基于 Apollo、LinkedIn 和 GPT-4o 的全面联系人信息丰富化
高级
这是一个Lead Generation, AI Summarization领域的自动化工作流，包含 24 个节点。主要使用 If, Set, Code, Sort, Limit 等节点。 基于 Apollo、LinkedIn 和 GPT-4o 的全面联系人信息丰富化，适用于 HubSpot
前置要求
- •HubSpot API Key
- •可能需要目标 API 的认证凭证
- •OpenAI API Key
使用的节点 (24)
工作流预览
可视化展示节点连接关系，支持缩放和平移
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": "t7e4sQzh96e0xaq7",
"meta": {
"instanceId": "219ad8a5631572c9ac16d4e2fec49250f591e94570ab0858fd2013d005c6d5c6",
"templateCredsSetupCompleted": true
},
"name": "联系人信息丰富化",
"tags": [
{
"id": "0zTM91F1nQEHLKzN",
"name": "template_upload",
"createdAt": "2025-07-17T15:01:40.334Z",
"updatedAt": "2025-07-17T15:01:40.334Z"
}
],
"nodes": [
{
"id": "dbf704c3-2b0e-4158-b5d8-ca1d138d141a",
"name": "当被其他工作流执行时",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-336,
752
],
"parameters": {
"workflowInputs": {
"values": [
{
"name": "name"
},
{
"name": "email"
}
]
}
},
"typeVersion": 1.1
},
{
"id": "747be8f0-cf2b-412d-b7a9-93051ff0a11b",
"name": "使用 Apollo 进行丰富化",
"type": "n8n-nodes-base.httpRequest",
"position": [
128,
752
],
"parameters": {
"url": "https://api.apollo.io/api/v1/people/match",
"method": "POST",
"options": {},
"sendQuery": true,
"sendHeaders": true,
"queryParameters": {
"parameters": [
{
"name": "reveal_personal_emails",
"value": "false"
},
{
"name": "reveal_phone_number",
"value": "false"
},
{
"name": "email",
"value": "={{ $json.email }}"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "Cache-Control",
"value": "no-cache"
},
{
"name": "accept",
"value": "application/json"
},
{
"name": "x-api-key",
"value": "YOUR-API-KEY"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "3630169c-2677-4124-a19b-f9d1a2bfdcce",
"name": "是否找到？",
"type": "n8n-nodes-base.if",
"position": [
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752
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "8037609c-e921-4ef9-9d34-375a6bd471ad",
"operator": {
"type": "string",
"operation": "exists",
"singleValue": true
},
"leftValue": "={{ $json.person && ($json.person.name || $json.person.email) }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "00a26c55-aa97-4255-b002-cb77bc1a8780",
"name": "在 HubSpot 中进行丰富化1",
"type": "n8n-nodes-base.hubspot",
"position": [
3168,
752
],
"parameters": {
"email": "={{ $json.output.email }}",
"options": {},
"authentication": "oAuth2",
"additionalFields": {
"city": "={{ $json.output.city }}",
"country": "={{ $json.output.country_region }}",
"jobTitle": "={{ $json.output.job_title }}",
"customPropertiesUi": {
"customPropertiesValues": [
{
"value": "={{ $json.output.linkedin_url }}",
"property": "linkedin_account"
},
{
"value": "={{ $json.output.experience_summary }}",
"property": "experience_summary"
},
{
"value": "={{ $json.output.education_summary }}",
"property": "education_summary"
},
{
"value": "={{ Math.floor(Date.now() / 86400000) * 86400000 }}",
"property": "last_enrichment_date"
},
{
"value": "={{ $json.output.recent_linkedin_activity }}",
"property": "recent_linkedin_posts"
}
]
}
}
},
"credentials": {
"hubspotOAuth2Api": {
"id": "DAzRcQP1uXfOKZuC",
"name": "HubSpot account"
}
},
"retryOnFail": true,
"typeVersion": 2.1
},
{
"id": "01dd3096-dcb3-4e0d-97ac-581a7d08b55f",
"name": "自动修复输出解析器2",
"type": "@n8n/n8n-nodes-langchain.outputParserAutofixing",
"position": [
2672,
912
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "b819ac28-783c-49d5-8125-fb824fd30661",
"name": "结构化输出解析器 2",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
2832,
1040
],
"parameters": {
"jsonSchemaExample": "{\n \"email\": \"testuser1@companydomain.com\",\n \"linkedin_url\": \"http://www.linkedin.com/in/testuser1\",\n \"job_title\": \"CEO & Co-Founder\",\n \"city\": \"Denver\",\n \"state\": \"Colorado\",\n \"country_region\": \"United States\",\n \"experience_summary\": \"User experience content\",\n \"education_summary\": \"Education Level content\",\n \"recent_linkedin_activity\": \"• Posted about winning 82 % of competitive deals and key sales mantras (6 hours ago)\\n• Shared guidance on setting realistic expectations for a new VP Sales’ impact (1 day ago)\"\n}"
},
"typeVersion": 1.2
},
{
"id": "06391ddf-2071-4b80-86ad-85c922ae33c8",
"name": "OpenAI 聊天模型5",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
2672,
1040
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "HH848Gw8QSS5TUKW",
"name": "OpenAi"
}
},
"typeVersion": 1.2
},
{
"id": "3aa928c0-6f12-4f5c-9acb-018b0b87667e",
"name": "丰富化摘要代理",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
2528,
752
],
"parameters": {
"text": "=Here’s the person data to enrich for CRM: \n{{ JSON.stringify($json, null, 2) }}\n\nHere's the recent activity to summarize for the CRM:\n{{ $json.recent_activity }}",
"messages": {
"messageValues": [
{
"message": "=# Overview\nYou are “HubSpot-Enrichment-Agent”, a data-cleaning assistant that converts messy person JSON enrichment data into concise, human-friendly fields ready to upsert into HubSpot. \n\n## OBJECTIVE\nReturn **one** JSON object with exactly these keys:\n\n{\n \"email\": string|null,\n \"linkedin_url\": string|null,\n \"job_title\": string|null, // title at the company that matches the email domain\n \"city\": string|null,\n \"state\": string|null,\n \"country_region\": string|null,\n \"experience_summary\": string|null, // 3-4 recent roles, bullet list, newest → oldest\n \"education_summary\": string|null, // highest degree(s) & institution(s), 1-2 lines\n \"recent_linkedin_activity\": string|null\n}\n\n## RULES & LOGIC\n\n1. **Email, LinkedIn URL, City, Country/Region** \n *Pass through directly if present; else `null`.*\n\n2. **Job Title (relevant)** \n - Use the employment record whose `organization_name` (or website) shares the **same email domain** as `email`. \n - If no domain match, pick the most recent item where `current == true`. \n - Strip membership-only titles (“Member”, “Angel Investor”) unless no better option exists.\n\n3. **Experience Summary** \n - Sort `employment_history` by `start_date` DESC. \n - Select the first **3–4** roles that are either `current == true` _or_ have an `end_date` within the last 10 years. \n - Format each as: \n `• <Title>, <Org> — <YYYY-start> → <\"Present\" | YYYY-end>` \n - Join with line breaks. Return `null` if list is empty.\n\n4. **Education Summary** \n - Look for the highest‐level degree (Doctorate > MBA/Master’s > Bachelor’s > Associate > Other). \n - Summarise as: \n `<Degree>, <Institution> (<YYYY-end>)`. \n - If multiple relevant degrees, include up to two lines. \n - If no education data, return `null`.\n\n5. **Recent LinkedIn Activity** \n - If a `recent_activity` or similar field exists, pull the latest **1-3** public posts or updates. \n - Summarise in plain text, e.g., \n `“Commented on procurement AI article” (3 days ago)` \n - If no activity data, set to `null`. Do NOT make up activity.\n\n6. **Null handling** \n - Any missing or empty field **must** be literal `null` (not empty string).\n\n7. **Output style** \n - Pure JSON, no markdown. \n - No additional keys, comments, or nested objects.\n\n## Grammar and Punctuation\n - ensure that your output is cleanly formatted for readiness to import into CRM, including removing uneceesary spaces or other symbols and capitalizing names. (e.g., \"sales director\" should become \"Sales Director\")\n\nReturn only that final JSON. Example output:\n{\n \"email\": \"dustin.c@gatekeeperhq.com\",\n \"linkedin_url\": \"https://www.linkedin.com/in/dustinclinard\",\n \"job_title\": \"Chief Revenue Officer (CRO)\",\n \"city\": \"Concord\",\n \"country_region\": \"United States\",\n \"experience_summary\": \"• Chief Revenue Officer, Gatekeeper — 2024 → Present\\n• Founding Member, Revenue Collective — 2019 → Present\\n• Member, Partnership Leaders — 2020 → Present\\n• Founder & Growth Consultant, Sales Growth Musings — 2016 → Present\",\n \"education_summary\": null,\n \"recent_linkedin_activity\": null\n}"
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.6
},
{
"id": "f3d84acb-fe43-4d14-ab74-97a0e5a336a7",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
48,
592
],
"parameters": {
"color": 5,
"width": 260,
"height": 400,
"content": "## 使用 Apollo 进行丰富化"
},
"typeVersion": 1
},
{
"id": "8f9f9c6c-e9b9-4502-97a2-b4bf3c5a396c",
"name": "便签2",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"color": 4,
"width": 540,
"height": 620,
"content": "## 总结丰富化数据"
},
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{
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"name": "便签3",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"color": 5,
"width": 260,
"height": 400,
"content": "## 丰富化 HubSpot"
},
"typeVersion": 1
},
{
"id": "6c4e7437-9a58-4aad-956c-268bb23920e8",
"name": "合并",
"type": "n8n-nodes-base.merge",
"position": [
2048,
752
],
"parameters": {
"mode": "combine",
"options": {
"includeUnpaired": true
},
"combineBy": "combineByPosition"
},
"typeVersion": 3.1
},
{
"id": "5f3436aa-94d0-46f9-83fd-e60726b8821c",
"name": "OpenAI 聊天模型6",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
2512,
912
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "o3",
"cachedResultName": "o3"
},
"options": {
"responseFormat": "json_object"
}
},
"credentials": {
"openAiApi": {
"id": "HH848Gw8QSS5TUKW",
"name": "OpenAi"
}
},
"typeVersion": 1.2
},
{
"id": "624d158e-08bc-42df-add7-9462b39cb8de",
"name": "获取近期帖子",
"type": "n8n-nodes-base.httpRequest",
"position": [
832,
912
],
"parameters": {
"url": "https://fresh-linkedin-profile-data.p.rapidapi.com/get-profile-posts",
"options": {},
"sendQuery": true,
"sendHeaders": true,
"queryParameters": {
"parameters": [
{
"name": "linkedin_url",
"value": "={{ $json.person.linkedin_url }}"
},
{
"name": "type",
"value": "posts"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "x-rapidapi-host",
"value": "fresh-linkedin-profile-data.p.rapidapi.com"
},
{
"name": "x-rapidapi-key",
"value": "<YOUR API KEY>"
}
]
}
},
"retryOnFail": true,
"typeVersion": 4.2,
"alwaysOutputData": true
},
{
"id": "45dfddb1-ad00-419b-8309-e91de878989d",
"name": "拆分输出",
"type": "n8n-nodes-base.splitOut",
"position": [
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],
"parameters": {
"options": {},
"fieldToSplitOut": "data"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "6b392b08-3dff-4201-ba66-a979ac7d301a",
"name": "过滤掉转发和旧帖子",
"type": "n8n-nodes-base.filter",
"position": [
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],
"parameters": {
"options": {},
"conditions": {
"options": {
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"leftValue": "",
"caseSensitive": true,
"typeValidation": "loose"
},
"combinator": "and",
"conditions": [
{
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"operator": {
"type": "boolean",
"operation": "false",
"singleValue": true
},
"leftValue": "={{ $json.reshared }}",
"rightValue": ""
},
{
"id": "8da29df1-cbfb-42cd-a85f-62b2b4647802",
"operator": {
"type": "dateTime",
"operation": "afterOrEquals"
},
"leftValue": "={{ $json.posted }}",
"rightValue": "={{$now.minus({ days: 30 }).toISO()}}"
}
]
},
"looseTypeValidation": true
},
"typeVersion": 2.2,
"alwaysOutputData": true
},
{
"id": "466bbf0f-54e8-4c2c-9637-f70925329b72",
"name": "限制为 3 条",
"type": "n8n-nodes-base.limit",
"position": [
1680,
912
],
"parameters": {
"maxItems": 3
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "fa0bd77e-310e-4a64-83c9-c4f3b0fddb7a",
"name": "按发帖日期排序",
"type": "n8n-nodes-base.sort",
"position": [
1488,
912
],
"parameters": {
"options": {},
"sortFieldsUi": {
"sortField": [
{
"order": "descending",
"fieldName": "posted"
}
]
}
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "b45d1bb7-5a3f-43f6-ae9c-7a912e3b1114",
"name": "聚合",
"type": "n8n-nodes-base.aggregate",
"position": [
1904,
912
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "c0e75ad6-08db-4bda-9c69-4067adcc4951",
"name": "遍历项目",
"type": "n8n-nodes-base.splitInBatches",
"position": [
624,
784
],
"parameters": {
"options": {}
},
"typeVersion": 3,
"alwaysOutputData": false
},
{
"id": "69bd14dd-f6d8-4220-ba6b-dd17d2d1ea8b",
"name": "提取字段",
"type": "n8n-nodes-base.set",
"position": [
2272,
752
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "19c926c1-da4e-44e2-abce-6d7d172cb0bb",
"name": "person_data",
"type": "string",
"value": "={{ $json.person }}"
},
{
"id": "62615717-7bc7-44c4-9304-7177dfcf3ee8",
"name": "recent_activity",
"type": "string",
"value": "={{ $json.data }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "5b9ce241-42a6-468a-be87-2462248836af",
"name": "便签说明5",
"type": "n8n-nodes-base.stickyNote",
"position": [
768,
848
],
"parameters": {
"color": 5,
"width": 1260,
"height": 220,
"content": "## 抓取近期 LinkedIn 帖子"
},
"typeVersion": 1
},
{
"id": "f76caacf-bf40-4aa7-b48c-60ebf9c772a8",
"name": "清理",
"type": "n8n-nodes-base.code",
"position": [
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752
],
"parameters": {
"jsCode": "/**********************************************************************\n * Deduplicate contacts by email & drop empties\n * Input: items[] (each item = { json: { … } })\n * Output: one item per unique, non-empty email\n **********************************************************************/\nconst normEmail = e => (typeof e === 'string' ? e.trim().toLowerCase() : '');\n\nconst seen = new Set();\nconst clean = [];\n\nfor (const itm of items) {\n // Handle both flat and nested structures\n const data = itm.json.output ?? itm.json; // supports {output:{…}} too\n const email = normEmail(data.email);\n\n if (!email || seen.has(email)) continue; // skip blanks & dupes\n\n seen.add(email);\n clean.push({\n json: {\n name: data.name ?? null,\n email // already normalised\n }\n });\n}\n\nreturn clean;"
},
"typeVersion": 2
},
{
"id": "e95c04cb-b9e3-433d-b510-77e1b9b7abf2",
"name": "便签1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1632,
848
],
"parameters": {
"height": 208,
"content": "决定要分析多少帖子"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {
"When Executed by Another Workflow": [
{
"json": {
"name": "People of intereset 1",
"email": "test@companymail.com"
}
},
{
"json": {
"name": "People of intereset 2",
"email": "test2@companymail.com"
}
}
]
},
"settings": {
"executionOrder": "v1"
},
"versionId": "ad6478e5-597d-450d-ab64-d506159731f2",
"connections": {
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]
},
"3aa928c0-6f12-4f5c-9acb-018b0b87667e": {
"main": [
[
{
"node": "00a26c55-aa97-4255-b002-cb77bc1a8780",
"type": "main",
"index": 0
}
]
]
},
"b819ac28-783c-49d5-8125-fb824fd30661": {
"ai_outputParser": [
[
{
"node": "01dd3096-dcb3-4e0d-97ac-581a7d08b55f",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"01dd3096-dcb3-4e0d-97ac-581a7d08b55f": {
"ai_outputParser": [
[
{
"node": "3aa928c0-6f12-4f5c-9acb-018b0b87667e",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"6b392b08-3dff-4201-ba66-a979ac7d301a": {
"main": [
[
{
"node": "fa0bd77e-310e-4a64-83c9-c4f3b0fddb7a",
"type": "main",
"index": 0
}
]
]
},
"dbf704c3-2b0e-4158-b5d8-ca1d138d141a": {
"main": [
[
{
"node": "f76caacf-bf40-4aa7-b48c-60ebf9c772a8",
"type": "main",
"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 - 潜在客户开发, AI 摘要总结
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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