V4.14.2 fearured (#5922)

* fix: chat agent template create (#5912)

* doc

* template market ui (#5917)

* Compress tool (#5919)

* Compress tool (#5914)

* rename file

* feat: agent call request

* perf: Agent call  (#5916)

* fix: interactive in tool call

* doc

* fix: merge node response

* fix: test

* fix：修改 message 对话中的压缩提示词 (#5918)

Co-authored-by: xxyyh <2289112474@qq>

* perf: compress code

* perf: agent call comment

---------

Co-authored-by: YeYuheng <57035043+YYH211@users.noreply.github.com>
Co-authored-by: xxyyh <2289112474@qq>

* remove pr

* feat: auto password

* perf: app template cache

* fix template market ui (#5921)

---------

Co-authored-by: heheer <heheer@sealos.io>
Co-authored-by: YeYuheng <57035043+YYH211@users.noreply.github.com>
Co-authored-by: xxyyh <2289112474@qq>

document/content/docs/introduction/development/openapi/chat.mdx

@@ -16,7 +16,7 @@ description: FastGPT OpenAPI 对话接口
 
 {/* * 对话现在有`v1`和`v2`两个接口，可以按需使用，v2 自 4.9.4 版本新增，v1 接口同时不再维护 */}
 
-## 请求简易应用和工作流
+## 请求对话 Agent 和工作流
 
 `v1`对话接口兼容`GPT`的接口！如果你的项目使用的是标准的`GPT`官方接口，可以直接通过修改`BaseUrl`和 `Authorization`来访问 FastGpt 应用，不过需要注意下面几个规则：
 

document/content/docs/toc.mdx

@@ -112,6 +112,7 @@ description: FastGPT 文档目录
 - [/docs/upgrading/4-13/4132](/docs/upgrading/4-13/4132)
 - [/docs/upgrading/4-14/4140](/docs/upgrading/4-14/4140)
 - [/docs/upgrading/4-14/4141](/docs/upgrading/4-14/4141)
+- [/docs/upgrading/4-14/4142](/docs/upgrading/4-14/4142)
 - [/docs/upgrading/4-8/40](/docs/upgrading/4-8/40)
 - [/docs/upgrading/4-8/41](/docs/upgrading/4-8/41)
 - [/docs/upgrading/4-8/42](/docs/upgrading/4-8/42)

document/content/docs/upgrading/4-14/4142.mdx

+---
+title: 'V4.14.2(进行中)'
+description: 'FastGPT V4.14.2 更新说明'
+---
+
+
+
+## 🚀 新增内容
+
+1. 封装底层 Agent Call 方式，支持工具连续调用时上下文的压缩，以及单个工具长响应的压缩。
+2. 模板市场新 UI。
+
+## ⚙️ 优化
+
+1. 30 分钟模板市场缓存时长。
+
+## 🐛 修复
+
+1. 简易应用模板未正常转化。
+2. 工具调用中，包含两个以上连续用户选择时候，第二个用户选择异常。

document/content/docs/upgrading/4-14/meta.json

 {
   "title": "4.14.x",
   "description": "",
-  "pages": ["4141", "4140"]
+  "pages": ["4142", "4141", "4140"]
 }

document/data/doc-last-modified.json

@@ -101,7 +101,7 @@
   "document/content/docs/protocol/terms.en.mdx": "2025-08-03T22:37:45+08:00",
   "document/content/docs/protocol/terms.mdx": "2025-08-03T22:37:45+08:00",
   "document/content/docs/toc.en.mdx": "2025-08-04T13:42:36+08:00",
-  "document/content/docs/toc.mdx": "2025-11-06T14:47:55+08:00",
+  "document/content/docs/toc.mdx": "2025-11-13T13:36:41+08:00",
   "document/content/docs/upgrading/4-10/4100.mdx": "2025-08-02T19:38:37+08:00",
   "document/content/docs/upgrading/4-10/4101.mdx": "2025-09-08T20:07:20+08:00",
   "document/content/docs/upgrading/4-11/4110.mdx": "2025-08-05T23:20:39+08:00",
@@ -115,7 +115,8 @@
   "document/content/docs/upgrading/4-13/4131.mdx": "2025-09-30T15:47:06+08:00",
   "document/content/docs/upgrading/4-13/4132.mdx": "2025-10-21T11:46:53+08:00",
   "document/content/docs/upgrading/4-14/4140.mdx": "2025-11-06T15:43:00+08:00",
-  "document/content/docs/upgrading/4-14/4141.mdx": "2025-11-11T14:05:02+08:00",
+  "document/content/docs/upgrading/4-14/4141.mdx": "2025-11-12T12:19:02+08:00",
+  "document/content/docs/upgrading/4-14/4142.mdx": "2025-11-13T20:49:04+08:00",
   "document/content/docs/upgrading/4-8/40.mdx": "2025-08-02T19:38:37+08:00",
   "document/content/docs/upgrading/4-8/41.mdx": "2025-08-02T19:38:37+08:00",
   "document/content/docs/upgrading/4-8/42.mdx": "2025-08-02T19:38:37+08:00",

packages/global/common/string/tools.ts

@@ -143,10 +143,7 @@ export const getRegQueryStr = (text: string, flags = 'i') => {
 
 /* slice json str */
 export const sliceJsonStr = (str: string) => {
-  str = str
-    .trim()
-    .replace(/(\\n|\\)/g, '')
-    .replace(/  /g, '');
+  str = str.trim();
 
   // Find first opening bracket
   let start = -1;

packages/global/core/ai/type.d.ts

@@ -80,7 +80,8 @@ export type CompletionFinishReason =
   | 'tool_calls'
   | 'content_filter'
   | 'function_call'
-  | null;
+  | null
+  | undefined;
 
 export default openai;
 export * from 'openai';

packages/global/core/app/type.d.ts

@@ -230,6 +230,8 @@ export type AppTemplateSchemaType = {
   type: string;
   author?: string;
   isActive?: boolean;
+  isPromoted?: boolean;
+  recommendText?: string;
   userGuide?: {
     type: 'markdown' | 'link';
     content?: string;
@@ -237,6 +239,7 @@ export type AppTemplateSchemaType = {
   };
   isQuickTemplate?: boolean;
   order?: number;
+  // TODO: 对于建议应用，是另一个格式
   workflow: WorkflowTemplateBasicType;
 };
 

packages/global/core/chat/utils.ts

@@ -213,21 +213,10 @@ export const getChatSourceByPublishChannel = (publishChannel: PublishChannelEnum
 export const mergeChatResponseData = (
   responseDataList: ChatHistoryItemResType[]
 ): ChatHistoryItemResType[] => {
-  // Merge children reponse data(Children has interactive response)
-  const responseWithMergedPlugins = responseDataList.map((item) => {
-    if (item.pluginDetail && item.pluginDetail.length > 1) {
-      return {
-        ...item,
-        pluginDetail: mergeChatResponseData(item.pluginDetail)
-      };
-    }
-    return item;
-  });
-
   const result: ChatHistoryItemResType[] = [];
   const mergeMap = new Map<string, number>(); // mergeSignId -> result index
 
-  for (const item of responseWithMergedPlugins) {
+  for (const item of responseDataList) {
     if (item.mergeSignId && mergeMap.has(item.mergeSignId)) {
       // Merge with existing item
       const existingIndex = mergeMap.get(item.mergeSignId)!;
@@ -238,9 +227,18 @@ export const mergeChatResponseData = (
         runningTime: +((existing.runningTime || 0) + (item.runningTime || 0)).toFixed(2),
         totalPoints: (existing.totalPoints || 0) + (item.totalPoints || 0),
         childTotalPoints: (existing.childTotalPoints || 0) + (item.childTotalPoints || 0),
-        toolDetail: [...(existing.toolDetail || []), ...(item.toolDetail || [])],
-        loopDetail: [...(existing.loopDetail || []), ...(item.loopDetail || [])],
-        pluginDetail: [...(existing.pluginDetail || []), ...(item.pluginDetail || [])]
+        toolDetail: mergeChatResponseData([
+          ...(existing.toolDetail || []),
+          ...(item.toolDetail || [])
+        ]),
+        loopDetail: mergeChatResponseData([
+          ...(existing.loopDetail || []),
+          ...(item.loopDetail || [])
+        ]),
+        pluginDetail: mergeChatResponseData([
+          ...(existing.pluginDetail || []),
+          ...(item.pluginDetail || [])
+        ])
       };
     } else {
       // Add new item

packages/global/core/workflow/runtime/type.d.ts

@@ -2,9 +2,9 @@ import type { ChatNodeUsageType } from '../../../support/wallet/bill/type';
 import type {
   ChatItemType,
   ToolRunResponseItemType,
-  AIChatItemValueItemType
+  AIChatItemValueItemType,
+  ChatHistoryItemResType
 } from '../../chat/type';
-import { NodeOutputItemType } from '../../chat/type';
 import type { FlowNodeInputItemType, FlowNodeOutputItemType } from '../type/io.d';
 import type { NodeToolConfigType, StoreNodeItemType } from '../type/node';
 import type { DispatchNodeResponseKeyEnum } from './constants';
@@ -112,7 +112,6 @@ export type RuntimeNodeItemType = {
   flowNodeType: StoreNodeItemType['flowNodeType'];
   showStatus?: StoreNodeItemType['showStatus'];
   isEntry?: boolean;
-  isStart?: boolean;
   version?: string;
 
   inputs: FlowNodeInputItemType[];

packages/global/core/workflow/runtime/utils.ts

@@ -20,6 +20,7 @@ import type { StoreNodeItemType } from '../type/node';
 import { isValidReferenceValueFormat } from '../utils';
 import type { RuntimeEdgeItemType, RuntimeNodeItemType } from './type';
 import { isSecretValue } from '../../../common/secret/utils';
+import { isChildInteractive } from '../template/system/interactive/constants';
 
 export const extractDeepestInteractive = (
   interactive: WorkflowInteractiveResponseType
@@ -28,11 +29,7 @@ export const extractDeepestInteractive = (
   let current = interactive;
   let depth = 0;
 
-  while (
-    depth < MAX_DEPTH &&
-    (current?.type === 'childrenInteractive' || current?.type === 'loopInteractive') &&
-    current.params?.childrenResponse
-  ) {
+  while (depth < MAX_DEPTH && 'childrenResponse' in current.params) {
     current = current.params.childrenResponse;
     depth++;
   }
@@ -181,10 +178,7 @@ export const getLastInteractiveValue = (
       return;
     }
 
-    if (
-      lastValue.interactive.type === 'childrenInteractive' ||
-      lastValue.interactive.type === 'loopInteractive'
-    ) {
+    if (isChildInteractive(lastValue.interactive.type)) {
       return lastValue.interactive;
     }
 
@@ -297,7 +291,6 @@ export const checkNodeRunStatus = ({
   node: RuntimeNodeItemType;
   runtimeEdges: RuntimeEdgeItemType[];
 }) => {
-  const filterRuntimeEdges = filterWorkflowEdges(runtimeEdges);
   const isStartNode = (nodeType: string) => {
     const map: Record<any, boolean> = {
       [FlowNodeTypeEnum.workflowStart]: true,
@@ -310,7 +303,7 @@ export const checkNodeRunStatus = ({
     const commonEdges: RuntimeEdgeItemType[] = [];
     const recursiveEdgeGroupsMap = new Map<string, RuntimeEdgeItemType[]>();
 
-    const sourceEdges = filterRuntimeEdges.filter((item) => item.target === targetNode.nodeId);
+    const sourceEdges = runtimeEdges.filter((item) => item.target === targetNode.nodeId);
 
     sourceEdges.forEach((sourceEdge) => {
       const stack: Array<{
@@ -333,7 +326,7 @@ export const checkNodeRunStatus = ({
         const sourceNode = nodesMap.get(edge.source);
         if (!sourceNode) continue;
 
-        if (isStartNode(sourceNode.flowNodeType) || sourceNode.isStart) {
+        if (isStartNode(sourceNode.flowNodeType) || sourceEdge.sourceHandle === 'selectedTools') {
           commonEdges.push(sourceEdge);
           continue;
         }
@@ -355,7 +348,7 @@ export const checkNodeRunStatus = ({
         newVisited.add(edge.source);
 
         // 查找目标节点的 source edges 并加入栈中
-        const nextEdges = filterRuntimeEdges.filter((item) => item.target === edge.source);
+        const nextEdges = runtimeEdges.filter((item) => item.target === edge.source);
 
         for (const nextEdge of nextEdges) {
           stack.push({

packages/global/core/workflow/template/system/interactive/constants.ts

+import type { InteractiveNodeResponseType } from './type';
+
+export const isChildInteractive = (type: InteractiveNodeResponseType['type']) => {
+  if (
+    type === 'childrenInteractive' ||
+    type === 'toolChildrenInteractive' ||
+    type === 'loopInteractive'
+  ) {
+    return true;
+  }
+  return false;
+};

packages/global/core/workflow/template/system/interactive/type.d.ts

@@ -9,11 +9,6 @@ type InteractiveBasicType = {
   memoryEdges: RuntimeEdgeItemType[];
   nodeOutputs: NodeOutputItemType[];
   skipNodeQueue?: { id: string; skippedNodeIdList: string[] }[]; // 需要记录目前在 queue 里的节点
-  toolParams?: {
-    entryNodeIds: string[]; // 记录工具中，交互节点的 Id，而不是起始工作流的入口
-    memoryMessages: ChatCompletionMessageParam[]; // 这轮工具中，产生的新的 messages
-    toolCallId: string; // 记录对应 tool 的id，用于后续交互节点可以替换掉 tool 的 response
-  };
 
   usageId?: string;
 };
@@ -27,7 +22,17 @@ type InteractiveNodeType = {
 type ChildrenInteractive = InteractiveNodeType & {
   type: 'childrenInteractive';
   params: {
-    childrenResponse?: WorkflowInteractiveResponseType;
+    childrenResponse: WorkflowInteractiveResponseType;
+  };
+};
+type ToolCallChildrenInteractive = InteractiveNodeType & {
+  type: 'toolChildrenInteractive';
+  params: {
+    childrenResponse: WorkflowInteractiveResponseType;
+    toolParams: {
+      memoryRequestMessages: ChatCompletionMessageParam[]; // 这轮工具中，产生的新的 messages
+      toolCallId: string; // 记录对应 tool 的id，用于后续交互节点可以替换掉 tool 的 response
+    };
   };
 };
 
@@ -94,6 +99,7 @@ export type InteractiveNodeResponseType =
   | UserSelectInteractive
   | UserInputInteractive
   | ChildrenInteractive
+  | ToolCallChildrenInteractive
   | LoopInteractive
   | PaymentPauseInteractive;
 

packages/service/core/ai/llm/agentCall/index.ts

+import type {
+  ChatCompletionMessageParam,
+  ChatCompletionTool,
+  ChatCompletionMessageToolCall,
+  CompletionFinishReason
+} from '@fastgpt/global/core/ai/type';
+import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
+import type {
+  ToolCallChildrenInteractive,
+  WorkflowInteractiveResponseType
+} from '@fastgpt/global/core/workflow/template/system/interactive/type';
+import type { CreateLLMResponseProps, ResponseEvents } from '../request';
+import { createLLMResponse } from '../request';
+import type { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
+import { compressRequestMessages } from '../compress';
+import { computedMaxToken } from '../../utils';
+import { filterGPTMessageByMaxContext } from '../utils';
+import { getLLMModel } from '../../model';
+import { filterEmptyAssistantMessages } from './utils';
+
+type RunAgentCallProps = {
+  maxRunAgentTimes: number;
+  compressTaskDescription?: string;
+
+  body: CreateLLMResponseProps['body'] & {
+    tools: ChatCompletionTool[];
+
+    temperature?: number;
+    top_p?: number;
+    stream?: boolean;
+  };
+
+  userKey?: CreateLLMResponseProps['userKey'];
+  isAborted?: CreateLLMResponseProps['isAborted'];
+
+  childrenInteractiveParams?: ToolCallChildrenInteractive['params'];
+  handleInteractiveTool: (e: ToolCallChildrenInteractive['params']) => Promise<{
+    response: string;
+    assistantMessages: ChatCompletionMessageParam[];
+    usages: ChatNodeUsageType[];
+    interactive?: WorkflowInteractiveResponseType;
+    stop?: boolean;
+  }>;
+
+  handleToolResponse: (e: {
+    call: ChatCompletionMessageToolCall;
+    messages: ChatCompletionMessageParam[];
+  }) => Promise<{
+    response: string;
+    assistantMessages: ChatCompletionMessageParam[];
+    usages: ChatNodeUsageType[];
+    interactive?: WorkflowInteractiveResponseType;
+    stop?: boolean;
+  }>;
+} & ResponseEvents;
+
+type RunAgentResponse = {
+  completeMessages: ChatCompletionMessageParam[]; // Step request complete messages
+  assistantMessages: ChatCompletionMessageParam[]; // Step assistant response messages
+  interactiveResponse?: ToolCallChildrenInteractive;
+
+  // Usage
+  inputTokens: number;
+  outputTokens: number;
+  subAppUsages: ChatNodeUsageType[];
+
+  finish_reason: CompletionFinishReason | undefined;
+};
+
+/* 
+  一个循环进行工具调用的 LLM 请求封装。
+
+  AssistantMessages 组成：
+  1. 调用 AI 时生成的 messages
+  2. tool 内部调用产生的 messages
+  3. tool 响应的值，role=tool，content=tool response
+
+  RequestMessages 为模型请求的消息，组成:
+  1. 历史对话记录
+  2. 调用 AI 时生成的 messages
+  3. tool 响应的值，role=tool，content=tool response
+
+  memoryRequestMessages 为上一轮中断时，requestMessages 的内容
+*/
+export const runAgentCall = async ({
+  maxRunAgentTimes,
+  body: { model, messages, max_tokens, tools, ...body },
+  userKey,
+  isAborted,
+
+  childrenInteractiveParams,
+  handleInteractiveTool,
+  handleToolResponse,
+
+  onReasoning,
+  onStreaming,
+  onToolCall,
+  onToolParam
+}: RunAgentCallProps): Promise<RunAgentResponse> => {
+  const modelData = getLLMModel(model);
+
+  let runTimes = 0;
+  let interactiveResponse: ToolCallChildrenInteractive | undefined;
+
+  // Init messages
+  const maxTokens = computedMaxToken({
+    model: modelData,
+    maxToken: max_tokens || 8000,
+    min: 100
+  });
+
+  // 本轮产生的 assistantMessages，包括 tool 内产生的
+  const assistantMessages: ChatCompletionMessageParam[] = [];
+  // 多轮运行时候的请求 messages
+  let requestMessages = (
+    await filterGPTMessageByMaxContext({
+      messages,
+      maxContext: modelData.maxContext - (maxTokens || 0) // filter token. not response maxToken
+    })
+  ).map((item) => {
+    if (item.role === 'assistant' && item.tool_calls) {
+      return {
+        ...item,
+        tool_calls: item.tool_calls.map((tool) => ({
+          id: tool.id,
+          type: tool.type,
+          function: tool.function
+        }))
+      };
+    }
+    return item;
+  });
+
+  let inputTokens: number = 0;
+  let outputTokens: number = 0;
+  let finish_reason: CompletionFinishReason | undefined;
+  const subAppUsages: ChatNodeUsageType[] = [];
+
+  // 处理 tool 里的交互
+  if (childrenInteractiveParams) {
+    const {
+      response,
+      assistantMessages: toolAssistantMessages,
+      usages,
+      interactive,
+      stop
+    } = await handleInteractiveTool(childrenInteractiveParams);
+
+    // 将 requestMessages 复原成上一轮中断时的内容，并附上 tool response
+    requestMessages = childrenInteractiveParams.toolParams.memoryRequestMessages.map((item) =>
+      item.role === 'tool' && item.tool_call_id === childrenInteractiveParams.toolParams.toolCallId
+        ? {
+            ...item,
+            content: response
+          }
+        : item
+    );
+
+    // 只需要推送本轮产生的 assistantMessages
+    assistantMessages.push(...filterEmptyAssistantMessages(toolAssistantMessages));
+    subAppUsages.push(...usages);
+
+    // 相同 tool 触发了多次交互, 调用的 toolId 认为是相同的
+    if (interactive) {
+      // console.dir(interactive, { depth: null });
+      interactiveResponse = {
+        type: 'toolChildrenInteractive',
+        params: {
+          childrenResponse: interactive,
+          toolParams: {
+            memoryRequestMessages: requestMessages,
+            toolCallId: childrenInteractiveParams.toolParams.toolCallId
+          }
+        }
+      };
+    }
+
+    if (interactiveResponse || stop) {
+      return {
+        inputTokens: 0,
+        outputTokens: 0,
+        subAppUsages,
+        completeMessages: requestMessages,
+        assistantMessages,
+        interactiveResponse,
+        finish_reason: 'stop'
+      };
+    }
+
+    // 正常完成该工具的响应，继续进行工具调用
+  }
+
+  // 自循环运行
+  while (runTimes < maxRunAgentTimes) {
+    // TODO: 费用检测
+
+    runTimes++;
+
+    // 1. Compress request messages
+    const result = await compressRequestMessages({
+      messages: requestMessages,
+      model: modelData
+    });
+    requestMessages = result.messages;
+    inputTokens += result.usage?.inputTokens || 0;
+    outputTokens += result.usage?.outputTokens || 0;
+
+    // 2. Request LLM
+    let {
+      reasoningText: reasoningContent,
+      answerText: answer,
+      toolCalls = [],
+      usage,
+      getEmptyResponseTip,
+      assistantMessage: llmAssistantMessage,
+      finish_reason: finishReason
+    } = await createLLMResponse({
+      body: {
+        ...body,
+        model,
+        messages: requestMessages,
+        tool_choice: 'auto',
+        toolCallMode: modelData.toolChoice ? 'toolChoice' : 'prompt',
+        tools,
+        parallel_tool_calls: true
+      },
+      userKey,
+      isAborted,
+      onReasoning,
+      onStreaming,
+      onToolCall,
+      onToolParam
+    });
+
+    finish_reason = finishReason;
+
+    if (!answer && !reasoningContent && !toolCalls.length) {
+      return Promise.reject(getEmptyResponseTip());
+    }
+
+    // 3. 更新 messages
+    const cloneRequestMessages = requestMessages.slice();
+    // 推送 AI 生成后的 assistantMessages
+    assistantMessages.push(...llmAssistantMessage);
+    requestMessages.push(...llmAssistantMessage);
+
+    // 4. Call tools
+    let toolCallStep = false;
+    for await (const tool of toolCalls) {
+      const {
+        response,
+        assistantMessages: toolAssistantMessages,
+        usages,
+        interactive,
+        stop
+      } = await handleToolResponse({
+        call: tool,
+        messages: cloneRequestMessages
+      });
+
+      const toolMessage: ChatCompletionMessageParam = {
+        tool_call_id: tool.id,
+        role: ChatCompletionRequestMessageRoleEnum.Tool,
+        content: response
+      };
+
+      // 5. Add tool response to messages
+      assistantMessages.push(toolMessage);
+      assistantMessages.push(...filterEmptyAssistantMessages(toolAssistantMessages)); // 因为 toolAssistantMessages 也需要记录成 AI 响应，所以这里需要推送。
+      requestMessages.push(toolMessage); // 请求的 Request 只需要工具响应，不需要工具中 assistant 的内容，所以不推送 toolAssistantMessages
+
+      subAppUsages.push(...usages);
+
+      if (interactive) {
+        interactiveResponse = {
+          type: 'toolChildrenInteractive',
+          params: {
+            childrenResponse: interactive,
+            toolParams: {
+              memoryRequestMessages: [],
+              toolCallId: tool.id
+            }
+          }
+        };
+      }
+      if (stop) {
+        toolCallStep = true;
+      }
+    }
+
+    // 6 Record usage
+    inputTokens += usage.inputTokens;
+    outputTokens += usage.outputTokens;
+
+    if (toolCalls.length === 0 || !!interactiveResponse || toolCallStep) {
+      break;
+    }
+  }
+
+  if (interactiveResponse) {
+    interactiveResponse.params.toolParams.memoryRequestMessages = requestMessages;
+  }
+
+  return {
+    inputTokens,
+    outputTokens,
+    subAppUsages,
+    completeMessages: requestMessages,
+    assistantMessages,
+    interactiveResponse,
+    finish_reason
+  };
+};

packages/service/core/ai/llm/agentCall/utils.ts

+import type { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type';
+
+export const filterEmptyAssistantMessages = (messages: ChatCompletionMessageParam[]) => {
+  return messages.filter((item) => {
+    if (item.role === 'assistant') {
+      if (!item.content) return false;
+      if (item.content.length === 0) return false;
+    }
+    return true;
+  });
+};

packages/service/core/ai/llm/compress/constants.ts

+/**
+ * Agent 上下文压缩配置常量
+ *
+ * ## 设计原则
+ *
+ * 1. **空间分配**
+ *    - 输出预留：30%（模型生成答案 + 缓冲）
+ *    - 系统提示词（Depends on）：15%
+ *    - Agent 对话历史：55%
+ *
+ * 2. **压缩策略**
+ *    - 触发阈值：接近空间上限时触发
+ *    - 压缩目标：激进压缩，预留增长空间
+ *    - 约束机制：单个 tool 有绝对大小限制
+ *
+ * 3. **协调关系**
+ *    - Depends on 使用完整 response，需要较大空间（15%）
+ *    - Agent 历史包含所有 tool responses，是动态主体（55%）
+ *    - 单个 tool 不能过大，避免挤占其他空间（10%）
+ */
+
+export const COMPRESSION_CONFIG = {
+  /**
+   * === Depends on（系统提示词中的步骤历史）===
+   *
+   * 触发场景：拼接依赖步骤的完整 response 后，token 数超过阈值
+   * 内容特点：包含多个步骤的完整执行结果（使用 response 而非 summary）
+   *
+   * 示例（maxContext=100k）：
+   * - 依赖 3 个步骤，每个 4k → 12k (12%) ✅ 不触发
+   * - 依赖 5 个步骤，每个 4k → 20k (20%) ⚠️ 触发压缩 → 12k
+   */
+  DEPENDS_ON_THRESHOLD: 0.15, // 15% 触发压缩
+  DEPENDS_ON_TARGET: 0.12, // 压缩到 12%（预留 3% 缓冲）
+
+  /**
+   * === 对话历史 ===
+   *
+   * 触发场景：对话历史（含所有 user/assistant/tool 消息）超过阈值
+   * 内容特点：动态累积，包含所有 tool responses
+   *
+   * 示例（maxContext=100k）：
+   * - 初始 20k + 6 轮对话(34k) = 54k (54%) ✅ 不触发
+   * - 再 1 轮 = 60k (60%) ⚠️ 触发压缩 → 30k
+   * - 预留：55k - 30k = 25k（还能跑 4 轮）
+   */
+  MESSAGE_THRESHOLD: 0.8, // 55% 触发压缩
+  MESSAGE_TARGET_RATIO: 0.5, // 压缩到 50%（即原 55% → 27.5%）
+
+  /**
+   * === 单个 tool response ===
+   *
+   * 触发场景：单个 tool 返回的内容超过绝对大小限制
+   * 内容特点：单次 tool 调用的响应（如搜索结果、文件内容等）
+   *
+   * 示例（maxContext=100k）：
+   * - tool response = 8k (8%) ✅ 不触发
+   * - tool response = 15k (15%) ⚠️ 触发压缩 → 7k
+   */
+  SINGLE_TOOL_MAX: 0.5,
+  SINGLE_TOOL_TARGET: 0.25,
+
+  /**
+   * === 分块压缩 ===
+   *
+   * 触发场景：当内容需要分块处理时（超过 LLM 单次处理能力）
+   * 用途：将超大内容切分成多个块，分别压缩后合并
+   *
+   * 示例（maxContext=100k）：
+   * - 单块最大：40k tokens
+   * - 50k 内容 → 切分成 2 块，每块约 25k
+   */
+  CHUNK_SIZE_RATIO: 0.5 // 40%（单块不超过此比例）
+} as const;
+
+/**
+ * 计算各场景的压缩阈值
+ * @param maxContext - 模型的最大上下文长度
+ * @returns 各场景的具体 token 数阈值
+ */
+export const calculateCompressionThresholds = (maxContext: number) => {
+  return {
+    // Depends on 压缩阈值
+    dependsOn: {
+      threshold: Math.floor(maxContext * COMPRESSION_CONFIG.DEPENDS_ON_THRESHOLD),
+      target: Math.floor(maxContext * COMPRESSION_CONFIG.DEPENDS_ON_TARGET)
+    },
+    // 对话历史压缩阈值
+    messages: {
+      threshold: Math.floor(maxContext * COMPRESSION_CONFIG.MESSAGE_THRESHOLD),
+      targetRatio: COMPRESSION_CONFIG.MESSAGE_TARGET_RATIO
+    },
+
+    // 单个 tool response 压缩阈值
+    singleTool: {
+      threshold: Math.floor(maxContext * COMPRESSION_CONFIG.SINGLE_TOOL_MAX),
+      target: Math.floor(maxContext * COMPRESSION_CONFIG.SINGLE_TOOL_TARGET)
+    },
+
+    // 分块大小
+    chunkSize: Math.floor(maxContext * COMPRESSION_CONFIG.CHUNK_SIZE_RATIO)
+  };
+};

packages/service/core/ai/llm/compress/index.ts

+import type { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
+import { countGptMessagesTokens } from '../../../../common/string/tiktoken';
+import { addLog } from '../../../../common/system/log';
+import { calculateCompressionThresholds } from './constants';
+import { createLLMResponse } from '../request';
+import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
+import type { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type';
+import { getCompressRequestMessagesPrompt } from './prompt';
+import type { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
+import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
+import { i18nT } from '../../../../../web/i18n/utils';
+import { parseToolArgs } from '../../utils';
+
+/**
+ * 压缩 对话历史
+ * 当 messages 的 token 长度超过阈值时，调用 LLM 进行压缩
+ */
+export const compressRequestMessages = async ({
+  messages,
+  model
+}: {
+  messages: ChatCompletionMessageParam[];
+  model: LLMModelItemType;
+}): Promise<{
+  messages: ChatCompletionMessageParam[];
+  usage?: ChatNodeUsageType;
+}> => {
+  if (!messages || messages.length === 0) {
+    return {
+      messages
+    };
+  }
+
+  // Save the system messages
+  const [systemMessages, otherMessages]: [
+    ChatCompletionMessageParam[],
+    ChatCompletionMessageParam[]
+  ] = [[], []];
+  messages.forEach((message) => {
+    if (message.role === ChatCompletionRequestMessageRoleEnum.System) {
+      systemMessages.push(message);
+    } else {
+      otherMessages.push(message);
+    }
+  });
+
+  const messageTokens = await countGptMessagesTokens(otherMessages);
+  const thresholds = calculateCompressionThresholds(model.maxContext).messages;
+
+  if (messageTokens < thresholds.threshold) {
+    return {
+      messages
+    };
+  }
+
+  addLog.info('[Compression messages] Start', {
+    tokens: messageTokens
+  });
+
+  const compressPrompt = await getCompressRequestMessagesPrompt({
+    messages: otherMessages,
+    rawTokens: messageTokens,
+    model
+  });
+
+  const userPrompt = '请执行压缩操作，严格按照JSON格式返回结果。';
+
+  try {
+    const { answerText, usage } = await createLLMResponse({
+      body: {
+        model,
+        messages: [
+          {
+            role: ChatCompletionRequestMessageRoleEnum.System,
+            content: compressPrompt
+          },
+          {
+            role: ChatCompletionRequestMessageRoleEnum.User,
+            content: userPrompt
+          }
+        ],
+        temperature: 0.1,
+        stream: true
+      }
+    });
+
+    if (!answerText) {
+      addLog.warn('[Compression messages] failed: empty response, return original messages');
+      return { messages };
+    }
+
+    const { totalPoints, modelName } = formatModelChars2Points({
+      model: model.model,
+      inputTokens: usage.inputTokens,
+      outputTokens: usage.outputTokens
+    });
+    const compressedUsage = {
+      moduleName: i18nT('account_usage:compress_llm_messages'),
+      model: modelName,
+      totalPoints,
+      inputTokens: usage.inputTokens,
+      outputTokens: usage.outputTokens
+    };
+
+    const compressResult = parseToolArgs<{
+      compressed_messages: ChatCompletionMessageParam[];
+      compression_summary: string;
+    }>(answerText);
+
+    if (
+      !compressResult ||
+      !Array.isArray(compressResult) ||
+      compressResult.compressed_messages.length === 0
+    ) {
+      addLog.warn('[Compression messages] failed: cannot parse JSON, return original messages', {
+        messages: compressResult?.compressed_messages
+      });
+      return { messages, usage: compressedUsage };
+    }
+
+    const compressedTokens = usage.outputTokens;
+    addLog.info('[Compression messages] successfully', {
+      originalTokens: messageTokens,
+      compressedTokens,
+      actualRatio: (compressedTokens / messageTokens).toFixed(2),
+      summary: compressResult.compression_summary
+    });
+
+    // 如果之前提取了 system 消息，现在插回去
+    const finalMessages = [...systemMessages, ...compressResult.compressed_messages];
+
+    return {
+      messages: finalMessages,
+      usage: compressedUsage
+    };
+  } catch (error) {
+    addLog.error('[Compression messages] failed', error);
+    return { messages };
+  }
+};

packages/service/core/ai/llm/request.ts

@@ -15,7 +15,7 @@ import { removeDatasetCiteText } from '@fastgpt/global/core/ai/llm/utils';
 import { getAIApi } from '../config';
 import type { OpenaiAccountType } from '@fastgpt/global/support/user/team/type';
 import { getNanoid } from '@fastgpt/global/common/string/tools';
-import { parsePromptToolCall, promptToolCallMessageRewrite } from './promptToolCall';
+import { parsePromptToolCall, promptToolCallMessageRewrite } from './promptCall';
 import { getLLMModel } from '../model';
 import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
 import { countGptMessagesTokens } from '../../../common/string/tiktoken/index';
@@ -26,14 +26,14 @@ import { i18nT } from '../../../../web/i18n/utils';
 import { getErrText } from '@fastgpt/global/common/error/utils';
 import json5 from 'json5';
 
-type ResponseEvents = {
+export type ResponseEvents = {
   onStreaming?: ({ text }: { text: string }) => void;
   onReasoning?: ({ text }: { text: string }) => void;
   onToolCall?: ({ call }: { call: ChatCompletionMessageToolCall }) => void;
   onToolParam?: ({ tool, params }: { tool: ChatCompletionMessageToolCall; params: string }) => void;
 };
 
-type CreateLLMResponseProps<T extends CompletionsBodyType> = {
+export type CreateLLMResponseProps<T extends CompletionsBodyType = CompletionsBodyType> = {
   userKey?: OpenaiAccountType;
   body: LLMRequestBodyType<T>;
   isAborted?: () => boolean | undefined;
@@ -86,7 +86,7 @@ export const createLLMResponse = async <T extends CompletionsBodyType>(
     messages: rewriteMessages
   });
 
-  // console.log(JSON.stringify(requestBody, null, 2));
+  // console.dir(requestBody, { depth: null });
   const { response, isStreamResponse, getEmptyResponseTip } = await createChatCompletion({
     body: requestBody,
     userKey,

packages/service/core/ai/utils.ts

@@ -2,6 +2,8 @@ import { type LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
 import type { CompletionFinishReason, CompletionUsage } from '@fastgpt/global/core/ai/type';
 import { getLLMDefaultUsage } from '@fastgpt/global/core/ai/constants';
 import { removeDatasetCiteText } from '@fastgpt/global/core/ai/llm/utils';
+import json5 from 'json5';
+import { sliceJsonStr } from '@fastgpt/global/common/string/tools';
 
 /* 
   Count response max token
@@ -317,3 +319,11 @@ export const parseLLMStreamResponse = () => {
     updateFinishReason
   };
 };
+
+export const parseToolArgs = <T = Record<string, any>>(toolArgs: string) => {
+  try {
+    return json5.parse(sliceJsonStr(toolArgs)) as T;
+  } catch {
+    return;
+  }
+};

packages/service/core/app/templates/register.ts

@@ -3,6 +3,7 @@ import { AppToolSourceEnum } from '@fastgpt/global/core/app/tool/constants';
 import { type AppTemplateSchemaType } from '@fastgpt/global/core/app/type';
 import { MongoAppTemplate } from './templateSchema';
 import { pluginClient } from '../../../thirdProvider/fastgptPlugin';
+import { addMinutes } from 'date-fns';
 
 const getFileTemplates = async (): Promise<AppTemplateSchemaType[]> => {
   const res = await pluginClient.workflow.getTemplateList();
@@ -11,9 +12,15 @@ const getFileTemplates = async (): Promise<AppTemplateSchemaType[]> => {
 };
 
 const getAppTemplates = async () => {
-  const communityTemplates = await getFileTemplates();
+  const originCommunityTemplates = await getFileTemplates();
+  const communityTemplates = originCommunityTemplates.map((template) => {
+    return {
+      ...template,
+      templateId: `${AppToolSourceEnum.community}-${template.templateId.split('.')[0]}`
+    };
+  });
 
-  const dbTemplates = await MongoAppTemplate.find();
+  const dbTemplates = await MongoAppTemplate.find().lean();
 
   // Merge db data to community templates
   const communityTemplateConfig = communityTemplates.map((template) => {
@@ -22,17 +29,12 @@ const getAppTemplates = async () => {
     if (config) {
       return {
         ...template,
-        isActive: config.isActive ?? template.isActive,
-        tags: config.tags ?? template.tags,
-        userGuide: config.userGuide ?? template.userGuide,
-        isQuickTemplate: config.isQuickTemplate ?? template.isQuickTemplate,
-        order: config.order ?? template.order
+        ...config
       };
     }
 
     return template;
   });
-
   const res = [
     ...communityTemplateConfig,
     ...dbTemplates.filter((t) => isCommercialTemaplte(t.templateId))
@@ -42,20 +44,31 @@ const getAppTemplates = async () => {
 };
 
 export const getAppTemplatesAndLoadThem = async (refresh = false) => {
-  if (isProduction && global.appTemplates && global.appTemplates.length > 0 && !refresh)
-    return global.appTemplates;
-
+  // 首次强制刷新
+  if (!global.templatesRefreshTime) {
+    global.templatesRefreshTime = Date.now() - 10000;
+  }
   if (!global.appTemplates) {
     global.appTemplates = [];
   }
 
+  if (
+    isProduction &&
+    // 有模板缓存
+    global.appTemplates.length > 0 &&
+    // 缓存时间未过期
+    global.templatesRefreshTime > Date.now() &&
+    !refresh
+  ) {
+    return global.appTemplates;
+  }
+
   try {
     const appTemplates = await getAppTemplates();
     global.appTemplates = appTemplates;
+    global.templatesRefreshTime = addMinutes(new Date(), 30).getTime(); // 缓存30分钟
     return appTemplates;
   } catch (error) {
-    // @ts-ignore
-    global.appTemplates = undefined;
     return [];
   }
 };
@@ -66,4 +79,5 @@ export const isCommercialTemaplte = (templateId: string) => {
 
 declare global {
   var appTemplates: AppTemplateSchemaType[];
+  var templatesRefreshTime: number;
 }

packages/service/core/app/templates/templateSchema.ts

@@ -19,6 +19,8 @@ const AppTemplateSchema = new Schema({
   },
   type: String,
   isActive: Boolean,
+  isPromoted: Boolean,
+  recommendText: String,
   userGuide: Object,
   isQuickTemplate: Boolean,
   order: {

packages/service/core/workflow/dispatch/ai/agent/index.ts

@@ -178,12 +178,11 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
 
     const {
       toolWorkflowInteractiveResponse,
-      dispatchFlowResponse, // tool flow response
+      toolDispatchFlowResponses, // tool flow response
       toolCallInputTokens,
       toolCallOutputTokens,
       completeMessages = [], // The actual message sent to AI(just save text)
       assistantResponses = [], // FastGPT system store assistant.value response
-      runTimes,
       finish_reason
     } = await (async () => {
       const adaptMessages = chats2GPTMessages({
@@ -191,22 +190,20 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
         reserveId: false
         // reserveTool: !!toolModel.toolChoice
       });
-      const requestParams = {
+
+      return runToolCall({
+        ...props,
         runtimeNodes,
         runtimeEdges,
         toolNodes,
         toolModel,
         messages: adaptMessages,
-        interactiveEntryToolParams: lastInteractive?.toolParams
-      };
-
-      return runToolCall({
-        ...props,
-        ...requestParams,
-        maxRunToolTimes: 100
+        childrenInteractiveParams:
+          lastInteractive?.type === 'toolChildrenInteractive' ? lastInteractive.params : undefined
       });
     })();
 
+    // Usage computed
     const { totalPoints: modelTotalPoints, modelName } = formatModelChars2Points({
       model,
       inputTokens: toolCallInputTokens,
@@ -214,12 +211,13 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
     });
     const modelUsage = externalProvider.openaiAccount?.key ? 0 : modelTotalPoints;
 
-    const toolUsages = dispatchFlowResponse.map((item) => item.flowUsages).flat();
+    const toolUsages = toolDispatchFlowResponses.map((item) => item.flowUsages).flat();
     const toolTotalPoints = toolUsages.reduce((sum, item) => sum + item.totalPoints, 0);
 
     // concat tool usage
     const totalPointsUsage = modelUsage + toolTotalPoints;
 
+    // Preview assistant responses
     const previewAssistantResponses = filterToolResponseToPreview(assistantResponses);
 
     return {
@@ -229,7 +227,10 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
           .map((item) => item.text?.content || '')
           .join('')
       },
-      [DispatchNodeResponseKeyEnum.runTimes]: runTimes,
+      [DispatchNodeResponseKeyEnum.runTimes]: toolDispatchFlowResponses.reduce(
+        (sum, item) => sum + item.runTimes,
+        0
+      ),
       [DispatchNodeResponseKeyEnum.assistantResponses]: previewAssistantResponses,
       [DispatchNodeResponseKeyEnum.nodeResponse]: {
         // 展示的积分消耗
@@ -244,7 +245,7 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
           10000,
           useVision
         ),
-        toolDetail: dispatchFlowResponse.map((item) => item.flowResponses).flat(),
+        toolDetail: toolDispatchFlowResponses.map((item) => item.flowResponses).flat(),
         mergeSignId: nodeId,
         finishReason: finish_reason
       },

packages/service/core/workflow/dispatch/ai/agent/type.d.ts

@@ -14,7 +14,11 @@ import type { DispatchFlowResponse } from '../../type';
 import type { AIChatItemValueItemType } from '@fastgpt/global/core/chat/type';
 import { ChatItemValueItemType } from '@fastgpt/global/core/chat/type';
 import type { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
-import type { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type';
+import type {
+  ToolCallChildrenInteractive,
+  InteractiveNodeResponseType,
+  WorkflowInteractiveResponseType
+} from '@fastgpt/global/core/workflow/template/system/interactive/type';
 import type { LLMModelItemType } from '@fastgpt/global/core/ai/model';
 import type { JSONSchemaInputType } from '@fastgpt/global/core/app/jsonschema';
 
@@ -37,18 +41,17 @@ export type DispatchToolModuleProps = ModuleDispatchProps<{
   messages: ChatCompletionMessageParam[];
   toolNodes: ToolNodeItemType[];
   toolModel: LLMModelItemType;
-  interactiveEntryToolParams?: WorkflowInteractiveResponseType['toolParams'];
+  childrenInteractiveParams?: ToolCallChildrenInteractive['params'];
 };
 
 export type RunToolResponse = {
-  dispatchFlowResponse: DispatchFlowResponse[];
+  toolDispatchFlowResponses: DispatchFlowResponse[];
   toolCallInputTokens: number;
   toolCallOutputTokens: number;
-  completeMessages?: ChatCompletionMessageParam[];
-  assistantResponses?: AIChatItemValueItemType[];
-  toolWorkflowInteractiveResponse?: WorkflowInteractiveResponseType;
-  [DispatchNodeResponseKeyEnum.runTimes]: number;
-  finish_reason?: CompletionFinishReason;
+  completeMessages: ChatCompletionMessageParam[];
+  assistantResponses: AIChatItemValueItemType[];
+  finish_reason: CompletionFinishReason;
+  toolWorkflowInteractiveResponse?: ToolCallChildrenInteractive;
 };
 export type ToolNodeItemType = RuntimeNodeItemType & {
   toolParams: RuntimeNodeItemType['inputs'];

packages/service/core/workflow/dispatch/ai/agent/utils.ts

@@ -62,12 +62,9 @@ export const initToolNodes = (
   nodes.forEach((node) => {
     if (entryNodeIds.includes(node.nodeId)) {
       node.isEntry = true;
-      node.isStart = true;
       if (startParams) {
         node.inputs = updateToolInputValue({ params: startParams, inputs: node.inputs });
       }
-    } else {
-      node.isStart = false;
     }
   });
 };

packages/service/core/workflow/dispatch/index.ts

@@ -747,6 +747,7 @@ export const runWorkflow = async (data: RunWorkflowProps): Promise<DispatchFlowR
 
         // Get next source edges and update status
         const skipHandleId = result[DispatchNodeResponseKeyEnum.skipHandleId] || [];
+
         const targetEdges = filterWorkflowEdges(runtimeEdges).filter(
           (item) => item.source === node.nodeId
         );
@@ -957,6 +958,7 @@ export const runWorkflow = async (data: RunWorkflowProps): Promise<DispatchFlowR
         entryNodeIds,
         memoryEdges: runtimeEdges.map((edge) => ({
           ...edge,
+          // 入口前面的边全部激活，保证下次进来一定能执行。
           status: entryNodeIds.includes(edge.target) ? 'active' : edge.status
         })),
         nodeOutputs,

packages/service/core/workflow/dispatch/tools/answer.ts

@@ -36,6 +36,7 @@ export const dispatchAnswer = (props: Record<string, any>): AnswerResponse => {
     [DispatchNodeResponseKeyEnum.answerText]: responseText,
     [DispatchNodeResponseKeyEnum.nodeResponse]: {
       textOutput: formatText
-    }
+    },
+    [DispatchNodeResponseKeyEnum.toolResponses]: responseText
   };
 };

packages/web/i18n/en/account_usage.json

@@ -4,6 +4,7 @@
   "app_name": "Application name",
   "auto_index": "Auto index",
   "billing_module": "Deduction module",
+  "compress_llm_messages": "AI history compression",
   "confirm_export": "A total of {{total}} pieces of data were filtered out. Are you sure to export?",
   "count": "Number of runs",
   "current_filter_conditions": "Current filter conditions",

packages/web/i18n/en/app.json

@@ -319,13 +319,14 @@
   "template.hard_strict_des": "Based on the question and answer template, stricter requirements are imposed on the model's answers.",
   "template.qa_template": "Q&A template",
   "template.qa_template_des": "A knowledge base suitable for QA question and answer structure, which allows AI to answer strictly according to preset content",
+  "template.recommended": "Promoted",
   "template.simple_robot": "Simple robot",
   "template.standard_strict": "Standard strict template",
   "template.standard_strict_des": "Based on the standard template, stricter requirements are imposed on the model's answers.",
   "template.standard_template": "Standard template",
   "template.standard_template_des": "Standard prompt words for knowledge bases with unfixed structures.",
   "templateMarket.Search_template": "Search Template",
-  "templateMarket.Use": "Use",
+  "templateMarket.Use": "Build now",
   "templateMarket.no_intro": "No introduction yet~",
   "templateMarket.templateTags.Recommendation": "Recommendation",
   "templateMarket.template_guide": "Guide",

packages/web/i18n/zh-CN/account_usage.json

@@ -5,6 +5,7 @@
   "app_name": "应用名",
   "auto_index": "索引增强",
   "billing_module": "扣费模块",
+  "compress_llm_messages": "AI 历史记录压缩",
   "confirm_export": "共筛选出 {{total}} 条数据，是否确认导出？",
   "count": "运行次数",
   "current_filter_conditions": "当前筛选条件：",

packages/web/i18n/zh-CN/app.json

@@ -333,16 +333,17 @@
   "template.hard_strict_des": "在问答模板基础上，对模型的回答做更严格的要求。",
   "template.qa_template": "问答模板",
   "template.qa_template_des": "适合 QA 问答结构的知识库，可以让AI较为严格的按预设内容回答",
+  "template.recommended": "精选",
   "template.simple_robot": "简易机器人",
   "template.standard_strict": "标准严格模板",
   "template.standard_strict_des": "在标准模板基础上，对模型的回答做更严格的要求。",
   "template.standard_template": "标准模板",
   "template.standard_template_des": "标准提示词，用于结构不固定的知识库。",
   "templateMarket.Search_template": "搜索模板",
-  "templateMarket.Use": "使用",
+  "templateMarket.Use": "立即搭建",
   "templateMarket.no_intro": "还没有介绍～",
   "templateMarket.templateTags.Recommendation": "推荐",
-  "templateMarket.template_guide": "模板说明",
+  "templateMarket.template_guide": "说明",
   "template_market": "模板市场",
   "template_market_description": "在模板市场探索更多玩法，配置教程与使用引导，带你理解并上手各种应用",
   "template_market_empty_data": "找不到合适的模板",

packages/web/i18n/zh-Hant/account_usage.json

@@ -4,6 +4,7 @@
   "app_name": "應用程式名",
   "auto_index": "索引增強",
   "billing_module": "扣費模組",
+  "compress_llm_messages": "AI 歷史記錄壓縮",
   "confirm_export": "共篩選出 {{total}} 條資料，是否確認匯出？",
   "count": "運行次數",
   "current_filter_conditions": "目前篩選條件：",

packages/web/i18n/zh-Hant/app.json

@@ -318,16 +318,17 @@
   "template.hard_strict_des": "在問答範本基礎上，對模型的回答做出更嚴格的要求。",
   "template.qa_template": "問答範本",
   "template.qa_template_des": "適合問答結構的知識庫，可以讓 AI 較為嚴格地按照預設內容回答",
+  "template.recommended": "精選",
   "template.simple_robot": "簡易機器人",
   "template.standard_strict": "標準嚴格範本",
   "template.standard_strict_des": "在標準範本基礎上，對模型的回答做出更嚴格的要求。",
   "template.standard_template": "標準範本",
   "template.standard_template_des": "標準提示詞，用於結構不固定的知識庫。",
   "templateMarket.Search_template": "搜尋範本",
-  "templateMarket.Use": "使用",
+  "templateMarket.Use": "立即搭建",
   "templateMarket.no_intro": "還沒有介紹～",
   "templateMarket.templateTags.Recommendation": "推薦",
-  "templateMarket.template_guide": "範本說明",
+  "templateMarket.template_guide": "說明",
   "template_market": "範本市集",
   "template_market_description": "在範本市集探索更多玩法，設定教學與使用指引，帶您理解並上手各種應用程式",
   "template_market_empty_data": "找不到合適的範本",

projects/app/public/imgs/app/templateBg.svg

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+<g clip-path="url(#clip0_29654_18158)">
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+</clipPath>
+</defs>
+</svg>
\ No newline at end of file

projects/app/public/imgs/app/templateCreateBg.svg

+<svg width="308" height="98" viewBox="0 0 308 98" fill="none" xmlns="http://www.w3.org/2000/svg">
+<g clip-path="url(#clip0_29676_18182)">
+<path d="M0 8C0 3.58172 3.58172 0 8 0H299.667C304.085 0 307.667 3.58172 307.667 8V90C307.667 94.4183 304.085 98 299.667 98H8C3.58172 98 0 94.4183 0 90V8Z" fill="white"/>
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+<defs>
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+<stop stop-color="#E39DFF"/>
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+<radialGradient id="paint3_radial_29676_18182" cx="0" cy="0" r="1" gradientTransform="matrix(171.751 -44.6637 19.1547 18.1293 153.833 35.1239)" gradientUnits="userSpaceOnUse">
+<stop stop-color="white"/>
+<stop offset="1" stop-color="white" stop-opacity="0"/>
+</radialGradient>
+<linearGradient id="paint4_linear_29676_18182" x1="260.313" y1="11.1353" x2="256.042" y2="21.2201" gradientUnits="userSpaceOnUse">
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+<stop offset="1" stop-color="white"/>
+</linearGradient>
+<linearGradient id="paint5_linear_29676_18182" x1="246.358" y1="36.1857" x2="252.554" y2="48.3697" gradientUnits="userSpaceOnUse">
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+<stop offset="1" stop-color="white"/>
+</linearGradient>
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+<stop stop-color="white"/>
+<stop offset="1" stop-color="white"/>
+</linearGradient>
+<clipPath id="clip0_29676_18182">
+<path d="M0 8C0 3.58172 3.58172 0 8 0H299.667C304.085 0 307.667 3.58172 307.667 8V90C307.667 94.4183 304.085 98 299.667 98H8C3.58172 98 0 94.4183 0 90V8Z" fill="white"/>
+</clipPath>
+</defs>
+</svg>
\ No newline at end of file

projects/app/src/components/Layout/navbar.tsx

@@ -123,7 +123,7 @@ const Navbar = ({ unread }: { unread: number }) => {
       w={'100%'}
       userSelect={'none'}
       pb={2}
-      bg={isSecondNavbarPage ? 'myGray.50' : 'transparent'}
+      bg={isSecondNavbarPage ? 'white' : 'transparent'}
     >
       {/* logo */}
       <Box flex={'0 0 auto'} mb={3}>

projects/app/src/pageComponents/dashboard/Container.tsx

@@ -232,14 +232,13 @@ const DashboardContainer = ({
           position={'fixed'}
           left={isPc ? navbarWidth : 0}
           top={0}
-          bg={'myGray.25'}
+          bg={'white'}
           w={`220px`}
           h={'full'}
           borderLeft={'1px solid'}
           borderRight={'1px solid'}
           borderColor={'myGray.200'}
           pt={4}
-          px={2.5}
           pb={2.5}
           zIndex={100}
           userSelect={'none'}
@@ -247,7 +246,12 @@ const DashboardContainer = ({
           flexDirection={'column'}
           justifyContent={'space-between'}
         >
-          <Box>
+          <Box
+            flex={1}
+            overflowY={'auto'}
+            px={2.5}
+            sx={{ '&::-webkit-scrollbar': { width: '4px' } }}
+          >
             {groupList.map((group) => {
               const selected = currentTab === group.groupId;
 
@@ -339,7 +343,7 @@ const DashboardContainer = ({
         </MyBox>
       )}
 
-      <Box h={'100%'} pl={isPc ? `220px` : 0} position={'relative'} bg={'myGray.25'}>
+      <Box h={'100%'} pl={isPc ? `220px` : 0} position={'relative'} bg={'white'}>
         {children({
           templateTags,
           templateList,

projects/app/src/pageComponents/dashboard/agent/TypeTag.tsx

 import React, { useRef } from 'react';
-import { AppTypeEnum } from '@fastgpt/global/core/app/constants';
+import type { AppTypeEnum } from '@fastgpt/global/core/app/constants';
 import MyIcon from '@fastgpt/web/components/common/Icon';
 import { Box, Flex } from '@chakra-ui/react';
 import { useTranslation } from 'next-i18next';
+import { appTypeTagMap } from '../constant';
 
 const AppTypeTag = ({ type }: { type: AppTypeEnum }) => {
   const { t } = useTranslation();
 
-  const map = useRef({
-    [AppTypeEnum.simple]: {
-      label: t('app:type.Chat_Agent'),
-      icon: 'core/app/type/simple'
-    },
-    [AppTypeEnum.workflow]: {
-      label: t('app:type.Workflow bot'),
-      icon: 'core/app/type/workflow'
-    },
-    [AppTypeEnum.workflowTool]: {
-      label: t('app:toolType_workflow'),
-      icon: 'core/app/type/plugin'
-    },
-    [AppTypeEnum.httpPlugin]: {
-      label: t('app:type.Http plugin'),
-      icon: 'core/app/type/httpPlugin'
-    },
-    [AppTypeEnum.httpToolSet]: {
-      label: t('app:toolType_http'),
-      icon: 'core/app/type/httpPlugin'
-    },
-    [AppTypeEnum.mcpToolSet]: {
-      label: t('app:toolType_mcp'),
-      icon: 'core/app/type/mcpTools'
-    },
-    [AppTypeEnum.tool]: undefined,
-    [AppTypeEnum.folder]: undefined,
-    [AppTypeEnum.hidden]: undefined,
-    [AppTypeEnum.agent]: undefined
-  });
-
-  const data = map.current[type as keyof typeof map.current];
+  const data = appTypeTagMap[type as keyof typeof appTypeTagMap];
 
   return data ? (
     <Flex
@@ -53,7 +23,7 @@ const AppTypeTag = ({ type }: { type: AppTypeEnum }) => {
     >
       <MyIcon name={data.icon as any} w={'14px'} color={'myGray.500'} />
       <Box ml={1} fontSize={'mini'}>
-        {data.label}
+        {t(data.label)}
       </Box>
     </Flex>
   ) : null;

projects/app/src/pageComponents/dashboard/constant.ts

+import { AppTypeEnum } from '@fastgpt/global/core/app/constants';
+import { i18nT } from '@fastgpt/web/i18n/utils';
+
+export const appTypeTagMap = {
+  [AppTypeEnum.simple]: {
+    label: i18nT('app:type.Chat_Agent'),
+    icon: 'core/app/type/simple'
+  },
+  [AppTypeEnum.workflow]: {
+    label: i18nT('app:type.Workflow bot'),
+    icon: 'core/app/type/workflow'
+  },
+  [AppTypeEnum.workflowTool]: {
+    label: i18nT('app:toolType_workflow'),
+    icon: 'core/app/type/plugin'
+  },
+  [AppTypeEnum.httpPlugin]: {
+    label: i18nT('app:type.Http plugin'),
+    icon: 'core/app/type/httpPlugin'
+  },
+  [AppTypeEnum.httpToolSet]: {
+    label: i18nT('app:toolType_http'),
+    icon: 'core/app/type/httpPlugin'
+  },
+  [AppTypeEnum.mcpToolSet]: {
+    label: i18nT('app:toolType_mcp'),
+    icon: 'core/app/type/mcpTools'
+  },
+  [AppTypeEnum.tool]: undefined,
+  [AppTypeEnum.folder]: undefined,
+  [AppTypeEnum.hidden]: undefined,
+  [AppTypeEnum.agent]: undefined
+};

projects/app/src/pageComponents/login/LoginForm/LoginForm.tsx

-import React, { type Dispatch } from 'react';
+import React, { useEffect, type Dispatch } from 'react';
 import { FormControl, Flex, Input, Button, Box } from '@chakra-ui/react';
 import { useForm } from 'react-hook-form';
 import { LoginPageTypeEnum } from '@/web/support/user/login/constants';
@@ -10,6 +10,10 @@ import { useTranslation } from 'next-i18next';
 import FormLayout from './FormLayout';
 import { useRequest2 } from '@fastgpt/web/hooks/useRequest';
 import PolicyTip from './PolicyTip';
+import { useSearchParams } from 'next/navigation';
+import { UserErrEnum } from '@fastgpt/global/common/error/code/user';
+import { useRouter } from 'next/router';
+import { useMount } from 'ahooks';
 
 interface Props {
   setPageType: Dispatch<`${LoginPageTypeEnum}`>;
@@ -25,6 +29,9 @@ const LoginForm = ({ setPageType, loginSuccess }: Props) => {
   const { t } = useTranslation();
   const { toast } = useToast();
   const { feConfigs } = useSystemStore();
+  const query = useSearchParams();
+  const router = useRouter();
+
   const {
     register,
     handleSubmit,
@@ -41,13 +48,28 @@ const LoginForm = ({ setPageType, loginSuccess }: Props) => {
           code
         })
       );
-      toast({
-        title: t('login:login_success'),
-        status: 'success'
-      });
     },
     {
-      refreshDeps: [loginSuccess]
+      refreshDeps: [loginSuccess],
+      successToast: t('login:login_success'),
+      onError: (error: any) => {
+        // 密码错误，需要清空 query 参数
+        if (error.statusText === UserErrEnum.account_psw_error) {
+          router.replace(
+            router.pathname,
+            {
+              query: {
+                ...router.query,
+                u: '',
+                p: ''
+              }
+            },
+            {
+              shallow: false
+            }
+          );
+        }
+      }
     }
   );
 
@@ -71,6 +93,17 @@ const LoginForm = ({ setPageType, loginSuccess }: Props) => {
       .join('/');
   })();
 
+  useMount(() => {
+    const username = query.get('u');
+    const password = query.get('p');
+    if (username && password) {
+      onclickLogin({
+        username,
+        password
+      });
+    }
+  });
+
   return (
     <FormLayout setPageType={setPageType} pageType={LoginPageTypeEnum.passwordLogin}>
       <Box

projects/app/src/pageComponents/login/index.tsx

@@ -11,7 +11,6 @@ import {
 import { LoginPageTypeEnum } from '@/web/support/user/login/constants';
 import { useSystemStore } from '@/web/common/system/useSystemStore';
 import type { LoginSuccessResponse } from '@/global/support/api/userRes.d';
-import { useUserStore } from '@/web/support/user/useUserStore';
 import { useChatStore } from '@/web/core/chat/context/useChatStore';
 import dynamic from 'next/dynamic';
 import Script from 'next/script';

projects/app/src/pages/api/core/app/template/list.ts

@@ -10,7 +10,7 @@ export type ListParams = {
   isQuickTemplate?: boolean;
   randomNumber?: number;
   type?: AppTypeEnum | 'all';
-  excludeIds?: string[];
+  excludeIds?: string;
 };
 
 export type ListResponse = {
@@ -24,20 +24,30 @@ async function handler(
 ): Promise<ListResponse> {
   await authCert({ req, authToken: true });
 
-  const { isQuickTemplate = false, randomNumber = 0, type = 'all', excludeIds = [] } = req.query;
+  const { isQuickTemplate = false, randomNumber = 0, type = 'all', excludeIds } = req.query;
 
+  const parsedExcludeIds: string[] = (() => {
+    if (!excludeIds) return [];
+    try {
+      return JSON.parse(excludeIds);
+    } catch (error) {
+      console.error('Failed to parse excludeIds:', error);
+      return [];
+    }
+  })();
   const templateMarketItems = await getAppTemplatesAndLoadThem();
 
   let filteredItems = templateMarketItems.filter((item) => {
     if (!item.isActive) return false;
-    if (type === 'all' && !ToolTypeList.includes(item.type as AppTypeEnum)) return true;
+    if (type === 'all' && !(ToolTypeList.includes(item.type as AppTypeEnum) && randomNumber > 0))
+      return true;
     if (item.type === type) return true;
     return false;
   });
   const total = filteredItems.length;
 
-  if (excludeIds && excludeIds.length > 0) {
-    filteredItems = filteredItems.filter((item) => !excludeIds.includes(item.templateId));
+  if (parsedExcludeIds && parsedExcludeIds.length > 0) {
+    filteredItems = filteredItems.filter((item) => !parsedExcludeIds.includes(item.templateId));
   }
 
   if (isQuickTemplate) {
@@ -63,6 +73,8 @@ async function handler(
       templateId: item.templateId,
       name: item.name,
       intro: item.intro,
+      recommendText: item.recommendText,
+      isPromoted: item.isPromoted,
       avatar: item.avatar,
       tags: item.tags,
       type: item.type,

projects/app/src/pages/dashboard/systemTool/index.tsx

@@ -146,7 +146,7 @@ const ToolKitProvider = ({ MenuIcon }: { MenuIcon: JSX.Element }) => {
   }, [tools, searchText, selectedTagIds, installedFilter, tags, i18n.language]);
 
   return (
-    <Box h={'full'} pr={6}>
+    <Box h={'full'}>
       <MyBox
         bg={'white'}
         h={'full'}

projects/app/src/web/core/app/api/template.ts

 import type { ListParams, ListResponse } from '@/pages/api/core/app/template/list';
-import { GET } from '@/web/common/api/request';
+import { GET, POST } from '@/web/common/api/request';
 import { useSystemStore } from '@/web/common/system/useSystemStore';
 import type { AppTemplateSchemaType, TemplateTypeSchemaType } from '@fastgpt/global/core/app/type';
 import { defaultTemplateTypes } from '@fastgpt/web/core/workflow/constants';

test/cases/service/core/ai/llm/toolCall.test.ts

 import {
   parsePromptToolCall,
   promptToolCallMessageRewrite
-} from '@fastgpt/service/core/ai/llm/promptToolCall';
+} from '@fastgpt/service/core/ai/llm/promptCall/index';
 import type { ChatCompletionMessageParam, ChatCompletionTool } from '@fastgpt/global/core/ai/type';
 import { describe, expect, it } from 'vitest';
 

test/cases/service/core/app/workflow/dispatch/checkNodeRunStatus.test.ts

@@ -1187,3 +1187,167 @@ describe('checkNodeRunStatus - 边界情况测试', () => {
     expect(checkNodeRunStatus({ nodesMap, node: nodeA, runtimeEdges: edges })).toBe('run');
   });
 });
+
+describe('checkNodeRunStatus - 工具调用场景测试', () => {
+  it('工具调用1: Tool节点作为入口节点 (无workflowStart时)', () => {
+    // 场景：当工作流中没有 workflowStart/pluginInput 节点时，tool 节点可以作为入口节点
+    // Tool → Process → End
+    const toolNode = createNode('tool1', FlowNodeTypeEnum.tool);
+    const processNode = createNode('process');
+    const endNode = createNode('end');
+
+    const nodesMap = new Map<string, RuntimeNodeItemType>([
+      ['tool1', toolNode],
+      ['process', processNode],
+      ['end', endNode]
+    ]);
+
+    // 场景1: Tool节点作为入口，无输入边
+    const edges1: RuntimeEdgeItemType[] = [
+      createEdge('tool1', 'process', 'waiting'),
+      createEdge('process', 'end', 'waiting')
+    ];
+
+    // Tool节点作为入口节点应该可以运行
+    expect(checkNodeRunStatus({ nodesMap, node: toolNode, runtimeEdges: edges1 })).toBe('run');
+    // 注意：由于tool节点没有输入边（是入口），process节点也会没有可追溯到start的边
+    // 因此process节点在这个场景下也会返回'run'（因为commonEdges和recursiveEdgeGroups都为空）
+    expect(checkNodeRunStatus({ nodesMap, node: processNode, runtimeEdges: edges1 })).toBe('run');
+
+    // 场景2: Tool节点执行完成后,process可以运行但end仍需等待
+    const edges2: RuntimeEdgeItemType[] = [
+      createEdge('tool1', 'process', 'active'),
+      createEdge('process', 'end', 'waiting')
+    ];
+
+    expect(checkNodeRunStatus({ nodesMap, node: processNode, runtimeEdges: edges2 })).toBe('run');
+    // end节点的输入边是waiting状态,需要等待process完成
+    expect(checkNodeRunStatus({ nodesMap, node: endNode, runtimeEdges: edges2 })).toBe('wait');
+
+    // 场景2.1: process完成后,end可以运行
+    const edges2_1: RuntimeEdgeItemType[] = [
+      createEdge('tool1', 'process', 'active'),
+      createEdge('process', 'end', 'active')
+    ];
+
+    expect(checkNodeRunStatus({ nodesMap, node: endNode, runtimeEdges: edges2_1 })).toBe('run');
+
+    // 场景3: 有workflowStart时，tool节点不再是入口节点
+    const startNode = createNode('start', FlowNodeTypeEnum.workflowStart);
+    const nodesMapWithStart = new Map<string, RuntimeNodeItemType>([
+      ['start', startNode],
+      ['tool1', toolNode],
+      ['process', processNode],
+      ['end', endNode]
+    ]);
+
+    const edges3: RuntimeEdgeItemType[] = [
+      createEdge('start', 'tool1', 'active'),
+      createEdge('tool1', 'process', 'waiting'),
+      createEdge('process', 'end', 'waiting')
+    ];
+
+    // 此时tool节点不再是入口节点，需要start激活才能运行
+    expect(
+      checkNodeRunStatus({ nodesMap: nodesMapWithStart, node: toolNode, runtimeEdges: edges3 })
+    ).toBe('run');
+    expect(
+      checkNodeRunStatus({ nodesMap: nodesMapWithStart, node: processNode, runtimeEdges: edges3 })
+    ).toBe('wait');
+
+    // Tool执行完成后，process可以运行
+    const edges4: RuntimeEdgeItemType[] = [
+      createEdge('start', 'tool1', 'active'),
+      createEdge('tool1', 'process', 'active'),
+      createEdge('process', 'end', 'waiting')
+    ];
+
+    expect(
+      checkNodeRunStatus({ nodesMap: nodesMapWithStart, node: processNode, runtimeEdges: edges4 })
+    ).toBe('run');
+  });
+
+  it('工具调用2: ToolSet节点与条件分支和循环组合 (Agent → ToolSet → Tool1/Tool2 → Result → Agent)', () => {
+    // 场景：Agent调用工具集，工具集根据条件选择不同工具执行，并支持循环调用
+    // Start → Agent → ToolSet → (Tool1 | Tool2) → Result → Agent (循环)
+    const nodeStart = createNode('start', FlowNodeTypeEnum.workflowStart);
+    const agentNode = createNode('agent', FlowNodeTypeEnum.agent);
+    const toolSetNode = createNode('toolSet', FlowNodeTypeEnum.toolSet);
+    const tool1Node = createNode('tool1', FlowNodeTypeEnum.tool);
+    const tool2Node = createNode('tool2', FlowNodeTypeEnum.tool);
+    const resultNode = createNode('result');
+
+    const nodesMap = new Map<string, RuntimeNodeItemType>([
+      ['start', nodeStart],
+      ['agent', agentNode],
+      ['toolSet', toolSetNode],
+      ['tool1', tool1Node],
+      ['tool2', tool2Node],
+      ['result', resultNode]
+    ]);
+
+    // 场景1: 第一次执行，Agent选择Tool1
+    const edges1: RuntimeEdgeItemType[] = [
+      createEdge('start', 'agent', 'active'),
+      createEdge('agent', 'toolSet', 'active'),
+      createEdge('toolSet', 'tool1', 'active'), // 选择Tool1
+      createEdge('toolSet', 'tool2', 'skipped'), // Tool2未选择
+      createEdge('tool1', 'result', 'waiting'),
+      createEdge('tool2', 'result', 'skipped'),
+      createEdge('result', 'agent', 'waiting') // 循环边等待
+    ];
+
+    expect(checkNodeRunStatus({ nodesMap, node: agentNode, runtimeEdges: edges1 })).toBe('wait');
+    expect(checkNodeRunStatus({ nodesMap, node: toolSetNode, runtimeEdges: edges1 })).toBe('run');
+    expect(checkNodeRunStatus({ nodesMap, node: tool1Node, runtimeEdges: edges1 })).toBe('run');
+    expect(checkNodeRunStatus({ nodesMap, node: tool2Node, runtimeEdges: edges1 })).toBe('skip');
+    expect(checkNodeRunStatus({ nodesMap, node: resultNode, runtimeEdges: edges1 })).toBe('wait');
+
+    // 场景2: Tool1执行完成，Result处理结果
+    const edges2: RuntimeEdgeItemType[] = [
+      createEdge('start', 'agent', 'active'),
+      createEdge('agent', 'toolSet', 'active'),
+      createEdge('toolSet', 'tool1', 'active'),
+      createEdge('toolSet', 'tool2', 'skipped'),
+      createEdge('tool1', 'result', 'active'), // Tool1完成
+      createEdge('tool2', 'result', 'skipped'),
+      createEdge('result', 'agent', 'waiting')
+    ];
+
+    expect(checkNodeRunStatus({ nodesMap, node: resultNode, runtimeEdges: edges2 })).toBe('run');
+
+    // 场景3: 循环回Agent，第二次调用选择Tool2
+    const edges3: RuntimeEdgeItemType[] = [
+      createEdge('start', 'agent', 'active'),
+      createEdge('agent', 'toolSet', 'active'),
+      createEdge('toolSet', 'tool1', 'skipped'), // Tool1未选择
+      createEdge('toolSet', 'tool2', 'active'), // 选择Tool2
+      createEdge('tool1', 'result', 'skipped'),
+      createEdge('tool2', 'result', 'active'), // Tool2完成
+      createEdge('result', 'agent', 'active') // 循环边激活
+    ];
+
+    // Agent有来自start和result的两条active边
+    expect(checkNodeRunStatus({ nodesMap, node: agentNode, runtimeEdges: edges3 })).toBe('run');
+    expect(checkNodeRunStatus({ nodesMap, node: tool1Node, runtimeEdges: edges3 })).toBe('skip');
+    expect(checkNodeRunStatus({ nodesMap, node: tool2Node, runtimeEdges: edges3 })).toBe('run');
+    expect(checkNodeRunStatus({ nodesMap, node: resultNode, runtimeEdges: edges3 })).toBe('run');
+
+    // 场景4: 循环退出，不再调用工具
+    const edges4: RuntimeEdgeItemType[] = [
+      createEdge('start', 'agent', 'active'),
+      createEdge('agent', 'toolSet', 'skipped'), // 不再调用工具集
+      createEdge('toolSet', 'tool1', 'skipped'),
+      createEdge('toolSet', 'tool2', 'skipped'),
+      createEdge('tool1', 'result', 'skipped'),
+      createEdge('tool2', 'result', 'skipped'),
+      createEdge('result', 'agent', 'skipped') // 循环退出
+    ];
+
+    expect(checkNodeRunStatus({ nodesMap, node: agentNode, runtimeEdges: edges4 })).toBe('run');
+    expect(checkNodeRunStatus({ nodesMap, node: toolSetNode, runtimeEdges: edges4 })).toBe('skip');
+    expect(checkNodeRunStatus({ nodesMap, node: tool1Node, runtimeEdges: edges4 })).toBe('skip');
+    expect(checkNodeRunStatus({ nodesMap, node: tool2Node, runtimeEdges: edges4 })).toBe('skip');
+    expect(checkNodeRunStatus({ nodesMap, node: resultNode, runtimeEdges: edges4 })).toBe('skip');
+  });
+});