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开启深度研究 - AI 驱动的自主研究工作流

高级

这是一个AI领域的自动化工作流，包含 17 个节点。主要使用 Code, HttpRequest, SplitInBatches, Agent, ChainLlm 等节点，结合人工智能技术实现智能自动化。 深度研究 - AI驱动的自主研究工作流

前置要求
  • •可能需要目标 API 的认证凭证

使用的节点 (17)

Code
StickyNote
HttpRequest
SplitInBatches
Agent
ChainLlm
ChatTrigger
ToolWikipedia
LmChatOpenRouter
MemoryBufferWindow

分类

人工智能
工作流预览
可视化展示节点连接关系，支持缩放和平移
React Flow
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
  "id": "WLSqXECfQF7rOj2A",
  "meta": {
    "instanceId": "cba4a4a2eb5d7683330e2944837278938831ed3c042e20da6f5049c07ad14798"
  },
  "name": "开启深度研究 - AI 驱动的自主研究工作流",
  "tags": [],
  "nodes": [
    {
      "id": "b7b70ba1-0267-4d2b-91f4-5cc4fd22fd03",
      "name": "聊天消息触发器",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -1940,
        160
      ],
      "webhookId": "cb0b9dbe-1f35-441a-b062-29624b0ebc6a",
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "55a8a512-f2d4-4aed-93e5-dd9bfa2dcaad",
      "name": "使用 LLM 生成搜索查询",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        -1760,
        160
      ],
      "parameters": {
        "text": "=User Query: {{ $('Chat Message Trigger').item.json.chatInput }}",
        "messages": {
          "messageValues": [
            {
              "message": "=You are an expert research assistant. Given a user's query, generate up to four distinct, precise search queries that would help gather comprehensive information on the topic. Return only a JSON list of strings, for example: ['query1', 'query2', 'query3']."
            }
          ]
        },
        "promptType": "define"
      },
      "typeVersion": 1.5
    },
    {
      "id": "5f92361a-b490-479d-8360-c87a100b470e",
      "name": "LLM 响应提供器 (OpenRouter)",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
      "position": [
        -1760,
        700
      ],
      "parameters": {
        "model": "google/gemini-2.0-flash-001",
        "options": {}
      },
      "credentials": {
        "openRouterApi": {
          "id": "WZWYWCfluxuKxZzV",
          "name": "OpenRouter account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "4ab360eb-858f-48b8-a00d-71867d4f0c93",
      "name": "解析与分块 JSON 数据",
      "type": "n8n-nodes-base.code",
      "position": [
        -1420,
        160
      ],
      "parameters": {
        "jsCode": "// Parse the input JSON string and split it into four chunks\nconst rawText = $json.text;\n\n// Remove Markdown JSON code blocks if present\nconst cleanedText = rawText.replace(/```json|```/g, '').trim();\n\ntry {\n    const jsonArray = JSON.parse(cleanedText);\n    if (!Array.isArray(jsonArray)) {\n        throw new Error('The JSON is not an array.');\n    }\n    const chunkSize = Math.ceil(jsonArray.length / 4);\n    const chunks = [];\n    for (let i = 0; i < jsonArray.length; i += chunkSize) {\n        chunks.push(jsonArray.slice(i, i + chunkSize));\n    }\n    return chunks.map(chunk => ({ json: { chunk } }));\n} catch (error) {\n    return [{ json: { error: error.message } }];\n}\n"
      },
      "typeVersion": 2
    },
    {
      "id": "5a3ac393-8355-449f-93cb-b98e8bee9b80",
      "name": "执行 SerpAPI 搜索请求",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -780,
        180
      ],
      "parameters": {
        "url": "https://serpapi.com/search",
        "options": {},
        "sendQuery": true,
        "queryParameters": {
          "parameters": [
            {
              "name": "q",
              "value": "={{ $('Parse and Chunk JSON Data').item.json.chunk }}"
            },
            {
              "name": "api_key",
              "value": "={{ $credentials.SerpAPI.key }}"
            },
            {
              "name": "engine",
              "value": "google"
            }
          ]
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "dad82469-830d-40fb-9f6b-b9fefef41267",
      "name": "执行 Jina AI 分析请求",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        80,
        160
      ],
      "parameters": {
        "url": "=https://r.jina.ai/{{ $json.url }}",
        "options": {},
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth"
      },
      "credentials": {
        "httpHeaderAuth": {
          "id": "iseKF5sPsvwtJhgT",
          "name": "Jina AI"
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "e21bbdf6-a903-491e-920c-ef7576f9ce80",
      "name": "格式化 SerpAPI 自然搜索结果",
      "type": "n8n-nodes-base.code",
      "position": [
        -460,
        140
      ],
      "parameters": {
        "jsCode": "// Format the organic search results from SerpAPI\nconst results = $input.first().json.organic_results;\nif (results.length === 0) {\n  return [{ json: { error: 'No search results found.' } }];\n}\nconst formattedResults = results.map(result => ({\n  title: result.title || 'No title available',\n  url: result.link || 'No link available',\n  source: result.source || result.displayed_link || 'Unknown source'\n}));\nreturn formattedResults.map(result => ({ json: result }));\n"
      },
      "typeVersion": 2
    },
    {
      "id": "a856c8e8-5c3c-4a2f-9086-66deee1afd06",
      "name": "通过 LLM 提取相关上下文",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        -1280,
        520
      ],
      "parameters": {
        "text": "=User Queries: {{ $('Parse and Chunk JSON Data').all().map(item => item.json.chunk[0]).join(', ') }}\nWebpage Contents: \n\"\"\"\n{{ $json.data }}\n\"\"\"",
        "options": {
          "systemMessage": "=You are an expert information extractor. Given the user's query, the search query that led to this page, and the webpage content, extract all relevant pieces of information that are useful to answer the query. Return only the relevant context as plain text without any additional commentary."
        },
        "promptType": "define"
      },
      "typeVersion": 1.7
    },
    {
      "id": "6d5c6698-0b4f-438c-91b9-3597f5d3e904",
      "name": "生成全面研究报告",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        -740,
        520
      ],
      "parameters": {
        "text": "=Extracted Contexts (Merged):\n\"\"\"\n{{ $json.output }}\n\"\"\"",
        "options": {
          "systemMessage": "You are an expert researcher and report writer. Based on the gathered contexts and the original user query, generate a comprehensive, well-structured report. Include all relevant insights and conclusions without unnecessary commentary.\n\nFormat the report in Markdown with clear headings. For example:\n\n# Research Report: [User Query]\n\n## Key Findings\n- Point 1\n- Point 2\n\n## Detailed Analysis\n### Aspect 1\nSummary of findings.\n_Source:_ [Source Name](URL)\n\n### Aspect 2\nSummary of findings.\n_Source:_ [Another Source](URL)\n\nNow, generate the complete report."
        },
        "promptType": "define"
      },
      "typeVersion": 1.7
    },
    {
      "id": "05fea6a1-791e-4980-8f2a-2960455066d7",
      "name": "为 SerpAPI 批处理拆分数据",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        -1100,
        160
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3
    },
    {
      "id": "df00e7e8-99b8-484a-8047-869474fefee9",
      "name": "为 Jina AI 批处理拆分数据",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        -220,
        140
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3
    },
    {
      "id": "2edc683b-65f7-40c3-a22d-7fbf5b67de0a",
      "name": "LLM 记忆缓冲区（输入上下文）",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        -1160,
        740
      ],
      "parameters": {
        "sessionKey": "my_test_session",
        "sessionIdType": "customKey",
        "contextWindowLength": 20
      },
      "typeVersion": 1.3
    },
    {
      "id": "23017ae7-72a7-45c7-8edf-d0ba72220675",
      "name": "LLM 记忆缓冲区（报告上下文）",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        -620,
        760
      ],
      "parameters": {
        "sessionKey": "my_test_session",
        "sessionIdType": "customKey",
        "contextWindowLength": 20
      },
      "typeVersion": 1.3
    },
    {
      "id": "6bc9533b-e265-47b3-b93a-3a4f86ba0541",
      "name": "获取维基百科信息",
      "type": "@n8n/n8n-nodes-langchain.toolWikipedia",
      "position": [
        -580,
        920
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "b25c148e-047d-40a7-8818-94c3504828dd",
      "name": "便签：SerpAPI 设置",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -940,
        -20
      ],
      "parameters": {
        "color": 7,
        "width": 420,
        "height": 140,
        "content": "## SerpAPI 设置说明"
      },
      "typeVersion": 1
    },
    {
      "id": "e69c9a85-31e4-42b9-a09a-683ec5bb97d1",
      "name": "便签：Jina AI 设置",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -60,
        -40
      ],
      "parameters": {
        "color": 7,
        "width": 420,
        "height": 140,
        "content": "## Jina AI 设置说明"
      },
      "typeVersion": 1
    },
    {
      "id": "dbd204e0-da8e-41d8-814b-f409a23e9573",
      "name": "便签：OpenRouter API 设置",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1680,
        460
      ],
      "parameters": {
        "color": 7,
        "width": 300,
        "height": 180,
        "content": "## OpenRouter API 设置说明"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "aa857bb3-84c1-4fe6-9464-90fc09163960",
  "connections": {
    "Chat Message Trigger": {
      "main": [
        [
          {
            "node": "Generate Search Queries using LLM",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Parse and Chunk JSON Data": {
      "main": [
        [
          {
            "node": "Split Data for SerpAPI Batching",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Fetch Wikipedia Information": {
      "ai_tool": [
        [
          {
            "node": "Generate Comprehensive Research Report",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Format SerpAPI Organic Results": {
      "main": [
        [
          {
            "node": "Split Data for Jina AI Batching",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Perform SerpAPI Search Request": {
      "main": [
        [
          {
            "node": "Split Data for SerpAPI Batching",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Split Data for Jina AI Batching": {
      "main": [
        [
          {
            "node": "Extract Relevant Context via LLM",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Perform Jina AI Analysis Request",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Split Data for SerpAPI Batching": {
      "main": [
        [
          {
            "node": "Format SerpAPI Organic Results",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Perform SerpAPI Search Request",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Extract Relevant Context via LLM": {
      "main": [
        [
          {
            "node": "Generate Comprehensive Research Report",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Perform Jina AI Analysis Request": {
      "main": [
        [
          {
            "node": "Split Data for Jina AI Batching",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Generate Search Queries using LLM": {
      "main": [
        [
          {
            "node": "Parse and Chunk JSON Data",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "LLM Memory Buffer (Input Context)": {
      "ai_memory": [
        [
          {
            "node": "Extract Relevant Context via LLM",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "LLM Memory Buffer (Report Context)": {
      "ai_memory": [
        [
          {
            "node": "Generate Comprehensive Research Report",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "LLM Response Provider (OpenRouter)": {
      "ai_languageModel": [
        [
          {
            "node": "Generate Search Queries using LLM",
            "type": "ai_languageModel",
            "index": 0
          },
          {
            "node": "Extract Relevant Context via LLM",
            "type": "ai_languageModel",
            "index": 0
          },
          {
            "node": "Generate Comprehensive Research Report",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    }
  }
}
常见问题

如何使用这个工作流？

复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。

这个工作流适合什么场景？

高级 - 人工智能

需要付费吗？

本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。

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难度等级
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节点数量17
分类1
节点类型10
难度说明

适合高级用户，包含 16+ 个节点的复杂工作流

作者
Leonard
外部链接
在 n8n.io 查看 →

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