Elay Guez This workflow corresponds to n8n.io template #10119 — we link there as the canonical source.
This workflow follows the Agent → Chainllm 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 →
{
"meta": {
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "14cd7efc-ef41-418a-af6d-bedef22b8085",
"name": "Respond to Webhook",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
-480,
304
],
"parameters": {
"options": {
"responseHeaders": {
"entries": [
{
"name": "Content-Type",
"value": "text/plain; charset=utf-8"
}
]
}
},
"respondWith": "text",
"responseBody": "={{ $json.output.summary }}"
},
"typeVersion": 1.4
},
{
"id": "b33b25ad-aff9-4192-946b-becd709dfe6c",
"name": "Bright Data Search Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-592,
-96
],
"parameters": {
"text": "=You are an AI agent specialized in web search and information retrieval.\n\nQuery: \"{{ $json.output }}\"\n\n## Objective: \nPerform a focused search and return the most relevant link in **JSON format only**: \n```json\n{\n \"link\": \"\"\n}\n\n## Execution Process:\n\n### 1. Run Search\nsearch_engine(\n query: [the query you constructed],\n engine: \"google\",\n limit: 10,\n country: \"us\" or \"il\" depending on relevance\n)\n\n### 2. Filter Results\n\nPrioritize reliable sources:\nMajor news websites (Reuters, Bloomberg, WSJ, Haaretz, Globes)\nOfficial websites (investor relations, about pages)\nOfficial financial reports (SEC filings, quarterly reports)\nTrusted data sources (Wikipedia, Crunchbase, LinkedIn)\n\nSkip:\nAds and irrelevant marketing content\nUnverified forums\nOutdated content (unless historical info is requested)\nBroken or invalid links\n\nFind a suitable link based on:\nRelevance to the query\nRecency of the source\nSource credibility\n\n### 3. Return Result\n\nRequired format \u2013 JSON only, no additional text:\n{\n \"link\": \"https://...\"\n}\n\nIf no high-quality result is found, search another source until you find one or return \"\".\n\n## Error Handling:\n\nIf search fails \u2192 try an alternative query\nIf no results found \u2192 return JSON with empty fields\nAlways return valid JSON, with no explanations or extra text",
"options": {},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 2.2
},
{
"id": "d018693a-0fdf-4ae8-ae22-1b4f79bed2a1",
"name": "Bright Data MCP",
"type": "@n8n/n8n-nodes-langchain.mcpClientTool",
"position": [
-528,
64
],
"parameters": {
"include": "selected",
"options": {
"timeout": 120000
},
"endpointUrl": "https://mcp.brightdata.com/mcp?token=YOUR_TOKEN_HERE&pro=1",
"includeTools": [
"search_engine"
],
"serverTransport": "httpStreamable"
},
"typeVersion": 1.1
},
{
"id": "ddf8a398-38ae-4da4-a2aa-0fe6a4b3e62b",
"name": "Webhook Call",
"type": "n8n-nodes-base.webhook",
"position": [
-1664,
-32
],
"parameters": {
"path": "brightdata-search",
"options": {},
"httpMethod": "POST",
"responseMode": "responseNode",
"authentication": "headerAuth"
},
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
},
"typeVersion": 2.1
},
{
"id": "8dfdd46c-ad8f-458a-a403-45910dcc13e5",
"name": "Adjust Query Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-1120,
-96
],
"parameters": {
"text": "=User prompt: {{ $json.userPrompt }}\nPrompt's referral: {{ $json.cellReference }}\n",
"options": {
"systemMessage": "=### 1. Request Analysis \n\nIdentify the query type: \n- **News/Updates**: \"News about X\", \"What\u2019s happening with X\" \n- **Financial Data**: \"Revenue\", \"Earnings\", \"Financial Reports\" \n- **Factual Information**: \"Who is the CEO\", \"How many employees\", \"Where is the HQ\" \n- **Analysis/Comparison**: \"Competitors\", \"Compare to Y\", \"Alternatives\" \n- **General Research**: Open-ended questions about a topic/field \n\n### 2. Search Query Construction \nBuild an optimal query based on the type:\n\n**News**: `\"{{ entity }}\" news [year/month if relevant]` \n**Financial Data**: `\"{{ entity }}\" revenue earnings \"Q[X] [year]\"` or `financial results` \n**Factual Information**: `\"{{ entity }}\" [specific fact] official` \n**Analysis**: `\"{{ entity }}\" analysis competitors market share` \n\n**Principles:** \n- Always translate to English to maximize results \n- Keep double quotes around specific names \n- Add relevant time \u2014 current time is {{ $now.format('dd/LL/yyyy') }} \n- Match keywords to the requested information type \n\n## Examples:\n\n**Input**: Prompt's referral=\"Apple\", user prompt=\"Search for news about the company\" \noutput example: \"Apple Inc\" news 2025 technology \n\n**Input**: Prompt's referral=\"Tesla\", user prompt=\"Monthly revenue April\" \noutput example: \"Tesla\" revenue \"April 2024\" OR \"Q2 2024\" financial results \n\n**Input**: Prompt's referral=\"United States\", user prompt=\"Who is the president\" \noutput example: \"United States\" president 19/10/2025 current \n"
},
"promptType": "define"
},
"typeVersion": 2.2
},
{
"id": "c12218ae-674c-4bf9-b35b-6a8d6b360c9a",
"name": "Bright Data - Data Extraction",
"type": "n8n-nodes-base.httpRequest",
"position": [
-192,
-48
],
"parameters": {
"url": "https://api.brightdata.com/request",
"method": "POST",
"options": {
"batching": {
"batch": {
"batchSize": 1,
"batchInterval": 2000
}
}
},
"sendBody": true,
"sendHeaders": true,
"bodyParameters": {
"parameters": [
{
"name": "zone",
"value": "mcp_unlocker"
},
{
"name": "url",
"value": "={{ $json.output.link }}"
},
{
"name": "format",
"value": "json"
},
{
"name": "method",
"value": "GET"
},
{
"name": "country",
"value": "il"
},
{
"name": "data_format",
"value": "markdown"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "Authorization",
"value": "Bearer YOUR_TOKEN_HERE"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "9f8b11b7-f997-4906-a929-2656cdee4204",
"name": "Extract Data",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"onError": "continueRegularOutput",
"position": [
-1600,
336
],
"parameters": {
"text": "=## Input\n### The user's original request:\n{{ $('Set Variables').item.json.cellReference }} - {{ $('Set Variables').item.json.userPrompt }}\n### Full content scanned from a website:\n{{ $json.body }}",
"batching": {},
"messages": {
"messageValues": [
{
"message": "=# You are an AI agent specialized in extracting relevant information. Your role is to receive:\n\n1. **The user's original request** \u2013 the question or topic requested \n2. **Full content scanned from a website** \u2013 raw information from a source \n\n## Objective\n\nExtract and summarize the most relevant information from the scanned content based on the user's query.\n\n## Workflow\n\n1. Carefully read the scanned content \n2. Identify information directly relevant to the user's query \n3. Create a focused and concise summary of only the relevant information \n4. Completely ignore unrelated content \n\n## Output Rules\n\n- **Return only valid JSON** according to the schema below \n- Do not include any text outside the JSON \n- The summary should include **only** information directly relevant to the user's query \n- Maximum preferred length: **400 characters** \n- If no relevant information is found, leave the `summary` field empty (`\"\"`) \n\n## Edge Case Handling\n\n- **Partial or corrupted content:** Extract only the existing and relevant information \n- **Multiple topics in content:** Focus only on the topic relevant to the user's query \n- **Different language:** Translate relevant information to {{ $('Set Variables').item.json.ouputLanguage }} \n- **No relevant information:** Return JSON with an empty `summary` \n- **Always return valid JSON**, no extra text \n\n## Key Principles\n\n** Extract factual, accurate, and focused information ** \n** Translate to {{ $('Set Variables').item.json.ouputLanguage }} when needed ** \n** Summarize concisely within 400 characters ** \n\n** Do not add information that is not in the original content ** \n** Do not include information unrelated to the query ** \n** Do not deviate from the JSON format ** \n"
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.7
},
{
"id": "d7a349a0-0158-4ec2-bb94-beebebcbe2bd",
"name": "Summarize Information",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-1120,
336
],
"parameters": {
"text": "=scraping summary information: {{ $json.output.summary }}\nthe actual user request/question: {{ $('Set Variables').item.json.cellReference }} - {{ $('Set Variables').item.json.userPrompt }}\n",
"options": {
"systemMessage": "=Generate a conclusion in {{ $('Set Variables').item.json.ouputLanguage }} based on all the information above. Filter and summarize the most relevant information according to the user's question into a maximum of 400 characters. \nAlways return valid JSON, with no explanations or additional text. \n"
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 2.2
},
{
"id": "4ca74428-5c4a-4c3f-8359-95a1df48a4b9",
"name": "Update Logs",
"type": "n8n-nodes-base.dataTable",
"position": [
-480,
496
],
"parameters": {
"columns": {
"value": {
"output": "={{ $json.output.summary }}",
"input_prompt": "={{ $('Set Variables').item.json.userPrompt }} - {{ $('Set Variables').item.json.cellReference }}"
},
"schema": [
{
"id": "input_prompt",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "input_prompt",
"defaultMatch": false
},
{
"id": "output",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "output",
"defaultMatch": false
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"logs"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"dataTableId": {
"__rl": true,
"mode": "list",
"value": "vAMxTuckOJDPlMN3",
"cachedResultUrl": "/projects/qoZIbTB1W6TCihY8/datatables/vAMxTuckOJDPlMN3",
"cachedResultName": "BrightData Test"
}
},
"typeVersion": 1
},
{
"id": "1abd9a03-bbb3-44cf-b23c-40363d54458e",
"name": "Structured Output Parser - 1",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-352,
64
],
"parameters": {
"jsonSchemaExample": "{\n \"link\": \"\"\n}"
},
"typeVersion": 1.3
},
{
"id": "0b99dc9d-bc59-4054-b97a-7bc708439a22",
"name": "Structured Output Parser - 2",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-1392,
496
],
"parameters": {
"jsonSchemaExample": "{\n \"summary\": \"\"\n}"
},
"typeVersion": 1.3
},
{
"id": "1987c731-3ef5-4977-8029-8415726355ea",
"name": "GPT 4o Mini - 1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-1664,
496
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini",
"cachedResultName": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "2c9c2b61-56f7-40ef-912c-1a1bf34688a2",
"name": "GPT 4o Mini - 2",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-1184,
496
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini",
"cachedResultName": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "7fd1cabe-0429-4ca4-81ba-8b2e6344279b",
"name": "GPT 4o - 1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-704,
64
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o",
"cachedResultName": "gpt-4o"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "95e48871-e554-442c-83fb-b028a020b342",
"name": "GPT 4.1 Mini - 1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-1184,
80
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "759b9362-6511-47a5-b96c-a7cb340bae16",
"name": "Structured Output Parser - 3",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
-912,
496
],
"parameters": {
"jsonSchemaExample": "{\n\t\"summary\": \"Intel was founded in 1968.\"\n}\n"
},
"typeVersion": 1.3
},
{
"id": "3b9a3797-81bd-4120-9c36-4d5d2c21ac01",
"name": "AI-Powered Web Scraping to Google Sheets with Bright Data",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2160,
-208
],
"parameters": {
"color": 6,
"width": 396,
"height": 860,
"content": "# AI-Powered Web Scraping to Google Sheets with Bright Data\n\n## Created by: [Elay Guez](https://www.linkedin.com/in/elay-g)\n\n## Features\n* \ud83d\udcca **Custom Function** in Google Sheets: `=BRIGHTDATA(cell,\"prompt\")`\n* \ud83e\udd16 **AI-optimized** search queries for better results\n* \ud83d\udd0d **Bright Data** enterprise scraping (bypasses bot detection)\n* \ud83c\udfaf **AI-filtered** results return only the best match\n* \u26a1 **<20 second** response time\n\n## Quick Setup\n1. Connect OpenAI API key (GPT-4.1 + GPT-4o Mini + GPT 4o)\n2. Add Bright Data API credentials\n3. Deploy Google Apps Script custom function\n4. Configure webhook authentication\n5. Write preffered Language output in \"Set Variables\" Node\n6. Test & activate! \ud83d\ude80\n\n## Cost per search: ~$0.02-0.05\n## Time saved: 3-5 minutes per search"
},
"typeVersion": 1
},
{
"id": "14a7088b-82e9-41b4-acbe-899b5eee195b",
"name": "Sticky Note22",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1744,
-208
],
"parameters": {
"color": 5,
"width": 500,
"height": 420,
"content": "# 1) Get Query & Organize Data"
},
"typeVersion": 1
},
{
"id": "97646605-6f57-49f1-9bd6-0afcf176bbe8",
"name": "Sticky Note20",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1232,
-208
],
"parameters": {
"color": 2,
"width": 468,
"height": 420,
"content": "# 2) Suit Query to Brightdata"
},
"typeVersion": 1
},
{
"id": "41e0d6fc-77ce-4541-8522-47f72d9d7782",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-752,
-208
],
"parameters": {
"color": 4,
"width": 724,
"height": 420,
"content": "# 3) Brightdata's Scraping "
},
"typeVersion": 1
},
{
"id": "40feaec4-3243-4c39-be4a-dbd9430707c8",
"name": "Sticky Note15",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1744,
224
],
"parameters": {
"color": 3,
"width": 1716,
"height": 424,
"content": "# 4) Infomation Extraction + Retrieving Summary & Updating Logs "
},
"typeVersion": 1
},
{
"id": "f6fd6c4c-24b2-4e9f-a45d-9e84af36b0dd",
"name": "Set Variables",
"type": "n8n-nodes-base.set",
"position": [
-1424,
-32
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "a4357b33-04b4-4246-ad30-f0d91b6687d2",
"name": "userPrompt",
"type": "string",
"value": "={{ $json.body.source }}"
},
{
"id": "977e4bde-3030-4d02-9c2f-3d46975901be",
"name": "cellReference",
"type": "string",
"value": "={{ $json.body.prompt }}"
},
{
"id": "c63d7766-11b9-4edb-92db-90e25535721b",
"name": "ouputLanguage",
"type": "string",
"value": "Hebrew"
}
]
}
},
"typeVersion": 3.4
}
],
"connections": {
"GPT 4o - 1": {
"ai_languageModel": [
[
{
"node": "Bright Data Search Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Extract Data": {
"main": [
[
{
"node": "Summarize Information",
"type": "main",
"index": 0
}
]
]
},
"Webhook Call": {
"main": [
[
{
"node": "Set Variables",
"type": "main",
"index": 0
}
]
]
},
"Set Variables": {
"main": [
[
{
"node": "Adjust Query Agent",
"type": "main",
"index": 0
}
]
]
},
"Bright Data MCP": {
"ai_tool": [
[
{
"node": "Bright Data Search Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"GPT 4o Mini - 1": {
"ai_languageModel": [
[
{
"node": "Extract Data",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"GPT 4o Mini - 2": {
"ai_languageModel": [
[
{
"node": "Summarize Information",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"GPT 4.1 Mini - 1": {
"ai_languageModel": [
[
{
"node": "Adjust Query Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Adjust Query Agent": {
"main": [
[
{
"node": "Bright Data Search Agent",
"type": "main",
"index": 0
}
]
]
},
"Summarize Information": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
},
{
"node": "Update Logs",
"type": "main",
"index": 0
}
]
]
},
"Bright Data Search Agent": {
"main": [
[
{
"node": "Bright Data - Data Extraction",
"type": "main",
"index": 0
}
]
]
},
"Structured Output Parser - 1": {
"ai_outputParser": [
[
{
"node": "Bright Data Search Agent",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Structured Output Parser - 2": {
"ai_outputParser": [
[
{
"node": "Extract Data",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Structured Output Parser - 3": {
"ai_outputParser": [
[
{
"node": "Summarize Information",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Bright Data - Data Extraction": {
"main": [
[
{
"node": "Extract Data",
"type": "main",
"index": 0
}
]
]
}
}
}
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.
httpHeaderAuthopenAiApi
Pro
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About this workflow
Transform any Google Sheets cell into an intelligent web scraper! Type and get AI-filtered result from every website in ~20 seconds.
Source: https://n8n.io/workflows/10119/ — original creator credit. Request a take-down →
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