This workflow follows the Agent → Google Gemini Chat recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"name": "UserProfiling",
"nodes": [
{
"parameters": {},
"type": "n8n-nodes-base.manualTrigger",
"typeVersion": 1,
"position": [
0,
0
],
"id": "06617531-047b-4ea9-82eb-cf73474c4531",
"name": "When clicking \u2018Execute workflow\u2019"
},
{
"parameters": {
"fileSelector": "E:/SalesBuddy/Linkedin_scraper/data/taufeeq",
"options": {}
},
"type": "n8n-nodes-base.readWriteFile",
"typeVersion": 1,
"position": [
208,
0
],
"id": "1dcb9085-8b5b-411f-9aa4-1aa95890f9a0",
"name": "Read/Write Files from Disk"
},
{
"parameters": {
"promptType": "define",
"text": "=You are an intelligent assistant.\n\nRead the following JSON object extracted from a LinkedIn company profile, and generate a very concise summary for company profiling.\n\nIt should only include:\n- company_name\n- industry\n- company_size\n- specialties (list of core focus areas)\n- key_markets (few major countries or regions where they operate)\n- linkedin_summary (one sentence summary of their typical activity or positioning)\n\nReturn the output in this JSON format only:\n{\n \"company_name\": \"\",\n \"industry\": \"\",\n \"company_size\": \"\",\n \"specialties\": [],\n \"key_markets\": [],\n \"linkedin_summary\": \"\"\n}\n\nHere\u2019s the data:\n{{ JSON.stringify($json) }}",
"hasOutputParser": true,
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 2,
"position": [
672,
0
],
"id": "259ea986-47f0-4393-9ec1-01eb390d95bb",
"name": "AI Agent"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"typeVersion": 1,
"position": [
672,
272
],
"id": "88c2b15a-2ba1-4ac4-8d83-1b1f8edb2722",
"name": "Google Gemini Chat Model",
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"operation": "fromJson",
"options": {}
},
"type": "n8n-nodes-base.extractFromFile",
"typeVersion": 1,
"position": [
448,
0
],
"id": "705505d2-a10d-484f-8d57-f118cbabd9a0",
"name": "Extract from File"
},
{
"parameters": {
"operation": "insert",
"collection": "leads",
"fields": "=output, {{ $json.output }}",
"options": {}
},
"type": "n8n-nodes-base.mongoDb",
"typeVersion": 1.2,
"position": [
1072,
0
],
"id": "ac67660b-1815-424c-867b-a6f962420945",
"name": "MongoDB",
"credentials": {
"mongoDb": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"company_name\": { \"type\": \"string\" },\n \"industry\": { \"type\": \"string\" },\n \"company_size\": { \"type\": \"string\" },\n \"specialties\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" }\n },\n \"key_markets\": {\n \"type\": \"array\",\n \"items\": { \"type\": \"string\" }\n },\n \"linkedin_summary\": { \"type\": \"string\" },\n \"website_link\": { \"type\": \"string\" }\n },\n \"required\": [\n \"company_name\",\n \"industry\",\n \"company_size\",\n \"specialties\",\n \"key_markets\",\n \"linkedin_summary\",\n \"website_link\"\n ]\n}",
"autoFix": true
},
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"typeVersion": 1.3,
"position": [
832,
224
],
"id": "5a31be9d-df21-4b77-81bf-aeb892c8dab6",
"name": "Structured Output Parser"
},
{
"parameters": {
"modelName": "models/gemini-2.5-flash-lite-preview-06-17",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"typeVersion": 1,
"position": [
912,
448
],
"id": "ebd43eb8-fbe4-42f6-9f28-d5f678a9a403",
"name": "Google Gemini Chat Model1",
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
}
}
],
"connections": {
"When clicking \u2018Execute workflow\u2019": {
"main": [
[
{
"node": "Read/Write Files from Disk",
"type": "main",
"index": 0
}
]
]
},
"Read/Write Files from Disk": {
"main": [
[
{
"node": "Extract from File",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Extract from File": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "MongoDB",
"type": "main",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "AI Agent",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Google Gemini Chat Model1": {
"ai_languageModel": [
[
{
"node": "Structured Output Parser",
"type": "ai_languageModel",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "d5eb19b3-676d-470f-be33-181ada4a4547",
"id": "U9sCQhStsL5hU32Y",
"tags": []
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
googlePalmApimongoDb
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About this workflow
UserProfiling. Uses readWriteFile, agent, lmChatGoogleGemini, mongoDb. Event-driven trigger; 8 nodes.
Source: https://github.com/Vinay-R-S/Linkedin_scraper/blob/4ae8e22862c756d004341768982ed49af5fa7a84/n8n/UserProfiling.json — original creator credit. Request a take-down →
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