refactor(agents): 移除reload_graph能力并简化agent初始化逻辑

统一agent初始化流程，直接从context获取system_prompt
清理不再使用的middleware导入和配置

Author: xerrors

server/routers/chat_router.py

@@ -865,7 +865,7 @@ def make_resume_chunk(content=None, **kwargs):
 async def save_agent_config(
     agent_id: str,
     config: dict = Body(...),
-    reload_graph: bool = Query(False),
+    reload_graph: bool = Query(True),
     current_user: User = Depends(get_required_user),
 ):
     """保存智能体配置到YAML文件（需要登录）"""

src/agents/chatbot/context.py

@@ -36,6 +36,7 @@ class Context(BaseContext):
             "options": lambda: list(MCP_SERVERS.keys()),
             "description": (
                 "MCP服务器列表，建议使用支持 SSE 的 MCP 服务器，"
-                "如果需要使用 uvx 或 npx 运行的服务器，也请在项目外部启动 MCP 服务器，并在项目中配置 MCP 服务器。"),
+                "如果需要使用 uvx 或 npx 运行的服务器，也请在项目外部启动 MCP 服务器，并在项目中配置 MCP 服务器。"
+            ),
         },
     )

src/agents/chatbot/graph.py

@@ -4,8 +4,6 @@
 from src.agents.common import BaseAgent, load_chat_model
 from src.agents.common.mcp import get_mcp_tools
 from src.agents.common.middlewares import (
-    context_aware_prompt,
-    context_based_model,
     inject_attachment_context,
 )
 from src.agents.common.tools import get_kb_based_tools
@@ -17,7 +15,7 @@
 class ChatbotAgent(BaseAgent):
     name = "智能体助手"
     description = "基础的对话机器人，可以回答问题，默认不使用任何工具，可在配置中启用需要的工具。"
-    capabilities = ["file_upload", "reload_graph"]  # 支持文件上传功能和重载图
+    capabilities = ["file_upload"]  # 支持文件上传功能
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
@@ -26,7 +24,6 @@ def __init__(self, **kwargs):
         self.context_schema = Context
 
     async def get_tools(self, tools: list[str] = None, mcps=None, knowledges=None):
-
         # 1. 基础工具 (从 context.tools 中筛选)
         all_basic_tools = get_tools()
         selected_tools = []
@@ -63,10 +60,9 @@ async def get_graph(self, **kwargs):
         graph = create_agent(
             model=load_chat_model(context.model),  # 使用 context 中的模型配置
             tools=await self.get_tools(context.tools, context.mcps, context.knowledges),
+            system_prompt=context.system_prompt,
             middleware=[
-                context_aware_prompt,  # 动态系统提示词
                 inject_attachment_context,  # 附件上下文注入
-                context_based_model,  # 动态模型选择
                 ModelRetryMiddleware(),  # 模型重试中间件
             ],
             checkpointer=await self._get_checkpointer(),
@@ -82,4 +78,4 @@ def main():
 
 if __name__ == "__main__":
     main()
-    # asyncio.run(main())
+    # asyncio.run(main())

src/agents/common/context.py

@@ -49,7 +49,7 @@ def update(self, data: dict):
         metadata={
             "name": "智能体模型",
             "options": [],
-            "description": "智能体的驱动模型，建议选择 Agent 能力较强的模型，不建议使用小参数模型。"
+            "description": "智能体的驱动模型，建议选择 Agent 能力较强的模型，不建议使用小参数模型。",
         },
     )
 

src/agents/deep_agent/graph.py

@@ -7,7 +7,7 @@
 from langchain.agents.middleware import ModelRequest, SummarizationMiddleware, TodoListMiddleware, dynamic_prompt
 
 from src.agents.common import BaseAgent, load_chat_model
-from src.agents.common.middlewares import context_based_model, inject_attachment_context
+from src.agents.common.middlewares import inject_attachment_context
 from src.agents.common.tools import search
 
 from .context import DeepContext
@@ -64,7 +64,6 @@ class DeepAgent(BaseAgent):
         "file_upload",
         "todo",
         "files",
-        "reload_graph",
     ]
 
     def __init__(self, **kwargs):
@@ -93,8 +92,8 @@ async def get_graph(self, **kwargs):
         graph = create_agent(
             model=model,
             tools=tools,
+            system_prompt=context.system_prompt,
             middleware=[
-                context_based_model,  # 动态模型选择
                 context_aware_prompt,  # 动态系统提示词
                 inject_attachment_context,  # 附件上下文注入
                 TodoListMiddleware(),
@@ -104,8 +103,8 @@ async def get_graph(self, **kwargs):
                     default_tools=tools,
                     subagents=[critique_sub_agent, research_sub_agent],
                     default_middleware=[
-                        TodoListMiddleware(),
-                        FilesystemMiddleware(),
+                        TodoListMiddleware(),  # 子智能体也有 todo 列表
+                        FilesystemMiddleware(),  # 当前的两个文件系统是隔离的
                         SummarizationMiddleware(
                             model=sub_model,
                             trigger=("tokens", 110000),

src/agents/mini_agent/graph.py

@@ -2,7 +2,6 @@
 
 from src import config
 from src.agents.common import BaseAgent, get_buildin_tools, load_chat_model
-from src.agents.common.middlewares import context_aware_prompt, context_based_model
 
 
 class MiniAgent(BaseAgent):
@@ -19,11 +18,13 @@ async def get_graph(self, **kwargs):
         if self.graph:
             return self.graph
 
+        context = self.context_schema.from_file(module_name=self.module_name)
+
         # 创建 MiniAgent
         graph = create_agent(
-            model=load_chat_model(config.default_model),  # 实际会被覆盖
+            model=load_chat_model(config.default_model),
+            system_prompt=context.system_prompt,
             tools=self.get_tools(),
-            middleware=[context_aware_prompt, context_based_model],
             checkpointer=await self._get_checkpointer(),
         )
 

src/agents/reporter/graph.py

@@ -2,7 +2,6 @@
 
 from src import config
 from src.agents.common import BaseAgent, get_mcp_tools, load_chat_model
-from src.agents.common.middlewares import context_aware_prompt, context_based_model
 from src.agents.common.toolkits.mysql import get_mysql_tools
 from src.utils import logger
 
@@ -25,11 +24,13 @@ async def get_graph(self, **kwargs):
         if self.graph:
             return self.graph
 
+        context = self.context_schema.from_file(module_name=self.module_name)
+
         # 创建 SqlReporterAgent
         graph = create_agent(
             model=load_chat_model(config.default_model),  # 默认模型，会被 middleware 覆盖
+            system_prompt=context.system_prompt,
             tools=await self.get_tools(),
-            middleware=[context_aware_prompt, context_based_model],
             checkpointer=await self._get_checkpointer(),
         )
 

web/src/components/AgentConfigSidebar.vue

@@ -220,7 +220,7 @@
       <div class="sidebar-footer" v-if="!isEmptyConfig">
         <div class="form-actions">
           <a-button @click="saveConfig" class="save-btn" :class="{'changed': agentStore.hasConfigChanges}">
-            {{ needsReload ? '保存配置并重新加载' : '保存配置' }}
+            保存配置并重新加载
           </a-button>
         </div>
       </div>
@@ -334,12 +334,6 @@ const isEmptyConfig = computed(() => {
   return !selectedAgentId.value || Object.keys(configurableItems.value).length === 0;
 });
 
-const needsReload = computed(() => {
-  return selectedAgent.value &&
-         selectedAgent.value.capabilities &&
-         selectedAgent.value.capabilities.includes('reload_graph');
-});
-
 const filteredTools = computed(() => {
   const toolsList = availableTools.value ? Object.values(availableTools.value) : [];
   if (!toolsSearchText.value) {
@@ -536,12 +530,7 @@ const saveConfig = async () => {
       message.info('检测到无效配置项，已自动过滤');
     }
 
-    const options = {};
-    if (selectedAgent.value && selectedAgent.value.capabilities && selectedAgent.value.capabilities.includes('reload_graph')) {
-      options.reload_graph = true;
-    }
-
-    await agentStore.saveAgentConfig(options);
+    await agentStore.saveAgentConfig();
     message.success('配置已保存到服务器');
   } catch (error) {
     console.error('保存配置到服务器出错:', error);