不存在,退出
print(colored(f"⚠️ 执行信息不存在,退出执行", "yellow"))
break
# 重置等待计数
empty_wait_count = 0
# 获取最新的总步骤数(用于显示)
execution_info = dynamic_execution_manager.get_execution_info(execution_id)
current_total_steps = execution_info.get("total_steps", total_steps) if execution_info else total_steps
# 执行步骤
async for event in execute_step_async_streaming(
stepDescrip,
plan["General Goal"],
AgentProfile_Dict,
KeyObjects,
step_index,
current_total_steps # 使用动态更新的总步骤数
):
if execution_state_manager.is_stopped():
await queue.put({
"type": "error",
"message": "执行已被用户停止"
})
return
await queue.put(event)
# 标记步骤完成
dynamic_execution_manager.mark_step_completed(execution_id)
# 更新KeyObjects
OutputName = stepDescrip.get("OutputObject", "")
if OutputName and OutputName in KeyObjects:
# 对象日志节点会在step_complete中发送
pass
step_index += 1
else:
# 非动态模式:按顺序执行所有步骤
for step_index, stepDescrip in enumerate(steps_to_run):
should_continue = await execution_state_manager.async_check_pause()
if not should_continue:
print(colored("🛑 用户请求停止执行", "red"))
await queue.put({
"type": "error",
"message": "执行已被用户停止"
})
return
async for event in execute_step_async_streaming(
stepDescrip,
plan["General Goal"],
AgentProfile_Dict,
KeyObjects,
step_index,
total_steps
):
if execution_state_manager.is_stopped():
await queue.put({
"type": "error",
"message": "执行已被用户停止"
})
return
await queue.put(event)
except Exception as e:
await queue.put({
"type": "error",
"message": f"执行出错: {str(e)}"
})
finally:
await queue.put(None)
# 运行异步任务并实时yield
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
queue = asyncio.Queue(maxsize=10)
producer_task = loop.create_task(produce_events(queue))
while True:
event = loop.run_until_complete(queue.get())
if event is None:
break
# 立即转换为SSE格式并发送
event_str = json.dumps(event, ensure_ascii=False)
yield f"data: {event_str}\n\n"
loop.run_until_complete(producer_task)
if not execution_state_manager.is_stopped():
complete_event = json.dumps({
"type": "execution_complete",
"total_steps": total_steps
}, ensure_ascii=False)
yield f"data: {complete_event}\n\n"
finally:
# 在关闭事件循环之前先清理执行记录
if execution_id:
# 清理执行记录
dynamic_execution_manager.cleanup(execution_id)
if 'producer_task' in locals():
if not producer_task.done():
producer_task.cancel()
# 确保所有任务都完成后再关闭事件循环
try:
pending = asyncio.all_tasks(loop)
for task in pending:
task.cancel()
loop.run_until_complete(asyncio.gather(*pending, return_exceptions=True))
except Exception:
pass # 忽略清理过程中的错误
loop.close()
# 保留旧版本函数以保持兼容性
executePlan_streaming = executePlan_streaming_dynamic

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

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

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
FAST_DESIGN_MODE: False
GROQ_API_KEY: ""
MISTRAL_API_KEY: ""
## options under experimentation, leave them as Fasle unless you know what it is for
USE_CACHE: True
USE_CACHE: False

View File

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

File diff suppressed because it is too large Load Diff

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

@@ -1,33 +1,33 @@
import { Box } from '@mui/material';
import React from 'react';
// 定义你的图标属性类型,这里可以扩展成任何你需要的属性
interface CustomIconProps {
size?: number | string;
color?: string;
// ...其他你需要的props
}
// 创建你的自定义SVG图标组件
const CheckIcon: React.FC<CustomIconProps> = ({
size = '100%',
color = 'currentColor',
}) => {
return (
<Box
component="svg"
width={size}
height="auto"
viewBox="0 0 11 9"
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<path
d="M10.7204 0C7.37522 1.94391 4.95071 4.40066 3.85635 5.63171L1.18331 3.64484L0 4.54699L4.61463 9C5.4068 7.07085 7.92593 3.30116 11 0.620227L10.7204 0Z"
fill={color}
/>
</Box>
);
};
export default CheckIcon;
import { Box } from '@mui/material';
import React from 'react';
// 定义你的图标属性类型,这里可以扩展成任何你需要的属性
interface CustomIconProps {
size?: number | string;
color?: string;
// ...其他你需要的props
}
// 创建你的自定义SVG图标组件
const CheckIcon: React.FC<CustomIconProps> = ({
size = '100%',
color = 'currentColor',
}) => {
return (
<Box
component="svg"
width={size}
height="auto"
viewBox="0 0 11 9"
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<path
d="M10.7204 0C7.37522 1.94391 4.95071 4.40066 3.85635 5.63171L1.18331 3.64484L0 4.54699L4.61463 9C5.4068 7.07085 7.92593 3.30116 11 0.620227L10.7204 0Z"
fill={color}
/>
</Box>
);
};
export default CheckIcon;

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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View File

@@ -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}.`;

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"
]
}

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# 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

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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"]

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# 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

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/* 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']
ElDrawer: typeof import('element-plus/es')['ElDrawer']
ElEmpty: typeof import('element-plus/es')['ElEmpty']
ElIcon: typeof import('element-plus/es')['ElIcon']
ElInput: typeof import('element-plus/es')['ElInput']
ElPopover: typeof import('element-plus/es')['ElPopover']
ElScrollbar: typeof import('element-plus/es')['ElScrollbar']
ElTag: typeof import('element-plus/es')['ElTag']
ElTooltip: typeof import('element-plus/es')['ElTooltip']
MultiLineTooltip: typeof import('./src/components/MultiLineTooltip/index.vue')['default']
Notification: typeof import('./src/components/Notification/Notification.vue')['default']
RouterLink: typeof import('vue-router')['RouterLink']
RouterView: typeof import('vue-router')['RouterView']
SvgIcon: typeof import('./src/components/SvgIcon/index.vue')['default']
}
export interface GlobalDirectives {
vLoading: typeof import('element-plus/es')['ElLoadingDirective']
}
}

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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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<!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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{
"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": {
"@element-plus/icons-vue": "^2.3.2",
"@jsplumb/browser-ui": "^6.2.10",
"@types/markdown-it": "^14.1.2",
"@vue-flow/background": "^1.3.2",
"@vue-flow/controls": "^1.1.3",
"@vue-flow/core": "^1.48.1",
"@vue-flow/minimap": "^1.5.4",
"@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",
"socket.io-client": "^4.8.3",
"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"
}
]

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{
"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"
},
"dev": true,
"apiBaseUrl": "http://localhost:8000"
}

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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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import type { Directive, DirectiveBinding } from 'vue'
import { useConfigStoreHook } from '@/stores'
/**
* 开发模式专用指令
* 只在开发模式下显示元素,生产模式下会移除该元素
*
* \@param binding.value - 是否开启该功能,默认为 true
* @example
* \!-- 默认开启,开发模式显示 --
* \!div v-dev-only开发模式内容</div>
*
* \!-- 传入参数控制 --
* <div v-dev-only="true">开启指令</div>
* <div v-dev-only="false">取消指令</div>
*/
export const devOnly: Directive = {
mounted(el: HTMLElement, binding: DirectiveBinding) {
checkAndRemoveElement(el, binding)
},
updated(el: HTMLElement, binding: DirectiveBinding) {
checkAndRemoveElement(el, binding)
},
}
const configStore = useConfigStoreHook()
/**
* 检查并移除元素的逻辑
*/
function checkAndRemoveElement(el: HTMLElement, binding: DirectiveBinding) {
const isDev = typeof configStore.config.dev === 'boolean' ? configStore.config.dev : import.meta.env.DEV
// 默认值为 true如果没有传值或者传值为 true 都启用
const shouldEnable = binding.value !== false
// 如果不是开发模式或者明确禁用,移除该元素
if (!isDev && shouldEnable) {
if (el.parentNode) {
el.parentNode.removeChild(el)
}
}
}

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import type { App } from 'vue'
import { devOnly } from './devOnly'
// 全局注册 directive
export function setupDirective(app: App<Element>) {
app.directive('dev-only', devOnly)
}

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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!')
})
})

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import request from '@/utils/request'
import websocket from '@/utils/websocket'
import type { Agent, IApiStepTask, IRawPlanResponse, IRawStepTask } from '@/stores'
import {
mockBackendAgentSelectModifyInit,
mockBackendAgentSelectModifyAddAspect,
type BackendAgentScoreResponse,
} from '@/layout/components/Main/TaskTemplate/TaskSyllabus/components/mock/AgentAssignmentBackendMock'
import {
mockBackendFillAgentTaskProcess,
type RawAgentTaskProcessResponse,
} from '@/layout/components/Main/TaskTemplate/TaskProcess/components/mock/AgentTaskProcessBackendMock'
import { mockBranchPlanOutlineAPI } from '@/layout/components/Main/TaskTemplate/TaskSyllabus/Branch/mock/branchPlanOutlineMock'
import { mockFillStepTaskAPI } from '@/layout/components/Main/TaskTemplate/TaskSyllabus/Branch/mock/fill-step-task-mock'
import {
mockBranchTaskProcessAPI,
type BranchAction,
} from '@/layout/components/Main/TaskTemplate/TaskSyllabus/Branch/mock/branchTaskProcessMock'
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[]
}
/**
* SSE 流式事件类型
*/
export type StreamingEvent =
| {
type: 'step_start'
step_index: number
total_steps: number
step_name: string
task_description?: string
}
| {
type: 'action_complete'
step_index: number
step_name: string
action_index: number
total_actions: number
completed_actions: number
action_result: ActionHistory
batch_info?: {
batch_index: number
batch_size: number
is_parallel: boolean
}
}
| {
type: 'step_complete'
step_index: number
step_name: string
step_log_node: any
object_log_node: any
}
| {
type: 'execution_complete'
total_steps: number
}
| {
type: 'error'
message: string
}
export interface IFillAgentSelectionRequest {
goal: string
stepTask: IApiStepTask
agents: string[]
}
class Api {
// 默认使用WebSocket
private useWebSocketDefault = true
setAgents = (data: Pick<Agent, 'Name' | 'Profile' | 'apiUrl' | 'apiKey' | 'apiModel'>[], useWebSocket: boolean = this.useWebSocketDefault) => {
// 如果启用WebSocket且已连接使用WebSocket
if (useWebSocket && websocket.connected) {
return websocket.send('set_agents', data)
}
// 否则使用REST API
return request({
url: '/setAgents',
data,
method: 'POST',
})
}
generateBasePlan = (data: {
goal: string
inputs: string[]
apiUrl?: string
apiKey?: string
apiModel?: string
useWebSocket?: boolean
onProgress?: (progress: { status: string; stage?: string; message?: string; [key: string]: any }) => void
}) => {
const useWs = data.useWebSocket !== undefined ? data.useWebSocket : this.useWebSocketDefault
// 如果启用WebSocket且已连接使用WebSocket
if (useWs && websocket.connected) {
return websocket.send('generate_base_plan', {
'General Goal': data.goal,
'Initial Input Object': data.inputs,
apiUrl: data.apiUrl,
apiKey: data.apiKey,
apiModel: data.apiModel,
}, undefined, data.onProgress)
}
// 否则使用REST API
return request<unknown, IRawPlanResponse>({
url: '/generate_basePlan',
method: 'POST',
data: {
'General Goal': data.goal,
'Initial Input Object': data.inputs,
apiUrl: data.apiUrl,
apiKey: data.apiKey,
apiModel: data.apiModel,
},
})
}
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,
})),
})),
},
},
})
}
/**
* 优化版流式执行计划(支持动态追加步骤)
* 步骤级流式 + 动作级智能并行 + 动态追加步骤
*/
executePlanOptimized = (
plan: IRawPlanResponse,
onMessage: (event: StreamingEvent) => void,
onError?: (error: Error) => void,
onComplete?: () => void,
useWebSocket?: boolean,
existingKeyObjects?: Record<string, any>,
enableDynamic?: boolean,
onExecutionStarted?: (executionId: string) => void,
executionId?: string,
restartFromStepIndex?: number, // 新增:从指定步骤重新执行的索引
rehearsalLog?: any[], // 新增:传递截断后的 RehearsalLog
) => {
const useWs = useWebSocket !== undefined ? useWebSocket : this.useWebSocketDefault
const data = {
RehearsalLog: rehearsalLog || [], // ✅ 使用传递的 RehearsalLog
num_StepToRun: null,
existingKeyObjects: existingKeyObjects || {},
enable_dynamic: enableDynamic || false,
execution_id: executionId || null,
restart_from_step_index: restartFromStepIndex ?? null, // 新增:传递重新执行索引
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,
})),
})),
},
}
// 如果启用WebSocket且已连接使用WebSocket
if (useWs && websocket.connected) {
websocket.subscribe(
'execute_plan_optimized',
data,
// onProgress
(progressData) => {
try {
let event: StreamingEvent
// 处理不同类型的progress数据
if (typeof progressData === 'string') {
event = JSON.parse(progressData)
} else {
event = progressData as StreamingEvent
}
// 处理特殊事件类型
if (event && typeof event === 'object') {
// 检查是否是execution_started事件
if ('status' in event && event.status === 'execution_started') {
if ('execution_id' in event && onExecutionStarted) {
onExecutionStarted(event.execution_id as string)
}
return
}
}
onMessage(event)
} catch (e) {
// Failed to parse WebSocket data
}
},
// onComplete
() => {
onComplete?.()
},
// onError
(error) => {
onError?.(error)
}
)
return
}
// 否则使用原有的SSE方式
fetch('/api/executePlanOptimized', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify(data),
})
.then(async (response) => {
if (!response.ok) {
throw new Error(`HTTP error! status: ${response.status}`)
}
const reader = response.body?.getReader()
const decoder = new TextDecoder()
if (!reader) {
throw new Error('Response body is null')
}
let buffer = ''
while (true) {
const { done, value } = await reader.read()
if (done) {
onComplete?.()
break
}
buffer += decoder.decode(value, { stream: true })
const lines = buffer.split('\n')
buffer = lines.pop() || ''
for (const line of lines) {
if (line.startsWith('data: ')) {
const data = line.slice(6)
try {
const event = JSON.parse(data)
onMessage(event)
} catch (e) {
// Failed to parse SSE data
}
}
}
}
})
.catch((error) => {
onError?.(error)
})
}
/**
* 分支任务大纲
*/
branchPlanOutline = (data: {
branch_Number: number
Modification_Requirement: string
Existing_Steps: IRawStepTask[]
Baseline_Completion: number
initialInputs: string[]
goal: string
useWebSocket?: boolean
onProgress?: (progress: { status: string; stage?: string; message?: string; [key: string]: any }) => void
}) => {
const useWs = data.useWebSocket !== undefined ? data.useWebSocket : this.useWebSocketDefault
// 如果启用WebSocket且已连接使用WebSocket
if (useWs && websocket.connected) {
return websocket.send('branch_plan_outline', {
branch_Number: data.branch_Number,
Modification_Requirement: data.Modification_Requirement,
Existing_Steps: data.Existing_Steps,
Baseline_Completion: data.Baseline_Completion,
'Initial Input Object': data.initialInputs,
'General Goal': data.goal,
}, undefined, data.onProgress)
}
// 否则使用REST API
return request<unknown, IRawPlanResponse>({
url: '/branch_PlanOutline',
method: 'POST',
data: {
branch_Number: data.branch_Number,
Modification_Requirement: data.Modification_Requirement,
Existing_Steps: data.Existing_Steps,
Baseline_Completion: data.Baseline_Completion,
'Initial Input Object': data.initialInputs,
'General Goal': data.goal,
},
})
}
/**
* 分支任务流程
*/
branchTaskProcess = (data: {
branch_Number: number
Modification_Requirement: string
Existing_Steps: BranchAction[]
Baseline_Completion: number
stepTaskExisting: any
goal: string
useWebSocket?: boolean
onProgress?: (progress: { status: string; stage?: string; message?: string; [key: string]: any }) => void
}) => {
const useWs = data.useWebSocket !== undefined ? data.useWebSocket : this.useWebSocketDefault
// 如果启用WebSocket且已连接使用WebSocket
if (useWs && websocket.connected) {
return websocket.send('branch_task_process', {
branch_Number: data.branch_Number,
Modification_Requirement: data.Modification_Requirement,
Existing_Steps: data.Existing_Steps,
Baseline_Completion: data.Baseline_Completion,
stepTaskExisting: data.stepTaskExisting,
'General Goal': data.goal,
}, undefined, data.onProgress)
}
// 否则使用REST API
return request<unknown, BranchAction[][]>({
url: '/branch_TaskProcess',
method: 'POST',
data: {
branch_Number: data.branch_Number,
Modification_Requirement: data.Modification_Requirement,
Existing_Steps: data.Existing_Steps,
Baseline_Completion: data.Baseline_Completion,
stepTaskExisting: data.stepTaskExisting,
'General Goal': data.goal,
},
})
}
fillStepTask = async (data: {
goal: string
stepTask: any
useWebSocket?: boolean
onProgress?: (progress: { status: string; stage?: string; message?: string; [key: string]: any }) => void
}): Promise<IRawStepTask> => {
const useWs = data.useWebSocket !== undefined ? data.useWebSocket : this.useWebSocketDefault
let response: any
// 如果启用WebSocket且已连接使用WebSocket
if (useWs && websocket.connected) {
response = await websocket.send('fill_step_task', {
'General Goal': data.goal,
stepTask: data.stepTask,
}, undefined, data.onProgress)
} else {
// 否则使用REST API
response = await request<
{
'General Goal': string
stepTask: any
},
{
AgentSelection?: string[]
Collaboration_Brief_FrontEnd?: {
template: string
data: Record<string, { text: string; color: number[] }>
}
InputObject_List?: string[]
OutputObject?: string
StepName?: string
TaskContent?: string
TaskProcess?: Array<{
ID: string
ActionType: string
AgentName: string
Description: string
ImportantInput: string[]
}>
}
>({
url: '/fill_stepTask',
method: 'POST',
data: {
'General Goal': data.goal,
stepTask: data.stepTask,
},
})
}
const vec2Hsl = (color: number[]): string => {
const [h, s, l] = color
return `hsl(${h}, ${s}%, ${l}%)`
}
const briefData: Record<string, { text: string; style?: Record<string, string> }> = {}
if (response.Collaboration_Brief_FrontEnd?.data) {
for (const [key, value] of Object.entries(response.Collaboration_Brief_FrontEnd.data)) {
briefData[key] = {
text: value.text,
style: {
background: vec2Hsl(value.color),
},
}
}
}
/**
* 构建前端格式的 IRawStepTask
*/
return {
StepName: response.StepName || '',
TaskContent: response.TaskContent || '',
InputObject_List: response.InputObject_List || [],
OutputObject: response.OutputObject || '',
AgentSelection: response.AgentSelection || [],
Collaboration_Brief_frontEnd: {
template: response.Collaboration_Brief_FrontEnd?.template || '',
data: briefData,
},
TaskProcess: response.TaskProcess || [],
}
}
fillStepTaskTaskProcess = async (data: {
goal: string
stepTask: IApiStepTask
agents: string[]
useWebSocket?: boolean
onProgress?: (progress: { status: string; stage?: string; message?: string; [key: string]: any }) => void
}): Promise<IApiStepTask> => {
const useWs = data.useWebSocket !== undefined ? data.useWebSocket : this.useWebSocketDefault
let response: any
// 如果启用WebSocket且已连接使用WebSocket
if (useWs && websocket.connected) {
response = await websocket.send('fill_step_task_process', {
'General Goal': data.goal,
stepTask_lackTaskProcess: {
StepName: data.stepTask.name,
TaskContent: data.stepTask.content,
InputObject_List: data.stepTask.inputs,
OutputObject: data.stepTask.output,
AgentSelection: data.agents,
},
}, undefined, data.onProgress)
} else {
// 否则使用REST API
response = await request<
{
'General Goal': string
stepTask_lackTaskProcess: {
StepName: string
TaskContent: string
InputObject_List: string[]
OutputObject: string
AgentSelection: string[]
}
},
{
StepName?: string
TaskContent?: string
InputObject_List?: string[]
OutputObject?: string
AgentSelection?: string[]
TaskProcess?: Array<{
ID: string
ActionType: string
AgentName: string
Description: string
ImportantInput: string[]
}>
Collaboration_Brief_FrontEnd?: {
template: string
data: Record<string, { text: string; color: number[] }>
}
}
>({
url: '/fill_stepTask_TaskProcess',
method: 'POST',
data: {
'General Goal': data.goal,
stepTask_lackTaskProcess: {
StepName: data.stepTask.name,
TaskContent: data.stepTask.content,
InputObject_List: data.stepTask.inputs,
OutputObject: data.stepTask.output,
AgentSelection: data.agents,
},
},
})
}
const vec2Hsl = (color: number[]): string => {
const [h, s, l] = color
return `hsl(${h}, ${s}%, ${l}%)`
}
const briefData: Record<string, { text: string; style: { background: string } }> = {}
if (response.Collaboration_Brief_FrontEnd?.data) {
for (const [key, value] of Object.entries(response.Collaboration_Brief_FrontEnd.data)) {
briefData[key] = {
text: value.text,
style: {
background: vec2Hsl(value.color),
},
}
}
}
const process = (response.TaskProcess || []).map((action: any) => ({
id: action.ID,
type: action.ActionType,
agent: action.AgentName,
description: action.Description,
inputs: action.ImportantInput,
}))
return {
name: response.StepName || '',
content: response.TaskContent || '',
inputs: response.InputObject_List || [],
output: response.OutputObject || '',
agents: response.AgentSelection || [],
brief: {
template: response.Collaboration_Brief_FrontEnd?.template || '',
data: briefData,
},
process,
}
}
/**
* 为每个智能体评分
*/
agentSelectModifyInit = async (data: {
goal: string
stepTask: any
useWebSocket?: boolean
onProgress?: (progress: { status: string; stage?: string; message?: string; [key: string]: any }) => void
}): Promise<Record<string, Record<string, { reason: string; score: number }>>> => {
const useWs = data.useWebSocket !== undefined ? data.useWebSocket : this.useWebSocketDefault
let response: Record<string, Record<string, { Reason: string; Score: number }>>
// 如果启用WebSocket且已连接使用WebSocket
if (useWs && websocket.connected) {
response = await websocket.send('agent_select_modify_init', {
'General Goal': data.goal,
stepTask: {
StepName: data.stepTask.StepName || data.stepTask.name,
TaskContent: data.stepTask.TaskContent || data.stepTask.content,
InputObject_List: data.stepTask.InputObject_List || data.stepTask.inputs,
OutputObject: data.stepTask.OutputObject || data.stepTask.output,
},
}, undefined, data.onProgress)
} else {
// 否则使用REST API
response = await request<
{
'General Goal': string
stepTask: any
},
Record<string, Record<string, { Reason: string; Score: number }>>
>({
url: '/agentSelectModify_init',
method: 'POST',
data: {
'General Goal': data.goal,
stepTask: {
StepName: data.stepTask.StepName || data.stepTask.name,
TaskContent: data.stepTask.TaskContent || data.stepTask.content,
InputObject_List: data.stepTask.InputObject_List || data.stepTask.inputs,
OutputObject: data.stepTask.OutputObject || data.stepTask.output,
},
},
})
}
const transformedData: Record<string, Record<string, { reason: string; score: number }>> = {}
for (const [aspect, agents] of Object.entries(response)) {
for (const [agentName, scoreInfo] of Object.entries(agents)) {
if (!transformedData[agentName]) {
transformedData[agentName] = {}
}
transformedData[agentName][aspect] = {
reason: scoreInfo.Reason,
score: scoreInfo.Score,
}
}
}
return transformedData
}
/**
* 添加新的评估维度
*/
agentSelectModifyAddAspect = async (data: {
aspectList: string[]
useWebSocket?: boolean
onProgress?: (progress: { status: string; stage?: string; message?: string; [key: string]: any }) => void
}): Promise<{
aspectName: string
agentScores: Record<string, { score: number; reason: string }>
}> => {
const useWs = data.useWebSocket !== undefined ? data.useWebSocket : this.useWebSocketDefault
let response: Record<string, Record<string, { Reason: string; Score: number }>>
// 如果启用WebSocket且已连接使用WebSocket
if (useWs && websocket.connected) {
response = await websocket.send('agent_select_modify_add_aspect', {
aspectList: data.aspectList,
}, undefined, data.onProgress)
} else {
// 否则使用REST API
response = await request<
{
aspectList: string[]
},
Record<string, Record<string, { Reason: string; Score: number }>>
>({
url: '/agentSelectModify_addAspect',
method: 'POST',
data: {
aspectList: data.aspectList,
},
})
}
/**
* 获取新添加的维度
*/
const newAspect = data.aspectList[data.aspectList.length - 1]
if (!newAspect) {
throw new Error('aspectList is empty')
}
const newAspectAgents = response[newAspect]
const agentScores: Record<string, { score: number; reason: string }> = {}
if (newAspectAgents) {
for (const [agentName, scoreInfo] of Object.entries(newAspectAgents)) {
agentScores[agentName] = {
score: scoreInfo.Score,
reason: scoreInfo.Reason,
}
}
}
return {
aspectName: newAspect,
agentScores,
}
}
/**
* ==================== Mock API开发阶段使用====================
*为每个智能体评分
*/
mockAgentSelectModifyInit = async (): Promise<
Record<string, Record<string, { reason: string; score: number }>>
> => {
const response: BackendAgentScoreResponse = await mockBackendAgentSelectModifyInit()
const transformedData: Record<string, Record<string, { reason: string; score: number }>> = {}
for (const [aspect, agents] of Object.entries(response)) {
for (const [agentName, scoreInfo] of Object.entries(agents)) {
if (!transformedData[agentName]) {
transformedData[agentName] = {}
}
transformedData[agentName][aspect] = {
reason: scoreInfo.Reason,
score: scoreInfo.Score,
}
}
}
return transformedData
}
mockAgentSelectModifyAddAspect = async (data: {
aspectList: string[]
}): Promise<{
aspectName: string
agentScores: Record<string, { score: number; reason: string }>
}> => {
const response: BackendAgentScoreResponse = await mockBackendAgentSelectModifyAddAspect(
data.aspectList,
)
const newAspect = data.aspectList[data.aspectList.length - 1]
if (!newAspect) {
throw new Error('aspectList is empty')
}
const newAspectAgents = response[newAspect]
const agentScores: Record<string, { score: number; reason: string }> = {}
if (newAspectAgents) {
for (const [agentName, scoreInfo] of Object.entries(newAspectAgents)) {
agentScores[agentName] = {
score: scoreInfo.Score,
reason: scoreInfo.Reason,
}
}
}
return {
aspectName: newAspect,
agentScores,
}
}
mockFillStepTaskTaskProcess = async (data: {
goal: string
stepTask: IApiStepTask
agents: string[]
}): Promise<IApiStepTask> => {
const response: RawAgentTaskProcessResponse = await mockBackendFillAgentTaskProcess(
data.goal,
data.stepTask,
data.agents,
)
const vec2Hsl = (color: number[]): string => {
const [h, s, l] = color
return `hsl(${h}, ${s}%, ${l}%)`
}
const briefData: Record<string, { text: string; style: { background: string } }> = {}
if (response.Collaboration_Brief_frontEnd?.data) {
for (const [key, value] of Object.entries(response.Collaboration_Brief_frontEnd.data)) {
briefData[key] = {
text: value.text,
style: {
background: vec2Hsl(value.color),
},
}
}
}
const process = (response.TaskProcess || []).map((action) => ({
id: action.ID,
type: action.ActionType,
agent: action.AgentName,
description: action.Description,
inputs: action.ImportantInput,
}))
return {
name: response.StepName || '',
content: response.TaskContent || '',
inputs: response.InputObject_List || [],
output: response.OutputObject || '',
agents: response.AgentSelection || [],
brief: {
template: response.Collaboration_Brief_frontEnd?.template || '',
data: briefData,
},
process,
}
}
mockBranchPlanOutline = async (data: {
branch_Number: number
Modification_Requirement: string
Existing_Steps: IRawStepTask[]
Baseline_Completion: number
initialInputs: string[]
goal: string
}): Promise<IRawPlanResponse> => {
const response = await mockBranchPlanOutlineAPI({
branch_Number: data.branch_Number,
Modification_Requirement: data.Modification_Requirement,
Existing_Steps: data.Existing_Steps,
Baseline_Completion: data.Baseline_Completion,
InitialObject_List: data.initialInputs,
General_Goal: data.goal,
})
return response
}
mockFillStepTask = async (data: { goal: string; stepTask: any }): Promise<any> => {
const response = await mockFillStepTaskAPI({
General_Goal: data.goal,
stepTask: data.stepTask,
})
return response
}
mockBranchTaskProcess = async (data: {
branch_Number: number
Modification_Requirement: string
Existing_Steps: BranchAction[]
Baseline_Completion: number
stepTaskExisting: any
goal: string
}): Promise<BranchAction[][]> => {
const response = await mockBranchTaskProcessAPI({
branch_Number: data.branch_Number,
Modification_Requirement: data.Modification_Requirement,
Existing_Steps: data.Existing_Steps,
Baseline_Completion: data.Baseline_Completion,
stepTaskExisting: data.stepTaskExisting,
General_Goal: data.goal,
})
return response
}
/**
* 向正在执行的任务追加新步骤
* @param executionId 执行ID
* @param newSteps 新步骤列表
* @returns 追加的步骤数量
*/
addStepsToExecution = async (executionId: string, newSteps: IRawStepTask[]): Promise<number> => {
if (!websocket.connected) {
throw new Error('WebSocket未连接')
}
const response = await websocket.send('add_steps_to_execution', {
execution_id: executionId,
new_steps: newSteps.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,
})),
})),
}) as { added_count: number }
return response?.added_count || 0
}
}
export default new Api()

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