From ba30161559ea2c15164934826689c38521d0ae49 Mon Sep 17 00:00:00 2001
From: zhaoying <yzhao96@best-inc.com>
Date: Thu, 15 Jan 2026 14:58:54 +0800
Subject: [PATCH 01/12] fix(web): stream api support refresh token

---
 web/src/utils/stream.ts | 131 ++++++++++++++++++++++++++--------------
 1 file changed, 87 insertions(+), 44 deletions(-)

diff --git a/web/src/utils/stream.ts b/web/src/utils/stream.ts
index 7688cdd5..e4179e25 100644
--- a/web/src/utils/stream.ts
+++ b/web/src/utils/stream.ts
@@ -1,8 +1,47 @@
 import { message } from 'antd';
 import i18n from '@/i18n'
 import { cookieUtils } from './request'
+import { refreshToken } from '@/api/user'
+import { clearAuthData } from './auth'
 const API_PREFIX = '/api'
 
+// Token refresh state
+let isRefreshing = false;
+let refreshPromise: Promise<string> | null = null;
+
+// Refresh token function for SSE
+const refreshTokenForSSE = async (): Promise<string> => {
+  if (isRefreshing && refreshPromise) {
+    return refreshPromise;
+  }
+  
+  isRefreshing = true;
+  refreshPromise = (async () => {
+    try {
+      const refresh_token = cookieUtils.get('refreshToken');
+      if (!refresh_token) {
+        throw new Error(i18n.t('common.refreshTokenNotExist'));
+      }
+      const response: any = await refreshToken();
+      const newToken = response.access_token;
+      cookieUtils.set('authToken', newToken);
+      return newToken;
+    } catch (error) {
+      clearAuthData();
+      message.warning(i18n.t('common.loginExpired'));
+      if (!window.location.hash.includes('#/login')) {
+        window.location.href = `/#/login`;
+      }
+      throw error;
+    } finally {
+      isRefreshing = false;
+      refreshPromise = null;
+    }
+  })();
+  
+  return refreshPromise;
+};
+
 export interface SSEMessage {
   event?: string
   data?: string | object
@@ -66,62 +105,66 @@ function parseDataContent(dataContent: string): string | object {
   }
 }
 
+const makeSSERequest = async (url: string, data: any, token: string, config = { headers: {} }) => {
+  return fetch(`${API_PREFIX}${url}`, {
+    method: 'POST',
+    headers: {
+      'Content-Type': 'application/json',
+      'Authorization': `Bearer ${token}`,
+      ...config.headers,
+    },
+    body: JSON.stringify(data)
+  });
+};
 
 export const handleSSE = async (url: string, data: any, onMessage?: (data: SSEMessage[]) => void, config = { headers: {} }) => {
   try {
-    const token = cookieUtils.get('authToken');
-    const response = await fetch(`${API_PREFIX}${url}`, {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-        'Authorization': `Bearer ${token}`,
-        ...config.headers,
-      },
-      body: JSON.stringify(data)
-    });
+    let token = cookieUtils.get('authToken');
+    let response = await makeSSERequest(url, data, token || '', config);
 
-    const { status } = response
-
-    switch(status) {
+    switch (response.status) {
       case 401:
         if (url?.includes('/public')) {
           return message.warning(i18n.t('common.publicApiCannotRefreshToken'));
         }
-        window.location.href = `/#/login`;
-        break;
-      default:
-        if (!response.body) throw new Error('No response body');
-        
-        const reader = response.body.getReader();
-        const decoder = new TextDecoder();
-        let buffer = ''; // 添加缓冲区来处理不完整的消息
-        
-        while (true) {
-          const { done, value } = await reader.read();
-          if (done) break;
-          
-          const chunk = decoder.decode(value, { stream: true });
-          buffer += chunk;
-          
-          // 处理完整的事件
-          const events = buffer.split('\n\n');
-          buffer = events.pop() || ''; // 保留最后一个可能不完整的事件
-          
-          for (const event of events) {
-            if (event.trim() && onMessage) {
-              onMessage(parseSSEToJSON(event) ?? {});
-            }
-          }
-        }
-        
-        // 处理剩余的缓冲区内容
-        if (buffer.trim() && onMessage) {
-          onMessage(parseSSEToJSON(buffer) ?? {});
+        try {
+          const newToken = await refreshTokenForSSE();
+          response = await makeSSERequest(url, data, newToken, config);
+        } catch (refreshError) {
+          return;
         }
         break;
     }
+    if (!response.body) throw new Error('No response body');
+
+    const reader = response.body.getReader();
+    const decoder = new TextDecoder();
+    let buffer = ''; // 添加缓冲区来处理不完整的消息
+
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+
+      const chunk = decoder.decode(value, { stream: true });
+      buffer += chunk;
+
+      // 处理完整的事件
+      const events = buffer.split('\n\n');
+      buffer = events.pop() || ''; // 保留最后一个可能不完整的事件
+
+      for (const event of events) {
+        if (event.trim() && onMessage) {
+          onMessage(parseSSEToJSON(event) ?? {});
+        }
+      }
+    }
+
+    // 处理剩余的缓冲区内容
+    if (buffer.trim() && onMessage) {
+      onMessage(parseSSEToJSON(buffer) ?? {});
+    }
   } catch (error) {
     console.error('Request failed:', error);
     throw error;
   }
-}
\ No newline at end of file
+};
\ No newline at end of file

From 67d0b196b8202215e5178baa8877d58268eda33e Mon Sep 17 00:00:00 2001
From: zhaoying <yzhao96@best-inc.com>
Date: Fri, 16 Jan 2026 13:56:36 +0800
Subject: [PATCH 02/12] =?UTF-8?q?fix(web):=20loop=E3=80=81iteration=20sub?=
 =?UTF-8?q?=20node=20move=20bugfix?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../views/Workflow/hooks/useWorkflowGraph.ts  | 90 +++++++++++++++++--
 1 file changed, 85 insertions(+), 5 deletions(-)

diff --git a/web/src/views/Workflow/hooks/useWorkflowGraph.ts b/web/src/views/Workflow/hooks/useWorkflowGraph.ts
index 77ea56ca..75bd2517 100644
--- a/web/src/views/Workflow/hooks/useWorkflowGraph.ts
+++ b/web/src/views/Workflow/hooks/useWorkflowGraph.ts
@@ -666,6 +666,77 @@ export const useWorkflowGraph = ({
       graphRef.current.resize(containerRef.current.offsetWidth, containerRef.current.offsetHeight);
     }
   };
+  
+  const nodeChangePosition = ({ node, options }: { node: Node; options: { skipParentHandler?: boolean } }) => {
+    const embedPadding = 50; // Define the embed padding constant
+    if (options.skipParentHandler) {
+      return
+    }
+
+    const children = node.getChildren()
+    if (children && children.length) {
+      node.prop('originPosition', node.getPosition())
+    }
+
+    const parent = node.getParent()
+    if (parent && parent.isNode()) {
+      let originSize = parent.prop('originSize')
+      if (originSize == null) {
+        originSize = parent.getSize()
+        parent.prop('originSize', originSize)
+      }
+
+      let originPosition = parent.prop('originPosition')
+      if (originPosition == null) {
+        originPosition = parent.getPosition()
+        parent.prop('originPosition', originPosition)
+      }
+
+      let x = originPosition.x
+      let y = originPosition.y
+      let cornerX = originPosition.x + originSize.width
+      let cornerY = originPosition.y + originSize.height
+      let hasChange = false
+
+      const children = parent.getChildren()
+      if (children) {
+        children.forEach((child) => {
+          const bbox = child.getBBox().inflate(embedPadding)
+          const corner = bbox.getCorner()
+
+          if (bbox.x < x) {
+            x = bbox.x
+            hasChange = true
+          }
+
+          if (bbox.y < y) {
+            y = bbox.y
+            hasChange = true
+          }
+
+          if (corner.x > cornerX) {
+            cornerX = corner.x
+            hasChange = true
+          }
+
+          if (corner.y > cornerY) {
+            cornerY = corner.y
+            hasChange = true
+          }
+        })
+      }
+
+      if (hasChange) {
+        parent.prop(
+          {
+            position: { x, y },
+            size: { width: cornerX - x, height: cornerY - y },
+          },
+          { skipParentHandler: true },
+        )
+      }
+    }
+  }
 
   // 初始化
   const init = () => {
@@ -764,10 +835,7 @@ export const useWorkflowGraph = ({
         },
       },
       embedding: {
-        enabled: true,
-        validate (this) {
-          return false
-        }
+        enabled: false,
       },
       translating: {
         restrict(view) {
@@ -783,6 +851,17 @@ export const useWorkflowGraph = ({
           return null
         },
       },
+      highlighting: {
+        embedding: {
+          name: 'stroke',
+          args: {
+            padding: -1,
+            attrs: {
+              stroke: '#73d13d',
+            },
+          },
+        },
+      },
     });
     // 使用插件
     setupPlugins();
@@ -824,7 +903,8 @@ export const useWorkflowGraph = ({
     // 监听缩放事件
     graphRef.current.on('scale', scaleEvent);
     // 监听节点移动事件
-    graphRef.current.on('node:moved', nodeMoved);
+    // graphRef.current.on('node:moved', nodeMoved);
+    graphRef.current.on('node:change:position', nodeChangePosition);
 
     // 监听画布变化事件
     const events = [

From a6a18b73046a66a16690260db42f4635b77df7af Mon Sep 17 00:00:00 2001
From: zhaoying <yzhao96@best-inc.com>
Date: Fri, 16 Jan 2026 13:57:46 +0800
Subject: [PATCH 03/12] feat(web): menu order adjustment

---
 web/src/store/menu.json | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/web/src/store/menu.json b/web/src/store/menu.json
index b49788a8..62f6c13c 100644
--- a/web/src/store/menu.json
+++ b/web/src/store/menu.json
@@ -332,21 +332,6 @@
         }
       ]
     },
-    {
-      "id": 19,
-      "parent": 0,
-      "code": "member",
-      "label": "成员管理",
-      "i18nKey": "menu.memberManagement",
-      "path": "/member",
-      "enable": true,
-      "display": true,
-      "level": 1,
-      "sort": 0,
-      "icon": null,
-      "iconActive": null,
-      "subs": null
-    },
     {
       "id": 10,
       "parent": 0,
@@ -377,6 +362,21 @@
       "iconActive": null,
       "subs": null
     },
+    {
+      "id": 19,
+      "parent": 0,
+      "code": "member",
+      "label": "成员管理",
+      "i18nKey": "menu.memberManagement",
+      "path": "/member",
+      "enable": true,
+      "display": true,
+      "level": 1,
+      "sort": 0,
+      "icon": null,
+      "iconActive": null,
+      "subs": null
+    },
     {
       "id": 12,
       "parent": 0,

From c2c2b306a28eade36e8391d53d6aff3d43a5902c Mon Sep 17 00:00:00 2001
From: zhaoying <yzhao96@best-inc.com>
Date: Fri, 16 Jan 2026 15:48:02 +0800
Subject: [PATCH 04/12] refactor: agent config refactor

---
 web/src/i18n/zh.ts                            |   2 +-
 web/src/views/ApplicationConfig/Agent.tsx     | 209 ++++++------------
 .../ApplicationConfig/components/Card.tsx     |   3 +
 .../components/ChatVariableConfigModal.tsx    | 101 +++++++++
 .../components/{ => Knowledge}/Knowledge.tsx  | 105 +++++----
 .../{ => Knowledge}/KnowledgeConfigModal.tsx  |  39 ++--
 .../KnowledgeGlobalConfigModal.tsx            |  19 +-
 .../{ => Knowledge}/KnowledgeListModal.tsx    |  17 +-
 .../components/Knowledge/types.ts             |  30 +++
 .../components/{ => ToolList}/ToolList.tsx    |  19 +-
 .../components/{ => ToolList}/ToolModal.tsx   |   0
 .../components/ToolList/types.ts              |  26 +++
 .../components/VariableList.tsx               | 131 -----------
 .../{ => VariableList}/ApiExtensionModal.tsx  |   2 +-
 .../{ => VariableList}/VariableEditModal.tsx  |  11 +-
 .../components/VariableList/VariableList.tsx  | 110 +++++++++
 .../components/VariableList/types.ts          |  28 +++
 web/src/views/ApplicationConfig/types.ts      |  92 +-------
 18 files changed, 501 insertions(+), 443 deletions(-)
 create mode 100644 web/src/views/ApplicationConfig/components/ChatVariableConfigModal.tsx
 rename web/src/views/ApplicationConfig/components/{ => Knowledge}/Knowledge.tsx (54%)
 rename web/src/views/ApplicationConfig/components/{ => Knowledge}/KnowledgeConfigModal.tsx (76%)
 rename web/src/views/ApplicationConfig/components/{ => Knowledge}/KnowledgeGlobalConfigModal.tsx (86%)
 rename web/src/views/ApplicationConfig/components/{ => Knowledge}/KnowledgeListModal.tsx (88%)
 create mode 100644 web/src/views/ApplicationConfig/components/Knowledge/types.ts
 rename web/src/views/ApplicationConfig/components/{ => ToolList}/ToolList.tsx (93%)
 rename web/src/views/ApplicationConfig/components/{ => ToolList}/ToolModal.tsx (100%)
 create mode 100644 web/src/views/ApplicationConfig/components/ToolList/types.ts
 delete mode 100644 web/src/views/ApplicationConfig/components/VariableList.tsx
 rename web/src/views/ApplicationConfig/components/{ => VariableList}/ApiExtensionModal.tsx (99%)
 rename web/src/views/ApplicationConfig/components/{ => VariableList}/VariableEditModal.tsx (96%)
 create mode 100644 web/src/views/ApplicationConfig/components/VariableList/VariableList.tsx
 create mode 100644 web/src/views/ApplicationConfig/components/VariableList/types.ts

diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts
index 028202d1..6b46084f 100644
--- a/web/src/i18n/zh.ts
+++ b/web/src/i18n/zh.ts
@@ -658,8 +658,8 @@ export const zh = {
       priority: '结构化整合',
       addTool: '添加工具',
       tool: '工具',
+      variableConfig: '配置变量'
     },
-    // 角色管理相关翻译
     role: {
       roleManagement: '角色管理',
       roleId: '角色ID',
diff --git a/web/src/views/ApplicationConfig/Agent.tsx b/web/src/views/ApplicationConfig/Agent.tsx
index 92170d55..9aab1110 100644
--- a/web/src/views/ApplicationConfig/Agent.tsx
+++ b/web/src/views/ApplicationConfig/Agent.tsx
@@ -13,26 +13,25 @@ import type {
   Config,
   ModelConfig,
   AgentRef,
-  KnowledgeBase,
-  KnowledgeConfig,
-  Variable,
   MemoryConfig,
   AiPromptModalRef,
   Source,
-  ToolOption
+  ChatVariableConfigModalRef
 } from './types'
+import type { Variable } from './components/VariableList/types'
+import type { KnowledgeConfig } from './components/Knowledge/types'
 import type { Model } from '@/views/ModelManagement/types'
 import { getModelList } from '@/api/models';
 import { saveAgentConfig } from '@/api/application'
-import Knowledge from './components/Knowledge'
-import VariableList from './components/VariableList'
+import Knowledge from './components/Knowledge/Knowledge'
+import VariableList from './components/VariableList/VariableList'
 import { getApplicationConfig } from '@/api/application'
-import { getKnowledgeBaseList } from '@/api/knowledgeBase'
 import { memoryConfigListUrl } from '@/api/memory'
 import CustomSelect from '@/components/CustomSelect'
 import aiPrompt from '@/assets/images/application/aiPrompt.png'
 import AiPromptModal from './components/AiPromptModal'
-import ToolList from './components/ToolList'
+import ToolList from './components/ToolList/ToolList'
+import ChatVariableConfigModal from './components/ChatVariableConfigModal';
 
 const DescWrapper: FC<{desc: string, className?: string}> = ({desc, className}) => {
   return (
@@ -66,7 +65,7 @@ const SwitchWrapper: FC<{ title: string, desc?: string, name: string | string[];
     </div>
   )
 }
-const SelectWrapper: FC<{ title: string, desc: string, name: string, url: string }> = ({ title, desc, name, url }) => {
+const SelectWrapper: FC<{ title: string, desc: string, name: string | string[], url: string }> = ({ title, desc, name, url }) => {
   const { t } = useTranslation();
   return (
     <>
@@ -77,6 +76,7 @@ const SelectWrapper: FC<{ title: string, desc: string, name: string, url: string
         className="rb:mb-0!"
       >
         <CustomSelect
+          placeholder={t('common.pleaseSelect')}
           url={url}
           hasAll={false}
           valueKey='config_id'
@@ -99,54 +99,22 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
   const [modelList, setModelList] = useState<Model[]>([])
   const [defaultModel, setDefaultModel] = useState<Model | null>(null)
   const [chatList, setChatList] = useState<ChatData[]>([])
-  const [formData, setFormData] = useState<{
-    default_model_config_id?: string,
-    model_parameters?: Config['model_parameters'],
-    tools: ToolOption[],
-  } | null>(null)
-  const values = Form.useWatch<{
-    memoryEnabled: boolean;
-    memory_content?: string | number;
-  } & Config>([], form)
-
-  const [knowledgeConfig, setKnowledgeConfig] = useState<KnowledgeConfig>({ knowledge_bases: [] })  
-  const [variableList, setVariableList] = useState<Variable[]>([])  
+  const values = Form.useWatch<Config>([], form) 
   const [isSave, setIsSave] = useState(false)
   const initialized = useRef(false)
-  const [toolList, setToolList] = useState<ToolOption[]>([])
   
   // 初始化完成标记
   useEffect(() => {
-    if (data && values && formData) {
+    if (data) {
       initialized.current = true
     }
-  }, [data, values, formData])
+  }, [data])
 
-  useEffect(() => {
-    if (!initialized.current) return
-    if (isSave) return
-    setIsSave(true)
-  }, [knowledgeConfig])
-  useEffect(() => {
-    if (!initialized.current) return
-    if (isSave) return
-    setIsSave(true)
-  }, [variableList])
-  useEffect(() => {
-    if (!initialized.current) return
-    if (isSave) return
-    setIsSave(true)
-  }, [formData])
   useEffect(() => {
     if (!initialized.current) return
     if (isSave) return
     setIsSave(true)
   }, [values])
-  useEffect(() => {
-    if (!initialized.current) return
-    if (isSave) return
-    setIsSave(true)
-  }, [toolList])
 
   useEffect(() => {
     getModels()
@@ -157,68 +125,19 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
     setLoading(true)
     getApplicationConfig(id as string).then(res => {
       const response = res as Config
-      setData({
-        ...response,
-        tools: Array.isArray(response.tools) ? response.tools : []
-      })
-      const { memory, tools } = response
+      let allTools = Array.isArray(response.tools) ? response.tools : []
       form.setFieldsValue({
         ...response,
-        memoryEnabled: memory?.enabled || false,
-        memory_content: memory?.memory_content ? Number(memory?.memory_content) : undefined,
-        tools: Array.isArray(tools) ? tools : []
+        tools: allTools
       })
-      setFormData({
-        default_model_config_id: response.default_model_config_id,
-        model_parameters: response.model_parameters || {},
-        tools: Array.isArray(tools) ? tools : []
+      setData({
+        ...response,
+        tools: allTools
       })
-      if (response?.knowledge_retrieval?.knowledge_bases?.length) {
-        getDefaultKnowledgeList(response)
-      }
-      if (response?.tools?.length) {
-        setToolList(response?.tools)
-      }
     }).finally(() => {
       setLoading(false)
     })
   }
-  const getDefaultKnowledgeList = (data: Config) => {
-    if (!data || !data.knowledge_retrieval || !data.knowledge_retrieval?.knowledge_bases?.length) {
-      return
-    }
-    const initialList = [...(data?.knowledge_retrieval?.knowledge_bases || [])]
-    getKnowledgeBaseList(undefined, {
-      kb_ids: initialList.map(vo => vo.kb_id).join(','),
-      page: 1,
-      pagesize: 100,
-    })
-      .then(res => {
-        const list = res.items || []
-        const knowledge_bases: KnowledgeBase[] = list.map(item => {
-          const filterItem = initialList.find(vo => vo.kb_id === item.id)
-          return {
-            ...item,
-            ...filterItem
-          }
-        })
-        setKnowledgeConfig(prev => ({
-          ...prev,
-          knowledge_bases: [...knowledge_bases]
-        }))
-        setData((prev) => {
-          prev = prev as Config
-          const knowledge_retrieval: KnowledgeConfig = {
-            ...(prev?.knowledge_retrieval || {}),
-            knowledge_bases: [...knowledge_bases]
-          }
-          return {
-            ...(prev || {}),
-            knowledge_retrieval
-          }
-        })
-      })
-  }
 
   const refresh = (vo: ModelConfig, type: Source) => {
     if (type === 'model') {
@@ -227,15 +146,7 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
         default_model_config_id,
         model_parameters: {...rest}
       })
-      setFormData((prevState) => {
-        const prev = prevState as Config
-        return {
-          ...(prev || {}),
-          default_model_config_id,
-          model_parameters: {...rest}
-        };
-      })
-      if (default_model_config_id === formData?.default_model_config_id) {
+      if (default_model_config_id === values?.default_model_config_id) {
         setChatList([{
           label: vo.label || '',
           model_config_id: default_model_config_id || '',
@@ -279,24 +190,20 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
   // 保存Agent配置
   const handleSave = (flag = true) => {
     if (!isSave || !data) return Promise.resolve()
-    const { memoryEnabled, memory_content, ...rest } = values
-    const { knowledge_bases = [], ...knowledgeRest } = knowledgeConfig || {}
-
-    
+    const { memory, knowledge_retrieval, tools, ...rest } = values
+    const { knowledge_bases = [], ...knowledgeRest } = knowledge_retrieval || {}
+    const { memory_content } = memory || {}
     // 从原数据中获取memory的其他必要属性
     const originalMemory = data.memory || ({} as MemoryConfig)
     
     const params: Config = {
       ...data,
       ...rest,
-      ...(formData || {}),
       memory: {
         ...originalMemory,
-        enabled: memoryEnabled,
+        ...memory,
         memory_content: memory_content ? String(memory_content) : '',
-        max_history: originalMemory.max_history || '',
       },
-      variables: variableList || [],
       knowledge_retrieval: knowledge_bases.length > 0 ? {
         ...data.knowledge_retrieval,
         ...knowledgeRest,
@@ -305,14 +212,12 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
           ...(item.config || {})
         }))
       } as KnowledgeConfig : null,
-      tools: toolList.map(vo => ({
+      tools: tools.map(vo => ({
         tool_id: vo.tool_id,
         operation: vo.operation,
         enabled: vo.enabled
       }))
     }
-
-    console.log('params', rest, params)
     
     return new Promise((resolve, reject) => {
       saveAgentConfig(data.app_id, params)
@@ -338,8 +243,8 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
     modelConfigModalRef.current?.handleOpen('chat')
   }
   useEffect(() => {
-    if (formData?.default_model_config_id && modelList.length > 0) {
-      const filterValue = modelList.find(item => item.id === formData.default_model_config_id)
+    if (values?.default_model_config_id && modelList.length > 0) {
+      const filterValue = modelList.find(item => item.id === values.default_model_config_id)
       setDefaultModel(filterValue as Model | null)
       setChatList([{
         label: filterValue?.name || '',
@@ -348,7 +253,7 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
         list: []
       }])
     }
-  }, [modelList, formData?.default_model_config_id])
+  }, [modelList, values?.default_model_config_id])
 
   useImperativeHandle(ref, () => ({
     handleSave
@@ -360,8 +265,31 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
   }
   const updatePrompt = (value: string) => {
     form.setFieldValue('system_prompt', value)
+    const variables = value.match(/\{\{([^}]+)\}\}/g)?.map(match => match.slice(2, -2)) || []
+    const uniqueVariables = [...new Set(variables)]
+    const newVariableList: Variable[] = uniqueVariables.map((name, index) => ({
+      index,
+      type: 'text',
+      name,
+      display_name: name,
+      required: false
+    }))
+    updateVariableList(newVariableList)
   }
 
+  const updateVariableList = (list: Variable[]) => {
+    form.setFieldValue('variables', [...list])
+    setChatVariables([...list])
+  }
+  const chatVariableConfigModalRef = useRef<ChatVariableConfigModalRef>(null)
+  const [chatVariables, setChatVariables] = useState<Variable[]>([])
+  const handleOpenVariableConfig = () => {
+    chatVariableConfigModalRef.current?.handleOpen(chatVariables)
+  }
+  const handleSaveChatVariable = (values: Variable[]) => {
+    setChatVariables(values)
+  }
+  console.log('values', values)
   return (
     <>
       {loading && <Spin fullscreen></Spin>}
@@ -379,8 +307,9 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
             </Space>
           </div>
           <Form form={form}>
+            <Form.Item name="default_model_config_id" hidden noStyle></Form.Item>
+            <Form.Item name="model_parameters" hidden noStyle></Form.Item>
             <Space size={16} direction="vertical" style={{ width: '100%' }}>
-              {/* 提示词 */}
               <Card title={t('application.promptConfiguration')}>
                 <div className="rb:flex rb:items-center rb:justify-between rb:mb-2.75">
                   <div className="rb:font-medium rb:leading-5">
@@ -406,36 +335,31 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
                 </Form.Item>
               </Card>
 
-              {/* 知识库 */}
-              <Knowledge
-                data={data?.knowledge_retrieval || { knowledge_bases: [] }} 
-                onUpdate={setKnowledgeConfig}
-              />
+              <Form.Item name="knowledge_retrieval" noStyle>
+                <Knowledge />
+              </Form.Item>
 
               {/* 记忆配置 */}
               <Card title={t('application.memoryConfiguration')}>
                 <Space size={24} direction='vertical' style={{ width: '100%' }}>
-                  <SwitchWrapper title="dialogueHistoricalMemory" desc="dialogueHistoricalMemoryDesc" name="memoryEnabled" />
+                  <SwitchWrapper title="dialogueHistoricalMemory" desc="dialogueHistoricalMemoryDesc" name={['memory', 'enabled']} />
                   <SelectWrapper 
                     title="selectMemoryContent" 
                     desc="selectMemoryContentDesc" 
-                    name="memory_content"
+                    name={['memory', 'memory_content']}
                     url={memoryConfigListUrl}
                   />
                 </Space>
               </Card>
 
-              {/* 变量配置 */}
-              <VariableList
-                data={data?.variables}
-                onUpdate={setVariableList}
-              />
+              <Form.Item name="variables">
+                <VariableList />
+              </Form.Item>
               
               {/* 工具配置 */}
-              <ToolList
-                data={data?.tools || []}
-                onUpdate={setToolList}
-              />
+              <Form.Item name="tools">
+                <ToolList />
+              </Form.Item>
             </Space>
           </Form>
         </Col>
@@ -444,6 +368,9 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
             {t('application.debuggingAndPreview')}
 
             <Space size={10}>
+              <Button type="primary" ghost onClick={handleOpenVariableConfig}>
+                {t('application.variableConfig')}
+              </Button>
               <Button type="primary" ghost onClick={handleAddModel}>
                 + {t('application.addModel')}
               </Button>
@@ -463,7 +390,7 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
 
       <ModelConfigModal
         modelList={modelList}
-        data={formData as Config}
+        data={values}
         ref={modelConfigModalRef}
         refresh={refresh}
       />
@@ -472,6 +399,10 @@ const Agent = forwardRef<AgentRef>((_props, ref) => {
         defaultModel={defaultModel}
         refresh={updatePrompt}
       />
+      <ChatVariableConfigModal
+        ref={chatVariableConfigModalRef}
+        refresh={handleSaveChatVariable}
+      />
     </>
   );
 });
diff --git a/web/src/views/ApplicationConfig/components/Card.tsx b/web/src/views/ApplicationConfig/components/Card.tsx
index 7d9328ea..f414848f 100644
--- a/web/src/views/ApplicationConfig/components/Card.tsx
+++ b/web/src/views/ApplicationConfig/components/Card.tsx
@@ -3,18 +3,21 @@ import RbCard from '@/components/RbCard/Card'
 
 interface CardProps {
   title?: string | ReactNode;
+  subTitle?: string | ReactNode;
   children: ReactNode;
   extra?: ReactNode;
 }
 
 const Card: FC<CardProps> = ({
   title,
+  subTitle,
   children,
   extra,
 }) => {
   return (
     <RbCard
       title={title}
+      subTitle={subTitle}
       extra={extra}
       headerType="borderL"
       headerClassName="rb:before:bg-[#155EEF]! rb:before:h-[19px]"
diff --git a/web/src/views/ApplicationConfig/components/ChatVariableConfigModal.tsx b/web/src/views/ApplicationConfig/components/ChatVariableConfigModal.tsx
new file mode 100644
index 00000000..abf33cb5
--- /dev/null
+++ b/web/src/views/ApplicationConfig/components/ChatVariableConfigModal.tsx
@@ -0,0 +1,101 @@
+import { forwardRef, useImperativeHandle, useState } from 'react';
+import { Form, Input, InputNumber } from 'antd';
+import { useTranslation } from 'react-i18next';
+
+import type { ChatVariableConfigModalRef } from '../types'
+import type { Variable } from './VariableList/types'
+import RbModal from '@/components/RbModal'
+
+interface VariableEditModalProps {
+  refresh: (values: Variable[]) => void;
+}
+
+const ChatVariableConfigModal = forwardRef<ChatVariableConfigModalRef, VariableEditModalProps>(({
+  refresh,
+}, ref) => {
+  const { t } = useTranslation();
+  const [visible, setVisible] = useState(false);
+  const [form] = Form.useForm<{variables: Variable[]}>();
+  const [loading, setLoading] = useState(false)
+  const [initialValues, setInitialValues] = useState<Variable[]>([])
+
+  // 封装取消方法，添加关闭弹窗逻辑
+  const handleClose = () => {
+    setVisible(false);
+    form.resetFields();
+    setLoading(false)
+  };
+
+  const handleOpen = (values: Variable[]) => {
+    console.log('values', values)
+    setVisible(true);
+    form.setFieldsValue({variables: values})
+    setInitialValues([...values])
+  };
+  // 封装保存方法，添加提交逻辑
+  const handleSave = () => {
+    form.validateFields().then((values) => {
+      refresh([
+        ...(values?.variables ?? []),
+      ])
+      handleClose()
+    })
+  }
+
+  // 暴露给父组件的方法
+  useImperativeHandle(ref, () => ({
+    handleOpen,
+    handleClose
+  }));
+
+  console.log(form.getFieldValue('variables'))
+
+  return (
+    <RbModal
+      title={t('application.variableConfig')}
+      open={visible}
+      onCancel={handleClose}
+      okText={t('common.save')}
+      onOk={handleSave}
+      confirmLoading={loading}
+    >
+      <Form
+        form={form}
+        layout="horizontal"
+        scrollToFirstError={{ behavior: 'instant', block: 'end', focus: true }}
+      >
+        <Form.List name="variables">
+          {(fields) => (
+            <>
+              {fields.map(({ name }, index) => {
+                const field = initialValues[index]
+                return (
+                  <Form.Item
+                    key={name}
+                    name={[name, 'value']}
+                    label={`${field.name}·${field.display_name}`}
+                    rules={[
+                      { required: field.required, message: t('common.pleaseEnter') },
+                    ]}
+                  >
+                    {
+                      field.type === 'text' && <Input placeholder={t('common.pleaseEnter')} />
+                    }
+                    {
+                      field.type === 'number' && <InputNumber placeholder={t('common.pleaseEnter')} className="rb:w-full!" onChange={(value) => form.setFieldValue(['variables', name, 'value'], value)} />
+                    }
+                    {
+                      field.type === 'paragraph' && <Input.TextArea placeholder={t('common.pleaseEnter')} />
+                    }
+                  </Form.Item>
+                )
+              })}
+            </>
+          )}
+        </Form.List>
+      </Form>
+    </RbModal>
+  );
+});
+
+export default ChatVariableConfigModal;
\ No newline at end of file
diff --git a/web/src/views/ApplicationConfig/components/Knowledge.tsx b/web/src/views/ApplicationConfig/components/Knowledge/Knowledge.tsx
similarity index 54%
rename from web/src/views/ApplicationConfig/components/Knowledge.tsx
rename to web/src/views/ApplicationConfig/components/Knowledge/Knowledge.tsx
index bc1207e4..1e59f26d 100644
--- a/web/src/views/ApplicationConfig/components/Knowledge.tsx
+++ b/web/src/views/ApplicationConfig/components/Knowledge/Knowledge.tsx
@@ -2,7 +2,6 @@ import { type FC, useRef, useState, useEffect } from 'react'
 import { useTranslation } from 'react-i18next'
 import { Space, Button, List } from 'antd'
 import knowledgeEmpty from '@/assets/images/application/knowledgeEmpty.svg'
-import Card from './Card'
 import type {
   KnowledgeConfigForm,
   KnowledgeConfig,
@@ -11,14 +10,16 @@ import type {
   KnowledgeModalRef,
   KnowledgeConfigModalRef,
   KnowledgeGlobalConfigModalRef,
-} from '../types'
+} from './types'
 import Empty from '@/components/Empty'
 import KnowledgeListModal from './KnowledgeListModal'
 import KnowledgeConfigModal from './KnowledgeConfigModal'
 import KnowledgeGlobalConfigModal from './KnowledgeGlobalConfigModal'
 import Tag from '@/components/Tag'
+import { getKnowledgeBaseList } from '@/api/knowledgeBase'
+import Card from '../Card'
 
-const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig) => void}> = ({data, onUpdate}) => {
+const Knowledge: FC<{value?: KnowledgeConfig; onChange?: (config: KnowledgeConfig) => void}> = ({value = {knowledge_bases: []}, onChange}) => {
   const { t } = useTranslation()
   const knowledgeModalRef = useRef<KnowledgeModalRef>(null)
   const knowledgeConfigModalRef = useRef<KnowledgeConfigModalRef>(null)
@@ -27,12 +28,31 @@ const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig)
   const [editConfig, setEditConfig] = useState<KnowledgeConfig>({} as KnowledgeConfig)
 
   useEffect(() => {
-    if (data) {
-      setEditConfig({ ...(data || {}) })
-      const knowledge_bases = [...(data.knowledge_bases || [])]
-      setKnowledgeList(knowledge_bases)
+    if (value && JSON.stringify(value) !== JSON.stringify(editConfig)) {
+      setEditConfig({ ...(value || {}) })
+      const knowledge_bases = [...(value.knowledge_bases || [])]
+      
+      // 检查是否有knowledge_bases缺少name字段
+      const basesWithoutName = knowledge_bases.filter(base => !base.name)
+      if (basesWithoutName.length > 0) {
+        // 调用接口获取完整的知识库信息
+        getKnowledgeBaseList().then(res => {
+          const fullBases = knowledge_bases.map(base => {
+            if (!base.name) {
+              const fullBase = res.items.find((item: any) => item.id === base.kb_id)
+              return fullBase ? { ...base, ...fullBase } : base
+            }
+            return base
+          })
+          setKnowledgeList(fullBases)
+        }).catch(() => {
+          setKnowledgeList(knowledge_bases)
+        })
+      } else {
+        setKnowledgeList(knowledge_bases)
+      }
     }
-  }, [data])
+  }, [value])
 
   const handleKnowledgeConfig = () => {
     knowledgeGlobalConfigModalRef.current?.handleOpen()
@@ -43,7 +63,7 @@ const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig)
   const handleDeleteKnowledge = (id: string) => {
     const list = knowledgeList.filter(item => item.id !== id)
     setKnowledgeList([...list])
-    onUpdate({
+    onChange && onChange({
       ...editConfig,
       knowledge_bases: [...list],
     })
@@ -65,7 +85,7 @@ const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig)
           list = [...values as KnowledgeBase[]]
         }
       setKnowledgeList([...list])
-      onUpdate({
+      onChange && onChange({
         ...editConfig,
         knowledge_bases: [...list],
       })
@@ -77,14 +97,14 @@ const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig)
         config: {...values as KnowledgeConfigForm}
       }
       setKnowledgeList([...list])
-      onUpdate({
+      onChange && onChange({
         ...editConfig,
         knowledge_bases: [...list],
       })
     } else if (type === 'rerankerConfig') {
       const rerankerValues = values as RerankerConfig
       setEditConfig(prev => ({ ...prev, ...rerankerValues }))
-      onUpdate({
+      onChange && onChange({
         ...editConfig,
         ...rerankerValues,
         reranker_id: rerankerValues.rerank_model ? rerankerValues.reranker_id : undefined,
@@ -93,55 +113,54 @@ const Knowledge: FC<{data: KnowledgeConfig; onUpdate: (config: KnowledgeConfig)
     }
   }
   return (
-    <Card 
+    <Card
       title={t('application.knowledgeBaseAssociation')}
       extra={
-        <Button style={{padding: '0 8px', height: '24px'}} onClick={() => handleKnowledgeConfig()}>{t('application.globalConfig')}</Button>
+        <Space>
+          <Button style={{ padding: '0 8px', height: '24px' }} onClick={handleKnowledgeConfig}>{t('workflow.config.knowledge-retrieval.recallConfig')}</Button>
+          <Button style={{ padding: '0 8px', height: '24px' }} onClick={handleAddKnowledge}>+</Button>
+        </Space>
       }
     >
-      <div className="rb:flex rb:items-center rb:justify-between rb:mb-3">
-        <div className="rb:font-medium rb:leading-5">{t('application.associatedKnowledgeBase')}</div>
-        <Button style={{padding: '0 8px', height: '24px'}} onClick={handleAddKnowledge}>+{t('application.addKnowledgeBase')}</Button>
-      </div>
-
       {knowledgeList.length === 0
         ? <Empty url={knowledgeEmpty} size={88} subTitle={t('application.knowledgeEmpty')} />
         : 
           <List
             grid={{ gutter: 12, column: 1 }}
             dataSource={knowledgeList}
-            renderItem={(item) => (
-              <List.Item>
-                <div key={item.id} className="rb:flex rb:items-center rb:justify-between rb:p-[12px_16px] rb:bg-[#FBFDFF] rb:border rb:border-[#DFE4ED] rb:rounded-lg">
-                  <div className="rb:font-medium rb:leading-4">
-                    {item.name}
-                    <Tag color={item.status === 1 ? 'success' : item.status === 0 ? 'default' : 'error'} className="rb:ml-2">
-                      {item.status === 1 ? t('common.enable') : item.status === 0 ? t('common.disabled') : t('common.deleted')}
-                    </Tag>
-                    <div className="rb:mt-1 rb:text-[12px] rb:text-[#5B6167] rb:font-regular rb:leading-5">{t('application.contains', {include_count: item.doc_num})}</div>
+            renderItem={(item) => {
+              if (!item.id) return null
+              return (
+                <List.Item>
+                  <div key={item.id} className="rb:flex rb:items-center rb:justify-between rb:p-[12px_16px] rb:bg-[#FBFDFF] rb:border rb:border-[#DFE4ED] rb:rounded-lg">
+                    <div className="rb:font-medium rb:leading-4">
+                      {item.name}
+                      <Tag color={item.status === 1 ? 'success' : item.status === 0 ? 'default' : 'error'} className="rb:ml-2">
+                        {item.status === 1 ? t('common.enable') : item.status === 0 ? t('common.disabled') : t('common.deleted')}
+                      </Tag>
+                      <div className="rb:mt-1 rb:text-[12px] rb:text-[#5B6167] rb:font-regular rb:leading-5">{t('application.contains', {include_count: item.doc_num})}</div>
+                    </div>
+                    <Space size={12}>
+                      <div 
+                        className="rb:w-6 rb:h-6 rb:cursor-pointer rb:bg-[url('@/assets/images/editBorder.svg')] rb:hover:bg-[url('@/assets/images/editBg.svg')]" 
+                        onClick={() => handleEditKnowledge(item)}
+                      ></div>
+                      <div 
+                        className="rb:w-6 rb:h-6 rb:cursor-pointer rb:bg-[url('@/assets/images/deleteBorder.svg')] rb:hover:bg-[url('@/assets/images/deleteBg.svg')]" 
+                        onClick={() => handleDeleteKnowledge(item.id)}
+                      ></div>
+                    </Space>
                   </div>
-                  <Space size={12}>
-                    <div 
-                      className="rb:w-6 rb:h-6 rb:cursor-pointer rb:bg-[url('@/assets/images/editBorder.svg')] rb:hover:bg-[url('@/assets/images/editBg.svg')]" 
-                      onClick={() => handleEditKnowledge(item)}
-                    ></div>
-                    <div 
-                      className="rb:w-6 rb:h-6 rb:cursor-pointer rb:bg-[url('@/assets/images/deleteBorder.svg')] rb:hover:bg-[url('@/assets/images/deleteBg.svg')]" 
-                      onClick={() => handleDeleteKnowledge(item.id)}
-                    ></div>
-                  </Space>
-                </div>
-              </List.Item>
-            )}
+                </List.Item>
+              )
+            }}
           />
       }
-      {/* 全局设置 */}
       <KnowledgeGlobalConfigModal
         data={editConfig}
         ref={knowledgeGlobalConfigModalRef}
         refresh={refresh}
       />
-      {/* 知识库列表 */}
       <KnowledgeListModal
         ref={knowledgeModalRef}
         selectedList={knowledgeList}
diff --git a/web/src/views/ApplicationConfig/components/KnowledgeConfigModal.tsx b/web/src/views/ApplicationConfig/components/Knowledge/KnowledgeConfigModal.tsx
similarity index 76%
rename from web/src/views/ApplicationConfig/components/KnowledgeConfigModal.tsx
rename to web/src/views/ApplicationConfig/components/Knowledge/KnowledgeConfigModal.tsx
index 8e9fa42b..abf56b18 100644
--- a/web/src/views/ApplicationConfig/components/KnowledgeConfigModal.tsx
+++ b/web/src/views/ApplicationConfig/components/Knowledge/KnowledgeConfigModal.tsx
@@ -2,7 +2,7 @@ import { forwardRef, useEffect, useImperativeHandle, useState } from 'react';
 import { Form, Select, InputNumber } from 'antd';
 import { useTranslation } from 'react-i18next';
 
-import type { KnowledgeConfigModalRef, KnowledgeBase, KnowledgeConfigForm } from '../types'
+import type { KnowledgeConfigModalRef, KnowledgeBase, KnowledgeConfigForm, RetrieveType } from './types'
 import RbModal from '@/components/RbModal'
 import RbSlider from '@/components/RbSlider'
 import { formatDateTime } from '@/utils/format';
@@ -12,7 +12,7 @@ const FormItem = Form.Item;
 interface KnowledgeConfigModalProps {
   refresh: (values: KnowledgeConfigForm, type: 'knowledgeConfig') => void;
 }
-const retrieveTypes = ['participle', 'semantic', 'hybrid']
+const retrieveTypes: RetrieveType[] = ['participle', 'semantic', 'hybrid']
 
 const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfigModalProps>(({
   refresh,
@@ -33,8 +33,11 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
 
   const handleOpen = (data: KnowledgeBase) => {
     form.setFieldsValue({
-      retrieve_type: retrieveTypes[0],
+      retrieve_type: data?.config?.retrieve_type || retrieveTypes[0],
       kb_id: data.id,
+      top_k: data?.config?.top_k || 5,
+      similarity_threshold: data?.config?.similarity_threshold || 0.5,
+      vector_similarity_weight: data?.config?.vector_similarity_weight || 0.5,
       ...(data || {}),
       ...(data?.config || {}),
     })
@@ -62,12 +65,10 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
 
   useEffect(() => {
     if (values?.retrieve_type) {
-      const initialValues = Object.keys(values).map(key => {
-        return {
-          [key as keyof KnowledgeConfigForm]: (key === 'kb_id' || key === 'retrieve_type') ? values[key] : undefined
-        }
-      })
-      form.resetFields(initialValues)
+      const fieldsToReset = Object.keys(values).filter(key => 
+        key !== 'kb_id' && key !== 'retrieve_type'
+      ) as (keyof KnowledgeConfigForm)[];
+      form.resetFields(fieldsToReset);
     }
   }, [values?.retrieve_type])
 
@@ -84,12 +85,12 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
         layout="vertical"
       >
         {data && (
-          <div className="rb:mb-[24px] rb:flex rb:items-center rb:justify-between rb:border rb:rounded-[8px] rb:p-[17px_16px] rb:cursor-pointer rb:bg-[#F0F3F8] rb:border-[#DFE4ED] rb:text-[#212332]">
-            <div className="rb:text-[16px] rb:leading-[22px]">
+          <div className="rb:mb-6 rb:flex rb:items-center rb:justify-between rb:border rb:rounded-lg rb:p-[17px_16px] rb:cursor-pointer rb:bg-[#F0F3F8] rb:border-[#DFE4ED] rb:text-[#212332]">
+            <div className="rb:text-[16px] rb:leading-5.5">
               {data.name}
-              <div className="rb:text-[12px] rb:leading-[16px] rb:text-[#5B6167] rb:mt-[8px]">{t('application.contains', {include_count: data.doc_num})}</div>
+              <div className="rb:text-[12px] rb:leading-4 rb:text-[#5B6167] rb:mt-2">{t('application.contains', {include_count: data.doc_num})}</div>
             </div>
-            <div className="rb:text-[12px] rb:leading-[16px] rb:text-[#5B6167]">{formatDateTime(data.updated_at, 'YYYY-MM-DD HH:mm:ss')}</div>
+            <div className="rb:text-[12px] rb:leading-4 rb:text-[#5B6167]">{formatDateTime(data.updated_at, 'YYYY-MM-DD HH:mm:ss')}</div>
           </div>
         )}
         <FormItem name="kb_id" hidden />
@@ -114,8 +115,14 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
           label={t('application.top_k')}
           rules={[{ required: true, message: t('common.pleaseEnter') }]}
           extra={t('application.top_k_desc')}
+          initialValue={5}
         >
-          <InputNumber style={{ width: '100%' }} />
+          <InputNumber
+            style={{ width: '100%' }}
+            min={1}
+            max={20}
+            onChange={(value) => form.setFieldValue('top_k', value)}
+          />
         </FormItem>
         {/* 语义相似度阈值 similarity_threshold */}
         {values?.retrieve_type === 'semantic' && (
@@ -123,6 +130,7 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
             name="similarity_threshold"
             label={t('application.similarity_threshold')}
             extra={t('application.similarity_threshold_desc')}
+            initialValue={0.5}
           >
             <RbSlider 
               max={1.0}
@@ -137,6 +145,7 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
             name="vector_similarity_weight"
             label={t('application.vector_similarity_weight')}
             extra={t('application.vector_similarity_weight_desc')}
+            initialValue={0.5}
           >
             <RbSlider 
               max={1.0}
@@ -152,6 +161,7 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
               name="similarity_threshold"
               label={t('application.similarity_threshold')}
               extra={t('application.similarity_threshold_desc1')}
+              initialValue={0.5}
             >
               <RbSlider 
                 max={1.0}
@@ -163,6 +173,7 @@ const KnowledgeConfigModal = forwardRef<KnowledgeConfigModalRef, KnowledgeConfig
               name="vector_similarity_weight"
               label={t('application.vector_similarity_weight')}
               extra={t('application.vector_similarity_weight_desc1')}
+              initialValue={0.5}
             >
               <RbSlider 
                 max={1.0}
diff --git a/web/src/views/ApplicationConfig/components/KnowledgeGlobalConfigModal.tsx b/web/src/views/ApplicationConfig/components/Knowledge/KnowledgeGlobalConfigModal.tsx
similarity index 86%
rename from web/src/views/ApplicationConfig/components/KnowledgeGlobalConfigModal.tsx
rename to web/src/views/ApplicationConfig/components/Knowledge/KnowledgeGlobalConfigModal.tsx
index 39e11581..2f349487 100644
--- a/web/src/views/ApplicationConfig/components/KnowledgeGlobalConfigModal.tsx
+++ b/web/src/views/ApplicationConfig/components/Knowledge/KnowledgeGlobalConfigModal.tsx
@@ -2,7 +2,7 @@ import { forwardRef, useImperativeHandle, useState, useEffect } from 'react';
 import { Form, InputNumber, Switch } from 'antd';
 import { useTranslation } from 'react-i18next';
 
-import type { RerankerConfig, KnowledgeGlobalConfigModalRef } from '../types'
+import type { RerankerConfig, KnowledgeGlobalConfigModalRef } from './types'
 import RbModal from '@/components/RbModal'
 import CustomSelect from '@/components/CustomSelect'
 import { getModelListUrl } from '@/api/models'
@@ -71,18 +71,18 @@ const KnowledgeGlobalConfigModal = forwardRef<KnowledgeGlobalConfigModalRef, Kno
         form={form}
         layout="vertical"
       >
-        <div className="rb:text-[#5B6167] rb:mb-[24px]">{t('application.globalConfigDesc')}</div>
+        <div className="rb:text-[#5B6167] rb:mb-6">{t('application.globalConfigDesc')}</div>
 
         {/* 结果重排 */}
-        <div className="rb:flex rb:items-center rb:justify-between rb:my-[24px]">
-          <div className="rb:text-[14px] rb:font-medium rb:leading-[20px]">
+        <div className="rb:flex rb:items-center rb:justify-between rb:my-6">
+          <div className="rb:text-[14px] rb:font-medium rb:leading-5">
             {t('application.rerankModel')}
-            <div className="rb:mt-[4px] rb:text-[12px] rb:text-[#5B6167] rb:font-regular rb:leading-[16px]">{t('application.rerankModelDesc')}</div>
+            <div className="rb:mt-1 rb:text-[12px] rb:text-[#5B6167] rb:font-regular rb:leading-4">{t('application.rerankModelDesc')}</div>
           </div>
           <FormItem
             name="rerank_model"
             valuePropName="checked"
-            className="rb:mb-[0px]!"
+            className="rb:mb-0!"
           >
             <Switch />
           </FormItem>
@@ -110,7 +110,12 @@ const KnowledgeGlobalConfigModal = forwardRef<KnowledgeGlobalConfigModalRef, Kno
             rules={[{ required: true, message: t('common.pleaseEnter') }]}
             extra={t('application.reranker_top_k_desc')}
           >
-            <InputNumber style={{ width: '100%' }} min={1} max={20} />
+            <InputNumber
+              style={{ width: '100%' }}
+              min={1}
+              max={20}
+              onChange={(value) => form.setFieldValue('reranker_top_k', value)}
+            />
           </FormItem>
         </>}
       </Form>
diff --git a/web/src/views/ApplicationConfig/components/KnowledgeListModal.tsx b/web/src/views/ApplicationConfig/components/Knowledge/KnowledgeListModal.tsx
similarity index 88%
rename from web/src/views/ApplicationConfig/components/KnowledgeListModal.tsx
rename to web/src/views/ApplicationConfig/components/Knowledge/KnowledgeListModal.tsx
index 0c7b47b2..f1ebd516 100644
--- a/web/src/views/ApplicationConfig/components/KnowledgeListModal.tsx
+++ b/web/src/views/ApplicationConfig/components/Knowledge/KnowledgeListModal.tsx
@@ -2,7 +2,7 @@ import { forwardRef, useEffect, useImperativeHandle, useState } from 'react';
 import { Space, List } from 'antd';
 import { useTranslation } from 'react-i18next';
 import clsx from 'clsx'
-import type { KnowledgeModalRef, KnowledgeBase } from '../types'
+import type { KnowledgeModalRef, KnowledgeBase } from './types'
 import type { KnowledgeBaseListItem } from '@/views/KnowledgeBase/types'
 import RbModal from '@/components/RbModal'
 import { getKnowledgeBaseList } from '@/api/knowledgeBase'
@@ -39,12 +39,13 @@ const KnowledgeListModal = forwardRef<KnowledgeModalRef, KnowledgeModalProps>(({
     setQuery({})
     setSelectedIds([])
     setSelectedRows([])
-    getList()
   };
 
   useEffect(() => {
-    getList()
-  }, [query.keywords])
+    if (visible) {
+      getList()
+    }
+  }, [query.keywords, visible])
   const getList = () => {
     getKnowledgeBaseList(undefined, {
       ...query,
@@ -124,15 +125,15 @@ const KnowledgeListModal = forwardRef<KnowledgeModalRef, KnowledgeModalProps>(({
               dataSource={filterList}
               renderItem={(item: KnowledgeBase) => (
                 <List.Item>
-                  <div key={item.id} className={clsx("rb:flex rb:items-center rb:justify-between rb:border rb:rounded-[8px] rb:p-[17px_16px] rb:cursor-pointer rb:hover:bg-[#F0F3F8]", {
+                  <div key={item.id} className={clsx("rb:flex rb:items-center rb:justify-between rb:border rb:rounded-lg rb:p-[17px_16px] rb:cursor-pointer rb:hover:bg-[#F0F3F8]", {
                     "rb:bg-[rgba(21,94,239,0.06)] rb:border-[#155EEF] rb:text-[#155EEF]": selectedIds.includes(item.id),
                     "rb:border-[#DFE4ED] rb:text-[#212332]": !selectedIds.includes(item.id),
                   })} onClick={() => handleSelect(item)}>
-                    <div className="rb:text-[16px] rb:leading-[22px]">
+                    <div className="rb:text-[16px] rb:leading-5.5">
                       {item.name}
-                      <div className="rb:text-[12px] rb:leading-[16px] rb:text-[#5B6167] rb:mt-[8px]">{t('application.contains', {include_count: item.doc_num})}</div>
+                      <div className="rb:text-[12px] rb:leading-4 rb:text-[#5B6167] rb:mt-2">{t('application.contains', {include_count: item.doc_num})}</div>
                     </div>
-                    <div className="rb:text-[12px] rb:leading-[16px] rb:text-[#5B6167]">{formatDateTime(item.created_at, 'YYYY-MM-DD HH:mm:ss')}</div>
+                    <div className="rb:text-[12px] rb:leading-4 rb:text-[#5B6167]">{formatDateTime(item.created_at, 'YYYY-MM-DD HH:mm:ss')}</div>
                   </div>
                 </List.Item>
               )}
diff --git a/web/src/views/ApplicationConfig/components/Knowledge/types.ts b/web/src/views/ApplicationConfig/components/Knowledge/types.ts
new file mode 100644
index 00000000..f4f9ed17
--- /dev/null
+++ b/web/src/views/ApplicationConfig/components/Knowledge/types.ts
@@ -0,0 +1,30 @@
+import type { KnowledgeBaseListItem } from '@/views/KnowledgeBase/types'
+export interface RerankerConfig {
+  rerank_model?: boolean | undefined;
+  reranker_id?: string | undefined;
+  reranker_top_k?: number | undefined;
+}
+export type RetrieveType = 'participle' | 'semantic' | 'hybrid'
+export interface KnowledgeConfigForm {
+  kb_id?: string;
+  similarity_threshold?: number;
+  vector_similarity_weight?: number;
+  top_k?: number;
+  retrieve_type?: RetrieveType;
+}
+export interface KnowledgeBase extends KnowledgeBaseListItem, KnowledgeConfigForm {
+  config?: KnowledgeConfigForm
+}
+export interface KnowledgeConfig extends RerankerConfig {
+  knowledge_bases: KnowledgeBase[];
+}
+
+export interface KnowledgeConfigModalRef {
+  handleOpen: (data: KnowledgeBase) => void;
+}
+export interface KnowledgeGlobalConfigModalRef {
+  handleOpen: () => void;
+}
+export interface KnowledgeModalRef {
+  handleOpen: (config?: KnowledgeConfig[]) => void;
+}
\ No newline at end of file
diff --git a/web/src/views/ApplicationConfig/components/ToolList.tsx b/web/src/views/ApplicationConfig/components/ToolList/ToolList.tsx
similarity index 93%
rename from web/src/views/ApplicationConfig/components/ToolList.tsx
rename to web/src/views/ApplicationConfig/components/ToolList/ToolList.tsx
index fde7286b..e914d879 100644
--- a/web/src/views/ApplicationConfig/components/ToolList.tsx
+++ b/web/src/views/ApplicationConfig/components/ToolList/ToolList.tsx
@@ -1,22 +1,22 @@
 import { type FC, useRef, useState, useEffect } from 'react'
 import { useTranslation } from 'react-i18next'
 import { Space, Button, List, Switch } from 'antd'
-import Card from './Card'
+import Card from '../Card'
 import type {
   ToolModalRef,
   ToolOption
-} from '../types'
+} from './types'
 import Empty from '@/components/Empty'
 import ToolModal from './ToolModal'
 import { getToolMethods, getToolDetail } from '@/api/tools'
 
-const ToolList: FC<{ data: ToolOption[]; onUpdate: (config: ToolOption[]) => void}> = ({data, onUpdate}) => {
+const ToolList: FC<{ value?: ToolOption[]; onChange?: (config: ToolOption[]) => void}> = ({value, onChange}) => {
   const { t } = useTranslation()
   const toolModalRef = useRef<ToolModalRef>(null)
   const [toolList, setToolList] = useState<ToolOption[]>([])
   useEffect(() => {
-    if (data) {
-      const processedData = data.map(async (item) => {
+    if (value) {
+      const processedData = value.map(async (item) => {
         if (!item.label && item.tool_id) {
           try {
             const [toolDetail, methods] = await Promise.all([
@@ -77,7 +77,7 @@ const ToolList: FC<{ data: ToolOption[]; onUpdate: (config: ToolOption[]) => voi
       
       Promise.all(processedData).then(setToolList)
     }
-  }, [data])
+  }, [value])
 
   const handleAddTool = () => {
     toolModalRef.current?.handleOpen()
@@ -85,12 +85,12 @@ const ToolList: FC<{ data: ToolOption[]; onUpdate: (config: ToolOption[]) => voi
   const updateTools = (tool: ToolOption) => {
     const list = [...toolList, tool]
     setToolList(list)
-    onUpdate(list)
+    onChange && onChange(list)
   }
   const handleDeleteTool = (index: number) => {
     const list = toolList.filter((_item, idx) => idx !== index)
     setToolList([...list])
-    onUpdate(list)
+    onChange && onChange(list)
   }
   const handleChangeEnabled = (index: number) => {
     const list = toolList.map((item, idx) => {
@@ -103,7 +103,7 @@ const ToolList: FC<{ data: ToolOption[]; onUpdate: (config: ToolOption[]) => voi
       return item
     })
     setToolList([...list])
-    onUpdate(list)
+    onChange && onChange(list)
   }
   return (
     <Card 
@@ -112,7 +112,6 @@ const ToolList: FC<{ data: ToolOption[]; onUpdate: (config: ToolOption[]) => voi
         <Button style={{ padding: '0 8px', height: '24px' }} onClick={handleAddTool}>+{t('application.addTool')}</Button>
       }
     >
-
       {toolList.length === 0
         ? <Empty size={88} />
         : 
diff --git a/web/src/views/ApplicationConfig/components/ToolModal.tsx b/web/src/views/ApplicationConfig/components/ToolList/ToolModal.tsx
similarity index 100%
rename from web/src/views/ApplicationConfig/components/ToolModal.tsx
rename to web/src/views/ApplicationConfig/components/ToolList/ToolModal.tsx
diff --git a/web/src/views/ApplicationConfig/components/ToolList/types.ts b/web/src/views/ApplicationConfig/components/ToolList/types.ts
new file mode 100644
index 00000000..142ffe26
--- /dev/null
+++ b/web/src/views/ApplicationConfig/components/ToolList/types.ts
@@ -0,0 +1,26 @@
+export interface ToolOption {
+  value?: string | number | null;
+  label?: React.ReactNode;
+  description?: string;
+  children?: ToolOption[];
+  isLeaf?: boolean;
+  method_id?: string;
+  operation?: string;
+  parameters?: Parameter[];
+  tool_id?: string;
+  enabled?: boolean;
+}
+export interface Parameter {
+  name: string;
+  type: string;
+  description: string;
+  required: boolean;
+  default: any;
+  enum: null | string[];
+  minimum: number;
+  maximum: number;
+  pattern: null | string;
+}
+export interface ToolModalRef {
+  handleOpen: () => void;
+}
\ No newline at end of file
diff --git a/web/src/views/ApplicationConfig/components/VariableList.tsx b/web/src/views/ApplicationConfig/components/VariableList.tsx
deleted file mode 100644
index fbadf2ea..00000000
--- a/web/src/views/ApplicationConfig/components/VariableList.tsx
+++ /dev/null
@@ -1,131 +0,0 @@
-import { type FC, useRef, useState, useEffect } from 'react'
-import { useTranslation } from 'react-i18next'
-import { Space, Button, Switch } from 'antd'
-import variablesEmpty from '@/assets/images/application/variablesEmpty.svg'
-import Card from './Card'
-import Table from '@/components/Table';
-import type { Variable, VariableEditModalRef } from '../types'
-import Empty from '@/components/Empty'
-import VariableEditModal from './VariableEditModal'
-
-interface VariableListProps {
-  data?: Variable[];
-  onUpdate: (data: Variable[]) => void;
-}
-const VariableList: FC<VariableListProps> = ({data = [], onUpdate}) => {
-  const { t } = useTranslation()
-  const variableEditModalRef = useRef<VariableEditModalRef>(null)
-  const [variableList, setVariableList] = useState<Variable[]>([])
-  const [maxIndex, setMaxIndex] = useState(0)
-
-  useEffect(() => {
-    if (!data || data.length === 0) return
-    const list = data.map((item, index) => ({
-      ...item,
-      index
-    }))
-    setVariableList(list)
-    onUpdate(list)
-    setMaxIndex(list.length)
-  }, [data])
-  
-  const handleAddVariable = () => {
-    variableEditModalRef.current?.handleOpen()
-  }
-  const handleSaveVariable = (value: Variable) => {
-    if (value.index !== undefined && value.index >= 0) {
-      const index = variableList.findIndex(item => item.index === value.index)
-      if (index !== -1) {
-        const newData = [...variableList]
-        newData[index] = value
-        setVariableList([...newData])
-        onUpdate([...newData])
-      }
-    } else {
-      const list = [...variableList, {
-        index: maxIndex + 1,
-        ...value
-      }]
-      setVariableList(list)
-      onUpdate([...list])
-      setMaxIndex(maxIndex + 1)
-    }
-  }
-  const handleDeleteVariable = (index: number) => {
-    const list = variableList.filter((_, i) => i !== index)
-    setVariableList(list)
-    onUpdate([...list])
-  }
-  return (
-    <Card title={t('application.variableConfiguration')}>
-      <div className="rb:flex rb:items-center rb:justify-between rb:mb-[11px]">
-        <div className="rb:font-medium rb:leading-[20px]">
-          {t('application.VariableManagement')}
-          <span className="rb:font-regular rb:text-[12px] rb:text-[#5B6167]"> ({t('application.VariableManagementDesc')})</span>
-        </div>
-        <Button style={{padding: '0 8px', height: '24px'}} onClick={handleAddVariable}>+{t('application.addVariables')}</Button>
-      </div>
-
-      {/* List */}
-      {variableList.length > 0
-        ? (
-          <div className="rb:mt-[12px]">
-            <Table
-              rowKey="index"
-              pagination={false}
-              columns={[
-                {
-                  title: t('application.variableType'),
-                  dataIndex: 'type',
-                  key: 'type',
-                  render: (type) => t(`application.${type}`)
-                },
-                {
-                  title: t('application.variableKey'),
-                  dataIndex: 'name',
-                  key: 'name',
-                },
-                {
-                  title: t('application.variableName'),
-                  dataIndex: 'display_name',
-                  key: 'display_name',
-                },
-                {
-                  title: t('application.optional'),
-                  dataIndex: 'required',
-                  key: 'required',
-                  render: (required) => <Switch checked={!required} disabled />
-                },
-                {
-                  title: t('common.operation'),
-                  key: 'action',
-                  render: (_, record, index: number) => (
-                    <Space size="middle">
-                      <Button
-                        type="link"
-                        onClick={() => variableEditModalRef.current?.handleOpen(record as Variable)}
-                      >
-                        {t('common.edit')}
-                      </Button>
-                      <Button type="link" danger onClick={() => handleDeleteVariable(index)}>
-                        {t('common.delete')}
-                      </Button>
-                    </Space>
-                  ),
-                },
-              ]}
-              initialData={variableList as unknown as Record<string, unknown>[]}
-              emptySize={88}
-            />
-          </div>
-        )
-        : <Empty url={variablesEmpty} size={88} subTitle={t('application.variablesEmpty')} />
-      }
-      <VariableEditModal
-        ref={variableEditModalRef}
-        refreshTable={handleSaveVariable}
-      />
-    </Card>
-  )
-}
-export default VariableList
\ No newline at end of file
diff --git a/web/src/views/ApplicationConfig/components/ApiExtensionModal.tsx b/web/src/views/ApplicationConfig/components/VariableList/ApiExtensionModal.tsx
similarity index 99%
rename from web/src/views/ApplicationConfig/components/ApiExtensionModal.tsx
rename to web/src/views/ApplicationConfig/components/VariableList/ApiExtensionModal.tsx
index b1c7450a..4f4f9047 100644
--- a/web/src/views/ApplicationConfig/components/ApiExtensionModal.tsx
+++ b/web/src/views/ApplicationConfig/components/VariableList/ApiExtensionModal.tsx
@@ -2,7 +2,7 @@ import { forwardRef, useImperativeHandle, useState } from 'react';
 import { Form, Input } from 'antd';
 import { useTranslation } from 'react-i18next';
 
-import type { ApiExtensionModalData, ApiExtensionModalRef } from '../types'
+import type { ApiExtensionModalData, ApiExtensionModalRef } from './types'
 import RbModal from '@/components/RbModal'
 
 const FormItem = Form.Item;
diff --git a/web/src/views/ApplicationConfig/components/VariableEditModal.tsx b/web/src/views/ApplicationConfig/components/VariableList/VariableEditModal.tsx
similarity index 96%
rename from web/src/views/ApplicationConfig/components/VariableEditModal.tsx
rename to web/src/views/ApplicationConfig/components/VariableList/VariableEditModal.tsx
index 3efd721c..69e213fb 100644
--- a/web/src/views/ApplicationConfig/components/VariableEditModal.tsx
+++ b/web/src/views/ApplicationConfig/components/VariableList/VariableEditModal.tsx
@@ -2,7 +2,7 @@ import { forwardRef, useImperativeHandle, useState, useRef } from 'react';
 import { Form, Input, Select, InputNumber, Checkbox, Tag, Divider, Button } from 'antd';
 import { useTranslation } from 'react-i18next';
 
-import type { ApiExtensionModalRef, Variable, VariableEditModalRef } from '../types'
+import type { ApiExtensionModalRef, Variable, VariableEditModalRef } from './types'
 import RbModal from '@/components/RbModal'
 import SortableList from '@/components/SortableList'
 import ApiExtensionModal from './ApiExtensionModal'
@@ -137,7 +137,14 @@ const VariableEditModal = forwardRef<VariableEditModalRef, VariableEditModalProp
               { pattern: /^[a-zA-Z_][a-zA-Z0-9_]*$/, message: t('application.invalidVariableName') },
             ]}
           >
-            <Input placeholder={t('common.enter')} />
+            <Input 
+              placeholder={t('common.enter')} 
+              onBlur={(e) => {
+                if (!form.getFieldValue('display_name')) {
+                  form.setFieldValue('display_name', e.target.value)
+                }
+              }}
+            />
           </FormItem>
           {/* 显示名称 */}
           <FormItem
diff --git a/web/src/views/ApplicationConfig/components/VariableList/VariableList.tsx b/web/src/views/ApplicationConfig/components/VariableList/VariableList.tsx
new file mode 100644
index 00000000..9a0e4b45
--- /dev/null
+++ b/web/src/views/ApplicationConfig/components/VariableList/VariableList.tsx
@@ -0,0 +1,110 @@
+import { type FC, useRef } from 'react'
+import { useTranslation } from 'react-i18next'
+import { Space, Button, Switch, Form } from 'antd'
+import variablesEmpty from '@/assets/images/application/variablesEmpty.svg'
+import Card from '../Card'
+import Table from '@/components/Table';
+import type { Variable, VariableEditModalRef } from './types'
+import Empty from '@/components/Empty'
+import VariableEditModal from './VariableEditModal'
+
+interface VariableListProps {
+  value?: Variable[];
+  onChange?: (value: Variable[]) => void;
+}
+const VariableList: FC<VariableListProps> = ({value = [], onChange}) => {
+  const { t } = useTranslation()
+  const variableEditModalRef = useRef<VariableEditModalRef>(null)
+  
+  const handleAddVariable = () => {
+    variableEditModalRef.current?.handleOpen()
+  }
+  const handleSaveVariable = (variable: Variable) => {
+    const newList = [...(value || [])]
+    if (variable.index !== undefined && variable.index >= 0) {
+      const index = newList.findIndex(item => item.index === variable.index)
+      if (index !== -1) {
+        newList[index] = variable
+      }
+    } else {
+      newList.push({ ...variable, index: Date.now() })
+    }
+    onChange?.(newList)
+  }
+  return (
+    <Card
+      title={<>
+        {t('application.variableConfiguration')}
+        <span className="rb:font-regular rb:text-[12px] rb:text-[#5B6167]"> ({t('application.VariableManagementDesc')})</span>
+      </>}
+      extra={<Button style={{ padding: '0 8px', height: '24px' }} onClick={handleAddVariable}>+ {t('application.addVariables')}</Button>}
+    >
+      <Form.List name="variables" initialValue={value}>
+        {(fields, { remove }) => {
+          return (
+            <>
+              {fields.length > 0 ? (
+                <div className="rb:mt-3">
+                  <Table
+                    rowKey="index"
+                    pagination={false}
+                    columns={[
+                      {
+                        title: t('application.variableType'),
+                        dataIndex: 'type',
+                        key: 'type',
+                        render: (type) => t(`application.${type}`)
+                      },
+                      {
+                        title: t('application.variableKey'),
+                        dataIndex: 'name',
+                        key: 'name',
+                      },
+                      {
+                        title: t('application.variableName'),
+                        dataIndex: 'display_name',
+                        key: 'display_name',
+                      },
+                      {
+                        title: t('application.optional'),
+                        dataIndex: 'required',
+                        key: 'required',
+                        render: (required) => <Switch checked={!required} disabled />
+                      },
+                      {
+                        title: t('common.operation'),
+                        key: 'action',
+                        render: (_, record, index: number) => (
+                          <Space size="middle">
+                            <Button
+                              type="link"
+                              onClick={() => variableEditModalRef.current?.handleOpen(record as Variable)}
+                            >
+                              {t('common.edit')}
+                            </Button>
+                            <Button type="link" danger onClick={() => remove(index)}>
+                              {t('common.delete')}
+                            </Button>
+                          </Space>
+                        ),
+                      },
+                    ]}
+                    initialData={value as unknown as Record<string, unknown>[]}
+                    emptySize={88}
+                  />
+                </div>
+              ) : (
+                <Empty url={variablesEmpty} size={88} subTitle={t('application.variablesEmpty')} />
+              )}
+            </>
+          )
+        }}
+      </Form.List>
+      <VariableEditModal
+        ref={variableEditModalRef}
+        refreshTable={handleSaveVariable}
+      />
+    </Card>
+  )
+}
+export default VariableList
\ No newline at end of file
diff --git a/web/src/views/ApplicationConfig/components/VariableList/types.ts b/web/src/views/ApplicationConfig/components/VariableList/types.ts
new file mode 100644
index 00000000..f262dda1
--- /dev/null
+++ b/web/src/views/ApplicationConfig/components/VariableList/types.ts
@@ -0,0 +1,28 @@
+export interface Variable {
+  index?: number;
+  name: string;
+  display_name: string;
+  type: string;
+  required: boolean;
+  max_length?: number;
+  description?: string;
+
+  key?: string;
+  default_value?: string;
+  options?: string[];
+  api_extension?: string;
+  hidden?: boolean;
+  value?: any;
+}
+export interface VariableEditModalRef {
+  handleOpen: (values?: Variable) => void;
+}
+
+export interface ApiExtensionModalData {
+  name: string;
+  apiEndpoint: string;
+  apiKey: string;
+}
+export interface ApiExtensionModalRef {
+  handleOpen: () => void;
+}
\ No newline at end of file
diff --git a/web/src/views/ApplicationConfig/types.ts b/web/src/views/ApplicationConfig/types.ts
index 6eb97f22..6f641ebb 100644
--- a/web/src/views/ApplicationConfig/types.ts
+++ b/web/src/views/ApplicationConfig/types.ts
@@ -1,4 +1,6 @@
-import type { KnowledgeBaseListItem } from '@/views/KnowledgeBase/types'
+import type { KnowledgeConfig } from './components/Knowledge/types'
+import type { Variable } from './components/VariableList/types'
+import type { ToolOption } from './components/ToolList/types'
 import type { ChatItem } from '@/components/Chat/types'
 import type { GraphRef } from '@/views/Workflow/types';
 import type { ApiKey } from '@/views/ApiKeyManagement/types'
@@ -14,55 +16,6 @@ export interface ModelConfig {
   n: number;
   stop?: string;
 }
-
-/*************** 知识库相关 ******************/
-export interface RerankerConfig {
-  rerank_model?: boolean | undefined;
-  reranker_id?: string | undefined;
-  reranker_top_k?: number | undefined;
-}
-export interface KnowledgeConfigForm {
-  kb_id?: string;
-  similarity_threshold?: number;
-  vector_similarity_weight?: number;
-  top_k?: number;
-  retrieve_type?: 'participle' | 'semantic' | 'hybrid';
-}
-export interface KnowledgeBase extends KnowledgeBaseListItem, KnowledgeConfigForm {
-  config?: KnowledgeConfigForm
-}
-export interface KnowledgeConfig extends RerankerConfig {
-  knowledge_bases: KnowledgeBase[];
-}
-
-export interface KnowledgeConfigModalRef {
-  handleOpen: (data: KnowledgeBase) => void;
-}
-export interface KnowledgeGlobalConfigModalRef {
-  handleOpen: () => void;
-}
-/*********** end 知识库相关 ******************/
-
-/*************** 变量相关 ******************/
-export interface Variable {
-  index?: number;
-  name: string;
-  display_name: string;
-  type: string;
-  required: boolean;
-  max_length?: number;
-  description?: string;
-
-  key: string;
-  default_value?: string;
-  options?: string[];
-  api_extension?: string;
-  hidden?: boolean;
-}
-export interface VariableEditModalRef {
-  handleOpen: (values?: Variable) => void;
-}
-/*********** end 变量相关 ******************/
 export interface MemoryConfig {
   enabled: boolean;
   memory_content?: string;
@@ -131,17 +84,6 @@ export interface ModelConfigModalData {
 export interface AiPromptModalRef {
   handleOpen: () => void;
 }
-export interface KnowledgeModalRef {
-  handleOpen: (config?: KnowledgeConfig[]) => void;
-}
-export interface ApiExtensionModalData {
-  name: string;
-  apiEndpoint: string;
-  apiKey: string;
-}
-export interface ApiExtensionModalRef {
-  handleOpen: () => void;
-}
 export interface ChatData {
   label?: string;
   model_config_id?: string;
@@ -206,30 +148,6 @@ export interface AiPromptForm {
   message?: string;
   current_prompt?: string;
 }
-export interface ToolModalRef {
-  handleOpen: () => void;
-}
-
-export interface ToolOption {
-  value?: string | number | null;
-  label?: React.ReactNode;
-  description?: string;
-  children?: ToolOption[];
-  isLeaf?: boolean;
-  method_id?: string;
-  operation?: string;
-  parameters?: Parameter[];
-  tool_id?: string;
-  enabled?: boolean;
-}
-export interface Parameter {
-  name: string;
-  type: string;
-  description: string;
-  required: boolean;
-  default: any;
-  enum: null | string[];
-  minimum: number;
-  maximum: number;
-  pattern: null | string;
+export interface ChatVariableConfigModalRef {
+  handleOpen: (values: Variable[]) => void;
 }
\ No newline at end of file

From 2d90b0c7527f288f71492198d86bcf4642614b1a Mon Sep 17 00:00:00 2001
From: zhaoying <yzhao96@best-inc.com>
Date: Mon, 19 Jan 2026 17:00:26 +0800
Subject: [PATCH 05/12] refactor: extract useVariableList; properties add
 output variable

---
 web/src/i18n/en.ts                            |    1 +
 web/src/i18n/zh.ts                            |    1 +
 .../Editor/plugin/InitialValuePlugin.tsx      |    6 +-
 .../Properties/GroupVariableList/index.tsx    |   16 +-
 .../components/Properties/VariableSelect.tsx  |    4 +-
 .../Properties/hooks/useVariableList.ts       |  209 +++
 .../Workflow/components/Properties/index.tsx  | 1155 +++++------------
 .../views/Workflow/hooks/useWorkflowGraph.ts  |    5 -
 8 files changed, 562 insertions(+), 835 deletions(-)
 create mode 100644 web/src/views/Workflow/components/Properties/hooks/useVariableList.ts

diff --git a/web/src/i18n/en.ts b/web/src/i18n/en.ts
index 1341bf55..bc757797 100644
--- a/web/src/i18n/en.ts
+++ b/web/src/i18n/en.ts
@@ -1967,6 +1967,7 @@ Memory Bear: After the rebellion, regional warlordism intensified for several re
         value: 'Value',
         addCase: 'Add Condition',
         addVariable: 'Add Variables',
+        output: 'Output Variable'
       },
 
       clear: 'Clear',
diff --git a/web/src/i18n/zh.ts b/web/src/i18n/zh.ts
index b6834fea..eeee6bc9 100644
--- a/web/src/i18n/zh.ts
+++ b/web/src/i18n/zh.ts
@@ -2061,6 +2061,7 @@ export const zh = {
         value: '值',
         addCase: '添加条件',
         addVariable: '添加变量',
+        output: '输出变量'
       },
 
       clear: '清空',
diff --git a/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx b/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx
index 5ad18dcd..22de9592 100644
--- a/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx
+++ b/web/src/views/Workflow/components/Editor/plugin/InitialValuePlugin.tsx
@@ -33,7 +33,8 @@ const InitialValuePlugin: React.FC<InitialValuePluginProps> = ({ value, options
 
   useEffect(() => {
     if (value !== prevValueRef.current && !isUserInputRef.current) {
-      editor.update(() => {
+      queueMicrotask(() => {
+        editor.update(() => {
         const root = $getRoot();
         root.clear();
 
@@ -98,7 +99,8 @@ const InitialValuePlugin: React.FC<InitialValuePluginProps> = ({ value, options
           });
           root.append(paragraph);
         }
-      }, { discrete: true });
+        }, { discrete: true });
+      });
     }
     
     prevValueRef.current = value;
diff --git a/web/src/views/Workflow/components/Properties/GroupVariableList/index.tsx b/web/src/views/Workflow/components/Properties/GroupVariableList/index.tsx
index 06ea9e86..81eac38e 100644
--- a/web/src/views/Workflow/components/Properties/GroupVariableList/index.tsx
+++ b/web/src/views/Workflow/components/Properties/GroupVariableList/index.tsx
@@ -17,7 +17,7 @@ const GroupVariableList: FC<GroupVariableListProps> = ({
   name,
   options = [],
   isCanAdd = false,
-  size = "middle"
+  size = "small"
 }) => {
   const { t } = useTranslation();
   const form = Form.useFormInstance();
@@ -37,16 +37,10 @@ const GroupVariableList: FC<GroupVariableListProps> = ({
     }
     
     return (
-      <div className="rb:mb-4">
-        <Row gutter={12} className="rb:mb-2!">
-          <Col span={12}>
-            <Form.Item
-              noStyle
-            >
-              {t('workflow.config.var-aggregator.variable')}
-            </Form.Item>
-          </Col>
-        </Row>
+      <div>
+        <div className="rb:font-medium rb:text-[12px] rb:mb-1">
+          {t('workflow.config.var-aggregator.variable')}
+        </div>
 
         <Form.Item
           name={name}
diff --git a/web/src/views/Workflow/components/Properties/VariableSelect.tsx b/web/src/views/Workflow/components/Properties/VariableSelect.tsx
index 7f5fc28a..59fd7d5a 100644
--- a/web/src/views/Workflow/components/Properties/VariableSelect.tsx
+++ b/web/src/views/Workflow/components/Properties/VariableSelect.tsx
@@ -34,9 +34,9 @@ const VariableSelect: FC<VariableSelectProps> = ({
     if (filterOption) {
       return (
         <span
-          className={clsx("rb:w-full rb:wrap-break-word rb:line-clamp-1 rb:border rb:border-[#DFE4ED] rb:rounded-md rb:bg-white rb:text-[12px] rb:inline-flex rb:items-center rb:px-1.5 rb:cursor-pointer", {
+          className={clsx("rb:max-w-full rb:wrap-break-word rb:line-clamp-1 rb:border rb:border-[#DFE4ED] rb:rounded-md rb:bg-white rb:text-[12px] rb:inline-flex rb:items-center rb:px-1.5 rb:cursor-pointer", {
             'rb:leading-5.5!': size !== 'small',
-            'rb:leading-4!': size === 'small'
+            'rb:leading-4! rb:text-[10px]!': size === 'small'
           })}
           contentEditable={false}
         >
diff --git a/web/src/views/Workflow/components/Properties/hooks/useVariableList.ts b/web/src/views/Workflow/components/Properties/hooks/useVariableList.ts
new file mode 100644
index 00000000..ab37fec9
--- /dev/null
+++ b/web/src/views/Workflow/components/Properties/hooks/useVariableList.ts
@@ -0,0 +1,209 @@
+import { useMemo, useEffect, useState } from 'react';
+import { Graph, Node } from '@antv/x6';
+import type { Suggestion } from '../../Editor/plugin/AutocompletePlugin';
+import type { ChatVariable } from '../../../types';
+
+const NODE_VARIABLES = {
+  llm: [{ label: 'output', dataType: 'string', field: 'output' }],
+  'jinja-render': [{ label: 'output', dataType: 'string', field: 'output' }],
+  tool: [{ label: 'data', dataType: 'string', field: 'data' }],
+  'knowledge-retrieval': [{ label: 'output', dataType: 'array[object]', field: 'output' }],
+  'parameter-extractor': [
+    { label: '__is_success', dataType: 'number', field: '__is_success' },
+    { label: '__reason', dataType: 'string', field: '__reason' }
+  ],
+  'http-request': [
+    { label: 'body', dataType: 'string', field: 'body' },
+    { label: 'status_code', dataType: 'number', field: 'status_code' }
+  ],
+  'question-classifier': [{ label: 'class_name', dataType: 'string', field: 'class_name' }],
+  'memory-read': [
+    { label: 'answer', dataType: 'string', field: 'answer' },
+    { label: 'intermediate_outputs', dataType: 'array[object]', field: 'intermediate_outputs' }
+  ]
+} as const;
+
+const addVariable = (
+  list: Suggestion[],
+  keys: Set<string>,
+  key: string,
+  label: string,
+  dataType: string,
+  value: string,
+  nodeData: any,
+  extra?: Partial<Suggestion>
+) => {
+  if (!keys.has(key)) {
+    keys.add(key);
+    list.push({ key, label, type: 'variable', dataType, value, nodeData, ...extra });
+  }
+};
+
+const processNodeVariables = (
+  nodeData: any,
+  dataNodeId: string,
+  variableList: Suggestion[],
+  addedKeys: Set<string>
+) => {
+  const { type, config } = nodeData;
+
+  if (type in NODE_VARIABLES) {
+    NODE_VARIABLES[type as keyof typeof NODE_VARIABLES].forEach(({ label, dataType, field }) => {
+      addVariable(variableList, addedKeys, `${dataNodeId}_${label}`, label, dataType, `${dataNodeId}.${field}`, nodeData);
+    });
+  }
+
+  switch (type) {
+    case 'start':
+      [...(config?.variables?.defaultValue ?? []), ...(config?.variables?.value ?? [])].forEach((v: any) => {
+        if (v?.name) addVariable(variableList, addedKeys, `${dataNodeId}_${v.name}`, v.name, v.type, `${dataNodeId}.${v.name}`, nodeData);
+      });
+      config?.variables?.sys?.forEach((v: any) => {
+        if (v?.name) addVariable(variableList, addedKeys, `${dataNodeId}_sys_${v.name}`, `sys.${v.name}`, v.type, `sys.${v.name}`, nodeData);
+      });
+      break;
+
+    case 'parameter-extractor':
+      (config?.params?.defaultValue || []).forEach((p: any) => {
+        if (p?.name) addVariable(variableList, addedKeys, `${dataNodeId}_${p.name}`, p.name, p.type || 'string', `${dataNodeId}.${p.name}`, nodeData);
+      });
+      break;
+
+    case 'var-aggregator':
+      if (config.group.defaultValue) {
+        (config.group_variables.defaultValue || []).forEach((gv: any) => {
+          if (gv?.key) {
+            let dt = 'string';
+            if (gv.value?.[0]) {
+              const fv = variableList.find(v => `{{${v.value}}}` === gv.value[0]);
+              if (fv) dt = fv.dataType;
+            }
+            addVariable(variableList, addedKeys, `${dataNodeId}_${gv.key}`, gv.key, dt, `${dataNodeId}.${gv.key}`, nodeData);
+          }
+        });
+      } else {
+        const fv = (config.group_variables.defaultValue || [])[0];
+        let dt = 'any';
+        if (fv) {
+          const found = variableList.find(v => `{{${v.value}}}` === fv);
+          if (found) dt = found.dataType;
+        }
+        addVariable(variableList, addedKeys, `${dataNodeId}_output`, 'output', dt, `${dataNodeId}.output`, nodeData);
+      }
+      break;
+
+    case 'iteration':
+      let dt = 'string';
+      if (nodeData.output) {
+        const sv = variableList.find(v => v.value === nodeData.output);
+        if (sv) dt = sv.dataType;
+      }
+      addVariable(variableList, addedKeys, `${dataNodeId}_output`, 'output', `array[${dt}]`, `${dataNodeId}.output`, nodeData);
+      break;
+
+    case 'loop':
+      (config.cycle_vars.defaultValue || []).forEach((cv: any) => {
+        if (cv.name?.trim()) addVariable(variableList, addedKeys, `${dataNodeId}_cycle_${cv.name}`, cv.name, cv.type || 'string', `${dataNodeId}.${cv.name}`, nodeData);
+      });
+      break;
+  }
+};
+
+const hasOutputNodeTypes = [
+  'llm',
+  'knowledge-retrieval',
+  'memory-read',
+  'question-classifier',
+  'var-aggregator',
+  'http-request',
+  'tool',
+  'jinja-render'
+]
+export const getCurrentNodeVariables = (nodeData: any, values: any): Suggestion[] => {
+  if (!nodeData || !hasOutputNodeTypes.includes(nodeData.type)) return [];
+  const list: Suggestion[] = [];
+  const keys = new Set<string>();
+  const dataNodeId = nodeData.id;
+
+  processNodeVariables({
+    ...nodeData,
+    config: {
+      ...nodeData.config,
+      ...values
+    }
+  }, dataNodeId, list, keys);
+  return nodeData.type === 'var-aggregator' && !nodeData.config.group.defaultValue ? [] : list;
+};
+
+export const useVariableList = (
+  selectedNode: Node | null | undefined,
+  graphRef: React.MutableRefObject<Graph | undefined>,
+  chatVariables: ChatVariable[]
+) => {
+  const [trigger, setTrigger] = useState(0);
+
+  const variableList = useMemo(() => {
+    if (!selectedNode || !graphRef?.current) return [];
+
+    const list: Suggestion[] = [];
+    const graph = graphRef.current;
+    const edges = graph.getEdges();
+    const nodes = graph.getNodes();
+    const keys = new Set<string>();
+
+    const getPreviousNodes = (nodeId: string, visited = new Set<string>()): string[] => {
+      if (visited.has(nodeId)) return [];
+      visited.add(nodeId);
+      const prev = edges.filter(e => e.getTargetCellId() === nodeId).map(e => e.getSourceCellId());
+      return [...prev, ...prev.flatMap(id => getPreviousNodes(id, visited))];
+    };
+
+    const getParentLoop = (nodeId: string): Node | null => {
+      const node = nodes.find(n => n.id === nodeId);
+      const cycle = node?.getData()?.cycle;
+      if (cycle) {
+        const parent = nodes.find(n => n.getData().id === cycle);
+        if (parent?.getData()?.type === 'loop' || parent?.getData()?.type === 'iteration') return parent;
+      }
+      return null;
+    };
+
+    const childIds = nodes.filter(n => n.getData()?.cycle === selectedNode.id).map(n => n.id);
+    const parentLoop = getParentLoop(selectedNode.id);
+    const relevantIds = [...getPreviousNodes(selectedNode.id), ...childIds, ...(parentLoop ? getPreviousNodes(parentLoop.id) : [])];
+
+    chatVariables?.forEach(v => addVariable(list, keys, `CONVERSATION_${v.name}`, v.name, v.type, `conv.${v.name}`, { type: 'CONVERSATION', name: 'CONVERSATION', icon: '' }, { group: 'CONVERSATION' }));
+
+    relevantIds.forEach(id => {
+      const node = nodes.find(n => n.id === id);
+      if (node) processNodeVariables(node.getData(), node.getData().id, list, keys);
+    });
+
+    if (parentLoop) {
+      const pd = parentLoop.getData();
+      const pid = pd.id;
+      if (pd.type === 'loop') {
+        (pd.cycle_vars || []).forEach((cv: any) => addVariable(list, keys, `${pid}_cycle_${cv.name}`, cv.name, cv.type || 'String', `${pid}.${cv.name}`, pd));
+      } else if (pd.type === 'iteration' && pd.config.input.defaultValue) {
+        let itemType = 'object';
+        const iv = list.find(v => `{{${v.value}}}` === pd.config.input.defaultValue);
+        if (iv?.dataType.startsWith('array[')) itemType = iv.dataType.replace(/^array\[(.+)\]$/, '$1');
+        addVariable(list, keys, `${pid}_item`, 'item', itemType, `${pid}.item`, pd);
+        addVariable(list, keys, `${pid}_index`, 'index', 'number', `${pid}.index`, pd);
+      }
+    }
+
+    return list;
+  }, [selectedNode, graphRef, trigger, chatVariables]);
+
+  useEffect(() => {
+    if (!graphRef?.current) return;
+    const graph = graphRef.current;
+    const handler = () => setTrigger(p => p + 1);
+    const events = ['edge:added', 'edge:removed', 'edge:changed', 'edge:connected', 'node:added', 'node:removed', 'node:change:data'];
+    events.forEach(e => graph.on(e, handler));
+    return () => events.forEach(e => graph.off(e, handler));
+  }, [graphRef]);
+
+  return variableList;
+};
diff --git a/web/src/views/Workflow/components/Properties/index.tsx b/web/src/views/Workflow/components/Properties/index.tsx
index 0ea5e284..6d4571dc 100644
--- a/web/src/views/Workflow/components/Properties/index.tsx
+++ b/web/src/views/Workflow/components/Properties/index.tsx
@@ -2,7 +2,8 @@ import { type FC, useEffect, useState, useRef, useMemo } from "react";
 import clsx from 'clsx'
 import { useTranslation } from 'react-i18next'
 import { Graph, Node } from '@antv/x6';
-import { Form, Input, Select, InputNumber, Switch } from 'antd'
+import { Form, Input, Select, InputNumber, Switch, Divider, Space } from 'antd'
+import { CaretDownOutlined, CaretRightOutlined } from '@ant-design/icons';
 
 import type { NodeConfig, NodeProperties, ChatVariable } from '../../types'
 import Empty from '@/components/Empty';
@@ -24,7 +25,7 @@ import AssignmentList from './AssignmentList'
 import ToolConfig from './ToolConfig'
 import MemoryConfig from './MemoryConfig'
 import VariableList from './VariableList'
-// import { calculateVariableList } from './utils/variableListCalculator'
+import { useVariableList, getCurrentNodeVariables } from './hooks/useVariableList'
 import styles from './properties.module.css'
 import Editor from "../Editor";
 import RbSlider from './RbSlider'
@@ -49,12 +50,12 @@ const Properties: FC<PropertiesProps> = ({
   const [form] = Form.useForm<NodeConfig>();
   const [configs, setConfigs] = useState<Record<string,NodeConfig>>({} as Record<string,NodeConfig>)
   const values = Form.useWatch([], form);
-  const [graphUpdateTrigger, setGraphUpdateTrigger] = useState(0)
   const prevMappingNamesRef = useRef<string[]>([])
   const prevTemplateVarsRef = useRef<string[]>([])
   const syncTimeoutRef = useRef<number | null>(null)
   const isSyncingRef = useRef(false)
   const lastSyncSourceRef = useRef<'mapping' | 'template' | null>(null)
+  const variableList = useVariableList(selectedNode, graphRef, chatVariables)
 
   useEffect(() => {
     if (selectedNode?.getData()?.id) {
@@ -62,6 +63,7 @@ const Properties: FC<PropertiesProps> = ({
       prevMappingNamesRef.current = []
       prevTemplateVarsRef.current = []
       lastSyncSourceRef.current = null
+      setOutputCollapsed(true)
     }
   }, [selectedNode?.getData()?.id])
 
@@ -244,513 +246,7 @@ const Properties: FC<PropertiesProps> = ({
     }
   }, [values, selectedNode, form])
 
-  const variableList = useMemo(() => {
-    if (!selectedNode || !graphRef?.current) return [];
-    
-    const variableList: Suggestion[] = [];
-    const graph = graphRef.current;
-    const edges = graph.getEdges();
-    const nodes = graph.getNodes();
-    const addedKeys = new Set<string>();
-    
-    // Find all connected previous nodes (recursive)
-    const getAllPreviousNodes = (nodeId: string, visited = new Set<string>()): string[] => {
-      if (visited.has(nodeId)) return [];
-      visited.add(nodeId);
-      
-      const directPrevious = edges
-        .filter(edge => edge.getTargetCellId() === nodeId)
-        .map(edge => edge.getSourceCellId());
-      
-      const allPrevious = [...directPrevious];
-      directPrevious.forEach(prevNodeId => {
-        allPrevious.push(...getAllPreviousNodes(prevNodeId, visited));
-      });
-      
-      return allPrevious;
-    };
-    
-    // Find child nodes (nodes whose cycle field equals current node's ID)
-    const getChildNodes = (nodeId: string): string[] => {
-      return nodes
-        .filter(node => node.getData()?.cycle === nodeId)
-        .map(node => node.id);
-    };
-    
-    // Find parent loop/iteration node if current node is a child
-    const getParentLoopNode = (nodeId: string): Node | null => {
-      const node = nodes.find(n => n.id === nodeId);
-      if (!node) return null;
-      
-      const nodeData = node.getData();
-      const cycle = nodeData?.cycle;
-      
-      if (cycle) {
-        const parentNode = nodes.find(n => n.getData().id === cycle);
-        if (parentNode) {
-          const parentData = parentNode.getData();
-          if (parentData?.type === 'loop' || parentData?.type === 'iteration') {
-            return parentNode;
-          }
-        }
-      }
-      return null;
-    };
-    
-    const allPreviousNodeIds = getAllPreviousNodes(selectedNode.id);
-    const childNodeIds = getChildNodes(selectedNode.id);
-    const parentLoopNode = getParentLoopNode(selectedNode.id);
-    
-    console.log('childNodeIds', selectedNode, childNodeIds)
-    let allRelevantNodeIds = [...allPreviousNodeIds, ...childNodeIds];
-    
-    // Add variables from nodes preceding the parent loop/iteration node if current node is a child
-    if (parentLoopNode) {
-      const parentPreviousNodeIds = getAllPreviousNodes(parentLoopNode.id);
-      allRelevantNodeIds.push(...parentPreviousNodeIds);
-    }
 
-    // Add conversation variables from global config
-    const conversationVariables = chatVariables || [];
-
-    conversationVariables.forEach((variable: any) => {
-      const key = `CONVERSATION_${variable.name}`;
-      if (!addedKeys.has(key)) {
-        addedKeys.add(key);
-        variableList.push({
-          key,
-          label: variable.name,
-          type: 'variable',
-          dataType: variable.type,
-          value: `conv.${variable.name}`,
-          nodeData: { type: 'CONVERSATION', name: 'CONVERSATION', icon: '' },
-          group: 'CONVERSATION'
-        });
-      }
-    });
-    
-    allRelevantNodeIds.forEach(nodeId => {
-      const node = nodes.find(n => n.id === nodeId);
-      if (!node) return;
-      
-      const nodeData = node.getData();
-      const dataNodeId = nodeData.id; // Use the data.id instead of node.id for consistency
-
-      switch(nodeData.type) {
-        case 'start':
-          const list = [
-            ...(nodeData.config?.variables?.defaultValue ?? []),
-            ...(nodeData.config?.variables?.value ?? [])
-          ]
-          list.forEach((variable: any) => {
-            if (!variable || !variable?.name) return;
-            const key = `${dataNodeId}_${variable.name}`;
-            if (!addedKeys.has(key)) {
-              addedKeys.add(key);
-              variableList.push({
-                key,
-                label: variable.name,
-                type: 'variable',
-                dataType: variable.type,
-                value: `${dataNodeId}.${variable.name}`,
-                nodeData: nodeData,
-              });
-            }
-          });
-          nodeData.config?.variables?.sys?.forEach((variable: any) => {
-            if (!variable || !variable?.name) return;
-            const key = `${dataNodeId}_sys_${variable.name}`;
-            if (!addedKeys.has(key)) {
-              addedKeys.add(key);
-              variableList.push({
-                key,
-                label: `sys.${variable.name}`,
-                type: 'variable',
-                dataType: variable.type,
-                value: `sys.${variable.name}`,
-                nodeData: nodeData,
-              });
-            }
-          });
-          break
-        case 'llm':
-          const llmKey = `${dataNodeId}_output`;
-          if (!addedKeys.has(llmKey)) {
-            addedKeys.add(llmKey);
-            variableList.push({
-              key: llmKey,
-              label: 'output',
-              type: 'variable',
-              dataType: 'string',
-              value: `${dataNodeId}.output`,
-              nodeData: nodeData,
-            });
-          }
-          break
-        case 'knowledge-retrieval':
-          const knowledgeKey = `${dataNodeId}_output`;
-          if (!addedKeys.has(knowledgeKey)) {
-            addedKeys.add(knowledgeKey);
-            variableList.push({
-              key: knowledgeKey,
-              label: 'output',
-              type: 'variable',
-              dataType: 'array[object]',
-              value: `${dataNodeId}.output`,
-              nodeData: nodeData,
-            });
-          }
-          break
-        case 'parameter-extractor':
-          const successKey = `${dataNodeId}___is_success`;
-          const reasonKey = `${dataNodeId}___reason`;
-          if (!addedKeys.has(successKey)) {
-            addedKeys.add(successKey);
-            variableList.push({
-              key: successKey,
-              label: '__is_success',
-              type: 'variable',
-              dataType: 'number',
-              value: `${dataNodeId}.__is_success`,
-              nodeData: nodeData,
-            });
-          }
-          if (!addedKeys.has(reasonKey)) {
-            addedKeys.add(reasonKey);
-            variableList.push({
-              key: reasonKey,
-              label: '__reason',
-              type: 'variable',
-              dataType: 'string',
-              value: `${dataNodeId}.__reason`,
-              nodeData: nodeData,
-            });
-          }
-          // Add params variables
-          const paramsList = nodeData.config?.params?.defaultValue || [];
-          paramsList.forEach((param: any) => {
-            if (!param || !param?.name) return;
-            const paramKey = `${dataNodeId}_${param.name}`;
-            if (!addedKeys.has(paramKey)) {
-              addedKeys.add(paramKey);
-              variableList.push({
-                key: paramKey,
-                label: param.name,
-                type: 'variable',
-                dataType: param.type || 'string',
-                value: `${dataNodeId}.${param.name}`,
-                nodeData: nodeData,
-              });
-            }
-          });
-          break
-        case 'var-aggregator':
-          if (nodeData.config.group.defaultValue) {
-            // If group=true, add variables from group_variables with key as variable name
-            const groupVariables = nodeData.config.group_variables.defaultValue || [];
-            groupVariables?.forEach((groupVar: any) => {
-              if (!groupVar || !groupVar.key) return;
-              
-              // Determine dataType from first variable in the group
-              let groupDataType = 'string';
-              if (groupVar.value && Array.isArray(groupVar.value) && groupVar.value.length > 0) {
-                const firstVariableValue = groupVar.value[0];
-                const firstVariable = variableList.find(v => `{{${v.value}}}` === firstVariableValue);
-                if (firstVariable) {
-                  groupDataType = firstVariable.dataType;
-                }
-              }
-              
-              const groupVarKey = `${dataNodeId}_${groupVar.key}`;
-              if (!addedKeys.has(groupVarKey)) {
-                addedKeys.add(groupVarKey);
-                variableList.push({
-                  key: groupVarKey,
-                  label: groupVar.key,
-                  type: 'variable',
-                  dataType: groupDataType,
-                  value: `${dataNodeId}.${groupVar.key}`,
-                  nodeData: nodeData,
-                });
-              }
-            });
-          } else {
-            // If group=false, add output variable with type from first group_variable
-            const groupVariables = nodeData.config.group_variables.defaultValue || [];
-            const firstVariable = groupVariables[0];
-            let outputDataType: string = 'any';
-            if (firstVariable) {
-              const filterVo = [...variableList].find(v => {
-                return `{{${v.value}}}` === firstVariable
-              })
-              if (filterVo) {
-                outputDataType = filterVo?.dataType
-              }
-            }
-            
-            const varAggregatorKey = `${dataNodeId}_output`;
-            if (!addedKeys.has(varAggregatorKey)) {
-              addedKeys.add(varAggregatorKey);
-              variableList.push({
-                key: varAggregatorKey,
-                label: 'output',
-                type: 'variable',
-                dataType: outputDataType,
-                value: `${dataNodeId}.output`,
-                nodeData: nodeData,
-              });
-            }
-          }
-          break
-        case 'http-request':
-          const httpBodyKey = `${dataNodeId}_body`;
-          const httpStatusKey = `${dataNodeId}_status_code`;
-          if (!addedKeys.has(httpBodyKey)) {
-            addedKeys.add(httpBodyKey);
-            variableList.push({
-              key: httpBodyKey,
-              label: 'body',
-              type: 'variable',
-              dataType: 'string',
-              value: `${dataNodeId}.body`,
-              nodeData: nodeData,
-            });
-          }
-          if (!addedKeys.has(httpStatusKey)) {
-            addedKeys.add(httpStatusKey);
-            variableList.push({
-              key: httpStatusKey,
-              label: 'status_code',
-              type: 'variable',
-              dataType: 'number',
-              value: `${dataNodeId}.status_code`,
-              nodeData: nodeData,
-            });
-          }
-          break
-        case 'jinja-render':
-          const jinjaOutputKey = `${dataNodeId}_output`;
-          if (!addedKeys.has(jinjaOutputKey)) {
-            addedKeys.add(jinjaOutputKey);
-            variableList.push({
-              key: jinjaOutputKey,
-              label: 'output',
-              type: 'variable',
-              dataType: 'string',
-              value: `${dataNodeId}.output`,
-              nodeData: nodeData,
-            });
-          }
-          break
-        case 'question-classifier':
-          const classNameKey = `${dataNodeId}_class_name`;
-          // const outputKey = `${dataNodeId}_output`;
-          if (!addedKeys.has(classNameKey)) {
-            addedKeys.add(classNameKey);
-            variableList.push({
-              key: classNameKey,
-              label: 'class_name',
-              type: 'variable',
-              dataType: 'string',
-              value: `${dataNodeId}.class_name`,
-              nodeData: nodeData,
-            });
-          }
-          // if (!addedKeys.has(outputKey)) {
-          //   addedKeys.add(outputKey);
-          //   variableList.push({
-          //     key: outputKey,
-          //     label: 'output',
-          //     type: 'variable',
-          //     dataType: 'string',
-          //     value: `${dataNodeId}.output`,
-          //     nodeData: nodeData,
-          //   });
-          // }
-          break
-        case 'iteration':
-          const iterationOutputKey = `${dataNodeId}_output`;
-          if (!addedKeys.has(iterationOutputKey)) {
-            addedKeys.add(iterationOutputKey);
-            // Get the data type from the output configuration, default to string
-            const outputConfig = nodeData.output;
-            let outputDataType = 'string';
-            if (outputConfig) {
-              // Find the selected variable from variableList to get its type
-              const selectedVariable = variableList.find(v => v.value === outputConfig);
-              if (selectedVariable) {
-                outputDataType = selectedVariable.dataType;
-              }
-            }
-            variableList.push({
-              key: iterationOutputKey,
-              label: 'output',
-              type: 'variable',
-              dataType: `array[${outputDataType}]`,
-              value: `${dataNodeId}.output`,
-              nodeData: nodeData,
-            });
-          }
-          break
-        case 'loop':
-          const cycleVars = nodeData.config.cycle_vars.defaultValue || [];
-          console.log('cycleVars', cycleVars)
-          cycleVars.forEach((cycleVar: any) => {
-            const cycleVarKey = `${dataNodeId}_cycle_${cycleVar.name}`;
-            if (!addedKeys.has(cycleVarKey)) {
-              addedKeys.add(cycleVarKey);
-              if (cycleVar.name && cycleVar.name.trim() !== '') {
-                variableList.push({
-                  key: cycleVarKey,
-                  label: cycleVar.name,
-                  type: 'variable',
-                  dataType: cycleVar.type || 'string',
-                  value: `${dataNodeId}.${cycleVar.name}`,
-                  nodeData: nodeData,
-                });
-              }
-            }
-          });
-          break
-        case 'tool':
-          const toolDataKey = `${dataNodeId}_data`;
-          if (!addedKeys.has(toolDataKey)) {
-            addedKeys.add(toolDataKey);
-            variableList.push({
-              key: toolDataKey,
-              label: 'data',
-              type: 'variable',
-              dataType: 'string',
-              value: `${dataNodeId}.data`,
-              nodeData: nodeData,
-            });
-          }
-          break
-        case 'memory-read':
-          const memoryReadAnswerKey = `${dataNodeId}_answer`;
-          const memoryReadIntermediateOutputs = `${dataNodeId}_intermediate_outputs`;
-          if (!addedKeys.has(memoryReadAnswerKey)) {
-            addedKeys.add(memoryReadAnswerKey);
-            variableList.push({
-              key: memoryReadAnswerKey,
-              label: 'answer',
-              type: 'variable',
-              dataType: 'string',
-              value: `${dataNodeId}.answer`,
-              nodeData: nodeData,
-            });
-          }
-          if (!addedKeys.has(memoryReadIntermediateOutputs)) {
-            addedKeys.add(memoryReadIntermediateOutputs);
-            variableList.push({
-              key: memoryReadIntermediateOutputs,
-              label: 'intermediate_outputs',
-              type: 'variable',
-              dataType: 'array[object]',
-              value: `${dataNodeId}.intermediate_outputs`,
-              nodeData: nodeData,
-            });
-          }
-          break
-      }
-    });
-
-
-    // Add parent loop/iteration node variables if current node is a child
-    if (parentLoopNode) {
-      const parentData = parentLoopNode.getData();
-      const parentNodeId = parentLoopNode.getData().id;
-
-      if (parentData.type === 'loop') {
-        const cycleVars = parentData.cycle_vars || [];
-        cycleVars.forEach((cycleVar: any) => {
-          const key = `${parentNodeId}_cycle_${cycleVar.name}`;
-          if (!addedKeys.has(key)) {
-            addedKeys.add(key);
-            variableList.push({
-              key,
-              label: cycleVar.name,
-              type: 'variable',
-              dataType: cycleVar.type || 'String',
-              value: `${parentNodeId}.${cycleVar.name}`,
-              nodeData: parentData,
-            });
-          }
-        });
-      } else if (parentData.type === 'iteration') {
-        // Add item and index variables for iteration parent only if input has value
-        if (parentData.config.input.defaultValue) {
-          const itemKey = `${parentNodeId}_item`;
-          const indexKey = `${parentNodeId}_index`;
-
-          // Determine item dataType from input variable
-          let itemDataType = 'object';
-          const inputVariable = variableList.find(v => `{{${v.value}}}` === parentData.config.input.defaultValue);
-          console.log('itemDataType defaultValue', parentData.config.input.defaultValue, variableList, inputVariable)
-          if (inputVariable && inputVariable.dataType.startsWith('array[')) {
-            itemDataType = inputVariable.dataType.replace(/^array\[(.+)\]$/, '$1');
-            console.log('itemDataType', itemDataType)
-          }
-
-
-          if (!addedKeys.has(itemKey)) {
-            addedKeys.add(itemKey);
-            variableList.push({
-              key: itemKey,
-              label: 'item',
-              type: 'variable',
-              dataType: itemDataType,
-              value: `${parentNodeId}.item`,
-              nodeData: parentData,
-            });
-          }
-
-          if (!addedKeys.has(indexKey)) {
-            addedKeys.add(indexKey);
-            variableList.push({
-              key: indexKey,
-              label: 'index',
-              type: 'variable',
-              dataType: 'number',
-              value: `${parentNodeId}.index`,
-              nodeData: parentData,
-            });
-          }
-        }
-      }
-    }
-
-    return variableList;
-  }, [selectedNode, graphRef, graphUpdateTrigger, chatVariables]);
-
-  // Trigger variableList update when graph edges or nodes change
-  useEffect(() => {
-    if (!graphRef?.current) return;
-    
-    const graph = graphRef.current;
-    const handleGraphChange = () => {
-      console.log('handleGraphChange')
-      // Force variableList recalculation by updating trigger
-      setGraphUpdateTrigger(prev => prev + 1);
-    };
-    
-    // Listen to graph changes
-    graph.on('edge:added', handleGraphChange);
-    graph.on('edge:removed', handleGraphChange);
-    graph.on('edge:changed', handleGraphChange);
-    graph.on('node:added', handleGraphChange);
-    graph.on('node:removed', handleGraphChange);
-    graph.on('node:change:data', handleGraphChange);
-    
-    return () => {
-      graph.off('edge:added', handleGraphChange);
-      graph.off('edge:removed', handleGraphChange);
-      graph.off('edge:changed', handleGraphChange);
-      graph.off('node:added', handleGraphChange);
-      graph.off('node:removed', handleGraphChange);
-      graph.off('node:change:data', handleGraphChange);
-    };
-  }, [graphRef]);
 
   // Filter out boolean type variables for loop and llm nodes
   const getFilteredVariableList = (nodeType?: string, key?: string) => {
@@ -994,324 +490,353 @@ const Properties: FC<PropertiesProps> = ({
   // const defaultVariableList = calculateVariableList(selectedNode as Node, graphRef, workflowConfig )
 
   console.log('values', values)
-  console.log('variableList', variableList)
+
+  const currentNodeVariables = useMemo(() => {
+    if (!selectedNode) return []
+    return getCurrentNodeVariables(selectedNode?.getData(), values)
+  }, [selectedNode?.getData(), values])
+
+  const [outputCollapsed, setOutputCollapsed] = useState(true)
+  const handleToggle = () => {
+    setOutputCollapsed((prev: boolean) => !prev)
+  }
+  console.log('variableList', variableList, currentNodeVariables)
 
   return (
     <div className={clsx("rb:w-75 rb:fixed rb:right-0 rb:top-16 rb:bottom-0 rb:p-3 rb:pb-6", styles.properties)}>
       <div className="rb:font-medium rb:leading-5 rb:pb-3 rb:mb-3 rb:border-b rb:border-b-[#DFE4ED]">{t('workflow.nodeProperties')}</div>
       {!selectedNode
         ? <Empty url={emptyIcon} size={140} className="rb:h-full rb:mx-15" title={t('workflow.empty')} />
-        : <Form form={form} size="small" layout="vertical" className="rb:h-[calc(100%-20px)] rb:overflow-x-hidden rb:overflow-y-auto">
-            <Form.Item name="name" label={t('workflow.nodeName')}>
-              <Input
-                placeholder={t('common.pleaseEnter')}
-                onChange={(e) => {
-                  updateNodeLabel(e.target.value);
-                }}
+        : <div className="rb:h-[calc(100%-20px)] rb:overflow-x-hidden rb:overflow-y-auto">
+        <Form form={form} size="small" layout="vertical">
+          <Form.Item name="name" label={t('workflow.nodeName')}>
+            <Input
+              placeholder={t('common.pleaseEnter')}
+              onChange={(e) => {
+                updateNodeLabel(e.target.value);
+              }}
+            />
+          </Form.Item>
+          <Form.Item name="id" label="ID">
+            <Input disabled />
+          </Form.Item>
+          
+          {selectedNode?.data?.type === 'http-request'
+            ? <HttpRequest 
+                options={variableList} 
+                selectedNode={selectedNode}
+                graphRef={graphRef}
               />
-            </Form.Item>
-            <Form.Item name="id" label="ID">
-              <Input disabled />
-            </Form.Item>
-            
-            {selectedNode?.data?.type === 'http-request'
-              ? <HttpRequest 
-                  options={variableList} 
-                  selectedNode={selectedNode}
-                  graphRef={graphRef}
-                />
-              : selectedNode?.data?.type === 'tool'
-              ? <ToolConfig options={variableList} />
-              : configs && Object.keys(configs).length > 0 && Object.keys(configs).map((key) => {
-                const config = configs[key] || {}
+            : selectedNode?.data?.type === 'tool'
+            ? <ToolConfig options={variableList} />
+            : configs && Object.keys(configs).length > 0 && Object.keys(configs).map((key) => {
+              const config = configs[key] || {}
 
-                if (config.dependsOn && (values as any)?.[config.dependsOn as string] !== config.dependsOnValue) {
-                  return null
-                }
-
-                if (selectedNode?.data?.type === 'start' && key === 'variables' && config.type === 'define') {
-                  return (
-                    <Form.Item key={key} name={key}>
-                      <VariableList
-                        parentName={key}
-                        selectedNode={selectedNode}
-                        config={config}
-                      />
-                    </Form.Item>
-                  )
-                }
-
-                if (selectedNode?.data?.type === 'llm' && key === 'messages' && config.type === 'define') {
-                  // 为llm节点且isArray=true时添加context变量支持
-                  let contextVariableList = [...getFilteredVariableList('llm')];
-                  const isArrayMode = config.isArray !== false; // 默认为true
-                  
-                  if (isArrayMode) {
-                    const contextKey = `${selectedNode.id}_context`;
-                    const hasContextVariable = contextVariableList.some(v => v.key === contextKey);
-                    
-                    if (!hasContextVariable) {
-                      contextVariableList.unshift({
-                        key: contextKey,
-                        label: 'context',
-                        type: 'variable',
-                        dataType: 'String',
-                        value: `context`,
-                        nodeData: selectedNode.getData(),
-                        isContext: true,
-                      });
-                    }
-                  }
-                  return (
-                    <Form.Item key={key} name={key}>
-                      <MessageEditor
-                        key={key}
-                        options={contextVariableList.filter(variable => variable.nodeData?.type !== 'knowledge-retrieval')} 
-                        parentName={key}
-                        placeholder={t(config.placeholder || 'common.pleaseSelect')}
-                        size="small"
-                      />
-                    </Form.Item>
-                  )
-                }
-                if (config.type === 'define') {
-                  return null
-                }
-
-                if (config.type === 'knowledge') {
-                  return (
-                    <Form.Item
-                      key={key}
-                      name={key}
-                    >
-                      <Knowledge />
-                    </Form.Item>
-                  )
-                }
-
-                if (config.type === 'messageEditor') {
-                  return (
-                    <Form.Item key={key} name={key}>
-                      <MessageEditor 
-                        title={t(`workflow.config.${selectedNode?.data?.type}.${key}`)}
-                        isArray={!!config.isArray} 
-                        parentName={key}
-                        enableJinja2={config.enableJinja2 as boolean}
-                        options={getFilteredVariableList(selectedNode?.data?.type, key)}
-                        titleVariant={config.titleVariant}
-                        size="small"
-                      />
-                    </Form.Item>
-                  )
-                }
-
-                if (config.type === 'paramList') {
-                  return (
-                    <Form.Item key={key} name={key}>
-                      <ParamsList
-                        label={t(`workflow.config.${selectedNode?.data?.type}.${key}`)}
-                      />
-                    </Form.Item>
-                  
-                  )
-                }
-                if (config.type === 'groupVariableList') {
-                  return (
-                    <Form.Item key={key} name={key}>
-                      <GroupVariableList
-                        name={key}
-                        options={getFilteredVariableList(selectedNode?.data?.type, key)}
-                        isCanAdd={!!(values as any)?.group}
-                        size="small"
-                      />
-                    </Form.Item>
-                  )
-                }
-                if (config.type === 'caseList') {
-                  return (
-                    <Form.Item key={key} name={key}>
-                      <CaseList
-                        name={key}
-                        options={getFilteredVariableList(selectedNode?.data?.type, key)}
-                        selectedNode={selectedNode}
-                        graphRef={graphRef}
-                      />
-                    </Form.Item>
-                  )
-                }
-
-                if (config.type === 'mappingList') {
-                  return (
-                    <Form.Item key={key} name={key} noStyle>
-                      <MappingList name={key} options={getFilteredVariableList(selectedNode?.data?.type, key)} />
-                    </Form.Item>
-                  
-                  )
-                }
-                if (config.type === 'cycleVarsList') {
-                  return (
-                    <Form.Item key={key} name={key}>
-                      <CycleVarsList
-                        size="small"
-                        parentName={key}
-                        options={getFilteredVariableList(selectedNode?.data?.type, key)}
-                      />
-                    </Form.Item>
-                  )
-                }
-                if (config.type === 'assignmentList') {
-                  return (
-                    <Form.Item key={key} name={key}>
-                      <AssignmentList
-                        parentName={key}
-                        options={(() => {
-                          if (config.filterLoopIterationVars) {
-                            const loopIterationVars: Suggestion[] = [];
-                            
-                            return [...getFilteredVariableList(selectedNode?.data?.type, key), ...loopIterationVars];
-                          }
-                          return getFilteredVariableList(selectedNode?.data?.type, key);
-                        })()
-                      }
-                    />
-                    </Form.Item>
-                  )
-                }
-                if (config.type === 'memoryConfig') {
-                  return (
-                    <Form.Item
-                      key={key}
-                      name={key}
-                      noStyle
-                    >
-                      <MemoryConfig
-                        parentName={key}
-                        options={getFilteredVariableList('llm')}
-                      />
-                    </Form.Item>
-                  )
-                }
-                if (config.type === 'conditionList') {
-                  return (
-                    <Form.Item
-                      key={key}
-                      name={key}
-                      noStyle
-                    >
-                      <ConditionList
-                        parentName={key}
-                        options={(() => {
-                          const cycleVars = values?.cycle_vars || [];
-                          const cycleVarSuggestions: Suggestion[] = cycleVars.filter(vo => vo.name && vo.name.trim() !== '').map((cycleVar: any) => ({
-                            key: `${selectedNode.id}_cycle_${cycleVar.name}`,
-                            label: cycleVar.name,
-                            type: 'variable',
-                            dataType: cycleVar.type || 'String',
-                            value: `${selectedNode.getData().id}.${cycleVar.name}`,
-                            nodeData: selectedNode.getData(),
-                          }));
-
-                          return [...getFilteredVariableList(selectedNode?.data?.type, key), ...cycleVarSuggestions];
-                        })()}
-                        selectedNode={selectedNode}
-                        graphRef={graphRef}
-                        addBtnText={t('workflow.config.addCase')}
-                      />
-                    </Form.Item>
-                  )
-                }
+              if (config.dependsOn && (values as any)?.[config.dependsOn as string] !== config.dependsOnValue) {
+                return null
+              }
 
+              if (selectedNode?.data?.type === 'start' && key === 'variables' && config.type === 'define') {
                 return (
-                  <Form.Item 
-                    key={key} 
-                    name={key}
-                    label={key === 'parallel_count' ? <span className="rb:text-[10px] rb:text-[#5B6167] rb:leading-3.5 rb:-mb-1!">{t(`workflow.config.${selectedNode?.data?.type}.${key}`)}</span> : t(`workflow.config.${selectedNode?.data?.type}.${key}`)}
-                    layout={config.type === 'switch' ? 'horizontal' : 'vertical'}
-                    className={key === 'parallel_count' ? 'rb:-mt-3! rb:leading-3.5!' : ''}
-                  >
-                    {config.type === 'input'
-                      ? <Input placeholder={t('common.pleaseEnter')} />
-                      : config.type === 'textarea'
-                      ? <Input.TextArea placeholder={t('common.pleaseEnter')} />
-                      : config.type === 'select'
-                      ? <Select
-                        options={config.needTranslation ? (config.options || []).map(vo => ({ ...vo, label: t(vo.label) })) : config.options}
-                        placeholder={t('common.pleaseSelect')}
-                      />
-                      : config.type === 'inputNumber'
-                      ? <InputNumber
-                          placeholder={t('common.pleaseEnter')}
-                          className="rb:w-full!"
-                          onChange={(value) => form.setFieldValue(key, value)}
-                        />
-                      : config.type === 'slider'
-                      ? <RbSlider min={config.min} max={config.max} step={config.step} />
-                      : config.type === 'customSelect'
-                      ? <CustomSelect
-                        placeholder={t('common.pleaseSelect')}
-                        url={config.url as string}
-                        params={config.params}
-                        hasAll={false}
-                        valueKey={config.valueKey}
-                        labelKey={config.labelKey}
-                        size="small"
-                      />
-                      : config.type === 'variableList'
-                      ? <VariableSelect
-                        placeholder={t(config.placeholder || 'common.pleaseSelect')}
-                        options={(() => {
-                          const baseVariableList = getFilteredVariableList(selectedNode?.data?.type, key);
-                          // Apply filtering if specified in config
-                          if (config.filterNodeTypes || config.filterVariableNames) {
-                            return baseVariableList.filter(variable => {
-                              const nodeTypeMatch = !config.filterNodeTypes || 
-                                (Array.isArray(config.filterNodeTypes) && config.filterNodeTypes.includes(variable.nodeData?.type));
-                              const variableNameMatch = !config.filterVariableNames || 
-                                (Array.isArray(config.filterVariableNames) && config.filterVariableNames.includes(variable.label));
-                              return nodeTypeMatch || variableNameMatch;
-                            });
-                          }
-                          // Filter child nodes for iteration output
-                          if (config.filterChildNodes && selectedNode) {
-                            const graph = graphRef.current;
-                            if (!graph) return [];
-                            
-                            const nodes = graph.getNodes();
-                            
-                            // Find child nodes whose cycle field equals parent node's ID
-                            const childNodes = nodes.filter(node => {
-                              const nodeData = node.getData();
-                              return nodeData?.cycle === selectedNode.id;
-                            });
-                            
-                            return baseVariableList.filter(variable => 
-                              childNodes.some(node => node.id === variable.nodeData?.id) || selectedNode?.data?.type === 'iteration' && key === 'output' && variable.value.includes('sys.')
-                            );
-                          }
-                          return baseVariableList;
-                        })()
-                        }
-                        size="small"
-                      />
-                      : config.type === 'switch'
-                      ? <Switch onChange={key === 'group' ? () => { form.setFieldValue('group_variables', []) } : undefined} />
-                      : config.type === 'categoryList'
-                      ? <CategoryList 
-                        parentName={key} 
-                        selectedNode={selectedNode}
-                        graphRef={graphRef}
-                        options={getFilteredVariableList(selectedNode?.data?.type, key)}
-                      />
-                      : config.type === 'editor'
-                      ? <Editor options={variableList} variant="outlined" size="small" />
-                      : null
-                    }
+                  <Form.Item key={key} name={key}>
+                    <VariableList
+                      parentName={key}
+                      selectedNode={selectedNode}
+                      config={config}
+                    />
                   </Form.Item>
                 )
-              })
-            }
-          </Form>
-      }
+              }
+
+              if (selectedNode?.data?.type === 'llm' && key === 'messages' && config.type === 'define') {
+                // 为llm节点且isArray=true时添加context变量支持
+                let contextVariableList = [...getFilteredVariableList('llm')];
+                const isArrayMode = config.isArray !== false; // 默认为true
+                
+                if (isArrayMode) {
+                  const contextKey = `${selectedNode.id}_context`;
+                  const hasContextVariable = contextVariableList.some(v => v.key === contextKey);
+                  
+                  if (!hasContextVariable) {
+                    contextVariableList.unshift({
+                      key: contextKey,
+                      label: 'context',
+                      type: 'variable',
+                      dataType: 'String',
+                      value: `context`,
+                      nodeData: selectedNode.getData(),
+                      isContext: true,
+                    });
+                  }
+                }
+                return (
+                  <Form.Item key={key} name={key}>
+                    <MessageEditor
+                      key={key}
+                      options={contextVariableList.filter(variable => variable.nodeData?.type !== 'knowledge-retrieval')} 
+                      parentName={key}
+                      placeholder={t(config.placeholder || 'common.pleaseSelect')}
+                      size="small"
+                    />
+                  </Form.Item>
+                )
+              }
+              if (config.type === 'define') {
+                return null
+              }
+
+              if (config.type === 'knowledge') {
+                return (
+                  <Form.Item
+                    key={key}
+                    name={key}
+                  >
+                    <Knowledge />
+                  </Form.Item>
+                )
+              }
+
+              if (config.type === 'messageEditor') {
+                return (
+                  <Form.Item key={key} name={key}>
+                    <MessageEditor 
+                      title={t(`workflow.config.${selectedNode?.data?.type}.${key}`)}
+                      isArray={!!config.isArray} 
+                      parentName={key}
+                      enableJinja2={config.enableJinja2 as boolean}
+                      options={getFilteredVariableList(selectedNode?.data?.type, key)}
+                      titleVariant={config.titleVariant}
+                      size="small"
+                    />
+                  </Form.Item>
+                )
+              }
+
+              if (config.type === 'paramList') {
+                return (
+                  <Form.Item key={key} name={key}>
+                    <ParamsList
+                      label={t(`workflow.config.${selectedNode?.data?.type}.${key}`)}
+                    />
+                  </Form.Item>
+                
+                )
+              }
+              if (config.type === 'groupVariableList') {
+                return (
+                  <Form.Item key={key} name={key}>
+                    <GroupVariableList
+                      name={key}
+                      options={getFilteredVariableList(selectedNode?.data?.type, key)}
+                      isCanAdd={!!(values as any)?.group}
+                      size="small"
+                    />
+                  </Form.Item>
+                )
+              }
+              if (config.type === 'caseList') {
+                return (
+                  <Form.Item key={key} name={key}>
+                    <CaseList
+                      name={key}
+                      options={getFilteredVariableList(selectedNode?.data?.type, key)}
+                      selectedNode={selectedNode}
+                      graphRef={graphRef}
+                    />
+                  </Form.Item>
+                )
+              }
+
+              if (config.type === 'mappingList') {
+                return (
+                  <Form.Item key={key} name={key} noStyle>
+                    <MappingList name={key} options={getFilteredVariableList(selectedNode?.data?.type, key)} />
+                  </Form.Item>
+                
+                )
+              }
+              if (config.type === 'cycleVarsList') {
+                return (
+                  <Form.Item key={key} name={key}>
+                    <CycleVarsList
+                      size="small"
+                      parentName={key}
+                      options={getFilteredVariableList(selectedNode?.data?.type, key)}
+                    />
+                  </Form.Item>
+                )
+              }
+              if (config.type === 'assignmentList') {
+                return (
+                  <Form.Item key={key} name={key}>
+                    <AssignmentList
+                      parentName={key}
+                      options={(() => {
+                        if (config.filterLoopIterationVars) {
+                          const loopIterationVars: Suggestion[] = [];
+                          
+                          return [...getFilteredVariableList(selectedNode?.data?.type, key), ...loopIterationVars];
+                        }
+                        return getFilteredVariableList(selectedNode?.data?.type, key);
+                      })()
+                    }
+                  />
+                  </Form.Item>
+                )
+              }
+              if (config.type === 'memoryConfig') {
+                return (
+                  <Form.Item
+                    key={key}
+                    name={key}
+                    noStyle
+                  >
+                    <MemoryConfig
+                      parentName={key}
+                      options={getFilteredVariableList('llm')}
+                    />
+                  </Form.Item>
+                )
+              }
+              if (config.type === 'conditionList') {
+                return (
+                  <Form.Item
+                    key={key}
+                    name={key}
+                    noStyle
+                  >
+                    <ConditionList
+                      parentName={key}
+                      options={(() => {
+                        const cycleVars = values?.cycle_vars || [];
+                        const cycleVarSuggestions: Suggestion[] = cycleVars.filter(vo => vo.name && vo.name.trim() !== '').map((cycleVar: any) => ({
+                          key: `${selectedNode.id}_cycle_${cycleVar.name}`,
+                          label: cycleVar.name,
+                          type: 'variable',
+                          dataType: cycleVar.type || 'String',
+                          value: `${selectedNode.getData().id}.${cycleVar.name}`,
+                          nodeData: selectedNode.getData(),
+                        }));
+
+                        return [...getFilteredVariableList(selectedNode?.data?.type, key), ...cycleVarSuggestions];
+                      })()}
+                      selectedNode={selectedNode}
+                      graphRef={graphRef}
+                      addBtnText={t('workflow.config.addCase')}
+                    />
+                  </Form.Item>
+                )
+              }
+
+              return (
+                <Form.Item 
+                  key={key} 
+                  name={key}
+                  label={key === 'parallel_count' ? <span className="rb:text-[10px] rb:text-[#5B6167] rb:leading-3.5 rb:-mb-1!">{t(`workflow.config.${selectedNode?.data?.type}.${key}`)}</span> : t(`workflow.config.${selectedNode?.data?.type}.${key}`)}
+                  layout={config.type === 'switch' ? 'horizontal' : 'vertical'}
+                  className={key === 'parallel_count' ? 'rb:-mt-3! rb:leading-3.5!' : ''}
+                >
+                  {config.type === 'input'
+                    ? <Input placeholder={t('common.pleaseEnter')} />
+                    : config.type === 'textarea'
+                    ? <Input.TextArea placeholder={t('common.pleaseEnter')} />
+                    : config.type === 'select'
+                    ? <Select
+                      options={config.needTranslation ? (config.options || []).map(vo => ({ ...vo, label: t(vo.label) })) : config.options}
+                      placeholder={t('common.pleaseSelect')}
+                    />
+                    : config.type === 'inputNumber'
+                    ? <InputNumber
+                        placeholder={t('common.pleaseEnter')}
+                        className="rb:w-full!"
+                        onChange={(value) => form.setFieldValue(key, value)}
+                      />
+                    : config.type === 'slider'
+                    ? <RbSlider min={config.min} max={config.max} step={config.step} />
+                    : config.type === 'customSelect'
+                    ? <CustomSelect
+                      placeholder={t('common.pleaseSelect')}
+                      url={config.url as string}
+                      params={config.params}
+                      hasAll={false}
+                      valueKey={config.valueKey}
+                      labelKey={config.labelKey}
+                      size="small"
+                    />
+                    : config.type === 'variableList'
+                    ? <VariableSelect
+                      placeholder={t(config.placeholder || 'common.pleaseSelect')}
+                      options={(() => {
+                        const baseVariableList = getFilteredVariableList(selectedNode?.data?.type, key);
+                        // Apply filtering if specified in config
+                        if (config.filterNodeTypes || config.filterVariableNames) {
+                          return baseVariableList.filter(variable => {
+                            const nodeTypeMatch = !config.filterNodeTypes || 
+                              (Array.isArray(config.filterNodeTypes) && config.filterNodeTypes.includes(variable.nodeData?.type));
+                            const variableNameMatch = !config.filterVariableNames || 
+                              (Array.isArray(config.filterVariableNames) && config.filterVariableNames.includes(variable.label));
+                            return nodeTypeMatch || variableNameMatch;
+                          });
+                        }
+                        // Filter child nodes for iteration output
+                        if (config.filterChildNodes && selectedNode) {
+                          const graph = graphRef.current;
+                          if (!graph) return [];
+                          
+                          const nodes = graph.getNodes();
+                          
+                          // Find child nodes whose cycle field equals parent node's ID
+                          const childNodes = nodes.filter(node => {
+                            const nodeData = node.getData();
+                            return nodeData?.cycle === selectedNode.id;
+                          });
+                          
+                          return baseVariableList.filter(variable => 
+                            childNodes.some(node => node.id === variable.nodeData?.id) || selectedNode?.data?.type === 'iteration' && key === 'output' && variable.value.includes('sys.')
+                          );
+                        }
+                        return baseVariableList;
+                      })()
+                      }
+                      size="small"
+                    />
+                    : config.type === 'switch'
+                    ? <Switch onChange={key === 'group' ? () => { form.setFieldValue('group_variables', []) } : undefined} />
+                    : config.type === 'categoryList'
+                    ? <CategoryList 
+                      parentName={key} 
+                      selectedNode={selectedNode}
+                      graphRef={graphRef}
+                      options={getFilteredVariableList(selectedNode?.data?.type, key)}
+                    />
+                    : config.type === 'editor'
+                    ? <Editor options={variableList} variant="outlined" size="small" />
+                    : null
+                  }
+                </Form.Item>
+              )
+            })
+          }
+        </Form>
+
+        {currentNodeVariables.length > 0 && !(!values?.group && selectedNode.getData().type === 'var-aggregator') &&
+          <div className="rb:pb-3">
+            <Divider />
+            <Space size={8} direction="vertical" className="rb:max-w-full!">
+              <div className="rb:font-medium rb:text-[12px] rb:leading-4.5 rb:cursor-pointer rb:ml-4" onClick={handleToggle}>
+                {t('workflow.config.output')}
+                {outputCollapsed ? <CaretRightOutlined /> : <CaretDownOutlined />}
+              </div>
+              {!outputCollapsed && currentNodeVariables.map(vo => (
+                <div key={vo.value} className="rb:ml-4 rb:text-[12px] rb:flex rb:gap-2">
+                  <span className="rb:font-medium">{vo.label}</span>
+                  <span className="rb:text-[#5B6167]">{vo.dataType}</span>
+                </div>
+              ))}
+            </Space>
+          </div>
+        }
+      </div>}
     </div>
   );
 };
diff --git a/web/src/views/Workflow/hooks/useWorkflowGraph.ts b/web/src/views/Workflow/hooks/useWorkflowGraph.ts
index 88723a4d..a7ebb29a 100644
--- a/web/src/views/Workflow/hooks/useWorkflowGraph.ts
+++ b/web/src/views/Workflow/hooks/useWorkflowGraph.ts
@@ -275,11 +275,6 @@ export const useWorkflowGraph = ({
       }, 100)
     }
     if (edges.length) {
-      // 计算loop和iteration类型节点的数量
-      const loopIterationCount = nodes.filter(node => 
-        node.type === 'loop' || node.type === 'iteration'
-      ).length;
-      
       // 去重处理：对于if-else和question-classifier节点，不同连接桩允许连接到相同节点
       const uniqueEdges = edges.filter((edge, index, arr) => {
         return arr.findIndex(e => {

From 12a27dbcf7bcb33897f761e88cfe21b9d32a89c0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B9=90=E5=8A=9B=E9=BD=90?=
 <162269739+lanceyq@users.noreply.github.com>
Date: Mon, 19 Jan 2026 17:56:52 +0800
Subject: [PATCH 06/12] Feature/memory redis (#152)

* [feature]Emotional memory cache

* [feature]Implicit memory cache

* [changes]Modify the expiration time of implicit memory to 24 hours.

* [feature]Emotional memory cache

* [feature]Implicit memory cache

* [changes]Modify the expiration time of implicit memory to 24 hours.

* [changes]Modify the code based on the AI review

* [feature]Emotional memory cache

* [feature]Implicit memory cache

* [changes]Modify the expiration time of implicit memory to 24 hours.

* [feature]Implicit memory cache

* [changes]Modify the code based on the AI review

* [changes]Modify the generated emotion cache to be "end_user_id"

* [feature]Emotional memory cache

* [feature]Implicit memory cache

* [changes]Modify the code based on the AI review

* [feature]Emotional memory cache

* [changes]Modify the code based on the AI review

* [changes]Modify the generated emotion cache to be "end_user_id"
---
 api/app/controllers/emotion_controller.py | 37 ++++-------------------
 api/app/schemas/emotion_schema.py         |  3 +-
 2 files changed, 7 insertions(+), 33 deletions(-)

diff --git a/api/app/controllers/emotion_controller.py b/api/app/controllers/emotion_controller.py
index 07e50774..b5cd7250 100644
--- a/api/app/controllers/emotion_controller.py
+++ b/api/app/controllers/emotion_controller.py
@@ -267,7 +267,7 @@ async def generate_emotion_suggestions(
     """生成个性化情绪建议（调用LLM并缓存）
 
     Args:
-        request: 包含 group_id、可选的 config_id 和 force_refresh
+        request: 包含 end_user_id
         db: 数据库会话
         current_user: 当前用户
 
@@ -275,47 +275,22 @@ async def generate_emotion_suggestions(
         新生成的个性化情绪建议响应
     """
     try:
-        # 验证 config_id（如果提供）
-        # 获取终端用户关联的配置
-        config_id = request.config_id
-        if config_id is None:
-            # 如果没有提供 config_id，尝试获取用户关联的配置
-            try:
-                from app.services.memory_agent_service import (
-                    get_end_user_connected_config,
-                )
-                connected_config = get_end_user_connected_config(request.group_id, db)
-                config_id = connected_config.get("memory_config_id")
-            except ValueError as e:
-                return fail(BizCode.INVALID_PARAMETER, "无法获取用户关联的配置", str(e))
-        else:
-            # 如果提供了 config_id，验证其有效性
-            from app.services.memory_config_service import MemoryConfigService
-            try:
-                config_service = MemoryConfigService(db)
-                config = config_service.get_config_by_id(config_id)
-                if not config:
-                    return fail(BizCode.INVALID_PARAMETER, "配置ID无效", f"配置 {config_id} 不存在")
-            except Exception as e:
-                return fail(BizCode.INVALID_PARAMETER, "配置ID验证失败", str(e))
-
         api_logger.info(
             f"用户 {current_user.username} 请求生成个性化情绪建议",
             extra={
-                "group_id": request.group_id,
-                "config_id": config_id
+                "end_user_id": request.end_user_id
             }
         )
 
         # 调用服务层生成建议
         data = await emotion_service.generate_emotion_suggestions(
-            end_user_id=request.group_id,
+            end_user_id=request.end_user_id,
             db=db
         )
 
         # 保存到缓存
         await emotion_service.save_suggestions_cache(
-            end_user_id=request.group_id,
+            end_user_id=request.end_user_id,
             suggestions_data=data,
             db=db,
             expires_hours=24
@@ -324,7 +299,7 @@ async def generate_emotion_suggestions(
         api_logger.info(
             "个性化建议生成成功",
             extra={
-                "group_id": request.group_id,
+                "end_user_id": request.end_user_id,
                 "suggestions_count": len(data.get("suggestions", []))
             }
         )
@@ -334,7 +309,7 @@ async def generate_emotion_suggestions(
     except Exception as e:
         api_logger.error(
             f"生成个性化建议失败: {str(e)}",
-            extra={"group_id": request.group_id},
+            extra={"end_user_id": request.end_user_id},
             exc_info=True
         )
         raise HTTPException(
diff --git a/api/app/schemas/emotion_schema.py b/api/app/schemas/emotion_schema.py
index 37e9a2e3..5175fed1 100644
--- a/api/app/schemas/emotion_schema.py
+++ b/api/app/schemas/emotion_schema.py
@@ -34,5 +34,4 @@ class EmotionSuggestionsRequest(BaseModel):
 
 class EmotionGenerateSuggestionsRequest(BaseModel):
     """生成个性化情绪建议请求"""
-    group_id: str = Field(..., description="组ID")
-    config_id: Optional[int] = Field(None, description="配置ID（用于指定LLM模型）")
+    end_user_id: str = Field(..., description="终端用户ID")

From c255be8d0972413eb1475122a6239f478028ba57 Mon Sep 17 00:00:00 2001
From: zhaoying <yzhao96@best-inc.com>
Date: Mon, 19 Jan 2026 18:51:50 +0800
Subject: [PATCH 07/12] feat(web): EMOTIONAL_MEMORY & IMPLICIT_MEMORY type user
 memory detail add refresh btn

---
 web/src/api/memory.ts                         | 126 +++++++++---------
 .../components/EmotionTags.tsx                |   2 +-
 .../UserMemoryDetail/components/Habits.tsx    |   9 +-
 .../components/InterestAreas.tsx              |   9 +-
 .../UserMemoryDetail/components/Portrait.tsx  |   9 +-
 .../components/Preferences.tsx                |   9 +-
 .../components/Suggestions.tsx                |   9 +-
 .../UserMemoryDetail/pages/ImplicitDetail.tsx |  37 ++++-
 .../pages/StatementDetail.tsx                 |  22 ++-
 .../views/UserMemoryDetail/pages/index.tsx    |  20 ++-
 10 files changed, 161 insertions(+), 91 deletions(-)

diff --git a/web/src/api/memory.ts b/web/src/api/memory.ts
index 0ac14451..bbd9f6b0 100644
--- a/web/src/api/memory.ts
+++ b/web/src/api/memory.ts
@@ -18,174 +18,180 @@ import type { TestParams } from '@/views/MemoryConversation'
 import type { EndUser } from '@/views/UserMemoryDetail/types'
 import { handleSSE, type SSEMessage } from '@/utils/stream'
 
-// 记忆对话
+// Memory conversation
 export const readService = (query: TestParams) => {
   return request.post('/memory/read_service', query)
 }
-/****************** 记忆看板 相关接口 *******************************/
-// 记忆看板-记忆总量
+/****************** Memory Dashboard APIs *******************************/
+// Memory Dashboard - Total memory count
 export const getTotalMemoryCount = () => {
   return request.get(`/dashboard/total_memory_count`)
 }
-// 记忆看板-知识库类型分布
+// Memory Dashboard - Knowledge base type distribution
 export const getKbTypes = () => {
   return request.get(`/memory/stats/types`)
 }
-// 记忆看板-热门记忆标签
+// Memory Dashboard - Hot memory tags
 export const getHotMemoryTags = () => {
   return request.get(`/memory-storage/analytics/hot_memory_tags`)
 }
-// 记忆看板-最近活动统计
+// Memory Dashboard - Recent activity statistics
 export const getRecentActivityStats = () => {
   return request.get(`/memory-storage/analytics/recent_activity_stats`)
 }
-// 记忆看板-记忆增长趋势
+// Memory Dashboard - Memory growth trend
 export const getMemoryIncrement = (limit: number) => {
   return request.get(`/dashboard/memory_increment`, { limit })
 }
-// 记忆看板-API调用趋势
+// Memory Dashboard - API call trend
 export const getApiTrend = () => {
   return request.get(`/dashboard/api_increment`)
 }
-// 记忆看板-总数据
+// Memory Dashboard - Total data
 export const getDashboardData = () => {
   return request.get(`/dashboard/dashboard_data`)
 }
-/*************** end 记忆看板 相关接口 ******************************/
+/*************** end Memory Dashboard APIs ******************************/
 
 
-/****************** 用户记忆 相关接口 *******************************/
+/****************** User Memory APIs *******************************/
 export const userMemoryListUrl = '/dashboard/end_users'
 export const getUserMemoryList = () => {
   return request.get(userMemoryListUrl)
 }
-// 用户记忆-用户记忆总量
+// User Memory - Total end users
 export const getTotalEndUsers = () => {
   return request.get(`/dashboard/total_end_users`)
 }
-// 用户记忆-用户详情
+// User Memory - User profile
 export const getUserProfile = (end_user_id: string) => {
   return request.get(`/memory/analytics/user_profile`, { end_user_id })
 }
 
-// 用户记忆-记忆洞察
+// User Memory - Memory insight
 export const getMemoryInsightReport = (end_user_id: string) => {
   return request.get(`/memory-storage/analytics/memory_insight/report`, { end_user_id })
 }
-// 用户记忆-用户摘要
+// User Memory - User summary
 export const getUserSummary = (end_user_id: string) => {
   return request.get(`/memory-storage/analytics/user_summary`, { end_user_id })
 }
-// 记忆分类
+// Memory classification
 export const getNodeStatistics = (end_user_id: string) => {
   return request.get(`/memory-storage/analytics/node_statistics`, { end_user_id })
 }
-// 基本信息
+// Basic information
 export const getEndUserProfile = (end_user_id: string) => {
   return request.get(`/memory-storage/read_end_user/profile`, { end_user_id })
 }
 export const updatedEndUserProfile = (values: EndUser) => {
   return request.post(`/memory-storage/updated_end_user/profile`, values)
 }
-// 用户记忆-关系网络
+// User Memory - Relationship network
 export const getMemorySearchEdges = (end_user_id: string) => {
   return request.get(`/memory-storage/analytics/graph_data`, { end_user_id })
 }
-// 用户记忆-用户兴趣分布
+// User Memory - User interest distribution
 export const getHotMemoryTagsByUser = (end_user_id: string) => {
   return request.get(`/memory/analytics/hot_memory_tags/by_user`, { end_user_id })
 }
-// 用户记忆-记忆总量
+// User Memory - Total memory count
 export const getTotalMemoryCountByUser = (end_user_id: string) => {
   return request.get(`/memory-storage/search`, { end_user_id })
 }
-// RAG 用户记忆-记忆总量
+// RAG User Memory - Total memory count
 export const getTotalRagMemoryCountByUser = (end_user_id: string) => {
   return request.get(`/dashboard/current_user_rag_total_num`, { end_user_id })
 }
-// RAG 用户记忆-用户摘要
+// RAG User Memory - User summary
 export const getChunkSummaryTag = (end_user_id: string) => {
   return request.get(`/dashboard/chunk_summary_tag`, { end_user_id })
 }
-// RAG 用户记忆-记忆洞察
+// RAG User Memory - Memory insight
 export const getChunkInsight = (end_user_id: string) => {
   return request.get(`/dashboard/chunk_insight`, { end_user_id })
 }
-// RAG 用户记忆-存储内容
+// RAG User Memory - Storage content
 export const getRagContent = (end_user_id: string) => {
   return request.get(`/dashboard/rag_content`, { end_user_id, limit: 20 })
 }
-// 情感分布分析
+// Emotion distribution analysis
 export const getWordCloud = (group_id: string) => {
   return request.post(`/memory/emotion-memory/wordcloud`, { group_id, limit: 20 })
 }
-// 高频情绪关键词
+// High-frequency emotion keywords
 export const getEmotionTags = (group_id: string) => {
   return request.post(`/memory/emotion-memory/tags`, { group_id, limit: 20 })
 }
-// 情绪健康指数
+// Emotion health index
 export const getEmotionHealth = (group_id: string) => {
   return request.post(`/memory/emotion-memory/health`, { group_id, limit: 20 })
 }
-// 个性化建议
+// Personalized suggestions
 export const getEmotionSuggestions = (group_id: string) => {
   return request.post(`/memory/emotion-memory/suggestions`, { group_id, limit: 20 })
 }
+export const generateSuggestions = (end_user_id: string) => {
+  return request.post(`/memory/emotion-memory/generate_suggestions`, { end_user_id })
+}
 export const analyticsRefresh = (end_user_id: string) => {
   return request.post('/memory-storage/analytics/generate_cache', { end_user_id })
 }
-// 遗忘
+// Forgetting stats
 export const getForgetStats = (group_id: string) => {
   return request.get(`/memory/forget-memory/stats`, { group_id })
 }
-// 隐性记忆-偏好
+// Implicit Memory - Preferences
 export const getImplicitPreferences = (end_user_id: string) => {
   return request.get(`/memory/implicit-memory/preferences/${end_user_id}`)
 }
-// 隐性记忆-核心特质
+// Implicit Memory - Core traits
 export const getImplicitPortrait = (end_user_id: string) => {
   return request.get(`/memory/implicit-memory/portrait/${end_user_id}`)
 }
-// 隐性记忆-兴趣领域分布
+// Implicit Memory - Interest areas distribution
 export const getImplicitInterestAreas = (end_user_id: string) => {
   return request.get(`/memory/implicit-memory/interest-areas/${end_user_id}`)
 }
-// 隐性记忆-用户习惯分析
+// Implicit Memory - User habits analysis
 export const getImplicitHabits = (end_user_id: string) => {
   return request.get(`/memory/implicit-memory/habits/${end_user_id}`)
 }
-// 短期记忆
+export const generateProfile = (end_user_id: string) => {
+  return request.post(`/memory/implicit-memory/generate_profile`, { end_user_id })
+}
+// Short-term memory
 export const getShortTerm = (end_user_id: string) => {
   return request.get(`/memory/short/short_term`, { end_user_id })
 }
-// 感知记忆-视觉记忆
+// Perceptual Memory - Visual memory
 export const getPerceptualLastVisual = (end_user: string) => {
   return request.get(`/memory/perceptual/${end_user}/last_visual`)
 }
-// 感知记忆-音频记忆
+// Perceptual Memory - Audio memory
 export const getPerceptualLastListen = (end_user: string) => {
   return request.get(`/memory/perceptual/${end_user}/last_listen`)
 }
-// 感知记忆-文本记忆
+// Perceptual Memory - Text memory
 export const getPerceptualLastText = (end_user: string) => {
   return request.get(`/memory/perceptual/${end_user}/last_text`)
 }
-// 感知记忆-感知记忆时间线
+// Perceptual Memory - Perceptual memory timeline
 export const getPerceptualTimeline = (end_user: string) => {
   return request.get(`/memory/perceptual/${end_user}/timeline`)
 }
-// 情景记忆-总览
+// Episodic Memory - Overview
 export const getEpisodicOverview = (data: { end_user_id: string; time_range: string; episodic_type: string; } ) => {
   return request.post(`/memory/episodic-memory/overview`, data)
 }
 export const getEpisodicDetail = (data: { end_user_id: string; summary_id: string; } ) => {
   return request.post(`/memory/episodic-memory/details`, data)
 }
-// 关系演化
+// Relationship evolution
 export const getRelationshipEvolution = (data: { id: string; label: string; } ) => {
   return request.get(`/memory-storage/memory_space/relationship_evolution`, data)
 }
-// 共同记忆时间线
+// Shared memory timeline
 export const getTimelineMemories = (data: { id: string; label: string; }) => {
   return request.get(`/memory-storage/memory_space/timeline_memories`, data)
 }
@@ -207,72 +213,72 @@ export const getConversationDetail = (end_user: string, conversation_id: string)
 export const forgetTrigger = (data: { max_merge_batch_size: number; min_days_since_access: number; end_user_id: string;}) => {
   return request.post(`/memory/forget-memory/trigger`, data)
 }
-/*************** end 用户记忆 相关接口 ******************************/
+/*************** end User Memory APIs ******************************/
 
-/****************** 记忆管理 相关接口 *******************************/
-// 记忆管理-获取所有配置
+/****************** Memory Management APIs *******************************/
+// Memory Management - Get all configurations
 export const memoryConfigListUrl = '/memory-storage/read_all_config'
 export const getMemoryConfigList = () => {
   return request.get(memoryConfigListUrl)
 }
-// 记忆管理-创建配置
+// Memory Management - Create configuration
 export const createMemoryConfig = (values: MemoryFormData) => {
   return request.post('/memory-storage/create_config', values)
 }
-// 记忆管理-更新配置
+// Memory Management - Update configuration
 export const updateMemoryConfig = (values: MemoryFormData) => {
   return request.post('/memory-storage/update_config', values)
 }
-// 记忆管理-删除配置
+// Memory Management - Delete configuration
 export const deleteMemoryConfig = (config_id: number) => {
   return request.delete(`/memory-storage/delete_config?config_id=${config_id}`)
 }
-// 遗忘引擎-获取配置
+// Forgetting Engine - Get configuration
 export const getMemoryForgetConfig = (config_id: number | string) => {
   return request.get('/memory/forget-memory/read_config', { config_id })
 }
-// 遗忘引擎-更新配置
+// Forgetting Engine - Update configuration
 export const updateMemoryForgetConfig = (values: ForgetConfigForm) => {
   return request.post('/memory/forget-memory/update_config', values)
 }
-// 记忆萃取引擎-获取配置
+// Memory Extraction Engine - Get configuration
 export const getMemoryExtractionConfig = (config_id: number | string) => {
   return request.get('/memory-storage/read_config_extracted', { config_id: config_id })
 }
-// 记忆萃取引擎-更新配置
+// Memory Extraction Engine - Update configuration
 export const updateMemoryExtractionConfig = (values: ExtractionConfigForm) => {
   return request.post('/memory-storage/update_config_extracted', values)
 }
-// 记忆萃取引擎-试运行
+// Memory Extraction Engine - Pilot run
 export const pilotRunMemoryExtractionConfig = (values: { config_id: number | string; dialogue_text: string; }, onMessage?: (data: SSEMessage[]) => void) => {
   return handleSSE('/memory-storage/pilot_run', values, onMessage)
 }
-// 情绪引擎-获取配置
+// Emotion Engine - Get configuration
 export const getMemoryEmotionConfig = (config_id: number | string) => {
   return request.get('/memory/emotion/read_config', { config_id: config_id })
 }
-// 情绪引擎-更新配置
+// Emotion Engine - Update configuration
 export const updateMemoryEmotionConfig = (values: EmotionConfig) => {
   return request.post('/memory/emotion/updated_config', values)
 }
-// 反思引擎-获取配置
+// Reflection Engine - Get configuration
 export const getMemoryReflectionConfig = (config_id: number | string) => {
   return request.get('/memory/reflection/configs', { config_id: config_id })
 }
-// 反思引擎-更新配置
+// Reflection Engine - Update configuration
 export const updateMemoryReflectionConfig = (values: SelfReflectionEngineConfig) => {
   return request.post('/memory/reflection/save', values)
 }
-// 反思引擎-试运行
+// Reflection Engine - Pilot run
 export const pilotRunMemoryReflectionConfig = (values: { config_id: number | string; language_type: string; }) => {
   return request.get('/memory/reflection/run', values)
 }
 
-/*************** end 记忆管理 相关接口 ******************************/
+/*************** end Memory Management APIs ******************************/
 
 
-/****************** API参数 相关接口 *******************************/
+/****************** API Parameters APIs *******************************/
 export const getMemoryApi = () => {
   return request.get('/memory/docs/api')
 }
-/*************** end API参数 相关接口 ******************************/
\ No newline at end of file
+/*************** end API Parameters APIs ******************************/
\ No newline at end of file
diff --git a/web/src/views/UserMemoryDetail/components/EmotionTags.tsx b/web/src/views/UserMemoryDetail/components/EmotionTags.tsx
index 5fc8f382..a6e6385a 100644
--- a/web/src/views/UserMemoryDetail/components/EmotionTags.tsx
+++ b/web/src/views/UserMemoryDetail/components/EmotionTags.tsx
@@ -121,7 +121,7 @@ const EmotionTags: FC = () => {
             })}
           </div>
         </div>
-        : <Empty size={88} className="rb:h-full" />
+        : <Empty size={88} className="rb:h-full rb:mb-4" />
       }
     </RbCard>
   )
diff --git a/web/src/views/UserMemoryDetail/components/Habits.tsx b/web/src/views/UserMemoryDetail/components/Habits.tsx
index 93ef817f..76756f9f 100644
--- a/web/src/views/UserMemoryDetail/components/Habits.tsx
+++ b/web/src/views/UserMemoryDetail/components/Habits.tsx
@@ -1,4 +1,4 @@
-import { type FC, useEffect, useState } from 'react'
+import { useEffect, useState, forwardRef, useImperativeHandle } from 'react'
 import { useTranslation } from 'react-i18next'
 import { useParams } from 'react-router-dom'
 import { Skeleton, Space, Progress } from 'antd';
@@ -20,7 +20,7 @@ interface HabitsItem {
   specific_examples: string[];
 }
 
-const Habits: FC = () => {
+const Habits = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
   const { t } = useTranslation()
   const { id } = useParams()
   const [loading, setLoading] = useState<boolean>(false)
@@ -43,6 +43,9 @@ const Habits: FC = () => {
       setLoading(false)
     })
   }
+  useImperativeHandle(ref, () => ({
+    handleRefresh: getData
+  }));
 
   return (
     <>
@@ -80,5 +83,5 @@ const Habits: FC = () => {
       </RbCard>
     </>
   )
-}
+})
 export default Habits
\ No newline at end of file
diff --git a/web/src/views/UserMemoryDetail/components/InterestAreas.tsx b/web/src/views/UserMemoryDetail/components/InterestAreas.tsx
index 6f74d255..df1a75c6 100644
--- a/web/src/views/UserMemoryDetail/components/InterestAreas.tsx
+++ b/web/src/views/UserMemoryDetail/components/InterestAreas.tsx
@@ -1,4 +1,4 @@
-import { type FC, useEffect, useState } from 'react'
+import { useEffect, useState, forwardRef, useImperativeHandle } from 'react'
 import { useTranslation } from 'react-i18next'
 import { useParams } from 'react-router-dom'
 import { Skeleton, Progress } from 'antd';
@@ -23,7 +23,7 @@ interface InterestAreasItem {
   art: Item;
 }
 
-const InterestAreas: FC = () => {
+const InterestAreas = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
   const { t } = useTranslation()
   const { id } = useParams()
   const [loading, setLoading] = useState<boolean>(false)
@@ -47,6 +47,9 @@ const InterestAreas: FC = () => {
     })
   }
 
+  useImperativeHandle(ref, () => ({
+    handleRefresh: getData
+  }));
   return (
     <RbCard
       title={t('implicitDetail.interestAreas')}
@@ -70,5 +73,5 @@ const InterestAreas: FC = () => {
         }
     </RbCard>
   )
-}
+})
 export default InterestAreas
\ No newline at end of file
diff --git a/web/src/views/UserMemoryDetail/components/Portrait.tsx b/web/src/views/UserMemoryDetail/components/Portrait.tsx
index 3164ae06..a14fee0d 100644
--- a/web/src/views/UserMemoryDetail/components/Portrait.tsx
+++ b/web/src/views/UserMemoryDetail/components/Portrait.tsx
@@ -1,4 +1,4 @@
-import { type FC, useEffect, useState } from 'react'
+import { useEffect, useState, forwardRef, useImperativeHandle } from 'react'
 import { useTranslation } from 'react-i18next'
 import { useParams } from 'react-router-dom'
 import { Skeleton, Progress } from 'antd';
@@ -25,7 +25,7 @@ interface PortraitItem {
   literature: Item;
 }
 
-const Portrait: FC = () => {
+const Portrait = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
   const { t } = useTranslation()
   const { id } = useParams()
   const [loading, setLoading] = useState<boolean>(false)
@@ -49,6 +49,9 @@ const Portrait: FC = () => {
     })
   }
 
+  useImperativeHandle(ref, () => ({
+    handleRefresh: getData
+  }));
   return (
     <RbCard
       title={t('implicitDetail.portrait')}
@@ -73,5 +76,5 @@ const Portrait: FC = () => {
         }
     </RbCard>
   )
-}
+})
 export default Portrait
\ No newline at end of file
diff --git a/web/src/views/UserMemoryDetail/components/Preferences.tsx b/web/src/views/UserMemoryDetail/components/Preferences.tsx
index 0644197f..4b8d1766 100644
--- a/web/src/views/UserMemoryDetail/components/Preferences.tsx
+++ b/web/src/views/UserMemoryDetail/components/Preferences.tsx
@@ -1,4 +1,4 @@
-import { type FC, useEffect, useState, useRef, useMemo } from 'react'
+import { useEffect, useState, useRef, useMemo, forwardRef, useImperativeHandle } from 'react'
 import { useTranslation } from 'react-i18next'
 import { useParams } from 'react-router-dom'
 import { Row, Col, Skeleton } from 'antd'
@@ -31,7 +31,7 @@ const generateCategoryColors = (categories: string[]) => {
   return colors
 }
 
-const Preferences: FC = () => {
+const Preferences = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
   const { t } = useTranslation()
   const { id } = useParams()
   const chartRef = useRef<HTMLDivElement>(null)
@@ -138,6 +138,9 @@ const Preferences: FC = () => {
     return selectedWord !== null && data[selectedWord].tag_name ? <>{data[selectedWord].tag_name}{t('implicitDetail.preferencesDetail')}</> : ''
   }, [selectedWord, data, t])
 
+  useImperativeHandle(ref, () => ({
+    handleRefresh: getData
+  }));
   return (
     <>
       <div className="rb:bg-[rgba(21,94,239,0.12)] rb:px-4 rb:py-2.5 rb:font-medium rb:leading-5 rb:mb-4 rb:mt-6 rb:rounded-md">{t('forgetDetail.overviewTitle')}</div>
@@ -184,6 +187,6 @@ const Preferences: FC = () => {
       </Row>
     </>
   )
-}
+})
 
 export default Preferences
\ No newline at end of file
diff --git a/web/src/views/UserMemoryDetail/components/Suggestions.tsx b/web/src/views/UserMemoryDetail/components/Suggestions.tsx
index 35fde91f..c2c8ca8b 100644
--- a/web/src/views/UserMemoryDetail/components/Suggestions.tsx
+++ b/web/src/views/UserMemoryDetail/components/Suggestions.tsx
@@ -1,4 +1,4 @@
-import { type FC, useEffect, useState } from 'react'
+import { useEffect, useState, forwardRef, useImperativeHandle } from 'react'
 import { useTranslation } from 'react-i18next'
 import { useParams } from 'react-router-dom'
 
@@ -18,7 +18,7 @@ interface Suggestions {
     actionable_steps: string[];
   }>;
 }
-const Suggestions: FC = () => {
+const Suggestions = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
   const { t } = useTranslation()
   const { id } = useParams()
   const [suggestions, setSuggestions] = useState<Suggestions | null>(null)
@@ -37,6 +37,9 @@ const Suggestions: FC = () => {
       })
   }
 
+  useImperativeHandle(ref, () => ({
+    handleRefresh: getSuggestionData
+  }));
   return (
     <RbCard
       title={t('statementDetail.suggestions')}
@@ -64,6 +67,6 @@ const Suggestions: FC = () => {
       }
     </RbCard>
   )
-}
+})
 
 export default Suggestions
\ No newline at end of file
diff --git a/web/src/views/UserMemoryDetail/pages/ImplicitDetail.tsx b/web/src/views/UserMemoryDetail/pages/ImplicitDetail.tsx
index ef23463a..d79407da 100644
--- a/web/src/views/UserMemoryDetail/pages/ImplicitDetail.tsx
+++ b/web/src/views/UserMemoryDetail/pages/ImplicitDetail.tsx
@@ -1,34 +1,57 @@
-import { type FC } from 'react'
+import { forwardRef, useImperativeHandle, useRef } from 'react'
 import { useTranslation } from 'react-i18next'
 import { Row, Col } from 'antd'
+import { useParams } from 'react-router-dom'
 
 import Preferences from '../components/Preferences'
 import Portrait from '../components/Portrait'
 import InterestAreas from '../components/InterestAreas'
 import Habits from '../components/Habits'
+import {
+  generateProfile,
+} from '@/api/memory'
 
-const ImplicitDetail: FC = () => {
+const ImplicitDetail = forwardRef<{ handleRefresh: () => void; }>((_props, ref) => {
   const { t } = useTranslation()
+  const { id } = useParams()
+  const preferencesRef = useRef<{ handleRefresh: () => void; }>(null)
+  const portraitRef = useRef<{ handleRefresh: () => void; }>(null)
+  const interestAreasRef = useRef<{ handleRefresh: () => void; }>(null)
+  const habitsRef = useRef<{ handleRefresh: () => void; }>(null)
+
+  const handleRefresh = () => {
+    if (!id) return
+    generateProfile(id)
+      .then(() => {
+        preferencesRef.current?.handleRefresh()
+        portraitRef.current?.handleRefresh()
+        interestAreasRef.current?.handleRefresh()
+        habitsRef.current?.handleRefresh()
+      })
+  }
+  useImperativeHandle(ref, () => ({
+    handleRefresh
+  }));
 
   return (
     <div className="rb:h-full rb:max-w-266 rb:mx-auto">
       <div className="rb:text-[#5B6167] rb:leading-5 rb:mt-3">{t('implicitDetail.title')}</div>
       
-      <Preferences />
+      <Preferences ref={preferencesRef} />
 
       <div className="rb:bg-[rgba(21,94,239,0.12)] rb:px-3 rb:py-2.5 rb:font-medium rb:leading-5 rb:mb-4 rb:mt-6 rb:rounded-md">{t('implicitDetail.portraitTitle')}</div>
       <div className="rb:my-3 rb:text-[#5B6167] rb:leading-5">{t('implicitDetail.portraitSubTitle')}</div>
       <Row gutter={[16, 16]} className="rb:mt-4">
         <Col span={12}>
-          <Portrait />
+          <Portrait ref={portraitRef} />
         </Col>
         <Col span={12}>
-          <InterestAreas />
+          <InterestAreas ref={interestAreasRef} />
         </Col>
       </Row>
 
-      <Habits />
+      <Habits ref={habitsRef} />
     </div>
   )
-}
+})
 export default ImplicitDetail
\ No newline at end of file
diff --git a/web/src/views/UserMemoryDetail/pages/StatementDetail.tsx b/web/src/views/UserMemoryDetail/pages/StatementDetail.tsx
index e6ddfd20..6515263e 100644
--- a/web/src/views/UserMemoryDetail/pages/StatementDetail.tsx
+++ b/web/src/views/UserMemoryDetail/pages/StatementDetail.tsx
@@ -1,13 +1,27 @@
-import { type FC } from 'react'
+import { forwardRef, useImperativeHandle, useRef } from 'react'
 import { Row, Col, Space } from 'antd';
+import { useParams } from 'react-router-dom'
 
 import WordCloud from '../components/WordCloud'
 import EmotionTags from '../components/EmotionTags'
 import Health from '../components/Health'
 import Suggestions from '../components/Suggestions'
+import { generateSuggestions } from '@/api/memory'
 
 
-const StatementDetail: FC = () => {
+const StatementDetail = forwardRef((_props, ref) => {
+  const { id } = useParams()
+  const suggestionsRef = useRef<{ handleRefresh: () => void; }>(null)
+  const handleRefresh = () => {
+    if (!id) return
+    generateSuggestions(id)
+      .then(() => {
+        suggestionsRef.current?.handleRefresh()
+      })
+  }
+  useImperativeHandle(ref, () => ({
+    handleRefresh
+  }));
   return (
     <Row gutter={[16, 16]}>
       <Col span={12}>
@@ -18,10 +32,10 @@ const StatementDetail: FC = () => {
         </Space>
       </Col>
       <Col span={12}>
-        <Suggestions />
+        <Suggestions ref={suggestionsRef} />
       </Col>
     </Row>
   )
-}
+})
 
 export default StatementDetail
\ No newline at end of file
diff --git a/web/src/views/UserMemoryDetail/pages/index.tsx b/web/src/views/UserMemoryDetail/pages/index.tsx
index f225b1f0..16004edc 100644
--- a/web/src/views/UserMemoryDetail/pages/index.tsx
+++ b/web/src/views/UserMemoryDetail/pages/index.tsx
@@ -24,6 +24,8 @@ const Detail: FC = () => {
   const navigate = useNavigate()
   const [name, setName] = useState<string>('')
   const forgetDetailRef = useRef<{ handleRefresh: () => void }>(null)
+  const statementDetailRef = useRef<{ handleRefresh: () => void }>(null)
+  const implicitDetailRef = useRef<{ handleRefresh: () => void }>(null)
 
   useEffect(() => {
     if (!id) return
@@ -45,7 +47,17 @@ const Detail: FC = () => {
     navigate(`/user-memory/detail/${id}/${key}`, { replace: true })
   }
   const handleRefresh = () => {
-    forgetDetailRef.current?.handleRefresh()
+    switch(type) {
+      case 'FORGET_MEMORY':
+        forgetDetailRef.current?.handleRefresh()
+        break;
+      case 'EMOTIONAL_MEMORY':
+        statementDetailRef.current?.handleRefresh()
+        break
+      case 'IMPLICIT_MEMORY':
+        implicitDetailRef.current?.handleRefresh()
+        break
+    }
   }
 
   if (type === 'GRAPH') {
@@ -67,16 +79,16 @@ const Detail: FC = () => {
             </div>
           </Dropdown>
         }
-        extra={type === 'FORGET_MEMORY' &&
+        extra={['FORGET_MEMORY', 'EMOTIONAL_MEMORY', 'IMPLICIT_MEMORY'].includes(type as string) &&
           <Button type="primary" ghost className="rb:group rb:h-6! rb:px-2!" onClick={handleRefresh}>
             <img src={refreshIcon} className="rb:w-4 rb:h-4" />
             {t('common.refresh')}
           </Button>}
       />
       <div className="rb:h-[calc(100vh-64px)] rb:overflow-y-auto rb:py-3 rb:px-4">
-        {type === 'EMOTIONAL_MEMORY' && <StatementDetail />}
+        {type === 'EMOTIONAL_MEMORY' && <StatementDetail ref={statementDetailRef} />}
         {type === 'FORGET_MEMORY' && <ForgetDetail ref={forgetDetailRef} />}
-        {type === 'IMPLICIT_MEMORY' && <ImplicitDetail />}
+        {type === 'IMPLICIT_MEMORY' && <ImplicitDetail ref={implicitDetailRef} />}
         {type === 'SHORT_TERM_MEMORY' && <ShortTermDetail />}
         {type === 'PERCEPTUAL_MEMORY' && <PerceptualDetail />}
         {type === 'EPISODIC_MEMORY' && <EpisodicDetail />}

From 3ced895c9cec7cf33aa165477690eb52695f3611 Mon Sep 17 00:00:00 2001
From: zhaoying <yzhao96@best-inc.com>
Date: Tue, 20 Jan 2026 10:15:12 +0800
Subject: [PATCH 08/12] refactor: CustomSelect component update

---
 web/src/components/CustomSelect/index.tsx | 59 +++++++++++------------
 1 file changed, 27 insertions(+), 32 deletions(-)

diff --git a/web/src/components/CustomSelect/index.tsx b/web/src/components/CustomSelect/index.tsx
index e9ccce74..1887d635 100644
--- a/web/src/components/CustomSelect/index.tsx
+++ b/web/src/components/CustomSelect/index.tsx
@@ -1,10 +1,13 @@
 import { useEffect, useState, type FC, type Key } from 'react';
-import { Select } from 'antd'
-import type { SelectProps, DefaultOptionType } from 'antd/es/select'
+import { Select } from 'antd';
+import type { SelectProps, DefaultOptionType } from 'antd/es/select';
 import { useTranslation } from 'react-i18next';
 import { request } from '@/utils/request';
 
-// 定义API响应类型
+interface OptionType {
+  [key: string]: Key | string | number;
+}
+
 interface ApiResponse<T> {
   items?: T[];
 }
@@ -20,19 +23,16 @@ interface CustomSelectProps extends Omit<SelectProps, 'filterOption'> {
   format?: (items: OptionType[]) => OptionType[];
   showSearch?: boolean;
   optionFilterProp?: string;
-  // 其他SelectProps属性
-  onChange?: SelectProps<Key, DefaultOptionType>['onChange'];
-  value?: SelectProps<Key, DefaultOptionType>['value'];
-  disabled?: boolean;
-  style?: React.CSSProperties;
-  className?: string;
   filterOption?: (inputValue: string, option?: DefaultOptionType) => boolean;
 }
-interface OptionType {
-  [key: string]: Key | string | number;
-}
+
+const defaultFilterOption = (inputValue: string, option?: DefaultOptionType): boolean => {
+  if (!option || !inputValue) return true;
+  const label = String(option.children || option.label || '');
+  return label.toLowerCase().includes(inputValue.toLowerCase());
+};
+
 const CustomSelect: FC<CustomSelectProps> = ({
-  onChange,
   url,
   params,
   valueKey = 'value',
@@ -42,42 +42,37 @@ const CustomSelect: FC<CustomSelectProps> = ({
   allTitle,
   format,
   showSearch = false,
-  optionFilterProp = 'label',
   filterOption,
   ...props
 }) => {
   const { t } = useTranslation();
-  const [options, setOptions] = useState<OptionType[]>([]); 
-  
-  // 默认模糊搜索函数
-  const defaultFilterOption = (inputValue: string, option?: DefaultOptionType) => {
-    if (!option || !inputValue) return true;
-    const label = String(option.children || option.label || '');
-    return label.toLowerCase().includes(inputValue.toLowerCase());
-  };
-  // 组件挂载时获取初始数据
+  const [options, setOptions] = useState<OptionType[]>([]);
+
   useEffect(() => {
     request.get<ApiResponse<OptionType>>(url, params).then((res) => {
-      const data = res;
-      setOptions(Array.isArray(data) ? data || [] : Array.isArray(data?.items) ? data.items || [] : []);
+      const data = Array.isArray(res) ? res : res?.items || [];
+      setOptions(data);
     });
-  }, []);
+  }, [url, params]);
+
+  const displayOptions = format ? format(options) : options;
+
   return (
-    <Select 
-      placeholder={placeholder ? placeholder : t('common.select')} 
-      onChange={onChange}
+    <Select
+      placeholder={placeholder || t('common.select')}
       defaultValue={hasAll ? null : undefined}
       showSearch={showSearch}
       filterOption={filterOption || defaultFilterOption}
       {...props}
     >
-      {hasAll && (<Select.Option>{allTitle || t('common.all')}</Select.Option>)}
-      {(format ? format(options) : options)?.map(option => (
+      {hasAll && <Select.Option value={null}>{allTitle || t('common.all')}</Select.Option>}
+      {displayOptions.map((option) => (
         <Select.Option key={option[valueKey]} value={option[valueKey]}>
           {String(option[labelKey])}
         </Select.Option>
       ))}
     </Select>
   );
-}
+};
+
 export default CustomSelect;
\ No newline at end of file

From cd1a50a1d1382a209f8ad6c8d9bfac3f9e75cb1a Mon Sep 17 00:00:00 2001
From: zhaoying <yzhao96@best-inc.com>
Date: Tue, 20 Jan 2026 10:21:00 +0800
Subject: [PATCH 09/12] fix(web): node cannot be connected to itself

---
 web/src/views/Workflow/hooks/useWorkflowGraph.ts | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/web/src/views/Workflow/hooks/useWorkflowGraph.ts b/web/src/views/Workflow/hooks/useWorkflowGraph.ts
index a7ebb29a..615cd3e5 100644
--- a/web/src/views/Workflow/hooks/useWorkflowGraph.ts
+++ b/web/src/views/Workflow/hooks/useWorkflowGraph.ts
@@ -729,6 +729,9 @@ export const useWorkflowGraph = ({
         validateConnection({ sourceCell, targetCell, targetMagnet }) {
           if (!targetMagnet) return false;
           
+          // 节点不能与自己连线
+          if (sourceCell?.id === targetCell?.id) return false;
+          
           const sourceType = sourceCell?.getData()?.type;
           const targetType = targetCell?.getData()?.type;
           

From 642587fc9714fc16a20494054a30bfc26986c444 Mon Sep 17 00:00:00 2001
From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com>
Date: Tue, 20 Jan 2026 10:36:30 +0800
Subject: [PATCH 10/12] Fix/memory mcp2 1 (#145)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* 去掉MCP框架，重构

* feat(celery): add comprehensive logging to worker and write task

- Initialize logging system in Celery worker entry point with LoggingConfig
- Add logger instance and startup message to celery_worker.py
- Reorganize imports in tasks.py for better readability and consistency
- Add detailed logging to write_message_task for debugging and monitoring
- Log task start with group_id, config_id, and storage_type parameters
- Log service execution and completion status with results
- Add exception handling with error logging and stack trace capture
- Log task completion time and Celery task ID for performance tracking
- Improves observability and troubleshooting of async task execution

* 去掉MCP框架，重构

* 去掉MCP框架，重构

---------

Co-authored-by: Ke Sun <kesun5@illinois.edu>
---
 api/app/celery_worker.py                      |   6 +
 api/app/core/config.py                        |   1 +
 api/app/core/memory/agent/__init__.py         |   0
 .../memory/agent/langgraph_graph/__init__.py  |  16 -
 .../agent/langgraph_graph/nodes/__init__.py   |   8 +-
 .../agent/langgraph_graph/nodes/data_nodes.py |  16 +
 .../agent/langgraph_graph/nodes/input_node.py | 150 ---
 .../langgraph_graph/nodes/problem_nodes.py    | 237 +++++
 .../langgraph_graph/nodes/retrieve_nodes.py   | 417 ++++++++
 .../langgraph_graph/nodes/summary_nodes.py    | 303 ++++++
 .../agent/langgraph_graph/nodes/tool_node.py  | 234 -----
 .../nodes/verification_nodes.py               |  85 ++
 .../langgraph_graph/nodes/write_nodes.py      |  50 +
 .../agent/langgraph_graph/read_graph.py       | 612 ++++--------
 .../agent/langgraph_graph/routing/__init__.py |  13 -
 .../agent/langgraph_graph/routing/routers.py  | 149 +--
 .../agent/langgraph_graph/state/__init__.py   |  13 -
 .../agent/langgraph_graph/state/extractors.py | 179 ----
 .../agent/langgraph_graph/tools/tool.py       | 320 +++++++
 .../agent/langgraph_graph/write_graph.py      | 103 +-
 .../core/memory/agent/mcp_server/__init__.py  |  28 -
 .../memory/agent/mcp_server/mcp_instance.py   |  11 -
 .../core/memory/agent/mcp_server/server.py    | 159 ----
 .../memory/agent/mcp_server/tools/__init__.py |  27 -
 .../agent/mcp_server/tools/data_tools.py      | 155 ---
 .../agent/mcp_server/tools/problem_tools.py   | 304 ------
 .../agent/mcp_server/tools/retrieval_tools.py | 294 ------
 .../agent/mcp_server/tools/summary_tools.py   | 640 -------------
 .../mcp_server/tools/verification_tools.py    | 174 ----
 .../agent/{mcp_server => }/models/__init__.py |   0
 .../{mcp_server => }/models/problem_models.py |   0
 .../models/retrieval_models.py                |   0
 .../{mcp_server => }/models/summary_models.py |   0
 .../models/verification_models.py             |   0
 .../memory/agent/multimodal/oss_picture.py    | 114 ---
 .../memory/agent/multimodal/speech_model.py   | 121 ---
 .../{mcp_server => }/services/__init__.py     |   0
 .../agent/services/optimized_llm_service.py   | 277 ++++++
 .../services/parameter_builder.py             |  22 +-
 .../services/search_service.py                |  66 +-
 .../services/session_service.py               |   0
 .../services/template_service.py              |  16 +-
 api/app/core/memory/agent/utils/__init__.py   |   7 -
 .../memory/agent/utils/llm_client_pool.py     |  56 ++
 api/app/core/memory/agent/utils/llm_tools.py  | 211 ++--
 api/app/core/memory/agent/utils/mcp_tools.py  |  33 -
 .../core/memory/agent/utils/messages_tool.py  | 260 -----
 .../core/memory/agent/utils/messages_tools.py | 194 ++++
 api/app/core/memory/agent/utils/model_tool.py |  38 -
 api/app/core/memory/agent/utils/multimodal.py | 131 ---
 .../memory/agent/utils/performance_monitor.py |  56 ++
 ...Problem_Extension_prompt_simplified.jinja2 |  81 ++
 .../prompt/Retrieve_Summary_prompt.jinja2     |   3 -
 .../utils/prompt/split_verify_prompt.jinja2   |   4 +-
 .../core/memory/agent/utils/session_tools.py  | 169 ++++
 .../core/memory/agent/utils/template_tools.py | 117 +++
 .../memory/agent/utils/type_classifier.py     |   3 +-
 .../memory/agent/utils/write_to_database.py   |  49 -
 .../core/memory/agent/utils/write_tools.py    |  12 +-
 api/app/services/memory_agent_service.py      | 899 ++++++------------
 api/app/tasks.py                              |  23 +-
 api/docker-compose.yml                        |  47 +-
 62 files changed, 3128 insertions(+), 4585 deletions(-)
 delete mode 100644 api/app/core/memory/agent/__init__.py
 delete mode 100644 api/app/core/memory/agent/langgraph_graph/__init__.py
 create mode 100644 api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py
 delete mode 100644 api/app/core/memory/agent/langgraph_graph/nodes/input_node.py
 create mode 100644 api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py
 create mode 100644 api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py
 create mode 100644 api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py
 delete mode 100644 api/app/core/memory/agent/langgraph_graph/nodes/tool_node.py
 create mode 100644 api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py
 create mode 100644 api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py
 delete mode 100644 api/app/core/memory/agent/langgraph_graph/routing/__init__.py
 delete mode 100644 api/app/core/memory/agent/langgraph_graph/state/__init__.py
 delete mode 100644 api/app/core/memory/agent/langgraph_graph/state/extractors.py
 create mode 100644 api/app/core/memory/agent/langgraph_graph/tools/tool.py
 delete mode 100644 api/app/core/memory/agent/mcp_server/__init__.py
 delete mode 100644 api/app/core/memory/agent/mcp_server/mcp_instance.py
 delete mode 100644 api/app/core/memory/agent/mcp_server/server.py
 delete mode 100644 api/app/core/memory/agent/mcp_server/tools/__init__.py
 delete mode 100644 api/app/core/memory/agent/mcp_server/tools/data_tools.py
 delete mode 100644 api/app/core/memory/agent/mcp_server/tools/problem_tools.py
 delete mode 100644 api/app/core/memory/agent/mcp_server/tools/retrieval_tools.py
 delete mode 100644 api/app/core/memory/agent/mcp_server/tools/summary_tools.py
 delete mode 100644 api/app/core/memory/agent/mcp_server/tools/verification_tools.py
 rename api/app/core/memory/agent/{mcp_server => }/models/__init__.py (100%)
 rename api/app/core/memory/agent/{mcp_server => }/models/problem_models.py (100%)
 rename api/app/core/memory/agent/{mcp_server => }/models/retrieval_models.py (100%)
 rename api/app/core/memory/agent/{mcp_server => }/models/summary_models.py (100%)
 rename api/app/core/memory/agent/{mcp_server => }/models/verification_models.py (100%)
 delete mode 100644 api/app/core/memory/agent/multimodal/oss_picture.py
 delete mode 100644 api/app/core/memory/agent/multimodal/speech_model.py
 rename api/app/core/memory/agent/{mcp_server => }/services/__init__.py (100%)
 create mode 100644 api/app/core/memory/agent/services/optimized_llm_service.py
 rename api/app/core/memory/agent/{mcp_server => }/services/parameter_builder.py (87%)
 rename api/app/core/memory/agent/{mcp_server => }/services/search_service.py (75%)
 rename api/app/core/memory/agent/{mcp_server => }/services/session_service.py (100%)
 rename api/app/core/memory/agent/{mcp_server => }/services/template_service.py (94%)
 delete mode 100644 api/app/core/memory/agent/utils/__init__.py
 create mode 100644 api/app/core/memory/agent/utils/llm_client_pool.py
 delete mode 100644 api/app/core/memory/agent/utils/mcp_tools.py
 delete mode 100644 api/app/core/memory/agent/utils/messages_tool.py
 create mode 100644 api/app/core/memory/agent/utils/messages_tools.py
 delete mode 100644 api/app/core/memory/agent/utils/model_tool.py
 delete mode 100644 api/app/core/memory/agent/utils/multimodal.py
 create mode 100644 api/app/core/memory/agent/utils/performance_monitor.py
 create mode 100644 api/app/core/memory/agent/utils/prompt/Problem_Extension_prompt_simplified.jinja2
 create mode 100644 api/app/core/memory/agent/utils/session_tools.py
 create mode 100644 api/app/core/memory/agent/utils/template_tools.py
 delete mode 100644 api/app/core/memory/agent/utils/write_to_database.py

diff --git a/api/app/celery_worker.py b/api/app/celery_worker.py
index baecdb3d..7d3ee686 100644
--- a/api/app/celery_worker.py
+++ b/api/app/celery_worker.py
@@ -3,6 +3,12 @@ Celery Worker 入口点
 用于启动 Celery Worker: celery -A app.celery_worker worker --loglevel=info
 """
 from app.celery_app import celery_app
+from app.core.logging_config import LoggingConfig, get_logger
+
+# Initialize logging system for Celery worker
+LoggingConfig.setup_logging()
+logger = get_logger(__name__)
+logger.info("Celery worker logging initialized")
 
 # 导入任务模块以注册任务
 import app.tasks
diff --git a/api/app/core/config.py b/api/app/core/config.py
index 01983457..9600b551 100644
--- a/api/app/core/config.py
+++ b/api/app/core/config.py
@@ -147,6 +147,7 @@ class Settings:
     # Celery configuration (internal)
     CELERY_BROKER: int = int(os.getenv("CELERY_BROKER", "1"))
     CELERY_BACKEND: int = int(os.getenv("CELERY_BACKEND", "2"))
+
     REFLECTION_INTERVAL_SECONDS: float = float(os.getenv("REFLECTION_INTERVAL_SECONDS", "300"))
     HEALTH_CHECK_SECONDS: float = float(os.getenv("HEALTH_CHECK_SECONDS", "600"))
     MEMORY_INCREMENT_INTERVAL_HOURS: float = float(os.getenv("MEMORY_INCREMENT_INTERVAL_HOURS", "24"))
diff --git a/api/app/core/memory/agent/__init__.py b/api/app/core/memory/agent/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/api/app/core/memory/agent/langgraph_graph/__init__.py b/api/app/core/memory/agent/langgraph_graph/__init__.py
deleted file mode 100644
index a0596e38..00000000
--- a/api/app/core/memory/agent/langgraph_graph/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""
-LangGraph Graph package for memory agent.
-
-This package provides the LangGraph workflow orchestrator with modular
-node implementations, routing logic, and state management.
-
-Package structure:
-- read_graph: Main graph factory for read operations
-- write_graph: Main graph factory for write operations
-- nodes: LangGraph node implementations
-- routing: State routing logic
-- state: State management utilities
-"""
-from app.core.memory.agent.langgraph_graph.read_graph import make_read_graph
-
-__all__ = ['make_read_graph']
\ No newline at end of file
diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/__init__.py b/api/app/core/memory/agent/langgraph_graph/nodes/__init__.py
index 4e808919..231a167c 100644
--- a/api/app/core/memory/agent/langgraph_graph/nodes/__init__.py
+++ b/api/app/core/memory/agent/langgraph_graph/nodes/__init__.py
@@ -4,7 +4,7 @@ LangGraph node implementations.
 This module contains custom node implementations for the LangGraph workflow.
 """
 
-from app.core.memory.agent.langgraph_graph.nodes.tool_node import ToolExecutionNode
-from app.core.memory.agent.langgraph_graph.nodes.input_node import create_input_message
-
-__all__ = ["ToolExecutionNode", "create_input_message"]
+# from app.core.memory.agent.langgraph_graph.nodes.tool_node import ToolExecutionNode
+# from app.core.memory.agent.langgraph_graph.nodes.input_node import create_input_message
+#
+# __all__ = ["ToolExecutionNode", "create_input_message"]
diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py
new file mode 100644
index 00000000..6595a2ce
--- /dev/null
+++ b/api/app/core/memory/agent/langgraph_graph/nodes/data_nodes.py
@@ -0,0 +1,16 @@
+from app.core.memory.agent.utils.llm_tools import ReadState, WriteState
+
+
+def content_input_node(state: ReadState) -> ReadState:
+    """开始节点 - 提取内容并保持状态信息"""
+
+    content = state['messages'][0].content if state.get('messages') else ''
+    # 返回内容并保持所有状态信息
+    return {"data": content}
+
+def content_input_write(state: WriteState) -> WriteState:
+    """开始节点 - 提取内容并保持状态信息"""
+
+    content = state['messages'][0].content if state.get('messages') else ''
+    # 返回内容并保持所有状态信息
+    return {"data": content}
\ No newline at end of file
diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/input_node.py b/api/app/core/memory/agent/langgraph_graph/nodes/input_node.py
deleted file mode 100644
index 3eed497f..00000000
--- a/api/app/core/memory/agent/langgraph_graph/nodes/input_node.py
+++ /dev/null
@@ -1,150 +0,0 @@
-"""
-Input node for LangGraph workflow entry point.
-
-This module provides the create_input_message function which processes initial
-user input with multimodal support and creates the first tool call message.
-"""
-
-import logging
-import re
-import uuid
-from datetime import datetime
-from typing import Any, Dict
-
-from app.core.memory.agent.utils.multimodal import MultimodalProcessor
-from app.schemas.memory_config_schema import MemoryConfig
-from langchain_core.messages import AIMessage
-
-logger = logging.getLogger(__name__)
-
-
-async def create_input_message(
-    state: Dict[str, Any],
-    tool_name: str,
-    session_id: str,
-    search_switch: str,
-    apply_id: str,
-    group_id: str,
-    multimodal_processor: MultimodalProcessor,
-    memory_config: MemoryConfig,
-) -> Dict[str, Any]:
-    """
-    Create initial tool call message from user input.
-    
-    This function:
-    1. Extracts the last message content from state
-    2. Processes multimodal inputs (images/audio) using the multimodal processor
-    3. Generates a unique message ID
-    4. Extracts namespace from session_id
-    5. Handles verified_data extraction for backward compatibility
-    6. Returns AIMessage with complete tool_calls structure
-    
-    Args:
-        state: LangGraph state dictionary containing messages
-        tool_name: Name of the tool to invoke (typically "Split_The_Problem")
-        session_id: Session identifier (format: "call_id_{namespace}")
-        search_switch: Search routing parameter
-        apply_id: Application identifier
-        group_id: Group identifier
-        multimodal_processor: Processor for handling image/audio inputs
-        memory_config: MemoryConfig object containing all configuration
-        
-    Returns:
-        State update with AIMessage containing tool_call
-        
-    Examples:
-        >>> state = {"messages": [HumanMessage(content="What is AI?")]}
-        >>> result = await create_input_message(
-        ...     state, "Split_The_Problem", "call_id_user123", "0", "app1", "group1", processor, config
-        ... )
-        >>> result["messages"][0].tool_calls[0]["name"]
-        'Split_The_Problem'
-    """
-    messages = state.get("messages", [])
-    
-    # Extract last message content
-    if messages:
-        last_message = messages[-1].content if hasattr(messages[-1], 'content') else str(messages[-1])
-    else:
-        logger.warning("[create_input_message] No messages in state, using empty string")
-        last_message = ""
-    
-    logger.debug(f"[create_input_message] Original input: {last_message[:100]}...")
-    
-    # Process multimodal input (images/audio)
-    try:
-        processed_content = await multimodal_processor.process_input(last_message)
-        if processed_content != last_message:
-            logger.info(
-                f"[create_input_message] Multimodal processing converted input "
-                f"from {len(last_message)} to {len(processed_content)} chars"
-            )
-        last_message = processed_content
-    except Exception as e:
-        logger.error(
-            f"[create_input_message] Multimodal processing failed: {e}",
-            exc_info=True
-        )
-        # Continue with original content
-    
-    # Generate unique message ID
-    uuid_str = uuid.uuid4()
-    time_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    
-    # Extract namespace from session_id
-    # Expected format: "call_id_{namespace}" or similar
-    try:
-        namespace = str(session_id).split('_id_')[1]
-    except (IndexError, AttributeError):
-        logger.warning(
-            f"[create_input_message] Could not extract namespace from session_id: {session_id}"
-        )
-        namespace = "unknown"
-    
-    # Handle verified_data extraction (backward compatibility)
-    # This regex-based extraction is kept for compatibility with existing data formats
-    if 'verified_data' in str(last_message):
-        try:
-            messages_last = str(last_message).replace('\\n', '').replace('\\', '')
-            query_match = re.findall(r'"query": "(.*?)",', messages_last)
-            if query_match:
-                last_message = query_match[0]
-                logger.debug(
-                    f"[create_input_message] Extracted query from verified_data: {last_message}"
-                )
-        except Exception as e:
-            logger.warning(
-                f"[create_input_message] Failed to extract query from verified_data: {e}"
-            )
-    
-    # Construct tool call message
-    tool_call_id = f"{session_id}_{uuid_str}"
-    
-    logger.info(
-        f"[create_input_message] Creating tool call for '{tool_name}' "
-        f"with ID: {tool_call_id}"
-    )
-    
-    # Build tool arguments
-    tool_args = {
-        "sentence": last_message,
-        "sessionid": session_id,
-        "messages_id": str(uuid_str),
-        "search_switch": search_switch,
-        "apply_id": apply_id,
-        "group_id": group_id,
-        "memory_config": memory_config,
-    }
-    
-    return {
-        "messages": [
-            AIMessage(
-                content="",
-                tool_calls=[{
-                    "name": tool_name,
-                    "args": tool_args,
-                    "id": tool_call_id
-                }]
-            )
-        ]
-    }
diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py
new file mode 100644
index 00000000..0c68a47e
--- /dev/null
+++ b/api/app/core/memory/agent/langgraph_graph/nodes/problem_nodes.py
@@ -0,0 +1,237 @@
+import json
+import time
+from app.core.logging_config import get_agent_logger
+from app.db import get_db
+
+from app.core.memory.agent.models.problem_models import ProblemExtensionResponse
+from app.core.memory.agent.utils.llm_tools import (
+    PROJECT_ROOT_,
+    ReadState,
+)
+from app.core.memory.agent.utils.redis_tool import store
+from app.core.memory.agent.utils.session_tools import SessionService
+from app.core.memory.agent.utils.template_tools import TemplateService
+from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
+
+template_root = PROJECT_ROOT_ + '/agent/utils/prompt'
+db_session = next(get_db())
+logger = get_agent_logger(__name__)
+
+class ProblemNodeService(LLMServiceMixin):
+    """问题处理节点服务类"""
+    
+    def __init__(self):
+        super().__init__()
+        self.template_service = TemplateService(template_root)
+
+# 创建全局服务实例
+problem_service = ProblemNodeService()
+
+async def Split_The_Problem(state: ReadState) -> ReadState:
+    """问题分解节点"""
+    # 从状态中获取数据
+    content = state.get('data', '')
+    group_id = state.get('group_id', '')
+    memory_config = state.get('memory_config', None)
+
+    history = await SessionService(store).get_history(group_id, group_id, group_id)
+    system_prompt = await problem_service.template_service.render_template(
+        template_name='problem_breakdown_prompt.jinja2',
+        operation_name='split_the_problem',
+        history=history,
+        sentence=content
+    )
+    
+    try:
+        # 使用优化的LLM服务
+        structured = await problem_service.call_llm_structured(
+            state=state,
+            db_session=db_session,
+            system_prompt=system_prompt,
+            response_model=ProblemExtensionResponse,
+            fallback_value=[]
+        )
+        
+        # 添加更详细的日志记录
+        logger.info(f"Split_The_Problem: 开始处理问题分解，内容长度: {len(content)}")
+        
+        # 验证结构化响应
+        if not structured or not hasattr(structured, 'root'):
+            logger.warning("Split_The_Problem: 结构化响应为空或格式不正确")
+            split_result = json.dumps([], ensure_ascii=False)
+        elif not structured.root:
+            logger.warning("Split_The_Problem: 结构化响应的root为空")
+            split_result = json.dumps([], ensure_ascii=False)
+        else:
+            split_result = json.dumps(
+                [item.model_dump() for item in structured.root],
+                ensure_ascii=False
+            )
+        
+        split_result_dict = []
+        for index, item in enumerate(json.loads(split_result)):
+            split_data = {
+                "id": f"Q{index+1}",
+                "question": item['extended_question'],
+                "type": item['type'],
+                "reason": item['reason']
+            }
+            split_result_dict.append(split_data)
+        
+        logger.info(f"Split_The_Problem: 成功生成 {len(structured.root) if structured.root else 0} 个分解项")
+
+        result = {
+            "context": split_result,
+            "original": content,
+            "_intermediate": {
+                "type": "problem_split",
+                "title": "问题拆分",
+                "data": split_result_dict,
+                "original_query": content
+            }
+        }
+        
+    except Exception as e:
+        logger.error(
+            f"Split_The_Problem failed: {e}",
+            exc_info=True
+        )
+        
+        # 提供更详细的错误信息
+        error_details = {
+            "error_type": type(e).__name__,
+            "error_message": str(e),
+            "content_length": len(content),
+            "llm_model_id": memory_config.llm_model_id if memory_config else None
+        }
+        
+        logger.error(f"Split_The_Problem error details: {error_details}")
+        
+        # 创建默认的空结果
+        result = {
+            "context": json.dumps([], ensure_ascii=False),
+            "original": content,
+            "error": str(e),
+            "_intermediate": {
+                "type": "problem_split",
+                "title": "问题拆分",
+                "data": [],
+                "original_query": content,
+                "error": error_details
+            }
+        }
+    
+    # 返回更新后的状态，包含spit_context字段
+    return {"spit_data": result}
+
+async def Problem_Extension(state: ReadState) -> ReadState:
+    """问题扩展节点"""
+    # 获取原始数据和分解结果
+    start = time.time()
+    content = state.get('data', '')
+    data = state.get('spit_data', '')['context']
+    group_id = state.get('group_id', '')
+    storage_type = state.get('storage_type', '')
+    user_rag_memory_id = state.get('user_rag_memory_id', '')
+    memory_config = state.get('memory_config', None)
+
+    databasets = {}
+    try:
+        data = json.loads(data)
+        for i in data:
+            databasets[i['extended_question']] = i['type']
+    except (json.JSONDecodeError, KeyError, TypeError) as e:
+        logger.error(f"Problem_Extension: 数据解析失败: {e}")
+        # 使用空字典作为fallback
+        databasets = {}
+        data = []
+
+    history = await SessionService(store).get_history(group_id, group_id, group_id)
+    system_prompt = await problem_service.template_service.render_template(
+        template_name='Problem_Extension_prompt.jinja2',
+        operation_name='problem_extension',
+        history=history,
+        questions=databasets
+    )
+
+    try:
+        # 使用优化的LLM服务
+        response_content = await problem_service.call_llm_structured(
+            state=state,
+            db_session=db_session,
+            system_prompt=system_prompt,
+            response_model=ProblemExtensionResponse,
+            fallback_value=[]
+        )
+
+        logger.info(f"Problem_Extension: 开始处理问题扩展，问题数量: {len(databasets)}")
+
+        # 验证结构化响应
+        if not response_content or not hasattr(response_content, 'root'):
+            logger.warning("Problem_Extension: 结构化响应为空或格式不正确")
+            aggregated_dict = {}
+        elif not response_content.root:
+            logger.warning("Problem_Extension: 结构化响应的root为空")
+            aggregated_dict = {}
+        else:
+            # Aggregate results by original question
+            aggregated_dict = {}
+            for item in response_content.root:
+                try:
+                    key = getattr(item, "original_question", None) or (
+                        item.get("original_question") if isinstance(item, dict) else None
+                    )
+                    value = getattr(item, "extended_question", None) or (
+                        item.get("extended_question") if isinstance(item, dict) else None
+                    )
+                    if not key or not value:
+                        logger.warning(f"Problem_Extension: 跳过无效项: key={key}, value={value}")
+                        continue
+                    aggregated_dict.setdefault(key, []).append(value)
+                except Exception as item_error:
+                    logger.warning(f"Problem_Extension: 处理项目时出错: {item_error}")
+                    continue
+
+        logger.info(f"Problem_Extension: 成功生成 {len(aggregated_dict)} 个扩展问题组")
+
+    except Exception as e:
+        logger.error(
+            f"LLM call failed for Problem_Extension: {e}",
+            exc_info=True
+        )
+
+        # 提供更详细的错误信息
+        error_details = {
+            "error_type": type(e).__name__,
+            "error_message": str(e),
+            "questions_count": len(databasets),
+            "llm_model_id": memory_config.llm_model_id if memory_config else None
+        }
+
+        logger.error(f"Problem_Extension error details: {error_details}")
+        aggregated_dict = {}
+
+    logger.info("Problem extension")
+    logger.info(f"Problem extension result: {aggregated_dict}")
+
+    # Emit intermediate output for frontend
+    print(time.time() - start)
+    result = {
+        "context": aggregated_dict,
+        "original": data,
+        "storage_type": storage_type,
+        "user_rag_memory_id": user_rag_memory_id,
+        "_intermediate": {
+            "type": "problem_extension",
+            "title": "问题扩展",
+            "data": aggregated_dict,
+            "original_query": content,
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id
+        }
+    }
+
+    return {"problem_extension": result}
+
+
+
diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py
new file mode 100644
index 00000000..14f8fa8b
--- /dev/null
+++ b/api/app/core/memory/agent/langgraph_graph/nodes/retrieve_nodes.py
@@ -0,0 +1,417 @@
+# ===== 标准库 =====
+import asyncio
+import json
+import os
+
+# ===== 第三方库 =====
+from langchain.agents import create_agent
+from langchain_openai import ChatOpenAI
+from app.core.logging_config import get_agent_logger
+from app.db import get_db, get_db_context
+
+from app.schemas import model_schema
+from app.services.memory_config_service import MemoryConfigService
+from app.services.model_service import ModelConfigService
+
+from app.core.memory.agent.services.search_service import SearchService
+from app.core.memory.agent.utils.llm_tools import (
+    COUNTState,
+    ReadState,
+    deduplicate_entries,
+    merge_to_key_value_pairs,
+)
+from app.core.memory.agent.langgraph_graph.tools.tool import (
+    create_hybrid_retrieval_tool_sync,
+    create_time_retrieval_tool,
+    extract_tool_message_content,
+)
+
+from app.core.rag.nlp.search import knowledge_retrieval
+
+logger = get_agent_logger(__name__)
+db = next(get_db())
+
+
+
+async def rag_config(state):
+    user_rag_memory_id = state.get('user_rag_memory_id', '')
+    kb_config = {
+        "knowledge_bases": [
+            {
+                "kb_id": user_rag_memory_id,
+                "similarity_threshold": 0.7,
+                "vector_similarity_weight": 0.5,
+                "top_k": 10,
+                "retrieve_type": "participle"
+            }
+        ],
+        "merge_strategy": "weight",
+        "reranker_id": os.getenv('reranker_id'),
+        "reranker_top_k": 10
+    }
+    return kb_config
+async def rag_knowledge(state,question):
+    kb_config = await rag_config(state)
+    group_id = state.get('group_id', '')
+    user_rag_memory_id=state.get("user_rag_memory_id",'')
+    retrieve_chunks_result = knowledge_retrieval(question, kb_config, [str(group_id)])
+    try:
+        retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
+        clean_content = '\n\n'.join(retrieval_knowledge)
+        cleaned_query = question
+        raw_results = clean_content
+        logger.info(f" Using RAG storage with memory_id={user_rag_memory_id}")
+    except  Exception :
+        retrieval_knowledge=[]
+        clean_content = ''
+        raw_results = ''
+        cleaned_query = question
+        logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
+    return retrieval_knowledge,clean_content,cleaned_query,raw_results
+
+
+async def llm_infomation(state: ReadState) -> ReadState:
+    memory_config = state.get('memory_config', None)
+    model_id = memory_config.llm_model_id
+    tenant_id = memory_config.tenant_id
+
+    # 使用现有的 memory_config 而不是重新查询数据库
+    # 或者使用线程安全的数据库访问
+    with get_db_context() as db:
+        result_orm = ModelConfigService.get_model_by_id(db=db, model_id=model_id, tenant_id=tenant_id)
+        result_pydantic = model_schema.ModelConfig.model_validate(result_orm)
+    return result_pydantic
+
+
+async def clean_databases(data) -> str:
+    """
+    简化的数据库搜索结果清理函数
+    
+    Args:
+        data: 搜索结果数据
+        
+    Returns:
+        清理后的内容字符串
+    """
+    try:
+        # 解析JSON字符串
+        if isinstance(data, str):
+            try:
+                data = json.loads(data)
+            except json.JSONDecodeError:
+                return data
+
+        if not isinstance(data, dict):
+            return str(data)
+
+        # 获取结果数据
+        # with open("搜索结果.json","w",encoding='utf-8') as f:
+        #     f.write(json.dumps(data, indent=4, ensure_ascii=False))
+        results = data.get('results', data)
+        if not isinstance(results, dict):
+            return str(results)
+
+        # 收集所有内容
+        content_list = []
+        
+        # 处理重排序结果
+        reranked = results.get('reranked_results', {})
+        if reranked:
+            for category in ['summaries', 'statements', 'chunks', 'entities']:
+                items = reranked.get(category, [])
+                if isinstance(items, list):
+                    content_list.extend(items)
+        # 处理时间搜索结果
+        time_search = results.get('time_search', {})
+        if time_search:
+            if isinstance(time_search, dict):
+                statements = time_search.get('statements', time_search.get('time_search', []))
+                if isinstance(statements, list):
+                    content_list.extend(statements)
+            elif isinstance(time_search, list):
+                content_list.extend(time_search)
+
+        # 提取文本内容
+        text_parts = []
+        for item in content_list:
+            if isinstance(item, dict):
+                text = item.get('statement') or item.get('content', '')
+                if text:
+                    text_parts.append(text)
+            elif isinstance(item, str):
+                text_parts.append(item)
+
+
+        return '\n'.join(text_parts).strip()
+
+    except Exception as e:
+        logger.error(f"clean_databases failed: {e}", exc_info=True)
+        return str(data)
+
+
+async def retrieve_nodes(state: ReadState) -> ReadState:
+
+    '''
+
+    模型信息
+    '''
+
+    problem_extension=state.get('problem_extension', '')['context']
+    storage_type=state.get('storage_type', '')
+    user_rag_memory_id=state.get('user_rag_memory_id', '')
+    group_id=state.get('group_id', '')
+    memory_config = state.get('memory_config', None)
+    original=state.get('data', '')
+    problem_list=[]
+    for key,values in problem_extension.items():
+        for data in values:
+            problem_list.append(data)
+    logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
+    # 创建异步任务处理单个问题
+    async def process_question_nodes(idx, question):
+        try:
+            # Prepare search parameters based on storage type
+            search_params = {
+                "group_id": group_id,
+                "question": question,
+                "return_raw_results": True
+            }
+            if storage_type == "rag" and user_rag_memory_id:
+                retrieval_knowledge, clean_content, cleaned_query, raw_results = await rag_knowledge(state, question)
+            else:
+                clean_content, cleaned_query, raw_results = await SearchService().execute_hybrid_search(
+                    **search_params, memory_config=memory_config
+                )
+
+            return {
+                "Query_small": cleaned_query,
+                "Result_small": clean_content,
+                "_intermediate": {
+                    "type": "search_result",
+                    "query": cleaned_query,
+                    "raw_results": raw_results,
+                    "index": idx + 1,
+                    "total": len(problem_list)
+                }
+            }
+
+        except Exception as e:
+            logger.error(
+                f"Retrieve: hybrid_search failed for question '{question}': {e}",
+                exc_info=True
+            )
+            # Return empty result for this question
+            return {
+                "Query_small": question,
+                "Result_small": "",
+                "_intermediate": {
+                    "type": "search_result",
+                    "query": question,
+                    "raw_results": [],
+                    "index": idx + 1,
+                    "total": len(problem_list)
+                }
+            }
+
+    # 并发处理所有问题
+    tasks = [process_question_nodes(idx, question) for idx, question in enumerate(problem_list)]
+    databases_anser = await asyncio.gather(*tasks)
+    databases_data = {
+        "Query": original,
+        "Expansion_issue": databases_anser
+    }
+
+    # Collect intermediate outputs before deduplication
+    intermediate_outputs = []
+    for item in databases_anser:
+        if '_intermediate' in item:
+            intermediate_outputs.append(item['_intermediate'])
+
+    # Deduplicate and merge results
+    deduplicated_data = deduplicate_entries(databases_data['Expansion_issue'])
+    deduplicated_data_merged = merge_to_key_value_pairs(
+        deduplicated_data,
+        'Query_small',
+        'Result_small'
+    )
+
+    # Restructure for Verify/Retrieve_Summary compatibility
+    keys, val = [], []
+    for item in deduplicated_data_merged:
+        for items_key, items_value in item.items():
+            keys.append(items_key)
+            val.append(items_value)
+
+    send_verify = []
+    for i, j in zip(keys, val, strict=False):
+        if j!=['']:
+            send_verify.append({
+                "Query_small": i,
+                "Answer_Small": j
+            })
+
+    dup_databases = {
+        "Query": original,
+        "Expansion_issue": send_verify,
+        "_intermediate_outputs": intermediate_outputs  # Preserve intermediate outputs
+    }
+
+    logger.info(f"Collected {len(intermediate_outputs)} intermediate outputs from search results")
+    return {'retrieve':dup_databases}
+
+
+
+
+async def retrieve(state: ReadState) -> ReadState:
+    # 从state中获取group_id
+    import time
+    start=time.time()
+    problem_extension = state.get('problem_extension', '')['context']
+    storage_type = state.get('storage_type', '')
+    user_rag_memory_id = state.get('user_rag_memory_id', '')
+    group_id = state.get('group_id', '')
+    memory_config = state.get('memory_config', None)
+    original = state.get('data', '')
+    problem_list = []
+    for key, values in problem_extension.items():
+        for data in values:
+            problem_list.append(data)
+    logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
+    databases_anser = []
+
+    async def get_llm_info():
+        with get_db_context() as db:  # 使用同步数据库上下文管理器
+            config_service = MemoryConfigService(db)
+            return await llm_infomation(state)
+    llm_config = await get_llm_info()
+    api_key_obj = llm_config.api_keys[0]
+    api_key = api_key_obj.api_key
+    api_base = api_key_obj.api_base
+    model_name = api_key_obj.model_name
+    llm = ChatOpenAI(
+        model=model_name,
+        api_key=api_key,
+        base_url=api_base,
+        temperature=0.2,
+    )
+
+    time_retrieval_tool = create_time_retrieval_tool(group_id)
+    search_params = { "group_id": group_id, "return_raw_results": True }
+    hybrid_retrieval=create_hybrid_retrieval_tool_sync(memory_config, **search_params)
+    agent = create_agent(
+        llm,
+        tools=[time_retrieval_tool,hybrid_retrieval],
+        system_prompt=f"我是检索专家，可以根据适合的工具进行检索。当前使用的group_id是: {group_id}"
+    )
+
+    # 创建异步任务处理单个问题
+    import asyncio
+
+    # 在模块级别定义信号量，限制最大并发数
+    SEMAPHORE = asyncio.Semaphore(5)  # 限制最多5个并发数据库操作
+
+    async def process_question(idx, question):
+        async with SEMAPHORE:  # 限制并发
+            try:
+                if storage_type == "rag" and user_rag_memory_id:
+                    retrieval_knowledge, clean_content, cleaned_query, raw_results = await rag_knowledge(state, question)
+                else:
+                    cleaned_query = question
+                    # 使用 asyncio 在线程池中运行同步的 agent.invoke
+                    import asyncio
+                    response = await asyncio.get_event_loop().run_in_executor(
+                        None,
+                        lambda: agent.invoke({"messages": question})
+                    )
+                    tool_results = extract_tool_message_content(response)
+                    if tool_results == None:
+                        raw_results = []
+                        clean_content = ''
+                    else:
+                        raw_results = tool_results['content']
+                        clean_content = await clean_databases(raw_results)
+
+                        try:
+                            raw_results = raw_results['results']
+                        except Exception:
+                            raw_results = []
+
+                return {
+                    "Query_small": cleaned_query,
+                    "Result_small": clean_content,
+                    "_intermediate": {
+                        "type": "search_result",
+                        "query": cleaned_query,
+                        "raw_results": raw_results,
+                        "index": idx + 1,
+                        "total": len(problem_list)
+                    }
+                }
+
+            except Exception as e:
+                logger.error(
+                    f"Retrieve: hybrid_search failed for question '{question}': {e}",
+                    exc_info=True
+                )
+                # Return empty result for this question
+                return {
+                    "Query_small": question,
+                    "Result_small": "",
+                    "_intermediate": {
+                        "type": "search_result",
+                        "query": question,
+                        "raw_results": [],
+                        "index": idx + 1,
+                        "total": len(problem_list)
+                    }
+                }
+
+    # 并发处理所有问题
+    import asyncio
+    tasks = [process_question(idx, question) for idx, question in enumerate(problem_list)]
+    databases_anser = await asyncio.gather(*tasks)
+    databases_data = {
+        "Query": original,
+        "Expansion_issue": databases_anser
+    }
+
+    # Collect intermediate outputs before deduplication
+    intermediate_outputs = []
+    for item in databases_anser:
+        if '_intermediate' in item:
+            intermediate_outputs.append(item['_intermediate'])
+
+    # Deduplicate and merge results
+    deduplicated_data = deduplicate_entries(databases_data['Expansion_issue'])
+    deduplicated_data_merged = merge_to_key_value_pairs(
+        deduplicated_data,
+        'Query_small',
+        'Result_small'
+    )
+
+    # Restructure for Verify/Retrieve_Summary compatibility
+    keys, val = [], []
+    for item in deduplicated_data_merged:
+        for items_key, items_value in item.items():
+            keys.append(items_key)
+            val.append(items_value)
+
+    send_verify = []
+    for i, j in zip(keys, val, strict=False):
+        if j != ['']:
+            send_verify.append({
+                "Query_small": i,
+                "Answer_Small": j
+            })
+
+    dup_databases = {
+        "Query": original,
+        "Expansion_issue": send_verify,
+        "_intermediate_outputs": intermediate_outputs  # Preserve intermediate outputs
+    }
+    # with open('retrieve_text.json', 'w') as f:
+    #     json.dump(dup_databases, f, indent=4)
+    logger.info(f"Collected {len(intermediate_outputs)} intermediate outputs from search results")
+    return {'retrieve': dup_databases}
+
+
diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py
new file mode 100644
index 00000000..7b727da5
--- /dev/null
+++ b/api/app/core/memory/agent/langgraph_graph/nodes/summary_nodes.py
@@ -0,0 +1,303 @@
+
+
+import time
+
+from app.core.logging_config import get_agent_logger, log_time
+from app.db import get_db
+
+from app.core.memory.agent.models.summary_models import (
+    RetrieveSummaryResponse,
+    SummaryResponse,
+)
+from app.core.memory.agent.services.search_service import SearchService
+from app.core.memory.agent.utils.llm_tools import (
+    PROJECT_ROOT_,
+    ReadState,
+)
+from app.core.memory.agent.utils.redis_tool import store
+from app.core.memory.agent.utils.session_tools import SessionService
+from app.core.memory.agent.utils.template_tools import TemplateService
+from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
+
+template_root = PROJECT_ROOT_ + '/agent/utils/prompt'
+logger = get_agent_logger(__name__)
+db_session = next(get_db())
+
+class SummaryNodeService(LLMServiceMixin):
+    """总结节点服务类"""
+    
+    def __init__(self):
+        super().__init__()
+        self.template_service = TemplateService(template_root)
+
+# 创建全局服务实例
+summary_service = SummaryNodeService()
+
+async def summary_history(state: ReadState) -> ReadState:
+    group_id = state.get("group_id", '')
+    history = await SessionService(store).get_history(group_id, group_id, group_id)
+    return history
+
+async def summary_llm(state: ReadState, history, retrieve_info, template_name, operation_name, response_model,search_mode) -> str:
+    """
+    增强的summary_llm函数，包含更好的错误处理和数据验证
+    """
+    data = state.get("data", '')
+    
+    # 构建系统提示词
+    if str(search_mode) == "0":
+        system_prompt = await summary_service.template_service.render_template(
+            template_name=template_name,
+            operation_name=operation_name,
+            data=retrieve_info,
+            query=data
+        )
+    else:
+        system_prompt = await summary_service.template_service.render_template(
+            template_name=template_name,
+            operation_name=operation_name,
+            query=data,
+            history=history,
+            retrieve_info=retrieve_info
+        )
+    try:
+        # 使用优化的LLM服务进行结构化输出
+        structured = await summary_service.call_llm_structured(
+            state=state,
+            db_session=db_session,
+            system_prompt=system_prompt,
+            response_model=response_model,
+            fallback_value=None
+        )
+        # 验证结构化响应
+        if structured is None:
+            logger.warning(f"LLM返回None，使用默认回答")
+            return "信息不足，无法回答"
+        
+        # 根据操作类型提取答案
+        if operation_name == "summary":
+            aimessages = getattr(structured, 'query_answer', None) or "信息不足，无法回答"
+        else:
+            # 处理RetrieveSummaryResponse
+            if hasattr(structured, 'data') and structured.data:
+                aimessages = getattr(structured.data, 'query_answer', None) or "信息不足，无法回答"
+            else:
+                logger.warning(f"结构化响应缺少data字段")
+                aimessages = "信息不足，无法回答"
+        
+        # 验证答案不为空
+        if not aimessages or aimessages.strip() == "":
+            aimessages = "信息不足，无法回答"
+            
+        return aimessages
+        
+    except Exception as e:
+        logger.error(f"结构化输出失败: {e}", exc_info=True)
+        
+        # 尝试非结构化输出作为fallback
+        try:
+            logger.info("尝试非结构化输出作为fallback")
+            response = await summary_service.call_llm_simple(
+                state=state,
+                db_session=db_session,
+                system_prompt=system_prompt,
+                fallback_message="信息不足，无法回答"
+            )
+            
+            if response and response.strip():
+                # 简单清理响应
+                cleaned_response = response.strip()
+                # 移除可能的JSON标记
+                if cleaned_response.startswith('```'):
+                    lines = cleaned_response.split('\n')
+                    cleaned_response = '\n'.join(lines[1:-1])
+                
+                return cleaned_response
+            else:
+                return "信息不足，无法回答"
+                
+        except Exception as fallback_error:
+            logger.error(f"Fallback也失败: {fallback_error}")
+            return "信息不足，无法回答"
+
+async def summary_redis_save(state: ReadState,aimessages) -> ReadState:
+    data = state.get("data", '')
+    group_id = state.get("group_id", '')
+    await SessionService(store).save_session(
+        user_id=group_id,
+        query=data,
+        apply_id=group_id,
+        group_id=group_id,
+        ai_response=aimessages
+    )
+    await SessionService(store).cleanup_duplicates()
+    logger.info(f"sessionid: {aimessages} 写入成功")
+async def summary_prompt(state: ReadState,aimessages,raw_results) -> ReadState:
+    storage_type=state.get("storage_type",'')
+    user_rag_memory_id=state.get("user_rag_memory_id",'')
+    data=state.get("data", '')
+    input_summary = {
+        "status": "success",
+        "summary_result": aimessages,
+        "storage_type": storage_type,
+        "user_rag_memory_id": user_rag_memory_id,
+        "_intermediate": {
+            "type": "input_summary",
+            "title": "快速答案",
+            "summary": aimessages,
+            "query": data,
+            "raw_results": raw_results,
+            "search_mode": "quick_search",
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id
+        }
+    }
+    retrieve={
+        "status": "success",
+        "summary_result": aimessages,
+        "storage_type": storage_type,
+        "user_rag_memory_id": user_rag_memory_id,
+        "_intermediate": {
+            "type": "retrieval_summary",
+            "title":"快速检索",
+            "summary": aimessages,
+            "query": data,
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id
+        }
+    }
+
+    return input_summary,retrieve
+
+async def Input_Summary(state: ReadState) -> ReadState:
+    start=time.time()
+    storage_type=state.get("storage_type",'')
+    memory_config = state.get('memory_config', None)
+    user_rag_memory_id=state.get("user_rag_memory_id",'')
+    data=state.get("data", '')
+    group_id=state.get("group_id", '')
+    logger.info(f"Input_Summary: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
+    history = await summary_history( state)
+    search_params = {
+        "group_id": group_id,
+        "question": data,
+        "return_raw_results": True
+    }
+
+    try:
+        retrieve_info, question, raw_results = await SearchService().execute_hybrid_search(**search_params, memory_config=memory_config)
+    except Exception as e:
+        logger.error( f"Input_Summary: hybrid_search failed, using empty results: {e}",  exc_info=True )
+        retrieve_info, question, raw_results = "", data, []
+
+
+    try:
+        # aimessages=await summary_llm(state,history,retrieve_info,'Retrieve_Summary_prompt.jinja2',
+        #                              'input_summary',RetrieveSummaryResponse)
+        # logger.info(f"快速答案总结==>>:{storage_type}--{user_rag_memory_id}--{aimessages}")
+        summary_result = await summary_prompt(state, retrieve_info, retrieve_info)
+        summary = summary_result[0]
+    except Exception as e:
+        logger.error( f"Input_Summary failed: {e}", exc_info=True )
+        summary= {
+            "status": "fail",
+            "summary_result": "信息不足，无法回答",
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id,
+            "error": str(e)
+        }
+    end = time.time()
+    try:
+        duration = end - start
+    except Exception:
+        duration = 0.0
+    log_time('检索', duration)
+    return {"summary":summary}
+
+async def Retrieve_Summary(state: ReadState)-> ReadState:
+    retrieve=state.get("retrieve", '')
+    history = await summary_history( state)
+    import json
+    with open("检索.json","w",encoding='utf-8') as f:
+        f.write(json.dumps(retrieve, indent=4, ensure_ascii=False))
+    retrieve=retrieve.get("Expansion_issue", [])
+    start=time.time()
+    retrieve_info_str=[]
+    for data in retrieve:
+        if data=='':
+            retrieve_info_str=''
+        else:
+            for key, value in data.items():
+                if key=='Answer_Small':
+                    for i in value:
+                        retrieve_info_str.append(i)
+    retrieve_info_str=list(set(retrieve_info_str))
+    retrieve_info_str='\n'.join(retrieve_info_str)
+
+    aimessages=await  summary_llm(state,history,retrieve_info_str,
+                                  'Retrieve_Summary_prompt.jinja2','retrieve_summary',RetrieveSummaryResponse,"1")
+    if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "":
+        await summary_redis_save(state, aimessages)
+    if aimessages == '':
+        aimessages = '信息不足，无法回答'
+    logger.info(f"Summary after retrieval: {aimessages}")
+    end = time.time()
+    try:
+        duration = end - start
+    except Exception:
+        duration = 0.0
+    log_time('Retrieval summary', duration)
+    
+    # 修复协程调用 - 先await，然后访问返回值
+    summary_result = await summary_prompt(state, aimessages, retrieve_info_str)
+    summary = summary_result[1]
+    return {"summary":summary}
+
+
+async def Summary(state: ReadState)-> ReadState:
+    start=time.time()
+    query = state.get("data", '')
+    verify=state.get("verify", '')
+    verify_expansion_issue=verify.get("verified_data", '')
+    retrieve_info_str=''
+    for data in verify_expansion_issue:
+        for key, value in data.items():
+            if key=='answer_small':
+                for i in value:
+                    retrieve_info_str+=i+'\n'
+    history=await summary_history(state)
+
+    data = {
+        "query": query,
+        "history": history,
+        "retrieve_info": retrieve_info_str
+    }
+    aimessages=await  summary_llm(state,history,data,
+                                  'summary_prompt.jinja2','summary',SummaryResponse,0)
+
+
+    if '信息不足，无法回答' not in str(aimessages) or str(aimessages) != "":
+        await summary_redis_save(state, aimessages)
+    if aimessages == '':
+        aimessages = '信息不足，无法回答'
+    try:
+        duration = time.time() - start
+    except Exception:
+        duration = 0.0
+    log_time('Retrieval summary', duration)
+
+    # 修复协程调用 - 先await，然后访问返回值
+    summary_result = await summary_prompt(state, aimessages, retrieve_info_str)
+    summary = summary_result[1]
+    return {"summary":summary}
+
+async def Summary_fails(state: ReadState)-> ReadState:
+    storage_type=state.get("storage_type", '')
+    user_rag_memory_id=state.get("user_rag_memory_id", '')
+    result= {
+        "status": "success",
+        "summary_result": "没有相关数据",
+        "storage_type": storage_type,
+        "user_rag_memory_id": user_rag_memory_id
+    }
+    return {"summary":result}
\ No newline at end of file
diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/tool_node.py b/api/app/core/memory/agent/langgraph_graph/nodes/tool_node.py
deleted file mode 100644
index 4727fb9c..00000000
--- a/api/app/core/memory/agent/langgraph_graph/nodes/tool_node.py
+++ /dev/null
@@ -1,234 +0,0 @@
-"""
-Tool execution node for LangGraph workflow.
-
-This module provides the ToolExecutionNode class which wraps tool execution
-with parameter transformation logic using the ParameterBuilder service.
-"""
-
-import logging
-import time
-from typing import Any, Callable, Dict
-
-from app.core.memory.agent.langgraph_graph.state.extractors import (
-    extract_content_payload,
-    extract_tool_call_id,
-)
-from app.core.memory.agent.mcp_server.services.parameter_builder import ParameterBuilder
-from app.schemas.memory_config_schema import MemoryConfig
-from langchain_core.messages import AIMessage
-from langgraph.prebuilt import ToolNode
-
-logger = logging.getLogger(__name__)
-
-
-class ToolExecutionNode:
-    """
-    Custom LangGraph node that wraps tool execution with parameter transformation.
-    
-    This node extracts content from previous tool results, transforms parameters
-    based on tool type using ParameterBuilder, and invokes the tool with the
-    correct argument structure.
-    
-    Attributes:
-        tool_node: LangGraph ToolNode wrapping the actual tool
-        id: Node identifier for message IDs
-        tool_name: Name of the tool being executed
-        namespace: Namespace for session management
-        search_switch: Search routing parameter
-        apply_id: Application identifier
-        group_id: Group identifier
-        parameter_builder: Service for building tool-specific arguments
-        memory_config: MemoryConfig object containing all configuration
-    """
-
-    def __init__(
-        self,
-        tool: Callable,
-        node_id: str,
-        namespace: str,
-        search_switch: str,
-        apply_id: str,
-        group_id: str,
-        parameter_builder: ParameterBuilder,
-        storage_type: str,
-        user_rag_memory_id: str,
-        memory_config: MemoryConfig,
-    ):
-        """
-        Initialize the tool execution node.
-        
-        Args:
-            tool: The tool function to execute
-            node_id: Identifier for this node (used in message IDs)
-            namespace: Namespace for session management
-            search_switch: Search routing parameter
-            apply_id: Application identifier
-            group_id: Group identifier
-            parameter_builder: Service for building tool-specific arguments
-            storage_type: Storage type for the workspace
-            user_rag_memory_id: User RAG memory identifier
-            memory_config: MemoryConfig object containing all configuration
-        """
-        self.tool_node = ToolNode([tool])
-        self.id = node_id
-        self.tool_name = tool.name if hasattr(tool, 'name') else str(tool)
-        self.namespace = namespace
-        self.search_switch = search_switch
-        self.apply_id = apply_id
-        self.group_id = group_id
-        self.parameter_builder = parameter_builder
-        self.storage_type = storage_type
-        self.user_rag_memory_id = user_rag_memory_id
-        self.memory_config = memory_config
-
-        logger.info(
-            f"[ToolExecutionNode] Initialized node '{self.id}' for tool '{self.tool_name}'"
-        )
-    
-    async def __call__(self, state: Dict[str, Any]) -> Dict[str, Any]:
-        """
-        Execute the tool with transformed parameters.
-        
-        This method:
-        1. Extracts the last message from state
-        2. Extracts tool call ID using state extractors
-        3. Extracts content payload using state extractors
-        4. Builds tool arguments using parameter builder
-        5. Constructs AIMessage with tool_calls
-        6. Invokes the tool and returns the result
-        
-        Args:
-            state: LangGraph state dictionary
-            
-        Returns:
-            Updated state with tool result in messages
-        """
-        messages = state.get("messages", [])
-        logger.debug( self.tool_name)
-        
-        if not messages:
-            logger.warning(f"[ToolExecutionNode] {self.id} - No messages in state")
-            return {"messages": [AIMessage(content="Error: No messages in state")]}
-        
-        last_message = messages[-1]
-        logger.debug(
-            f"[ToolExecutionNode] {self.id} - Processing message at {time.time()}"
-        )
-        
-        try:
-            # Extract tool call ID using state extractors
-            tool_call_id = extract_tool_call_id(last_message)
-            logger.debug(f"[ToolExecutionNode] {self.id} - Extracted tool_call_id: {tool_call_id}")
-            
-        except ValueError as e:
-            logger.error(
-                f"[ToolExecutionNode] {self.id} - Failed to extract tool call ID: {e}"
-            )
-            return {"messages": [AIMessage(content=f"Error: {str(e)}")]}
-        
-        try:
-            # Extract content payload using state extractors
-            content = extract_content_payload(last_message)
-            logger.debug(
-                f"[ToolExecutionNode] {self.id} - Extracted content type: {type(content)}, content_keys: {list(content.keys()) if isinstance(content, dict) else 'N/A'}"
-            )
-            # Log raw message content for debugging
-            if hasattr(last_message, 'content'):
-                raw = last_message.content
-                logger.debug(f"[ToolExecutionNode] {self.id} - Raw message content (first 500 chars): {str(raw)[:500]}")
-            
-        except Exception as e:
-            logger.error(
-                f"[ToolExecutionNode] {self.id} - Failed to extract content: {e}",
-                exc_info=True
-            )
-            content = {}
-        
-        try:
-            # Build tool arguments using parameter builder
-            tool_args = self.parameter_builder.build_tool_args(
-                tool_name=self.tool_name,
-                content=content,
-                tool_call_id=tool_call_id,
-                search_switch=self.search_switch,
-                apply_id=self.apply_id,
-                group_id=self.group_id,
-                memory_config=self.memory_config,
-                storage_type=self.storage_type,
-                user_rag_memory_id=self.user_rag_memory_id,
-            )
-            logger.debug(
-                f"[ToolExecutionNode] {self.id} - Built tool args with keys: {list(tool_args.keys())}"
-            )
-            
-        except Exception as e:
-            logger.error(
-                f"[ToolExecutionNode] {self.id} - Failed to build tool args: {e}",
-                exc_info=True
-            )
-            return {"messages": [AIMessage(content=f"Error building arguments: {str(e)}")]}
-        
-        # Construct tool input message
-        tool_input = {
-            "messages": [
-                AIMessage(
-                    content="",
-                    tool_calls=[{
-                        "name": self.tool_name,
-                        "args": tool_args,
-                        "id": f"{self.id}_{tool_call_id}",
-                    }]
-                )
-            ]
-        }
-        
-        try:
-            # Invoke the tool
-            result = await self.tool_node.ainvoke(tool_input)
-            
-            logger.debug(
-                f"[ToolExecutionNode] {self.id} - Tool execution completed"
-            )
-            
-            # Check for error in tool response
-            error_entry = None
-            if result and "messages" in result:
-                for msg in result["messages"]:
-                    if hasattr(msg, 'content'):
-                        try:
-                            import json
-                            content = msg.content
-                            if isinstance(content, str):
-                                parsed = json.loads(content)
-                                if isinstance(parsed, dict) and "error" in parsed:
-                                    error_msg = parsed["error"]
-                                    logger.warning(
-                                        f"[ToolExecutionNode] {self.id} - Tool returned error: {error_msg}"
-                                    )
-                                    error_entry = {"tool": self.tool_name, "error": error_msg, "node_id": self.id}
-                        except (json.JSONDecodeError, TypeError):
-                            pass
-            
-            # Return result with error tracking if error was found
-            if error_entry:
-                result["errors"] = [error_entry]
-            
-            return result
-            
-        except Exception as e:
-            logger.error(
-                f"[ToolExecutionNode] {self.id} - Tool execution failed: {e}",
-                exc_info=True
-            )
-            # Track error in state and return error message
-            from langchain_core.messages import ToolMessage
-            error_entry = {"tool": self.tool_name, "error": str(e), "node_id": self.id}
-            return {
-                "messages": [
-                    ToolMessage(
-                        content=f"Error executing tool: {str(e)}",
-                        tool_call_id=f"{self.id}_{tool_call_id}"
-                    )
-                ],
-                "errors": [error_entry]
-            }
diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py
new file mode 100644
index 00000000..f3a39afb
--- /dev/null
+++ b/api/app/core/memory/agent/langgraph_graph/nodes/verification_nodes.py
@@ -0,0 +1,85 @@
+
+from app.core.logging_config import get_agent_logger
+from app.db import get_db
+
+from app.core.memory.agent.models.verification_models import VerificationResult
+from app.core.memory.agent.utils.llm_tools import (
+    PROJECT_ROOT_,
+    ReadState,
+)
+from app.core.memory.agent.utils.redis_tool import store
+from app.core.memory.agent.utils.session_tools import SessionService
+from app.core.memory.agent.utils.template_tools import TemplateService
+from app.core.memory.agent.services.optimized_llm_service import LLMServiceMixin
+
+template_root = PROJECT_ROOT_ + '/agent/utils/prompt'
+db_session = next(get_db())
+logger = get_agent_logger(__name__)
+
+class VerificationNodeService(LLMServiceMixin):
+    """验证节点服务类"""
+    
+    def __init__(self):
+        super().__init__()
+        self.template_service = TemplateService(template_root)
+
+# 创建全局服务实例
+verification_service = VerificationNodeService()
+
+async def Verify_prompt(state: ReadState,messages_deal):
+    storage_type = state.get('storage_type', '')
+    user_rag_memory_id = state.get('user_rag_memory_id', '')
+    data = state.get('data', '')
+    Verify_result = {
+        "status": messages_deal.split_result,
+        "verified_data": messages_deal.expansion_issue,
+        "storage_type": storage_type,
+        "user_rag_memory_id": user_rag_memory_id,
+        "_intermediate": {
+            "type": "verification",
+            "title": "Data Verification",
+            "result": messages_deal.split_result,
+            "reason": messages_deal.reason,
+            "query": data,
+            "verified_count": len(messages_deal.expansion_issue),
+            "storage_type": storage_type,
+            "user_rag_memory_id": user_rag_memory_id
+        }
+    }
+    return Verify_result
+async def Verify(state: ReadState):
+    content = state.get('data', '')
+    group_id = state.get('group_id', '')
+    memory_config = state.get('memory_config', None)
+
+    history = await SessionService(store).get_history(group_id, group_id, group_id)
+
+    retrieve = state.get("retrieve", '')
+    retrieve = retrieve.get("Expansion_issue", [])
+    messages = {
+        "Query": content,
+        "Expansion_issue": retrieve
+    }
+
+    system_prompt = await verification_service.template_service.render_template(
+        template_name='split_verify_prompt.jinja2',
+        operation_name='split_verify_prompt',
+        history=history,
+        sentence=messages
+    )
+    
+    # 使用优化的LLM服务
+    structured = await verification_service.call_llm_structured(
+        state=state,
+        db_session=db_session,
+        system_prompt=system_prompt,
+        response_model=VerificationResult,
+        fallback_value={
+            "split_result": "fail",
+            "expansion_issue": [],
+            "reason": "验证失败"
+        }
+    )
+    
+    result = await Verify_prompt(state, structured)
+    return {"verify": result}
\ No newline at end of file
diff --git a/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py
new file mode 100644
index 00000000..8421d059
--- /dev/null
+++ b/api/app/core/memory/agent/langgraph_graph/nodes/write_nodes.py
@@ -0,0 +1,50 @@
+
+from app.core.memory.agent.utils.llm_tools import  WriteState
+from app.core.memory.agent.utils.write_tools import write
+from app.core.logging_config import get_agent_logger
+
+logger = get_agent_logger(__name__)
+async def write_node(state: WriteState) -> WriteState:
+    """
+        Write data to the database/file system.
+
+        Args:
+            ctx: FastMCP context for dependency injection
+            content: Data content to write
+            user_id: User identifier
+            apply_id: Application identifier
+            group_id: Group identifier
+            memory_config: MemoryConfig object containing all configuration
+
+        Returns:
+            dict: Contains 'status', 'saved_to', and 'data' fields
+        """
+    content=state.get('data','')
+    group_id=state.get('group_id','')
+    memory_config=state.get('memory_config', '')
+    try:
+        result=await write(
+            content=content,
+            user_id=group_id,
+            apply_id=group_id,
+            group_id=group_id,
+            memory_config=memory_config,
+        )
+        logger.info(f"Write completed successfully! Config: {memory_config.config_name}")
+
+        write_result= {
+            "status": "success",
+            "data": content,
+            "config_id": memory_config.config_id,
+            "config_name": memory_config.config_name,
+        }
+        return {"write_result":write_result}
+
+
+    except Exception as e:
+        logger.error(f"Data_write failed: {e}", exc_info=True)
+        write_result= {
+            "status": "error",
+            "message": str(e),
+        }
+        return {"write_result": write_result}
diff --git a/api/app/core/memory/agent/langgraph_graph/read_graph.py b/api/app/core/memory/agent/langgraph_graph/read_graph.py
index c29b5d86..19011a5f 100644
--- a/api/app/core/memory/agent/langgraph_graph/read_graph.py
+++ b/api/app/core/memory/agent/langgraph_graph/read_graph.py
@@ -1,469 +1,177 @@
-import json
-import os
-import re
-import time
-import warnings
+#!/usr/bin/env python3
 from contextlib import asynccontextmanager
-from typing import Literal
 
-from app.core.logging_config import get_agent_logger
-from app.core.memory.agent.langgraph_graph.nodes import (
-    ToolExecutionNode,
-    create_input_message,
-)
-from app.core.memory.agent.mcp_server.services.parameter_builder import ParameterBuilder
-from app.core.memory.agent.utils.llm_tools import COUNTState, ReadState
-from app.core.memory.agent.utils.multimodal import MultimodalProcessor
-from app.schemas.memory_config_schema import MemoryConfig
-from dotenv import load_dotenv
-from langchain_core.messages import AIMessage
-from langgraph.checkpoint.memory import InMemorySaver
-from langgraph.constants import END, START
+from langchain_core.messages import HumanMessage
+from langgraph.constants import START, END
 from langgraph.graph import StateGraph
-from langgraph.prebuilt import ToolNode
-
-logger = get_agent_logger(__name__)
-
-warnings.filterwarnings("ignore", category=RuntimeWarning)
-load_dotenv()
-redishost=os.getenv("REDISHOST")
-redisport=os.getenv('REDISPORT')
-redisdb=os.getenv('REDISDB')
-redispassword=os.getenv('REDISPASSWORD')
-counter = COUNTState(limit=3)
-
-# Update loop count in workflow
-async def update_loop_count(state):
-    """Update loop counter"""
-    current_count = state.get("loop_count", 0)
-    return {"loop_count": current_count + 1}
 
 
-def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]:
-    messages = state["messages"]
+from app.db import get_db
+from app.services.memory_config_service import MemoryConfigService
 
-    # Add boundary check
-    if not messages:
-        return END
-    counter.add(1)  # Increment by 1
+from app.core.memory.agent.utils.llm_tools import ReadState
+from app.core.memory.agent.langgraph_graph.nodes.data_nodes import content_input_node
+from app.core.memory.agent.langgraph_graph.nodes.problem_nodes import (
+    Split_The_Problem,
+    Problem_Extension,
+)
+from app.core.memory.agent.langgraph_graph.nodes.retrieve_nodes import (
+    retrieve,
+)
+from app.core.memory.agent.langgraph_graph.nodes.summary_nodes import (
+    Input_Summary,
+    Retrieve_Summary,
+    Summary_fails,
+    Summary,
+)
+from app.core.memory.agent.langgraph_graph.nodes.verification_nodes import Verify
+from app.core.memory.agent.langgraph_graph.routing.routers import (
+    Split_continue,
+    Retrieve_continue,
+    Verify_continue,
+)
 
-    loop_count = counter.get_total()
-    logger.debug(f"[should_continue] Current loop count: {loop_count}")
-
-    last_message = messages[-1]
-    last_message_str = str(last_message).replace('\\', '')
-    status_tools = re.findall(r'"split_result": "(.*?)"', last_message_str)
-    logger.debug(f"Status tools: {status_tools}")
-
-    if "success" in status_tools:
-        counter.reset()
-        return "Summary"
-    elif "failed" in status_tools:
-        if loop_count < 2:  # Maximum loop count is 3
-            return "content_input"
-        else:
-            counter.reset()
-            return "Summary_fails"
-    else:
-        # Add default return value to avoid returning None
-        counter.reset()
-        return "Summary"  # Default based on business requirements
-
-
-def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]:
-    """
-    Determine routing based on search_switch value.
-
-    Args:
-        state: State dictionary containing search_switch
-
-    Returns:
-        Next node to execute
-    """
-    # Direct dictionary access instead of regex parsing
-    search_switch = state.get("search_switch")
-
-    # Handle case where search_switch might be in messages
-    if search_switch is None and "messages" in state:
-        messages = state.get("messages", [])
-        if messages:
-            last_message = messages[-1]
-            # Try to extract from tool_calls args
-            if hasattr(last_message, "tool_calls") and last_message.tool_calls:
-                for tool_call in last_message.tool_calls:
-                    if isinstance(tool_call, dict) and "args" in tool_call:
-                        search_switch = tool_call["args"].get("search_switch")
-                        break
-
-    # Convert to string for comparison if needed
-    if search_switch is not None:
-        search_switch = str(search_switch)
-        if search_switch == '0':
-            return 'Verify'
-        elif search_switch == '1':
-            return 'Retrieve_Summary'
-
-    # Add default return value to avoid returning None
-    return 'Retrieve_Summary'  # Default based on business logic
-
-
-def Split_continue(state) -> Literal["Split_The_Problem", "Input_Summary"]:
-    """
-    Determine routing based on search_switch value.
-
-    Args:
-        state: State dictionary containing search_switch
-
-    Returns:
-        Next node to execute
-    """
-    logger.debug(f"Split_continue state: {state}")
-
-    # Direct dictionary access instead of regex parsing
-    search_switch = state.get("search_switch")
-
-    # Handle case where search_switch might be in messages
-    if search_switch is None and "messages" in state:
-        messages = state.get("messages", [])
-        if messages:
-            last_message = messages[-1]
-            # Try to extract from tool_calls args
-            if hasattr(last_message, "tool_calls") and last_message.tool_calls:
-                for tool_call in last_message.tool_calls:
-                    if isinstance(tool_call, dict) and "args" in tool_call:
-                        search_switch = tool_call["args"].get("search_switch")
-                        break
-
-    # Convert to string for comparison if needed
-    if search_switch is not None:
-        search_switch = str(search_switch)
-        if search_switch == '2':
-            return 'Input_Summary'
-    return 'Split_The_Problem'  # Default case
-
-
-class ProblemExtensionNode:
-    def __init__(self, tool, id, namespace, search_switch, apply_id, group_id, storage_type="", user_rag_memory_id=""):
-        self.tool_node = ToolNode([tool])
-        self.id = id
-        self.tool_name = tool.name if hasattr(tool, 'name') else str(tool)
-        self.namespace = namespace
-        self.search_switch = search_switch
-        self.apply_id = apply_id
-        self.group_id = group_id
-        self.storage_type = storage_type
-        self.user_rag_memory_id = user_rag_memory_id
-
-    async def __call__(self, state):
-        messages = state["messages"]
-        last_message = messages[-1] if messages else ""
-        logger.debug(f"ProblemExtensionNode {self.id} - Current time: {time.time()} - Message: {last_message}")
-        if self.tool_name == 'Input_Summary':
-            tool_call = re.findall("'id': '(.*?)'", str(last_message))[0]
-        else:
-            tool_call = str(re.findall(r"tool_call_id=.*?'(.*?)'", str(last_message))[0]).replace('\\', '').split('_id')[1]
-        
-        # Try to extract actual content payload from previous tool result
-        raw_msg = last_message.content if hasattr(last_message, 'content') else str(last_message)
-        extracted_payload = None
-        # Capture ToolMessage content field (supports single/double quotes), avoid greedy matching
-        m = re.search(r"content=(?:\"|\')(.*?)(?:\"|\'),\s*name=", raw_msg, flags=re.S)
-        if m:
-            extracted_payload = m.group(1)
-        else:
-            # Fallback: use raw string directly
-            extracted_payload = raw_msg
-
-        # Try to parse content as JSON first
-        try:
-            content = json.loads(extracted_payload)
-        except Exception:
-            # Try to extract JSON fragment from text and parse
-            parsed = None
-            candidates = re.findall(r"[\[{].*[\]}]", extracted_payload, flags=re.S)
-            for cand in candidates:
-                try:
-                    parsed = json.loads(cand)
-                    break
-                except Exception:
-                    continue
-            # If still fails, use raw string as content
-            content = parsed if parsed is not None else extracted_payload
-
-        # Build correct parameters based on tool name
-        tool_args = {}
-
-        if self.tool_name == "Verify":
-            # Verify tool requires context and usermessages parameters
-            if isinstance(content, dict):
-                tool_args["context"] = content
-            else:
-                tool_args["context"] = {"content": content}
-            tool_args["usermessages"] = str(tool_call)
-            tool_args["apply_id"] = str(self.apply_id)
-            tool_args["group_id"] = str(self.group_id)
-        elif self.tool_name == "Retrieve":
-            # Retrieve tool requires context and usermessages parameters
-            if isinstance(content, dict):
-                tool_args["context"] = content
-            else:
-                tool_args["context"] = {"content": content}
-            tool_args["usermessages"] = str(tool_call)
-            tool_args["search_switch"] = str(self.search_switch)
-            tool_args["apply_id"] = str(self.apply_id)
-            tool_args["group_id"] = str(self.group_id)
-        elif self.tool_name == "Summary":
-            # Summary tool requires string type context parameter
-            if isinstance(content, dict):
-                # Convert dict to JSON string
-                tool_args["context"] = json.dumps(content, ensure_ascii=False)
-            else:
-                tool_args["context"] = str(content)
-            tool_args["usermessages"] = str(tool_call)
-            tool_args["apply_id"] = str(self.apply_id)
-            tool_args["group_id"] = str(self.group_id)
-        elif self.tool_name == "Summary_fails":
-            # Summary_fails tool requires string type context parameter
-            if isinstance(content, dict):
-                # Convert dict to JSON string
-                tool_args["context"] = json.dumps(content, ensure_ascii=False)
-            else:
-                tool_args["context"] = str(content)
-            tool_args["usermessages"] = str(tool_call)
-            tool_args["apply_id"] = str(self.apply_id)
-            tool_args["group_id"] = str(self.group_id)
-        elif self.tool_name == 'Input_Summary':
-            tool_args["context"] = str(last_message)
-            tool_args["usermessages"] = str(tool_call)
-            tool_args["search_switch"] = str(self.search_switch)
-            tool_args["apply_id"] = str(self.apply_id)
-            tool_args["group_id"] = str(self.group_id)
-            tool_args["storage_type"] = getattr(self, 'storage_type', "")
-            tool_args["user_rag_memory_id"] = getattr(self, 'user_rag_memory_id', "")
-        elif self.tool_name == 'Retrieve_Summary':
-            # Retrieve_Summary expects dict directly, not JSON string
-            # content might be a JSON string, try to parse it
-            if isinstance(content, str):
-                try:
-                    parsed_content = json.loads(content)
-                    # Check if it has a "context" key
-                    if isinstance(parsed_content, dict) and "context" in parsed_content:
-                        tool_args["context"] = parsed_content["context"]
-                    else:
-                        tool_args["context"] = parsed_content
-                except json.JSONDecodeError:
-                    # If parsing fails, wrap the string
-                    tool_args["context"] = {"content": content}
-            elif isinstance(content, dict):
-                # Check if content has a "context" key that needs unwrapping
-                if "context" in content:
-                    tool_args["context"] = content["context"]
-                else:
-                    tool_args["context"] = content
-            else:
-                tool_args["context"] = {"content": str(content)}
-
-            tool_args["usermessages"] = str(tool_call)
-            tool_args["apply_id"] = str(self.apply_id)
-            tool_args["group_id"] = str(self.group_id)
-        else:
-            # Other tools use context parameter
-            if isinstance(content, dict):
-                tool_args["context"] = content
-            else:
-                tool_args["context"] = {"content": content}
-            tool_args["usermessages"] = str(tool_call)
-            tool_args["apply_id"] = str(self.apply_id)
-            tool_args["group_id"] = str(self.group_id)
-
-
-        tool_input = {
-            "messages": [
-                AIMessage(
-                    content="",
-                    tool_calls=[{
-                        "name": self.tool_name,
-                        "args": tool_args,
-                        "id": self.id + f"{tool_call}",
-                    }]
-                )
-            ]
-        }
-        result = await self.tool_node.ainvoke(tool_input)
-        result_text = str(result)
-
-        return {"messages": [AIMessage(content=result_text)]}
 
 
 @asynccontextmanager
-async def make_read_graph(namespace, tools, search_switch, apply_id, group_id, memory_config: MemoryConfig, storage_type=None, user_rag_memory_id=None):
-    """
-    Create a read graph workflow for memory operations.
-    
-    Args:
-        namespace: Namespace identifier
-        tools: MCP tools loaded from session
-        search_switch: Search mode switch ("0", "1", or "2")
-        apply_id: Application identifier
-        group_id: Group identifier
-        memory_config: MemoryConfig object containing all configuration
-        storage_type: Storage type (optional)
-        user_rag_memory_id: User RAG memory ID (optional)
-    """
-    memory = InMemorySaver()
-    tool = [i.name for i in tools]
-    logger.info(f"Initializing read graph with tools: {tool}")
-    logger.info(f"Using memory_config: {memory_config.config_name} (id={memory_config.config_id})")
-    
-    # Extract tool functions
-    Split_The_Problem_ = next((t for t in tools if t.name == "Split_The_Problem"), None)
-    Problem_Extension_ = next((t for t in tools if t.name == "Problem_Extension"), None)
-    Retrieve_ = next((t for t in tools if t.name == "Retrieve"), None)
-    Verify_ = next((t for t in tools if t.name == "Verify"), None)
-    Summary_ = next((t for t in tools if t.name == "Summary"), None)
-    Summary_fails_ = next((t for t in tools if t.name == "Summary_fails"), None)
-    Retrieve_Summary_ = next((t for t in tools if t.name == "Retrieve_Summary"), None)
-    Input_Summary_ = next((t for t in tools if t.name == "Input_Summary"), None)
-    
-    # Instantiate services
-    parameter_builder = ParameterBuilder()
-    multimodal_processor = MultimodalProcessor()
-    
-    # Create nodes using new modular components
-    Split_The_Problem_node = ToolNode([Split_The_Problem_])
-    
-    Problem_Extension_node = ToolExecutionNode(
-        tool=Problem_Extension_,
-        node_id="Problem_Extension_id",
-        namespace=namespace,
-        search_switch=search_switch,
-        apply_id=apply_id,
-        group_id=group_id,
-        parameter_builder=parameter_builder,
-        storage_type=storage_type,
-        user_rag_memory_id=user_rag_memory_id,
-        memory_config=memory_config,
+async def make_read_graph():
+    """创建并返回 LangGraph 工作流"""
+    try:
+        # Build workflow graph
+        workflow = StateGraph(ReadState)
+        workflow.add_node("content_input", content_input_node)
+        workflow.add_node("Split_The_Problem", Split_The_Problem)
+        workflow.add_node("Problem_Extension", Problem_Extension)
+        workflow.add_node("Input_Summary", Input_Summary)
+        # workflow.add_node("Retrieve", retrieve_nodes)
+        workflow.add_node("Retrieve", retrieve)
+        workflow.add_node("Verify", Verify)
+        workflow.add_node("Retrieve_Summary", Retrieve_Summary)
+        workflow.add_node("Summary", Summary)
+        workflow.add_node("Summary_fails", Summary_fails)
+        
+        # 添加边
+        workflow.add_edge(START, "content_input")
+        workflow.add_conditional_edges("content_input", Split_continue)
+        workflow.add_edge("Input_Summary", END)
+        workflow.add_edge("Split_The_Problem", "Problem_Extension")
+        workflow.add_edge("Problem_Extension", "Retrieve")
+        workflow.add_conditional_edges("Retrieve", Retrieve_continue)
+        workflow.add_edge("Retrieve_Summary", END)
+        workflow.add_conditional_edges("Verify", Verify_continue)
+        workflow.add_edge("Summary_fails", END)
+        workflow.add_edge("Summary", END)
+
+
+        '''-----'''
+        # workflow.add_edge("Retrieve", END)
+        
+        # 编译工作流
+        graph = workflow.compile()
+        yield graph
+        
+    except Exception as e:
+        print(f"创建工作流失败: {e}")
+        raise
+    finally:
+        print("工作流创建完成")
+
+async def main():
+    """主函数 - 运行工作流"""
+    message = "昨天有什么好看的电影"
+    group_id = '88a459f5_text09'  # 组ID
+    storage_type = 'neo4j'  # 存储类型
+    search_switch = '1'  # 搜索开关
+    user_rag_memory_id = 'wwwwwwww'  # 用户RAG记忆ID
+
+    # 获取数据库会话
+    db_session = next(get_db())
+    config_service = MemoryConfigService(db_session)
+    memory_config = config_service.load_memory_config(
+        config_id=17,  # 改为整数
+        service_name="MemoryAgentService"
     )
+    import time
+    start=time.time()
+    try:
+        async with make_read_graph() as graph:
+            config = {"configurable": {"thread_id": group_id}}
+            # 初始状态 - 包含所有必要字段
+            initial_state = {"messages": [HumanMessage(content=message)] ,"search_switch":search_switch,"group_id":group_id
+                             ,"storage_type":storage_type,"user_rag_memory_id":user_rag_memory_id,"memory_config":memory_config}
+            # 获取节点更新信息
+            _intermediate_outputs = []
+            summary = ''
+            
+            async for update_event in graph.astream(
+                    initial_state,
+                    stream_mode="updates",
+                    config=config
+            ):
+                for node_name, node_data in update_event.items():
+                    print(f"处理节点: {node_name}")
+                    
+                    # 处理不同Summary节点的返回结构
+                    if 'Summary' in node_name:
+                        if 'InputSummary' in node_data and 'summary_result' in node_data['InputSummary']:
+                            summary = node_data['InputSummary']['summary_result']
+                        elif 'RetrieveSummary' in node_data and 'summary_result' in node_data['RetrieveSummary']:
+                            summary = node_data['RetrieveSummary']['summary_result']
+                        elif 'summary' in node_data and 'summary_result' in node_data['summary']:
+                            summary = node_data['summary']['summary_result']
+                        elif 'SummaryFails' in node_data and 'summary_result' in node_data['SummaryFails']:
+                            summary = node_data['SummaryFails']['summary_result']
 
-    Retrieve_node = ToolExecutionNode(
-        tool=Retrieve_,
-        node_id="Retrieve_id",
-        namespace=namespace,
-        search_switch=search_switch,
-        apply_id=apply_id,
-        group_id=group_id,
-        parameter_builder=parameter_builder,
-        storage_type=storage_type,
-        user_rag_memory_id=user_rag_memory_id,
-        memory_config=memory_config,
-    )
+                    spit_data = node_data.get('spit_data', {}).get('_intermediate', None)
+                    if spit_data and spit_data != [] and spit_data != {}:
+                        _intermediate_outputs.append(spit_data)
+                    
+                    # Problem_Extension 节点
+                    problem_extension = node_data.get('problem_extension', {}).get('_intermediate', None)
+                    if problem_extension and problem_extension != [] and problem_extension != {}:
+                        _intermediate_outputs.append(problem_extension)
+                    
+                    # Retrieve 节点
+                    retrieve_node = node_data.get('retrieve', {}).get('_intermediate_outputs', None)
+                    if retrieve_node and retrieve_node != [] and retrieve_node != {}:
+                        _intermediate_outputs.extend(retrieve_node)
+                    
+                    # Verify 节点
+                    verify_n = node_data.get('verify', {}).get('_intermediate', None)
+                    if verify_n and verify_n != [] and verify_n != {}:
+                        _intermediate_outputs.append(verify_n)
 
-    Verify_node = ToolExecutionNode(
-        tool=Verify_,
-        node_id="Verify_id",
-        namespace=namespace,
-        search_switch=search_switch,
-        apply_id=apply_id,
-        group_id=group_id,
-        parameter_builder=parameter_builder,
-        storage_type=storage_type,
-        user_rag_memory_id=user_rag_memory_id,
-        memory_config=memory_config,
-    )
-    
-    Summary_node = ToolExecutionNode(
-        tool=Summary_,
-        node_id="Summary_id",
-        namespace=namespace,
-        search_switch=search_switch,
-        apply_id=apply_id,
-        group_id=group_id,
-        parameter_builder=parameter_builder,
-        storage_type=storage_type,
-        user_rag_memory_id=user_rag_memory_id,
-        memory_config=memory_config,
-    )
+                    
+                    # Summary 节点
+                    summary_n = node_data.get('summary', {}).get('_intermediate', None)
+                    if summary_n and summary_n != [] and summary_n != {}:
+                        _intermediate_outputs.append(summary_n)
 
-    Summary_fails_node = ToolExecutionNode(
-        tool=Summary_fails_,
-        node_id="Summary_fails_id",
-        namespace=namespace,
-        search_switch=search_switch,
-        apply_id=apply_id,
-        group_id=group_id,
-        parameter_builder=parameter_builder,
-        storage_type=storage_type,
-        user_rag_memory_id=user_rag_memory_id,
-        memory_config=memory_config,
-    )
+            # # 过滤掉空值
+            # _intermediate_outputs = [item for item in _intermediate_outputs if item and item != [] and item != {}]
+            #
+            # # 优化搜索结果
+            # print("=== 开始优化搜索结果 ===")
+            # optimized_outputs = merge_multiple_search_results(_intermediate_outputs)
+            # result=reorder_output_results(optimized_outputs)
+            # # 保存优化后的结果到文件
+            # with open('_intermediate_outputs_optimized.json', 'w', encoding='utf-8') as f:
+            #     import json
+            #     f.write(json.dumps(result, indent=4, ensure_ascii=False))
+            #
+            print(f"=== 最终摘要 ===")
+            print(summary)
+                
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
 
-    Retrieve_Summary_node = ToolExecutionNode(
-        tool=Retrieve_Summary_,
-        node_id="Retrieve_Summary_id",
-        namespace=namespace,
-        search_switch=search_switch,
-        apply_id=apply_id,
-        group_id=group_id,
-        parameter_builder=parameter_builder,
-        storage_type=storage_type,
-        user_rag_memory_id=user_rag_memory_id,
-        memory_config=memory_config,
-    )
+    end=time.time()
+    print(100*'y')
+    print(f"总耗时: {end-start}s")
+    print(100*'y')
 
-    Input_Summary_node = ToolExecutionNode(
-        tool=Input_Summary_,
-        node_id="Input_Summary_id",
-        namespace=namespace,
-        search_switch=search_switch,
-        apply_id=apply_id,
-        group_id=group_id,
-        parameter_builder=parameter_builder,
-        storage_type=storage_type,
-        user_rag_memory_id=user_rag_memory_id,
-        memory_config=memory_config,
-    )
 
-    async def content_input_node(state):
-        state_search_switch = state.get("search_switch", search_switch)
-
-        tool_name = "Input_Summary" if state_search_switch == '2' else "Split_The_Problem"
-        session_prefix = "input_summary_call_id" if state_search_switch == '2' else "split_call_id"
-
-        return await create_input_message(
-            state=state,
-            tool_name=tool_name,
-            session_id=f"{session_prefix}_{namespace}",
-            search_switch=search_switch,
-            apply_id=apply_id,
-            group_id=group_id,
-            multimodal_processor=multimodal_processor,
-            memory_config=memory_config,
-        )
-
-    
-    # Build workflow graph
-    workflow = StateGraph(ReadState)
-    workflow.add_node("content_input", content_input_node)
-    workflow.add_node("Split_The_Problem", Split_The_Problem_node)
-    workflow.add_node("Problem_Extension", Problem_Extension_node)
-    workflow.add_node("Retrieve", Retrieve_node)
-    workflow.add_node("Verify", Verify_node)
-    workflow.add_node("Summary", Summary_node)
-    workflow.add_node("Summary_fails", Summary_fails_node)
-    workflow.add_node("Retrieve_Summary", Retrieve_Summary_node)
-    workflow.add_node("Input_Summary", Input_Summary_node)
-
-    # Add edges using imported routers
-    workflow.add_edge(START, "content_input")
-    workflow.add_conditional_edges("content_input", Split_continue)
-    workflow.add_edge("Input_Summary", END)
-    workflow.add_edge("Split_The_Problem", "Problem_Extension")
-    workflow.add_edge("Problem_Extension", "Retrieve")
-    workflow.add_conditional_edges("Retrieve", Retrieve_continue)
-    workflow.add_edge("Retrieve_Summary", END)
-    workflow.add_conditional_edges("Verify", Verify_continue)
-    workflow.add_edge("Summary_fails", END)
-    workflow.add_edge("Summary", END)
-
-    graph = workflow.compile(checkpointer=memory)
-    yield graph
+if __name__ == "__main__":
+    import asyncio
+    asyncio.run(main())
diff --git a/api/app/core/memory/agent/langgraph_graph/routing/__init__.py b/api/app/core/memory/agent/langgraph_graph/routing/__init__.py
deleted file mode 100644
index a9366bd0..00000000
--- a/api/app/core/memory/agent/langgraph_graph/routing/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""LangGraph routing logic."""
-
-from app.core.memory.agent.langgraph_graph.routing.routers import (
-    Verify_continue,
-    Retrieve_continue,
-    Split_continue,
-)
-
-__all__ = [
-    "Verify_continue",
-    "Retrieve_continue",
-    "Split_continue",
-]
diff --git a/api/app/core/memory/agent/langgraph_graph/routing/routers.py b/api/app/core/memory/agent/langgraph_graph/routing/routers.py
index c8abd544..c0b01be1 100644
--- a/api/app/core/memory/agent/langgraph_graph/routing/routers.py
+++ b/api/app/core/memory/agent/langgraph_graph/routing/routers.py
@@ -1,123 +1,62 @@
-"""
-Routing functions for LangGraph conditional edges.
 
-This module provides routing functions that determine the next node to execute
-based on state values. All functions return Literal types for type safety.
-"""
-
-import logging
-import re
 from typing import Literal
 
-from app.core.memory.agent.langgraph_graph.state.extractors import extract_search_switch
+from app.core.logging_config import get_agent_logger
 from app.core.memory.agent.utils.llm_tools import ReadState, COUNTState
 
-logger = logging.getLogger(__name__)
 
-# Global counter for Verify routing
+logger = get_agent_logger(__name__)
 counter = COUNTState(limit=3)
-
-
-def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]:
+def Split_continue(state:ReadState) -> Literal["Split_The_Problem", "Input_Summary"]:
     """
-    Determine routing after Verify node based on verification result.
-    
-    This function checks the verification result in the last message and routes to:
-    - Summary: if verification succeeded
-    - content_input: if verification failed and retry limit not reached
-    - Summary_fails: if verification failed and retry limit reached
-    
+    Determine routing based on search_switch value.
+
     Args:
-        state: LangGraph state containing messages
-        
+        state: State dictionary containing search_switch
+
     Returns:
-        Next node name as Literal type
+        Next node to execute
     """
-    messages = state.get("messages", [])
-    
-    # Boundary check
-    if not messages:
-        logger.warning("[Verify_continue] No messages in state, defaulting to Summary")
-        counter.reset()
-        return "Summary"
-    
-    # Increment counter
-    counter.add(1)
+    logger.debug(f"Split_continue state: {state}")
+    search_switch = state.get('search_switch', '')
+    if search_switch is not None:
+        search_switch = str(search_switch)
+        if search_switch == '2':
+            return 'Input_Summary'
+    return 'Split_The_Problem'  # 默认情况
+
+def Retrieve_continue(state) -> Literal["Verify", "Retrieve_Summary"]:
+    """
+    Determine routing based on search_switch value.
+
+    Args:
+        state: State dictionary containing search_switch
+
+    Returns:
+        Next node to execute
+    """
+    search_switch = state.get('search_switch', '')
+    if search_switch is not None:
+        search_switch = str(search_switch)
+        if search_switch == '0':
+            return 'Verify'
+        elif search_switch == '1':
+            return 'Retrieve_Summary'
+    return 'Retrieve_Summary'  # Default based on business logic
+def Verify_continue(state: ReadState) -> Literal["Summary", "Summary_fails", "content_input"]:
+    status=state.get('verify', '')['status']
     loop_count = counter.get_total()
-    logger.debug(f"[Verify_continue] Current loop count: {loop_count}")
-    
-    # Extract verification result from last message
-    last_message = messages[-1]
-    last_message_str = str(last_message).replace('\\', '')
-    status_tools = re.findall(r'"split_result": "(.*?)"', last_message_str)
-    logger.debug(f"[Verify_continue] Status tools: {status_tools}")
-    
-    # Route based on verification result
-    if "success" in status_tools:
+    print(status)
+    if "success" in status:
         counter.reset()
         return "Summary"
-    elif "failed" in status_tools:
-        if loop_count < 2:  # Max retry count is 2
+    elif "failed" in status:
+        if loop_count < 2:  # Maximum loop count is 3
             return "content_input"
         else:
             counter.reset()
             return "Summary_fails"
-    else:
-        # Default to Summary if status is unclear
-        counter.reset()
-        return "Summary"
-
-
-def Retrieve_continue(state: dict) -> Literal["Verify", "Retrieve_Summary"]:
-    """
-    Determine routing after Retrieve node based on search_switch value.
-    
-    This function routes based on the search_switch parameter:
-    - search_switch == '0': Route to Verify (verification needed)
-    - search_switch == '1': Route to Retrieve_Summary (direct summary)
-    
-    Args:
-        state: LangGraph state dictionary
-        
-    Returns:
-        Next node name as Literal type
-    """
-    search_switch = extract_search_switch(state)
-    
-    logger.debug(f"[Retrieve_continue] search_switch: {search_switch}")
-    
-    if search_switch == '0':
-        return 'Verify'
-    elif search_switch == '1':
-        return 'Retrieve_Summary'
-    
-    # Default to Retrieve_Summary
-    logger.debug("[Retrieve_continue] No valid search_switch, defaulting to Retrieve_Summary")
-    return 'Retrieve_Summary'
-
-
-def Split_continue(state: dict) -> Literal["Split_The_Problem", "Input_Summary"]:
-    """
-    Determine routing after content_input node based on search_switch value.
-    
-    This function routes based on the search_switch parameter:
-    - search_switch == '2': Route to Input_Summary (direct input summary)
-    - Otherwise: Route to Split_The_Problem (problem decomposition)
-    
-    Args:
-        state: LangGraph state dictionary
-        
-    Returns:
-        Next node name as Literal type
-    """
-    logger.debug(f"[Split_continue] state keys: {state.keys()}")
-    
-    search_switch = extract_search_switch(state)
-    
-    logger.debug(f"[Split_continue] search_switch: {search_switch}")
-    
-    if search_switch == '2':
-        return 'Input_Summary'
-    
-    # Default to Split_The_Problem
-    return 'Split_The_Problem'
+    # else:
+    #     # Add default return value to avoid returning None
+    #     counter.reset()
+    #     return "Summary"  # Default based on business requirements
diff --git a/api/app/core/memory/agent/langgraph_graph/state/__init__.py b/api/app/core/memory/agent/langgraph_graph/state/__init__.py
deleted file mode 100644
index 279c6463..00000000
--- a/api/app/core/memory/agent/langgraph_graph/state/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""LangGraph state management utilities."""
-
-from app.core.memory.agent.langgraph_graph.state.extractors import (
-    extract_search_switch,
-    extract_tool_call_id,
-    extract_content_payload,
-)
-
-__all__ = [
-    "extract_search_switch",
-    "extract_tool_call_id",
-    "extract_content_payload",
-]
diff --git a/api/app/core/memory/agent/langgraph_graph/state/extractors.py b/api/app/core/memory/agent/langgraph_graph/state/extractors.py
deleted file mode 100644
index f5a32f5d..00000000
--- a/api/app/core/memory/agent/langgraph_graph/state/extractors.py
+++ /dev/null
@@ -1,179 +0,0 @@
-"""
-State extraction utilities for type-safe access to LangGraph state values.
-
-This module provides utility functions for extracting values from LangGraph state
-dictionaries with proper error handling and sensible defaults.
-"""
-
-import json
-import logging
-from typing import Any, Optional
-
-logger = logging.getLogger(__name__)
-
-def extract_search_switch(state: dict) -> Optional[str]:
-    """
-    Extract search_switch from state or messages.
-    """
-
-    search_switch = state.get("search_switch")
-
-    if search_switch is not None:
-        return str(search_switch)
-
-    # Try to extract from messages
-    messages = state.get("messages", [])
-    if not messages:
-        return None
-
-    # 从最新的消息开始查找
-    for message in reversed(messages):
-        # 尝试从 tool_calls 中提取
-        if hasattr(message, "tool_calls") and message.tool_calls:
-            for tool_call in message.tool_calls:
-                if isinstance(tool_call, dict):
-                    # 从 tool_call 的 args 中提取
-                    if "args" in tool_call and isinstance(tool_call["args"], dict):
-                        search_switch = tool_call["args"].get("search_switch")
-                        if search_switch is not None:
-                            return str(search_switch)
-                    # 直接从 tool_call 中提取
-                    search_switch = tool_call.get("search_switch")
-                    if search_switch is not None:
-                        return str(search_switch)
-
-        # 尝试从 content 中提取（如果是 JSON 格式）
-        if hasattr(message, "content"):
-            try:
-                import json
-                if isinstance(message.content, str):
-                    content_data = json.loads(message.content)
-                    if isinstance(content_data, dict):
-                        search_switch = content_data.get("search_switch")
-                        if search_switch is not None:
-                            return str(search_switch)
-            except (json.JSONDecodeError, ValueError):
-                pass
-
-    return None
-
-
-def extract_tool_call_id(message: Any) -> str:
-    """
-    Extract tool call ID from message using structured attributes.
-    
-    This function extracts the tool call ID from a message object, handling both
-    direct attribute access and tool_calls list structures.
-    
-    Args:
-        message: Message object (typically ToolMessage or AIMessage)
-        
-    Returns:
-        Tool call ID as string
-        
-    Raises:
-        ValueError: If tool call ID cannot be extracted
-        
-    Examples:
-        >>> message = ToolMessage(content="...", tool_call_id="call_123")
-        >>> extract_tool_call_id(message)
-        'call_123'
-    """
-    # Try direct attribute access for ToolMessage
-    if hasattr(message, "tool_call_id"):
-        tool_call_id = message.tool_call_id
-        if tool_call_id:
-            return str(tool_call_id)
-    
-    # Try extracting from tool_calls list for AIMessage
-    if hasattr(message, "tool_calls") and message.tool_calls:
-        tool_call = message.tool_calls[0]
-        if isinstance(tool_call, dict) and "id" in tool_call:
-            return str(tool_call["id"])
-    
-    # Try extracting from id attribute
-    if hasattr(message, "id"):
-        message_id = message.id
-        if message_id:
-            return str(message_id)
-    
-    # If all else fails, raise an error
-    raise ValueError(f"Could not extract tool call ID from message: {type(message)}")
-
-
-def extract_content_payload(message: Any) -> Any:
-    """
-    Extract content payload from ToolMessage, parsing JSON if needed.
-    
-    This function extracts the content from a message and attempts to parse it as JSON
-    if it appears to be a JSON string. It handles various message formats and provides
-    sensible fallbacks.
-    
-    Args:
-        message: Message object (typically ToolMessage)
-        
-    Returns:
-        Parsed content (dict, list, or str)
-        
-    Examples:
-        >>> message = ToolMessage(content='{"key": "value"}')
-        >>> extract_content_payload(message)
-        {'key': 'value'}
-        
-        >>> message = ToolMessage(content='plain text')
-        >>> extract_content_payload(message)
-        'plain text'
-    """
-    # Extract raw content
-    # For ToolMessages (responses from tools), extract from content
-    if hasattr(message, "content"):
-        raw_content = message.content
-        logger.info(f"extract_content_payload: raw_content type={type(raw_content)}, value={str(raw_content)[:500]}")
-        
-        # Handle MCP content format: [{'type': 'text', 'text': '...'}]
-        if isinstance(raw_content, list):
-            for block in raw_content:
-                if isinstance(block, dict) and block.get('type') == 'text':
-                    raw_content = block.get('text', '')
-                    logger.info(f"extract_content_payload: extracted text from MCP format: {str(raw_content)[:300]}")
-                    break
-        
-        # If content is empty and this is an AIMessage with tool_calls,
-        # extract from args (this handles the initial tool call from content_input)
-        if not raw_content and hasattr(message, "tool_calls") and message.tool_calls:
-            tool_call = message.tool_calls[0]
-            if isinstance(tool_call, dict) and "args" in tool_call:
-                return tool_call["args"]
-    else:
-        raw_content = str(message)
-    
-    # If content is already a dict or list, return it directly
-    if isinstance(raw_content, (dict, list)):
-        logger.info(f"extract_content_payload: returning raw dict/list with keys={list(raw_content.keys()) if isinstance(raw_content, dict) else 'list'}")
-        return raw_content
-    
-    # Try to parse as JSON
-    if isinstance(raw_content, str):
-        # First, try direct JSON parsing
-        try:
-            parsed = json.loads(raw_content)
-            logger.info(f"extract_content_payload: parsed JSON, keys={list(parsed.keys()) if isinstance(parsed, dict) else 'list'}")
-            return parsed
-        except (json.JSONDecodeError, ValueError):
-            pass
-        
-        # If that fails, try to extract JSON from the string
-        # This handles cases where the content is embedded in a larger string
-        import re
-        json_candidates = re.findall(r'[\[{].*[\]}]', raw_content, flags=re.DOTALL)
-        for candidate in json_candidates:
-            try:
-                parsed = json.loads(candidate)
-                logger.info(f"extract_content_payload: parsed JSON from candidate, keys={list(parsed.keys()) if isinstance(parsed, dict) else 'list'}")
-                return parsed
-            except (json.JSONDecodeError, ValueError):
-                continue
-    
-    # If all parsing attempts fail, return the raw content
-    logger.info(f"extract_content_payload: returning raw content (parsing failed)")
-    return raw_content
diff --git a/api/app/core/memory/agent/langgraph_graph/tools/tool.py b/api/app/core/memory/agent/langgraph_graph/tools/tool.py
new file mode 100644
index 00000000..ce6d5dd4
--- /dev/null
+++ b/api/app/core/memory/agent/langgraph_graph/tools/tool.py
@@ -0,0 +1,320 @@
+import asyncio
+import json
+from datetime import datetime, timedelta
+
+
+from langchain.tools import tool
+from pydantic import BaseModel, Field
+
+
+from app.core.memory.src.search import (
+    search_by_temporal,
+    search_by_keyword_temporal,
+)
+
+def extract_tool_message_content(response):
+    """从agent响应中提取ToolMessage内容和工具名称"""
+    messages = response.get('messages', [])
+
+    for message in messages:
+        if hasattr(message, 'tool_call_id') and hasattr(message, 'content'):
+            # 这是一个ToolMessage
+            tool_content = message.content
+            tool_name = None
+
+            # 尝试获取工具名称
+            if hasattr(message, 'name'):
+                tool_name = message.name
+            elif hasattr(message, 'tool_name'):
+                tool_name = message.tool_name
+
+            try:
+                # 解析JSON内容
+                parsed_content = json.loads(tool_content)
+                return {
+                    'tool_name': tool_name,
+                    'content': parsed_content
+                }
+            except json.JSONDecodeError:
+                # 如果不是JSON格式，直接返回内容
+                return {
+                    'tool_name': tool_name,
+                    'content': tool_content
+                }
+
+    return None
+
+
+class TimeRetrievalInput(BaseModel):
+    """时间检索工具的输入模式"""
+    context: str = Field(description="用户输入的查询内容")
+    group_id: str = Field(default="88a459f5_text09", description="组ID，用于过滤搜索结果")
+
+def create_time_retrieval_tool(group_id: str):
+    """
+    创建一个带有特定group_id的TimeRetrieval工具（同步版本），用于按时间范围搜索语句(Statements)
+    """
+    
+    def clean_temporal_result_fields(data):
+        """
+        清理时间搜索结果中不需要的字段，并修改结构
+        
+        Args:
+            data: 要清理的数据
+            
+        Returns:
+            清理后的数据
+        """
+        # 需要过滤的字段列表
+        fields_to_remove = {
+            'id', 'apply_id', 'user_id', 'chunk_id', 'created_at', 
+            'valid_at', 'invalid_at', 'statement_ids'
+        }
+        
+        if isinstance(data, dict):
+            cleaned = {}
+            for key, value in data.items():
+                if key == 'statements' and isinstance(value, dict) and 'statements' in value:
+                    # 将 statements: {"statements": [...]} 改为 time_search: {"statements": [...]}
+                    cleaned_value = clean_temporal_result_fields(value)
+                    # 进一步将内部的 statements 改为 time_search
+                    if 'statements' in cleaned_value:
+                        cleaned['results'] = {
+                            'time_search': cleaned_value['statements']
+                        }
+                    else:
+                        cleaned['results'] = cleaned_value
+                elif key not in fields_to_remove:
+                    cleaned[key] = clean_temporal_result_fields(value)
+            return cleaned
+        elif isinstance(data, list):
+            return [clean_temporal_result_fields(item) for item in data]
+        else:
+            return data
+    
+    @tool
+    def TimeRetrievalWithGroupId(context: str, start_date: str = None, end_date: str = None, group_id_param: str = None, clean_output: bool = True) -> str:
+        """
+        优化的时间检索工具，只结合时间范围搜索（同步版本），自动过滤不需要的元数据字段
+        显式接收参数：
+        - context: 查询上下文内容
+        - start_date: 开始时间（可选，格式：YYYY-MM-DD）
+        - end_date: 结束时间（可选，格式：YYYY-MM-DD）
+        - group_id_param: 组ID（可选，用于覆盖默认组ID）
+        - clean_output: 是否清理输出中的元数据字段
+        -end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d")
+        """
+        async def _async_search():
+            # 使用传入的参数或默认值
+            actual_group_id = group_id_param or group_id
+            actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d")
+            actual_start_date = start_date or (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")
+            
+            # 基本时间搜索
+            results = await search_by_temporal(
+                group_id=actual_group_id,
+                start_date=actual_start_date,
+                end_date=actual_end_date,
+                limit=10
+            )
+            
+            # 清理结果中不需要的字段
+            if clean_output:
+                cleaned_results = clean_temporal_result_fields(results)
+            else:
+                cleaned_results = results
+
+            return json.dumps(cleaned_results, ensure_ascii=False, indent=2)
+        
+        return asyncio.run(_async_search())
+
+    @tool
+    def KeywordTimeRetrieval(context: str, days_back: int = 7, start_date: str = None, end_date: str = None, clean_output: bool = True) -> str:
+        """
+        优化的关键词时间检索工具，结合关键词和时间范围搜索（同步版本），自动过滤不需要的元数据字段
+        显式接收参数：
+        - context: 查询内容
+        - days_back: 向前搜索的天数，默认7天
+        - start_date: 开始时间（可选，格式：YYYY-MM-DD）
+        - end_date: 结束时间（可选，格式：YYYY-MM-DD）
+        - clean_output: 是否清理输出中的元数据字段
+        - end_date 需要根据用户的描述获取结束的时间，输出格式用strftime("%Y-%m-%d")
+        """
+        async def _async_search():
+            actual_end_date = end_date or datetime.now().strftime("%Y-%m-%d")
+            actual_start_date = start_date or (datetime.now() - timedelta(days=days_back)).strftime("%Y-%m-%d")
+
+            # 关键词时间搜索
+            results = await search_by_keyword_temporal(
+                query_text=context,
+                group_id=group_id,
+                start_date=actual_start_date,
+                end_date=actual_end_date,
+                limit=15
+            )
+            
+            # 清理结果中不需要的字段
+            if clean_output:
+                cleaned_results = clean_temporal_result_fields(results)
+            else:
+                cleaned_results = results
+
+            return json.dumps(cleaned_results, ensure_ascii=False, indent=2)
+
+        return asyncio.run(_async_search())
+    
+    return TimeRetrievalWithGroupId
+
+
+def create_hybrid_retrieval_tool_async(memory_config, **search_params):
+    """
+    创建混合检索工具，使用run_hybrid_search进行混合检索，优化输出格式并过滤不需要的字段
+    
+    Args:
+        memory_config: 内存配置对象
+        **search_params: 搜索参数，包含group_id, limit, include等
+    """
+    
+    def clean_result_fields(data):
+        """
+        递归清理结果中不需要的字段
+        
+        Args:
+            data: 要清理的数据（可能是字典、列表或其他类型）
+            
+        Returns:
+            清理后的数据
+        """
+        # 需要过滤的字段列表
+        fields_to_remove = {
+            'invalid_at', 'valid_at', 'chunk_id_from_rel', 'entity_ids', 
+            'expired_at', 'created_at', 'chunk_id', 'id', 'apply_id', 
+            'user_id', 'statement_ids', 'updated_at',"chunk_ids","fact_summary"
+        }
+        
+        if isinstance(data, dict):
+            # 对字典进行清理
+            cleaned = {}
+            for key, value in data.items():
+                if key not in fields_to_remove:
+                    cleaned[key] = clean_result_fields(value)  # 递归清理嵌套数据
+            return cleaned
+        elif isinstance(data, list):
+            # 对列表中的每个元素进行清理
+            return [clean_result_fields(item) for item in data]
+        else:
+            # 其他类型直接返回
+            return data
+    
+    @tool
+    async def HybridSearch(
+        context: str, 
+        search_type: str = "hybrid",
+        limit: int = 10,
+        group_id: str = None,
+        rerank_alpha: float = 0.6,
+        use_forgetting_rerank: bool = False,
+        use_llm_rerank: bool = False,
+        clean_output: bool = True  # 新增：是否清理输出字段
+    ) -> str:
+        """
+        优化的混合检索工具，支持关键词、向量和混合搜索，自动过滤不需要的元数据字段
+        
+        Args:
+            context: 查询内容
+            search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
+            limit: 结果数量限制
+            group_id: 组ID，用于过滤搜索结果
+            rerank_alpha: 重排序权重参数
+            use_forgetting_rerank: 是否使用遗忘重排序
+            use_llm_rerank: 是否使用LLM重排序
+            clean_output: 是否清理输出中的元数据字段
+        """
+        try:
+            # 导入run_hybrid_search函数
+            from app.core.memory.src.search import run_hybrid_search
+            
+            # 合并参数，优先使用传入的参数
+            final_params = {
+                "query_text": context,
+                "search_type": search_type,
+                "group_id": group_id or search_params.get("group_id"),
+                "limit": limit or search_params.get("limit", 10),
+                "include": search_params.get("include", ["summaries", "statements", "chunks", "entities"]),
+                "output_path": None,  # 不保存到文件
+                "memory_config": memory_config,
+                "rerank_alpha": rerank_alpha,
+                "use_forgetting_rerank": use_forgetting_rerank,
+                "use_llm_rerank": use_llm_rerank
+            }
+            
+            # 执行混合检索
+            raw_results = await run_hybrid_search(**final_params)
+            
+            # 清理结果中不需要的字段
+            if clean_output:
+                cleaned_results = clean_result_fields(raw_results)
+            else:
+                cleaned_results = raw_results
+            
+            # 格式化返回结果
+            formatted_results = {
+                "search_query": context,
+                "search_type": search_type,
+                "results": cleaned_results
+            }
+            
+            return json.dumps(formatted_results, ensure_ascii=False, indent=2, default=str)
+            
+        except Exception as e:
+            error_result = {
+                "error": f"混合检索失败: {str(e)}",
+                "search_query": context,
+                "search_type": search_type,
+                "timestamp": datetime.now().isoformat()
+            }
+            return json.dumps(error_result, ensure_ascii=False, indent=2)
+    
+    return HybridSearch
+
+
+def create_hybrid_retrieval_tool_sync(memory_config, **search_params):
+    """
+    创建同步版本的混合检索工具，优化输出格式并过滤不需要的字段
+    
+    Args:
+        memory_config: 内存配置对象
+        **search_params: 搜索参数
+    """
+    @tool
+    def HybridSearchSync(
+        context: str, 
+        search_type: str = "hybrid",
+        limit: int = 10,
+        group_id: str = None,
+        clean_output: bool = True
+    ) -> str:
+        """
+        优化的混合检索工具（同步版本），自动过滤不需要的元数据字段
+        
+        Args:
+            context: 查询内容
+            search_type: 搜索类型 ('keyword', 'embedding', 'hybrid')
+            limit: 结果数量限制
+            group_id: 组ID，用于过滤搜索结果
+            clean_output: 是否清理输出中的元数据字段
+        """
+        async def _async_search():
+            # 创建异步工具并执行
+            async_tool = create_hybrid_retrieval_tool_async(memory_config, **search_params)
+            return await async_tool.ainvoke({
+                "context": context,
+                "search_type": search_type,
+                "limit": limit,
+                "group_id": group_id,
+                "clean_output": clean_output
+            })
+        
+        return asyncio.run(_async_search())
+    
+    return HybridSearchSync
\ No newline at end of file
diff --git a/api/app/core/memory/agent/langgraph_graph/write_graph.py b/api/app/core/memory/agent/langgraph_graph/write_graph.py
index ae333e84..5a6f1e28 100644
--- a/api/app/core/memory/agent/langgraph_graph/write_graph.py
+++ b/api/app/core/memory/agent/langgraph_graph/write_graph.py
@@ -1,30 +1,32 @@
+
 import asyncio
-import json
 import sys
 import warnings
 from contextlib import asynccontextmanager
 
-from app.core.logging_config import get_agent_logger
-from app.core.memory.agent.utils.llm_tools import WriteState
-from app.schemas.memory_config_schema import MemoryConfig
-from langchain_core.messages import AIMessage
+
+from langchain_core.messages import HumanMessage
 from langgraph.constants import END, START
 from langgraph.graph import StateGraph
-from langgraph.prebuilt import ToolNode
+
+
+from app.db import get_db
+from app.core.logging_config import get_agent_logger
+from app.core.memory.agent.utils.llm_tools import WriteState
+from app.core.memory.agent.langgraph_graph.nodes.write_nodes import write_node
+from app.core.memory.agent.langgraph_graph.nodes.data_nodes import content_input_write
+from app.services.memory_config_service import MemoryConfigService
 
 warnings.filterwarnings("ignore", category=RuntimeWarning)
-
 logger = get_agent_logger(__name__)
 
 if sys.platform.startswith("win"):
     asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
-
-
 @asynccontextmanager
-async def make_write_graph(user_id, tools, apply_id, group_id, memory_config: MemoryConfig):
+async def make_write_graph():
     """
     Create a write graph workflow for memory operations.
-    
+
     Args:
         user_id: User identifier
         tools: MCP tools loaded from session
@@ -32,43 +34,8 @@ async def make_write_graph(user_id, tools, apply_id, group_id, memory_config: Me
         group_id: Group identifier
         memory_config: MemoryConfig object containing all configuration
     """
-    logger.info("Loading MCP tools: %s", [t.name for t in tools])
-    logger.info(f"Using memory_config: {memory_config.config_name} (id={memory_config.config_id})")
-
-    data_write_tool = next((t for t in tools if t.name == "Data_write"), None)
-
-    if not data_write_tool:
-        logger.error("Data_write tool not found", exc_info=True)
-        raise ValueError("Data_write tool not found")
-
-    write_node = ToolNode([data_write_tool])
-
-    async def call_model(state):
-        messages = state["messages"]
-        last_message = messages[-1]
-        content = last_message[1] if isinstance(last_message, tuple) else last_message.content
-
-        # Call Data_write directly with memory_config
-        write_params = {
-            "content": content,
-            "apply_id": apply_id,
-            "group_id": group_id,
-            "user_id": user_id,
-            "memory_config": memory_config,
-        }
-        logger.debug(f"Passing memory_config to Data_write: {memory_config.config_id}")
-
-        write_result = await data_write_tool.ainvoke(write_params)
-
-        if isinstance(write_result, dict):
-            result_content = write_result.get("data", str(write_result))
-        else:
-            result_content = str(write_result)
-        logger.info("Write content: %s", result_content)
-        return {"messages": [AIMessage(content=result_content)]}
-
     workflow = StateGraph(WriteState)
-    workflow.add_node("content_input", call_model)
+    workflow.add_node("content_input", content_input_write)
     workflow.add_node("save_neo4j", write_node)
     workflow.add_edge(START, "content_input")
     workflow.add_edge("content_input", "save_neo4j")
@@ -76,5 +43,45 @@ async def make_write_graph(user_id, tools, apply_id, group_id, memory_config: Me
 
     graph = workflow.compile()
 
-
     yield graph
+
+
+async def main():
+    """主函数 - 运行工作流"""
+    message = "今天周一"
+    group_id = 'new_2025test1103'  # 组ID
+
+
+    # 获取数据库会话
+    db_session = next(get_db())
+    config_service = MemoryConfigService(db_session)
+    memory_config = config_service.load_memory_config(
+        config_id=17,  # 改为整数
+        service_name="MemoryAgentService"
+    )
+    try:
+        async with make_write_graph() as graph:
+            config = {"configurable": {"thread_id": group_id}}
+            # 初始状态 - 包含所有必要字段
+            initial_state = {"messages": [HumanMessage(content=message)],  "group_id": group_id, "memory_config": memory_config}
+
+            # 获取节点更新信息
+            async for update_event in graph.astream(
+                    initial_state,
+                    stream_mode="updates",
+                    config=config
+            ):
+                for node_name, node_data in update_event.items():
+                    if 'save_neo4j'==node_name:
+                        massages=node_data
+            massages=massages.get('write_result')['status']
+            print(massages)  # | 更新数据: {node_data}
+
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    import asyncio
+    asyncio.run(main())
\ No newline at end of file
diff --git a/api/app/core/memory/agent/mcp_server/__init__.py b/api/app/core/memory/agent/mcp_server/__init__.py
deleted file mode 100644
index efd03773..00000000
--- a/api/app/core/memory/agent/mcp_server/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-"""
-MCP Server package for memory agent.
-
-This package provides the FastMCP server implementation with context-based
-dependency injection for tool functions.
-
-Package structure:
-- server: FastMCP server initialization and context setup
-- tools: MCP tool implementations
-- models: Pydantic response models
-- services: Business logic services
-"""
-# from app.core.memory.agent.mcp_server.server import (
-#     mcp,
-#     initialize_context,
-#     main,
-#     get_context_resource
-# )
-
-# # Import tools to register them (but don't export them)
-# from app.core.memory.agent.mcp_server import tools
-
-# __all__ = [
-#     'mcp',
-#     'initialize_context',
-#     'main',
-#     'get_context_resource',
-# ]
\ No newline at end of file
diff --git a/api/app/core/memory/agent/mcp_server/mcp_instance.py b/api/app/core/memory/agent/mcp_server/mcp_instance.py
deleted file mode 100644
index 3a2eeb78..00000000
--- a/api/app/core/memory/agent/mcp_server/mcp_instance.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""
-MCP Server Instance
-
-This module contains the FastMCP server instance that is shared across all modules.
-It's in a separate file to avoid circular import issues.
-"""
-from mcp.server.fastmcp import FastMCP
-
-# Initialize FastMCP server instance
-# This instance is shared across all tool modules
-mcp = FastMCP('data_flow')
diff --git a/api/app/core/memory/agent/mcp_server/server.py b/api/app/core/memory/agent/mcp_server/server.py
deleted file mode 100644
index 26f24824..00000000
--- a/api/app/core/memory/agent/mcp_server/server.py
+++ /dev/null
@@ -1,159 +0,0 @@
-"""
-MCP Server initialization with FastMCP context setup.
-
-This module initializes the FastMCP server and registers shared resources
-in the context for dependency injection into tool functions.
-"""
-import os
-import sys
-
-from app.core.config import settings
-from app.core.logging_config import get_agent_logger
-from app.core.memory.agent.mcp_server.mcp_instance import mcp
-from app.core.memory.agent.mcp_server.services.search_service import SearchService
-from app.core.memory.agent.mcp_server.services.session_service import SessionService
-from app.core.memory.agent.mcp_server.services.template_service import TemplateService
-from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_
-from app.core.memory.agent.utils.redis_tool import store
-
-logger = get_agent_logger(__name__)
-
-
-def get_context_resource(ctx, resource_name: str):
-    """
-    Helper function to retrieve a resource from the FastMCP context.
-    
-    Args:
-        ctx: FastMCP Context object (passed to tool functions)
-        resource_name: Name of the resource to retrieve
-        
-    Returns:
-        The requested resource
-        
-    Raises:
-        AttributeError: If the resource doesn't exist
-        
-    Example:
-        @mcp.tool()
-        async def my_tool(ctx: Context):
-            template_service = get_context_resource(ctx, 'template_service')
-            llm_client = get_context_resource(ctx, 'llm_client')
-    """
-    if not hasattr(ctx, 'fastmcp') or ctx.fastmcp is None:
-        raise RuntimeError("Context does not have fastmcp attribute")
-    
-    if not hasattr(ctx.fastmcp, resource_name):
-        raise AttributeError(
-            f"Resource '{resource_name}' not found in context. "
-            f"Available resources: {[k for k in dir(ctx.fastmcp) if not k.startswith('_')]}"
-        )
-    
-    return getattr(ctx.fastmcp, resource_name)
-
-
-def initialize_context():
-    """
-    Initialize and register shared resources in FastMCP context.
-    
-    This function sets up all shared resources that will be available
-    to tool functions via dependency injection through the context parameter.
-    
-    Resources are stored as attributes on the FastMCP instance and can be
-    accessed via ctx.fastmcp in tool functions.
-    
-    Resources registered:
-    - session_store: RedisSessionStore for session management
-    - llm_client: LLM client for structured API calls
-    - app_settings: Application settings (renamed to avoid conflict with FastMCP settings)
-    - template_service: Service for template rendering
-    - search_service: Service for hybrid search
-    - session_service: Service for session operations
-    """
-    try:
-        # Register Redis session store
-        logger.info("Registering session_store in context")
-        mcp.session_store = store
-        
-        # Note: LLM client is NOT loaded at server startup
-        # It should be loaded dynamically when needed, with config_id passed explicitly
-        # to make_write_graph or make_read_graph functions
-        logger.info("LLM client will be loaded dynamically with config_id when needed")
-        mcp.llm_client = None  # Placeholder - actual client loaded per-request with config_id
-        
-        # Register application settings (renamed to avoid conflict with FastMCP's settings)
-        logger.info("Registering app_settings in context")
-        mcp.app_settings = settings
-        
-        # Register template service
-        template_root = PROJECT_ROOT_ + '/agent/utils/prompt'
-        # logger.info(f"Registering template_service in context with root: {template_root}")
-        template_service = TemplateService(template_root)
-        mcp.template_service = template_service
-        
-        # Register search service
-        # logger.info("Registering search_service in context")
-        search_service = SearchService()
-        mcp.search_service = search_service
-        
-        # Register session service
-        # logger.info("Registering session_service in context")
-        session_service = SessionService(store)
-        mcp.session_service = session_service
-        
-        # logger.info("All context resources registered successfully")
-        
-    except Exception as e:
-        logger.error(f"Failed to initialize context: {e}", exc_info=True)
-        raise
-
-
-def main():
-    """
-    Main entry point for the MCP server.
-    
-    Initializes context and starts the server with SSE transport.
-    """
-    try:
-        logger.info("Starting MCP server initialization")
-        # Initialize context resources
-        initialize_context()
-        
-        # Import and register tools (imports trigger tool registration)
-        from app.core.memory.agent.mcp_server.tools import (  # noqa: F401
-            data_tools,
-            problem_tools,
-            retrieval_tools,
-            summary_tools,
-            verification_tools,
-        )
-        
-        # Tools are registered via imports above
-        
-        # Get MCP port from environment (default: 8081)
-        mcp_port = int(os.getenv("MCP_PORT", "8081"))
-        logger.info(f"Starting MCP server on {settings.SERVER_IP}:{mcp_port} with SSE transport")
-        
-        # Configure DNS rebinding protection for Docker container compatibility
-        from mcp.server.fastmcp.server import TransportSecuritySettings
-        
-        # Disable DNS rebinding protection to allow Docker container hostnames
-        # This allows containers to connect using service names like 'mcp-server'
-        mcp.settings.transport_security = TransportSecuritySettings(
-            enable_dns_rebinding_protection=False,
-        )
-        logger.info("DNS rebinding protection: disabled for Docker container compatibility")
-        
-        # logger.info(f"Starting MCP server on {settings.SERVER_IP}:{mcp_port} with SSE transport")
-        
-        # Run the server with SSE transport for HTTP connections
-        import uvicorn
-        app = mcp.sse_app()
-        uvicorn.run(app, host=settings.SERVER_IP, port=mcp_port, log_level="info")
-        
-    except Exception as e:
-        logger.error(f"Failed to start MCP server: {e}", exc_info=True)
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/api/app/core/memory/agent/mcp_server/tools/__init__.py b/api/app/core/memory/agent/mcp_server/tools/__init__.py
deleted file mode 100644
index 5ce04ef3..00000000
--- a/api/app/core/memory/agent/mcp_server/tools/__init__.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""
-MCP Tools module.
-
-This module contains all MCP tool implementations organized by functionality.
-
-Tools are organized into the following modules:
-- problem_tools: Question segmentation and extension
-- retrieval_tools: Database and context retrieval
-- verification_tools: Data verification
-- summary_tools: Summarization and summary retrieval
-- data_tools: Data type differentiation and writing
-"""
-
-# Import all tool modules to register them with the MCP server
-from . import problem_tools
-from . import retrieval_tools
-from . import verification_tools
-from . import summary_tools
-from . import data_tools
-
-__all__ = [
-    'problem_tools',
-    'retrieval_tools',
-    'verification_tools',
-    'summary_tools',
-    'data_tools',
-]
diff --git a/api/app/core/memory/agent/mcp_server/tools/data_tools.py b/api/app/core/memory/agent/mcp_server/tools/data_tools.py
deleted file mode 100644
index 631f7fd7..00000000
--- a/api/app/core/memory/agent/mcp_server/tools/data_tools.py
+++ /dev/null
@@ -1,155 +0,0 @@
-"""
-Data Tools for data type differentiation and writing.
-
-This module contains MCP tools for distinguishing data types and writing data.
-"""
-
-import os
-
-from app.core.logging_config import get_agent_logger
-from app.core.memory.agent.mcp_server.mcp_instance import mcp
-from app.core.memory.agent.mcp_server.models.retrieval_models import (
-    DistinguishTypeResponse,
-)
-from app.core.memory.agent.mcp_server.server import get_context_resource
-from app.core.memory.agent.utils.write_tools import write
-from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-from app.db import get_db_context
-from app.schemas.memory_config_schema import MemoryConfig
-from mcp.server.fastmcp import Context
-
-logger = get_agent_logger(__name__)
-
-
-@mcp.tool()
-async def Data_type_differentiation(
-    ctx: Context,
-    context: str,
-    memory_config: MemoryConfig,
-) -> dict:
-    """
-    Distinguish the type of data (read or write).
-    
-    Args:
-        ctx: FastMCP context for dependency injection
-        context: Text to analyze for type differentiation
-        memory_config: MemoryConfig object containing LLM configuration
-        
-    Returns:
-        dict: Contains 'context' with the original text and 'type' field
-    """
-    try:
-        # Extract services from context
-        template_service = get_context_resource(ctx, 'template_service')
-        
-        # Get LLM client from memory_config using factory pattern
-        with get_db_context() as db:
-            factory = MemoryClientFactory(db)
-            llm_client = factory.get_llm_client_from_config(memory_config)
-        
-        # Render template
-        try:
-            system_prompt = await template_service.render_template(
-                template_name='distinguish_types_prompt.jinja2',
-                operation_name='status_typle',
-                user_query=context
-            )
-        except Exception as e:
-            logger.error(
-                f"Template rendering failed for Data_type_differentiation: {e}",
-                exc_info=True
-            )
-            return {
-                "type": "error",
-                "message": f"Prompt rendering failed: {str(e)}"
-            }
-
-        # Call LLM with structured response
-        try:
-            structured = await llm_client.response_structured(
-                messages=[{"role": "system", "content": system_prompt}],
-                response_model=DistinguishTypeResponse
-            )
-            
-            result = structured.model_dump()
-            
-            # Add context to result
-            result["context"] = context
-            
-            return result
-            
-        except Exception as e:
-            logger.error(
-                f"LLM call failed for Data_type_differentiation: {e}",
-                exc_info=True
-            )
-            return {
-                "context": context,
-                "type": "error",
-                "message": f"LLM call failed: {str(e)}"
-            }
-            
-    except Exception as e:
-        logger.error(
-            f"Data_type_differentiation failed: {e}",
-            exc_info=True
-        )
-        return {
-            "context": context,
-            "type": "error",
-            "message": str(e)
-        }
-
-
-@mcp.tool()
-async def Data_write(
-    ctx: Context,
-    content: str,
-    user_id: str,
-    apply_id: str,
-    group_id: str,
-    memory_config: MemoryConfig,
-) -> dict:
-    """
-    Write data to the database/file system.
-    
-    Args:
-        ctx: FastMCP context for dependency injection
-        content: Data content to write
-        user_id: User identifier
-        apply_id: Application identifier
-        group_id: Group identifier
-        memory_config: MemoryConfig object containing all configuration
-        
-    Returns:
-        dict: Contains 'status', 'saved_to', and 'data' fields
-    """
-    try:
-        # Ensure output directory exists
-        os.makedirs("data_output", exist_ok=True)
-        file_path = os.path.join("data_output", "user_data.csv")
-
-        # Write data - clients are constructed inside write() from memory_config
-        await write(
-            content=content,
-            user_id=user_id,
-            apply_id=apply_id,
-            group_id=group_id,
-            memory_config=memory_config,
-        )
-        logger.info(f"Write completed successfully! Config: {memory_config.config_name}")
-
-        return {
-            "status": "success",
-            "saved_to": file_path,
-            "data": content,
-            "config_id": memory_config.config_id,
-            "config_name": memory_config.config_name,
-        }
-
-    except Exception as e:
-        logger.error(f"Data_write failed: {e}", exc_info=True)
-        return {
-            "status": "error",
-            "message": str(e),
-        }
diff --git a/api/app/core/memory/agent/mcp_server/tools/problem_tools.py b/api/app/core/memory/agent/mcp_server/tools/problem_tools.py
deleted file mode 100644
index 49812e38..00000000
--- a/api/app/core/memory/agent/mcp_server/tools/problem_tools.py
+++ /dev/null
@@ -1,304 +0,0 @@
-"""
-Problem Tools for question segmentation and extension.
-
-This module contains MCP tools for breaking down and extending user questions.
-LLM clients are constructed from MemoryConfig when needed.
-"""
-
-import json
-import time
-
-from app.core.logging_config import get_agent_logger, log_time
-from app.core.memory.agent.mcp_server.mcp_instance import mcp
-from app.core.memory.agent.mcp_server.models.problem_models import (
-    ProblemBreakdownResponse,
-    ProblemExtensionResponse,
-)
-from app.core.memory.agent.mcp_server.server import get_context_resource
-from app.core.memory.agent.utils.messages_tool import Problem_Extension_messages_deal
-from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-from app.db import get_db_context
-from app.schemas.memory_config_schema import MemoryConfig
-from mcp.server.fastmcp import Context
-
-logger = get_agent_logger(__name__)
-
-
-@mcp.tool()
-async def Split_The_Problem(
-    ctx: Context,
-    sentence: str,
-    sessionid: str,
-    messages_id: str,
-    apply_id: str,
-    group_id: str,
-    memory_config: MemoryConfig,
-) -> dict:
-    """
-    Segment the dialogue or sentence into sub-problems.
-    
-    Args:
-        ctx: FastMCP context for dependency injection
-        sentence: Original sentence to split
-        sessionid: Session identifier
-        messages_id: Message identifier
-        apply_id: Application identifier
-        group_id: Group identifier
-        memory_config: MemoryConfig object containing all configuration
-        
-    Returns:
-        dict: Contains 'context' (JSON string of split results) and 'original' sentence
-    """
-    start = time.time()
-
-    try:
-        # Extract services from context
-        template_service = get_context_resource(ctx, "template_service")
-        session_service = get_context_resource(ctx, "session_service")
-
-        # Get LLM client from memory_config
-        with get_db_context() as db:
-            factory = MemoryClientFactory(db)
-            llm_client = factory.get_llm_client_from_config(memory_config)
-        
-        # Extract user ID from session
-        user_id = session_service.resolve_user_id(sessionid)
-        
-        # Get conversation history
-        history = await session_service.get_history(user_id, apply_id, group_id)
-        # Override with empty list for now (as in original)
-        history = []
-        
-        # Render template
-        try:
-            system_prompt = await template_service.render_template(
-                template_name='problem_breakdown_prompt.jinja2',
-                operation_name='split_the_problem',
-                history=history,
-                sentence=sentence
-            )
-        except Exception as e:
-            logger.error(
-                f"Template rendering failed for Split_The_Problem: {e}",
-                exc_info=True
-            )
-            return {
-                "context": json.dumps([], ensure_ascii=False),
-                "original": sentence,
-                "error": f"Prompt rendering failed: {str(e)}"
-            }
-        
-        # Call LLM with structured response
-        try:
-            structured = await llm_client.response_structured(
-                messages=[{"role": "system", "content": system_prompt}],
-                response_model=ProblemBreakdownResponse
-            )
-            
-            # Handle RootModel response with .root attribute access
-            if structured is None:
-                # LLM returned None, use empty list as fallback
-                split_result = json.dumps([], ensure_ascii=False)
-            elif hasattr(structured, 'root') and structured.root is not None:
-                split_result = json.dumps(
-                    [item.model_dump() for item in structured.root],
-                    ensure_ascii=False
-                )
-            elif isinstance(structured, list):
-                # Fallback: treat structured itself as the list
-                split_result = json.dumps(
-                    [item.model_dump() for item in structured],
-                    ensure_ascii=False
-                )
-            else:
-                # Last resort: use empty list
-                split_result = json.dumps([], ensure_ascii=False)
-                
-        except Exception as e:
-            logger.error(
-                f"LLM call failed for Split_The_Problem: {e}",
-                exc_info=True
-            )
-            split_result = json.dumps([], ensure_ascii=False)
-        
-        logger.info("Problem splitting")
-        logger.info(f"Problem split result: {split_result}")
-        
-        # Emit intermediate output for frontend
-        result = {
-            "context": split_result,
-            "original": sentence,
-            "_intermediate": {
-                "type": "problem_split",
-                "data": json.loads(split_result) if split_result else [],
-                "original_query": sentence
-            }
-        }
-        
-        return result
-        
-    except Exception as e:
-        logger.error(
-            f"Split_The_Problem failed: {e}",
-            exc_info=True
-        )
-        return {
-            "context": json.dumps([], ensure_ascii=False),
-            "original": sentence,
-            "error": str(e)
-        }
-        
-    finally:
-        # Log execution time
-        end = time.time()
-        try:
-            duration = end - start
-        except Exception:
-            duration = 0.0
-        log_time('Problem splitting', duration)
-
-
-@mcp.tool()
-async def Problem_Extension(
-    ctx: Context,
-    context: dict,
-    usermessages: str,
-    apply_id: str,
-    group_id: str,
-    memory_config: MemoryConfig,
-    storage_type: str = "",
-    user_rag_memory_id: str = "",
-) -> dict:
-    """
-    Extend the problem with additional sub-questions.
-    
-    Args:
-        ctx: FastMCP context for dependency injection
-        context: Dictionary containing split problem results
-        usermessages: User messages identifier
-        apply_id: Application identifier
-        group_id: Group identifier
-        memory_config: MemoryConfig object containing all configuration
-        storage_type: Storage type for the workspace (optional)
-        user_rag_memory_id: User RAG memory identifier (optional)
-        
-    Returns:
-        dict: Contains 'context' (aggregated questions) and 'original' question
-    """
-    start = time.time()
-
-    try:
-        # Extract services from context
-        template_service = get_context_resource(ctx, "template_service")
-        session_service = get_context_resource(ctx, "session_service")
-
-        # Get LLM client from memory_config
-        with get_db_context() as db:
-            factory = MemoryClientFactory(db)
-            llm_client = factory.get_llm_client_from_config(memory_config)
-        
-        # Resolve session ID from usermessages
-        from app.core.memory.agent.utils.messages_tool import Resolve_username
-        sessionid = Resolve_username(usermessages)
-        
-        # Get conversation history
-        history = await session_service.get_history(sessionid, apply_id, group_id)
-        # Override with empty list for now (as in original)
-        history = []
-        
-        # Process context to extract questions
-        extent_quest, original = await Problem_Extension_messages_deal(context)
-        
-        # Format questions for template rendering
-        questions_formatted = []
-        for msg in extent_quest:
-            if msg.get("role") == "user":
-                questions_formatted.append(msg.get("content", ""))
-        
-        # Render template
-        try:
-            system_prompt = await template_service.render_template(
-                template_name='Problem_Extension_prompt.jinja2',
-                operation_name='problem_extension',
-                history=history,
-                questions=questions_formatted
-            )
-        except Exception as e:
-            logger.error(
-                f"Template rendering failed for Problem_Extension: {e}",
-                exc_info=True
-            )
-            return {
-                "context": {},
-                "original": original,
-                "error": f"Prompt rendering failed: {str(e)}"
-            }
-        
-        # Call LLM with structured response
-        try:
-            response_content = await llm_client.response_structured(
-                messages=[{"role": "system", "content": system_prompt}],
-                response_model=ProblemExtensionResponse
-            )
-            
-            # Aggregate results by original question
-            aggregated_dict = {}
-            for item in response_content.root:
-                key = getattr(item, "original_question", None) or (
-                    item.get("original_question") if isinstance(item, dict) else None
-                )
-                value = getattr(item, "extended_question", None) or (
-                    item.get("extended_question") if isinstance(item, dict) else None
-                )
-                if not key or not value:
-                    continue
-                aggregated_dict.setdefault(key, []).append(value)
-                
-        except Exception as e:
-            logger.error(
-                f"LLM call failed for Problem_Extension: {e}",
-                exc_info=True
-            )
-            aggregated_dict = {}
-        
-        logger.info("Problem extension")
-        logger.info(f"Problem extension result: {aggregated_dict}")
-        
-        # Emit intermediate output for frontend
-        result = {
-            "context": aggregated_dict,
-            "original": original,
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id,
-            "_intermediate": {
-                "type": "problem_extension",
-                "data": aggregated_dict,
-                "original_query": original,
-                "storage_type": storage_type,
-                "user_rag_memory_id": user_rag_memory_id
-            }
-        }
-        
-        return result
-        
-    except Exception as e:
-        logger.error(
-            f"Problem_Extension failed: {e}",
-            exc_info=True
-        )
-        return {
-            "context": {},
-            "original": context.get("original", ""),
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id,
-            "error": str(e)
-        }
-        
-    finally:
-        # Log execution time
-        end = time.time()
-        try:
-            duration = end - start
-        except Exception:
-            duration = 0.0
-        log_time('Problem extension', duration)
diff --git a/api/app/core/memory/agent/mcp_server/tools/retrieval_tools.py b/api/app/core/memory/agent/mcp_server/tools/retrieval_tools.py
deleted file mode 100644
index db18ba04..00000000
--- a/api/app/core/memory/agent/mcp_server/tools/retrieval_tools.py
+++ /dev/null
@@ -1,294 +0,0 @@
-"""
-Retrieval Tools for database and context retrieval.
-
-This module contains MCP tools for retrieving data using hybrid search.
-"""
-
-import os
-import time
-
-from app.core.logging_config import get_agent_logger, log_time
-from app.core.memory.agent.mcp_server.mcp_instance import mcp
-from app.core.memory.agent.mcp_server.server import get_context_resource
-from app.core.memory.agent.utils.llm_tools import (
-    deduplicate_entries,
-    merge_to_key_value_pairs,
-)
-from app.core.memory.agent.utils.messages_tool import Retriev_messages_deal
-from app.core.rag.nlp.search import knowledge_retrieval
-from app.schemas.memory_config_schema import MemoryConfig
-from dotenv import load_dotenv
-from mcp.server.fastmcp import Context
-
-load_dotenv()
-logger = get_agent_logger(__name__)
-
-
-@mcp.tool()
-async def Retrieve(
-    ctx: Context,
-    context,
-    usermessages: str,
-    apply_id: str,
-    group_id: str,
-    memory_config: MemoryConfig,
-    storage_type: str = "",
-    user_rag_memory_id: str = "",
-) -> dict:
-    """
-    Retrieve data from the database using hybrid search.
-    
-    Args:
-        ctx: FastMCP context for dependency injection
-        context: Dictionary or string containing query information
-        usermessages: User messages identifier
-        apply_id: Application identifier
-        group_id: Group identifier
-        memory_config: MemoryConfig object containing all configuration
-        storage_type: Storage type for the workspace (e.g., 'rag', 'vector')
-        user_rag_memory_id: User RAG memory identifier
-        
-    Returns:
-        dict: Contains 'context' with Query and Expansion_issue results
-    """
-    kb_config = {
-        "knowledge_bases": [
-            {
-                "kb_id": user_rag_memory_id,
-                "similarity_threshold": 0.7,
-                "vector_similarity_weight": 0.5,
-                "top_k": 10,
-                "retrieve_type": "participle"
-            }
-        ],
-        "merge_strategy": "weight",
-        "reranker_id": os.getenv('reranker_id'),
-        "reranker_top_k": 10
-    }
-    start = time.time()
-    logger.info(f"Retrieve: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
-    logger.info(f"Retrieve: context type={type(context)}, context={str(context)[:500]}")
-    
-    try:
-        # Extract services from context
-        search_service = get_context_resource(ctx, 'search_service')
-        
-        databases_anser = []
-        
-        # Handle both dict and string context
-        if isinstance(context, dict):
-            # Process dict context with extended questions
-            all_items = []
-            logger.info(f"Retrieve: context keys={list(context.keys())}")
-            content, original = await Retriev_messages_deal(context)
-            logger.info(f"Retrieve: after Retriev_messages_deal - content_type={type(content)}, content={str(content)[:300]}")
-            logger.info(f"Retrieve: original='{original[:100] if original else 'EMPTY'}'")
-            
-            if not original:
-                logger.warning(f"Retrieve: original query is empty! context={context}")
-            
-            # Extract all query items from content
-            # content is like {original_question: [extended_questions...], ...}
-            for key, values in content.items():
-                if isinstance(values, list):
-                    all_items.extend(values)
-                elif isinstance(values, str):
-                    all_items.append(values)
-                elif values is not None:
-                    # Fallback: convert non-empty non-list values to string
-                    all_items.append(str(values))
-            
-            # Execute search for each question
-            for idx, question in enumerate(all_items):
-                try:
-                    # Prepare search parameters based on storage type
-                    search_params = {
-                        "group_id": group_id,
-                        "question": question,
-                        "return_raw_results": True
-                    }
-
-                    # Add storage-specific parameters
-                    if storage_type == "rag" and user_rag_memory_id:
-                        retrieve_chunks_result = knowledge_retrieval(question, kb_config,[str(group_id)])
-                        try:
-                            retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
-                            clean_content = '\n\n'.join(retrieval_knowledge)
-                            cleaned_query=question
-                            raw_results=clean_content
-                            logger.info(f" Using RAG storage with memory_id={user_rag_memory_id}")
-                        except:
-                            clean_content = ''
-                            raw_results=''
-                            cleaned_query = question
-                            logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
-                    else:
-                        clean_content, cleaned_query, raw_results = await search_service.execute_hybrid_search(
-                            **search_params, memory_config=memory_config
-                        )
-
-                    databases_anser.append({
-                        "Query_small": cleaned_query,
-                        "Result_small": clean_content,
-                        "_intermediate": {
-                            "type": "search_result",
-                            "query": cleaned_query,
-                            "raw_results": raw_results,  
-                            "index": idx + 1,
-                            "total": len(all_items)
-                        }
-                    })
-                except Exception as e:
-                    logger.error(
-                        f"Retrieve: hybrid_search failed for question '{question}': {e}",
-                        exc_info=True
-                    )
-                    # Continue with empty result for this question
-                    databases_anser.append({
-                        "Query_small": question,
-                        "Result_small": ""
-                    })
-            
-            # Build initial database data structure
-            databases_data = {
-                "Query": original,
-                "Expansion_issue": databases_anser
-            }
-            
-            # Collect intermediate outputs before deduplication
-            intermediate_outputs = []
-            for item in databases_anser:
-                if '_intermediate' in item:
-                    intermediate_outputs.append(item['_intermediate'])
-            
-            # Deduplicate and merge results
-            deduplicated_data = deduplicate_entries(databases_data['Expansion_issue'])
-            deduplicated_data_merged = merge_to_key_value_pairs(
-                deduplicated_data,
-                'Query_small',
-                'Result_small'
-            )
-            
-            # Restructure for Verify/Retrieve_Summary compatibility
-            keys, val = [], []
-            for item in deduplicated_data_merged:
-                for items_key, items_value in item.items():
-                    keys.append(items_key)
-                    val.append(items_value)
-            
-            send_verify = []
-            for i, j in zip(keys, val, strict=False):
-                send_verify.append({
-                    "Query_small": i,
-                    "Answer_Small": j
-                })
-            
-            dup_databases = {
-                "Query": original,
-                "Expansion_issue": send_verify,
-                "_intermediate_outputs": intermediate_outputs  # Preserve intermediate outputs
-            }
-            
-            logger.info(f"Collected {len(intermediate_outputs)} intermediate outputs from search results")
-            
-        else:
-            # Handle string context (simple query)
-            query = str(context).strip()
-            
-            try:
-                # Prepare search parameters based on storage type
-                search_params = {
-                    "group_id": group_id,
-                    "question": query,
-                    "return_raw_results": True
-                }
-                
-                # Add storage-specific parameters
-                if storage_type == "rag" and user_rag_memory_id:
-                    retrieve_chunks_result = knowledge_retrieval(query, kb_config,[str(group_id)])
-                    try:
-                        retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
-                        clean_content = '\n\n'.join(retrieval_knowledge)
-                        cleaned_query = query
-                        raw_results = clean_content
-                        logger.info(f" Using RAG storage with memory_id={user_rag_memory_id}")
-                    except:
-                        clean_content = ''
-                        raw_results = ''
-                        cleaned_query = query
-                        logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
-                else:
-                    clean_content, cleaned_query, raw_results = await search_service.execute_hybrid_search(
-                        **search_params, memory_config=memory_config
-                    )
-                # Keep structure for Verify/Retrieve_Summary compatibility
-                dup_databases = {
-                    "Query": cleaned_query,
-                    "Expansion_issue": [{
-                        "Query_small": cleaned_query,
-                        "Answer_Small": clean_content,
-                        "_intermediate": {
-                            "type": "search_result",
-                            "query": cleaned_query,
-                            "raw_results": raw_results,
-                            "index": 1,
-                            "total": 1
-                        }
-                    }]
-                }
-            except Exception as e:
-                logger.error(
-                    f"Retrieve: hybrid_search failed for query '{query}': {e}",
-                    exc_info=True
-                )
-                # Return empty results on failure
-                dup_databases = {
-                    "Query": query,
-                    "Expansion_issue": []
-                }
-        
-        logger.info(
-            f"Retrieval: {storage_type}--{user_rag_memory_id}--Query={dup_databases.get('Query', '')}, "
-            f"Expansion_issue count={len(dup_databases.get('Expansion_issue', []))}"
-        )
-        
-        # Build result with intermediate outputs
-        result = {
-            "context": dup_databases,
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id
-        }
-        
-        # Add intermediate outputs list if they exist
-        intermediate_outputs = dup_databases.get('_intermediate_outputs', [])
-        if intermediate_outputs:
-            result['_intermediates'] = intermediate_outputs
-            logger.info(f"Adding {len(intermediate_outputs)} intermediate outputs to result")
-        else:
-            logger.warning("No intermediate outputs found in dup_databases")
-        
-        return result
-        
-    except Exception as e:
-        logger.error(
-            f"Retrieve failed: {e}",
-            exc_info=True
-        )
-        return {
-            "context": {
-                "Query": "",
-                "Expansion_issue": []
-            },
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id,
-            "error": str(e)
-        }
-        
-    finally:
-        # Log execution time
-        end = time.time()
-        try:
-            duration = end - start
-        except Exception:
-            duration = 0.0
-        log_time('Retrieval', duration)
diff --git a/api/app/core/memory/agent/mcp_server/tools/summary_tools.py b/api/app/core/memory/agent/mcp_server/tools/summary_tools.py
deleted file mode 100644
index 0f306572..00000000
--- a/api/app/core/memory/agent/mcp_server/tools/summary_tools.py
+++ /dev/null
@@ -1,640 +0,0 @@
-"""
-Summary Tools for data summarization.
-
-This module contains MCP tools for summarizing retrieved data and generating responses.
-LLM clients are constructed from MemoryConfig when needed.
-"""
-
-import json
-import os
-import re
-import time
-
-from app.core.logging_config import get_agent_logger, log_time
-from app.core.memory.agent.mcp_server.mcp_instance import mcp
-from app.core.memory.agent.mcp_server.models.summary_models import (
-    RetrieveSummaryResponse,
-    SummaryResponse,
-)
-from app.core.memory.agent.mcp_server.server import get_context_resource
-from app.core.memory.agent.utils.messages_tool import (
-    Resolve_username,
-    Summary_messages_deal,
-)
-from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
-from app.core.rag.nlp.search import knowledge_retrieval
-from app.db import get_db_context
-from app.schemas.memory_config_schema import MemoryConfig
-from dotenv import load_dotenv
-from mcp.server.fastmcp import Context
-
-load_dotenv()
-logger = get_agent_logger(__name__)
-
-
-@mcp.tool()
-async def Summary(
-    ctx: Context,
-    context: str,
-    usermessages: str,
-    apply_id: str,
-    group_id: str,
-    memory_config: MemoryConfig,
-    storage_type: str = "",
-    user_rag_memory_id: str = "",
-) -> dict:
-    """
-    Summarize the verified data.
-    
-    Args:
-        ctx: FastMCP context for dependency injection
-        context: JSON string containing verified data
-        usermessages: User messages identifier
-        apply_id: Application identifier
-        group_id: Group identifier
-        memory_config: MemoryConfig object containing all configuration
-        storage_type: Storage type for the workspace (optional)
-        user_rag_memory_id: User RAG memory identifier (optional)
-        
-    Returns:
-        dict: Contains 'status' and 'summary_result'
-    """
-    start = time.time()
-
-    try:
-        # Extract services from context
-        template_service = get_context_resource(ctx, "template_service")
-        session_service = get_context_resource(ctx, "session_service")
-
-        # Get LLM client from memory_config
-        with get_db_context() as db:
-            factory = MemoryClientFactory(db)
-            llm_client = factory.get_llm_client_from_config(memory_config)
-        
-        # Resolve session ID
-        sessionid = Resolve_username(usermessages)
-        
-        # Process context to extract answer and query
-        answer_small, query = await Summary_messages_deal(context)
-
-        
-        start_time= time.time()
-        history = await session_service.get_history(sessionid, apply_id, group_id)
-        end_time=time.time()
-        logger.info(f"Retrieve_Summary-REDIS搜索：{end_time - start_time}")
-        data = {
-            "query": query,
-            "history": history,
-            "retrieve_info": answer_small
-        }
-
-    except Exception as e:
-        logger.error(
-            f"Summary: initialization failed: {e}",
-            exc_info=True
-        )
-        return {
-            "status": "error",
-            "summary_result": "信息不足，无法回答"
-        }
-
-    try:
-        # Render template
-        system_prompt = await template_service.render_template(
-            template_name='summary_prompt.jinja2',
-            operation_name='summary',
-            data=data,
-            query=query
-        )
-    except Exception as e:
-        logger.error(
-            f"Template rendering failed for Summary: {e}",
-            exc_info=True
-        )
-        return {
-            "status": "error",
-            "message": f"Prompt rendering failed: {str(e)}"
-        }
-
-    try:
-        # Call LLM with structured response
-        structured = await llm_client.response_structured(
-            messages=[{"role": "system", "content": system_prompt}],
-            response_model=SummaryResponse
-        )
-
-        aimessages = structured.query_answer or ""
-
-    except Exception as e:
-        logger.error(
-            f"LLM call failed for Summary: {e}",
-            exc_info=True
-        )
-        aimessages = ""
-
-    try:
-        # Save session
-        if aimessages != "":
-            await session_service.save_session(
-            user_id=sessionid,
-            query=query,
-            apply_id=apply_id,
-            group_id=group_id,
-            ai_response=aimessages
-        )
-        logger.info(f"sessionid: {aimessages} 写入成功")
-    except Exception as e:
-        logger.error(
-            f"sessionid: {sessionid} 写入失败，错误信息：{str(e)}",
-            exc_info=True
-        )
-        return {
-            "status": "error",
-            "message": str(e)
-        }
-
-    # Cleanup duplicate sessions
-    await session_service.cleanup_duplicates()
-
-    # Use fallback if empty
-    if aimessages == '':
-        aimessages = '信息不足，无法回答'
-
-    logger.info(f"Summary after verification: {aimessages}")
-
-    # Log execution time
-    end = time.time()
-    try:
-        duration = end - start
-    except Exception:
-        duration = 0.0
-    log_time('Summary', duration)
-
-    return {
-        "status": "success",
-        "summary_result": aimessages,
-        "storage_type": storage_type,
-        "user_rag_memory_id": user_rag_memory_id
-    }
-
-
-@mcp.tool()
-async def Retrieve_Summary(
-    ctx: Context,
-    context: dict,
-    usermessages: str,
-    apply_id: str,
-    group_id: str,
-    memory_config: MemoryConfig,
-    storage_type: str = "",
-    user_rag_memory_id: str = "",
-) -> dict:
-    """
-    Summarize data directly from retrieval results.
-
-    Args:
-        ctx: FastMCP context for dependency injection
-        context: Dictionary containing Query and Expansion_issue from Retrieve
-        usermessages: User messages identifier
-        apply_id: Application identifier
-        group_id: Group identifier
-        memory_config: MemoryConfig object containing all configuration
-        storage_type: Storage type for the workspace (optional)
-        user_rag_memory_id: User RAG memory identifier (optional)
-
-    Returns:
-        dict: Contains 'status' and 'summary_result'
-    """
-    start = time.time()
-
-    try:
-        # Extract services from context
-        template_service = get_context_resource(ctx, "template_service")
-        session_service = get_context_resource(ctx, "session_service")
-
-        # Get LLM client from memory_config
-        with get_db_context() as db:
-            factory = MemoryClientFactory(db)
-            llm_client = factory.get_llm_client_from_config(memory_config)
-
-        # Resolve session ID
-        sessionid = Resolve_username(usermessages)
-
-
-
-        # Handle both 'content' and 'context' keys (LangGraph uses 'content')
-        logger.debug(f"Retrieve_Summary: raw context type={type(context)}, keys={list(context.keys()) if isinstance(context, dict) else 'N/A'}")
-        
-        if isinstance(context, dict):
-            if "content" in context:
-                inner = context["content"]
-                # If it's a JSON string, parse it
-                if isinstance(inner, str):
-                    try:
-                        parsed = json.loads(inner)
-                        logger.info("Retrieve_Summary: successfully parsed JSON")
-                    except json.JSONDecodeError:
-                        # Try unescaping first
-                        try:
-                            unescaped = inner.encode('utf-8').decode('unicode_escape')
-                            parsed = json.loads(unescaped)
-                            logger.info("Retrieve_Summary: parsed after unescaping")
-                        except (json.JSONDecodeError, UnicodeDecodeError) as e:
-                            logger.error(
-                                f"Retrieve_Summary: parsing failed even after unescape: {e}"
-                            )
-                            context_dict = {"Query": "", "Expansion_issue": []}
-                            parsed = None
-
-                    if parsed:
-                        # Check if parsed has 'context' wrapper
-                        if isinstance(parsed, dict) and "context" in parsed:
-                            context_dict = parsed["context"]
-                        else:
-                            context_dict = parsed
-                elif isinstance(inner, dict):
-                    context_dict = inner
-                else:
-                    context_dict = {"Query": "", "Expansion_issue": []}
-            elif "context" in context:
-                context_dict = context["context"] if isinstance(context["context"], dict) else context
-            else:
-                context_dict = context
-        else:
-            context_dict = {"Query": "", "Expansion_issue": []}
-
-        query = context_dict.get("Query", "")
-        expansion_issue = context_dict.get("Expansion_issue", [])
-        
-        logger.debug(f"Retrieve_Summary: query='{query}', expansion_issue count={len(expansion_issue)}")
-        logger.debug(f"Retrieve_Summary: expansion_issue={expansion_issue[:2] if expansion_issue else 'empty'}")
-
-        # Extract retrieve_info from expansion_issue
-        retrieve_info = []
-        for item in expansion_issue:
-            # Check for both Answer_Small and Answer_Small (typo) for backward compatibility
-            answer = None
-            if isinstance(item, dict):
-                if "Answer_Small" in item:
-                    answer = item["Answer_Small"]
-
-
-                if answer is not None:
-                    # Handle both string and list formats
-                    if isinstance(answer, list):
-                        # Join list of characters/strings into a single string
-                        retrieve_info.append(''.join(str(x) for x in answer))
-                    elif isinstance(answer, str):
-                        retrieve_info.append(answer)
-                    else:
-                        retrieve_info.append(str(answer))
-
-        # Join all retrieve_info into a single string
-        retrieve_info_str = '\n\n'.join(retrieve_info) if retrieve_info else ""
-
-        start_time=time.time()
-        history = await session_service.get_history(sessionid, apply_id, group_id)
-        # Override with empty list for now (as in original)
-        end_time=time.time()
-        logger.info(f"Retrieve_Summary-REDIS搜索：{end_time - start_time}")
-    except Exception as e:
-        logger.error(
-            f"Retrieve_Summary: initialization failed: {e}",
-            exc_info=True
-        )
-        return {
-            "status": "error",
-            "summary_result": "信息不足，无法回答"
-        }
-
-    try:
-        # Render template
-        system_prompt = await template_service.render_template(
-            template_name='Retrieve_Summary_prompt.jinja2',
-            operation_name='retrieve_summary',
-            query=query,
-            history=history,
-            retrieve_info=retrieve_info_str
-        )
-    except Exception as e:
-        logger.error(
-            f"Template rendering failed for Retrieve_Summary: {e}",
-            exc_info=True
-        )
-        return {
-            "status": "error",
-            "message": f"Prompt rendering failed: {str(e)}"
-        }
-
-    try:
-        # Call LLM with structured response
-        structured = await llm_client.response_structured(
-            messages=[{"role": "system", "content": system_prompt}],
-            response_model=RetrieveSummaryResponse
-        )
-
-        # Handle case where structured response might be None or incomplete
-        if structured and hasattr(structured, 'data') and structured.data:
-            aimessages = structured.data.query_answer or ""
-        else:
-            logger.warning("Structured response is None or incomplete, using default message")
-            aimessages = "信息不足，无法回答"
-
-
-        # Check for insufficient information response
-        if '信息不足，无法回答' not  in str(aimessages) or str(aimessages)!="":
-            # Save session
-            await session_service.save_session(
-                user_id=sessionid,
-                query=query,
-                apply_id=apply_id,
-                group_id=group_id,
-                ai_response=aimessages
-            )
-            logger.info(f"sessionid: {aimessages} 写入成功")
-    except Exception as e:
-        logger.error(
-            f"Retrieve_Summary: LLM call failed: {e}",
-            exc_info=True
-        )
-        aimessages = ""
-    # Cleanup duplicate sessions
-    await session_service.cleanup_duplicates()
-
-    # Use fallback if empty
-    if aimessages == '':
-        aimessages = '信息不足，无法回答'
-
-    logger.info(f"Summary after retrieval: {aimessages}")
-
-    # Log execution time
-    end = time.time()
-    try:
-        duration = end - start
-    except Exception:
-        duration = 0.0
-    log_time('Retrieval summary', duration)
-
-    # Emit intermediate output for frontend
-    return {
-        "status": "success",
-        "summary_result": aimessages,
-        "storage_type": storage_type,
-        "user_rag_memory_id": user_rag_memory_id,
-        "_intermediate": {
-            "type": "retrieval_summary",
-            "summary": aimessages,
-            "query": query,
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id
-        }
-    }
-
-
-@mcp.tool()
-async def Input_Summary(
-    ctx: Context,
-    context: str,
-    usermessages: str,
-    search_switch: str,
-    apply_id: str,
-    group_id: str,
-    memory_config: MemoryConfig,
-    storage_type: str = "",
-    user_rag_memory_id: str = "",
-) -> dict:
-    """
-    Generate a quick summary for direct input without verification.
-
-    Args:
-        ctx: FastMCP context for dependency injection
-        context: String containing the input sentence
-        usermessages: User messages identifier
-        search_switch: Search switch value for routing ('2' for summaries only)
-        apply_id: Application identifier
-        group_id: Group identifier
-        memory_config: MemoryConfig object containing all configuration
-        storage_type: Storage type for the workspace (e.g., 'rag', 'vector')
-        user_rag_memory_id: User RAG memory identifier
-
-    Returns:
-        dict: Contains 'query_answer' with the summary result
-    """
-    start = time.time()
-    logger.info(f"Input_Summary: storage_type={storage_type}, user_rag_memory_id={user_rag_memory_id}")
-
-    try:
-        # Extract services from context
-        session_service = get_context_resource(ctx, "session_service")
-        search_service = get_context_resource(ctx, "search_service")
-
-        # Resolve session ID
-        sessionid = Resolve_username(usermessages) or ""
-        sessionid = sessionid.replace('call_id_', '')
-
-        start_time=time.time()
-        history = await session_service.get_history(
-            str(sessionid),
-            str(apply_id),
-            str(group_id)
-        )
-        end_time=time.time()
-        logger.info(f"Input_Summary-REDIS搜索：{end_time - start_time}")
-        # Override with empty list for now (as in original)
-
-        # Log the raw context for debugging
-        logger.info(f"Input_Summary: Received context type={type(context)}, value={context[:200] if isinstance(context, str) else context}")
-
-        # Extract sentence from context
-        # Context can be a string or might contain the sentence in various formats
-        try:
-            # Try to parse as JSON first
-            if isinstance(context, str) and (context.startswith('{') or context.startswith('[')):
-                try:
-                    import json
-                    context_dict = json.loads(context)
-                    if isinstance(context_dict, dict):
-                        query = context_dict.get('sentence', context_dict.get('content', context))
-                    else:
-                        query = context
-                except json.JSONDecodeError:
-                    # Not valid JSON, try regex
-                    match = re.search(r"'sentence':\s*['\"]?(.*?)['\"]?\s*,", context)
-                    query = match.group(1) if match else context
-            else:
-                query = context
-        except Exception as e:
-            logger.warning(f"Failed to extract query from context: {e}")
-            query = context
-
-        # Clean query
-        query = str(query).strip().strip("\"'")
-
-        logger.debug(f"Input_Summary: Extracted query='{query}' from context type={type(context)}")
-
-        # Execute search based on search_switch and storage_type
-        try:
-            logger.info(f"search_switch: {search_switch}, storage_type: {storage_type}")
-
-            # Prepare search parameters based on storage type
-            search_params = {
-                "group_id": group_id,
-                "question": query,
-                "return_raw_results": True
-            }
-
-            # Add storage-specific parameters
-
-            # Retrieval
-            if search_switch == '2':
-                search_params["include"] = ["summaries"]
-                if storage_type == "rag" and user_rag_memory_id:
-                    raw_results = []
-                    retrieve_info = ""
-                    kb_config={
-                        "knowledge_bases": [
-                            {
-                                "kb_id": user_rag_memory_id,
-                                "similarity_threshold": 0.7,
-                                "vector_similarity_weight": 0.5,
-                                "top_k": 10,
-                                "retrieve_type": "participle"
-                            }
-                        ],
-                        "merge_strategy": "weight",
-                        "reranker_id":os.getenv('reranker_id'),
-                        "reranker_top_k": 10
-                    }
-
-                    retrieve_chunks_result = knowledge_retrieval(query, kb_config,[str(group_id)])
-                    try:
-                        retrieval_knowledge = [i.page_content for i in retrieve_chunks_result]
-                        retrieve_info = '\n\n'.join(retrieval_knowledge)
-                        raw_results=[retrieve_info]
-                        logger.info(f"Input_Summary: Using RAG storage with memory_id={user_rag_memory_id}")
-                    except:
-                        retrieve_info=''
-                        raw_results=['']
-                        logger.info(f"No content retrieved from knowledge base: {user_rag_memory_id}")
-                else:
-                    retrieve_info, question, raw_results = await search_service.execute_hybrid_search(
-                        **search_params, memory_config=memory_config
-                    )
-                logger.info("Input_Summary: Using summary for retrieval")
-            else:
-                retrieve_info, question, raw_results = await search_service.execute_hybrid_search(
-                    **search_params, memory_config=memory_config
-                )
-
-        except Exception as e:
-            logger.error(
-                f"Input_Summary: hybrid_search failed, using empty results: {e}",
-                exc_info=True
-            )
-            retrieve_info, question, raw_results = "", query, []
-
-        # Return retrieved information directly without LLM processing
-        # Use the raw retrieved info as the answer
-        aimessages = retrieve_info if retrieve_info else "信息不足，无法回答"
-
-        logger.info(f"Quick answer (no LLM): {storage_type}--{user_rag_memory_id}--{aimessages[:500]}...")
-
-        # Emit intermediate output for frontend
-        return {
-            "status": "success",
-            "summary_result": aimessages,
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id,
-            "_intermediate": {
-                "type": "input_summary",
-                "title": "快速答案",
-                "summary": aimessages,
-                "query": query,
-                "raw_results": raw_results,
-                "search_mode": "quick_search",
-                "storage_type": storage_type,
-                "user_rag_memory_id": user_rag_memory_id
-            }
-        }
-
-    except Exception as e:
-        logger.error(
-            f"Input_Summary failed: {e}",
-            exc_info=True
-        )
-        return {
-            "status": "fail",
-            "summary_result": "信息不足，无法回答",
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id,
-            "error": str(e)
-        }
-
-    finally:
-        # Log execution time
-        end = time.time()
-        try:
-            duration = end - start
-        except Exception:
-            duration = 0.0
-        log_time('Retrieval', duration)
-
-
-@mcp.tool()
-async def Summary_fails(
-    ctx: Context,
-    context: str,
-    usermessages: str,
-    apply_id: str,
-    group_id: str,
-    storage_type: str = "",
-    user_rag_memory_id: str = ""
-) -> dict:
-    """
-    Handle workflow failure when summary cannot be generated.
-
-    Args:
-        ctx: FastMCP context for dependency injection
-        context: Failure context string
-        usermessages: User messages identifier
-        apply_id: Application identifier
-        group_id: Group identifier
-        storage_type: Storage type for the workspace (optional)
-        user_rag_memory_id: User RAG memory identifier (optional)
-
-    Returns:
-        dict: Contains 'query_answer' with failure message
-    """
-    try:
-        # Extract services from context
-        session_service = get_context_resource(ctx, 'session_service')
-
-        # Parse session ID from usermessages
-        usermessages_parts = usermessages.split('_')[1:]
-        sessionid = '_'.join(usermessages_parts[:-1])
-
-        # Cleanup duplicate sessions
-        await session_service.cleanup_duplicates()
-
-        logger.info("没有相关数据")
-        logger.debug(f"Summary_fails called with apply_id: {apply_id}, group_id: {group_id}")
-        
-        return {
-            "status": "success",
-            "summary_result": "没有相关数据",
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id
-        }
-        
-    except Exception as e:
-        logger.error(
-            f"Summary_fails failed: {e}",
-            exc_info=True
-        )
-        return {
-            "status": "fail",
-            "summary_result": "没有相关数据",
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id,
-            "error": str(e)
-        }
diff --git a/api/app/core/memory/agent/mcp_server/tools/verification_tools.py b/api/app/core/memory/agent/mcp_server/tools/verification_tools.py
deleted file mode 100644
index cb6af5bd..00000000
--- a/api/app/core/memory/agent/mcp_server/tools/verification_tools.py
+++ /dev/null
@@ -1,174 +0,0 @@
-"""
-Verification Tools for data verification.
-
-This module contains MCP tools for verifying retrieved data.
-"""
-import time
-
-from app.core.logging_config import get_agent_logger, log_time
-from app.core.memory.agent.mcp_server.mcp_instance import mcp
-from app.core.memory.agent.mcp_server.server import get_context_resource
-from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_
-from app.core.memory.agent.utils.messages_tool import (
-    Resolve_username,
-    Retrieve_verify_tool_messages_deal,
-    Verify_messages_deal,
-)
-from app.core.memory.agent.utils.verify_tool import VerifyTool
-from app.schemas.memory_config_schema import MemoryConfig
-from jinja2 import Template
-from mcp.server.fastmcp import Context
-
-logger = get_agent_logger(__name__)
-
-
-@mcp.tool()
-async def Verify(
-    ctx: Context,
-    context: dict,
-    usermessages: str,
-    apply_id: str,
-    group_id: str,
-    memory_config: MemoryConfig,
-    storage_type: str = "",
-    user_rag_memory_id: str = ""
-) -> dict:
-    """
-    Verify the retrieved data.
-    
-    Args:
-        ctx: FastMCP context for dependency injection
-        context: Dictionary containing query and expansion issues
-        usermessages: User messages identifier
-        apply_id: Application identifier
-        group_id: Group identifier
-        memory_config: MemoryConfig object containing all configuration
-        storage_type: Storage type for the workspace (optional)
-        user_rag_memory_id: User RAG memory identifier (optional)
-        
-    Returns:
-        dict: Contains 'status' and 'verified_data' with verification results
-    """
-    start = time.time()
-
-
-    try:
-        # Extract services from context
-        session_service = get_context_resource(ctx, 'session_service')
-        
-        # Load verification prompt template
-        file_path = PROJECT_ROOT_ + '/agent/utils/prompt/split_verify_prompt.jinja2'
-        
-        # Read template file directly (VerifyTool expects raw template content)
-        from app.core.memory.agent.utils.messages_tool import read_template_file
-        system_prompt = await read_template_file(file_path)
-
-
-        
-        # Resolve session ID
-        sessionid = Resolve_username(usermessages)
-        
-        # Get conversation history
-        history = await session_service.get_history(sessionid, apply_id, group_id)
-
-        template = Template(system_prompt)
-        system_prompt = template.render(history=history, sentence=context)
-        
-        # Process context to extract query and results
-        Query_small, Result_small, query = await Verify_messages_deal(context)
-        
-        # Build query list for verification
-        query_list = []
-        for query_small, anser in zip(Query_small, Result_small, strict=False):
-            query_list.append({
-                'Query_small': query_small,
-                'Answer_Small': anser
-            })
-        
-        messages = {
-            "Query": query,
-            "Expansion_issue": query_list
-        }
-
-
-        
-        # Call verification workflow with LLM model ID from memory_config
-        verify_tool = VerifyTool(
-            system_prompt=system_prompt,
-            verify_data=messages,
-            llm_model_id=str(memory_config.llm_model_id)
-        )
-        verify_result = await verify_tool.verify()
-        
-        # Parse LLM verification result with error handling
-        try:
-            messages_deal = await Retrieve_verify_tool_messages_deal(
-                verify_result,
-                history,
-                query
-            )
-        except Exception as e:
-            logger.error(
-                f"Retrieve_verify_tool_messages_deal parsing failed: {e}",
-                exc_info=True
-            )
-            # Fallback to avoid 500 errors
-            messages_deal = {
-                "data": {
-                    "query": query,
-                    "expansion_issue": []
-                },
-                "split_result": "failed",
-                "reason": str(e),
-                "history": history,
-            }
-        
-        logger.info(f"Verification result: {messages_deal}")
-        
-        # Emit intermediate output for frontend
-        return {
-            "status": "success",
-            "verified_data": messages_deal,
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id,
-            "_intermediate": {
-                "type": "verification",
-                "title": "Data Verification",
-                "result": messages_deal.get("split_result", "unknown"),
-                "reason": messages_deal.get("reason", ""),
-                "query": query,
-                "verified_count": len(query_list),
-                "storage_type": storage_type,
-                "user_rag_memory_id": user_rag_memory_id
-            }
-        }
-        
-    except Exception as e:
-        logger.error(
-            f"Verify failed: {e}",
-            exc_info=True
-        )
-        return {
-            "status": "error",
-            "message": str(e),
-            "storage_type": storage_type,
-            "user_rag_memory_id": user_rag_memory_id,
-            "verified_data": {
-                "data": {
-                    "query": "",
-                    "expansion_issue": []
-                },
-                "split_result": "failed",
-                "reason": str(e),
-                "history": [],
-            }
-        }
-        
-    finally:
-        # Log execution time
-        end = time.time()
-        try:
-            duration = end - start
-        except Exception:
-            duration = 0.0
-        log_time('Verification', duration)
diff --git a/api/app/core/memory/agent/mcp_server/models/__init__.py b/api/app/core/memory/agent/models/__init__.py
similarity index 100%
rename from api/app/core/memory/agent/mcp_server/models/__init__.py
rename to api/app/core/memory/agent/models/__init__.py
diff --git a/api/app/core/memory/agent/mcp_server/models/problem_models.py b/api/app/core/memory/agent/models/problem_models.py
similarity index 100%
rename from api/app/core/memory/agent/mcp_server/models/problem_models.py
rename to api/app/core/memory/agent/models/problem_models.py
diff --git a/api/app/core/memory/agent/mcp_server/models/retrieval_models.py b/api/app/core/memory/agent/models/retrieval_models.py
similarity index 100%
rename from api/app/core/memory/agent/mcp_server/models/retrieval_models.py
rename to api/app/core/memory/agent/models/retrieval_models.py
diff --git a/api/app/core/memory/agent/mcp_server/models/summary_models.py b/api/app/core/memory/agent/models/summary_models.py
similarity index 100%
rename from api/app/core/memory/agent/mcp_server/models/summary_models.py
rename to api/app/core/memory/agent/models/summary_models.py
diff --git a/api/app/core/memory/agent/mcp_server/models/verification_models.py b/api/app/core/memory/agent/models/verification_models.py
similarity index 100%
rename from api/app/core/memory/agent/mcp_server/models/verification_models.py
rename to api/app/core/memory/agent/models/verification_models.py
diff --git a/api/app/core/memory/agent/multimodal/oss_picture.py b/api/app/core/memory/agent/multimodal/oss_picture.py
deleted file mode 100644
index b5b4bd6b..00000000
--- a/api/app/core/memory/agent/multimodal/oss_picture.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import os
-import sys
-import traceback
-
-import requests
-
-# from qcloud_cos import CosConfig, CosS3Client
-# from qcloud_cos.cos_exception import CosClientError, CosServiceError
-
-# from config.paths import BASE_DIR
-BASE_DIR = os.path.dirname(os.path.realpath(sys.argv[0]))
-
-class OSSUploader:
-    """对象存储文件上传工具类"""
-
-    def __init__(self, env):
-        api = {
-            "test": "https://testlingqi.redbearai.com/api/user/file/common/upload/v2/anon",
-            "prod": "https://lingqi.redbearai.com/api/user/file/common/upload/v2/anon"
-        }
-        self.api = api.get(env, "https://testlingqi.redbearai.com/api/user/file/common/upload/v2/anon")
-        self.privacy = "false"
-        self.headers = {
-            "User-Agent": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
-                          'AppleWebKit/537.36 (KHTML, like Gecko)'
-                          ' Chrome/133.0.6833.84 Safari/537.36'
-        }
-
-    @staticmethod
-    def _generate_object_key(file_path, prefix='xhs_'):
-        """
-        生成对象存储的Key
-
-        :param file_path: 本地文件路径
-        :param prefix: 存储前缀，用于分类存储
-        :return: 生成的对象Key
-        """
-        # 文件md5值.后缀名
-        filename = os.path.basename(file_path)
-        filename = f"{filename}"
-
-        # 组合成完整的对象Key
-        return f"{prefix}{filename}"
-
-    def upload_image(self, file_name, prefix='jd_'):
-        """
-        上传文件到COS并返回可访问的URL
-
-        :param file_url: 文件路径
-        :param file_name: 文件名称
-        :param media_type: 文件类型
-        :param prefix: 存储前缀，用于分类存储
-        :return: 文件访问URL
-        """
-        # 检查文件是否存在
-
-
-
-        file_path = os.path.join(BASE_DIR, file_name)
-
-        # response = requests.get(url, headers=self.headers, stream=True)
-
-        # if response.status_code == 200:
-        #     with open(file_path, "wb") as f:
-        #         for chunk in response.iter_content(1024):  # 分块写入，避免内存占用过大
-        #             f.write(chunk)
-        # else:
-        #     raise Exception(f"文件下载失败,{file_name}")
-
-        # 生成对象Key
-        object_key = self._generate_object_key(file_path, prefix +file_name.split('.')[-1])
-
-        try:
-            upload_response = requests.post(
-                self.api,
-                data={
-                    "privacy": self.privacy,
-                    "fileName": object_key,
-                }
-            )
-
-            if upload_response.status_code != 200:
-                raise Exception('上传接口请求失败')
-            resp = upload_response.json()
-            name = resp["data"]["name"]
-            file_url = resp["data"]["path"]
-            policy = resp["data"]["policy"]
-            with open(file_path, 'rb') as f:
-                oss_push_resp = requests.post(
-                    policy["host"],
-                    files={
-                        "key": policy["dir"],
-                        "OSSAccessKeyId": policy["accessid"],
-                        "name": name,
-                        "policy": policy["policy"],
-                        "success_action_status": 200,
-                        "signature": policy["signature"],
-                        "file": f,
-                    }
-                )
-                if oss_push_resp.status_code == 200:
-                    return file_url
-            raise Exception("OSS上传失败")
-        except Exception:
-            raise Exception(f"上传失败: \n{traceback.format_exc()}")
-        finally:
-            print('success')
-            # os.remove(file_path)
-
-
-if __name__ == '__main__':
-    cos_uploader = OSSUploader("prod")
-    url =cos_uploader.upload_image('./example01.jpg')
-    print(url)
diff --git a/api/app/core/memory/agent/multimodal/speech_model.py b/api/app/core/memory/agent/multimodal/speech_model.py
deleted file mode 100644
index 2df32dd0..00000000
--- a/api/app/core/memory/agent/multimodal/speech_model.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import asyncio
-import re
-
-from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_, picture_model_requests,Picture_recognize, Voice_recognize
-from app.core.memory.agent.utils.messages_tool import read_template_file
-
-import requests
-import json
-import os
-import time
-# file_urls = [
-#     "https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_female2.wav",
-#     "https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_male2.wav",
-# ]
-class Vico_recognition:
-    def __init__(self,file_urls):
-        self.api_key=''
-        self.backend_model_name=''
-        self.api_base=''
-        self.file_urls=file_urls
-
-    # 提交文件转写任务，包含待转写文件url列表
-    async  def submit_task(self) -> str:
-        self.api_key, self.backend_model_name, self.api_base =await Voice_recognize()
-
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json",
-            "X-DashScope-Async": "enable",
-        }
-        data = {
-            "model": self.backend_model_name,
-            "input": {"file_urls": self.file_urls},
-            "parameters": {
-                "channel_id": [0],
-                "vocabulary_id": "vocab-Xxxx",
-            },
-        }
-        # 录音文件转写服务url
-        service_url = (
-            "https://dashscope.aliyuncs.com/api/v1/services/audio/asr/transcription"
-        )
-        response = requests.post(
-            service_url, headers=headers, data=json.dumps(data)
-        )
-
-        # 打印响应内容
-        if response.status_code == 200:
-            return response.json()["output"]["task_id"]
-        else:
-            print("task failed!")
-            print(response.json())
-            return None
-
-    async def download_transcription_result(self, transcription_url):
-        """
-        Args:
-            transcription_url (str): 转写结果文件URL
-        Returns:
-            dict: 转写结果内容
-        """
-        try:
-            response = requests.get(transcription_url)
-            response.raise_for_status()
-            return response.json()
-        except Exception as e:
-            print(f"下载转写结果失败: {e}")
-            return None
-
-    # 循环查询任务状态直到成功
-    async def wait_for_complete(self,task_id):
-        self.api_key, self.backend_model_name, self.api_base = await Voice_recognize()
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json",
-            "X-DashScope-Async": "enable",
-        }
-
-        pending = True
-        while pending:
-            # 查询任务状态服务url
-            service_url = f"https://dashscope.aliyuncs.com/api/v1/tasks/{task_id}"
-            response = requests.post(
-                service_url, headers=headers
-            )
-            if response.status_code == 200:
-                status = response.json()['output']['task_status']
-                if status == 'SUCCEEDED':
-                    print("task succeeded!")
-                    pending = False
-                    return response.json()['output']['results']
-                elif status == 'RUNNING' or status == 'PENDING':
-                    pass
-                else:
-                    print("task failed!")
-                    pending = False
-            else:
-                print("query failed!")
-                pending = False
-            time.sleep(0.1)
-    async def run(self):
-        self.api_key, self.backend_model_name, self.api_base = await Voice_recognize()
-        task_id=await self.submit_task()
-        result=await self.wait_for_complete(task_id)
-        result_context=[]
-        for  i in result:
-            transcription_url=i['transcription_url']
-            print(f"转写URL: {transcription_url}")
-
-            # 下载并打印转写内容
-            content = await self.download_transcription_result(transcription_url)
-            if content:
-                content=json.dumps(content, indent=2, ensure_ascii=False)
-                context=re.findall(r'"text": "(.*?)"', content)
-                result_context.append(context[0])
-        result=''.join(result_context)
-        return (result)
-
-
-
-
diff --git a/api/app/core/memory/agent/mcp_server/services/__init__.py b/api/app/core/memory/agent/services/__init__.py
similarity index 100%
rename from api/app/core/memory/agent/mcp_server/services/__init__.py
rename to api/app/core/memory/agent/services/__init__.py
diff --git a/api/app/core/memory/agent/services/optimized_llm_service.py b/api/app/core/memory/agent/services/optimized_llm_service.py
new file mode 100644
index 00000000..6942d421
--- /dev/null
+++ b/api/app/core/memory/agent/services/optimized_llm_service.py
@@ -0,0 +1,277 @@
+"""
+优化的LLM服务类，用于压缩和统一LLM调用
+"""
+
+import asyncio
+from typing import Any, Dict, List, Optional, Type, TypeVar, Union
+from pydantic import BaseModel
+from sqlalchemy.orm import Session
+
+from app.core.logging_config import get_agent_logger
+from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
+from app.core.memory.llm_tools.openai_client import OpenAIClient
+
+T = TypeVar('T', bound=BaseModel)
+
+logger = get_agent_logger(__name__)
+
+
+class OptimizedLLMService:
+    """
+    优化的LLM服务类，提供统一的LLM调用接口
+    
+    特性：
+    1. 客户端复用 - 避免重复创建LLM客户端
+    2. 批量处理 - 支持并发处理多个请求
+    3. 错误处理 - 统一的错误处理和降级策略
+    4. 性能优化 - 缓存和连接池优化
+    """
+    
+    def __init__(self, db_session: Session):
+        self.db_session = db_session
+        self.client_factory = MemoryClientFactory(db_session)
+        self._client_cache: Dict[str, OpenAIClient] = {}
+        
+    def _get_cached_client(self, llm_model_id: str) -> OpenAIClient:
+        """获取缓存的LLM客户端，避免重复创建"""
+        if llm_model_id not in self._client_cache:
+            self._client_cache[llm_model_id] = self.client_factory.get_llm_client(llm_model_id)
+        return self._client_cache[llm_model_id]
+    
+    async def structured_response(
+        self,
+        llm_model_id: str,
+        system_prompt: str,
+        response_model: Type[T],
+        user_message: Optional[str] = None,
+        fallback_value: Optional[Any] = None
+    ) -> T:
+        """
+        统一的结构化响应接口
+        
+        Args:
+            llm_model_id: LLM模型ID
+            system_prompt: 系统提示词
+            response_model: 响应模型类
+            user_message: 用户消息（可选）
+            fallback_value: 失败时的降级值
+            
+        Returns:
+            结构化响应对象
+        """
+        try:
+            llm_client = self._get_cached_client(llm_model_id)
+            
+            messages = [{"role": "system", "content": system_prompt}]
+            if user_message:
+                messages.append({"role": "user", "content": user_message})
+            
+            logger.debug(f"LLM调用: model={llm_model_id}, prompt_length={len(system_prompt)}")
+            
+            structured = await llm_client.response_structured(
+                messages=messages,
+                response_model=response_model
+            )
+            
+            if structured is None:
+                logger.warning(f"LLM返回None，使用降级值")
+                return self._create_fallback_response(response_model, fallback_value)
+            
+            return structured
+            
+        except Exception as e:
+            logger.error(f"结构化响应失败: {e}", exc_info=True)
+            return self._create_fallback_response(response_model, fallback_value)
+    
+    async def batch_structured_response(
+        self,
+        llm_model_id: str,
+        requests: List[Dict[str, Any]],
+        response_model: Type[T],
+        max_concurrent: int = 5
+    ) -> List[T]:
+        """
+        批量处理结构化响应
+        
+        Args:
+            llm_model_id: LLM模型ID
+            requests: 请求列表，每个请求包含system_prompt等参数
+            response_model: 响应模型类
+            max_concurrent: 最大并发数
+            
+        Returns:
+            结构化响应列表
+        """
+        semaphore = asyncio.Semaphore(max_concurrent)
+        
+        async def process_single_request(request: Dict[str, Any]) -> T:
+            async with semaphore:
+                return await self.structured_response(
+                    llm_model_id=llm_model_id,
+                    system_prompt=request.get('system_prompt', ''),
+                    response_model=response_model,
+                    user_message=request.get('user_message'),
+                    fallback_value=request.get('fallback_value')
+                )
+        
+        tasks = [process_single_request(req) for req in requests]
+        return await asyncio.gather(*tasks)
+    
+    async def simple_response(
+        self,
+        llm_model_id: str,
+        system_prompt: str,
+        user_message: Optional[str] = None,
+        fallback_message: str = "信息不足，无法回答"
+    ) -> str:
+        """
+        简单的文本响应接口
+        
+        Args:
+            llm_model_id: LLM模型ID
+            system_prompt: 系统提示词
+            user_message: 用户消息（可选）
+            fallback_message: 失败时的降级消息
+            
+        Returns:
+            响应文本
+        """
+        try:
+            llm_client = self._get_cached_client(llm_model_id)
+            
+            messages = [{"role": "system", "content": system_prompt}]
+            if user_message:
+                messages.append({"role": "user", "content": user_message})
+            
+            response = await llm_client.response(messages=messages)
+            
+            if not response or not response.strip():
+                return fallback_message
+            
+            return response.strip()
+            
+        except Exception as e:
+            logger.error(f"简单响应失败: {e}", exc_info=True)
+            return fallback_message
+    
+    def _create_fallback_response(self, response_model: Type[T], fallback_value: Optional[Any]) -> T:
+        """创建降级响应"""
+        try:
+            if fallback_value is not None:
+                if isinstance(fallback_value, response_model):
+                    return fallback_value
+                elif isinstance(fallback_value, dict):
+                    return response_model(**fallback_value)
+            
+            # 尝试创建空的响应模型
+            if hasattr(response_model, 'root'):
+                # RootModel类型
+                return response_model([])
+            else:
+                # 普通BaseModel类型
+                return response_model()
+                
+        except Exception as e:
+            logger.error(f"创建降级响应失败: {e}")
+            # 最后的降级策略
+            if hasattr(response_model, 'root'):
+                return response_model([])
+            else:
+                return response_model()
+    
+    def clear_cache(self):
+        """清理客户端缓存"""
+        self._client_cache.clear()
+
+
+class LLMServiceMixin:
+    """
+    LLM服务混入类，为节点提供便捷的LLM调用方法
+    """
+    
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._llm_service: Optional[OptimizedLLMService] = None
+    
+    def get_llm_service(self, db_session: Session) -> OptimizedLLMService:
+        """获取LLM服务实例"""
+        if self._llm_service is None:
+            self._llm_service = OptimizedLLMService(db_session)
+        return self._llm_service
+    
+    async def call_llm_structured(
+        self,
+        state: Dict[str, Any],
+        db_session: Session,
+        system_prompt: str,
+        response_model: Type[T],
+        user_message: Optional[str] = None,
+        fallback_value: Optional[Any] = None
+    ) -> T:
+        """
+        便捷的结构化LLM调用方法
+        
+        Args:
+            state: 状态字典，包含memory_config
+            db_session: 数据库会话
+            system_prompt: 系统提示词
+            response_model: 响应模型类
+            user_message: 用户消息（可选）
+            fallback_value: 失败时的降级值
+            
+        Returns:
+            结构化响应对象
+        """
+        memory_config = state.get('memory_config')
+        if not memory_config:
+            raise ValueError("State中缺少memory_config")
+        
+        llm_model_id = memory_config.llm_model_id
+        if not llm_model_id:
+            raise ValueError("Memory config中缺少llm_model_id")
+        
+        llm_service = self.get_llm_service(db_session)
+        return await llm_service.structured_response(
+            llm_model_id=llm_model_id,
+            system_prompt=system_prompt,
+            response_model=response_model,
+            user_message=user_message,
+            fallback_value=fallback_value
+        )
+    
+    async def call_llm_simple(
+        self,
+        state: Dict[str, Any],
+        db_session: Session,
+        system_prompt: str,
+        user_message: Optional[str] = None,
+        fallback_message: str = "信息不足，无法回答"
+    ) -> str:
+        """
+        便捷的简单LLM调用方法
+        
+        Args:
+            state: 状态字典，包含memory_config
+            db_session: 数据库会话
+            system_prompt: 系统提示词
+            user_message: 用户消息（可选）
+            fallback_message: 失败时的降级消息
+            
+        Returns:
+            响应文本
+        """
+        memory_config = state.get('memory_config')
+        if not memory_config:
+            raise ValueError("State中缺少memory_config")
+        
+        llm_model_id = memory_config.llm_model_id
+        if not llm_model_id:
+            raise ValueError("Memory config中缺少llm_model_id")
+        
+        llm_service = self.get_llm_service(db_session)
+        return await llm_service.simple_response(
+            llm_model_id=llm_model_id,
+            system_prompt=system_prompt,
+            user_message=user_message,
+            fallback_message=fallback_message
+        )
\ No newline at end of file
diff --git a/api/app/core/memory/agent/mcp_server/services/parameter_builder.py b/api/app/core/memory/agent/services/parameter_builder.py
similarity index 87%
rename from api/app/core/memory/agent/mcp_server/services/parameter_builder.py
rename to api/app/core/memory/agent/services/parameter_builder.py
index d5305dc6..a58fcf1a 100644
--- a/api/app/core/memory/agent/mcp_server/services/parameter_builder.py
+++ b/api/app/core/memory/agent/services/parameter_builder.py
@@ -4,22 +4,19 @@ Parameter Builder for constructing tool call arguments.
 This service provides tool-specific parameter transformation logic
 to build correct arguments for each tool type.
 """
-
 from typing import Any, Dict, Optional
-
 from app.core.logging_config import get_agent_logger
-from app.schemas.memory_config_schema import MemoryConfig
 
 logger = get_agent_logger(__name__)
 
 
 class ParameterBuilder:
     """Service for building tool call arguments based on tool type."""
-
+    
     def __init__(self):
         """Initialize the parameter builder."""
         logger.info("ParameterBuilder initialized")
-
+    
     def build_tool_args(
         self,
         tool_name: str,
@@ -28,9 +25,8 @@ class ParameterBuilder:
         search_switch: str,
         apply_id: str,
         group_id: str,
-        memory_config: MemoryConfig,
         storage_type: Optional[str] = None,
-        user_rag_memory_id: Optional[str] = None,
+        user_rag_memory_id: Optional[str] = None
     ) -> Dict[str, Any]:
         """
         Build tool arguments based on tool type.
@@ -49,7 +45,6 @@ class ParameterBuilder:
             search_switch: Search routing parameter
             apply_id: Application identifier
             group_id: Group identifier
-            memory_config: MemoryConfig object containing all configuration
             storage_type: Storage type for the workspace (optional)
             user_rag_memory_id: User RAG memory ID for knowledge base retrieval (optional)
             
@@ -60,19 +55,18 @@ class ParameterBuilder:
         base_args = {
             "usermessages": tool_call_id,
             "apply_id": apply_id,
-            "group_id": group_id,
-            "memory_config": memory_config,
+            "group_id": group_id
         }
-
+        
         # Always add storage_type and user_rag_memory_id (with defaults if None)
         base_args["storage_type"] = storage_type if storage_type is not None else ""
         base_args["user_rag_memory_id"] = user_rag_memory_id if user_rag_memory_id is not None else ""
         
         # Tool-specific argument construction
-        if tool_name in ["Verify", "Summary", "Summary_fails", "Retrieve_Summary", "Problem_Extension"]:
-            # These tools expect dict context
+        if tool_name in ["Verify","Summary", "Summary_fails",'Retrieve_Summary']:
+            # Verify expects dict context
             return {
-                "context": content if isinstance(content, dict) else {"content": content},
+                "context": content if isinstance(content, dict) else {},
                 **base_args
             }
 
diff --git a/api/app/core/memory/agent/mcp_server/services/search_service.py b/api/app/core/memory/agent/services/search_service.py
similarity index 75%
rename from api/app/core/memory/agent/mcp_server/services/search_service.py
rename to api/app/core/memory/agent/services/search_service.py
index 47295f87..8a2e7cfe 100644
--- a/api/app/core/memory/agent/mcp_server/services/search_service.py
+++ b/api/app/core/memory/agent/services/search_service.py
@@ -4,31 +4,21 @@ Search Service for executing hybrid search and processing results.
 This service provides clean search result processing with content extraction
 and deduplication.
 """
-
-from typing import TYPE_CHECKING, List, Optional, Tuple
+from typing import List, Tuple, Optional
 
 from app.core.logging_config import get_agent_logger
 from app.core.memory.src.search import run_hybrid_search
 from app.core.memory.utils.data.text_utils import escape_lucene_query
 
-if TYPE_CHECKING:
-    from app.schemas.memory_config_schema import MemoryConfig
 
 logger = get_agent_logger(__name__)
 
 
 class SearchService:
     """Service for executing hybrid search and processing results."""
-
-    def __init__(self, memory_config: "MemoryConfig" = None):
-        """
-        Initialize the search service.
-        
-        Args:
-            memory_config: Optional MemoryConfig for embedding model configuration.
-                          If not provided, must be passed to execute_hybrid_search.
-        """
-        self.memory_config = memory_config
+    
+    def __init__(self):
+        """Initialize the search service."""
         logger.info("SearchService initialized")
     
     def extract_content_from_result(self, result: dict) -> str:
@@ -103,49 +93,40 @@ class SearchService:
         self,
         group_id: str,
         question: str,
-        limit: int = 15,
+        limit: int = 5,
         search_type: str = "hybrid",
         include: Optional[List[str]] = None,
-        rerank_alpha: float = 0.6,
-        activation_boost_factor: float = 0.8,
+        rerank_alpha: float = 0.4,
         output_path: str = "search_results.json",
         return_raw_results: bool = False,
-        memory_config: "MemoryConfig" = None,
+        memory_config = None
     ) -> Tuple[str, str, Optional[dict]]:
         """
-        Execute hybrid search with two-stage ranking.
-        
-        Stage 1: Filter by content relevance (BM25 + Embedding)
-        Stage 2: Rerank by activation values (ACTR)
+        Execute hybrid search and return clean content.
         
         Args:
-            group_id: Group identifier for filtering
+            group_id: Group identifier for filtering results
             question: Search query text
-            limit: Max results per category (default: 15)
-            search_type: "hybrid", "keyword", or "embedding" (default: "hybrid")
-            include: Result types (default: ["statements", "chunks", "entities", "summaries"])
-            rerank_alpha: BM25 weight (default: 0.6)
-            activation_boost_factor: Activation impact on memory strength (default: 0.8)
-            output_path: JSON output path (default: "search_results.json")
-            return_raw_results: Return full metadata (default: False)
-            memory_config: MemoryConfig for embedding model
+            limit: Maximum number of results to return (default: 5)
+            search_type: Type of search - "hybrid", "keyword", or "embedding" (default: "hybrid")
+            include: List of result types to include (default: ["statements", "chunks", "entities", "summaries"])
+            rerank_alpha: Weight for BM25 scores in reranking (default: 0.4)
+            output_path: Path to save search results (default: "search_results.json")
+            return_raw_results: If True, also return the raw search results as third element (default: False)
+            memory_config: Memory configuration object (required)
         
         Returns:
-            Tuple[str, str, Optional[dict]]: (clean_content, cleaned_query, raw_results)
+            Tuple of (clean_content, cleaned_query, raw_results)
+            raw_results is None if return_raw_results=False
         """
         if include is None:
             include = ["statements", "chunks", "entities", "summaries"]
-
-        # Use provided memory_config or fall back to instance config
-        config = memory_config or self.memory_config
-        if not config:
-            raise ValueError("memory_config is required for search - either pass it to __init__ or execute_hybrid_search")
-
+        
         # Clean query
         cleaned_query = self.clean_query(question)
-
+        
         try:
-            # Execute search using memory_config
+            # Execute search
             answer = await run_hybrid_search(
                 query_text=cleaned_query,
                 search_type=search_type,
@@ -153,9 +134,8 @@ class SearchService:
                 limit=limit,
                 include=include,
                 output_path=output_path,
-                memory_config=config,
-                rerank_alpha=rerank_alpha,
-                activation_boost_factor=activation_boost_factor,
+                memory_config=memory_config,
+                rerank_alpha=rerank_alpha
             )
             
             # Extract results based on search type and include parameter
diff --git a/api/app/core/memory/agent/mcp_server/services/session_service.py b/api/app/core/memory/agent/services/session_service.py
similarity index 100%
rename from api/app/core/memory/agent/mcp_server/services/session_service.py
rename to api/app/core/memory/agent/services/session_service.py
diff --git a/api/app/core/memory/agent/mcp_server/services/template_service.py b/api/app/core/memory/agent/services/template_service.py
similarity index 94%
rename from api/app/core/memory/agent/mcp_server/services/template_service.py
rename to api/app/core/memory/agent/services/template_service.py
index 95223f0b..1bf86375 100644
--- a/api/app/core/memory/agent/mcp_server/services/template_service.py
+++ b/api/app/core/memory/agent/services/template_service.py
@@ -3,12 +3,22 @@ Template Service for loading and rendering Jinja2 templates.
 
 This service provides centralized template management with caching and error handling.
 """
+
 import os
 from functools import lru_cache
-from typing import Optional
-from jinja2 import Environment, FileSystemLoader, Template, TemplateNotFound
 
-from app.core.logging_config import get_agent_logger, log_prompt_rendering
+from jinja2 import (
+    Environment,
+    FileSystemLoader,
+    Template,
+    TemplateNotFound,
+)
+
+from app.core.logging_config import (
+    get_agent_logger,
+    log_prompt_rendering,
+)
+
 
 
 logger = get_agent_logger(__name__)
diff --git a/api/app/core/memory/agent/utils/__init__.py b/api/app/core/memory/agent/utils/__init__.py
deleted file mode 100644
index 2b77e240..00000000
--- a/api/app/core/memory/agent/utils/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-"""Agent utilities."""
-
-from app.core.memory.agent.utils.multimodal import MultimodalProcessor
-
-__all__ = [
-    "MultimodalProcessor",
-]
diff --git a/api/app/core/memory/agent/utils/llm_client_pool.py b/api/app/core/memory/agent/utils/llm_client_pool.py
new file mode 100644
index 00000000..fddd54f6
--- /dev/null
+++ b/api/app/core/memory/agent/utils/llm_client_pool.py
@@ -0,0 +1,56 @@
+
+import asyncio
+from typing import Dict, Optional
+from app.core.memory.utils.llm.llm_utils import get_llm_client_fast
+from app.db import get_db
+from app.core.logging_config import get_agent_logger
+
+logger = get_agent_logger(__name__)
+
+class LLMClientPool:
+    """LLM客户端连接池"""
+    
+    def __init__(self, max_size: int = 5):
+        self.max_size = max_size
+        self.pools: Dict[str, asyncio.Queue] = {}
+        self.active_clients: Dict[str, int] = {}
+        
+    async def get_client(self, llm_model_id: str):
+        """获取LLM客户端"""
+        if llm_model_id not in self.pools:
+            self.pools[llm_model_id] = asyncio.Queue(maxsize=self.max_size)
+            self.active_clients[llm_model_id] = 0
+            
+        pool = self.pools[llm_model_id]
+        
+        try:
+            # 尝试从池中获取客户端
+            client = pool.get_nowait()
+            logger.debug(f"从池中获取LLM客户端: {llm_model_id}")
+            return client
+        except asyncio.QueueEmpty:
+            # 池为空，创建新客户端
+            if self.active_clients[llm_model_id] < self.max_size:
+                db_session = next(get_db())
+                client = get_llm_client_fast(llm_model_id, db_session)
+                self.active_clients[llm_model_id] += 1
+                logger.debug(f"创建新LLM客户端: {llm_model_id}")
+                return client
+            else:
+                # 等待可用客户端
+                logger.debug(f"等待LLM客户端可用: {llm_model_id}")
+                return await pool.get()
+                
+    async def return_client(self, llm_model_id: str, client):
+        """归还LLM客户端到池中"""
+        if llm_model_id in self.pools:
+            try:
+                self.pools[llm_model_id].put_nowait(client)
+                logger.debug(f"归还LLM客户端到池: {llm_model_id}")
+            except asyncio.QueueFull:
+                # 池已满，丢弃客户端
+                self.active_clients[llm_model_id] -= 1
+                logger.debug(f"池已满，丢弃LLM客户端: {llm_model_id}")
+
+# 全局客户端池
+llm_client_pool = LLMClientPool()
diff --git a/api/app/core/memory/agent/utils/llm_tools.py b/api/app/core/memory/agent/utils/llm_tools.py
index ec22b628..8dd2f1d3 100644
--- a/api/app/core/memory/agent/utils/llm_tools.py
+++ b/api/app/core/memory/agent/utils/llm_tools.py
@@ -1,40 +1,12 @@
-import asyncio
-import json
-import logging
 import os
 from collections import defaultdict
 from typing import Annotated, TypedDict
 
-from app.core.memory.agent.utils.messages_tool import read_template_file
-from app.core.memory.utils.config.config_utils import (
-    get_picture_config,
-    get_voice_config,
-)
-
-# Removed global variable imports - use dependency injection instead
-from dotenv import load_dotenv
 from langchain_core.messages import AnyMessage
 from langgraph.graph import add_messages
-from openai import OpenAI
 
 PROJECT_ROOT_ = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-logger = logging.getLogger(__name__)
 
-load_dotenv()
-
-
-async def picture_model_requests(image_url):
-    '''
-
-    Args:
-        image_url:
-    Returns:
-
-    '''
-    file_path = PROJECT_ROOT_ + '/agent/utils/prompt/Template_for_image_recognition_prompt.jinja2 '
-    system_prompt = await read_template_file(file_path)
-    result = await Picture_recognize(image_url,system_prompt)
-    return (result)
 class WriteState(TypedDict):
     '''
     Langgrapg Writing TypedDict
@@ -44,39 +16,69 @@ class WriteState(TypedDict):
     apply_id:str
     group_id:str
     errors: list[dict]  # Track errors: [{"tool": "tool_name", "error": "message"}]
+    memory_config: object
+    write_result: dict
+    data:str
 
 class ReadState(TypedDict):
-    '''
-       Langgrapg READING TypedDict
-       name:
-       id:user id
-       loop_count:Traverse times
-       search_switch：type
-       config_id: configuration id for filtering results
-       errors: list of errors that occurred during workflow execution
-       '''
-    messages: Annotated[list[AnyMessage], add_messages] #消息追加的模式增加消息
-    name: str
-    id: str
-    loop_count:int
+    """
+    LangGraph 工作流状态定义
+
+    Attributes:
+        messages: 消息列表，支持自动追加
+        loop_count: 遍历次数
+        search_switch: 搜索类型开关
+        group_id: 组标识
+        config_id: 配置ID，用于过滤结果
+        data: 从content_input_node传递的内容数据
+        spit_data: 从Split_The_Problem传递的分解结果
+        tool_calls: 工具调用请求列表
+        tool_results: 工具执行结果列表
+        memory_config: 内存配置对象
+    """
+    messages: Annotated[list[AnyMessage], add_messages]  # 消息追加模式
+    loop_count: int
     search_switch: str
-    user_id: str
-    apply_id: str
     group_id: str
     config_id: str
-    errors: list[dict]  # Track errors: [{"tool": "tool_name", "error": "message"}]
-
-
+    data: str  # 新增字段用于传递内容
+    spit_data: dict  # 新增字段用于传递问题分解结果
+    problem_extension:dict
+    storage_type: str
+    user_rag_memory_id: str
+    llm_id: str
+    embedding_id: str
+    memory_config: object  # 新增字段用于传递内存配置对象
+    retrieve:dict
+    RetrieveSummary: dict
+    InputSummary: dict
+    verify: dict
+    SummaryFails: dict
+    summary: dict
 class COUNTState:
-    '''
-    The number of times the workflow dialogue retrieval content has no correct message recall traversal
-    '''
+    """
+    工作流对话检索内容计数器
+
+    用于记录工作流对话检索内容没有正确消息召回遍历的次数。
+    """
+
     def __init__(self, limit: int = 5):
+        """
+        初始化计数器
+
+        Args:
+            limit: 最大计数限制，默认为5
+        """
         self.total: int = 0  # 当前累加值
         self.limit: int = limit  # 最大上限
 
-    def add(self, value: int = 1):
-        """累加数字，如果达到上限就保持最大值"""
+    def add(self, value: int = 1) -> None:
+        """
+        累加数字，如果达到上限就保持最大值
+
+        Args:
+            value: 要累加的值，默认为1
+        """
         self.total += value
         print(f"[COUNTState] 当前值: {self.total}")
         if self.total >= self.limit:
@@ -84,21 +86,19 @@ class COUNTState:
             self.total = self.limit  # 达到上限不再增加
 
     def get_total(self) -> int:
-        """获取当前累加值"""
+        """
+        获取当前累加值
+
+        Returns:
+            当前累加值
+        """
         return self.total
 
-    def reset(self):
+    def reset(self) -> None:
         """手动重置累加值"""
         self.total = 0
         print("[COUNTState] 已重置为 0")
 
-
-def merge_to_key_value_pairs(data, query_key, result_key):
-    grouped = defaultdict(list)
-    for item in data:
-        grouped[item[query_key]].append(item[result_key])
-    return [{key: values} for key, values in grouped.items()]
-
 def deduplicate_entries(entries):
     seen = set()
     deduped = []
@@ -109,70 +109,37 @@ def deduplicate_entries(entries):
             deduped.append(entry)
     return deduped
 
+def merge_to_key_value_pairs(data, query_key, result_key):
+    grouped = defaultdict(list)
+    for item in data:
+        grouped[item[query_key]].append(item[result_key])
+    return [{key: values} for key, values in grouped.items()]
 
 
-async def Picture_recognize(image_path, PROMPT_TICKET_EXTRACTION, picture_model_name: str) -> str:
+def convert_extended_question_to_question(data):
     """
-    Updated to eliminate global variables in favor of explicit parameters.
-    
+    递归地将数据中的 extended_question 字段转换为 question 字段
+
     Args:
-        image_path: Path to image file
-        PROMPT_TICKET_EXTRACTION: Extraction prompt
-        picture_model_name: Picture model name (required, no longer from global variables)
+        data: 要转换的数据（可能是字典、列表或其他类型）
+
+    Returns:
+        转换后的数据
     """
-    try:
-        model_config = get_picture_config(picture_model_name)
-    except Exception as e:
-            err = f"LLM配置不可用：{str(e)}。请检查 config.json 和 runtime.json。"
-            logger.error(err)
-            return err
-    api_key = os.getenv(model_config["api_key"])  # 从环境变量读取对应后端的 API key
-    backend_model_name = model_config["llm_name"].split("/")[-1]
-    api_base=model_config['api_base']
-
-    logger.info(f"model_name: {backend_model_name}")
-    logger.info(f"api_key set: {'yes' if api_key else 'no'}")
-    logger.info(f"base_url: {model_config['api_base']}")
-
-    client = OpenAI(
-        api_key=api_key, base_url=api_base,
-    )
-    completion = client.chat.completions.create(
-        model=backend_model_name,
-        messages=[
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "type": "image_url",
-                            "image_url":image_path,
-                        },
-                        {"type": "text",
-                         "text": PROMPT_TICKET_EXTRACTION}
-                    ]
-                }
-            ])
-    picture_text = completion.choices[0].message.content
-    picture_text = picture_text.replace('```json', '').replace('```', '')
-    picture_text = json.loads(picture_text)
-    return (picture_text['statement'])
-
-async def Voice_recognize(voice_model_name: str):
-    """
-    Updated to eliminate global variables in favor of explicit parameters.
-    
-    Args:
-        voice_model_name: Voice model name (required, no longer from global variables)
-    """
-    try:
-        model_config = get_voice_config(voice_model_name)
-    except Exception as e:
-            err = f"LLM配置不可用：{str(e)}。请检查 config.json 和 runtime.json。"
-            logger.error(err)
-            return err
-    api_key = os.getenv(model_config["api_key"])  # 从环境变量读取对应后端的 API key
-    backend_model_name = model_config["llm_name"].split("/")[-1]
-    api_base = model_config['api_base']
-    return api_key,backend_model_name,api_base
-
-
+    if isinstance(data, dict):
+        # 创建新字典来存储转换后的数据
+        converted = {}
+        for key, value in data.items():
+            if key == 'extended_question':
+                # 将 extended_question 转换为 question
+                converted['question'] = convert_extended_question_to_question(value)
+            else:
+                # 递归处理其他字段
+                converted[key] = convert_extended_question_to_question(value)
+        return converted
+    elif isinstance(data, list):
+        # 递归处理列表中的每个元素
+        return [convert_extended_question_to_question(item) for item in data]
+    else:
+        # 其他类型直接返回
+        return data
\ No newline at end of file
diff --git a/api/app/core/memory/agent/utils/mcp_tools.py b/api/app/core/memory/agent/utils/mcp_tools.py
deleted file mode 100644
index 7ede9843..00000000
--- a/api/app/core/memory/agent/utils/mcp_tools.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import os
-from app.core.config import settings
-
-def get_mcp_server_config():
-    """
-    Get the MCP server configuration.
-    
-    Uses MCP_SERVER_URL environment variable if set (for Docker),
-    otherwise falls back to SERVER_IP and MCP_PORT (for local development).
-    """
-    # Get MCP port from environment (default: 8081)
-    mcp_port = os.getenv("MCP_PORT", "8081")
-    
-    # In Docker: MCP_SERVER_URL=http://mcp-server:8081
-    # In local dev: uses SERVER_IP (127.0.0.1 or localhost)
-    mcp_server_url = os.getenv("MCP_SERVER_URL")
-    
-    if mcp_server_url:
-        # Docker environment: use full URL from environment
-        base_url = mcp_server_url
-    else:
-        # Local development: build URL from SERVER_IP and MCP_PORT
-        base_url = f"http://{settings.SERVER_IP}:{mcp_port}"
-    
-    mcp_server_config = {
-        "data_flow": {
-            "url": f"{base_url}/sse",
-            "transport": "sse",
-            "timeout": 15000,
-            "sse_read_timeout": 15000,
-        }
-    }
-    return mcp_server_config
diff --git a/api/app/core/memory/agent/utils/messages_tool.py b/api/app/core/memory/agent/utils/messages_tool.py
deleted file mode 100644
index 769e795a..00000000
--- a/api/app/core/memory/agent/utils/messages_tool.py
+++ /dev/null
@@ -1,260 +0,0 @@
-import json
-import logging
-import re
-from typing import Any, List
-
-from app.core.logging_config import get_agent_logger
-from langchain_core.messages import AnyMessage
-
-logger = get_agent_logger(__name__)
-
-
-def _to_openai_messages(msgs: List[AnyMessage]) -> List[dict]:
-    out = []
-    for m in msgs:
-        if hasattr(m, "content"):
-            out.append({"role": "user", "content": getattr(m, "content", "")})
-        elif isinstance(m, dict) and "role" in m and "content" in m:
-            out.append(m)
-        else:
-            out.append({"role": "user", "content": str(m)})
-    return out
-
-
-def _extract_content(resp: Any) -> str:
-    """Extract LLM content and sanitize to raw JSON/text.
-
-    - Supports both object and dict response shapes.
-    - Removes leading role labels (e.g., "Assistant:").
-    - Strips Markdown code fences like ```json ... ```.
-    - Attempts to isolate the first valid JSON array/object block when extra text is present.
-    """
-
-    def _to_text(r: Any) -> str:
-        try:
-            # 对象形式: resp.choices[0].message.content
-            if hasattr(r, "choices") and getattr(r, "choices", None):
-                msg = r.choices[0].message
-                if hasattr(msg, "content"):
-                    return msg.content
-                if isinstance(msg, dict) and "content" in msg:
-                    return msg["content"]
-            # 字典形式: resp["choices"][0]["message"]["content"]
-            if isinstance(r, dict):
-                return r.get("choices", [{}])[0].get("message", {}).get("content", "")
-        except Exception:
-            pass
-        return str(r)
-
-    def _clean_text(text: str) -> str:
-        s = str(text).strip()
-        # 移除可能的角色前缀
-        s = re.sub(r"^\s*(Assistant|assistant)\s*:\s*", "", s)
-        # 提取 ```json ... ``` 代码块
-        m = re.search(r"```json\s*(.*?)\s*```", s, flags=re.S | re.I)
-        if m:
-            s = m.group(1).strip()
-        # 如果仍然包含多余文本，尝试截取第一个 JSON 数组/对象片段
-        if not (s.startswith("{") or s.startswith("[")):
-            left = s.find("[")
-            right = s.rfind("]")
-            if left != -1 and right != -1 and right > left:
-                s = s[left:right + 1].strip()
-            else:
-                left = s.find("{")
-                right = s.rfind("}")
-                if left != -1 and right != -1 and right > left:
-                    s = s[left:right + 1].strip()
-        return s
-
-    raw = _to_text(resp)
-    return _clean_text(raw)
-
-def Resolve_username(usermessages):
-    '''
-    Extract username
-    Args:
-        usermessages: user name
-
-    Returns:
-
-    '''
-    usermessages = usermessages.split('_')[1:]
-    sessionid = '_'.join(usermessages[:-1])
-    return sessionid
-
-
-# TODO: USE app.core.memory.src.utils.render_template instead
-async def read_template_file(template_path: str) -> str:
-    """
-    读取模板文件
-
-    Args:
-        template_path: 模板文件路径
-
-    Returns:
-        模板内容字符串
-
-    Note:
-        建议使用 app.core.memory.utils.template_render 中的统一模板渲染功能
-    """
-    try:
-        with open(template_path, "r", encoding="utf-8") as f:
-            return f.read()
-    except FileNotFoundError:
-        logger.error(f"模板文件未找到: {template_path}")
-        raise
-    except IOError as e:
-        logger.error(f"读取模板文件失败: {template_path}, 错误: {str(e)}", exc_info=True)
-        raise
-
-
-async def Problem_Extension_messages_deal(context):
-    '''
-    Extract data
-    Args:
-        context:
-    Returns:
-    '''
-    extent_quest = []
-    original = context.get('original', '')
-    messages = context.get('context', '')
-    
-    # Handle empty or non-string messages
-    if not messages:
-        return extent_quest, original
-    
-    if isinstance(messages, str):
-        try:
-            messages = json.loads(messages)
-        except json.JSONDecodeError:
-            # If JSON parsing fails, return empty list
-            return extent_quest, original
-    
-    if isinstance(messages, list):
-        for message in messages:
-            question = message.get('question', '')
-            type = message.get('type', '')
-            extent_quest.append({"role": "user", "content": f"问题:{question}；问题类型：{type}"})
-
-    return extent_quest, original
-
-
-async def Retriev_messages_deal(context):
-    '''
-    Extract data
-    Args:
-        context:
-    Returns:
-    '''
-    logger.info(f"Retriev_messages_deal input: type={type(context)}, value={str(context)[:500]}")
-    
-    if isinstance(context, dict):
-        logger.info(f"Retriev_messages_deal: context is dict with keys={list(context.keys())}")
-        if 'context' in context or 'original' in context:
-            content = context.get('context', {})
-            original = context.get('original', '')
-            logger.info(f"Retriev_messages_deal output: content_type={type(content)}, content={str(content)[:300]}, original='{original[:50] if original else ''}'")
-            return content, original
-    
-    # Return empty defaults if context is not a dict or doesn't have expected keys
-    logger.warning(f"Retriev_messages_deal: context missing expected keys, returning empty defaults")
-    return {}, ''
-
-async  def Verify_messages_deal(context):
-    '''
-    Extract data
-    Args:
-        context:
-    Returns:
-    '''
-
-    query = context['context']['Query']
-    Query_small_list = context['context']['Expansion_issue']
-    Result_small = []
-    Query_small = []
-    for i in Query_small_list:
-        Result_small.append(i['Answer_Small'][0])
-        Query_small.append(i['Query_small'])
-    return Query_small, Result_small, query
-
-
-async def Summary_messages_deal(context):
-    '''
-    Extract data
-    Args:
-        context:
-    Returns:
-    '''
-    messages = str(context).replace('\\n', '').replace('\n', '').replace('\\', '')
-    query = re.findall(r'"query": (.*?),', messages)[0]
-    query = query.replace('[', '').replace(']', '').strip()
-    matches = re.findall(r'"answer_small"\s*:\s*"(\[.*?\])"', messages)
-    answer_small_texts = []
-    for m in matches:
-        try:
-            parsed = json.loads(m)
-            for item in parsed:
-                answer_small_texts.append(item.strip().replace('\\', '').replace('[', '').replace(']', ''))
-        except Exception:
-            answer_small_texts.append(m.strip().replace('\\', '').replace('[', '').replace(']', ''))
-
-    return answer_small_texts, query
-
-
-async def VerifyTool_messages_deal(context):
-    '''
-    Extract data
-    Args:
-        context:
-    Returns:
-    '''
-    messages = str(context).replace('\\n', '').replace('\n', '').replace('\\', '')
-    content_messages = messages.split('"context":')[1].replace('""', '"')
-    messages = str(content_messages).split("name='Retrieve'")[0]
-    query = re.findall('"Query": "(.*?)"', messages)[0]
-    Query_small = re.findall('"Query_small": "(.*?)"', messages)
-    Result_small = re.findall('"Result_small": "(.*?)"', messages)
-    return Query_small, Result_small, query
-
-
-async def Retrieve_Summary_messages_deal(context):
-    pass
-
-
-async def Retrieve_verify_tool_messages_deal(context, history, query):
-    '''
-    Extract data
-    Args:
-        context:
-    Returns:
-    '''
-    results = []
-    # 统一转为字符串，避免 None 或非字符串导致正则报错
-    text = str(context)
-    blocks = re.findall(r'\{(.*?)\}', text, flags=re.S)
-    for block in blocks:
-        query_small = re.search(r'"Query_small"\s*:\s*"([^"]*)"', block)
-        answer_small = re.search(r'"Answer_Small"\s*:\s*(\[[^\]]*\])', block)
-        status = re.search(r'"status"\s*:\s*"([^"]*)"', block)
-        query_answer = re.search(r'"Query_answer"\s*:\s*"([^"]*)"', block)
-
-        results.append({
-            "query_small": query_small.group(1) if query_small else None,
-            "answer_small": answer_small.group(1) if answer_small else None,
-            # 将缺失的 status 统一为空字符串，后续用字符串判定，避免 NoneType 错误
-            "status": status.group(1) if status else "",
-            "query_answer": query_answer.group(1) if query_answer else None
-        })
-    result = []
-    for r in results:
-        # 统一按字符串判定状态，兼容大小写和缺失情况
-        status_str = str(r.get('status', '')).strip().lower()
-        if status_str == 'false':
-            continue
-        else:
-            result.append(r)
-    split_result = 'failed' if not result else 'success'
-    result = {"data": {"query": query, "expansion_issue": result}, "split_result": split_result, "reason": "",
-              "history": history}
-    return result
diff --git a/api/app/core/memory/agent/utils/messages_tools.py b/api/app/core/memory/agent/utils/messages_tools.py
new file mode 100644
index 00000000..db95319f
--- /dev/null
+++ b/api/app/core/memory/agent/utils/messages_tools.py
@@ -0,0 +1,194 @@
+from typing import List, Dict, Any
+from app.core.logging_config import get_agent_logger
+
+logger = get_agent_logger(__name__)
+async def read_template_file(template_path: str) -> str:
+    """
+    读取模板文件
+
+    Args:
+        template_path: 模板文件路径
+
+    Returns:
+        模板内容字符串
+
+    Note:
+        建议使用 app.core.memory.utils.template_render 中的统一模板渲染功能
+    """
+    try:
+        with open(template_path, "r", encoding="utf-8") as f:
+            return f.read()
+    except FileNotFoundError:
+        logger.error(f"模板文件未找到: {template_path}")
+        raise
+    except IOError as e:
+        logger.error(f"读取模板文件失败: {template_path}, 错误: {str(e)}", exc_info=True)
+        raise
+
+def reorder_output_results(results: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    重新排序输出结果，将 retrieval_summary 类型的数据放到最后面
+
+    Args:
+        results: 原始输出结果列表
+
+    Returns:
+        重新排序后的结果列表
+    """
+    retrieval_summaries = []
+    other_results = []
+
+    # 分离 retrieval_summary 和其他类型的结果
+    for result in results:
+        if 'summary' in result.get('type'):
+            retrieval_summaries.append(result)
+        else:
+            other_results.append(result)
+
+    # 将 retrieval_summary 放到最后
+    return other_results + retrieval_summaries
+
+def optimize_search_results(intermediate_outputs):
+    """
+    优化检索结果，合并多个搜索结果，过滤空结果，统一格式
+
+    Args:
+        intermediate_outputs: 原始的中间输出列表
+
+    Returns:
+        优化后的检索结果列表
+    """
+    optimized_results = []
+
+    for item in intermediate_outputs:
+        if not item or item == [] or item == {}:
+            continue
+
+        # 检查是否是搜索结果类型
+        if isinstance(item, dict) and item.get('type') == 'search_result':
+            raw_results = item.get('raw_results', {})
+
+            # 如果 raw_results 为空，跳过
+            if not raw_results or raw_results == [] or raw_results == {}:
+                continue
+
+            # 创建优化后的结果结构
+            optimized_item = {
+                "type": "search_result",
+                "title": f"检索结果 ({item.get('index', 1)}/{item.get('total', 1)})",
+                "query": item.get('query', ''),
+                "raw_results": {},
+                "index": item.get('index', 1),
+                "total": item.get('total', 1)
+            }
+
+            # 合并所有搜索结果类型到一个 raw_results 中
+            merged_raw_results = {}
+
+            # 处理 time_search
+            if 'time_search' in raw_results and raw_results['time_search']:
+                merged_raw_results['time_search'] = raw_results['time_search']
+
+            # 处理 keyword_search
+            if 'keyword_search' in raw_results and raw_results['keyword_search']:
+                merged_raw_results['keyword_search'] = raw_results['keyword_search']
+
+            # 处理 embedding_search
+            if 'embedding_search' in raw_results and raw_results['embedding_search']:
+                merged_raw_results['embedding_search'] = raw_results['embedding_search']
+
+            # 处理 combined_summary
+            if 'combined_summary' in raw_results and raw_results['combined_summary']:
+                merged_raw_results['combined_summary'] = raw_results['combined_summary']
+
+            # 处理 reranked_results
+            if 'reranked_results' in raw_results and raw_results['reranked_results']:
+                merged_raw_results['reranked_results'] = raw_results['reranked_results']
+
+            # 如果合并后的结果不为空，添加到优化结果中
+            if merged_raw_results:
+                optimized_item['raw_results'] = merged_raw_results
+                optimized_results.append(optimized_item)
+        else:
+            # 非搜索结果类型，直接添加
+            optimized_results.append(item)
+
+    return optimized_results
+
+
+def merge_multiple_search_results(intermediate_outputs):
+    """
+    将多个搜索结果合并为一个统一的搜索结果
+
+    Args:
+        intermediate_outputs: 原始的中间输出列表
+
+    Returns:
+        合并后的结果列表
+    """
+    search_results = []
+    other_results = []
+
+    # 分离搜索结果和其他结果
+    for item in intermediate_outputs:
+        if isinstance(item, dict) and item.get('type') == 'search_result':
+            raw_results = item.get('raw_results', {})
+            # 只保留有内容的搜索结果
+            if raw_results and raw_results != [] and raw_results != {}:
+                search_results.append(item)
+        else:
+            other_results.append(item)
+
+    # 如果没有搜索结果，返回原始结果
+    if not search_results:
+        return intermediate_outputs
+
+    # 如果只有一个搜索结果，优化格式后返回
+    if len(search_results) == 1:
+        optimized = optimize_search_results(search_results)
+        return other_results + optimized
+
+    # 合并多个搜索结果
+    merged_raw_results = {}
+    all_queries = []
+
+    for result in search_results:
+        query = result.get('query', '')
+        if query:
+            all_queries.append(query)
+
+        raw_results = result.get('raw_results', {})
+
+        # 合并各种搜索类型的结果
+        for search_type in ['time_search', 'keyword_search', 'embedding_search', 'combined_summary',
+                            'reranked_results']:
+            if search_type in raw_results and raw_results[search_type]:
+                if search_type not in merged_raw_results:
+                    merged_raw_results[search_type] = raw_results[search_type]
+                else:
+                    # 如果是字典类型，需要合并
+                    if isinstance(raw_results[search_type], dict) and isinstance(merged_raw_results[search_type], dict):
+                        for key, value in raw_results[search_type].items():
+                            if key not in merged_raw_results[search_type]:
+                                merged_raw_results[search_type][key] = value
+                            elif isinstance(value, list) and isinstance(merged_raw_results[search_type][key], list):
+                                merged_raw_results[search_type][key].extend(value)
+                    elif isinstance(raw_results[search_type], list):
+                        if isinstance(merged_raw_results[search_type], list):
+                            merged_raw_results[search_type].extend(raw_results[search_type])
+                        else:
+                            merged_raw_results[search_type] = raw_results[search_type]
+
+    # 创建合并后的结果
+    if merged_raw_results:
+        merged_result = {
+            "type": "search_result",
+            "title": f"合并检索结果 (共{len(search_results)}个查询)",
+            "query": " | ".join(all_queries),
+            "raw_results": merged_raw_results,
+            "index": 1,
+            "total": 1
+        }
+        return other_results + [merged_result]
+
+    return other_results
diff --git a/api/app/core/memory/agent/utils/model_tool.py b/api/app/core/memory/agent/utils/model_tool.py
deleted file mode 100644
index 969a2a91..00000000
--- a/api/app/core/memory/agent/utils/model_tool.py
+++ /dev/null
@@ -1,38 +0,0 @@
-
-
-# project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-# sys.path.insert(0, project_root)
-
-# load_dotenv()
-
-# async def llm_client_chat(messages: List[dict]) -> str:
-#     """使用 OpenAI 兼容接口进行对话，返回内容字符串。"""
-#     try:
-#         cfg = get_model_config(SELECTED_LLM_ID)
-#         rb_config = RedBearModelConfig(
-#         model_name=cfg["model_name"],
-#         provider=cfg["provider"],
-#         api_key=cfg["api_key"],
-#         base_url=cfg["base_url"],
-#     )
-#         client = OpenAIClient(model_config=rb_config, type_="chat")
-
-#     except Exception as e:
-#         logger.error(f"获取模型配置失败：{e}")
-#         err = f"获取模型配置失败：{str(e)}。请检查!!!"
-#         return err
-#     try:
-#         response = await client.chat(messages)
-#         print(f"model_tool's llm_client_chat response ======>:\n {response}")
-#         return _extract_content(response)
-#         # return _extract_content(result)
-#     except Exception as e:
-#         logger.error(f"LLM调用失败：{str(e)}。请检查 model_name、api_key、api_base 是否正确。")
-#     return f"LLM调用失败：{str(e)}。请检查 model_name、api_key、api_base 是否正确。"
-
-# async def main(image_url):
-#     await llm_client_chat(image_url)
-#
-# # 运行主函数
-# asyncio.run(main(['https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_male2.wav']))
-#
diff --git a/api/app/core/memory/agent/utils/multimodal.py b/api/app/core/memory/agent/utils/multimodal.py
deleted file mode 100644
index 0fc52634..00000000
--- a/api/app/core/memory/agent/utils/multimodal.py
+++ /dev/null
@@ -1,131 +0,0 @@
-"""
-Multimodal input processor for handling image and audio content.
-
-This module provides utilities for detecting and processing multimodal inputs
-(images and audio files) by converting them to text using appropriate models.
-"""
-
-import logging
-from typing import List
-
-from app.core.memory.agent.multimodal.speech_model import Vico_recognition
-from app.core.memory.agent.utils.llm_tools import picture_model_requests
-
-logger = logging.getLogger(__name__)
-
-
-class MultimodalProcessor:
-    """
-    Processor for handling multimodal inputs (images and audio).
-    
-    This class detects image and audio file paths in input content and converts
-    them to text using appropriate recognition models.
-    """
-    
-    # Supported file extensions
-    IMAGE_EXTENSIONS = ['.jpg', '.png']
-    AUDIO_EXTENSIONS = [
-        'aac', 'amr', 'avi', 'flac', 'flv', 'm4a', 'mkv', 'mov',
-        'mp3', 'mp4', 'mpeg', 'ogg', 'opus', 'wav', 'webm', 'wma', 'wmv'
-    ]
-    
-    def __init__(self):
-        """Initialize the multimodal processor."""
-        pass
-    
-    def is_image(self, content: str) -> bool:
-        """
-        Check if content is an image file path.
-        
-        Args:
-            content: Input string to check
-            
-        Returns:
-            True if content ends with a supported image extension
-            
-        Examples:
-            >>> processor = MultimodalProcessor()
-            >>> processor.is_image("photo.jpg")
-            True
-            >>> processor.is_image("document.pdf")
-            False
-        """
-        if not isinstance(content, str):
-            return False
-        
-        content_lower = content.lower()
-        return any(content_lower.endswith(ext) for ext in self.IMAGE_EXTENSIONS)
-    
-    def is_audio(self, content: str) -> bool:
-        """
-        Check if content is an audio file path.
-        
-        Args:
-            content: Input string to check
-            
-        Returns:
-            True if content ends with a supported audio extension
-            
-        Examples:
-            >>> processor = MultimodalProcessor()
-            >>> processor.is_audio("recording.mp3")
-            True
-            >>> processor.is_audio("video.mp4")
-            True
-            >>> processor.is_audio("document.txt")
-            False
-        """
-        if not isinstance(content, str):
-            return False
-        
-        content_lower = content.lower()
-        return any(content_lower.endswith(f'.{ext}') for ext in self.AUDIO_EXTENSIONS)
-    
-    async def process_input(self, content: str) -> str:
-        """
-        Process input content, converting images/audio to text if needed.
-        
-        This method detects if the input is an image or audio file and converts
-        it to text using the appropriate recognition model. If processing fails
-        or the content is not multimodal, it returns the original content.
-        
-        Args:
-            content: Input string (may be file path or regular text)
-            
-        Returns:
-            Text content (original or converted from image/audio)
-            
-        Examples:
-            >>> processor = MultimodalProcessor()
-            >>> await processor.process_input("photo.jpg")
-            "Recognized text from image..."
-            
-            >>> await processor.process_input("Hello world")
-            "Hello world"
-        """
-        if not isinstance(content, str):
-            logger.warning(f"[MultimodalProcessor] Content is not a string: {type(content)}")
-            return str(content)
-        
-        try:
-            # Check for image input
-            if self.is_image(content):
-                logger.info(f"[MultimodalProcessor] Detected image input: {content}")
-                result = await picture_model_requests(content)
-                logger.info(f"[MultimodalProcessor] Image recognition result: {result[:100]}...")
-                return result
-            
-            # Check for audio input
-            if self.is_audio(content):
-                logger.info(f"[MultimodalProcessor] Detected audio input: {content}")
-                result = await Vico_recognition([content]).run()
-                logger.info(f"[MultimodalProcessor] Audio recognition result: {result[:100]}...")
-                return result
-        
-        except Exception as e:
-            logger.error(f"[MultimodalProcessor] Error processing multimodal input: {e}", exc_info=True)
-            logger.info("[MultimodalProcessor] Falling back to original content")
-            return content
-        
-        # Return original content if not multimodal
-        return content
diff --git a/api/app/core/memory/agent/utils/performance_monitor.py b/api/app/core/memory/agent/utils/performance_monitor.py
new file mode 100644
index 00000000..d2d9fdfa
--- /dev/null
+++ b/api/app/core/memory/agent/utils/performance_monitor.py
@@ -0,0 +1,56 @@
+
+import time
+import json
+from collections import defaultdict
+from typing import Dict, List
+from app.core.logging_config import get_agent_logger
+
+logger = get_agent_logger(__name__)
+
+class ProblemExtensionMonitor:
+    """Problem_Extension性能监控器"""
+    
+    def __init__(self):
+        self.metrics = defaultdict(list)
+        self.slow_queries = []
+        self.error_count = 0
+        
+    def record_execution(self, duration: float, question_count: int, success: bool):
+        """记录执行指标"""
+        self.metrics['durations'].append(duration)
+        self.metrics['question_counts'].append(question_count)
+        
+        if not success:
+            self.error_count += 1
+            
+        # 记录慢查询（超过10秒）
+        if duration > 10.0:
+            self.slow_queries.append({
+                'duration': duration,
+                'question_count': question_count,
+                'timestamp': time.time()
+            })
+            
+    def get_stats(self) -> Dict:
+        """获取统计信息"""
+        durations = self.metrics['durations']
+        if not durations:
+            return {"message": "暂无数据"}
+            
+        return {
+            "total_executions": len(durations),
+            "avg_duration": sum(durations) / len(durations),
+            "max_duration": max(durations),
+            "min_duration": min(durations),
+            "slow_queries_count": len(self.slow_queries),
+            "error_rate": self.error_count / len(durations) if durations else 0,
+            "recent_slow_queries": self.slow_queries[-5:]  # 最近5个慢查询
+        }
+        
+    def log_stats(self):
+        """记录统计信息到日志"""
+        stats = self.get_stats()
+        logger.info(f"Problem_Extension性能统计: {json.dumps(stats, indent=2)}")
+
+# 全局监控器实例
+performance_monitor = ProblemExtensionMonitor()
diff --git a/api/app/core/memory/agent/utils/prompt/Problem_Extension_prompt_simplified.jinja2 b/api/app/core/memory/agent/utils/prompt/Problem_Extension_prompt_simplified.jinja2
new file mode 100644
index 00000000..a0e21fbd
--- /dev/null
+++ b/api/app/core/memory/agent/utils/prompt/Problem_Extension_prompt_simplified.jinja2
@@ -0,0 +1,81 @@
+
+你是一个高效的问题拆分助手，任务是根据用户提供的原始问题和问题类型，生成可操作的扩展问题，用于精确回答原问题。请严格遵循以下规则：
+
+角色：
+- 你是“问题拆分专家”，专注于逻辑、信息完整性和可操作性。
+- 你能够结合【历史信息】、【上下文】、【背景知识】进行分析，以保持问题拆分的连贯性和相关性。
+- 如果历史信息或上下文与当前问题无关，可忽略。
+
+---
+
+### 历史信息参考
+在生成扩展问题时，你可以参考以下历史数据（如果提供）：
+- 历史对话或任务的主题；
+- 历史中出现的关键实体（时间、人物、地点、研究主题等）；
+- 历史中已解答的问题（避免重复）；
+- 历史推理链（保持逻辑一致性）。
+
+> 如果没有提供历史信息，则仅根据当前输入问题进行分析。
+输入历史信息内容:{{history}}
+
+## User Input
+{% if questions is string %}
+{{ questions }}
+{% else %}
+{% for question in questions %}
+- {{ question }}
+{% endfor %}
+{% endif %}
+
+需求：
+- 如果问题是单跳问题（单步可答），直接保留原问题提取重要提问部分作为拆分/扩展问题。
+- 如果问题是多跳问题（需多个信息点才能回答），对问题进行扩展拆分。
+- 扩展问题必须完整覆盖原问题的所有关键要素，包括时间、主体、动作、目标等，不得遗漏。
+- 扩展问题不得冗余：避免重复询问相同信息或过度拆分同一主题。
+- 扩展问题必须高度相关：每个子问题直接服务于原问题，不引入未提及的新概念、人物或细节。
+- 扩展问题必须可操作：每个子问题能在有限资源下独立解答。
+- 子问题数量不超过4个。
+- 拆分问题的时候可以考虑输入的历史内容，以保持逻辑连贯。
+    比如：输入历史信息内容:[{'Query': '4月27日，我和你推荐过一本书，书名是什么？', 'ANswer': '张曼玉推荐了《小王子》'}]
+    拆分问题：4月27日，我和你推荐过一本书，书名是什么？，可以拆分为：4月27日，张曼玉推荐过一本书，书名是什么？
+
+
+
+输出要求：
+- 仅输出 JSON 数组，不要包含任何解释或代码块。
+- 每个元素包含：
+  - `original_question`: 原始问题
+  - `extended_question`: 扩展后的问题
+  - `type`: 类型（事实检索/澄清/定义/比较/行动建议）
+  - `reason`: 生成该扩展问题的简短理由
+- 使用标准 ASCII 双引号，无换行；确保字符串正确关闭并以逗号分隔。
+
+示例：
+输入：
+[
+  "问题：今年诺贝尔物理学奖的获奖者是谁，他们因为什么贡献获奖？；问题类型：多跳",
+]
+
+输出：
+[
+  {
+    "original_question": "今年诺贝尔物理学奖的获奖者是谁，他们因为什么贡献获奖？",
+    "extended_question": "今年诺贝尔物理学奖的获奖者有哪些人？",
+    "type": "多跳",
+    "reason": "输出原问题的关键要素"
+  },
+  {
+    "original_question": "今年诺贝尔物理学奖的获奖者是谁，他们因为什么贡献获奖？",
+    "extended_question": "今年诺贝尔物理学奖的获奖者是因哪些具体贡献获奖的？",
+    "type": "多跳",
+    "reason": "输出原问题的关键要素"
+  }
+]
+**Output format**
+**CRITICAL JSON FORMATTING REQUIREMENTS:**
+1. Use only standard ASCII double quotes (") for JSON structure - never use Chinese quotation marks ("") or other Unicode quotes
+2. If the extracted statement text contains quotation marks, escape them properly using backslashes (\")
+3. Ensure all JSON strings are properly closed and comma-separated
+4. Do not include line breaks within JSON string values
+
+The output language should always be the same as the input language.{{ json_schema }}
diff --git a/api/app/core/memory/agent/utils/prompt/Retrieve_Summary_prompt.jinja2 b/api/app/core/memory/agent/utils/prompt/Retrieve_Summary_prompt.jinja2
index 1fa71df3..5fbe8574 100644
--- a/api/app/core/memory/agent/utils/prompt/Retrieve_Summary_prompt.jinja2
+++ b/api/app/core/memory/agent/utils/prompt/Retrieve_Summary_prompt.jinja2
@@ -1,13 +1,10 @@
 # 角色
 你是一个专业的问答助手，擅长基于检索信息和历史对话回答用户问题。
-
 # 任务
 根据提供的上下文信息回答用户的问题。
-
 # 输入信息
 - 历史对话：{{history}}
 - 检索信息：{{retrieve_info}}
-
 ## User Query
 {{query}}
 
diff --git a/api/app/core/memory/agent/utils/prompt/split_verify_prompt.jinja2 b/api/app/core/memory/agent/utils/prompt/split_verify_prompt.jinja2
index f4d4665c..d6ad8cab 100644
--- a/api/app/core/memory/agent/utils/prompt/split_verify_prompt.jinja2
+++ b/api/app/core/memory/agent/utils/prompt/split_verify_prompt.jinja2
@@ -9,8 +9,8 @@
 3. 判断Answer_Small和Query_Small之间分析出来的关系状态
 4. 如果是True保留，否则不要相对应的问题和回答
 5. 输出，需要严格按照模版
-输入：{{history}}
-历史消息：{"history":{{sentence}}}
+输入：{{sentence}}
+历史消息：{"history":{{history}}}
 ### 第一步 获取用户的输入
 获取用户的输入提取对应的Query_Small和Answer_Small
 ### 第二步 分析验证
diff --git a/api/app/core/memory/agent/utils/session_tools.py b/api/app/core/memory/agent/utils/session_tools.py
new file mode 100644
index 00000000..b2d4f0ff
--- /dev/null
+++ b/api/app/core/memory/agent/utils/session_tools.py
@@ -0,0 +1,169 @@
+"""
+Session Service for managing user sessions and conversation history.
+
+This service provides clean Redis interactions with error handling and
+session management utilities.
+"""
+from typing import List, Optional
+
+from app.core.logging_config import get_agent_logger
+from app.core.memory.agent.utils.redis_tool import RedisSessionStore
+
+
+logger = get_agent_logger(__name__)
+
+
+class SessionService:
+    """Service for managing user sessions and conversation history."""
+    
+    def __init__(self, store: RedisSessionStore):
+        """
+        Initialize the session service.
+        
+        Args:
+            store: Redis session store instance
+        """
+        self.store = store
+        logger.info("SessionService initialized")
+    
+    def resolve_user_id(self, session_string: str) -> str:
+        """
+        Extract user ID from session string.
+        
+        Handles formats like:
+        - 'call_id_user123' -> 'user123'
+        - 'prefix_id_user456_suffix' -> 'user456_suffix'
+        
+        Args:
+            session_string: Session identifier string
+            
+        Returns:
+            Extracted user ID
+        """
+        try:
+            # Split by '_id_' and take everything after it
+            parts = session_string.split('_id_')
+            if len(parts) > 1:
+                return parts[1]
+            
+            # Fallback: return original string
+            return session_string
+            
+        except Exception as e:
+            logger.warning(
+                f"Failed to parse user ID from session string '{session_string}': {e}"
+            )
+            return session_string
+    
+    async def get_history(
+        self,
+        user_id: str,
+        apply_id: str,
+        group_id: str
+    ) -> List[dict]:
+        """
+        Retrieve conversation history from Redis.
+        
+        Args:
+            user_id: User identifier
+            apply_id: Application identifier
+            group_id: Group identifier
+            
+        Returns:
+            List of conversation history items with Query and Answer keys
+            Returns empty list if no history found or on error
+        """
+        try:
+            history = self.store.find_user_apply_group(user_id, apply_id, group_id)
+            
+            # Validate history structure
+            if not isinstance(history, list):
+                logger.warning(
+                    f"Invalid history format for user {user_id}, "
+                    f"apply {apply_id}, group {group_id}: expected list, got {type(history)}"
+                )
+                return []
+            
+            return history
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to retrieve history for user {user_id}, "
+                f"apply {apply_id}, group {group_id}: {e}",
+                exc_info=True
+            )
+            # Return empty list on error to allow execution to continue
+            return []
+    
+    async def save_session(
+        self,
+        user_id: str,
+        query: str,
+        apply_id: str,
+        group_id: str,
+        ai_response: str
+    ) -> Optional[str]:
+        """
+        Save conversation turn to Redis.
+        
+        Args:
+            user_id: User identifier
+            query: User query/message
+            apply_id: Application identifier
+            group_id: Group identifier
+            ai_response: AI response/answer
+            
+        Returns:
+            Session ID if successful, None on error
+        """
+        try:
+            # Validate required fields
+            if not user_id:
+                logger.warning("Cannot save session: user_id is empty")
+                return None
+            
+            if not query:
+                logger.warning("Cannot save session: query is empty")
+                return None
+            
+            # Save session
+            session_id = self.store.save_session(
+                userid=user_id,
+                messages=query,
+                apply_id=apply_id,
+                group_id=group_id,
+                aimessages=ai_response
+            )
+            
+            logger.info(f"Session saved successfully: {session_id}")
+            return session_id
+            
+        except Exception as e:
+            logger.error(
+                f"Failed to save session for user {user_id}: {e}",
+                exc_info=True
+            )
+            return None
+    
+    async def cleanup_duplicates(self) -> int:
+        """
+        Remove duplicate session entries.
+        
+        Duplicates are identified by matching:
+        - sessionid
+        - user_id (id field)
+        - group_id
+        - messages
+        - aimessages
+        
+        Returns:
+            Number of duplicate sessions deleted
+        """
+        try:
+            deleted_count = self.store.delete_duplicate_sessions()
+            logger.info(f"Cleaned up {deleted_count} duplicate sessions")
+            return deleted_count
+            
+        except Exception as e:
+            logger.error(f"Failed to cleanup duplicate sessions: {e}", exc_info=True)
+            return 0
diff --git a/api/app/core/memory/agent/utils/template_tools.py b/api/app/core/memory/agent/utils/template_tools.py
new file mode 100644
index 00000000..854c5383
--- /dev/null
+++ b/api/app/core/memory/agent/utils/template_tools.py
@@ -0,0 +1,117 @@
+"""
+Template Service for loading and rendering Jinja2 templates.
+
+This service provides centralized template management with caching and error handling.
+"""
+# 标准库
+import os
+from functools import lru_cache
+
+from jinja2 import Environment, FileSystemLoader, Template, TemplateNotFound
+
+from app.core.logging_config import get_agent_logger, log_prompt_rendering
+
+
+logger = get_agent_logger(__name__)
+
+
+class TemplateRenderError(Exception):
+    """Exception raised when template rendering fails."""
+    
+    def __init__(self, template_name: str, error: Exception, variables: dict):
+        self.template_name = template_name
+        self.error = error
+        self.variables = variables
+        super().__init__(
+            f"Failed to render template '{template_name}': {str(error)}"
+        )
+
+
+class TemplateService:
+    """Service for loading and rendering Jinja2 templates with caching."""
+    
+    def __init__(self, template_root: str):
+        """
+        Initialize the template service.
+        
+        Args:
+            template_root: Root directory containing template files
+        """
+        self.template_root = template_root
+        self.env = Environment(
+            loader=FileSystemLoader(template_root),
+            autoescape=False  # Disable autoescape for prompt templates
+        )
+        logger.info(f"TemplateService initialized with root: {template_root}")
+    
+    @lru_cache(maxsize=128)
+    def _load_template(self, template_name: str) -> Template:
+        """
+        Load a template from disk with caching.
+        
+        Args:
+            template_name: Relative path to template file
+            
+        Returns:
+            Loaded Jinja2 Template object
+            
+        Raises:
+            TemplateNotFound: If template file doesn't exist
+        """
+        try:
+            return self.env.get_template(template_name)
+        except TemplateNotFound as e:
+            expected_path = os.path.join(self.template_root, template_name)
+            logger.error(
+                f"Template not found: {template_name}. "
+                f"Expected path: {expected_path}"
+            )
+            raise
+    
+    async def render_template(
+        self,
+        template_name: str,
+        operation_name: str,
+        **variables
+    ) -> str:
+        """
+        Load and render a Jinja2 template.
+        
+        Args:
+            template_name: Relative path to template file
+            operation_name: Name for logging (e.g., "split_the_problem")
+            **variables: Template variables to render
+            
+        Returns:
+            Rendered template string
+            
+        Raises:
+            TemplateRenderError: If template loading or rendering fails
+        """
+        try:
+            # Load template (cached)
+            template = self._load_template(template_name)
+            
+            # Render template
+            rendered = template.render(**variables)
+            
+            # Log rendered prompt
+            log_prompt_rendering(operation_name, rendered)
+            
+            return rendered
+            
+        except TemplateNotFound as e:
+            logger.error(
+                f"Template rendering failed for {operation_name} "
+                f"({template_name}): Template not found",
+                exc_info=True
+            )
+            raise TemplateRenderError(template_name, e, variables)
+            
+        except Exception as e:
+            logger.error(
+                f"Template rendering failed for {operation_name} "
+                f"({template_name}): {e}",
+                exc_info=True
+            )
+            raise TemplateRenderError(template_name, e, variables)
diff --git a/api/app/core/memory/agent/utils/type_classifier.py b/api/app/core/memory/agent/utils/type_classifier.py
index 3e5358bd..f1df6f04 100644
--- a/api/app/core/memory/agent/utils/type_classifier.py
+++ b/api/app/core/memory/agent/utils/type_classifier.py
@@ -1,10 +1,9 @@
 """
 Type classification utility for distinguishing read/write operations.
 """
-from app.core.config import settings
 from app.core.logging_config import get_agent_logger, log_prompt_rendering
 from app.core.memory.agent.utils.llm_tools import PROJECT_ROOT_
-from app.core.memory.agent.utils.messages_tool import read_template_file
+from app.core.memory.agent.utils.messages_tools import read_template_file
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.db import get_db_context
 from jinja2 import Template
diff --git a/api/app/core/memory/agent/utils/write_to_database.py b/api/app/core/memory/agent/utils/write_to_database.py
deleted file mode 100644
index bd78fe9d..00000000
--- a/api/app/core/memory/agent/utils/write_to_database.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import os
-import uuid
-from datetime import datetime
-from typing import Any
-from sqlalchemy.orm import Session
-import logging
-import json
-
-from app.db import get_db
-from app.models.retrieval_info import RetrievalInfo
-
-logger = logging.getLogger(__name__)
-
-async def write_to_database(host_id: uuid.UUID, data: Any) -> str:
-    """
-    将数据写入数据库
-    :param host_id: 宿主 ID
-    :param data: 要写入的数据
-    :return: 写入数据库的结果
-    """
-    # 从数据库会话中获取会话
-    db: Session = next(get_db())
-    try:
-        if isinstance(data, (dict, list)):
-            serialized = json.dumps(data, ensure_ascii=False)
-        elif isinstance(data, str):
-            serialized = data
-        else:
-            serialized = str(data)
-
-        new_retrieval_info = RetrievalInfo(
-            # host_id=host_id,
-            host_id=uuid.UUID("2f6ff1eb-50c7-4765-8e89-e4566be19122"),
-            retrieve_info=serialized,
-            created_at=datetime.now()
-        )
-        db.add(new_retrieval_info)
-        db.commit()
-        logger.info(f"success to write data to database, host_id: {host_id}, retrieve_info: {serialized}")
-        return "success to write data to database"
-    except Exception as e:
-        db.rollback()
-        logger.error(f"failed to write data to database, host_id: {host_id}, retrieve_info: {data}, error: {e}")
-        raise e
-    finally:
-        try:
-            db.close()
-        except Exception:
-            pass
diff --git a/api/app/core/memory/agent/utils/write_tools.py b/api/app/core/memory/agent/utils/write_tools.py
index f09b35e8..53c941ad 100644
--- a/api/app/core/memory/agent/utils/write_tools.py
+++ b/api/app/core/memory/agent/utils/write_tools.py
@@ -7,14 +7,12 @@ pipeline. Only MemoryConfig is needed - clients are constructed internally.
 import time
 from datetime import datetime
 
+from dotenv import load_dotenv
+
 from app.core.logging_config import get_agent_logger
 from app.core.memory.agent.utils.get_dialogs import get_chunked_dialogs
-from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import (
-    ExtractionOrchestrator,
-)
-from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import (
-    memory_summary_generation,
-)
+from app.core.memory.storage_services.extraction_engine.extraction_orchestrator import ExtractionOrchestrator
+from app.core.memory.storage_services.extraction_engine.knowledge_extraction.memory_summary import memory_summary_generation
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.core.memory.utils.log.logging_utils import log_time
 from app.db import get_db_context
@@ -23,7 +21,7 @@ from app.repositories.neo4j.add_nodes import add_memory_summary_nodes
 from app.repositories.neo4j.graph_saver import save_dialog_and_statements_to_neo4j
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_config_schema import MemoryConfig
-from dotenv import load_dotenv
+
 
 load_dotenv()
 
diff --git a/api/app/services/memory_agent_service.py b/api/app/services/memory_agent_service.py
index f0756764..c9230a26 100644
--- a/api/app/services/memory_agent_service.py
+++ b/api/app/services/memory_agent_service.py
@@ -9,30 +9,27 @@ import os
 import re
 import time
 import uuid
-
 from typing import Any, AsyncGenerator, Dict, List, Optional
-
 import redis
+from langchain_core.messages import HumanMessage
+
 from app.core.config import settings
 from app.core.logging_config import get_config_logger, get_logger
 from app.core.memory.agent.langgraph_graph.read_graph import make_read_graph
 from app.core.memory.agent.langgraph_graph.write_graph import make_write_graph
 from app.core.memory.agent.logger_file.log_streamer import LogStreamer
-from app.core.memory.agent.utils.mcp_tools import get_mcp_server_config
+from app.core.memory.agent.utils.messages_tools import merge_multiple_search_results, reorder_output_results
 from app.core.memory.agent.utils.type_classifier import status_typle
 from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags
 from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
 from app.db import get_db_context
 from app.models.knowledge_model import Knowledge, KnowledgeType
-from app.repositories.memory_short_repository import ShortTermMemoryRepository
 from app.repositories.neo4j.neo4j_connector import Neo4jConnector
 from app.schemas.memory_config_schema import ConfigurationError
 from app.services.memory_config_service import MemoryConfigService
 from app.services.memory_konwledges_server import (
     write_rag,
 )
-from langchain_mcp_adapters.client import MultiServerMCPClient
-from langchain_mcp_adapters.tools import load_mcp_tools
 from pydantic import BaseModel, Field
 from sqlalchemy import func
 from sqlalchemy.orm import Session
@@ -50,21 +47,17 @@ _neo4j_connector = Neo4jConnector()
 
 class MemoryAgentService:
     """Service for memory agent operations"""
-    
 
-
-    def writer_messages_deal(self,messages,start_time,group_id,config_id,message):
-        messages = str(messages).replace("'", '"').replace('\\n', '').replace('\n', '').replace('\\', '')
-        countext = re.findall(r'"status": "(.*?)",', messages)[0]
+    def writer_messages_deal(self, messages, start_time, group_id, config_id, message, context):
         duration = time.time() - start_time
 
-        if countext == 'success':
+        if str(messages) == 'success':
             logger.info(f"Write operation successful for group {group_id} with config_id {config_id}")
             # 记录成功的操作
             if audit_logger:
                 audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=True,
                                            duration=duration, details={"message_length": len(message)})
-            return countext
+            return context
         else:
             logger.warning(f"Write operation failed for group {group_id}")
 
@@ -80,9 +73,9 @@ class MemoryAgentService:
                 )
 
             raise ValueError(f"写入失败: {messages}")
-    
 
-    
+
+
     def extract_tool_call_info(self, event: Dict) -> bool:
         """Extract tool call information from event"""
         last_message = event["messages"][-1]
@@ -119,15 +112,15 @@ class MemoryAgentService:
             return True
 
         return False
-    
+
     async def get_health_status(self) -> Dict:
         """
         Get latest health status from Redis cache
-        
+
         Returns health status information written by Celery periodic task
         """
         logger.info("Checking health status")
-        
+
         client = redis.Redis(
             host=settings.REDIS_HOST,
             port=settings.REDIS_PORT,
@@ -135,34 +128,51 @@ class MemoryAgentService:
             password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None
         )
         payload = client.hgetall("memsci:health:read_service") or {}
-        
+
         if payload:
             # decode bytes to str
             decoded = {k.decode("utf-8"): v.decode("utf-8") for k, v in payload.items()}
             status = decoded.get("status", "unknown")
         else:
             status = "unknown"
-        
+
+        # Add database connection pool status
+        try:
+            from app.db import get_pool_status
+            pool_status = get_pool_status()
+            logger.info(f"Database pool status: {pool_status}")
+
+            # Check if pool usage is too high
+            if pool_status.get("usage_percent", 0) > 80:
+                logger.warning(f"High database pool usage: {pool_status['usage_percent']}%")
+                status = "warning"
+
+        except Exception as e:
+            logger.error(f"Failed to get pool status: {e}")
+            pool_status = {"error": str(e)}
+
         logger.info(f"Health status: {status}")
-        return {"status": status}
+        return {
+            "status": status,
+            "database_pool": pool_status
+        }
 
     def get_log_content(self) -> str:
         """
         Read and return agent service log file content
-        
-        Returns cleaned log content using the same cleaning logic as transmission mode        
+
+        Returns cleaned log content using the same cleaning logic as transmission mode
 
         Returns cleaned log content using the same cleaning logic as transmission mode
         """
         logger.info("Reading log file")
 
-        # Use project root directory for logs
-        # Get the project root (redbear-mem directory)
+
         current_file = os.path.abspath(__file__)  # app/services/memory_agent_service.py
         app_dir = os.path.dirname(os.path.dirname(current_file))  # app directory
         project_root = os.path.dirname(app_dir)  # redbear-mem directory
         log_path = os.path.join(project_root, "logs", "agent_service.log")
-        
+
         summer = ''
 
         with open(log_path, "r", encoding="utf-8") as infile:
@@ -176,83 +186,83 @@ class MemoryAgentService:
 
         logger.info(f"Log content retrieved, size: {len(summer)} bytes")
         return summer
-    
+
     async def stream_log_content(self) -> AsyncGenerator[str, None]:
         """
         Stream log content in real-time using Server-Sent Events (SSE)
-        
+
         This method establishes a streaming connection and transmits log entries
         as they are written to the log file. It uses the LogStreamer to watch
         the file and yields SSE-formatted messages.
-        
+
         Yields:
             SSE-formatted strings with the following event types:
             - log: Contains log content and timestamp
             - keepalive: Periodic keepalive messages to maintain connection
             - error: Error information if streaming fails
             - done: Indicates streaming has completed
-            
+
         Raises:
             FileNotFoundError: If log file doesn't exist at stream start
             Exception: For other unexpected errors during streaming
         """
         logger.info("Starting log content streaming")
-        
+
         # Get log file path - use project root directory
         current_file = os.path.abspath(__file__)  # app/services/memory_agent_service.py
         app_dir = os.path.dirname(os.path.dirname(current_file))  # app directory
         project_root = os.path.dirname(app_dir)  # redbear-mem directory
         log_path = os.path.join(project_root, "logs", "agent_service.log")
-        
+
         # Check if file exists before starting stream
         if not os.path.exists(log_path):
             logger.error(f"Log file not found: {log_path}")
             # Send error event in SSE format
             yield f"event: error\ndata: {json.dumps({'code': 4006, 'message': '日志文件不存在', 'error': f'File not found: {log_path}'})}\n\n"
             return
-        
+
         streamer = None
         try:
             # Initialize LogStreamer with keepalive interval from settings (default 300 seconds)
             keepalive_interval = getattr(settings, 'LOG_STREAM_KEEPALIVE_INTERVAL', 300)
             streamer = LogStreamer(log_path, keepalive_interval=keepalive_interval)
-            
+
             logger.info(f"LogStreamer initialized for {log_path}")
-            
+
             # Stream log content using read_existing_and_stream to get all existing content first
             async for message in streamer.read_existing_and_stream():
                 event_type = message.get("event")
                 data = message.get("data")
-                
+
                 # Format as SSE message
                 # SSE format: "event: <type>\ndata: <json_data>\n\n"
                 sse_message = f"event: {event_type}\ndata: {json.dumps(data)}\n\n"
-                
+
                 logger.debug(f"Streaming event: {event_type}")
                 yield sse_message
-                
+
                 # If error or done event, stop streaming
                 if event_type in ["error", "done"]:
                     logger.info(f"Stream ended with event: {event_type}")
                     break
-                    
+
         except FileNotFoundError as e:
             logger.error(f"Log file not found during streaming: {e}")
             yield f"event: error\ndata: {json.dumps({'code': 4006, 'message': '日志文件在流式传输期间变得不可用', 'error': str(e)})}\n\n"
-            
+
         except Exception as e:
             logger.error(f"Unexpected error during log streaming: {e}", exc_info=True)
             yield f"event: error\ndata: {json.dumps({'code': 8001, 'message': '流式传输期间发生错误', 'error': str(e)})}\n\n"
-            
+
         finally:
             # Resource cleanup
             logger.info("Log streaming completed, cleaning up resources")
             # LogStreamer uses context manager for file handling, so cleanup is automatic
-    
+
     async def write_memory(self, group_id: str, message: str, config_id: Optional[str], db: Session, storage_type: str, user_rag_memory_id: str) -> str:
         """
         Process write operation with config_id
-        
+
         Args:
             group_id: Group identifier (also used as end_user_id)
             message: Message to write
@@ -260,10 +270,10 @@ class MemoryAgentService:
             db: SQLAlchemy database session
             storage_type: Storage type (neo4j or rag)
             user_rag_memory_id: User RAG memory ID
-            
+
         Returns:
             Write operation result status
-            
+
         Raises:
             ValueError: If config loading fails or write operation fails
         """
@@ -279,7 +289,7 @@ class MemoryAgentService:
                     raise  # Re-raise our specific error
                 logger.error(f"Failed to get connected config for end_user {group_id}: {e}")
                 raise ValueError(f"Unable to determine memory configuration for end_user {group_id}: {e}")
-        
+
         import time
         start_time = time.time()
 
@@ -294,61 +304,49 @@ class MemoryAgentService:
         except ConfigurationError as e:
             error_msg = f"Failed to load configuration for config_id: {config_id}: {e}"
             logger.error(error_msg)
-            
+
             # Log failed operation
             if audit_logger:
                 duration = time.time() - start_time
                 audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg)
-            
+
             raise ValueError(error_msg)
-        mcp_config = get_mcp_server_config()
-        client = MultiServerMCPClient(mcp_config)
-
-        if storage_type == "rag":
-            result = await write_rag(group_id, message, user_rag_memory_id)
-            return result
-        else:
-            async with client.session("data_flow") as session:
-                logger.debug("Connected to MCP Server: data_flow")
-                tools = await load_mcp_tools(session)
-                workflow_errors = []  # Track errors from workflow
-
-                # Pass memory_config to the graph workflow
-                async with make_write_graph(group_id, tools, group_id, group_id, memory_config=memory_config) as graph:
-                    logger.debug("Write graph created successfully")
 
+        try:
+            if storage_type == "rag":
+                result = await write_rag(group_id, message, user_rag_memory_id)
+                return result
+            else:
+                async with make_write_graph() as graph:
                     config = {"configurable": {"thread_id": group_id}}
+                    # 初始状态 - 包含所有必要字段
+                    initial_state = {"messages": [HumanMessage(content=message)], "group_id": group_id,
+                                     "memory_config": memory_config}
 
-                    async for event in graph.astream(
-                            {"messages": message, "memory_config": memory_config, "errors": []},
-                            stream_mode="values",
+                    # 获取节点更新信息
+                    async for update_event in graph.astream(
+                            initial_state,
+                            stream_mode="updates",
                             config=config
                     ):
-                        messages = event.get('messages')
-                        # Capture any errors from the state
-                        if event.get('errors'):
-                            workflow_errors.extend(event.get('errors', []))
-            
-            # Check for workflow errors
-            if workflow_errors:
-                error_details = "; ".join([f"{e['tool']}: {e['error']}" for e in workflow_errors])
-                logger.error(f"Write workflow failed with errors: {error_details}")
-                
-                if audit_logger:
-                    duration = time.time() - start_time
-                    audit_logger.log_operation(
-                        operation="WRITE",
-                        config_id=config_id,
-                        group_id=group_id,
-                        success=False,
-                        duration=duration,
-                        error=error_details
-                    )
-                
-                raise ValueError(f"Write workflow failed: {error_details}")
-            
-            return self.writer_messages_deal(messages, start_time, group_id, config_id, message)
-    
+                        for node_name, node_data in update_event.items():
+                            if 'save_neo4j' == node_name:
+                                massages = node_data
+                    massagesstatus = massages.get('write_result')['status']
+                    contents = massages.get('write_result')
+                    return self.writer_messages_deal(massagesstatus, start_time, group_id, config_id, message, contents)
+        except Exception as e:
+            # Ensure proper error handling and logging
+            error_msg = f"Write operation failed: {str(e)}"
+            logger.error(error_msg)
+            if audit_logger:
+                duration = time.time() - start_time
+                audit_logger.log_operation(operation="WRITE", config_id=config_id, group_id=group_id, success=False, duration=duration, error=error_msg)
+            raise ValueError(error_msg)
+
+
+
+
     async def read_memory(
         self,
         group_id: str,
@@ -362,12 +360,12 @@ class MemoryAgentService:
     ) -> Dict:
         """
         Process read operation with config_id
-        
+
         search_switch values:
         - "0": Requires verification
         - "1": No verification, direct split
         - "2": Direct answer based on context
-        
+
         Args:
             group_id: Group identifier (also used as end_user_id)
             message: User message
@@ -377,18 +375,17 @@ class MemoryAgentService:
             db: SQLAlchemy database session
             storage_type: Storage type (neo4j or rag)
             user_rag_memory_id: User RAG memory ID
-            
+
         Returns:
             Dict with 'answer' and 'intermediate_outputs' keys
-            
+
         Raises:
             ValueError: If config loading fails
         """
 
         import time
         start_time = time.time()
-        ori_message=message
-        end_user_id=group_id
+
         # Resolve config_id if None using end_user's connected config
         if config_id is None:
             try:
@@ -410,6 +407,7 @@ class MemoryAgentService:
         except ImportError:
             audit_logger = None
 
+
         try:
             config_service = MemoryConfigService(db)
             memory_config = config_service.load_memory_config(
@@ -440,326 +438,128 @@ class MemoryAgentService:
         logger.debug(f"Group ID:{group_id}, Message:{message}, History:{history}, Config ID:{config_id}")
 
         # Step 3: Initialize MCP client and execute read workflow
-        mcp_config = get_mcp_server_config()
-        client = MultiServerMCPClient(mcp_config)
-
-        async with client.session('data_flow') as session:
-            session_start = time.time()
-            logger.debug("Connected to MCP Server: data_flow")
-
-            tools_start = time.time()
-            tools = await load_mcp_tools(session)
-            tools_time = time.time() - tools_start
-            logger.info(f"[PERF] MCP tools loading took: {tools_time:.4f}s")
-
-            outputs = []
-            intermediate_outputs = []
-            seen_intermediates = set()  # Track seen intermediate outputs to avoid duplicates
-
-            # Pass memory_config to the graph workflow
-            graph_start = time.time()
-            async with make_read_graph(group_id, tools, search_switch, group_id, group_id, memory_config=memory_config, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id) as graph:
-                graph_init_time = time.time() - graph_start
-                logger.info(f"[PERF] Graph initialization took: {graph_init_time:.4f}s")
-
-                start = time.time()
+        try:
+            async with make_read_graph() as graph:
                 config = {"configurable": {"thread_id": group_id}}
-                workflow_errors = []  # Track errors from workflow
-
-                event_count = 0
-                async for event in graph.astream(
-                        {"messages": history, "memory_config": memory_config, "errors": []},
-                        stream_mode="values",
+                # 初始状态 - 包含所有必要字段
+                initial_state = {"messages": [HumanMessage(content=message)], "search_switch": search_switch,
+                                 "group_id": group_id
+                    , "storage_type": storage_type, "user_rag_memory_id": user_rag_memory_id,
+                                 "memory_config": memory_config}
+                # 获取节点更新信息
+                _intermediate_outputs = []
+                summary = ''
+                async for update_event in graph.astream(
+                        initial_state,
+                        stream_mode="updates",
                         config=config
                 ):
-                    event_count += 1
-                    event_start = time.time()
-                    messages = event.get('messages')
-                    # Capture any errors from the state
-                    if event.get('errors'):
-                        workflow_errors.extend(event.get('errors', []))
+                    for node_name, node_data in update_event.items():
+                        # if 'save_neo4j' == node_name:
+                        #     massages = node_data
+                        print(f"处理节点: {node_name}")
 
-                    for msg in messages:
-                        msg_content = msg.content
-                        msg_role = msg.__class__.__name__.lower().replace("message", "")
-                        outputs.append({
-                            "role": msg_role,
-                            "content": msg_content
-                        })
+                        # 处理不同Summary节点的返回结构
+                        if 'Summary' in node_name:
+                            if 'InputSummary' in node_data and 'summary_result' in node_data['InputSummary']:
+                                summary = node_data['InputSummary']['summary_result']
+                            elif 'RetrieveSummary' in node_data and 'summary_result' in node_data['RetrieveSummary']:
+                                summary = node_data['RetrieveSummary']['summary_result']
+                            elif 'summary' in node_data and 'summary_result' in node_data['summary']:
+                                summary = node_data['summary']['summary_result']
+                            elif 'SummaryFails' in node_data and 'summary_result' in node_data['SummaryFails']:
+                                summary = node_data['SummaryFails']['summary_result']
 
-                        # Extract intermediate outputs
-                        if hasattr(msg, 'content'):
-                            try:
-                                # Handle MCP content format: [{'type': 'text', 'text': '...'}]
-                                content_to_parse = msg_content
-                                if isinstance(msg_content, list):
-                                    for block in msg_content:
-                                        if isinstance(block, dict) and block.get('type') == 'text':
-                                            content_to_parse = block.get('text', '')
-                                            break
-                                    else:
-                                        continue  # No text block found
+                        spit_data = node_data.get('spit_data', {}).get('_intermediate', None)
+                        if spit_data and spit_data != [] and spit_data != {}:
+                            _intermediate_outputs.append(spit_data)
 
-                                # Try to parse content as JSON
-                                if isinstance(content_to_parse, str):
-                                    try:
-                                        parsed = json.loads(content_to_parse)
-                                        if isinstance(parsed, dict):
-                                            # Check for single intermediate output
-                                            if '_intermediate' in parsed:
-                                                intermediate_data = parsed['_intermediate']
-                                                output_key = self._create_intermediate_key(intermediate_data)
+                        # Problem_Extension 节点
+                        problem_extension = node_data.get('problem_extension', {}).get('_intermediate', None)
+                        if problem_extension and problem_extension != [] and problem_extension != {}:
+                            _intermediate_outputs.append(problem_extension)
 
-                                                if output_key not in seen_intermediates:
-                                                    seen_intermediates.add(output_key)
-                                                    intermediate_outputs.append(self._format_intermediate_output(intermediate_data))
+                        # Retrieve 节点
+                        retrieve_node = node_data.get('retrieve', {}).get('_intermediate_outputs', None)
+                        if retrieve_node and retrieve_node != [] and retrieve_node != {}:
+                            _intermediate_outputs.extend(retrieve_node)
 
-                                            # Check for multiple intermediate outputs (from Retrieve)
-                                            if '_intermediates' in parsed:
-                                                for intermediate_data in parsed['_intermediates']:
-                                                    output_key = self._create_intermediate_key(intermediate_data)
+                        # Verify 节点
+                        verify_n = node_data.get('verify', {}).get('_intermediate', None)
+                        if verify_n and verify_n != [] and verify_n != {}:
+                            _intermediate_outputs.append(verify_n)
 
-                                                    if output_key not in seen_intermediates:
-                                                        seen_intermediates.add(output_key)
-                                                        intermediate_outputs.append(self._format_intermediate_output(intermediate_data))
-                                    except (json.JSONDecodeError, ValueError):
-                                        pass
-                            except Exception as e:
-                                logger.debug(f"Failed to extract intermediate output: {e}")
+                        # Summary 节点
+                        summary_n = node_data.get('summary', {}).get('_intermediate', None)
+                        if summary_n and summary_n != [] and summary_n != {}:
+                            _intermediate_outputs.append(summary_n)
 
-                    event_time = time.time() - event_start
-                    logger.info(f"[PERF] Event {event_count} processing took: {event_time:.4f}s")
+                _intermediate_outputs = [item for item in _intermediate_outputs if item and item != [] and item != {}]
 
-        workflow_duration = time.time() - start
-        session_duration = time.time() - session_start
-        logger.info(f"[PERF] Read graph workflow completed in {workflow_duration}s")
-        logger.info(f"[PERF] Total session duration: {session_duration:.4f}s")
-        logger.info(f"[PERF] Total events processed: {event_count}")
-        # Extract final answer
-        final_answer = ""
-        for messages in outputs:
-            if messages['role'] == 'tool':
-                message = messages['content']
+                optimized_outputs = merge_multiple_search_results(_intermediate_outputs)
+                result = reorder_output_results(optimized_outputs)
 
-                # Handle MCP content format: [{'type': 'text', 'text': '...'}]
-                if isinstance(message, list):
-                    # Extract text from MCP content blocks
-                    for block in message:
-                        if isinstance(block, dict) and block.get('type') == 'text':
-                            message = block.get('text', '')
-                            break
-                    else:
-                        continue  # No text block found
-
-                try:
-                    parsed = json.loads(message) if isinstance(message, str) else message
-                    if isinstance(parsed, dict):
-                        if parsed.get('status') == 'success':
-                            summary_result = parsed.get('summary_result')
-                            if summary_result:
-                                final_answer = summary_result
-                except (json.JSONDecodeError, ValueError):
-                    pass
-
-        # 记录成功的操作
-        total_duration = time.time() - start_time
-
-        # Check for workflow errors
-        if workflow_errors:
-            error_details = "; ".join([f"{e['tool']}: {e['error']}" for e in workflow_errors])
-            logger.warning(f"Read workflow completed with errors: {error_details}")
+                # Log successful operation
+                if audit_logger:
+                    duration = time.time() - start_time
+                    audit_logger.log_operation(
+                        operation="READ",
+                        config_id=config_id,
+                        group_id=group_id,
+                        success=True,
+                        duration=duration
+                    )
 
+                return {
+                    "answer": summary,
+                    "intermediate_outputs": result
+                }
+        except Exception as e:
+            # Ensure proper error handling and logging
+            error_msg = f"Read operation failed: {str(e)}"
+            logger.error(error_msg)
             if audit_logger:
+                duration = time.time() - start_time
                 audit_logger.log_operation(
                     operation="READ",
                     config_id=config_id,
                     group_id=group_id,
                     success=False,
-                    duration=total_duration,
-                    error=error_details,
-                    details={
-                        "search_switch": search_switch,
-                        "history_length": len(history),
-                        "intermediate_outputs_count": len(intermediate_outputs),
-                        "has_answer": bool(final_answer),
-                        "errors": workflow_errors
-                    }
+                    duration=duration,
+                    error=error_msg
                 )
-
-            # Raise error if no answer was produced
-            if not final_answer:
-                raise ValueError(f"Read workflow failed: {error_details}")
-
-        if audit_logger and not workflow_errors:
-            audit_logger.log_operation(
-                operation="READ",
-                config_id=config_id,
-                group_id=group_id,
-                success=True,
-                duration=total_duration,
-                details={
-                    "search_switch": search_switch,
-                    "history_length": len(history),
-                    "intermediate_outputs_count": len(intermediate_outputs),
-                    "has_answer": bool(final_answer)
-                }
-            )
-        retrieved_content=[]
-        repo = ShortTermMemoryRepository(db)
-        if str(search_switch)!="2":
-            for intermediate in intermediate_outputs:
-                print(intermediate)
-                intermediate_type=intermediate['type']
-                if intermediate_type=="search_result":
-                    query=intermediate['query']
-                    raw_results=intermediate['raw_results']
-                    reranked_results=raw_results.get('reranked_results',[])
-                    try:
-                        statements=[statement['statement'] for statement in reranked_results.get('statements', [])]
-                    except Exception:
-                        statements=[]
-                    statements=list(set(statements))
-                    retrieved_content.append({query:statements})
-        if retrieved_content==[]:
-            retrieved_content=''
-        if   '信息不足，无法回答。' != str(final_answer) and str(search_switch).strip() != "2":#and retrieved_content!=[]
-            # 使用 upsert 方法
-            repo.upsert(
-                end_user_id=end_user_id,  # 确保这个变量在作用域内
-                messages=ori_message,
-                aimessages=final_answer,
-                retrieved_content=retrieved_content,
-                search_switch=str(search_switch)
-            )
-            print("写入成功")
+            raise ValueError(error_msg)
 
 
 
-        return {
-            "answer": final_answer,
-            "intermediate_outputs": intermediate_outputs
-        }
-
-    def _create_intermediate_key(self, output: Dict) -> str:
-        """
-        Create a unique key for an intermediate output to detect duplicates.
-        
-        Args:
-            output: Intermediate output dictionary
-            
-        Returns:
-            Unique string key for this output
-        """
-        output_type = output.get('type', 'unknown')
-        
-        if output_type == 'problem_split':
-            # Use type + original query as key
-            return f"split:{output.get('original_query', '')}"
-        elif output_type == 'problem_extension':
-            # Use type + original query as key
-            return f"extension:{output.get('original_query', '')}"
-        elif output_type == 'search_result':
-            # Use type + query + index as key
-            return f"search:{output.get('query', '')}:{output.get('index', 0)}"
-        elif output_type == 'retrieval_summary':
-            # Use type + query as key
-            return f"summary:{output.get('query', '')}"
-        elif output_type == 'verification':
-            # Use type + query as key
-            return f"verification:{output.get('query', '')}"
-        elif output_type == 'input_summary':
-            # Use type + query as key
-            return f"input_summary:{output.get('query', '')}"
-        else:
-            # Fallback: use JSON representation
-            import json
-            return json.dumps(output, sort_keys=True)
-    
-    def _format_intermediate_output(self, output: Dict) -> Dict:
-        """Format intermediate output for frontend display."""
-        output_type = output.get('type', 'unknown')
-        
-        if output_type == 'problem_split':
-            return {
-                'type': 'problem_split',
-                'title': '问题拆分',
-                'data': output.get('data', []),
-                'original_query': output.get('original_query', '')
-            }
-        elif output_type == 'problem_extension':
-            return {
-                'type': 'problem_extension',
-                'title': '问题扩展',
-                'data': output.get('data', {}),
-                'original_query': output.get('original_query', '')
-            }
-        elif output_type == 'search_result':
-            return {
-                'type': 'search_result',
-                'title': f'检索结果 ({output.get("index", 0)}/{output.get("total", 0)})',
-                'query': output.get('query', ''),
-                'raw_results': output.get('raw_results', ''),
-                'index': output.get('index', 0),
-                'total': output.get('total', 0)
-            }
-        elif output_type == 'retrieval_summary':
-            return {
-                'type': 'retrieval_summary',
-                'title': '检索总结',
-                'summary': output.get('summary', ''),
-                'query': output.get('query', ''),
-                'raw_results': output.get('raw_results'),
-
-            }
-        elif output_type == 'verification':
-            return {
-                'type': 'verification',
-                'title': '数据验证',
-                'result': output.get('result', 'unknown'),
-                'reason': output.get('reason', ''),
-                'query': output.get('query', ''),
-                'verified_count': output.get('verified_count', 0)
-            }
-        elif output_type == 'input_summary':
-            return {
-                'type': 'input_summary',
-                'title': '快速答案',
-                'summary': output.get('summary', ''),
-                'query': output.get('query', ''),
-                'raw_results': output.get('raw_results'),
-
-            }
-        else:
-            return output
-    
     async def classify_message_type(self, message: str, config_id: int, db: Session) -> Dict:
         """
         Determine the type of user message (read or write)
         Updated to eliminate global variables in favor of explicit parameters.
-        
+
         Args:
             message: User message to classify
             config_id: Configuration ID to load LLM model from database
             db: Database session
-            
+
         Returns:
             Type classification result
         """
         logger.info("Classifying message type")
-        
+
         # Load configuration to get LLM model ID
         config_service = MemoryConfigService(db)
         memory_config = config_service.load_memory_config(
             config_id=config_id,
             service_name="MemoryAgentService"
         )
-        
+
         status = await status_typle(message, memory_config.llm_model_id)
         logger.debug(f"Message type: {status}")
         return status
-    
+
     # ==================== 新增的三个接口方法 ====================
-    
+
     async def get_knowledge_type_stats(
         self,
         end_user_id: Optional[str] = None,
@@ -772,13 +572,13 @@ class MemoryAgentService:
         1. PostgreSQL 中的知识库类型：General, Web, Third-party, Folder（根据 workspace_id 过滤）
         2. Neo4j 中的 memory 类型（仅统计 Chunk 数量，根据 end_user_id/group_id 过滤）
         3. total: 所有类型的总和
-        
+
         参数：
         - end_user_id: 用户组ID（可选，未提供时 memory 统计为 0）
         - only_active: 是否仅统计有效记录
         - current_workspace_id: 当前工作空间ID（可选，未提供时知识库统计为 0）
         - db: 数据库会话
-        
+
         返回格式：
         {
             "General": count,
@@ -790,18 +590,18 @@ class MemoryAgentService:
         }
         """
         result = {}
-        
+
         # 1. 统计 PostgreSQL 中的知识库类型
         try:
             if db is None:
                 from app.db import get_db
                 db_gen = get_db()
                 db = next(db_gen)
-            
+
             # 初始化所有标准类型为 0
             for kb_type in KnowledgeType:
                 result[kb_type.value] = 0
-            
+
             # 如果提供了 workspace_id，则按 workspace_id 过滤
             if current_workspace_id:
                 # 构建查询条件
@@ -809,47 +609,48 @@ class MemoryAgentService:
                     Knowledge.type,
                     func.count(Knowledge.id).label('count')
                 ).filter(Knowledge.workspace_id == current_workspace_id)
-                
+
                 # 检查 Knowledge 模型是否有 status 字段
                 if only_active and hasattr(Knowledge, 'status'):
                     query = query.filter(Knowledge.status == 1)
-                
+
                 # 按类型分组
                 type_counts = query.group_by(Knowledge.type).all()
-                
+
                 # 只填充标准类型的统计值，忽略其他类型
                 valid_types = {kb_type.value for kb_type in KnowledgeType}
                 for type_name, count in type_counts:
                     if type_name in valid_types:
                         result[type_name] = count
-                        
+
                 logger.info(f"知识库类型统计成功 (workspace_id={current_workspace_id}): {result}")
             else:
                 # 没有提供 workspace_id，所有知识库类型返回 0
                 logger.info("未提供 workspace_id，知识库类型统计全部为 0")
-                    
+
         except Exception as e:
             logger.error(f"知识库类型统计失败: {e}")
             raise Exception(f"知识库类型统计失败: {e}")
-        
+
         # 2. 统计 Neo4j 中的 memory 总量（统计当前空间下所有宿主的 Chunk 总数）
         try:
             if current_workspace_id:
                 # 获取当前空间下的所有宿主
                 from app.repositories import app_repository, end_user_repository
                 from app.schemas.app_schema import App as AppSchema
-                
+                from app.schemas.end_user_schema import EndUser as EndUserSchema
+
                 # 查询应用并转换为 Pydantic 模型
                 apps_orm = app_repository.get_apps_by_workspace_id(db, current_workspace_id)
                 apps = [AppSchema.model_validate(h) for h in apps_orm]
                 app_ids = [app.id for app in apps]
-                
+
                 # 获取所有宿主
                 end_users = []
                 for app_id in app_ids:
                     end_user_orm_list = end_user_repository.get_end_users_by_app_id(db, app_id)
                     end_users.extend(h for h in end_user_orm_list)
-                
+
                 # 统计所有宿主的 Chunk 总数
                 total_chunks = 0
                 for end_user in end_users:
@@ -864,27 +665,27 @@ class MemoryAgentService:
                     chunk_count = neo4j_result[0]["Count"] if neo4j_result else 0
                     total_chunks += chunk_count
                     logger.debug(f"EndUser {end_user_id_str} Chunk数量: {chunk_count}")
-                
+
                 result["memory"] = total_chunks
                 logger.info(f"Neo4j memory统计成功: 总Chunk数={total_chunks}, 宿主数={len(end_users)}")
             else:
                 # 没有 workspace_id 时，返回 0
                 result["memory"] = 0
                 logger.info("未提供 workspace_id，memory 统计为 0")
-            
+
         except Exception as e:
             logger.error(f"Neo4j memory统计失败: {e}", exc_info=True)
             # 如果 Neo4j 查询失败，memory 设为 0
             result["memory"] = 0
-        
+
         # 3. 计算知识库类型总和（不包括 memory）
         result["total"] = (
-            result.get("General", 0) + 
-            result.get("Web", 0) + 
-            result.get("Third-party", 0) + 
+            result.get("General", 0) +
+            result.get("Web", 0) +
+            result.get("Third-party", 0) +
             result.get("Folder", 0)
         )
-        
+
         return result
 
 
@@ -895,11 +696,11 @@ class MemoryAgentService:
     ) -> List[Dict[str, Any]]:
         """
         获取指定用户的热门记忆标签
-        
+
         参数：
         - end_user_id: 用户ID（可选），对应Neo4j中的group_id字段
         - limit: 返回标签数量限制
-        
+
         返回格式：
         [
             {"name": "标签名", "frequency": 频次},
@@ -928,13 +729,13 @@ class MemoryAgentService:
         1. 用户名字（直接使用 end_user_name)
         2. 用户标签（从摘要中用LLM总结3个标签）
         3. 热门记忆标签（从hot_memory_tags获取前4个）
-        
+
         参数：
         - end_user_id: 用户ID（可选）
         - current_user_id: 当前登录用户的ID（保留参数）
         - llm_id: LLM模型ID（用于生成标签，可选，如果不提供则跳过标签生成）
         - db: 数据库会话（可选）
-        
+
         返回格式：
         {
             "name": "用户名",
@@ -947,13 +748,13 @@ class MemoryAgentService:
         }
         """
         result = {}
-        
+
         # 1. 根据 end_user_id 获取 end_user_name
         try:
             if end_user_id and db:
                 from app.repositories import end_user_repository
                 from app.schemas.end_user_schema import EndUser as EndUserSchema
-                
+
                 end_user_orm = end_user_repository.get_end_user_by_id(db, end_user_id)
                 if end_user_orm:
                     end_user = EndUserSchema.model_validate(end_user_orm)
@@ -965,14 +766,14 @@ class MemoryAgentService:
         except Exception as e:
             logger.error(f"Failed to get end_user_name: {e}")
             end_user_name = "默认用户"
-             
+
         result["name"] = end_user_name
         logger.debug(f"The end_user is: {end_user_name}")
-        
+
         # 2. 使用LLM从语句和实体中提取标签
         try:
             connector = Neo4jConnector()
-            
+
             # 查询该用户的语句
             query = (
                 "MATCH (s:Statement) "
@@ -982,7 +783,7 @@ class MemoryAgentService:
             )
             rows = await connector.execute_query(query, group_id=end_user_id)
             statements = [r.get("statement", "") for r in rows if r.get("statement")]
-            
+
             # 查询该用户的热门实体
             entity_query = (
                 "MATCH (e:ExtractedEntity) "
@@ -992,9 +793,9 @@ class MemoryAgentService:
             )
             entity_rows = await connector.execute_query(entity_query, group_id=end_user_id)
             entities = [f"{r['name']} ({r['frequency']})" for r in entity_rows]
-            
+
             await connector.close()
-            
+
             if not statements or not llm_id:
                 result["tags"] = []
                 if not llm_id and statements:
@@ -1003,16 +804,16 @@ class MemoryAgentService:
                 # 构建摘要文本
                 summary_text = f"用户语句样本：{' | '.join(statements[:20])}\n核心实体：{', '.join(entities)}"
                 logger.debug(f"User data found: {len(statements)} statements, {len(entities)} entities")
-                
+
                 # 使用LLM提取标签
                 with get_db_context() as db:
                     factory = MemoryClientFactory(db)
                     llm_client = factory.get_llm_client(llm_id)
-                
+
                 # 定义标签提取的结构
                 class UserTags(BaseModel):
                     tags: list[str] = Field(..., description="3个描述用户特征的标签，如：产品设计师、旅行爱好者、摄影发烧友")
-                
+
                 messages = [
                     {
                         "role": "system",
@@ -1023,20 +824,20 @@ class MemoryAgentService:
                         "content": f"请从以下用户信息中提取3个标签：\n\n{summary_text}"
                     }
                 ]
-                
+
                 user_tags = await llm_client.response_structured(
                     messages=messages,
                     response_model=UserTags
                 )
-                
+
                 result["tags"] = user_tags.tags
                 logger.debug(f"Extracted tags: {user_tags.tags}")
-            
+
         except Exception as e:
             # 如果提取失败，使用默认值
             logger.error(f"Failed to extract user tags: {e}")
             result["tags"] = []
-        
+
         try:
             # 3. 获取热门记忆标签（前4个）
             connector = Neo4jConnector()
@@ -1049,18 +850,18 @@ class MemoryAgentService:
                 "ORDER BY frequency DESC LIMIT 4"
             )
             hot_tag_rows = await connector.execute_query(
-                hot_tag_query, 
-                group_id=end_user_id, 
+                hot_tag_query,
+                group_id=end_user_id,
                 names_to_exclude=names_to_exclude
             )
             await connector.close()
-            
+
             result["hot_tags"] = [{"name": r["name"], "frequency": r["frequency"]} for r in hot_tag_rows]
             logger.debug(f"Hot tags found: {len(result['hot_tags'])} tags")
         except Exception as e:
             logger.error(f"Failed to get hot tags: {e}")
             result["hot_tags"] = []
-        
+
         return result
 
     async def stream_log_content(self) -> AsyncGenerator[str, None]:
@@ -1135,79 +936,40 @@ class MemoryAgentService:
             logger.info("Log streaming completed, cleaning up resources")
             # LogStreamer uses context manager for file handling, so cleanup is automatic
 
-# async def get_api_docs(self, file_path: Optional[str] = None) -> Dict[str, Any]:
-#     """
-#     Parse and return API documentation
-    
-#     Args:
-#         file_path: Optional path to API docs file. If None, uses default path.
-        
-#     Returns:
-#         Dict containing parsed API documentation or error information
-#     """
-#     try:
-#         target = file_path or get_default_docs_path()
-        
-#         if not os.path.isfile(target):
-#             return {
-#                 "success": False,
-#                 "msg": "API文档文件不存在",
-#                 "error_code": "DOC_NOT_FOUND",
-#                 "data": {"path": target}
-#             }
-        
-#         data = parse_api_docs(target)
-#         return {
-#             "success": True,
-#             "msg": "解析成功",
-#             "data": data
-#         }
-#     except Exception as e:
-#         logger.error(f"Failed to parse API docs: {e}")
-#         return {
-#             "success": False,
-#             "msg": "解析失败",
-#             "error_code": "DOC_PARSE_ERROR",
-#             "data": {"error": str(e)}
-#         }
-
-
 def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, Any]:
     """
     获取终端用户关联的记忆配置
-    
+
     通过以下流程获取配置：
     1. 根据 end_user_id 获取用户的 app_id
     2. 获取该应用的最新发布版本
     3. 从发布版本的 config 字段中提取 memory_config_id
-    4. 根据 memory_config_id 查询配置名称
-    
+
     Args:
         end_user_id: 终端用户ID
         db: 数据库会话
-        
+
     Returns:
-        包含 memory_config_id、config_name 和相关信息的字典
-        
+        包含 memory_config_id 和相关信息的字典
+
     Raises:
         ValueError: 当终端用户不存在或应用未发布时
     """
     from app.models.app_release_model import AppRelease
-    from app.models.data_config_model import DataConfig
     from app.models.end_user_model import EndUser
     from sqlalchemy import select
-    
+
     logger.info(f"Getting connected config for end_user: {end_user_id}")
-    
+
     # 1. 获取 end_user 及其 app_id
     end_user = db.query(EndUser).filter(EndUser.id == end_user_id).first()
     if not end_user:
         logger.warning(f"End user not found: {end_user_id}")
         raise ValueError(f"终端用户不存在: {end_user_id}")
-    
+
     app_id = end_user.app_id
     logger.debug(f"Found end_user app_id: {app_id}")
-    
+
     # 2. 获取该应用的最新发布版本
     stmt = (
         select(AppRelease)
@@ -1215,170 +977,135 @@ def get_end_user_connected_config(end_user_id: str, db: Session) -> Dict[str, An
         .order_by(AppRelease.version.desc())
     )
     latest_release = db.scalars(stmt).first()
-    
+
     if not latest_release:
         logger.warning(f"No active release found for app: {app_id}")
         raise ValueError(f"应用未发布: {app_id}")
-    
+
     logger.debug(f"Found latest release: version={latest_release.version}, id={latest_release.id}")
-    
+
     # 3. 从 config 中提取 memory_config_id
     config = latest_release.config or {}
     memory_obj = config.get('memory', {})
     memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None
-    
-    # 4. 根据 memory_config_id 查询配置名称
-    config_name = None
-    if memory_config_id:
-        try:
-            # memory_config_id 可能是整数或字符串，需要转换
-            config_id = int(memory_config_id) if isinstance(memory_config_id, str) else memory_config_id
-            data_config = db.query(DataConfig).filter(DataConfig.config_id == config_id).first()
-            if data_config:
-                config_name = data_config.config_name
-                logger.debug(f"Found config_name: {config_name} for config_id: {config_id}")
-            else:
-                logger.warning(f"DataConfig not found for config_id: {config_id}")
-        except (ValueError, TypeError) as e:
-            logger.warning(f"Invalid memory_config_id format: {memory_config_id}, error: {str(e)}")
-    
+
     result = {
         "end_user_id": str(end_user_id),
         "app_id": str(app_id),
         "release_id": str(latest_release.id),
         "release_version": latest_release.version,
-        "memory_config_id": memory_config_id,
-        "memory_config_name": config_name
+        "memory_config_id": memory_config_id
     }
-    
-    logger.info(f"Successfully retrieved connected config: memory_config_id={memory_config_id}, config_name={config_name}")
+
+    logger.info(f"Successfully retrieved connected config: memory_config_id={memory_config_id}")
     return result
 
 
 def get_end_users_connected_configs_batch(end_user_ids: List[str], db: Session) -> Dict[str, Dict[str, Any]]:
     """
-    批量获取多个终端用户关联的记忆配置
-    
-    通过优化的查询减少数据库往返次数：
-    1. 一次性查询所有 end_user 及其 app_id
-    2. 批量查询所有相关的 app_release
-    3. 批量查询所有相关的 data_config
-    
+    批量获取多个终端用户关联的记忆配置（优化版本，减少数据库查询次数）
+
+    通过以下流程获取配置：
+    1. 批量查询所有 end_user_id 对应的 app_id
+    2. 批量获取这些应用的最新发布版本
+    3. 从发布版本的 config 字段中提取 memory_config_id
+
     Args:
         end_user_ids: 终端用户ID列表
         db: 数据库会话
-        
+
     Returns:
-        字典，key 为 end_user_id，value 为配置信息字典
-        对于查询失败的用户，value 包含 error 字段
+        字典，key 为 end_user_id，value 为包含 memory_config_id 和 memory_config_name 的字典
+        格式: {
+            "user_id_1": {"memory_config_id": "xxx", "memory_config_name": "xxx"},
+            "user_id_2": {"memory_config_id": None, "memory_config_name": None},
+            ...
+        }
     """
     from app.models.app_release_model import AppRelease
-    from app.models.data_config_model import DataConfig
     from app.models.end_user_model import EndUser
+    from app.models.memory_config_model import MemoryConfig
     from sqlalchemy import select
-    
-    logger.info(f"Batch getting connected configs for {len(end_user_ids)} end users")
-    
+
+    logger.info(f"Batch getting connected configs for {len(end_user_ids)} end_users")
+
     result = {}
-    
+
+    # 如果列表为空，直接返回空字典
+    if not end_user_ids:
+        return result
+
     # 1. 批量查询所有 end_user 及其 app_id
     end_users = db.query(EndUser).filter(EndUser.id.in_(end_user_ids)).all()
     
-    # 构建 end_user_id -> end_user 的映射
-    end_user_map = {str(user.id): user for user in end_users}
+    # 创建 end_user_id -> app_id 的映射
+    user_to_app = {str(eu.id): eu.app_id for eu in end_users}
     
-    # 记录不存在的用户
-    for user_id in end_user_ids:
-        if user_id not in end_user_map:
-            result[user_id] = {
-                "end_user_id": user_id,
-                "memory_config_id": None,
-                "memory_config_name": None,
-                "error": f"终端用户不存在: {user_id}"
-            }
-    
-    if not end_users:
-        logger.warning("No valid end users found")
+    # 记录未找到的用户
+    found_user_ids = set(user_to_app.keys())
+    missing_user_ids = set(end_user_ids) - found_user_ids
+    if missing_user_ids:
+        logger.warning(f"End users not found: {missing_user_ids}")
+        for user_id in missing_user_ids:
+            result[user_id] = {"memory_config_id": None, "memory_config_name": None}
+
+    # 2. 批量获取所有相关应用的最新发布版本
+    app_ids = list(user_to_app.values())
+    if not app_ids:
         return result
-    
-    # 2. 批量查询所有相关应用的最新发布版本
-    app_ids = [user.app_id for user in end_users]
-    
-    # 使用子查询找到每个 app 的最新版本
-    from sqlalchemy import and_
-    
-    # 查询所有相关的活跃发布版本
-    releases = db.query(AppRelease).filter(
-        and_(
-            AppRelease.app_id.in_(app_ids),
-            AppRelease.is_active.is_(True)
-        )
-    ).order_by(AppRelease.app_id, AppRelease.version.desc()).all()
-    
-    # 构建 app_id -> latest_release 的映射（每个 app 只保留最新版本）
-    app_release_map = {}
+
+    # 查询所有活跃的发布版本
+    stmt = (
+        select(AppRelease)
+        .where(AppRelease.app_id.in_(app_ids), AppRelease.is_active.is_(True))
+        .order_by(AppRelease.app_id, AppRelease.version.desc())
+    )
+    releases = db.scalars(stmt).all()
+
+    # 创建 app_id -> latest_release 的映射（每个 app 只保留最新版本）
+    app_to_release = {}
     for release in releases:
-        app_id_str = str(release.app_id)
-        if app_id_str not in app_release_map:
-            app_release_map[app_id_str] = release
-    
-    # 3. 收集所有 memory_config_id
+        if release.app_id not in app_to_release:
+            app_to_release[release.app_id] = release
+
+    # 3. 收集所有 memory_config_id 并批量查询配置名称
     memory_config_ids = []
-    for release in app_release_map.values():
-        config = release.config or {}
-        memory_obj = config.get('memory', {})
-        memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None
-        if memory_config_id:
-            try:
-                config_id = int(memory_config_id) if isinstance(memory_config_id, str) else memory_config_id
-                memory_config_ids.append(config_id)
-            except (ValueError, TypeError):
-                pass
-    
-    # 4. 批量查询所有 data_config
-    config_name_map = {}
+    for end_user_id, app_id in user_to_app.items():
+        release = app_to_release.get(app_id)
+        if release:
+            config = release.config or {}
+            memory_obj = config.get('memory', {})
+            memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None
+            if memory_config_id:
+                memory_config_ids.append(memory_config_id)
+
+    # 批量查询 memory_config_name
+    config_id_to_name = {}
     if memory_config_ids:
-        data_configs = db.query(DataConfig).filter(
-            DataConfig.config_id.in_(memory_config_ids)
-        ).all()
-        config_name_map = {config.config_id: config.config_name for config in data_configs}
-    
-    # 5. 组装结果
-    for user in end_users:
-        user_id = str(user.id)
-        app_id = str(user.app_id)
+        memory_configs = db.query(MemoryConfig).filter(MemoryConfig.id.in_(memory_config_ids)).all()
+        config_id_to_name = {str(mc.id): mc.config_name for mc in memory_configs}
+
+    # 4. 构建最终结果
+    for end_user_id, app_id in user_to_app.items():
+        release = app_to_release.get(app_id)
         
-        # 检查是否有发布版本
-        if app_id not in app_release_map:
-            result[user_id] = {
-                "end_user_id": user_id,
-                "memory_config_id": None,
-                "memory_config_name": None,
-                "error": f"应用未发布: {app_id}"
-            }
+        if not release:
+            logger.warning(f"No active release found for app: {app_id} (end_user: {end_user_id})")
+            result[end_user_id] = {"memory_config_id": None, "memory_config_name": None}
             continue
-        
-        release = app_release_map[app_id]
-        
-        # 提取 memory_config_id
+
+        # 从 config 中提取 memory_config_id
         config = release.config or {}
         memory_obj = config.get('memory', {})
         memory_config_id = memory_obj.get('memory_content') if isinstance(memory_obj, dict) else None
         
-        # 获取 config_name
-        config_name = None
-        if memory_config_id:
-            try:
-                config_id = int(memory_config_id) if isinstance(memory_config_id, str) else memory_config_id
-                config_name = config_name_map.get(config_id)
-            except (ValueError, TypeError):
-                pass
-        
-        result[user_id] = {
-            "end_user_id": user_id,
+        # 获取配置名称
+        memory_config_name = config_id_to_name.get(memory_config_id) if memory_config_id else None
+
+        result[end_user_id] = {
             "memory_config_id": memory_config_id,
-            "memory_config_name": config_name
+            "memory_config_name": memory_config_name
         }
-    
-    logger.info(f"Successfully retrieved batch configs: total={len(result)}, with_config={sum(1 for v in result.values() if v.get('memory_config_id'))}")
+
+    logger.info(f"Successfully retrieved {len(result)} connected configs")
     return result
\ No newline at end of file
diff --git a/api/app/tasks.py b/api/app/tasks.py
index 28a882b7..fba9f290 100644
--- a/api/app/tasks.py
+++ b/api/app/tasks.py
@@ -1,27 +1,27 @@
 import asyncio
-import trio
 import json
 import os
+import re
 import time
 import uuid
 from datetime import datetime, timezone
 from math import ceil
 from typing import Any, Dict, List, Optional
-import re
 
 import redis
 import requests
+import trio
 
 # Import a unified Celery instance
 from app.celery_app import celery_app
 from app.core.config import settings
+from app.core.rag.graphrag.general.index import init_graphrag, run_graphrag_for_kb
 from app.core.rag.graphrag.utils import get_llm_cache, set_llm_cache
 from app.core.rag.llm.chat_model import Base
 from app.core.rag.llm.cv_model import QWenCV
 from app.core.rag.llm.embedding_model import OpenAIEmbed
 from app.core.rag.llm.sequence2txt_model import QWenSeq2txt
 from app.core.rag.models.chunk import DocumentChunk
-from app.core.rag.graphrag.general.index import init_graphrag, run_graphrag_for_kb
 from app.core.rag.prompts.generator import question_proposal
 from app.core.rag.vdb.elasticsearch.elasticsearch_vector import (
     ElasticSearchVectorFactory,
@@ -486,6 +486,10 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
     Raises:
         Exception on failure
     """
+    from app.core.logging_config import get_logger
+    logger = get_logger(__name__)
+    
+    logger.info(f"[CELERY WRITE] Starting write task - group_id={group_id}, config_id={config_id}, storage_type={storage_type}")
     start_time = time.time()
     
     # Resolve config_id if None
@@ -506,8 +510,14 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
     async def _run() -> str:
         db = next(get_db())
         try:
+            logger.info(f"[CELERY WRITE] Executing MemoryAgentService.write_memory")
             service = MemoryAgentService()
-            return await service.write_memory(group_id, message, actual_config_id, db, storage_type, user_rag_memory_id)
+            result = await service.write_memory(group_id, message, actual_config_id, db, storage_type, user_rag_memory_id)
+            logger.info(f"[CELERY WRITE] Write completed successfully: {result}")
+            return result
+        except Exception as e:
+            logger.error(f"[CELERY WRITE] Write failed: {e}", exc_info=True)
+            raise
         finally:
             db.close()
 
@@ -532,6 +542,8 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
         result = loop.run_until_complete(_run())
         elapsed_time = time.time() - start_time
         
+        logger.info(f"[CELERY WRITE] Task completed successfully - elapsed_time={elapsed_time:.2f}s, task_id={self.request.id}")
+        
         return {
             "status": "SUCCESS",
             "result": result,
@@ -548,6 +560,9 @@ def write_message_task(self, group_id: str, message: str, config_id: str,storage
             detailed_error = "; ".join(error_messages)
         else:
             detailed_error = str(e)
+        
+        logger.error(f"[CELERY WRITE] Task failed - elapsed_time={elapsed_time:.2f}s, error={detailed_error}", exc_info=True)
+        
         return {
             "status": "FAILURE",
             "error": detailed_error,
diff --git a/api/docker-compose.yml b/api/docker-compose.yml
index 8470a5d1..8bc19f3a 100644
--- a/api/docker-compose.yml
+++ b/api/docker-compose.yml
@@ -1,32 +1,5 @@
-version: '3.9'
-
 services:
-  # MCP Server - standalone service
-  mcp-server:
-    image: redbear-mem-open:latest
-    container_name: mcp-server
-    ports:
-      - "8081:8081"  # MCP server port
-    env_file:
-      - .env
-    environment:
-      - SERVER_IP=0.0.0.0  # Bind to all interfaces
-    volumes:
-      - ./files:/files
-      - /etc/localtime:/etc/localtime:ro
-    command: python -m app.core.memory.agent.mcp_server.server
-    healthcheck:
-      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8081/sse')"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-      start_period: 30s
-    restart: unless-stopped
-    networks:
-      - default
-      - celery
-
-  # FastAPI application - connects to  MCP server
+  # FastAPI application
   api:
     image: redbear-mem-open:latest
     container_name: api
@@ -35,37 +8,31 @@ services:
     env_file:
       - .env
     environment:
-      - MCP_SERVER_URL=http://mcp-server:8081  # Back to using container name
-      - SERVER_IP=0.0.0.0  # Ensure MCP server binds to all interfaces
+      - SERVER_IP=0.0.0.0
+      # 如果代码里必须要 MCP_SERVER_URL，可以先注释或指向占位
+      # - MCP_SERVER_URL=
     volumes:
       - ./files:/files
       - /etc/localtime:/etc/localtime:ro
     command: uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload --log-level debug
-    depends_on:
-      mcp-server:
-        condition: service_healthy
     restart: unless-stopped
     networks:
       - default
       - celery
 
-  # Celery worker - connects to MCP server
+  # Celery worker
   worker:
     image: redbear-mem-open:latest
     container_name: worker
     env_file:
       - .env
-    environment:
-      - MCP_SERVER_URL=http://mcp-server:8081  # Back to using container name
     volumes:
       - ./files:/files
       - /etc/localtime:/etc/localtime:ro
     command: celery -A app.celery_worker.celery_app worker --loglevel=info
-    depends_on:
-      mcp-server:
-        condition: service_healthy
     restart: unless-stopped
     networks:
       - celery
+
 networks:
-  celery:
\ No newline at end of file
+  celery:

From e518b57deab18d3b9d635a67808311e5e1ef9692 Mon Sep 17 00:00:00 2001
From: lixinyue11 <94037597+lixinyue11@users.noreply.github.com>
Date: Tue, 20 Jan 2026 10:39:12 +0800
Subject: [PATCH 11/12] Fix/memory bug fix (#150)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* 图谱数据量限制数量去掉

* 图谱数据量限制数量去掉

* 图谱数据量限制数量去掉

* 用户详情优化

* 用户详情优化

* 用户详情优化

* 用户详情优化

* 用户详情优化

* 用户详情优化

* 读取的接口，去掉全局锁

* 输出数组

* 反思优化1.0（优化隐私输出、时间检索）

* 反思优化1.0（优化隐私输出、时间检索）

* 反思优化1.0（优化隐私输出、时间检索）
---
 .../memory_reflection_controller.py           |  95 +++++-----------
 .../reflection_engine/self_reflexion.py       |  36 +++----
 .../utils/prompt/prompts/evaluate.jinja2      |   3 +-
 .../utils/prompt/prompts/reflexion.jinja2     |  28 +++--
 .../repositories/data_config_repository.py    | 102 ++++++++++--------
 api/app/repositories/neo4j/cypher_queries.py  |   4 +
 api/app/repositories/neo4j/neo4j_update.py    |  55 +++++++---
 api/app/schemas/memory_storage_schema.py      |  10 +-
 api/app/services/memory_reflection_service.py |  10 +-
 9 files changed, 173 insertions(+), 170 deletions(-)

diff --git a/api/app/controllers/memory_reflection_controller.py b/api/app/controllers/memory_reflection_controller.py
index b0287d80..24c143b9 100644
--- a/api/app/controllers/memory_reflection_controller.py
+++ b/api/app/controllers/memory_reflection_controller.py
@@ -1,10 +1,11 @@
 import asyncio
 import time
+import uuid
 
 from app.core.logging_config import get_api_logger
 from app.core.memory.storage_services.reflection_engine.self_reflexion import (
     ReflectionConfig,
-    ReflectionEngine,
+    ReflectionEngine, ReflectionRange, ReflectionBaseline,
 )
 from app.core.response_utils import success
 from app.db import get_db
@@ -39,9 +40,6 @@ async def save_reflection_config(
     db: Session = Depends(get_db),
 ) -> dict:
     """Save reflection configuration to data_comfig table"""
-    
-
-    
     try:
         config_id = request.config_id
         if not config_id:
@@ -52,51 +50,30 @@ async def save_reflection_config(
 
         api_logger.info(f"用户 {current_user.username} 保存反思配置，config_id: {config_id}")
 
-        update_params = {
-            "enable_self_reflexion": request.reflection_enabled,
-            "iteration_period": request.reflection_period_in_hours,
-            "reflexion_range": request.reflexion_range,
-            "baseline": request.baseline,
-            "reflection_model_id": request.reflection_model_id,
-            "memory_verify": request.memory_verify,
-            "quality_assessment": request.quality_assessment,
-        }
+        data_config = DataConfigRepository.update_reflection_config(
+            db,
+            config_id=config_id,
+            enable_self_reflexion=request.reflection_enabled,
+            iteration_period=request.reflection_period_in_hours,
+            reflexion_range=request.reflexion_range,
+            baseline=request.baseline,
+            reflection_model_id=request.reflection_model_id,
+            memory_verify=request.memory_verify,
+            quality_assessment=request.quality_assessment
+        )
 
-
-
-        query, params = DataConfigRepository.build_update_reflection(config_id, **update_params)
-
-        result = db.execute(text(query), params)
-        if result.rowcount == 0:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail=f"未找到config_id为 {config_id} 的配置"
-            )
-        
         db.commit()
-        
-        # 查询更新后的配置
-        select_query, select_params = DataConfigRepository.build_select_reflection(config_id)
-        result = db.execute(text(select_query), select_params).fetchone()
-        
-        if not result:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail=f"更新后未找到config_id为 {config_id} 的配置"
-            )
-        
-        api_logger.info(f"成功保存反思配置到数据库，config_id: {config_id}")
+        db.refresh(data_config)
 
         reflection_result={
-                "config_id": result.config_id,
-                "enable_self_reflexion": result.enable_self_reflexion,
-                "iteration_period": result.iteration_period,
-                "reflexion_range": result.reflexion_range,
-                "baseline": result.baseline,
-                "reflection_model_id": result.reflection_model_id,
-                "memory_verify": result.memory_verify,
-                "quality_assessment": result.quality_assessment,
-                "user_id": result.user_id}
+                "config_id": data_config.config_id,
+                "enable_self_reflexion": data_config.enable_self_reflexion,
+                "iteration_period": data_config.iteration_period,
+                "reflexion_range": data_config.reflexion_range,
+                "baseline": data_config.baseline,
+                "reflection_model_id": data_config.reflection_model_id,
+                "memory_verify": data_config.memory_verify,
+                "quality_assessment": data_config.quality_assessment}
 
         return success(data=reflection_result, msg="反思配置成功")
         
@@ -116,9 +93,8 @@ async def save_reflection_config(
         )
 
 
-@router.post("/reflection")
+@router.get("/reflection")
 async def start_workspace_reflection(
-    config_id: int,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
 ) -> dict:
@@ -178,17 +154,7 @@ async def start_reflection_configs(
     """通过config_id查询data_config表中的反思配置信息"""
     try:
         api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
-        
-        # 使用DataConfigRepository查询反思配置
-        select_query, select_params = DataConfigRepository.build_select_reflection(config_id)
-        result = db.execute(text(select_query), select_params).fetchone()
-        
-        if not result:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail=f"未找到config_id为 {config_id} 的配置"
-            )
-        
+        result = DataConfigRepository.query_reflection_config_by_id(db, config_id)
         # 构建返回数据
         reflection_config = {
             "config_id": result.config_id,
@@ -198,8 +164,7 @@ async def start_reflection_configs(
             "baseline": result.baseline,
             "reflection_model_id": result.reflection_model_id,
             "memory_verify": result.memory_verify,
-            "quality_assessment": result.quality_assessment,
-            "user_id": result.user_id
+            "quality_assessment": result.quality_assessment
         }
         api_logger.info(f"成功查询反思配置，config_id: {config_id}")
         return success(data=reflection_config, msg="反思配置查询成功")
@@ -227,9 +192,7 @@ async def reflection_run(
     api_logger.info(f"用户 {current_user.username} 查询反思配置，config_id: {config_id}")
 
     # 使用DataConfigRepository查询反思配置
-    select_query, select_params = DataConfigRepository.build_select_reflection(config_id)
-    result = db.execute(text(select_query), select_params).fetchone()
-
+    result = DataConfigRepository.query_reflection_config_by_id(db, config_id)
     if not result:
         raise HTTPException(
             status_code=status.HTTP_404_NOT_FOUND,
@@ -242,7 +205,7 @@ async def reflection_run(
     model_id = result.reflection_model_id
     if model_id:
         try:
-            ModelConfigService.get_model_by_id(db=db, model_id=model_id)
+            ModelConfigService.get_model_by_id(db=db, model_id=uuid.UUID(model_id))
             api_logger.info(f"模型ID验证成功: {model_id}")
         except Exception as e:
             api_logger.warning(f"模型ID '{model_id}' 不存在，将使用默认模型: {str(e)}")
@@ -252,8 +215,8 @@ async def reflection_run(
     config = ReflectionConfig(
         enabled=result.enable_self_reflexion,
         iteration_period=result.iteration_period,
-        reflexion_range=result.reflexion_range,
-        baseline=result.baseline,
+        reflexion_range=ReflectionRange(result.reflexion_range),
+        baseline=ReflectionBaseline(result.baseline),
         output_example='',
         memory_verify=result.memory_verify,
         quality_assessment=result.quality_assessment,
diff --git a/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py b/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py
index e9fb8855..bd3a9190 100644
--- a/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py
+++ b/api/app/core/memory/storage_services/reflection_engine/self_reflexion.py
@@ -24,15 +24,9 @@ from app.core.memory.utils.config.get_data import (
     get_data,
     get_data_statement,
 )
-from app.core.memory.utils.llm.llm_utils import get_llm_client
-from app.core.memory.utils.prompt.template_render import (
-    render_evaluate_prompt,
-    render_reflexion_prompt,
-)
+
 from app.core.models.base import RedBearModelConfig
-from app.core.response_utils import success
 from app.repositories.neo4j.cypher_queries import (
-    UPDATE_STATEMENT_INVALID_AT,
     neo4j_query_all,
     neo4j_query_part,
     neo4j_statement_all,
@@ -160,12 +154,11 @@ class ReflectionEngine:
             self.neo4j_connector = Neo4jConnector()
 
         if self.llm_client is None:
-            from app.core.memory.utils.config import definitions as config_defs
             from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
             from app.db import get_db_context
             with get_db_context() as db:
                 factory = MemoryClientFactory(db)
-                self.llm_client = factory.get_llm_client(config_defs.SELECTED_LLM_ID)
+                self.llm_client = factory.get_llm_client(self.config.model_id)
         elif isinstance(self.llm_client, str):
             # 如果 llm_client 是字符串（model_id），则用它初始化客户端
             from app.core.memory.utils.llm.llm_utils import MemoryClientFactory
@@ -263,25 +256,23 @@ class ReflectionEngine:
 
             # 2. 检测冲突（基于事实的反思）
             conflict_data = await self._detect_conflicts(reflexion_data, statement_databasets)
-            print(100 * '-')
-            print(conflict_data)
-            print(100 * '-')
-            # # 检查是否真的有冲突
-            conflicts_found=''
+            conflict_list=[]
+            for i  in conflict_data:
+                conflict_list.append(i['data'])
 
-            conflicts_found=''
+
+
+            conflicts_found=0
             # 3. 解决冲突
-            solved_data = await self._resolve_conflicts(conflict_data, statement_databasets)
+            solved_data = await self._resolve_conflicts(conflict_list, statement_databasets)
+
             if not solved_data:
                 return ReflectionResult(
                     success=False,
-                    message="反思失败，未解决冲突",
+                    message=f"没有{self.config.baseline}相关的冲突数据",
                     conflicts_found=conflicts_found,
                     execution_time=asyncio.get_event_loop().time() - start_time
                 )
-            print(100 * '*')
-            print(solved_data)
-            print(100 * '*')
 
             conflicts_resolved = len(solved_data)
             logging.info(f"解决了 {conflicts_resolved} 个冲突")
@@ -386,7 +377,7 @@ class ReflectionEngine:
             memory_verifies.append(item['memory_verify'])
         result_data['memory_verifies'] = memory_verifies
         result_data['quality_assessments'] = quality_assessments
-        conflicts_found=''
+        conflicts_found = 0  # 初始化为整数0而不是空字符串
         REMOVE_KEYS = {"created_at", "expired_at","relationship","predicate","statement_id","id","statement_id","relationship_statement_id"}
         # Clearn conflict_data，And memory_verify和quality_assessment
         cleaned_conflict_data = []
@@ -414,7 +405,7 @@ class ReflectionEngine:
             cleaned_conflict_data_.append(cleaned_item)
         print(cleaned_conflict_data_)
         # 3. 解决冲突
-        solved_data = await self._resolve_conflicts(cleaned_conflict_data, source_data)
+        solved_data = await self._resolve_conflicts(cleaned_conflict_data_, source_data)
         if not solved_data:
             return ReflectionResult(
                 success=False,
@@ -739,4 +730,3 @@ class ReflectionEngine:
 
             raise ValueError(f"未知的反思基线: {self.config.baseline}")
 
-
diff --git a/api/app/core/memory/utils/prompt/prompts/evaluate.jinja2 b/api/app/core/memory/utils/prompt/prompts/evaluate.jinja2
index e649897a..5da6d4b5 100644
--- a/api/app/core/memory/utils/prompt/prompts/evaluate.jinja2
+++ b/api/app/core/memory/utils/prompt/prompts/evaluate.jinja2
@@ -24,7 +24,8 @@
 - **身份冲突**: 同一实体被赋予不同类型或角色
 - **隐私审核**: 存在隐私信息也作为冲突输出当{{ memory_verify }}是true的时候
 ### 混合冲突
-检测所有逻辑不一致或相互矛盾的记录。
+- 检测所有逻辑不一致或相互矛盾的记录。
+- **隐私审核**: 存在隐私信息也作为冲突输出当{{ memory_verify }}是true的时候
 **检测原则**:
 - 重点检查相同实体的记录
 - 分析description字段语义冲突
diff --git a/api/app/core/memory/utils/prompt/prompts/reflexion.jinja2 b/api/app/core/memory/utils/prompt/prompts/reflexion.jinja2
index ed3aad32..99660aa4 100644
--- a/api/app/core/memory/utils/prompt/prompts/reflexion.jinja2
+++ b/api/app/core/memory/utils/prompt/prompts/reflexion.jinja2
@@ -63,7 +63,7 @@
 **脱敏字段**: name、entity1_name、entity2_name、description、relationship
 
 ## 4. 处理流程
-
+###如果存在冲突数据执行以下步骤，不存在返回【】在data中
 ### 步骤1: 类型匹配验证
 **匹配规则**:
 - baseline="TIME": 只处理时间相关冲突(涉及时间表达式、日期、时间点)
@@ -78,7 +78,7 @@
 
 ### 步骤2: 冲突数据分组
 **分组策略**:
-- 时间冲突组: 涉及用户时间的记录
+- 时间冲突组: 涉及用户时间的记录比如（生日在2月17...）
 - 活动时间冲突组: 同一活动不同时间的记录  
 - 事实冲突组: 同一实体不同属性的记录
 - 其他冲突组: 其他类型冲突记录
@@ -97,11 +97,12 @@
 ### 处理规则
 
 ** baseline是TIME
-    -保留正确记录不变修改错误记录的expired_at为当前时间(2025-12-16T12:00:00),以及name需要修改成正确的
-** baseline不是TIME
+    - 只处理时间相关的内容，比如时间表达式、日期、时间点
+    -保留正确记录不变修改错误记录的expired_at为当前时间，比如(2025-12-16T12:00:00)
+** baseline是FACT或者HYBRID
+    - 处理不是时间相关的内容
     - 修改字段内容（ name、entity1_name、entity2_name、description、relationship）字段内容是否正确，如果不正确，需要对这些字段的内容重新生成，则不需要修改expired_at字段,
     如果涉及到修改entity1_name/entity2_name字段的时候，同时也需要修改description字段，输出修改前和修改后的放入change里面的field
-
 **核心原则**:
 - 只输出需要修改的记录
 - 优先保留策略: 时间冲突保留最可信created_at时间，事实冲突选择最新且可信度最高记录
@@ -110,22 +111,26 @@
 - 脱敏变更记录: 隐私脱敏变更也必须在change字段中记录{% endif %}
 - 不可修改数据: 数据被判定为正确时不可修改，无数据可输出时为空
 - 输出的结果reflexion字段中的reason字段和solution不允许含有（expired_at设为2024-01-01T00:00:00Z、memory_verify=true、memory_verify=false)等原数据字段以及涉及需要修改的字段以及内容，
-    ，如果是FACT，只记录事实冲突相关的数据；如果是TIME，只记录时间冲突相关的数据；如果是HYBRID，则记录所有冲突相关的数据
+    ，如果是FACT，只记录事实冲突相关的数据；如果是TIME，只记录时间冲突相关的数据；如果是HYBRID，则记录所有冲突相关的数据,如果存在隐私审核，隐私审核是true，也需要放到reflexion的reason字段和solution
 
 **变更记录格式**:
 ```json
 "change": [
   {
     "field": [
-      {"id":修改字段对应的ID}
-      {"statement_id":需要修改的对象对应的statement_id}
-      {"字段名1": ["修改前的值1","修改后的值1"]},
-      {"字段名2": ["修改前的值2","修改后的值2"]}
+      {"id": "修改字段对应的ID"},
+      {"字段名1": ["修改前的值1", "修改后的值1"]},
+      {"字段名2": ["修改前的值2", "修改后的值2"]}
     ]
   }
 ]
 ```
 
+**resolved_memory格式说明**:
+- 对于TIME类型冲突: 只需expired_at字段即可
+- 对于FACT/HYBRID类型冲突: 需要包含完整的记录对象（包括name、entity1_name、entity2_name、description、relationship等所有相关字段）
+- resolved_memory中只包含需要修改的记录，不需要修改的记录不要包含在内
+
 **类型不匹配处理**:
 - 冲突类型与baseline不匹配时，resolved设为null
 - reflexion.reason说明类型不匹配原因
@@ -157,7 +162,8 @@
         "conflict": true
       },
       "reflexion": {
-        "reason": "该冲突类型的原因分析，如果是FACT就是存在事实冲突，分析该冲突原因，如果是TIME就是存在时间冲突，分析该冲突原因，如果是HYBRID，可以输出存在时间与事实的混合冲突再添加上原因分析，
+        "reason": "该冲突类型的原因分析，如果是FACT就是存在事实冲突，分析该冲突原因，如果是TIME就是存在时间冲突，分析该冲突原因，如果是HYBRID，可以输出存在时间与事实的混合冲突再添加上原因分析，如果
+        隐私审核打开的时候如果存在冲突，分析该冲突的原因
         不可以随意分配冲突类型以及原因，不允许输出字段比如（statement、description、entity1_name、entity2_name、name、memory_verify、expired_at、conflict）等类似这种",
         "solution": "该冲突类型的解决方案（不允许输出字段比如（statement、description、entity1_name、entity2_name、name、memory_verify、expired_at、conflict）等类似这种）"
       },
diff --git a/api/app/repositories/data_config_repository.py b/api/app/repositories/data_config_repository.py
index 135c0063..d26058b2 100644
--- a/api/app/repositories/data_config_repository.py
+++ b/api/app/repositories/data_config_repository.py
@@ -10,7 +10,7 @@ Classes:
 
 import uuid
 from typing import Dict, List, Optional, Tuple
-
+from app.core.exceptions import BusinessException
 from app.core.logging_config import get_config_logger, get_db_logger
 from app.models.data_config_model import DataConfig
 from app.schemas.memory_storage_schema import (
@@ -20,7 +20,7 @@ from app.schemas.memory_storage_schema import (
     ConfigUpdateExtracted,
     ConfigUpdateForget,
 )
-from sqlalchemy import desc
+from sqlalchemy import desc, select
 from sqlalchemy.orm import Session
 
 # 获取数据库专用日志器
@@ -136,72 +136,88 @@ class DataConfigRepository:
         id: m.id
       } AS targetNode
     """
-
-    # ==================== SQLAlchemy ORM 数据库操作方法 ====================
     @staticmethod
-    def build_update_reflection(config_id: int, **kwargs) -> Tuple[str, Dict]:
+    def update_reflection_config(
+            db: Session,
+            config_id: int,
+            enable_self_reflexion: bool,
+            iteration_period: str,
+            reflexion_range: str,
+            baseline: str,
+            reflection_model_id: str,
+            memory_verify: bool,
+            quality_assessment: bool
+    ) -> DataConfig:
         """构建反思配置更新语句（SQLAlchemy text() 命名参数）
 
         Args:
+            quality_assessment:
+            memory_verify:
+            reflection_model_id:
+            baseline:
+            reflexion_range:
+            iteration_period:
+            enable_self_reflexion:
+            db: database object
             config_id: 配置ID
-            **kwargs: 反思配置参数
 
         Returns:
-            Tuple[str, Dict]: (SQL查询字符串, 参数字典)
+            Data
 
         Raises:
             ValueError: 没有字段需要更新时抛出
         """
         db_logger.debug(f"构建反思配置更新语句: config_id={config_id}")
+        stmt = select(DataConfig).where(DataConfig.config_id == config_id)
+        data_config_obj = db.scalars(stmt).first()
+        if not data_config_obj:
+            raise BusinessException
+        data_config_obj.enable_self_reflexion = enable_self_reflexion
+        data_config_obj.iteration_period = iteration_period
+        data_config_obj.reflexion_range = reflexion_range
+        data_config_obj.baseline = baseline
+        data_config_obj.reflection_model_id = reflection_model_id
+        data_config_obj.memory_verify = memory_verify
+        data_config_obj.quality_assessment = quality_assessment
 
-        key_where = "config_id = :config_id"
-        set_fields: List[str] = []
-        params: Dict = {
-            "config_id": config_id,
-        }
-
-        # 反思配置字段映射
-        mapping = {
-            "enable_self_reflexion": "enable_self_reflexion",
-            "iteration_period": "iteration_period",
-            "reflexion_range": "reflexion_range",
-            "baseline": "baseline",
-            "reflection_model_id": "reflection_model_id",
-            "memory_verify": "memory_verify",
-            "quality_assessment": "quality_assessment",
-        }
-
-        for api_field, db_col in mapping.items():
-            if api_field in kwargs and kwargs[api_field] is not None:
-                set_fields.append(f"{db_col} = :{api_field}")
-                params[api_field] = kwargs[api_field]
-
-        if not set_fields:
-            raise ValueError("No fields to update")
-
-        set_fields.append("updated_at = timezone('Asia/Shanghai', now())")
-        query = f"UPDATE {TABLE_NAME} SET " + ", ".join(set_fields) + f" WHERE {key_where}"
-        return query, params
+        return data_config_obj
 
     @staticmethod
-    def build_select_reflection(config_id: int) -> Tuple[str, Dict]:
+    def query_reflection_config_by_id(db: Session, config_id: int) -> DataConfig:
         """构建反思配置查询语句，通过config_id查询反思配置（SQLAlchemy text() 命名参数）
 
         Args:
+            db: database object
             config_id: 配置ID
 
         Returns:
             Tuple[str, Dict]: (SQL查询字符串, 参数字典)
         """
         db_logger.debug(f"构建反思配置查询语句: config_id={config_id}")
+        stmt = select(DataConfig).where(DataConfig.config_id == config_id)
+        data_config = db.scalars(stmt).first()
+        if not data_config:
+            raise RuntimeError("reflection config not found")
+        return data_config
+    @staticmethod
+    def query_reflection_config_by_workspace_id(db: Session, workspace_id: uuid.UUID) -> DataConfig:
+        """构建查询所有配置的语句（SQLAlchemy text() 命名参数）
+
+        Args:
+            db: database object
+            workspace_id: 工作空间ID
+
+        Returns:
+            Tuple[str, Dict]: (SQL查询字符串, 参数字典)
+        """
+        db_logger.debug(f"构建查询所有配置语句: workspace_id={workspace_id}")
+
+        stmt = select(DataConfig).where(DataConfig.workspace_id == workspace_id)
+        data_config = db.scalars(stmt).first()
+        if not data_config:
+            raise RuntimeError("reflection config not found")
+        return data_config
 
-        query = (
-            f"SELECT config_id, enable_self_reflexion, iteration_period, reflexion_range, baseline, "
-            f"reflection_model_id, memory_verify, quality_assessment, user_id "
-            f"FROM {TABLE_NAME} WHERE config_id = :config_id"
-        )
-        params = {"config_id": config_id}
-        return query, params
 
     @staticmethod
     def build_select_all(workspace_id: uuid.UUID) -> Tuple[str, Dict]:
diff --git a/api/app/repositories/neo4j/cypher_queries.py b/api/app/repositories/neo4j/cypher_queries.py
index c91c2e80..cd3cbed7 100644
--- a/api/app/repositories/neo4j/cypher_queries.py
+++ b/api/app/repositories/neo4j/cypher_queries.py
@@ -837,12 +837,14 @@ neo4j_query_part = """
             WITH DISTINCT m
             OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity)
             RETURN 
+             elementId(m) as id,
             m.name as entity1_name,
             m.description as description,
             m.statement_id as statement_id,
             m.created_at as created_at,
             m.expired_at as expired_at,
             CASE WHEN rel IS NULL THEN "NO_RELATIONSHIP" ELSE type(rel) END as relationship_type,
+              elementId(rel) as rel_id,
             rel.predicate as predicate,
             rel.statement as relationship,
             rel.statement_id as relationship_statement_id,
@@ -855,12 +857,14 @@ neo4j_query_all = """
                 WITH DISTINCT m
                 OPTIONAL MATCH (m)-[rel]-(other:ExtractedEntity)
                 RETURN 
+                 elementId(m) as id,
                 m.name as entity1_name,
                 m.description as description,
                 m.statement_id as statement_id,
                 m.created_at as created_at,
                 m.expired_at as expired_at,
                 CASE WHEN rel IS NULL THEN "NO_RELATIONSHIP" ELSE type(rel) END as relationship_type,
+                  elementId(rel) as rel_id,
                 rel.predicate as predicate,
                 rel.statement as relationship,
                 rel.statement_id as relationship_statement_id,
diff --git a/api/app/repositories/neo4j/neo4j_update.py b/api/app/repositories/neo4j/neo4j_update.py
index 73b44396..753ae256 100644
--- a/api/app/repositories/neo4j/neo4j_update.py
+++ b/api/app/repositories/neo4j/neo4j_update.py
@@ -11,22 +11,28 @@ async def update_neo4j_data(neo4j_dict_data, update_databases):
         update_databases: update
     """
     try:
-        # 构建WHERE条件
+        # 构建WHERE条件 - 只使用elementId
         where_conditions = []
         params = {}
 
-        for key, value in neo4j_dict_data.items():
-            if value is not None:
-                param_name = f"param_{key}"
-                where_conditions.append(f"e.{key} = ${param_name}")
-                params[param_name] = value
+        # 优先使用id作为elementId进行查询
+        if 'id' in neo4j_dict_data and neo4j_dict_data['id'] is not None:
+            where_conditions.append(f"elementId(e) = $param_id")
+            params['param_id'] = neo4j_dict_data['id']
+        else:
+            # 如果没有id，使用其他字段作为条件
+            for key, value in neo4j_dict_data.items():
+                if value is not None:
+                    param_name = f"param_{key}"
+                    where_conditions.append(f"e.{key} = ${param_name}")
+                    params[param_name] = value
 
         where_clause = " AND ".join(where_conditions) if where_conditions else "1=1"
 
-        # 构建SET条件
+        # 构建SET条件 - 排除id字段
         set_conditions = []
         for key, value in update_databases.items():
-            if value is not None:
+            if value is not None and key != 'id':  # 不更新id字段
                 param_name = f"update_{key}"
                 set_conditions.append(f"e.{key} = ${param_name}")
                 params[param_name] = value
@@ -76,22 +82,28 @@ async def update_neo4j_data_edge(neo4j_dict_data, update_databases):
         update_databases: update
     """
     try:
-        # 构建WHERE条件
+        # 构建WHERE条件 - 只使用elementId
         where_conditions = []
         params = {}
 
-        for key, value in neo4j_dict_data.items():
-            if value is not None:
-                param_name = f"param_{key}"
-                where_conditions.append(f"r.{key} = ${param_name}")
-                params[param_name] = value
+        # 优先使用id作为elementId进行查询
+        if 'id' in neo4j_dict_data and neo4j_dict_data['id'] is not None:
+            where_conditions.append(f"elementId(r) = $param_id")
+            params['param_id'] = neo4j_dict_data['id']
+        else:
+            # 如果没有id，使用其他字段作为条件
+            for key, value in neo4j_dict_data.items():
+                if value is not None:
+                    param_name = f"param_{key}"
+                    where_conditions.append(f"r.{key} = ${param_name}")
+                    params[param_name] = value
 
         where_clause = " AND ".join(where_conditions) if where_conditions else "1=1"
 
-        # 构建SET条件
+        # 构建SET条件 - 排除id字段
         set_conditions = []
         for key, value in update_databases.items():
-            if value is not None:
+            if value is not None and key != 'id':  # 不更新id字段
                 param_name = f"update_{key}"
                 set_conditions.append(f"r.{key} = ${param_name}")
                 params[param_name] = value
@@ -242,7 +254,16 @@ async def neo4j_data(solved_data):
                 if key=='expired_at':
                     updat_expired_at[key] = values[1]
 
-            elif key == 'statement_id':
+            elif key == 'id':
+                ori_edge[key] = values
+                updata_edge[key] = values
+
+                ori_entity[key] = values
+                updata_entity[key] = values
+
+                ori_expired_at[key] = values
+            elif key == 'rel_id':
+                key='id'
                 ori_edge[key] = values
                 updata_edge[key] = values
 
diff --git a/api/app/schemas/memory_storage_schema.py b/api/app/schemas/memory_storage_schema.py
index ecb1570f..d17a9f2c 100644
--- a/api/app/schemas/memory_storage_schema.py
+++ b/api/app/schemas/memory_storage_schema.py
@@ -35,10 +35,10 @@ class BaseDataSchema(BaseModel):
     expired_at: Optional[str] = Field(None, description="The expiration timestamp in ISO 8601 format.")
     description: Optional[str] = Field(None, description="The description of the data entry.")
 
-    # 新增字段以匹配实际输入数据
-    entity1_name: str = Field(..., description="The first entity name.")
+    # 新增字段以匹配实际输入数据 - 改为可选以支持resolved_memory场景
+    entity1_name: Optional[str] = Field(None, description="The first entity name.")
     entity2_name: Optional[str] = Field(None, description="The second entity name.")
-    statement_id: str = Field(..., description="The statement identifier.")
+    statement_id: Optional[str] = Field(None, description="The statement identifier.")
     # 新增字段 - 设为可选以保持向后兼容性
     predicate: Optional[str] = Field(None, description="The predicate describing the relationship between entities.")
     relationship_statement_id: Optional[str] = Field(None, description="The relationship statement identifier.")
@@ -108,13 +108,13 @@ class ChangeRecordSchema(BaseModel):
     """Schema for individual change records
     
     字段值格式说明：
-    - id 和 statement_id: 字符串或 None
+    - id: 字符串，表示修改字段对应的记录ID
     - 其他字段: 可以是字符串、None，数组 [修改前的值, 修改后的值]，或嵌套字典结构
     - entity2等嵌套对象的字段也遵循 [old_value, new_value] 格式
     """
     field: List[Dict[str, Any]] = Field(
         ..., 
-        description="List of field changes. First item: {id: value or None}, second: {statement_id: value}, followed by changed fields as {field_name: [old_value, new_value]} or {field_name: new_value} or nested structures like {entity2: {field_name: [old, new]}}"
+        description="List of field changes. First item: {id: value}, followed by changed fields as {field_name: [old_value, new_value]} or {field_name: new_value} or nested structures like {entity2: {field_name: [old, new]}}"
     )
 
 class ResolvedSchema(BaseModel):
diff --git a/api/app/services/memory_reflection_service.py b/api/app/services/memory_reflection_service.py
index 0f8fb569..015cc08a 100644
--- a/api/app/services/memory_reflection_service.py
+++ b/api/app/services/memory_reflection_service.py
@@ -120,10 +120,12 @@ class WorkspaceAppService:
     def _get_data_config(self, memory_content: str) -> Dict[str, Any]:
         """Retrieve data_comfig information based on memory_comtent"""
         try:
-            data_config_query, data_config_params = DataConfigRepository.build_select_reflection(memory_content)
-            data_config_result = self.db.execute(text(data_config_query), data_config_params).fetchone()
-            if data_config_result is None:
-                return None
+            data_config_result = DataConfigRepository.query_reflection_config_by_id(self.db, int(memory_content))
+
+            # data_config_query, data_config_params = DataConfigRepository.build_select_reflection(memory_content)
+            # data_config_result = self.db.execute(text(data_config_query), data_config_params).fetchone()
+            # if data_config_result is None:
+            #     return None
             
             if data_config_result:
                 return {

From 804d87bca2f9a7b2fbd341efb851d7da23778949 Mon Sep 17 00:00:00 2001
From: zhaoying <yzhao96@best-inc.com>
Date: Tue, 20 Jan 2026 10:42:13 +0800
Subject: [PATCH 12/12] refactor: extract jinja render's form

---
 .../Workflow/components/Editor/index.tsx      |  36 +--
 .../components/Editor/nodes/VariableNode.tsx  |   2 +-
 .../Editor/plugin/AutocompletePlugin.tsx      |   8 +-
 .../components/Editor/plugin/BlurPlugin.tsx   |  33 +++
 .../Properties/JinjaRender/index.tsx          | 206 ++++++++++++++++++
 .../Workflow/components/Properties/index.tsx  | 145 +-----------
 6 files changed, 279 insertions(+), 151 deletions(-)
 create mode 100644 web/src/views/Workflow/components/Editor/plugin/BlurPlugin.tsx
 create mode 100644 web/src/views/Workflow/components/Properties/JinjaRender/index.tsx

diff --git a/web/src/views/Workflow/components/Editor/index.tsx b/web/src/views/Workflow/components/Editor/index.tsx
index ba2e3a41..fd3e937b 100644
--- a/web/src/views/Workflow/components/Editor/index.tsx
+++ b/web/src/views/Workflow/components/Editor/index.tsx
@@ -16,6 +16,7 @@ import InitialValuePlugin from './plugin/InitialValuePlugin';
 import CommandPlugin from './plugin/CommandPlugin';
 import Jinja2HighlightPlugin from './plugin/Jinja2HighlightPlugin';
 import LineNumberPlugin from './plugin/LineNumberPlugin';
+import BlurPlugin from './plugin/BlurPlugin';
 import { VariableNode } from './nodes/VariableNode'
 
 interface LexicalEditorProps {
@@ -113,8 +114,10 @@ const Editor: FC<LexicalEditorProps> =({
             display: flex;
             align-items: flex-start;
           }
-          .editor-content-with-numbers {
+          .editor-content-wrapper {
             flex: 1;
+          }
+          .editor-content-with-numbers {
             white-space: pre-wrap;
           }
           .editor-content-with-numbers p {
@@ -174,18 +177,20 @@ const Editor: FC<LexicalEditorProps> =({
                 <div className="line-numbers">
                   <div>1</div>
                 </div>
-                <ContentEditable
-                  className="editor-content-with-numbers"
-                  style={{
-                    minHeight: minheight,
-                    padding: '4px 0',
-                    outline: 'none',
-                    resize: 'none',
-                    fontSize: fontSize,
-                    lineHeight: lineHeight,
-                    border: 'none',
-                  }}
-                />
+                <div className="editor-content-wrapper">
+                  <ContentEditable
+                    className="editor-content-with-numbers"
+                    style={{
+                      minHeight: minheight,
+                      padding: '4px 0',
+                      outline: 'none',
+                      resize: 'none',
+                      fontSize: fontSize,
+                      lineHeight: lineHeight,
+                      border: 'none',
+                    }}
+                  />
+                </div>
               </div>
             ) : (
               <ContentEditable
@@ -207,8 +212,8 @@ const Editor: FC<LexicalEditorProps> =({
               style={{
                 minHeight: placeHolderMinheight,
                 position: 'absolute',
-                top: variant === 'borderless' ? '0' : '6px',
-                left: enableJinja2 ? '59px' : (variant === 'borderless' ? '0' : '11px'),
+                top: enableJinja2 ? '4px' : variant === 'borderless' ? '0' : '6px',
+                left: enableJinja2 ? '16px' : (variant === 'borderless' ? '0' : '11px'),
                 color: '#A8A9AA',
                 fontSize: fontSize,
                 lineHeight: placeHolderMinheight,
@@ -227,6 +232,7 @@ const Editor: FC<LexicalEditorProps> =({
         <AutocompletePlugin options={options} enableJinja2={enableJinja2} />
         <CharacterCountPlugin setCount={(count) => { setCount(count) }} onChange={onChange} />
         <InitialValuePlugin value={value} options={options} enableJinja2={enableJinja2} />
+        {enableJinja2 && <BlurPlugin />}
       </div>
     </LexicalComposer>
   );
diff --git a/web/src/views/Workflow/components/Editor/nodes/VariableNode.tsx b/web/src/views/Workflow/components/Editor/nodes/VariableNode.tsx
index 13d12ee1..d29fba4c 100644
--- a/web/src/views/Workflow/components/Editor/nodes/VariableNode.tsx
+++ b/web/src/views/Workflow/components/Editor/nodes/VariableNode.tsx
@@ -36,7 +36,7 @@ const VariableComponent: React.FC<{ nodeKey: NodeKey; data: Suggestion }> = ({
   return (
     <span
       onClick={handleClick}
-      className={clsx('rb:border rb:rounded-md rb:bg-white rb:text-[12px] rb:inline-flex rb:items-center rb:py-0.5 rb:px-1.5 rb:mx-0.5 rb:cursor-pointer', {
+      className={clsx('rb:border rb:rounded-md rb:bg-white rb:text-[10px] rb:inline-flex rb:items-center rb:py-0 rb:px-1.5 rb:mx-0.5 rb:cursor-pointer', {
         'rb:border-[#155EEF]': isSelected,
         'rb:border-[#DFE4ED]': !isSelected
       })}
diff --git a/web/src/views/Workflow/components/Editor/plugin/AutocompletePlugin.tsx b/web/src/views/Workflow/components/Editor/plugin/AutocompletePlugin.tsx
index 34ef3196..7811cc7b 100644
--- a/web/src/views/Workflow/components/Editor/plugin/AutocompletePlugin.tsx
+++ b/web/src/views/Workflow/components/Editor/plugin/AutocompletePlugin.tsx
@@ -1,6 +1,6 @@
 import { useEffect, useState, type FC } from 'react';
 import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
-import { $getSelection, $isRangeSelection } from 'lexical';
+import { $getSelection, $isRangeSelection, $isTextNode } from 'lexical';
 
 import { INSERT_VARIABLE_COMMAND } from '../commands';
 import type { NodeProperties } from '../../../types'
@@ -96,7 +96,9 @@ const AutocompletePlugin: FC<{ options: Suggestion[], enableJinja2?: boolean }>
           const textAfter = nodeText.substring(anchorOffset);
           const newText = textBefore + `{{${suggestion.value}}}` + textAfter;
           
-          anchorNode.setTextContent(newText);
+          if ($isTextNode(anchorNode)) {
+            anchorNode.setTextContent(newText);
+          }
           
           // 设置光标位置到插入文本之后
           const newOffset = textBefore.length + `{{${suggestion.value}}}`.length;
@@ -129,6 +131,8 @@ const AutocompletePlugin: FC<{ options: Suggestion[], enableJinja2?: boolean }>
   }
   return (
     <div
+      data-autocomplete-popup="true"
+      onMouseDown={(e) => e.preventDefault()}
       style={{
         position: 'fixed',
         top: popupPosition.top,
diff --git a/web/src/views/Workflow/components/Editor/plugin/BlurPlugin.tsx b/web/src/views/Workflow/components/Editor/plugin/BlurPlugin.tsx
new file mode 100644
index 00000000..b636605b
--- /dev/null
+++ b/web/src/views/Workflow/components/Editor/plugin/BlurPlugin.tsx
@@ -0,0 +1,33 @@
+import { useLexicalComposerContext } from '@lexical/react/LexicalComposerContext';
+import { useEffect } from 'react';
+import { $setSelection } from 'lexical';
+
+export default function BlurPlugin() {
+  const [editor] = useLexicalComposerContext();
+
+  useEffect(() => {
+    return editor.registerRootListener((rootElement) => {
+      if (rootElement) {
+        const handleBlur = (e: FocusEvent) => {
+          // 检查是否点击了自动完成弹窗
+          const target = e.target as HTMLElement;
+          console.log('target', target)
+          if (target?.closest('[data-autocomplete-popup="true"]')) {
+            return;
+          }
+          
+          editor.update(() => {
+            $setSelection(null);
+          });
+        };
+
+        rootElement.addEventListener('blur', handleBlur);
+        return () => {
+          rootElement.removeEventListener('blur', handleBlur);
+        };
+      }
+    });
+  }, [editor]);
+
+  return null;
+}
diff --git a/web/src/views/Workflow/components/Properties/JinjaRender/index.tsx b/web/src/views/Workflow/components/Properties/JinjaRender/index.tsx
new file mode 100644
index 00000000..a2c9da37
--- /dev/null
+++ b/web/src/views/Workflow/components/Properties/JinjaRender/index.tsx
@@ -0,0 +1,206 @@
+import { type FC, useEffect, useRef } from 'react'
+import { useTranslation } from 'react-i18next'
+import { Form } from 'antd'
+import { Node } from '@antv/x6'
+import type { Suggestion } from '../../Editor/plugin/AutocompletePlugin'
+import MappingList from '../MappingList'
+import MessageEditor from '../MessageEditor'
+
+interface MappingItem {
+  name?: string
+  value?: string
+}
+
+interface JinjaRenderProps {
+  options: Suggestion[]
+  templateOptions: Suggestion[]
+  selectedNode: Node
+}
+
+const extractTemplateVars = (template: string): string[] => {
+  return (template.match(/{{\s*([\w.]+)\s*}}/g) || [])
+    .map(m => m.replace(/{{\s*|\s*}}/g, ''))
+}
+
+const getMappingNames = (mapping: MappingItem[]): string[] => {
+  return mapping.filter(item => item?.name).map(item => item.name!)
+}
+
+const JinjaRender: FC<JinjaRenderProps> = ({ selectedNode, options, templateOptions }) => {
+  const { t } = useTranslation()
+  const form = Form.useFormInstance()
+  const values = Form.useWatch([], form) || {}
+
+  console.log('JinjaRender values', values)
+
+  const prevMappingNamesRef = useRef<string[]>([])
+  const prevTemplateVarsRef = useRef<string[]>([])
+  const syncTimeoutRef = useRef<number | null>(null)
+  const isSyncingRef = useRef(false)
+  const lastSyncSourceRef = useRef<'mapping' | 'template' | null>(null)
+
+  // Reset refs when node changes
+  useEffect(() => {
+    if (selectedNode?.getData()?.id) {
+      prevMappingNamesRef.current = []
+      prevTemplateVarsRef.current = []
+      lastSyncSourceRef.current = null
+    }
+  }, [selectedNode?.getData()?.id])
+
+  // Sync template when mapping names change
+  useEffect(() => {
+    if (
+      isSyncingRef.current ||
+      lastSyncSourceRef.current === 'mapping' ||
+      selectedNode?.data?.type !== 'jinja-render' ||
+      !values?.mapping ||
+      !values?.template
+    ) return
+
+    const currentMappingNames = Array.isArray(values.mapping) ? getMappingNames(values.mapping) : []
+    const prevNames = prevMappingNamesRef.current
+
+    if (prevNames.length === 0) {
+      prevMappingNamesRef.current = currentMappingNames
+      return
+    }
+
+    if (JSON.stringify(prevNames) === JSON.stringify(currentMappingNames)) return
+
+    if (syncTimeoutRef.current) clearTimeout(syncTimeoutRef.current)
+    const activeElement = document.activeElement as HTMLElement
+
+    syncTimeoutRef.current = setTimeout(() => {
+      let updatedTemplate = String(form.getFieldValue('template') || '')
+
+      prevNames.forEach((oldName, index) => {
+        const newName = currentMappingNames[index]
+        if (newName && oldName !== newName) {
+          updatedTemplate = updatedTemplate.replace(
+            new RegExp(`{{\\s*${oldName}\\s*}}`, 'g'),
+            `{{${newName}}}`
+          )
+        }
+      })
+
+      if (updatedTemplate !== form.getFieldValue('template')) {
+        isSyncingRef.current = true
+        lastSyncSourceRef.current = 'mapping'
+        
+        prevTemplateVarsRef.current = extractTemplateVars(updatedTemplate)
+        prevMappingNamesRef.current = currentMappingNames
+        form.setFieldValue('template', updatedTemplate)
+
+        requestAnimationFrame(() => {
+          activeElement?.focus?.()
+          setTimeout(() => {
+            isSyncingRef.current = false
+            lastSyncSourceRef.current = null
+          }, 50)
+        })
+      } else {
+        prevMappingNamesRef.current = currentMappingNames
+      }
+    }, 0)
+  }, [values?.mapping, selectedNode?.data?.type, form])
+
+  // Sync mapping when template variables change
+  useEffect(() => {
+    console.log('values?.template', values?.template)
+    if (
+      isSyncingRef.current ||
+      lastSyncSourceRef.current === 'template' ||
+      selectedNode?.data?.type !== 'jinja-render' ||
+      !values?.template ||
+      !values?.mapping
+    ) return
+
+    const templateVars = extractTemplateVars(String(values.template))
+    if (JSON.stringify(prevTemplateVarsRef.current) === JSON.stringify(templateVars)) return
+
+    const isTemplateEditor = document.activeElement?.closest('[data-editor-type="template"]')
+    if (!isTemplateEditor) {
+      prevTemplateVarsRef.current = templateVars
+      return
+    }
+
+    const updatedMapping: MappingItem[] = Array.isArray(values.mapping)
+      ? [...values.mapping.filter((item: MappingItem) => item)]
+      : []
+    const existingNames = getMappingNames(updatedMapping)
+    let updatedTemplate = String(values.template)
+
+    // Update existing mapping names based on position
+    if (prevTemplateVarsRef.current.length > 0) {
+      prevTemplateVarsRef.current.forEach((oldVar, index) => {
+        const newVar = templateVars[index]
+        if (newVar && oldVar !== newVar && updatedMapping[index]) {
+          updatedMapping[index] = { ...updatedMapping[index], name: newVar }
+        }
+      })
+    }
+
+    // Add new mappings and normalize template
+    templateVars.forEach(varName => {
+      const existingMapping = updatedMapping.find(item => item.value === `{{${varName}}}`)
+      const regex = new RegExp(`{{\\s*${varName.replace(/\./g, '\\.')}\\s*}}`, 'g')
+
+      if (existingMapping) {
+        updatedTemplate = updatedTemplate.replace(regex, `{{${existingMapping.name}}}`)
+      } else if (!existingNames.includes(varName)) {
+        const mappingName = varName.includes('.') ? varName.split('.').pop() || varName : varName
+        updatedMapping.push({ name: mappingName, value: `{{${varName}}}` })
+        updatedTemplate = updatedTemplate.replace(regex, `{{${mappingName}}}`)
+      }
+    })
+
+    // Remove unused mappings and duplicates
+    const seenNames = new Set<string>()
+    const finalMapping = updatedMapping.filter(item => {
+      const isUsed = templateVars.some(v => item.name === v || item.value === `{{${v}}}`)
+      if (!isUsed || !item.name || seenNames.has(item.name)) return false
+      seenNames.add(item.name)
+      return true
+    })
+
+    isSyncingRef.current = true
+    lastSyncSourceRef.current = 'template'
+    prevMappingNamesRef.current = getMappingNames(finalMapping)
+    prevTemplateVarsRef.current = templateVars
+
+    if (JSON.stringify(finalMapping) !== JSON.stringify(values.mapping)) {
+      form.setFieldValue('mapping', finalMapping)
+    }
+    if (updatedTemplate !== String(values.template)) {
+      form.setFieldValue('template', updatedTemplate)
+    }
+
+    setTimeout(() => {
+      isSyncingRef.current = false
+      lastSyncSourceRef.current = null
+    }, 50)
+  }, [values?.template, selectedNode?.data?.type, form])
+
+  return (
+    <>
+      <Form.Item name="mapping" noStyle>
+        <MappingList name="mapping" options={options} />
+      </Form.Item>
+
+      <Form.Item name="template">
+        <MessageEditor
+          title={t('workflow.config.jinja-render.template')}
+          isArray={false}
+          parentName="template"
+          enableJinja2={true}
+          options={templateOptions}
+          titleVariant="borderless"
+          size="small"
+        />
+      </Form.Item>
+    </>
+  )
+}
+
+export default JinjaRender
diff --git a/web/src/views/Workflow/components/Properties/index.tsx b/web/src/views/Workflow/components/Properties/index.tsx
index 6d4571dc..d55e1d9e 100644
--- a/web/src/views/Workflow/components/Properties/index.tsx
+++ b/web/src/views/Workflow/components/Properties/index.tsx
@@ -1,4 +1,4 @@
-import { type FC, useEffect, useState, useRef, useMemo } from "react";
+import { type FC, useEffect, useState, useMemo } from "react";
 import clsx from 'clsx'
 import { useTranslation } from 'react-i18next'
 import { Graph, Node } from '@antv/x6';
@@ -17,7 +17,6 @@ import ParamsList from './ParamsList';
 import GroupVariableList from './GroupVariableList'
 import CaseList from './CaseList'
 import HttpRequest from './HttpRequest';
-import MappingList from './MappingList'
 import CategoryList from './CategoryList'
 import ConditionList from './ConditionList'
 import CycleVarsList from './CycleVarsList'
@@ -29,6 +28,7 @@ import { useVariableList, getCurrentNodeVariables } from './hooks/useVariableLis
 import styles from './properties.module.css'
 import Editor from "../Editor";
 import RbSlider from './RbSlider'
+import JinjaRender from './JinjaRender'
 
 interface PropertiesProps {
   selectedNode?: Node | null; 
@@ -50,136 +50,16 @@ const Properties: FC<PropertiesProps> = ({
   const [form] = Form.useForm<NodeConfig>();
   const [configs, setConfigs] = useState<Record<string,NodeConfig>>({} as Record<string,NodeConfig>)
   const values = Form.useWatch([], form);
-  const prevMappingNamesRef = useRef<string[]>([])
-  const prevTemplateVarsRef = useRef<string[]>([])
-  const syncTimeoutRef = useRef<number | null>(null)
-  const isSyncingRef = useRef(false)
-  const lastSyncSourceRef = useRef<'mapping' | 'template' | null>(null)
   const variableList = useVariableList(selectedNode, graphRef, chatVariables)
 
   useEffect(() => {
     if (selectedNode?.getData()?.id) {
-      form.resetFields()
-      prevMappingNamesRef.current = []
-      prevTemplateVarsRef.current = []
-      lastSyncSourceRef.current = null
       setOutputCollapsed(true)
+    } else {
+      form.resetFields()
     }
   }, [selectedNode?.getData()?.id])
 
-  // Sync template when mapping names change
-  useEffect(() => {
-    if (isSyncingRef.current || lastSyncSourceRef.current === 'mapping' || selectedNode?.data?.type !== 'jinja-render' || !values?.mapping || !values?.template) return
-    
-    const currentMappingNames = Array.isArray(values.mapping) ? values.mapping.filter(item => item && item.name).map((item: any) => item.name) : []
-    const prevNames = prevMappingNamesRef.current
-    
-    if (prevNames.length === 0) {
-      prevMappingNamesRef.current = currentMappingNames
-      return
-    }
-    
-    if (JSON.stringify(prevNames) === JSON.stringify(currentMappingNames)) return
-    
-    if (syncTimeoutRef.current) clearTimeout(syncTimeoutRef.current)
-    const activeElement = document.activeElement as HTMLElement
-    
-    syncTimeoutRef.current = setTimeout(() => {
-      let updatedTemplate = String(form.getFieldValue('template') || '')
-      
-      prevNames.forEach((oldName, index) => {
-        const newName = currentMappingNames[index]
-        if (newName && oldName !== newName) {
-          updatedTemplate = updatedTemplate.replace(new RegExp(`{{\\s*${oldName}\\s*}}`, 'g'), `{{${newName}}}`)
-        }
-      })
-      
-      if (updatedTemplate !== form.getFieldValue('template')) {
-        isSyncingRef.current = true
-        lastSyncSourceRef.current = 'mapping'
-        const newTemplateVars = (updatedTemplate.match(/{{\s*([\w.]+)\s*}}/g) || []).map(m => m.replace(/{{\s*|\s*}}/g, ''))
-        prevTemplateVarsRef.current = newTemplateVars
-        prevMappingNamesRef.current = currentMappingNames
-        form.setFieldValue('template', updatedTemplate)
-        
-        requestAnimationFrame(() => {
-          activeElement?.focus?.()
-          setTimeout(() => { 
-            isSyncingRef.current = false
-            lastSyncSourceRef.current = null
-          }, 50)
-        })
-      } else {
-        prevMappingNamesRef.current = currentMappingNames
-      }
-    }, 0)
-  }, [values?.mapping, selectedNode?.data?.type, form])
-
-  // Sync mapping when template variables change
-  useEffect(() => {
-    if (isSyncingRef.current || lastSyncSourceRef.current === 'template' || selectedNode?.data?.type !== 'jinja-render' || !values?.template || !values?.mapping) return
-    
-    const templateVars = (String(values.template).match(/{{\s*([\w.]+)\s*}}/g) || []).map(m => m.replace(/{{\s*|\s*}}/g, ''))
-    if (JSON.stringify(prevTemplateVarsRef.current) === JSON.stringify(templateVars)) return
-    
-    const isTemplateEditor = document.activeElement?.closest('[data-editor-type="template"]')
-    if (!isTemplateEditor) {
-      prevTemplateVarsRef.current = templateVars
-      return
-    }
-    
-    const updatedMapping = Array.isArray(values.mapping) ? [...values.mapping.filter(item => item)] : []
-    const existingNames = updatedMapping.filter(item => item && item.name).map(item => item.name)
-    let updatedTemplate = String(values.template)
-    
-    if (prevTemplateVarsRef.current.length > 0) {
-      prevTemplateVarsRef.current.forEach((oldVar, index) => {
-        const newVar = templateVars[index]
-        if (newVar && oldVar !== newVar && updatedMapping[index]) {
-          updatedMapping[index] = { ...updatedMapping[index], name: newVar }
-        }
-      })
-    }
-    
-    templateVars.forEach(varName => {
-      const existingMapping = updatedMapping.find(item => item.value === `{{${varName}}}`)
-      const regex = new RegExp(`{{\\s*${varName.replace(/\./g, '\\.')}\\s*}}`, 'g')
-      
-      if (existingMapping) {
-        updatedTemplate = updatedTemplate.replace(regex, `{{${existingMapping.name}}}`)
-      } else if (!existingNames.includes(varName)) {
-        const mappingName = varName.includes('.') ? varName.split('.').pop() || varName : varName
-        updatedMapping.push({ name: mappingName, value: `{{${varName}}}` })
-        updatedTemplate = updatedTemplate.replace(regex, `{{${mappingName}}}`)
-      }
-    })
-    
-    const seenNames = new Set<string>()
-    const finalMapping = updatedMapping.filter(item => {
-      const isUsed = templateVars.some(v => item.name === v || item.value === `{{${v}}}`)
-      if (!isUsed || seenNames.has(item.name)) return false
-      seenNames.add(item.name)
-      return true
-    })
-    
-    isSyncingRef.current = true
-    lastSyncSourceRef.current = 'template'
-    prevMappingNamesRef.current = finalMapping.filter(item => item && item.name).map((item: any) => item.name)
-    prevTemplateVarsRef.current = templateVars
-    
-    if (JSON.stringify(finalMapping) !== JSON.stringify(values.mapping)) {
-      form.setFieldValue('mapping', finalMapping)
-    }
-    if (updatedTemplate !== String(values.template)) {
-      form.setFieldValue('template', updatedTemplate)
-    }
-    
-    setTimeout(() => { 
-      isSyncingRef.current = false
-      lastSyncSourceRef.current = null
-    }, 50)
-  }, [values?.template, selectedNode?.data?.type, form])
-
   useEffect(() => {
     if (selectedNode && form) {
       const { type = 'default', name = '', config } = selectedNode.getData() || {}
@@ -197,6 +77,8 @@ const Properties: FC<PropertiesProps> = ({
         ...initialValue,
       })
       setConfigs(config || {})
+    } else {
+      form.resetFields()
     }
   }, [selectedNode, form])
 
@@ -529,6 +411,12 @@ const Properties: FC<PropertiesProps> = ({
               />
             : selectedNode?.data?.type === 'tool'
             ? <ToolConfig options={variableList} />
+            : selectedNode?.data.type === 'jinja-render'
+            ? <JinjaRender
+              selectedNode={selectedNode}
+              options={getFilteredVariableList(selectedNode?.data?.type, 'mapping')}
+              templateOptions={getFilteredVariableList(selectedNode?.data?.type, 'template')}
+            />
             : configs && Object.keys(configs).length > 0 && Object.keys(configs).map((key) => {
               const config = configs[key] || {}
 
@@ -646,15 +534,6 @@ const Properties: FC<PropertiesProps> = ({
                   </Form.Item>
                 )
               }
-
-              if (config.type === 'mappingList') {
-                return (
-                  <Form.Item key={key} name={key} noStyle>
-                    <MappingList name={key} options={getFilteredVariableList(selectedNode?.data?.type, key)} />
-                  </Form.Item>
-                
-                )
-              }
               if (config.type === 'cycleVarsList') {
                 return (
                   <Form.Item key={key} name={key}>