This workflow follows the Agent → Google Sheets 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 →
{
"nodes": [
{
"parameters": {
"rules": {
"values": [
{
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "loose",
"version": 2
},
"conditions": [
{
"leftValue": "={{ $json.content.isNotEmpty() && $json.links.length > 0}}",
"rightValue": "",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
},
"id": "a33e576f-7b88-4c5c-8590-fa7f36c1991c"
}
],
"combinator": "and"
},
"renameOutput": true,
"outputKey": "OK"
},
{
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "loose",
"version": 2
},
"conditions": [
{
"id": "2872a05a-8f62-4a4a-aad5-6954f2d3747c",
"leftValue": "={{ $json.links}}",
"rightValue": "0",
"operator": {
"type": "array",
"operation": "empty",
"singleValue": true
}
}
],
"combinator": "and"
},
"renameOutput": true,
"outputKey": "NullHyperlink"
},
{
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "loose",
"version": 2
},
"conditions": [
{
"id": "73f7f831-5ad9-42d8-95fb-ba65552ff91c",
"leftValue": "={{ $json.content.isEmpty() }}",
"rightValue": "",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
}
}
],
"combinator": "and"
},
"renameOutput": true,
"outputKey": "NullContent"
}
]
},
"looseTypeValidation": true,
"options": {}
},
"type": "n8n-nodes-base.switch",
"typeVersion": 3.3,
"position": [
2928,
2176
],
"id": "8458420a-709b-41ed-8cc8-eadc5f70cd39",
"name": "Switch",
"alwaysOutputData": false
},
{
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "const puppeteer = require('puppeteer-extra');\nconst stealthPlugin = require('puppeteer-extra-plugin-stealth');\n\n// Aplica o plugin Stealth\npuppeteer.use(stealthPlugin());\n\nasync function scrapeSubpage(url) {\n // ... (toda a sua fun\u00e7\u00e3o scrapeSubpage at\u00e9 o try...catch) ...\n // ... (o browser, page, setRequestInterception, etc. continuam iguais) ...\n let browser = null;\n\n try {\n console.log(`\ud83d\ude80 Iniciando browser...`);\n browser = await puppeteer.launch({\n headless: true,\n args: [\n '--no-sandbox',\n '--disable-setuid-sandbox',\n '--disable-dev-shm-usage',\n '--disable-gpu',\n ],\n });\n\n const page = await browser.newPage();\n page.setDefaultNavigationTimeout(30000);\n\n // Bloqueia recursos desnecess\u00e1rios\n await page.setRequestInterception(true);\n page.on('request', (req) => {\n if (['image', 'stylesheet', 'font'].includes(req.resourceType())) {\n req.abort();\n } else {\n req.continue();\n }\n });\n \n console.log(`\ud83d\udd78\ufe0f Scraping subpage: ${url}`);\n \n // Adiciona uma verifica\u00e7\u00e3o para garantir que a URL \u00e9 uma string\n if (typeof url !== 'string' || !url.startsWith('http')) {\n throw new Error(`URL inv\u00e1lida ou n\u00e3o \u00e9 uma string: ${url}`);\n }\n\n await page.goto(url, { waitUntil: 'networkidle2' });\n\n // Extrai texto limpo\n const content = await page.evaluate(() => {\n //const text = document.body?.innerText || '';\n //return text.replace(/\\s+/g, ' ').trim();\n return document.documentElement.outerHTML;\n });\n\n await browser.close();\n\n return {\n json: {\n success: true,\n url,\n content,\n length: content.length,\n timestamp: new Date().toISOString(),\n }\n };\n } catch (error) {\n // Garante que o browser feche mesmo se o goto() falhar\n if (browser) {\n await browser.close();\n }\n \n // ****** MUDAN\u00c7A PRINCIPAL AQUI ******\n // Use 'return' em vez de 'throw'\n // Isso passa o erro adiante sem parar o workflow do n8n\n return {\n json: { \n success: false, \n url, \n error: error.message,\n timestamp: new Date().toISOString(),\n }\n };\n }\n}\n\n// ===========================\n// Execu\u00e7\u00e3o principal do node\n// ===========================\n\n// O Split Out envia o objeto no formato: { \"links\": \"https://...\" }\nconst url = $json.url;\n\n// ****** MUDAN\u00c7A PRINCIPAL AQUI ******\n// O resultado da fun\u00e7\u00e3o j\u00e1 \u00e9 o objeto que queremos.\n// N\u00e3o precisamos envolv\u00ea-lo em { json: ... }\nreturn scrapeSubpage(url);"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
4224,
1712
],
"id": "0b2b1ed3-c3d8-4e72-a71d-4f2aca6424a1",
"name": "Subpage Scrapper",
"onError": "continueErrorOutput"
},
{
"parameters": {
"jsCode": "/**\n * Puppeteer Scraper v3 (Corrigido)\n *\n * - N\u00e3o usa 'puppeteer-extra-plugin-user-preferences'.\n * - Usa page.evaluate() para extrair texto e links absolutos (corrigindo o problema original).\n * - Corrige a inicializa\u00e7\u00e3o do Puppeteer com Stealth.\n * - Limpa o fluxo de retorno e o fechamento do browser.\n */\n\nconst puppeteer = require('puppeteer-extra');\nconst stealthPlugin = require('puppeteer-extra-plugin-stealth');\n\n// Aplica o plugin Stealth\npuppeteer.use(stealthPlugin());\n\nasync function scrapePage(urlToScrape) {\n let browser = null;\n\n try {\n console.log(`\ud83d\ude80 Iniciando browser...`);\n browser = await puppeteer.launch({\n headless: true,\n args: [\n '--no-sandbox',\n '--disable-setuid-sandbox',\n '--disable-dev-shm-usage',\n '--disable-gpu',\n ],\n });\n\n const page = await browser.newPage();\n page.setDefaultNavigationTimeout(30000);\n\n // Bloqueia recursos desnecess\u00e1rios\n await page.setRequestInterception(true);\n page.on('request', (req) => {\n if (['image', 'stylesheet', 'font'].includes(req.resourceType())) {\n req.abort();\n } else {\n req.continue();\n }\n });\n\n console.log(`\ud83d\udd78\ufe0f Acessando p\u00e1gina: ${urlToScrape}`);\n await page.goto(urlToScrape, { waitUntil: 'networkidle2' });\n\n // 1. Verifica\u00e7\u00e3o de CAPTCHA (baseada no HTML bruto)\n const pageContent = await page.content();\n if (pageContent.toLowerCase().includes('captcha') || pageContent.toLowerCase().includes('are you a robot')) {\n return { success: false, url: urlToScrape, content: null, links: [], error: 'Captcha detectado' };\n }\n\n // 2. Extra\u00e7\u00e3o de Dados (via page.evaluate)\n // 'a.href' dentro do evaluate() retorna o link ABSOLUTO.\n const result = await page.evaluate(() => {\n // Extrai o texto limpo\n //const content = (document.body?.innerText || '').replace(/\\s+/g, ' ').trim();\n const content = document.documentElement.outerHTML;\n \n // Extrai links, garante que s\u00e3o \u00fanicos e pega os 10 primeiros\n const allLinks = Array.from(document.querySelectorAll('a[href]'));\n const uniqueAbsoluteLinks = [...new Set(\n allLinks\n .map(a => a.href) // 'a.href' resolve caminhos relativos!\n .filter(href => href.startsWith('http')) // Garante que s\u00e3o links web\n )];\n \n const links = uniqueAbsoluteLinks.slice(0, 10);\n\n return { content, links };\n });\n\n // 3. Verifica\u00e7\u00f5es de Sucesso (baseadas no 'result')\n if (!result.content || result.content.length < 50) {\n return { success: false, url: urlToScrape, content: result.content, links: result.links, error: 'Conte\u00fado n\u00e3o encontrado ou muito curto' };\n }\n\n if (result.links.length === 0) {\n return { success: false, url: urlToScrape, content: result.content, links: result.links, error: 'Nenhum hyperlink encontrado' };\n }\n\n // 4. Sucesso!\n return {\n success: true,\n url: urlToScrape,\n content: result.content,\n links: result.links,\n timestamp: new Date().toISOString(),\n };\n\n } catch (error) {\n console.error(`\u274c Erro no scrape: ${error.message}`);\n return {\n success: false,\n url: urlToScrape,\n error: error.message,\n timestamp: new Date().toISOString(),\n };\n } finally {\n // 5. Garante que o browser sempre ser\u00e1 fechado\n if (browser) {\n console.log(`\ud83d\udd12 Fechando browser...`);\n await browser.close();\n }\n }\n}\n\n// ===========================\n// Execu\u00e7\u00e3o principal do node\n// ===========================\nconst url = $input.first().json.url\n\nif (!url) {\n return { json: { success: false, error: \"Nenhuma 'url' fornecida no input do Webhook.\" } };\n}\n\nconst output = await scrapePage(url);\n\n// Retorna um \u00fanico objeto JSON\nreturn { json: output };"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
2752,
2192
],
"id": "0570d10f-9aad-4bff-b851-944e9cc1cb25",
"name": "PuppeteerScraper"
},
{
"parameters": {
"aggregate": "aggregateAllItemData",
"options": {}
},
"type": "n8n-nodes-base.aggregate",
"typeVersion": 1,
"position": [
4336,
2080
],
"id": "3eb2ca5b-6fcd-4146-b5ac-b13c894e4cc7",
"name": "AggregateSubpages"
},
{
"parameters": {
"url": "=http://api.scrape.do",
"authentication": "genericCredentialType",
"genericAuthType": "httpQueryAuth",
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "url",
"value": "={{ $json.url }}"
}
]
},
"options": {
"timeout": 120000
}
},
"id": "bbc24172-aee7-4d16-b853-fa02af9c95ae",
"name": "Scrape.do1",
"type": "n8n-nodes-base.httpRequest",
"position": [
3104,
2304
],
"retryOnFail": true,
"typeVersion": 4.2,
"waitBetweenTries": 5000,
"credentials": {
"httpQueryAuth": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"url": "=http://api.scrape.do",
"authentication": "genericCredentialType",
"genericAuthType": "httpQueryAuth",
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "url",
"value": "={{ $('Webhook').item.json.body.url }}"
}
]
},
"options": {
"timeout": 120000
}
},
"id": "d4fed834-0122-41d0-a7bf-600c7ca50e90",
"name": "Scrape.do2",
"type": "n8n-nodes-base.httpRequest",
"position": [
4416,
1920
],
"retryOnFail": true,
"typeVersion": 4.2,
"waitBetweenTries": 5000,
"credentials": {
"httpQueryAuth": {
"name": "<your credential>"
}
},
"onError": "continueErrorOutput"
},
{
"parameters": {
"fieldToSplitOut": "links",
"include": "allOtherFields",
"options": {}
},
"type": "n8n-nodes-base.splitOut",
"typeVersion": 1,
"position": [
3616,
1712
],
"id": "b3f192a7-19a8-4322-b658-a17278404493",
"name": "Split Out"
},
{
"parameters": {},
"type": "n8n-nodes-base.manualTrigger",
"typeVersion": 1,
"position": [
2432,
2192
],
"id": "8256e5c9-12c1-4c20-8c4e-96d125dd5a55",
"name": "When clicking \u2018Execute workflow\u2019"
},
{
"parameters": {
"documentId": {
"__rl": true,
"value": "18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4",
"mode": "list",
"cachedResultName": "sheets-tegra-n8n",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "webpages_links",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4/edit#gid=0"
},
"filtersUI": {
"values": [
{
"lookupColumn": "status",
"lookupValue": "pending"
}
]
},
"options": {
"returnFirstMatch": true
}
},
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.7,
"position": [
2592,
2192
],
"id": "fdb18e5d-9750-4bda-9d5c-5a82d3433e38",
"name": "GetPendingURL",
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"operation": "append",
"documentId": {
"__rl": true,
"value": "18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4",
"mode": "list",
"cachedResultName": "sheets-tegra-n8n",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "webpages_links",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4/edit#gid=0"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"url": "={{ $json.url }}",
"instructions": "={{ $json.instructions }}",
"status": "={{ $json.status }}",
"last_updated": "={{ $json.last_updated }}",
"error_notes": "={{ $json.error_notes }}"
},
"matchingColumns": [],
"schema": [
{
"id": "url",
"displayName": "url",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "instructions",
"displayName": "instructions",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "status",
"displayName": "status",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "last_updated",
"displayName": "last_updated",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "error_notes",
"displayName": "error_notes",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.7,
"position": [
3952,
1712
],
"id": "2b257242-7fb4-4277-b3d0-15cbc5793a70",
"name": "Append row in sheet",
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "/*\n * SCRIPT: Code in JavaScript1 (Fallback Subp\u00e1gina)\n * * INPUT: HTML bruto do n\u00f3 Scrape.do2.\n * OUTPUT: Formato id\u00eantico ao Subpage Scraper { success, url, content, error_notes }\n */\n\n// --- Execu\u00e7\u00e3o Principal ---\n\nconst scrapeDoResult = $input.first().json; // Recebe o HTML (ou erro)\n// A URL da subp\u00e1gina vem do item que entrou neste loop\n// Assumindo que o Split Out passou { links: \"url_subpagina\" }\n// Se o seu Split Out passou { url: \"url_subpagina\" }, mude abaixo\nconst subpageUrl = $input.first().json.links || $input.first().json.url; \n\nif (!subpageUrl) {\n throw new Error(\"N\u00e3o foi poss\u00edvel encontrar a URL da subp\u00e1gina no input do loop.\");\n}\n\n// Verifica se o Scrape.do2 retornou um erro\nif (typeof scrapeDoResult !== 'string') {\n return {\n json: {\n success: false,\n url: subpageUrl,\n content: null,\n error_notes: `Fallback Scrape.do2 falhou: ${JSON.stringify(scrapeDoResult)}`\n }\n };\n}\n\nconst htmlContent = scrapeDoResult;\n\n// Verifica conte\u00fado m\u00ednimo\nif (!htmlContent || htmlContent.length < 50) {\n return {\n json: {\n success: false,\n url: subpageUrl,\n content: htmlContent,\n error_notes: 'Fallback Scrape.do2 retornou conte\u00fado muito curto ou vazio.'\n }\n };\n}\n\n// Sucesso no Fallback\nreturn {\n json: {\n success: true,\n url: subpageUrl,\n content: htmlContent, // O HTML bruto\n error_notes: 'Used Scrape.do2 Fallback' // Nota opcional\n }\n};"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
4656,
1920
],
"id": "aec92ce1-8804-40a5-8151-f31044bed7a8",
"name": "HTML Extractor",
"onError": "continueErrorOutput"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "d23c65be-ba06-46f4-8d1f-cf478e6e58b2",
"name": "url",
"value": "={{ $json.links }}",
"type": "string"
},
{
"id": "6b753e76-ba03-4bc6-b780-cbbafead681f",
"name": "instructions",
"value": "={{ $('GetPendingURL').item.json.instructions }}",
"type": "string"
},
{
"id": "959001e5-e4bf-41ec-b5e3-262abb358018",
"name": "status",
"value": "pending",
"type": "string"
},
{
"id": "15f45a34-6eb4-44d6-ba5b-bc30b7954b74",
"name": "last_updated",
"value": "={{ $now.toISO() }}",
"type": "string"
},
{
"id": "ac36c06a-4505-4516-b3a2-577ed19a0465",
"name": "error_notes",
"value": "",
"type": "string"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
3776,
1712
],
"id": "7e8af143-84eb-40c2-a61a-9391bb9a05d4",
"name": "Set hyperlink fields"
},
{
"parameters": {
"operation": "update",
"documentId": {
"__rl": true,
"value": "18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4",
"mode": "list",
"cachedResultName": "sheets-tegra-n8n",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "webpages_links",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4/edit#gid=0"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"url": "={{ $json.url }}",
"last_updated": "={{ $json.last_updated }}",
"error_notes": "={{ $json.error_notes }}",
"instructions": "={{ $json.instructions }}",
"status": "={{ $json.status }}"
},
"matchingColumns": [
"url"
],
"schema": [
{
"id": "url",
"displayName": "url",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
},
{
"id": "instructions",
"displayName": "instructions",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "status",
"displayName": "status",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
},
{
"id": "last_updated",
"displayName": "last_updated",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "error_notes",
"displayName": "error_notes",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "row_number",
"displayName": "row_number",
"required": false,
"defaultMatch": false,
"display": true,
"type": "number",
"canBeUsedToMatch": true,
"readOnly": true,
"removed": true
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.7,
"position": [
5120,
2192
],
"id": "4d1067c0-6872-4f74-aac5-45185867d78b",
"name": "Update row in sheet",
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "// Assume que 'data' vem do input do n8n (ou de outra fonte)\nconst data = $json.data\n\n/**\n * Extrai texto limpo e leg\u00edvel de uma string HTML.\n * @param {string} html - A string HTML bruta.\n * @returns {string} O texto extra\u00eddo e limpo.\n */\nfunction extractTextFromHTML(html) {\n if (typeof html !== 'string') return '';\n\n const cleanedHTML = html\n // Remove blocos de conte\u00fado n\u00e3o vis\u00edveis ou indesejados\n .replace(/<script[\\s\\S]*?>[\\s\\S]*?<\\/script>/gi, ' ')\n .replace(/<style[\\s\\S]*?>[\\s\\S]*?<\\/style>/gi, ' ')\n .replace(/<head[\\s\\S]*?>[\\s\\S]*?<\\/head>/gi, ' ')\n .replace(/<nav[\\s\\S]*?>[\\s\\S]*?<\\/nav>/gi, ' ')\n .replace(/<footer[\\s\\S]*?>[\\s\\S]*?<\\/footer>/gi, ' ')\n \n // Substitui todas as tags HTML restantes por um espa\u00e7o\n // Isso evita que palavras se juntem (ex: <p>Ol\u00e1</p><p>Mundo</p> -> \"Ol\u00e1 Mundo\")\n .replace(/<[^>]+>/g, ' ')\n \n // Substitui m\u00faltiplos espa\u00e7os, tabs ou quebras de linha por um \u00fanico espa\u00e7o\n .replace(/\\s+/g, ' ')\n .trim();\n \n return cleanedHTML;\n}\n\n/**\n * Extrai todos os URLs (atributos href) de uma string HTML.\n * @param {string} html - A string HTML bruta.\n * @returns {string[]} Um array de URLs.\n */\nfunction extractLinksFromHTML(html) {\n if (typeof html !== 'string') return [];\n \n const links = [];\n // Regex para encontrar tags <a> e capturar o atributo href\n // Lida com aspas simples ou duplas\n const linkRegex = /<a\\s+[^>]*?href\\s*=\\s*[\"']([^\"']*)[\"']/gi;\n \n // Usamos matchAll para encontrar todas as ocorr\u00eancias\n const matches = html.matchAll(linkRegex);\n\n for (const match of matches) {\n const url = match[1]; // O grupo capturado (a URL)\n \n // Filtra links vazios, \u00e2ncoras de p\u00e1gina (#) ou a\u00e7\u00f5es javascript\n if (url && !url.startsWith('#') && !url.toLowerCase().startsWith('javascript:')) {\n links.push(url);\n }\n // Para o loop assim que atingir 10 links\n if (links.length >= 10) {\n break;\n }\n }\n \n // Retorna apenas links \u00fanicos\n return [...new Set(links)];\n}\n\n// --- Execu\u00e7\u00e3o ---\n\n// Extrai as duas informa\u00e7\u00f5es do HTML\nconst extractedText = extractTextFromHTML(data);\nconst extractedLinks = extractLinksFromHTML(data);\n\n// Retorna no formato JSON desejado\nreturn {\n success: $json.data ? true : false,\n content: extractedText,\n links: extractedLinks\n};"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
3248,
2304
],
"id": "5e8c705e-db1e-44c1-8026-4d5e57fc4fcf",
"name": "ExtractHtmlSubpage"
},
{
"parameters": {
"jsCode": "/*\n * SCRIPT 1: FORMATADOR DA P\u00c1GINA PRINCIPAL\n * Coloque este n\u00f3 logo ap\u00f3s o Main_Page_AI_Agent\n */\n\n// --- Fun\u00e7\u00e3o Helper para parsear o output da IA ---\nfunction parseAiOutput(aiItemJson) {\n try {\n // aiItemJson \u00e9 o { output: \"```json...\" }\n const jsonString = aiItemJson.output.replace(/```json\\n|```/g, '').trim();\n const parsed = JSON.parse(jsonString);\n // Retorna o primeiro item (ou vazio) pois a IA foi instru\u00edda a agregar por URL\n return (parsed.extracted_data && parsed.extracted_data[0]) ? parsed.extracted_data[0] : {};\n } catch (e) {\n console.error(\"Falha ao parsear JSON da IA (Main):\", e.message, aiItemJson.output);\n return {};\n }\n}\n\n// --- 1. Obter todas as fontes de dados ---\n\n// Fonte A: Output da IA (Input deste n\u00f3)\nconst aiOutput = $input.first().json;\nconst aiData = parseAiOutput(aiOutput);\n\n// Fonte B: Dados Iniciais (do in\u00edcio)\n// !!! MUDE \"GetPendingURL\" para o nome do seu n\u00f3 inicial !!!\nconst startData = $node[\"GetPendingURL\"].json;\nconst instructions = startData.instructions;\n\n// Fonte C: Dados do Scrape da P\u00c1GINA PRINCIPAL (do Merge ANTES da IA)\n// !!! MUDE \"Merge_Main_Scrape\" para o nome do n\u00f3 Merge que uniu Scraper/Fallback !!!\nconst mainPageScrapeData = $node[\"Merge\"].json;\nif (!mainPageScrapeData) {\n throw new Error(\"N\u00e3o foi poss\u00edvel encontrar os dados de scrape da P\u00e1gina Principal no n\u00f3 Merge_Main_Scrape.\");\n}\nconst page = mainPageScrapeData;\n\n// --- 2. Preparar e retornar o item ---\nconst now = new Date().toISOString();\n\nreturn {\n json: {\n url: page.url,\n instructions: instructions,\n status: page.success ? 'completed' : 'failed',\n last_updated: now,\n error_notes: page.error_notes || '',\n extracted_content: aiData.extracted_content || '' // Conte\u00fado da IA principal\n }\n};"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
4144,
2464
],
"id": "b0312eef-bf39-430d-8c2a-53fc05cbac34",
"name": "CodeFormatMainPage"
},
{
"parameters": {},
"type": "n8n-nodes-base.merge",
"typeVersion": 3.2,
"position": [
3424,
2176
],
"id": "c374f589-0969-434f-b6e2-7a38eb1ece63",
"name": "Merge"
},
{
"parameters": {
"promptType": "define",
"text": "=INSTRUCTIONS:\n{{ $('GetPendingURL').item.json.instructions }}\nEXECUTION_TIMESTAMP:\n{{ $now.toISO() }}\n\nCONTENT_TO_ANALYZE:\n\n[Subpage]\nURL: {{ $json.url }}\nContent: {{ $json.content }}\n[End of Subpage]",
"needsFallback": true,
"options": {
"systemMessage": "# AI Agent Configuration: Content Extractor\n\n## 1. Role\n\nContent Extractor\n\n## 2. Context\n\nYou are an AI data extraction agent. Your purpose is to analyze the provided `CONTENT_TO_ANALYZE`, which is split into `[Main Page]` and `[Subpages]`.\n\nYou must strictly follow the `INSTRUCTIONS` to identify, extract \"as-is\" (verbatim), and correlate *only* the relevant text segments.\n\nThe content you extract will be used to feed a vector database (via the `scrape_vector_store` tool). Your final answer *must* be a formatted JSON object.\n\n## 3. Tools\n\n(This defines the available tool for the agent.)\n\n```json\n[\n {\n \"name\": \"scrape_vector_store\",\n \"description\": \"Stores the extracted content and its source URL in a Supabase vector database. Use this tool to save the final data.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"data_to_store\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"source_url\": {\n \"type\": \"string\",\n \"description\": \"The URL where the content was extracted from.\"\n },\n \"content_chunk\": {\n \"type\": \"string\",\n \"description\": \"The extracted text chunk.\"\n },\n \"timestamp\": {\n \"type\": \"string\",\n \"description\": \"ISO timestamp of the extraction.\"\n }\n },\n \"required\": [\"source_url\", \"content_chunk\", \"timestamp\"]\n }\n }\n },\n \"required\": [\"data_to_store\"]\n }\n }\n]```\n\n## 4. Reasoning (Chain of Thought)\n(This is the internal reasoning the agent will follow.)\n\n1. First, I will read and understand the `INSTRUCTIONS`.\n2. I will identify the `EXECUTION_TIMESTAMP`.\n3. I will scan the `Content` (which is HTML) for relevant information based on the `INSTRUCTIONS` (e.g., product titles and prices).\n4. I will temporarily store *all* extracted segments as `{ url: \"source_url\", content: \"extracted_segment\" }`.\n5. After analyzing all `[Main Page]` and `[Subpages]` blocks, I will **group all temporarily stored segments by their unique `url`**.\n6. For each unique `url`, I will **aggregate all its corresponding `content` segments into a single string**. I will use a newline character (`\\n`) as a separator.\n7. I will format my final answer as a JSON object, following the `Output Rules`, creating *only one object* per unique `source_url`.\n## 5. Output Rules (Structured Output Parser)\n(Defines the mandatory JSON output format.)\n## Output rules\n- The output format must be *exclusively* a JSON object.\n- The `extracted_data` array must contain *only one object per unique source_url*.\n- The `extracted_content` field MUST contain all segments found for that URL, joined together into a single string (separated by newlines `\\n`).\n\n- Example of a *good* `extracted_content` (all segments joined): \n `\"Title: A Light in the ..., Price: \u00a351.77\\nTitle: Tipping the Velvet, Price: \u00a353.74\"`\n\n- Example of a *bad* structure (which is now forbidden):\n `...{ \"source_url\": \"url1\", \"extracted_content\": \"chunk A\" }, { \"source_url\": \"url1\", \"extracted_content\": \"chunk B\" }...`\n\n- The final JSON structure must be:\n{\n \"extracted_data\": [\n {\n \"source_url\": \"https://books.toscrape.com/index.html\",\n \"extracted_content\": \"Title: A Light in the ..., Price: \u00a351.77\\nTitle: Tipping the Velvet, Price: \u00a353.74\\nTitle: Soumission, Price: \u00a350.10\",\n \"timestamp\": \"the_value_of_the_EXECUTION_TIMESTAMP\"\n },\n {\n \"source_url\": \"https://books.toscrape.com/catalogue/category/books/travel_2/index.html\",\n \"extracted_content\": \"Title: It's Only the Himalayas, Price: \u00a345.17\\nTitle: Full Moon over ..., Price: \u00a349.43\",\n \"timestamp\": \"the_value_of_the_EXECUTION_TIMESTAMP\"\n }\n ]\n}\n\n- If no relevant content is found, return an empty array:\n{\n \"extracted_data\": []\n}"
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 2.2,
"position": [
3808,
2080
],
"id": "95b2ec2b-e217-4071-8716-952a68c7e010",
"name": "AIAgentSubpage",
"retryOnFail": true,
"waitBetweenTries": 5000,
"onError": "continueErrorOutput"
},
{
"parameters": {
"promptType": "define",
"text": "=INSTRU\u00c7\u00d5ES:\n{{ $('GetPendingURL').item.json.instructions }}\n\nCONTE\u00daDO:\n[P\u00e1gina principal]{{ $json.content }}\n[FIM P\u00e1gina principal]\n",
"options": {
"systemMessage": "# AI Agent Configuration: Content Extractor\n\n## 1. Role\n\nYou are an AI data extraction agent. Your purpose is to analyze the provided `CONTENT_TO_ANALYZE`, which is split into `[Main Page]` and `[Subpages]`.\nYou must strictly follow the `INSTRUCTIONS` to identify, extract \"as-is\" (verbatim), and correlate *only* the relevant text segments.\n\nYou are an AI agent that parses raw **HTML content**, not plain text. You must navigate the HTML structure to find data, connecting related pieces of information (like a product title and its price).\n\n**Reasoning:**\n1. First, I will read and understand the `INSTRUCTIONS`.\n2. I will identify the `EXECUTION_TIMESTAMP`.\n3. I will scan the `Content` (which is HTML) for relevant information based on the `INSTRUCTIONS` (e.g., product titles and prices).\n4. I will temporarily store *all* extracted segments as `{ url: \"source_url\", content: \"extracted_segment\" }`.\n5. After analyzing all `[Main Page]` and `[Subpages]` blocks, I will **group all temporarily stored segments by their unique `url`**.\n6. For each unique `url`, I will **aggregate all its corresponding `content` segments into a single string**. I will use a newline character (`\\n`) as a separator.\n7. I will format my final answer as a JSON object, following the `Output Rules`, creating *only one object* per unique `source_url`.\n\n**Output Rules:**\n- The output format must be *exclusively* a JSON object.\n- The `extracted_data` array must contain *only one object per unique source_url*.\n- The `extracted_content` field MUST contain all segments found for that URL, joined together into a single string (separated by newlines `\\n`).\n- Example `extracted_content`: \"Title: A Light in the ..., Price: \u00a351.77\\nTitle: Tipping the Velvet, Price: \u00a353.74\"\n\n- The final JSON structure must be:\n{\n \"extracted_data\": [\n {\n \"source_url\": \"url_of_the_source_page\",\n \"extracted_content\": \"all_relevant_segments_joined_by_newline\",\n \"timestamp\": \"the_value_of_the_EXECUTION_TIMESTAMP\"\n }\n ]\n}\n\n- If no relevant content is found, return an empty array:\n{\n \"extracted_data\": []\n}"
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 2.2,
"position": [
3808,
2464
],
"id": "9eab0b9d-5878-447a-8e09-e3854951aa73",
"name": "MainPageAIAgent"
},
{
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "/*\n * SCRIPT 2: FORMATADOR DAS SUBP\u00c1GINAS (DENTRO DO LOOP)\n * Coloque este n\u00f3 logo ap\u00f3s o AI_Agent_Subpage (dentro do loop)\n */\n\n// --- Fun\u00e7\u00e3o Helper para parsear o output da IA ---\nfunction parseAiOutput(aiItemJson) {\n try {\n // aiItemJson \u00e9 o { output: \"```json...\" }\n const jsonString = aiItemJson.output.replace(/```json\\n|```/g, '').trim();\n const parsed = JSON.parse(jsonString);\n // Retorna o primeiro item (ou vazio)\n return (parsed.extracted_data && parsed.extracted_data[0]) ? parsed.extracted_data[0] : {};\n } catch (e) {\n console.error(\"Falha ao parsear JSON da IA (Subpage):\", e.message, aiItemJson.output);\n return {};\n }\n}\n\n// --- 1. Obter todas as fontes de dados ---\n\n// Fonte A: Output da IA (Input deste n\u00f3)\n// O n\u00f3 da IA passa adiante os dados que ele recebeu (scrape data)\nconst aiOutput = $json\nconst aiData = parseAiOutput(aiOutput);\n\n// Fonte B: Dados Iniciais (do in\u00edcio)\n// !!! MUDE \"GetPendingURL\" para o nome do seu n\u00f3 inicial !!!\nconst startData = $('GetPendingURL').item.json\nconst instructions = startData.instructions;\n\n// Fonte C: Dados do Scrape da SUBP\u00c1GINA (est\u00e3o no input, passados pela IA)\n// O n\u00f3 da IA deveria ter recebido { success, url, content, error_notes } do Merge_Subpage_Result\nconst scrapeData = $('Subpage Scrapper').item.json\n//if (!aiData || !aiData.source_url) {\n// throw new Error(\"Input para Code_Format_Subpage n\u00e3o cont\u00e9m dados de scrape (url, success). Verifique o n\u00f3 Merge anterior \u00e0 IA.\");\n\n\n// --- 2. Preparar e retornar o item ---\nconst now = new Date().toISOString();\n\nreturn {\n json: {\n url: scrapeData.url, // Veio do input (scrape)\n instructions: instructions, // Veio do n\u00f3 inicial\n status: scrapeData.success ? 'completed' : 'failed', // Veio do input (scrape)\n last_updated: now,\n error_notes: scrapeData.error_notes || '', // Veio do input (scrape)\n content: aiData.extracted_content || '' // Veio do output da IA\n }\n};"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
4160,
2080
],
"id": "bdb740b6-6fa4-44fb-a247-954fec10928a",
"name": "SubPageFormatter"
},
{
"parameters": {},
"type": "n8n-nodes-base.merge",
"typeVersion": 3.2,
"position": [
4800,
1712
],
"id": "4ba308bb-d799-4c5f-a557-cf2ae47497db",
"name": "Merge1"
},
{
"parameters": {},
"type": "n8n-nodes-base.merge",
"typeVersion": 3.2,
"position": [
4480,
2336
],
"id": "b1380c13-ba4c-4387-b21f-44be9aa40bfc",
"name": "Merge2"
},
{
"parameters": {
"jsCode": "const dataArray = $input.first().json.data\nif (!Array.isArray(dataArray)) return [];\n\nreturn dataArray.map(item => ({ json: item }));\n"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
5136,
2416
],
"id": "5fc249dc-321d-4f80-b8bb-e15fc8808b84",
"name": "FlattenResult"
},
{
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "// Este c\u00f3digo roda para CADA item (URL) do n\u00f3 anterior\nconst item = $json;\nconst outputItems = [];\n\n// 1. \"Explode\" o campo 'content' em linhas\nconst chunks = item.content.split('\\n')\n .map(line => line.trim())\n .filter(line => line.length > 0);\n\nconst totalChunks = chunks.length;\n\n// 2. Cria um novo item de sa\u00edda para CADA chunk (t\u00edtulo)\nfor (let i = 0; i < chunks.length; i++) {\n const chunkText = chunks[i];\n \n // Formato obrigat\u00f3rio para o n\u00f3 'Supabase Vector Store'\n const newItem = {\n // pageContent \u00e9 a chave padr\u00e3o que o n\u00f3 procura para embedar\n chunk_text: chunkText,\n source_url: item.url,\n chunk_index: i,\n total_chunks: totalChunks,\n last_updated: item.last_updated,\n // metadata \u00e9 onde colocamos todos os dados extras\n metadata: {\n instructions: item.instructions,\n status: item.status \n }\n };\n\n outputItems.push({ json: newItem });\n}\n\n// 4. Retorna um array de itens. \n// O n8n vai criar m\u00faltiplos itens (um para cada t\u00edtulo de livro)\nreturn {json: {outputItems}};"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
5328,
2416
],
"id": "96accfac-c8a9-49e9-844f-70417b1b547e",
"name": "ChunkContent"
},
{
"parameters": {
"jsCode": "/*\n * SCRIPT 3: MESCLAGEM FINAL (ap\u00f3s Page + Subpages)\n * Entrada 0 \u2192 P\u00e1gina principal formatada (CodeFormatMainPage)\n * Entrada 1 \u2192 Subp\u00e1ginas agregadas (AggregateSubpages)\n */\n\nconst finalItemsToUpdate = [];\n\n// Entrada 0: P\u00e1gina principal\nconst mainInput = $input.first().json\nif (mainInput) {\n finalItemsToUpdate.push({ json: mainInput });\n} else {\n console.warn(\"\u26a0\ufe0f Nenhum dado da p\u00e1gina principal encontrado.\");\n}\n\n// Entrada 1: Subp\u00e1ginas agregadas\nconst subInput = $input.last().json.data \nif (subInput && Array.isArray(subInput.data)) {\n for (const subpage of subInput.data) {\n finalItemsToUpdate.push({ json: subpage });\n }\n} else if (Array.isArray(subInput)) {\n // Caso o input j\u00e1 seja um array direto (sem wrapper data)\n for (const subpage of subInput) {\n finalItemsToUpdate.push({ json: subpage });\n }\n} else if (subInput && typeof subInput === 'object') {\n // Caso seja apenas um objeto \u00fanico\n finalItemsToUpdate.push({ json: subInput });\n} else {\n console.warn(\"\u26a0\ufe0f Nenhum dado de subp\u00e1ginas encontrado na entrada 1.\");\n}\n\nreturn finalItemsToUpdate;\n"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
4736,
2336
],
"id": "684651b6-b00b-416e-a717-e683ca6972a1",
"name": "FinalFormatter"
},
{
"parameters": {
"fieldToSplitOut": "data",
"options": {}
},
"type": "n8n-nodes-base.splitOut",
"typeVersion": 1,
"position": [
4928,
2192
],
"id": "d5462f18-5fea-4ead-b54f-e8edb3430c62",
"name": "Split Out1"
},
{
"parameters": {
"operation": "appendOrUpdate",
"documentId": {
"__rl": true,
"value": "18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4",
"mode": "list",
"cachedResultName": "sheets-tegra-n8n",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4/edit?usp=drivesdk"
},
"sheetName": {
"__rl": true,
"value": "gid=0",
"mode": "list",
"cachedResultName": "webpages_links",
"cachedResultUrl": "https://docs.google.com/spreadsheets/d/18ERlfLcL5JITmWumxFxwBFcgMCYcRXDR84T6BTgrMP4/edit#gid=0"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"url": "={{ $json.url }}",
"instructions": "={{ $('GetPendingURL').item.json.instructions }}",
"status": "={{ $('PuppeteerScraper').item.json.success ? 'completed' : 'failed' }}",
"last_updated": "={{ $('PuppeteerScraper').item.json.timestamp }}",
"error_notes": "={{ $json.error ? $json.error:''}}"
},
"matchingColumns": [
"url"
],
"schema": [
{
"id": "url",
"displayName": "url",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true,
"removed": false
},
{
"id": "instructions",
"displayName": "instructions",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "status",
"displayName": "status",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "last_updated",
"displayName": "last_updated",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
},
{
"id": "error_notes",
"displayName": "error_notes",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"canBeUsedToMatch": true
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.7,
"position": [
3168,
2000
],
"id": "63e7086a-b58a-48c0-994a-dee9f3a1ed54",
"name": "Append or update row in sheet",
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"fieldToSplitOut": "outputItems",
"options": {}
},
"type": "n8n-nodes-base.splitOut",
"typeVersion": 1,
"position": [
5536,
2416
],
"id": "2017dd43-5378-4b9d-a082-4717d5e22e38",
"name": "Split Out2"
},
{
"parameters": {
"tableId": "scraped_content",
"dataToSend": "autoMapInputData"
},
"type": "n8n-nodes-base.supabase",
"typeVersion": 1,
"position": [
6160,
2416
],
"id": "192bc6d2-44e6-4816-8b2e-bb8df918ae82",
"name": "Create a row",
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"method": "POST",
"url": "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:embedContent",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={\n \"model\": \"models/gemini-embedding-001\",\n \"content\": { \n \"parts\": [{ \"text\": \"{{ $json.json.chunk_text }}\" }]\n },\n \"outputDimensionality\": 1536\n}",
"options": {
"batching": {
"batch": {
"batchSize": 2,
"batchInterval": 3000
}
}
}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
5744,
2416
],
"id": "c95dddbc-1f11-46a6-a8da-95eaf5a95f93",
"name": "HTTP Request",
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
},
"httpHeaderAuth": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "// O 'item' cont\u00e9m todos os dados do n\u00f3 anterior\n// (chunk_text, source_url, etc. + a resposta da API 'embedding')\nconst item = $('Split Out2').item.json.json\n\n// 1. O HTTP Request retorna o embedding dentro de { \"embedding\": { \"values\": [...] } }\n// N\u00f3s precisamos extrair apenas o array 'values'.\nconst embeddingVector =$json.embedding.values;\n\n// 2. Opcional: Agrupar o metadata em um \u00fanico objeto JSONB\n// (Assumindo que sua tabela tem uma coluna 'metadata' como no exemplo anterior)\nconst metadata = {\n instructions: item.metadata.instructions,\n status: item.metadata.status,\n last_updated: item.last_updated\n // Adicione quaisquer outros campos que voc\u00ea queira guardar no JSONB\n};\n\n// 3. Montar o objeto final limpo.\n// As chaves (ex: 'source_url') devem ser ID\u00caNTICAS\n// aos nomes das colunas da sua tabela Supabase.\nconst rowData = {\n source_url: item.source_url,\n chunk_text: item.chunk_text,\n chunk_index: item.chunk_index,\n total_chunks: item.total_chunks,\n embedding: embeddingVector, // <-- O vetor limpo\n metadata: metadata // <-- O objeto JSONB\n};\n\n// 4. Retornar o objeto formatado\nreturn { json: rowData };"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
5952,
2416
],
"id": "79434a31-5376-4e10-812b-62a5f40128c2",
"name": "Code in JavaScript"
},
{
"parameters": {
"modelName": "models/gemini-2.0-flash-001",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"typeVersion": 1,
"position": [
3968,
2288
],
"id": "58116084-e52b-4df5-b41d-bdde8f2864cd",
"name": "Fallback Model",
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"modelName": "models/gemini-2.0-flash",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"typeVersion": 1,
"position": [
3808,
2288
],
"id": "4b8751b8-e6f1-4178-a3d0-133cbcc226cc",
"name": "Embedding Model",
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
}
}
],
"connections": {
"Switch": {
"main": [
[
{
"node": "Append or update row in sheet",
"type": "main",
"index": 0
},
{
"node": "Merge",
"type": "main",
"index": 0
}
],
[
{
"node": "Scrape.do1",
"type": "main",
"index": 0
}
],
[
{
"node": "Scrape.do1",
"type": "main",
"index": 0
}
]
]
},
"Subpage Scrapper": {
"main": [
[
{
"node": "Merge1",
"type": "main",
"index": 0
}
],
[
{
"node": "Scrape.do2",
"type": "main",
"index": 0
}
]
]
},
"PuppeteerScraper": {
"main": [
[
{
"node": "Switch",
"type": "main",
"index": 0
}
]
]
},
"AggregateSubpages": {
"main": [
[
{
"node": "Merge2",
"type": "main",
"index": 0
}
]
]
},
"Scrape.do1": {
"main": [
[
{
"node": "ExtractHtmlSubpage",
"type": "main",
"index": 0
}
]
]
},
"Scrape.do2": {
"main": [
[
{
"node": "HTML Extractor",
"type": "main",
"index": 0
}
]
]
},
"Split Out": {
"main": [
[
{
"node": "Set hyperlink fields",
"type": "main",
"index": 0
}
]
]
},
"When clicking \u2018Execute workflow\u2019": {
"main": [
[
{
"node": "GetPendingURL",
"type": "main",
"index": 0
}
]
]
},
"GetPendingURL": {
"main": [
[
{
"node": "PuppeteerScraper",
"type": "main",
"index": 0
}
]
]
},
"Append row in sheet": {
"main": [
[
{
"node": "Subpage Scrapper",
"type": "main",
"index": 0
}
]
]
},
"HTML Extractor": {
"main": [
[
{
"node": "Merge1",
"type": "main",
"index": 1
}
]
]
},
"Set hyperlink fields": {
"main": [
[
{
"node": "Append row in sheet",
"type": "main",
"index": 0
}
]
]
},
"ExtractHtmlSubpage": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 1
}
]
]
},
"CodeFormatMainPage": {
"main": [
[
{
"node": "Merge2",
"type": "main",
"index": 1
}
]
]
},
"Merge": {
"main": [
[
{
"node": "MainPageAIAgent",
"type": "main",
"index": 0
},
{
"node": "Split Out",
"type": "main",
"index": 0
}
]
]
},
"AIAgentSubpage": {
"main": [
[
{
"node": "SubPageFormatter",
"type": "main",
"index": 0
}
],
[]
]
},
"MainPageAIAgent": {
"main": [
[
{
"node": "CodeFormatMainPage",
"type": "main",
"index": 0
}
]
]
},
"SubPageFormatter": {
"main": [
[
{
"node": "AggregateSubpages",
"type": "main",
"index": 0
}
]
]
},
"Merge1": {
"main": [
[
{
"node": "AIAgentSubpage",
"type": "main",
"index": 0
}
]
]
},
"Merge2": {
"main": [
[
{
"node": "FinalFormatter",
"type": "main",
"index": 0
}
]
]
},
"FlattenResult": {
"main": [
[
{
"node": "ChunkContent",
"type": "main",
"index": 0
}
]
]
},
"ChunkContent": {
"main": [
[
{
"node": "Split Out2",
"type": "main",
"index": 0
}
]
]
},
"FinalFormatter": {
"main": [
[
{
"node": "FlattenResult",
"type": "main",
"index": 0
},
{
"node": "Split Out1",
"type": "main",
"index": 0
}
]
]
},
"Split Out1": {
"main": [
[
{
"node": "Update row in sheet",
"type": "main",
"index": 0
}
]
]
},
"Append or update row in sheet": {
"main": [
[]
]
},
"Split Out2": {
"main": [
[
{
"node": "HTTP Request",
"type": "main",
"index": 0
}
]
]
},
"Create a row": {
"main": [
[]
]
},
"HTTP Request": {
"main": [
[
{
"node": "Code in JavaScript",
"type": "main",
"index": 0
}
]
]
},
"Code in JavaScript": {
"main": [
[
{
"node": "Create a row",
"type": "main",
"index": 0
}
]
]
},
"Fallback Model": {
"ai_languageModel": [
[
{
"node": "AIAgentSubpage",
"type": "ai_languageModel",
"index": 1
}
]
]
},
"Embedding Model": {
"ai_languageModel": [
[
{
"node": "AIAgentSubpage",
"type": "ai_languageModel",
"index": 0
},
{
"node": "MainPageAIAgent",
"type": "ai_languageModel",
"index": 0
}
]
]
}
},
"meta": {
"templateCredsSetupCompleted": true
}
}
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.
googlePalmApigoogleSheetsOAuth2ApihttpHeaderAuthhttpQueryAuthsupabaseApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
Tegra Scraper. Uses httpRequest, googleSheets, agent, supabase. Event-driven trigger; 32 nodes.
Source: https://github.com/jpmartins201/tegra-scraper/blob/f171f08ac7f787b5b773ba4098028b0464d636b8/workflow.json — original creator credit. Request a take-down →
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