refactor: 重构智能体管理和基础代理类

- 将 AgentManager 类中的方法重命名为更具描述性的名称，并优化了代理的注册和实例化逻辑。
- 添加了线程ID和用户ID字段到基础代理类，并改进了获取历史消息的逻辑，增加了错误处理。
- 在 ChatbotAgent 中确保创建图时设置 checkpointer，并处理可能的异常。

server/routers/chat_router.py

@@ -31,7 +31,7 @@ async def get_default_agent(current_user: User = Depends(get_required_user)):
         default_agent_id = config.default_agent_id
         # 如果没有设置默认智能体，尝试获取第一个可用的智能体
         if not default_agent_id:
-            agents = [agent.get_info() for agent in agent_manager.agents.values()]
+            agents = await agent_manager.get_agents_info()
             if agents:
                 default_agent_id = agents[0].get("name", "")
 
@@ -45,7 +45,7 @@ async def set_default_agent(agent_id: str = Body(..., embed=True), current_user
     """设置默认智能体ID (仅管理员)"""
     try:
         # 验证智能体是否存在
-        agents = [agent.get_info() for agent in agent_manager.agents.values()]
+        agents = await agent_manager.get_agents_info()
         agent_ids = [agent.get("name", "") for agent in agents]
 
         if agent_id not in agent_ids:
@@ -161,7 +161,8 @@ async def call(query: str = Body(...), meta: dict = Body(None), current_user: Us
 @chat.get("/agent")
 async def get_agent(current_user: User = Depends(get_required_user)):
     """获取所有可用智能体（需要登录）"""
-    agents = [agent.get_info() for agent in agent_manager.agents.values()]
+    agents = await agent_manager.get_agents_info()
+    # logger.debug(f"agents: {agents}")
     return {"agents": agents}
 
 @chat.post("/agent/{agent_name}")
@@ -195,7 +196,7 @@ async def chat_agent(agent_name: str,
         yield make_chunk(status="init", meta=meta, msg=HumanMessage(content=query).model_dump())
 
         try:
-            agent = agent_manager.get_runnable_agent(agent_name)
+            agent = agent_manager.get_agent(agent_name)
         except Exception as e:
             logger.error(f"Error getting agent {agent_name}: {e}, {traceback.format_exc()}")
             yield make_chunk(message=f"Error getting agent {agent_name}: {e}", status="error")
@@ -284,7 +285,7 @@ async def get_agent_history(
     """获取智能体历史消息（需要登录）"""
     try:
         # 获取Agent实例和配置类
-        agent = agent_manager.get_runnable_agent(agent_name)
+        agent = agent_manager.get_agent(agent_name)
         if not agent:
             raise HTTPException(status_code=404, detail=f"智能体 {agent_name} 不存在")
 

src/agents/__init__.py

@@ -1,37 +1,42 @@
+import asyncio
 from src.agents.chatbot import ChatbotAgent
 from src.agents.react import ReActAgent
 
 class AgentManager:
     def __init__(self):
-        self.agents = {}
+        self._classes = {}
-        self.agent_instances = {}  # 存储已创建的 agent 实例
+        self._instances = {}  # 存储已创建的 agent 实例
 
-    def add_agent(self, agent_id, agent_class):
+    def register_agent(self, agent_id, agent_class):
-        self.agents[agent_id] = agent_class
+        self._classes[agent_id] = agent_class
 
-    def get_runnable_agent(self, agent_id, **kwargs):
+    def init_all_agents(self):
+        for agent_id, agent_class in self._classes.items():
+            self.get_agent(agent_id)
+
+    def get_agent(self, agent_id, **kwargs):
         # 检查是否已经创建了该 agent 的实例
-        if agent_id not in self.agent_instances:
+        if agent_id not in self._instances:
-            agent_class = self.get_agent(agent_id)
+            agent_class = self._classes[agent_id]
-            self.agent_instances[agent_id] = agent_class()
+            self._instances[agent_id] = agent_class()
-        return self.agent_instances[agent_id]
 
-    def get_agent(self, agent_id):
+        return self._instances[agent_id]
-        return self.agents[agent_id]
+
+    def get_agents(self):
+        return list(self._instances.values())
+
+    async def get_agents_info(self):
+        agents = self.get_agents()
+        return await asyncio.gather(*[a.get_info() for a in agents])
 
 
 agent_manager = AgentManager()
-agent_manager.add_agent("chatbot", ChatbotAgent)
+agent_manager.register_agent("chatbot", ChatbotAgent)
-# agent_manager.add_agent("react", ReActAgent)  # 暂时屏蔽 ReActAgent
+agent_manager.register_agent("react", ReActAgent)  # 暂时屏蔽 ReActAgent
+agent_manager.init_all_agents()
 
 __all__ = ["agent_manager"]
 
 
 if __name__ == "__main__":
-    agent = agent_manager.get_agent("chatbot")
+    pass
-    conf = agent_manager.get_configuration("chatbot")
-    agent_info = {
-        "name": agent.name,
-        "description": agent.description,
-    }
-    print(agent_info)

src/agents/chatbot/configuration.py

@@ -15,24 +15,6 @@ class ChatbotConfiguration(Configuration):
     configurable 为 False 的配置项不能被用户配置，只能由开发者预设。
     """
 
-    thread_id: str = field(
-        default_factory=lambda: str(uuid.uuid4()),
-        metadata={
-            "name": "线程ID",
-            "configurable": False,
-            "description": "用来描述智能体的角色和行为"
-        },
-    )
-
-    user_id: str = field(
-        default_factory=lambda: str(uuid.uuid4()),
-        metadata={
-            "name": "用户ID",
-            "configurable": False,
-            "description": "用来描述智能体的角色和行为"
-        },
-    )
-
     system_prompt: str = field(
         default="You are a helpful assistant.",
         metadata={

src/agents/chatbot/graph.py

@@ -75,11 +75,18 @@ class ChatbotAgent(BaseAgent):
         workflow.add_edge("tools", "chatbot")
         workflow.add_edge("chatbot", END)
 
-        # 创建数据库连接
+        # 创建数据库连接并确保设置 checkpointer
-        sqlite_checkpointer = AsyncSqliteSaver(await self.get_async_conn())
+        try:
-        graph = workflow.compile(checkpointer=sqlite_checkpointer)
+            sqlite_checkpointer = AsyncSqliteSaver(await self.get_async_conn())
-        self.graph = graph
+            graph = workflow.compile(checkpointer=sqlite_checkpointer)
-        return graph
+            self.graph = graph
+            return graph
+        except Exception as e:
+            logger.error(f"构建 Graph 设置 checkpointer 时出错: {e}")
+            # 即使出错也返回一个可用的图实例，只是无法保存历史
+            graph = workflow.compile()
+            self.graph = graph
+            return graph
 
     async def get_async_conn(self) -> aiosqlite.Connection:
         """获取异步数据库连接"""

src/agents/react/configuration.py

@@ -1,36 +1,12 @@
-from dataclasses import dataclass, field
-from datetime import datetime, timezone
 
-from langchain_core.tools import tool
+from dataclasses import dataclass
-from langchain_openai import ChatOpenAI
 
 from src.agents.registry import Configuration
 
-def get_default_requirements():
-    return ["TAVILY_API_KEY"]
-
-@tool
-def multiply(first_int: int, second_int: int) -> int:
-    """Multiply two integers together."""
-    return first_int * second_int
 
 @dataclass(kw_only=True)
 class ReActConfiguration(Configuration):
-    """ReAct 的配置"""
+    """配置"""
 
-    system_prompt: str = field(
+    pass
-        default=f"You are a helpful assistant. Now is {datetime.now(tz=timezone.utc).isoformat()}",
-        metadata={
-            "description": "The system prompt to use for the agent's interactions. "
-            "This prompt sets the context and behavior for the agent."
-        },
-    )
-
-    model: str = field(
-        default="zhipu/glm-4-plus",
-        metadata={
-            "description": "The name of the language model to use for the agent's main interactions. "
-            "Should be in the form: provider/model-name."
-        },
-    )
 

src/agents/react/graph.py

@@ -2,28 +2,22 @@ import asyncio
 import uuid
 
 
+from src.utils import logger
 from src.agents.registry import BaseAgent
 from src.agents.react.configuration import ReActConfiguration
 
 class ReActAgent(BaseAgent):
     name = "react"
     description = "A react agent that can answer questions and help with tasks."
+    config_schema = ReActConfiguration
 
-    def get_graph(self, **kwargs):
+    async def get_graph(self, **kwargs):
-        """构建图"""
         from .workflows import graph
         return graph
 
-def main():
-    agent = ReActAgent(ReActConfiguration())
 
-    thread_id = str(uuid.uuid4())
-    config = {"configurable": {"thread_id": thread_id}}
 
-    from src.agents.utils import agent_cli
-    asyncio.run(agent_cli(agent, config))
 
 
 if __name__ == "__main__":
-    main()
+    pass
-    # asyncio.run(main())

src/agents/react/workflows.py

@@ -1,8 +1,7 @@
 import os
 
 from langchain_openai import ChatOpenAI
-
+from langgraph.checkpoint.memory import InMemorySaver
-from src import graph_base
 
 model = ChatOpenAI(model="glm-4-plus",
                    api_key=os.getenv("ZHIPUAI_API_KEY"),
@@ -23,4 +22,4 @@ tools = []
 
 from langgraph.prebuilt import create_react_agent
 
-graph = create_react_agent(model, tools=tools)
+graph = create_react_agent(model, tools=tools, checkpointer=InMemorySaver())

src/agents/registry.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import os
 import yaml
+import uuid
 from pathlib import Path
 from typing import Type, Annotated, Optional, TypedDict
 from enum import Enum
@@ -139,6 +140,23 @@ class Configuration(dict):
         return confs
 
 
+    thread_id: str = field(
+        default_factory=lambda: str(uuid.uuid4()),
+        metadata={
+            "name": "线程ID",
+            "configurable": False,
+            "description": "用来描述智能体的角色和行为"
+        },
+    )
+
+    user_id: str = field(
+        default_factory=lambda: str(uuid.uuid4()),
+        metadata={
+            "name": "用户ID",
+            "configurable": False,
+            "description": "用来描述智能体的角色和行为"
+        },
+    )
 
 class BaseAgent():
 
@@ -146,66 +164,85 @@ class BaseAgent():
     定义一个基础 Agent 供 各类 graph 继承
     """
 
-    name: str = field(default="base_agent")
+    name = "base_agent"
-    description: str = field(default="base_agent")
+    description = "base_agent"
     config_schema: Configuration = Configuration
     requirements: list[str]
 
     def __init__(self, **kwargs):
         self.check_requirements()
 
-    @classmethod
+    async def get_info(self):
-    def get_info(cls):
         return {
-            "name": cls.name,
+            "name": self.name if hasattr(self, "name") else "Unknown",
-            "description": cls.description,
+            "description": self.description if hasattr(self, "description") else "Unknown",
-            "config_schema": cls.config_schema.to_dict(),
+            "config_schema": self.config_schema.to_dict(),
-            "requirements": cls.requirements if hasattr(cls, "requirements") else [],
+            "requirements": self.requirements if hasattr(self, "requirements") else [],
-            "all_tools": cls.all_tools if hasattr(cls, "all_tools") else [],
+            "all_tools": self.all_tools if hasattr(self, "all_tools") else [],
+            "has_checkpointer": await self.check_checkpointer(),
+            "met_requirements": self.check_requirements(),
         }
 
     def check_requirements(self):
         if not hasattr(self, "requirements") or not self.requirements:
-            return
+            return True
         for requirement in self.requirements:
             if requirement not in os.environ:
                 raise ValueError(f"没有配置{requirement} 环境变量，请在 src/.env 文件中配置，并重新启动服务")
+        return True
 
     async def stream_values(self, messages: list[str], config_schema: RunnableConfig = None, **kwargs):
-        graph = await self.get_graph(config_schema=config_schema, **kwargs)
+        graph = await self.get_graph()
         logger.debug(f"stream_values: {config_schema}")
         for event in graph.astream({"messages": messages}, stream_mode="values", config=config_schema):
             yield event["messages"]
 
     async def stream_messages(self, messages: list[str], config_schema: RunnableConfig = None, **kwargs):
-        graph = await self.get_graph(config_schema=config_schema, **kwargs)
+        graph = await self.get_graph()
         logger.debug(f"stream_messages: {config_schema}")
 
         async for msg, metadata in graph.astream({"messages": messages}, stream_mode="messages", config=config_schema):
             yield msg, metadata
 
+    async def check_checkpointer(self):
+        app = await self.get_graph()
+        if not hasattr(app, "checkpointer") or app.checkpointer is None:
+            logger.warning(f"智能体 {self.name} 的 Graph 未配置 checkpointer，无法获取历史记录")
+            return False
+        return True
+
     async def get_history(self, user_id, thread_id) -> list[dict]:
         """获取历史消息"""
-        # 获取LangGraph应用实例
+        try:
-        app = await self.get_graph()
+            app = await self.get_graph()
-        # 构建配置信息
-        config = {"configurable": {"thread_id": thread_id, "user_id": user_id}}
-        # 获取状态
-        state = await app.aget_state(config)
 
-        result = []
+            if not await self.check_checkpointer():
-        if state:
+                return []
-            messages = state.values.get('messages', [])
-            for msg in messages:
-                if hasattr(msg, 'model_dump'):
-                    msg_dict = msg.model_dump()  # 转换成字典
-                else:
-                    # 如果消息没有model_dump方法，尝试转成dict
-                    msg_dict = dict(msg) if hasattr(msg, '__dict__') else {"content": str(msg)}
-                result.append(msg_dict)
 
-        return result
+            config = {"configurable": {"thread_id": thread_id, "user_id": user_id}}
+            state = await app.aget_state(config)
+
+            result = []
+            if state:
+                messages = state.values.get('messages', [])
+                for msg in messages:
+                    if hasattr(msg, 'model_dump'):
+                        msg_dict = msg.model_dump()  # 转换成字典
+                    else:
+                        msg_dict = dict(msg) if hasattr(msg, '__dict__') else {"content": str(msg)}
+                    result.append(msg_dict)
+
+            return result
+
+        except Exception as e:
+            logger.error(f"获取智能体 {self.name} 历史消息出错: {e}")
+            return []
 
     @abstractmethod
-    def get_graph(self, **kwargs) -> CompiledStateGraph:
+    async def get_graph(self, **kwargs) -> CompiledStateGraph:
+        """
+        获取并编译对话图实例。
+        必须确保在编译时设置 checkpointer，否则将无法获取历史记录。
+        例如: graph = workflow.compile(checkpointer=sqlite_checkpointer)
+        """
         pass

web/src/components/AgentChatComponent.vue

@@ -44,7 +44,7 @@
         <span>正在加载历史记录...</span>
       </div>
 
-      <div v-else-if="convs.length === 0" class="chat-examples">
+      <div v-else-if="convs.length === 0 && !onGoingConv.messages.length" class="chat-examples">
         <h1>{{ currentAgent ? currentAgent.name : '请选择一个智能体开始对话' }}</h1>
         <p>{{ currentAgent ? currentAgent.description : '不同的智能体有不同的专长和能力' }}</p>
       </div>

web/src/components/UserInfoComponent.vue

@@ -35,7 +35,7 @@ import { useRouter } from 'vue-router';
 import { useUserStore } from '@/stores/user';
 import { UserOutlined, LogoutOutlined } from '@ant-design/icons-vue';
 import { message } from 'ant-design-vue';
-import { CircleUser, LogOut } from 'lucide-vue-next';
+import { CircleUser, UserRoundCheck } from 'lucide-vue-next';
 
 const router = useRouter();
 const userStore = useUserStore();

web/src/views/AgentView.vue

@@ -59,14 +59,14 @@
       <div class="sidebar" :class="{ 'is-open': state.agentSiderbarConfigOpen }">
         <div class="agent-info">
           <h3 @click="toggleDebugMode">描述</h3>
-          <p>{{ agents[selectedAgentId]?.description }}</p>
+          <p>{{ selectedAgent.description }}</p>
+          <pre v-if="state.debug_mode">{{ selectedAgent }}</pre>
 
           <div v-if="selectedAgentId && configSchema" class="config-modal-content">
             <!-- 配置表单 -->
             <a-form :model="agentConfig" layout="vertical">
-              <div class="empty-config" v-if="state.isEmptyConfig">
+              <a-alert  v-if="state.isEmptyConfig" type="warning" message="该智能体没有配置项" show-icon/>
-                <a-alert type="warning" message="该智能体没有配置项" show-icon/>
+              <a-alert v-if="!selectedAgent.has_checkpointer" type="error" message="该智能体没有配置 Checkpointer，功能无法正常使用，参考：https://langchain-ai.github.io/langgraph/concepts/persistence/" show-icon/>
-              </div>
               <!-- 统一显示所有配置项 -->
               <template v-for="(value, key) in configurableItems" :key="key">
                 <a-form-item
@@ -185,7 +185,9 @@ const state = reactive({
     Object.keys(configurableItems.value).length === 0
   )
 });
-const configSchema = computed(() => agents.value[selectedAgentId.value]?.config_schema || {});
+
+const selectedAgent = computed(() => agents.value[selectedAgentId.value] || {});
+const configSchema = computed(() => selectedAgent.value.config_schema || {});
 const configurableItems = computed(() => configSchema.value.configurable_items || {});
 
 // 配置状态
@@ -617,6 +619,11 @@ const toggleSidebar = () => {
 .config-modal-content {
   max-height: 70vh;
   overflow-y: auto;
+  user-select: text;
+
+  div[role="alert"] {
+    margin-bottom: 10px;
+  }
 
   .description {
     font-size: 12px;