feat(agent): 添加智能体配置保存时的graph重载功能

在保存智能体配置时新增reload_graph选项，用于强制清空graph缓存并重新构建
修改前后端接口以支持该功能，并在UI中添加相应提示
为DeepAgent添加subagents_model配置项，优化子智能体模型选择

server/routers/chat_router.py

@@ -3,7 +3,7 @@ import json
 import traceback
 import uuid
 
-from fastapi import APIRouter, Body, Depends, HTTPException, UploadFile, File
+from fastapi import APIRouter, Body, Depends, HTTPException, Query, UploadFile, File
 from fastapi.responses import StreamingResponse
 from langchain.messages import AIMessageChunk, HumanMessage
 from langgraph.types import Command
@@ -849,7 +849,12 @@ async def resume_agent_chat(
 
 
 @chat.post("/agent/{agent_id}/config")
-async def save_agent_config(agent_id: str, config: dict = Body(...), current_user: User = Depends(get_required_user)):
+async def save_agent_config(
+    agent_id: str,
+    config: dict = Body(...),
+    reload_graph: bool = Query(False),
+    current_user: User = Depends(get_required_user),
+):
     """保存智能体配置到YAML文件（需要登录）"""
     try:
         # 获取Agent实例和配置类
@@ -860,6 +865,8 @@ async def save_agent_config(agent_id: str, config: dict = Body(...), current_use
         result = agent.context_schema.save_to_file(config, agent.module_name)
 
         if result:
+            if reload_graph:
+                agent_manager.get_agent(agent_id, reload_graph=True)
             return {"success": True, "message": f"智能体 {agent.name} 配置已保存"}
         else:
             raise HTTPException(status_code=500, detail="保存智能体配置失败")

src/agents/__init__.py

@@ -20,12 +20,16 @@ class AgentManager(metaclass=SingletonMeta):
         for agent_id in self._classes.keys():
             self.get_agent(agent_id)
 
-    def get_agent(self, agent_id, reload=False, **kwargs):
+    def get_agent(self, agent_id, reload=False, reload_graph=False, **kwargs):
         # 检查是否已经创建了该 agent 的实例
         if reload or agent_id not in self._instances:
             agent_class = self._classes[agent_id]
             self._instances[agent_id] = agent_class()
 
+        # 如果仅需要重新加载 graph，则清空 graph 缓存
+        if reload_graph and agent_id in self._instances:
+            self._instances[agent_id].reload_graph()
+
         return self._instances[agent_id]
 
     def get_agents(self):

src/agents/common/base.py

@@ -132,6 +132,11 @@ class BaseAgent:
             logger.error(f"获取智能体 {self.name} 历史消息出错: {e}")
             return []
 
+    def reload_graph(self):
+        """重置 graph 缓存，强制下次调用 get_graph 时重新构建"""
+        self.graph = None
+        logger.info(f"{self.name} graph 缓存已清空，将在下次调用时重新构建")
+
     @abstractmethod
     async def get_graph(self, **kwargs) -> CompiledStateGraph:
         """

src/agents/deep_agent/context.py

@@ -1,6 +1,7 @@
 """Deep Agent Context - 基于BaseContext的深度分析上下文配置"""
 
 from dataclasses import dataclass, field
+from typing import Annotated
 
 from src.agents.common.context import BaseContext
 
@@ -102,3 +103,10 @@ class DeepContext(BaseContext):
         default=DEEP_PROMPT,
         metadata={"name": "系统提示词", "description": "Deep智能体的角色和行为指导"},
     )
+    subagents_model: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field(
+        default="siliconflow/deepseek-ai/DeepSeek-V3.2",
+        metadata={
+            "name": "Sub-agent Model",
+            "description": "The model used by sub-agents (e.g., critique-agent, research-agent).",
+        },
+    )

src/agents/deep_agent/graph.py

@@ -10,6 +10,7 @@ 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.tools import search
 
+from .context import DeepContext
 from .prompts import DEEP_PROMPT
 
 search_tools = [search]
@@ -57,10 +58,12 @@ def context_aware_prompt(request: ModelRequest) -> str:
 class DeepAgent(BaseAgent):
     name = "深度分析智能体"
     description = "具备规划、深度分析和子智能体协作能力的智能体，可以处理复杂的多步骤任务"
+    context_schema = DeepContext
     capabilities = [
         "file_upload",
         "todo",
         "files",
+        "reload_graph",
     ]
 
     def __init__(self, **kwargs):
@@ -82,6 +85,7 @@ class DeepAgent(BaseAgent):
         context = self.context_schema.from_file(module_name=self.module_name)
 
         model = load_chat_model(context.model)
+        sub_model = load_chat_model(context.subagents_model)
         tools = await self.get_tools()
 
         # 使用 create_deep_agent 创建深度智能体
@@ -95,15 +99,14 @@ class DeepAgent(BaseAgent):
                 TodoListMiddleware(),
                 FilesystemMiddleware(),
                 SubAgentMiddleware(
-                    default_model=load_chat_model(context.model),
+                    default_model=sub_model,
                     default_tools=tools,
                     subagents=[critique_sub_agent, research_sub_agent],
                     default_middleware=[
-                        context_based_model,  # 动态模型选择
                         TodoListMiddleware(),
                         FilesystemMiddleware(),
                         SummarizationMiddleware(
-                            model=model,
+                            model=sub_model,
                             trigger=("tokens", 110000),
                             keep=("messages", 10),
                             trim_tokens_to_summarize=None,

web/src/apis/agent_api.js

@@ -126,10 +126,13 @@ export const agentApi = {
    * 保存智能体配置
    * @param {string} agentName - 智能体名称
    * @param {Object} config - 配置对象
+   * @param {Object} options - 额外参数 (e.g., { reload_graph: true })
    * @returns {Promise} - 保存结果
    */
-  saveAgentConfig: async (agentName, config) => {
+  saveAgentConfig: async (agentName, config, options = {}) => {
-    return apiAdminPost(`/api/chat/agent/${agentName}/config`, config)
+    const queryParams = new URLSearchParams(options).toString();
+    const url = `/api/chat/agent/${agentName}/config` + (queryParams ? `?${queryParams}` : '');
+    return apiAdminPost(url, config)
   },
 
   /**

web/src/components/AgentConfigSidebar.vue

@@ -55,7 +55,7 @@
                 <!-- 模型选择 -->
                 <div v-if="value.template_metadata.kind === 'llm'" class="model-selector">
                   <ModelSelectorComponent
-                    @select-model="handleModelChange"
+                    @select-model="(spec) => handleModelChange(key, spec)"
                     :model_spec="agentConfig[key] || ''"
                   />
                 </div>
@@ -219,7 +219,7 @@
       <div class="sidebar-footer" v-if="!isEmptyConfig">
         <div class="form-actions">
           <a-button @click="saveConfig" class="save-btn" :class="{'changed': agentStore.hasConfigChanges}">
-            保存配置
+            {{ needsReload ? '保存配置并重新加载' : '保存配置' }}
           </a-button>
         </div>
       </div>
@@ -333,6 +333,12 @@ 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) {
@@ -363,10 +369,10 @@ const getPlaceholder = (key, value) => {
   return `（默认: ${value.default}）`;
 };
 
-const handleModelChange = (spec) => {
+const handleModelChange = (key, spec) => {
   if (typeof spec !== 'string' || !spec) return;
   agentStore.updateAgentConfig({
-    model: spec
+    [key]: spec
   });
 };
 
@@ -529,7 +535,12 @@ const saveConfig = async () => {
       message.info('检测到无效配置项，已自动过滤');
     }
 
-    await agentStore.saveAgentConfig();
+    const options = {};
+    if (selectedAgent.value && selectedAgent.value.capabilities && selectedAgent.value.capabilities.includes('reload_graph')) {
+      options.reload_graph = true;
+    }
+
+    await agentStore.saveAgentConfig(options);
     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);

web/src/components/ToolCallingResult/ToolCallRenderer.vue

@@ -88,6 +88,7 @@ const isTaskResult = computed(() => {
 
 const isKnowledgeBaseResult = computed(() => {
   const currentTool = tool.value;
+
   if (currentTool && currentTool.metadata) {
     const metadata = currentTool.metadata;
     const hasKnowledgebaseTag = metadata.tag && metadata.tag.includes('knowledgebase');

web/src/stores/agent.js

@@ -249,13 +249,14 @@ export const useAgentStore = defineStore('agent', () => {
 
   /**
    * 保存智能体配置
+   * @param {Object} options - 额外参数 (e.g., { reload_graph: true })
    */
-  async function saveAgentConfig(agentId = null) {
+  async function saveAgentConfig(options = {}) {
-    const targetAgentId = agentId || selectedAgentId.value
+    const targetAgentId = selectedAgentId.value
     if (!targetAgentId) return
 
     try {
-      await agentApi.saveAgentConfig(targetAgentId, agentConfig.value)
+      await agentApi.saveAgentConfig(targetAgentId, agentConfig.value, options)
       originalAgentConfig.value = { ...agentConfig.value }
     } catch (err) {
       console.error('Failed to save agent config:', err)