refactor: agent call (#5572)

* feat: Add relevant functions related to agent invocation, including plan parsing, state management and tool invocation

* Refactor agent call logic and utilities

- Simplified the `runAgentCall` function by removing unnecessary complexity and restructuring the flow for better readability and maintainability.
- Introduced helper functions to create tools from tool nodes and to prepare agent messages, enhancing modularity.
- Removed the `utils.ts` file as its functions were integrated into the main logic, streamlining the codebase.
- Updated the dispatch logic in `index.ts` to utilize the new helper functions and improve clarity.
- Adjusted the handling of interactive modes and tool calls to ensure proper response formatting and error handling.

* refactor: clean up the processing logic of the interactive mode and remove the unused tool creation functions

* feat: add relevant constants for proxy configuration and update the proxy call logic

* refactor: remove unused configuration variables from workflow properties

* refactor: remove unused configuration variables from dispatchRunAgents props

* fix: build error

* refactor: update FlowNodeTypeEnum values and consolidate utility functions

* refactor: simplify conditional checks in tool call and reasoning handlers

* feat: add default agent prompt for improved response handling

* refactor: rename directory with agent->tool, agentCall->agnet

* refactor: rename dispatchRunAgents to dispatchRunAgent for consistency

* refactor: rename toolCall to tools for consistency in FlowNodeTypeEnum

* refactor: rename agents to toolCall for consistency in nodeTypes mapping

* refactor: remove unused runtimeEdges parameter from dispatchRunAgent

* refactor: update runAgentCall and dispatchRunAgent to use structured requestProps and workflowProps

* refactor: streamline requestProps and handleToolResponse in runAgentCall and dispatchRunAgent

* refactor: restructure RunAgentCallProps and update requestProps to requestParams for clarity

* refactor: enhance interactiveEntryToolParams handling in runAgentCall for improved response management

* refactor: flatten RunAgentCallProps structure and update dispatchRunAgent to use direct properties

* fix: correct initialization of interactiveResponse in runAgentCall

packages/global/core/workflow/constants.ts

@@ -168,6 +168,11 @@ export enum NodeInputKeyEnum {
   aiChatResponseFormat = 'aiChatResponseFormat',
   aiChatJsonSchema = 'aiChatJsonSchema',
 
+  // agent
+  subAgentConfig = 'subConfig',
+  planAgentConfig = 'planConfig',
+  modelAgentConfig = 'modelConfig',
+
   // dataset
   datasetSelectList = 'datasets',
   datasetSimilarity = 'similarity',

packages/global/core/workflow/node/constant.ts

@@ -137,7 +137,8 @@ export enum FlowNodeTypeEnum {
   pluginInput = 'pluginInput',
   pluginOutput = 'pluginOutput',
   queryExtension = 'cfr',
-  agent = 'tools',
+  agent = 'agent',
+  toolCall = 'tools',
   stopTool = 'stopTool',
   toolParams = 'toolParams',
   lafModule = 'lafModule',

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

@@ -0,0 +1,410 @@
+import type {
+  ChatCompletionToolMessageParam,
+  ChatCompletionMessageParam,
+  ChatCompletionTool,
+  CompletionFinishReason
+} from '@fastgpt/global/core/ai/type';
+import { responseWriteController } from '../../../../../common/response';
+import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
+import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
+import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
+import type { ToolNodeItemType } from './type';
+import type { DispatchFlowResponse, WorkflowResponseType } from '../../type';
+import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
+import type { AIChatItemType, AIChatItemValueItemType } from '@fastgpt/global/core/chat/type';
+import { computedMaxToken } from '../../../../ai/utils';
+import { sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
+import type { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type';
+import { ChatItemValueTypeEnum } from '@fastgpt/global/core/chat/constants';
+import { getErrText } from '@fastgpt/global/common/error/utils';
+import { createLLMResponse } from '../../../../ai/llm/request';
+import { toolValueTypeList, valueTypeJsonSchemaMap } from '@fastgpt/global/core/workflow/constants';
+import type { RunAgentResponse } from './type';
+import type { ExternalProviderType } from '@fastgpt/global/core/workflow/runtime/type';
+import type { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
+import type { NextApiResponse } from 'next/types';
+
+type ToolRunResponseType = {
+  toolRunResponse?: DispatchFlowResponse;
+  toolMsgParams: ChatCompletionToolMessageParam;
+}[];
+
+type RunAgentCallProps = {
+  messages: ChatCompletionMessageParam[];
+  agentModel: LLMModelItemType;
+  toolNodes: ToolNodeItemType[];
+  maxRunAgentTimes: number;
+  res?: NextApiResponse;
+  workflowStreamResponse?: WorkflowResponseType;
+  interactiveEntryToolParams?: WorkflowInteractiveResponseType['toolParams'];
+  requestParams: {
+    temperature: number;
+    maxToken: number;
+    externalProvider: ExternalProviderType;
+    requestOrigin?: string;
+    stream?: boolean;
+    retainDatasetCite?: boolean;
+    useVision?: boolean;
+    top_p?: number;
+    response_format?: {
+      type?: string;
+      json_schema?: string;
+    };
+    stop?: string;
+    reasoning?: boolean;
+  };
+  handleToolResponse: ({ args, nodeId }: { args: string; nodeId: string }) => Promise<string>;
+};
+
+export const runAgentCall = async (props: RunAgentCallProps): Promise<RunAgentResponse> => {
+  const { requestParams, handleToolResponse, ...workflowProps } = props;
+  const {
+    messages,
+    agentModel,
+    toolNodes,
+    interactiveEntryToolParams,
+    maxRunAgentTimes,
+    res,
+    workflowStreamResponse
+  } = workflowProps;
+  const { stream, maxToken, externalProvider, reasoning } = requestParams;
+
+  const toolNodesMap = new Map<string, ToolNodeItemType>(
+    toolNodes.map((item) => [item.nodeId, item])
+  );
+  const tools: ChatCompletionTool[] = [
+    // ...createBuiltinTools(),
+    ...createToolFromToolNodes(toolNodes)
+  ];
+
+  const max_tokens = computedMaxToken({
+    model: agentModel,
+    maxToken: maxToken,
+    min: 100
+  });
+
+  const write = res ? responseWriteController({ res, readStream: stream }) : undefined;
+
+  // 统计信息
+  const allToolsRunResponse: ToolRunResponseType = [];
+  const assistantResponses: AIChatItemValueItemType[] = [];
+  const dispatchFlowResponse: DispatchFlowResponse[] = [];
+  let agentWorkflowInteractiveResponse: WorkflowInteractiveResponseType | undefined;
+  let allCompleteMessages: ChatCompletionMessageParam[] = messages;
+  let finish_reason: CompletionFinishReason = null;
+  let currRunAgentTimes: number = maxRunAgentTimes;
+  let inputTokens: number = 0;
+  let outputTokens: number = 0;
+  let runTimes: number = 0;
+
+  if (interactiveEntryToolParams) {
+    // TODO: mock data, wait for ask interactive node implemented
+    const interactiveResponse = ' ';
+
+    workflowStreamResponse?.({
+      event: SseResponseEventEnum.toolResponse,
+      data: {
+        tool: {
+          id: interactiveEntryToolParams.toolCallId,
+          toolName: '',
+          toolAvatar: '',
+          params: '',
+          response: sliceStrStartEnd(interactiveResponse, 5000, 5000)
+        }
+      }
+    });
+
+    // const hasStopSignal = toolRunResponse.flowResponses?.some((item) => item.toolStop);
+    // const workflowInteractiveResponse = toolRunResponse.workflowInteractiveResponse;
+
+    allCompleteMessages.push(
+      ...interactiveEntryToolParams.memoryMessages.map((item) =>
+        item.role === 'tool' && item.tool_call_id === interactiveEntryToolParams?.toolCallId
+          ? { ...item, content: interactiveResponse }
+          : item
+      )
+    );
+
+    // 累积 interactive 工具的结果
+    // dispatchFlowResponse.push(toolRunResponse);
+    // assistantResponses.push(...toolRunResponse.assistantResponses);
+    // runTimes += toolRunResponse.runTimes;
+
+    // if (hasStopSignal || workflowInteractiveResponse) {
+    //   if (workflowInteractiveResponse) {
+    //     agentWorkflowInteractiveResponse = {
+    //       ...workflowInteractiveResponse,
+    //       toolParams: {
+    //         entryNodeIds: workflowInteractiveResponse.entryNodeIds,
+    //         toolCallId: interactiveEntryToolParams?.toolCallId || '',
+    //         memoryMessages: interactiveEntryToolParams?.memoryMessages || []
+    //       }
+    //     };
+    //   }
+    // }
+
+    currRunAgentTimes--;
+  }
+
+  // ------------------------------------------------------------
+
+  while (currRunAgentTimes > 0) {
+    const currToolsRunResponse: ToolRunResponseType = [];
+
+    // TODO: Context agent compression
+
+    let {
+      reasoningText: reasoningContent,
+      answerText: answer,
+      toolCalls = [],
+      finish_reason: currFinishReason,
+      usage,
+      getEmptyResponseTip,
+      assistantMessage,
+      completeMessages
+    } = await createLLMResponse({
+      body: {
+        model: agentModel.model,
+        messages: allCompleteMessages,
+        tool_choice: 'auto',
+        toolCallMode: agentModel.toolChoice ? 'toolChoice' : 'prompt',
+        tools,
+        parallel_tool_calls: true,
+        max_tokens,
+        ...requestParams
+      },
+      userKey: externalProvider.openaiAccount,
+      isAborted: () => res?.closed,
+      onReasoning({ text }) {
+        if (!reasoning) return;
+        workflowStreamResponse?.({
+          write,
+          event: SseResponseEventEnum.answer,
+          data: textAdaptGptResponse({
+            reasoning_content: text
+          })
+        });
+      },
+      onStreaming({ text }) {
+        workflowStreamResponse?.({
+          write,
+          event: SseResponseEventEnum.answer,
+          data: textAdaptGptResponse({
+            text
+          })
+        });
+      },
+      onToolCall({ call }) {
+        const toolNode = toolNodesMap.get(call.function.name);
+        if (!toolNode) return;
+        workflowStreamResponse?.({
+          event: SseResponseEventEnum.toolCall,
+          data: {
+            tool: {
+              id: call.id,
+              toolName: toolNode?.name || call.function.name,
+              toolAvatar: toolNode?.avatar || '',
+              functionName: call.function.name,
+              params: call.function.arguments ?? '',
+              response: ''
+            }
+          }
+        });
+      }
+    });
+
+    if (!answer && !reasoningContent && !toolCalls.length) {
+      return Promise.reject(getEmptyResponseTip());
+    }
+
+    for await (const tool of toolCalls) {
+      const toolNode = toolNodesMap.get(tool.function?.name);
+      let toolRunResponse, stringToolResponse;
+
+      try {
+        if (!toolNode) continue;
+        stringToolResponse = handleToolResponse({
+          args: tool.function.arguments,
+          nodeId: toolNode.nodeId
+        });
+      } catch (error) {
+        stringToolResponse = getErrText(error);
+      }
+      workflowStreamResponse?.({
+        event: SseResponseEventEnum.toolResponse,
+        data: {
+          tool: {
+            id: tool.id,
+            toolName: '',
+            toolAvatar: '',
+            params: '',
+            response: sliceStrStartEnd(stringToolResponse || '', 5000, 5000)
+          }
+        }
+      });
+
+      currToolsRunResponse.push({
+        toolRunResponse,
+        toolMsgParams: {
+          tool_call_id: tool.id,
+          role: ChatCompletionRequestMessageRoleEnum.Tool,
+          name: tool.function.name,
+          content: sliceStrStartEnd(stringToolResponse || '', 5000, 5000)
+        }
+      });
+    }
+
+    const currFlatToolsResponseData = currToolsRunResponse
+      .flatMap((item) => item.toolRunResponse ?? [])
+      .filter(Boolean);
+
+    // 累积工具调用的响应结果
+    allToolsRunResponse.push(...currToolsRunResponse);
+    dispatchFlowResponse.push(...currFlatToolsResponseData);
+    inputTokens += usage.inputTokens;
+    outputTokens += usage.outputTokens;
+    finish_reason = currFinishReason;
+
+    // handle sub apps
+    if (toolCalls.length > 0) {
+      allCompleteMessages = [
+        ...completeMessages,
+        ...currToolsRunResponse.map((item) => item?.toolMsgParams)
+      ];
+
+      const agentNodeAssistant = GPTMessages2Chats({
+        messages: [...assistantMessage, ...currToolsRunResponse.map((item) => item?.toolMsgParams)],
+        getToolInfo: (id) => {
+          const toolNode = toolNodesMap.get(id);
+          return {
+            name: toolNode?.name || '',
+            avatar: toolNode?.avatar || ''
+          };
+        }
+      })[0] as AIChatItemType;
+      const agentChildAssistants = currFlatToolsResponseData
+        .map((item) => item.assistantResponses)
+        .flat()
+        .filter((item) => item.type !== ChatItemValueTypeEnum.interactive); // 交互节点留着下次记录
+
+      assistantResponses.push(...agentNodeAssistant.value, ...agentChildAssistants);
+
+      runTimes += currFlatToolsResponseData.reduce((sum, { runTimes }) => sum + runTimes, 0);
+
+      const hasStopSignal = currFlatToolsResponseData.some((item) =>
+        item.flowResponses?.some((flow) => flow.toolStop)
+      );
+      // Check interactive response(Only 1 interaction is reserved)
+      const workflowInteractiveResponseItem = currToolsRunResponse.find(
+        (item) => item.toolRunResponse?.workflowInteractiveResponse
+      );
+
+      if (hasStopSignal || workflowInteractiveResponseItem) {
+        // Get interactive tool data
+        const workflowInteractiveResponse =
+          workflowInteractiveResponseItem?.toolRunResponse?.workflowInteractiveResponse;
+
+        // Flashback traverses completeMessages, intercepting messages that know the first user
+        const firstUserIndex = allCompleteMessages.findLastIndex((item) => item.role === 'user');
+        const newMessages = allCompleteMessages.slice(firstUserIndex + 1);
+
+        if (workflowInteractiveResponse) {
+          agentWorkflowInteractiveResponse = {
+            ...workflowInteractiveResponse,
+            toolParams: {
+              entryNodeIds: workflowInteractiveResponse.entryNodeIds,
+              toolCallId: workflowInteractiveResponseItem?.toolMsgParams.tool_call_id,
+              memoryMessages: newMessages
+            }
+          };
+        }
+
+        break;
+      }
+
+      currRunAgentTimes--;
+    } else {
+      const agentNodeAssistant = GPTMessages2Chats({
+        messages: assistantMessage
+      })[0] as AIChatItemType;
+      assistantResponses.push(...agentNodeAssistant.value);
+      runTimes++;
+
+      break;
+    }
+  }
+
+  return {
+    dispatchFlowResponse,
+    agentCallInputTokens: inputTokens,
+    agentCallOutputTokens: outputTokens,
+    completeMessages: allCompleteMessages,
+    assistantResponses,
+    agentWorkflowInteractiveResponse,
+    runTimes,
+    finish_reason
+  };
+};
+
+const createToolFromToolNodes = (toolNodes: ToolNodeItemType[]): ChatCompletionTool[] => {
+  return toolNodes.map((item: ToolNodeItemType) => {
+    if (item.jsonSchema) {
+      return {
+        type: 'function',
+        function: {
+          name: item.nodeId,
+          description: item.intro || item.name,
+          parameters: item.jsonSchema
+        }
+      };
+    }
+
+    const properties: Record<string, any> = {};
+    item.toolParams.forEach((param) => {
+      const jsonSchema = param.valueType
+        ? valueTypeJsonSchemaMap[param.valueType] || toolValueTypeList[0].jsonSchema
+        : toolValueTypeList[0].jsonSchema;
+
+      properties[param.key] = {
+        ...jsonSchema,
+        description: param.toolDescription || '',
+        enum: param.enum?.split('\n').filter(Boolean) || undefined
+      };
+    });
+
+    return {
+      type: 'function',
+      function: {
+        name: item.nodeId,
+        description: item.toolDescription || item.intro || item.name,
+        parameters: {
+          type: 'object',
+          properties,
+          required: item.toolParams.filter((param) => param.required).map((param) => param.key)
+        }
+      }
+    };
+  });
+};
+
+// const createBuiltinTools = (): ChatCompletionTool[] => {
+//   return [
+//     {
+//       type: 'function',
+//       function: {
+//         name: 'plan_agent',
+//         description: '',
+//         parameters: {
+//           type: 'object',
+//           properties: {
+//             instruction: {
+//               type: 'string',
+//               description: ''
+//             }
+//           },
+//           required: ['instruction']
+//         }
+//       }
+//     }
+//   ];
+// };

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

@@ -1,14 +1,25 @@
-import { replaceVariable } from '@fastgpt/global/common/string/tools';
+export const getTopAgentDefaultPrompt = () => {
+  return `你是一位Supervisor Agent，具备以下核心能力：
 
-export const getMultiplePrompt = (obj: {
-  fileCount: number;
-  imgCount: number;
-  question: string;
-}) => {
-  const prompt = `Number of session file inputs：
-Document：{{fileCount}}
-Image：{{imgCount}}
-------
-{{question}}`;
-  return replaceVariable(prompt, obj);
+## 核心能力
+1. **计划制定与管理**：根据用户需求制定详细的执行计划，并实时跟踪和调整计划进度
+2. **工具调用编排**：可以调用各种工具来完成特定任务，支持并行和串行工具调用
+3. **上下文理解**：能够理解对话历史、文档内容和当前状态
+4. **自主决策**：根据当前情况和计划进度做出最优决策
+
+## 工作流程
+1. **需求分析**：深入理解用户需求，识别关键目标和约束条件
+2. **计划制定**：使用 plan_agent 工具制定详细的执行计划
+3. **工具编排**：根据计划选择和调用合适的工具
+4. **结果处理**：分析工具返回结果，判断是否满足预期
+5. **计划调整**：根据执行结果动态调整计划
+6. **最终输出**：给出完整、准确的回答
+
+## 特殊指令
+- 对于复杂任务，必须先使用 plan_agent 制定计划
+- 在执行过程中如需调整计划，再次调用 plan_agent
+- 始终保持计划的可见性和可追踪性
+- 遇到错误时要有容错和重试机制
+
+请始终保持专业、准确、有条理的回答风格，确保用户能够清楚了解执行进度和结果。`;
 };

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

@@ -2,15 +2,14 @@ import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workfl
 import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
 import type {
   ChatDispatchProps,
-  DispatchNodeResultType,
-  RuntimeNodeItemType
+  DispatchNodeResultType
 } from '@fastgpt/global/core/workflow/runtime/type';
 import { getLLMModel } from '../../../../ai/model';
 import { filterToolNodeIdByEdges, getNodeErrResponse, getHistories } from '../../utils';
-import { runToolCall } from './toolCall';
-import { type DispatchToolModuleProps, type ToolNodeItemType } from './type';
+import { runAgentCall } from './agentCall';
+import { type DispatchAgentModuleProps } from './type';
 import { type ChatItemType, type UserChatItemValueItemType } from '@fastgpt/global/core/chat/type';
-import { ChatItemValueTypeEnum, ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
+import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
 import {
   GPTMessages2Chats,
   chatValue2RuntimePrompt,
@@ -21,22 +20,27 @@ import {
 import { formatModelChars2Points } from '../../../../../support/wallet/usage/utils';
 import { getHistoryPreview } from '@fastgpt/global/core/chat/utils';
 import { replaceVariable } from '@fastgpt/global/common/string/tools';
-import { getMultiplePrompt } from './constants';
-import { filterToolResponseToPreview } from './utils';
+import {
+  filterToolResponseToPreview,
+  formatToolResponse,
+  getToolNodesByIds,
+  initToolNodes,
+  toolCallMessagesAdapt
+} from '../utils';
 import { getFileContentFromLinks, getHistoryFileLinks } from '../../tools/readFiles';
 import { parseUrlToFileType } from '@fastgpt/global/common/file/tools';
 import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
 import { getDocumentQuotePrompt } from '@fastgpt/global/core/ai/prompt/AIChat';
 import { postTextCensor } from '../../../../chat/postTextCensor';
-import type { FlowNodeInputItemType } from '@fastgpt/global/core/workflow/type/io';
-import type { McpToolDataType } from '@fastgpt/global/core/app/mcpTools/type';
-import type { JSONSchemaInputType } from '@fastgpt/global/core/app/jsonschema';
+import { getTopAgentDefaultPrompt } from './constants';
+import { runWorkflow } from '../..';
+import json5 from 'json5';
 
 type Response = DispatchNodeResultType<{
   [NodeOutputKeyEnum.answerText]: string;
 }>;
 
-export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<Response> => {
+export const dispatchRunAgent = async (props: DispatchAgentModuleProps): Promise<Response> => {
   let {
     node: { nodeId, name, isEntry, version, inputs },
     runtimeNodes,
@@ -49,6 +53,9 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
     runningUserInfo,
     externalProvider,
     usageId,
+    stream,
+    res,
+    workflowStreamResponse,
     params: {
       model,
       systemPrompt,
@@ -56,52 +63,31 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
       history = 6,
       fileUrlList: fileLinks,
       aiChatVision,
-      aiChatReasoning
+      aiChatReasoning,
+      temperature,
+      maxToken,
+      aiChatTopP,
+      aiChatResponseFormat,
+      aiChatJsonSchema,
+      aiChatStopSign
     }
   } = props;
 
   try {
-    const toolModel = getLLMModel(model);
-    const useVision = aiChatVision && toolModel.vision;
+    const agentModel = getLLMModel(model);
+    const useVision = aiChatVision && agentModel.vision;
     const chatHistories = getHistories(history, histories);
 
-    props.params.aiChatVision = aiChatVision && toolModel.vision;
-    props.params.aiChatReasoning = aiChatReasoning && toolModel.reasoning;
+    props.params.aiChatVision = aiChatVision && agentModel.vision;
+    props.params.aiChatReasoning = aiChatReasoning && agentModel.reasoning;
+
     const fileUrlInput = inputs.find((item) => item.key === NodeInputKeyEnum.fileUrlList);
     if (!fileUrlInput || !fileUrlInput.value || fileUrlInput.value.length === 0) {
       fileLinks = undefined;
     }
 
     const toolNodeIds = filterToolNodeIdByEdges({ nodeId, edges: runtimeEdges });
-
-    // Gets the module to which the tool is connected
-    const toolNodes = toolNodeIds
-      .map((nodeId) => {
-        const tool = runtimeNodes.find((item) => item.nodeId === nodeId);
-        return tool;
-      })
-      .filter(Boolean)
-      .map<ToolNodeItemType>((tool) => {
-        const toolParams: FlowNodeInputItemType[] = [];
-        // Raw json schema(MCP tool)
-        let jsonSchema: JSONSchemaInputType | undefined = undefined;
-        tool?.inputs.forEach((input) => {
-          if (input.toolDescription) {
-            toolParams.push(input);
-          }
-
-          if (input.key === NodeInputKeyEnum.toolData || input.key === 'toolData') {
-            const value = input.value as McpToolDataType;
-            jsonSchema = value.inputSchema;
-          }
-        });
-
-        return {
-          ...(tool as RuntimeNodeItemType),
-          toolParams,
-          jsonSchema
-        };
-      });
+    const toolNodes = getToolNodesByIds({ toolNodeIds, runtimeNodes });
 
     // Check interactive entry
     props.node.isEntry = false;
@@ -122,53 +108,24 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
       usageId
     });
 
-    const concatenateSystemPrompt = [
-      toolModel.defaultSystemChatPrompt,
+    const messages: ChatItemType[] = prepareAgentMessages({
+      systemPromptParams: {
         systemPrompt,
-      documentQuoteText
-        ? replaceVariable(getDocumentQuotePrompt(version), {
-            quote: documentQuoteText
-          })
-        : ''
-    ]
-      .filter(Boolean)
-      .join('\n\n===---===---===\n\n');
-
-    const messages: ChatItemType[] = (() => {
-      const value: ChatItemType[] = [
-        ...getSystemPrompt_ChatItemType(concatenateSystemPrompt),
-        // Add file input prompt to histories
-        ...chatHistories.map((item) => {
-          if (item.obj === ChatRoleEnum.Human) {
-            return {
-              ...item,
-              value: toolCallMessagesAdapt({
-                userInput: item.value,
-                skip: !hasReadFilesTool
-              })
-            };
+        documentQuoteText,
+        version
+      },
+      conversationParams: {
+        chatHistories,
+        hasReadFilesTool,
+        userChatInput,
+        userFiles,
+        lastInteractive,
+        isEntry: isEntry ?? false
       }
-          return item;
-        }),
-        {
-          obj: ChatRoleEnum.Human,
-          value: toolCallMessagesAdapt({
-            skip: !hasReadFilesTool,
-            userInput: runtimePrompt2ChatsValue({
-              text: userChatInput,
-              files: userFiles
-            })
-          })
-        }
-      ];
-      if (lastInteractive && isEntry) {
-        return value.slice(0, -2);
-      }
-      return value;
-    })();
+    });
 
     // censor model and system key
-    if (toolModel.censor && !externalProvider.openaiAccount?.key) {
+    if (agentModel.censor && !externalProvider.openaiAccount?.key) {
       await postTextCensor({
         text: `${systemPrompt}
           ${userChatInput}
@@ -176,41 +133,66 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
       });
     }
 
-    const {
-      toolWorkflowInteractiveResponse,
-      dispatchFlowResponse, // 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({
       messages,
       reserveId: false
-        // reserveTool: !!toolModel.toolChoice
     });
     const requestParams = {
-        runtimeNodes,
-        runtimeEdges,
-        toolNodes,
-        toolModel,
-        messages: adaptMessages,
-        interactiveEntryToolParams: lastInteractive?.toolParams
+      temperature,
+      maxToken,
+      stream,
+      requestOrigin,
+      externalProvider,
+      retainDatasetCite: true,
+      useVision: aiChatVision,
+      top_p: aiChatTopP,
+      response_format: {
+        type: aiChatResponseFormat,
+        json_schema: aiChatJsonSchema
+      },
+      stop: aiChatStopSign,
+      reasoning: aiChatReasoning
     };
 
-      return runToolCall({
-        ...props,
-        ...requestParams,
-        maxRunToolTimes: 100
-      });
+    const {
+      agentWorkflowInteractiveResponse,
+      dispatchFlowResponse,
+      agentCallInputTokens,
+      agentCallOutputTokens,
+      completeMessages = [],
+      assistantResponses = [],
+      runTimes,
+      finish_reason
+    } = await runAgentCall({
+      messages: adaptMessages,
+      toolNodes,
+      agentModel,
+      maxRunAgentTimes: 100,
+      res,
+      workflowStreamResponse,
+      interactiveEntryToolParams: lastInteractive?.toolParams,
+      requestParams,
+      handleToolResponse: async ({ args, nodeId }) => {
+        const startParams = (() => {
+          try {
+            return json5.parse(args);
+          } catch {
+            return {};
+          }
         })();
+        initToolNodes(runtimeNodes, [nodeId], startParams);
+        const toolRunResponse = await runWorkflow({
+          ...props,
+          isToolCall: true
+        });
+        return formatToolResponse(toolRunResponse.toolResponses);
+      }
+    });
 
     const { totalPoints: modelTotalPoints, modelName } = formatModelChars2Points({
       model,
-      inputTokens: toolCallInputTokens,
-      outputTokens: toolCallOutputTokens
+      inputTokens: agentCallInputTokens,
+      outputTokens: agentCallOutputTokens
     });
     const modelUsage = externalProvider.openaiAccount?.key ? 0 : modelTotalPoints;
 
@@ -234,8 +216,8 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
       [DispatchNodeResponseKeyEnum.nodeResponse]: {
         // 展示的积分消耗
         totalPoints: totalPointsUsage,
-        toolCallInputTokens: toolCallInputTokens,
-        toolCallOutputTokens: toolCallOutputTokens,
+        toolCallInputTokens: agentCallInputTokens,
+        toolCallOutputTokens: agentCallOutputTokens,
         childTotalPoints: toolTotalPoints,
         model: modelName,
         query: userChatInput,
@@ -254,13 +236,13 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
           moduleName: name,
           model: modelName,
           totalPoints: modelUsage,
-          inputTokens: toolCallInputTokens,
-          outputTokens: toolCallOutputTokens
+          inputTokens: agentCallInputTokens,
+          outputTokens: agentCallOutputTokens
         },
         // 工具的消耗
         ...toolUsages
       ],
-      [DispatchNodeResponseKeyEnum.interactive]: toolWorkflowInteractiveResponse
+      [DispatchNodeResponseKeyEnum.interactive]: agentWorkflowInteractiveResponse
     };
   } catch (error) {
     return getNodeErrResponse({ error });
@@ -324,51 +306,72 @@ const getMultiInput = async ({
   };
 };
 
-/*
-Tool call， auth add file prompt to question。
-Guide the LLM to call tool.
-*/
-const toolCallMessagesAdapt = ({
-  userInput,
-  skip
+const prepareAgentMessages = ({
+  systemPromptParams,
+  conversationParams
 }: {
-  userInput: UserChatItemValueItemType[];
-  skip?: boolean;
-}): UserChatItemValueItemType[] => {
-  if (skip) return userInput;
+  systemPromptParams: {
+    systemPrompt: string;
+    documentQuoteText: string;
+    version?: string;
+  };
+  conversationParams: {
+    chatHistories: ChatItemType[];
+    hasReadFilesTool: boolean;
+    userChatInput: string;
+    userFiles: UserChatItemValueItemType['file'][];
+    isEntry: boolean;
+    lastInteractive?: any;
+  };
+}): ChatItemType[] => {
+  const { systemPrompt, documentQuoteText, version } = systemPromptParams;
+  const { chatHistories, hasReadFilesTool, userChatInput, userFiles, lastInteractive, isEntry } =
+    conversationParams;
 
-  const files = userInput.filter((item) => item.type === 'file');
+  const agentPrompt = systemPrompt || getTopAgentDefaultPrompt();
 
-  if (files.length > 0) {
-    const filesCount = files.filter((file) => file.file?.type === 'file').length;
-    const imgCount = files.filter((file) => file.file?.type === 'image').length;
+  const finalSystemPrompt = [
+    agentPrompt,
+    documentQuoteText
+      ? replaceVariable(getDocumentQuotePrompt(version || ''), {
+          quote: documentQuoteText
+        })
+      : ''
+  ]
+    .filter(Boolean)
+    .join('\n\n===---===---===\n\n');
 
-    if (userInput.some((item) => item.type === 'text')) {
-      return userInput.map((item) => {
-        if (item.type === 'text') {
-          const text = item.text?.content || '';
+  const systemMessages = getSystemPrompt_ChatItemType(finalSystemPrompt);
+
+  const processedHistories = chatHistories.map((item) => {
+    if (item.obj !== ChatRoleEnum.Human) return item;
 
     return {
       ...item,
-            text: {
-              content: getMultiplePrompt({ fileCount: filesCount, imgCount, question: text })
-            }
+      value: toolCallMessagesAdapt({
+        userInput: item.value,
+        skip: !hasReadFilesTool
+      })
     };
-        }
-        return item;
   });
-    }
 
-    // Every input is a file
-    return [
-      {
-        type: ChatItemValueTypeEnum.text,
-        text: {
-          content: getMultiplePrompt({ fileCount: filesCount, imgCount, question: '' })
-        }
-      }
-    ];
-  }
-
-  return userInput;
+  const currentUserMessage: ChatItemType = {
+    obj: ChatRoleEnum.Human,
+    value: toolCallMessagesAdapt({
+      skip: !hasReadFilesTool,
+      userInput: runtimePrompt2ChatsValue({
+        text: userChatInput,
+        files: userFiles
+      })
+    })
+  };
+
+  const allMessages: ChatItemType[] = [
+    ...systemMessages,
+    ...processedHistories,
+    currentUserMessage
+  ];
+
+  // 交互模式下且为入口节点时，移除最后两条消息
+  return lastInteractive && isEntry ? allMessages.slice(0, -2) : allMessages;
 };

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

@@ -6,7 +6,7 @@ import type { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
 import { NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
 import type {
   ModuleDispatchProps,
-  DispatchNodeResponseType
+  DispatchNodeResultType
 } from '@fastgpt/global/core/workflow/runtime/type';
 import type { RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type';
 import { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
@@ -18,7 +18,7 @@ import type { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workf
 import type { LLMModelItemType } from '@fastgpt/global/core/ai/model';
 import type { JSONSchemaInputType } from '@fastgpt/global/core/app/jsonschema';
 
-export type DispatchToolModuleProps = ModuleDispatchProps<{
+export type DispatchAgentModuleProps = ModuleDispatchProps<{
   [NodeInputKeyEnum.history]?: ChatItemType[];
   [NodeInputKeyEnum.userChatInput]: string;
 
@@ -33,23 +33,23 @@ export type DispatchToolModuleProps = ModuleDispatchProps<{
   [NodeInputKeyEnum.aiChatStopSign]?: string;
   [NodeInputKeyEnum.aiChatResponseFormat]?: string;
   [NodeInputKeyEnum.aiChatJsonSchema]?: string;
-}> & {
-  messages: ChatCompletionMessageParam[];
-  toolNodes: ToolNodeItemType[];
-  toolModel: LLMModelItemType;
-  interactiveEntryToolParams?: WorkflowInteractiveResponseType['toolParams'];
-};
 
-export type RunToolResponse = {
+  [NodeInputKeyEnum.subAgentConfig]?: Record<string, any>;
+  [NodeInputKeyEnum.planAgentConfig]?: Record<string, any>;
+  [NodeInputKeyEnum.modelAgentConfig]?: Record<string, any>;
+}>;
+
+export type RunAgentResponse = {
   dispatchFlowResponse: DispatchFlowResponse[];
-  toolCallInputTokens: number;
-  toolCallOutputTokens: number;
+  agentCallInputTokens: number;
+  agentCallOutputTokens: number;
   completeMessages?: ChatCompletionMessageParam[];
   assistantResponses?: AIChatItemValueItemType[];
-  toolWorkflowInteractiveResponse?: WorkflowInteractiveResponseType;
+  agentWorkflowInteractiveResponse?: WorkflowInteractiveResponseType;
   [DispatchNodeResponseKeyEnum.runTimes]: number;
   finish_reason?: CompletionFinishReason;
 };
+
 export type ToolNodeItemType = RuntimeNodeItemType & {
   toolParams: RuntimeNodeItemType['inputs'];
   jsonSchema?: JSONSchemaInputType;

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

@@ -1,70 +0,0 @@
-import { sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
-import { ChatItemValueTypeEnum } from '@fastgpt/global/core/chat/constants';
-import { type AIChatItemValueItemType } from '@fastgpt/global/core/chat/type';
-import { type FlowNodeInputItemType } from '@fastgpt/global/core/workflow/type/io';
-import { type RuntimeEdgeItemType } from '@fastgpt/global/core/workflow/type/edge';
-import { type RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type';
-
-export const updateToolInputValue = ({
-  params,
-  inputs
-}: {
-  params: Record<string, any>;
-  inputs: FlowNodeInputItemType[];
-}) => {
-  return inputs.map((input) => ({
-    ...input,
-    value: params[input.key] ?? input.value
-  }));
-};
-
-export const filterToolResponseToPreview = (response: AIChatItemValueItemType[]) => {
-  return response.map((item) => {
-    if (item.type === ChatItemValueTypeEnum.tool) {
-      const formatTools = item.tools?.map((tool) => {
-        return {
-          ...tool,
-          response: sliceStrStartEnd(tool.response, 500, 500)
-        };
-      });
-      return {
-        ...item,
-        tools: formatTools
-      };
-    }
-
-    return item;
-  });
-};
-
-export const formatToolResponse = (toolResponses: any) => {
-  if (typeof toolResponses === 'object') {
-    return JSON.stringify(toolResponses, null, 2);
-  }
-
-  return toolResponses ? String(toolResponses) : 'none';
-};
-
-// 在原参上改变值，不修改原对象，tool workflow 中，使用的还是原对象
-export const initToolCallEdges = (edges: RuntimeEdgeItemType[], entryNodeIds: string[]) => {
-  edges.forEach((edge) => {
-    if (entryNodeIds.includes(edge.target)) {
-      edge.status = 'active';
-    }
-  });
-};
-
-export const initToolNodes = (
-  nodes: RuntimeNodeItemType[],
-  entryNodeIds: string[],
-  startParams?: Record<string, any>
-) => {
-  nodes.forEach((node) => {
-    if (entryNodeIds.includes(node.nodeId)) {
-      node.isEntry = true;
-      if (startParams) {
-        node.inputs = updateToolInputValue({ params: startParams, inputs: node.inputs });
-      }
-    }
-  });
-};

packages/service/core/workflow/dispatch/ai/tool/constants.ts

@@ -0,0 +1,14 @@
+import { replaceVariable } from '@fastgpt/global/common/string/tools';
+
+export const getMultiplePrompt = (obj: {
+  fileCount: number;
+  imgCount: number;
+  question: string;
+}) => {
+  const prompt = `Number of session file inputs：
+Document：{{fileCount}}
+Image：{{imgCount}}
+------
+{{question}}`;
+  return replaceVariable(prompt, obj);
+};

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

@@ -0,0 +1,287 @@
+import { NodeInputKeyEnum, NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
+import { DispatchNodeResponseKeyEnum } from '@fastgpt/global/core/workflow/runtime/constants';
+import type {
+  ChatDispatchProps,
+  DispatchNodeResultType
+} from '@fastgpt/global/core/workflow/runtime/type';
+import { getLLMModel } from '../../../../ai/model';
+import { filterToolNodeIdByEdges, getNodeErrResponse, getHistories } from '../../utils';
+import { runToolCall } from './toolCall';
+import type { DispatchToolModuleProps } from './type';
+import type { ChatItemType, UserChatItemValueItemType } from '@fastgpt/global/core/chat/type';
+import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
+import {
+  GPTMessages2Chats,
+  chatValue2RuntimePrompt,
+  chats2GPTMessages,
+  getSystemPrompt_ChatItemType,
+  runtimePrompt2ChatsValue
+} from '@fastgpt/global/core/chat/adapt';
+import { formatModelChars2Points } from '../../../../../support/wallet/usage/utils';
+import { getHistoryPreview } from '@fastgpt/global/core/chat/utils';
+import { replaceVariable } from '@fastgpt/global/common/string/tools';
+import { filterToolResponseToPreview, toolCallMessagesAdapt, getToolNodesByIds } from '../utils';
+import { getFileContentFromLinks, getHistoryFileLinks } from '../../tools/readFiles';
+import { parseUrlToFileType } from '@fastgpt/global/common/file/tools';
+import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
+import { getDocumentQuotePrompt } from '@fastgpt/global/core/ai/prompt/AIChat';
+import { postTextCensor } from '../../../../chat/postTextCensor';
+
+type Response = DispatchNodeResultType<{
+  [NodeOutputKeyEnum.answerText]: string;
+}>;
+
+export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<Response> => {
+  let {
+    node: { nodeId, name, isEntry, version, inputs },
+    runtimeNodes,
+    runtimeEdges,
+    histories,
+    query,
+    requestOrigin,
+    chatConfig,
+    lastInteractive,
+    runningUserInfo,
+    externalProvider,
+    params: {
+      model,
+      systemPrompt,
+      userChatInput,
+      history = 6,
+      fileUrlList: fileLinks,
+      aiChatVision,
+      aiChatReasoning
+    }
+  } = props;
+
+  try {
+    const toolModel = getLLMModel(model);
+    const useVision = aiChatVision && toolModel.vision;
+    const chatHistories = getHistories(history, histories);
+
+    props.params.aiChatVision = aiChatVision && toolModel.vision;
+    props.params.aiChatReasoning = aiChatReasoning && toolModel.reasoning;
+    const fileUrlInput = inputs.find((item) => item.key === NodeInputKeyEnum.fileUrlList);
+    if (!fileUrlInput || !fileUrlInput.value || fileUrlInput.value.length === 0) {
+      fileLinks = undefined;
+    }
+
+    const toolNodeIds = filterToolNodeIdByEdges({ nodeId, edges: runtimeEdges });
+    const toolNodes = getToolNodesByIds({ toolNodeIds, runtimeNodes });
+
+    // Check interactive entry
+    props.node.isEntry = false;
+    const hasReadFilesTool = toolNodes.some(
+      (item) => item.flowNodeType === FlowNodeTypeEnum.readFiles
+    );
+
+    const globalFiles = chatValue2RuntimePrompt(query).files;
+    const { documentQuoteText, userFiles } = await getMultiInput({
+      runningUserInfo,
+      histories: chatHistories,
+      requestOrigin,
+      maxFiles: chatConfig?.fileSelectConfig?.maxFiles || 20,
+      customPdfParse: chatConfig?.fileSelectConfig?.customPdfParse,
+      fileLinks,
+      inputFiles: globalFiles,
+      hasReadFilesTool
+    });
+
+    const concatenateSystemPrompt = [
+      toolModel.defaultSystemChatPrompt,
+      systemPrompt,
+      documentQuoteText
+        ? replaceVariable(getDocumentQuotePrompt(version), {
+            quote: documentQuoteText
+          })
+        : ''
+    ]
+      .filter(Boolean)
+      .join('\n\n===---===---===\n\n');
+
+    const messages: ChatItemType[] = (() => {
+      const value: ChatItemType[] = [
+        ...getSystemPrompt_ChatItemType(concatenateSystemPrompt),
+        // Add file input prompt to histories
+        ...chatHistories.map((item) => {
+          if (item.obj === ChatRoleEnum.Human) {
+            return {
+              ...item,
+              value: toolCallMessagesAdapt({
+                userInput: item.value,
+                skip: !hasReadFilesTool
+              })
+            };
+          }
+          return item;
+        }),
+        {
+          obj: ChatRoleEnum.Human,
+          value: toolCallMessagesAdapt({
+            skip: !hasReadFilesTool,
+            userInput: runtimePrompt2ChatsValue({
+              text: userChatInput,
+              files: userFiles
+            })
+          })
+        }
+      ];
+      if (lastInteractive && isEntry) {
+        return value.slice(0, -2);
+      }
+      return value;
+    })();
+
+    // censor model and system key
+    if (toolModel.censor && !externalProvider.openaiAccount?.key) {
+      await postTextCensor({
+        text: `${systemPrompt}
+          ${userChatInput}
+        `
+      });
+    }
+
+    const {
+      toolWorkflowInteractiveResponse,
+      dispatchFlowResponse, // 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({
+        messages,
+        reserveId: false
+        // reserveTool: !!toolModel.toolChoice
+      });
+      const requestParams = {
+        runtimeNodes,
+        runtimeEdges,
+        toolNodes,
+        toolModel,
+        messages: adaptMessages,
+        interactiveEntryToolParams: lastInteractive?.toolParams
+      };
+
+      return runToolCall({
+        ...props,
+        ...requestParams,
+        maxRunToolTimes: 100
+      });
+    })();
+
+    const { totalPoints: modelTotalPoints, modelName } = formatModelChars2Points({
+      model,
+      inputTokens: toolCallInputTokens,
+      outputTokens: toolCallOutputTokens
+    });
+    const modelUsage = externalProvider.openaiAccount?.key ? 0 : modelTotalPoints;
+
+    const toolUsages = dispatchFlowResponse.map((item) => item.flowUsages).flat();
+    const toolTotalPoints = toolUsages.reduce((sum, item) => sum + item.totalPoints, 0);
+
+    // concat tool usage
+    const totalPointsUsage = modelUsage + toolTotalPoints;
+
+    const previewAssistantResponses = filterToolResponseToPreview(assistantResponses);
+
+    return {
+      data: {
+        [NodeOutputKeyEnum.answerText]: previewAssistantResponses
+          .filter((item) => item.text?.content)
+          .map((item) => item.text?.content || '')
+          .join('')
+      },
+      [DispatchNodeResponseKeyEnum.runTimes]: runTimes,
+      [DispatchNodeResponseKeyEnum.assistantResponses]: previewAssistantResponses,
+      [DispatchNodeResponseKeyEnum.nodeResponse]: {
+        // 展示的积分消耗
+        totalPoints: totalPointsUsage,
+        toolCallInputTokens: toolCallInputTokens,
+        toolCallOutputTokens: toolCallOutputTokens,
+        childTotalPoints: toolTotalPoints,
+        model: modelName,
+        query: userChatInput,
+        historyPreview: getHistoryPreview(
+          GPTMessages2Chats({ messages: completeMessages, reserveTool: false }),
+          10000,
+          useVision
+        ),
+        toolDetail: dispatchFlowResponse.map((item) => item.flowResponses).flat(),
+        mergeSignId: nodeId,
+        finishReason: finish_reason
+      },
+      [DispatchNodeResponseKeyEnum.nodeDispatchUsages]: [
+        // 模型本身的积分消耗
+        {
+          moduleName: name,
+          model: modelName,
+          totalPoints: modelUsage,
+          inputTokens: toolCallInputTokens,
+          outputTokens: toolCallOutputTokens
+        },
+        // 工具的消耗
+        ...toolUsages
+      ],
+      [DispatchNodeResponseKeyEnum.interactive]: toolWorkflowInteractiveResponse
+    };
+  } catch (error) {
+    return getNodeErrResponse({ error });
+  }
+};
+
+const getMultiInput = async ({
+  runningUserInfo,
+  histories,
+  fileLinks,
+  requestOrigin,
+  maxFiles,
+  customPdfParse,
+  inputFiles,
+  hasReadFilesTool
+}: {
+  runningUserInfo: ChatDispatchProps['runningUserInfo'];
+  histories: ChatItemType[];
+  fileLinks?: string[];
+  requestOrigin?: string;
+  maxFiles: number;
+  customPdfParse?: boolean;
+  inputFiles: UserChatItemValueItemType['file'][];
+  hasReadFilesTool: boolean;
+}) => {
+  // Not file quote
+  if (!fileLinks || hasReadFilesTool) {
+    return {
+      documentQuoteText: '',
+      userFiles: inputFiles
+    };
+  }
+
+  const filesFromHistories = getHistoryFileLinks(histories);
+  const urls = [...fileLinks, ...filesFromHistories];
+
+  if (urls.length === 0) {
+    return {
+      documentQuoteText: '',
+      userFiles: []
+    };
+  }
+
+  // Get files from histories
+  const { text } = await getFileContentFromLinks({
+    // Concat fileUrlList and filesFromHistories; remove not supported files
+    urls,
+    requestOrigin,
+    maxFiles,
+    customPdfParse,
+    teamId: runningUserInfo.teamId,
+    tmbId: runningUserInfo.tmbId
+  });
+
+  return {
+    documentQuoteText: text,
+    userFiles: fileLinks.map((url) => parseUrlToFileType(url)).filter(Boolean)
+  };
+};

packages/service/core/workflow/dispatch/ai/tool/toolCall.ts

@@ -14,7 +14,7 @@ import json5 from 'json5';
 import type { DispatchFlowResponse } from '../../type';
 import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
 import type { AIChatItemType } from '@fastgpt/global/core/chat/type';
-import { formatToolResponse, initToolCallEdges, initToolNodes } from './utils';
+import { formatToolResponse, initToolCallEdges, initToolNodes } from '../utils';
 import { computedMaxToken } from '../../../../ai/utils';
 import { sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
 import type { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type';
@@ -328,7 +328,7 @@ export const runToolCall = async (
     },
     onToolCall({ call }) {
       const toolNode = toolNodesMap.get(call.function.name);
-      if (toolNode) {
+      if (!toolNode) return;
       workflowStreamResponse?.({
         event: SseResponseEventEnum.toolCall,
         data: {
@@ -342,7 +342,6 @@ export const runToolCall = async (
           }
         }
       });
-      }
     },
     onToolParam({ tool, params }) {
       workflowStreamResponse?.({

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

@@ -0,0 +1,56 @@
+import type {
+  ChatCompletionMessageParam,
+  CompletionFinishReason
+} from '@fastgpt/global/core/ai/type';
+import type { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
+import { NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
+import type {
+  ModuleDispatchProps,
+  DispatchNodeResponseType
+} from '@fastgpt/global/core/workflow/runtime/type';
+import type { RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type';
+import { ChatNodeUsageType } from '@fastgpt/global/support/wallet/bill/type';
+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 { LLMModelItemType } from '@fastgpt/global/core/ai/model';
+import type { JSONSchemaInputType } from '@fastgpt/global/core/app/jsonschema';
+
+export type DispatchToolModuleProps = ModuleDispatchProps<{
+  [NodeInputKeyEnum.history]?: ChatItemType[];
+  [NodeInputKeyEnum.userChatInput]: string;
+
+  [NodeInputKeyEnum.fileUrlList]?: string[];
+  [NodeInputKeyEnum.aiModel]: string;
+  [NodeInputKeyEnum.aiSystemPrompt]: string;
+  [NodeInputKeyEnum.aiChatTemperature]: number;
+  [NodeInputKeyEnum.aiChatMaxToken]: number;
+  [NodeInputKeyEnum.aiChatVision]?: boolean;
+  [NodeInputKeyEnum.aiChatReasoning]?: boolean;
+  [NodeInputKeyEnum.aiChatTopP]?: number;
+  [NodeInputKeyEnum.aiChatStopSign]?: string;
+  [NodeInputKeyEnum.aiChatResponseFormat]?: string;
+  [NodeInputKeyEnum.aiChatJsonSchema]?: string;
+}> & {
+  messages: ChatCompletionMessageParam[];
+  toolNodes: ToolNodeItemType[];
+  toolModel: LLMModelItemType;
+  interactiveEntryToolParams?: WorkflowInteractiveResponseType['toolParams'];
+};
+
+export type RunToolResponse = {
+  dispatchFlowResponse: DispatchFlowResponse[];
+  toolCallInputTokens: number;
+  toolCallOutputTokens: number;
+  completeMessages?: ChatCompletionMessageParam[];
+  assistantResponses?: AIChatItemValueItemType[];
+  toolWorkflowInteractiveResponse?: WorkflowInteractiveResponseType;
+  [DispatchNodeResponseKeyEnum.runTimes]: number;
+  finish_reason?: CompletionFinishReason;
+};
+export type ToolNodeItemType = RuntimeNodeItemType & {
+  toolParams: RuntimeNodeItemType['inputs'];
+  jsonSchema?: JSONSchemaInputType;
+};

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

@@ -0,0 +1,161 @@
+import { sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
+import { ChatItemValueTypeEnum } from '@fastgpt/global/core/chat/constants';
+import type {
+  AIChatItemValueItemType,
+  UserChatItemValueItemType
+} from '@fastgpt/global/core/chat/type';
+import type { FlowNodeInputItemType } from '@fastgpt/global/core/workflow/type/io';
+import type { RuntimeEdgeItemType } from '@fastgpt/global/core/workflow/type/edge';
+import type { RuntimeNodeItemType } from '@fastgpt/global/core/workflow/runtime/type';
+import { NodeInputKeyEnum } from '@fastgpt/global/core/workflow/constants';
+import type { McpToolDataType } from '@fastgpt/global/core/app/mcpTools/type';
+import type { JSONSchemaInputType } from '@fastgpt/global/core/app/jsonschema';
+import { getMultiplePrompt } from './tool/constants';
+import type { ToolNodeItemType } from './tool/type';
+
+export const updateToolInputValue = ({
+  params,
+  inputs
+}: {
+  params: Record<string, any>;
+  inputs: FlowNodeInputItemType[];
+}) => {
+  return inputs.map((input) => ({
+    ...input,
+    value: params[input.key] ?? input.value
+  }));
+};
+
+export const filterToolResponseToPreview = (response: AIChatItemValueItemType[]) => {
+  return response.map((item) => {
+    if (item.type === ChatItemValueTypeEnum.tool) {
+      const formatTools = item.tools?.map((tool) => {
+        return {
+          ...tool,
+          response: sliceStrStartEnd(tool.response, 500, 500)
+        };
+      });
+      return {
+        ...item,
+        tools: formatTools
+      };
+    }
+
+    return item;
+  });
+};
+
+export const formatToolResponse = (toolResponses: any) => {
+  if (typeof toolResponses === 'object') {
+    return JSON.stringify(toolResponses, null, 2);
+  }
+
+  return toolResponses ? String(toolResponses) : 'none';
+};
+
+// 在原参上改变值，不修改原对象，tool workflow 中，使用的还是原对象
+export const initToolCallEdges = (edges: RuntimeEdgeItemType[], entryNodeIds: string[]) => {
+  edges.forEach((edge) => {
+    if (entryNodeIds.includes(edge.target)) {
+      edge.status = 'active';
+    }
+  });
+};
+
+export const initToolNodes = (
+  nodes: RuntimeNodeItemType[],
+  entryNodeIds: string[],
+  startParams?: Record<string, any>
+) => {
+  nodes.forEach((node) => {
+    if (entryNodeIds.includes(node.nodeId)) {
+      node.isEntry = true;
+      if (startParams) {
+        node.inputs = updateToolInputValue({ params: startParams, inputs: node.inputs });
+      }
+    }
+  });
+};
+
+/*
+Tool call， auth add file prompt to question。
+Guide the LLM to call tool.
+*/
+export const toolCallMessagesAdapt = ({
+  userInput,
+  skip
+}: {
+  userInput: UserChatItemValueItemType[];
+  skip?: boolean;
+}): UserChatItemValueItemType[] => {
+  if (skip) return userInput;
+
+  const files = userInput.filter((item) => item.type === 'file');
+
+  if (files.length > 0) {
+    const filesCount = files.filter((file) => file.file?.type === 'file').length;
+    const imgCount = files.filter((file) => file.file?.type === 'image').length;
+
+    if (userInput.some((item) => item.type === 'text')) {
+      return userInput.map((item) => {
+        if (item.type === 'text') {
+          const text = item.text?.content || '';
+
+          return {
+            ...item,
+            text: {
+              content: getMultiplePrompt({ fileCount: filesCount, imgCount, question: text })
+            }
+          };
+        }
+        return item;
+      });
+    }
+
+    // Every input is a file
+    return [
+      {
+        type: ChatItemValueTypeEnum.text,
+        text: {
+          content: getMultiplePrompt({ fileCount: filesCount, imgCount, question: '' })
+        }
+      }
+    ];
+  }
+
+  return userInput;
+};
+
+export const getToolNodesByIds = ({
+  toolNodeIds,
+  runtimeNodes
+}: {
+  toolNodeIds: string[];
+  runtimeNodes: RuntimeNodeItemType[];
+}): ToolNodeItemType[] => {
+  const nodeMap = new Map(runtimeNodes.map((node) => [node.nodeId, node]));
+
+  return toolNodeIds
+    .map((nodeId) => nodeMap.get(nodeId))
+    .filter((tool): tool is RuntimeNodeItemType => Boolean(tool))
+    .map((tool) => {
+      const toolParams: FlowNodeInputItemType[] = [];
+      let jsonSchema: JSONSchemaInputType | undefined;
+
+      for (const input of tool.inputs) {
+        if (input.toolDescription) {
+          toolParams.push(input);
+        }
+
+        if (input.key === NodeInputKeyEnum.toolData) {
+          jsonSchema = (input.value as McpToolDataType).inputSchema;
+        }
+      }
+
+      return {
+        ...tool,
+        toolParams,
+        jsonSchema
+      };
+    });
+};

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

@@ -2,9 +2,9 @@ import { FlowNodeTypeEnum } from '@fastgpt/global/core/workflow/node/constant';
 import { dispatchAppRequest } from './abandoned/runApp';
 import { dispatchClassifyQuestion } from './ai/classifyQuestion';
 import { dispatchContentExtract } from './ai/extract';
-import { dispatchRunTools } from './ai/agent/index';
-import { dispatchStopToolCall } from './ai/agent/stopTool';
-import { dispatchToolParams } from './ai/agent/toolParams';
+import { dispatchRunTools } from './ai/tool/index';
+import { dispatchStopToolCall } from './ai/tool/stopTool';
+import { dispatchToolParams } from './ai/tool/toolParams';
 import { dispatchChatCompletion } from './ai/chat';
 import { dispatchCodeSandbox } from './tools/codeSandbox';
 import { dispatchDatasetConcat } from './dataset/concat';
@@ -30,6 +30,7 @@ import { dispatchIfElse } from './tools/runIfElse';
 import { dispatchLafRequest } from './tools/runLaf';
 import { dispatchUpdateVariable } from './tools/runUpdateVar';
 import { dispatchTextEditor } from './tools/textEditor';
+import { dispatchRunAgent } from './ai/agent';
 
 export const callbackMap: Record<FlowNodeTypeEnum, Function> = {
   [FlowNodeTypeEnum.workflowStart]: dispatchWorkflowStart,
@@ -45,7 +46,8 @@ export const callbackMap: Record<FlowNodeTypeEnum, Function> = {
   [FlowNodeTypeEnum.pluginInput]: dispatchPluginInput,
   [FlowNodeTypeEnum.pluginOutput]: dispatchPluginOutput,
   [FlowNodeTypeEnum.queryExtension]: dispatchQueryExtension,
-  [FlowNodeTypeEnum.agent]: dispatchRunTools,
+  [FlowNodeTypeEnum.agent]: dispatchRunAgent,
+  [FlowNodeTypeEnum.toolCall]: dispatchRunTools,
   [FlowNodeTypeEnum.stopTool]: dispatchStopToolCall,
   [FlowNodeTypeEnum.toolParams]: dispatchToolParams,
   [FlowNodeTypeEnum.lafModule]: dispatchLafRequest,

projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/index.tsx

@@ -44,6 +44,7 @@ const nodeTypes: Record<FlowNodeTypeEnum, any> = {
   [FlowNodeTypeEnum.pluginOutput]: dynamic(() => import('./nodes/NodePluginIO/PluginOutput')),
   [FlowNodeTypeEnum.pluginModule]: NodeSimple,
   [FlowNodeTypeEnum.queryExtension]: NodeSimple,
+  [FlowNodeTypeEnum.toolCall]: undefined,
   [FlowNodeTypeEnum.agent]: dynamic(() => import('./nodes/NodeAgent')),
   [FlowNodeTypeEnum.stopTool]: (data: NodeProps<FlowNodeItemType>) => (
     <NodeSimple {...data} minW={'100px'} maxW={'300px'} />