Robin Geuens This workflow corresponds to n8n.io template #9056 — we link there as the canonical source.
This workflow follows the Form Trigger → HTTP Request 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 →
{
"id": "ASCZ9YZJvNkHsxOF",
"name": "SEO optimized outline generator using SerpAPI & AI",
"tags": [],
"nodes": [
{
"id": "bddced98-6feb-4327-a4a6-98e76ff0c6c3",
"name": "Create outline",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
1808,
-32
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "gpt-4o",
"cachedResultName": "GPT-4O"
},
"options": {
"temperature": 0.7
},
"messages": {
"values": [
{
"content": "=Please create an outline for an article with the keyword: \"{{ $('Extract URLs from JSON').item.json.Keyword }}\".\n\nBelow are the 3 article you should base the outline on:\n\n===ARTICLE 1===\n{{ ($('Put all articles into one item').item.json.markdown[0] == null) ? \"\" : $('Put all articles into one item').item.json.markdown[0]}}\n===END ARTICLE 1===\n===ARTICLE 2===\n{{ ($('Put all articles into one item').item.json.markdown[1] == null) ? \"\" : $('Put all articles into one item').item.json.markdown[1]}}\n===END ARTICLE 2===\n===ARTICLE 3===\n{{ ($('Put all articles into one item').item.json.markdown[2] == null) ? \"\" : $('Put all articles into one item').item.json.markdown[2]}}\n===END ARTICLE 3"
},
{
"role": "system",
"content": "=You are an SEO Content Strategist AI. \nYour task is to analyze three provided articles and a keyword, and generate a **MECE-structured (mutually exclusive, collectively exhaustive), SEO-optimized outline** for a new article. \n\nFollow these rules precisely:\n\n---\n\n### INPUT:\n- Three full-length articles on a similar topic.\n- One keyword (the target topic for SEO).\n\n---\n\n### TASKS:\n\n1. **Analyze the Articles:**\n - Extract all factual information.\n - Identify recurring themes, structures, headings, and subtopics across all three.\n - Do **not** fabricate or replace facts with your own.\n - Do **not** summarize\u2014focus on content structure and topic relationships.\n\n2. **Keyword Integration:**\n - Use the given keyword to guide topic relevance.\n - Ensure every section in the outline directly supports the keyword.\n\n3. **Create a MECE Outline:**\n - Organize topics into high-level sections and sub-sections that are:\n - **Mutually Exclusive**: No overlapping content or themes.\n - **Collectively Exhaustive**: Together, the sections should fully cover the topic.\n\n4. **Optimize for SEO:**\n - Use heading structures (H1, H2, H3).\n - Incorporate natural language variants and LSI keywords when visible from the articles.\n - Focus on user search intent as inferred from article content.\n\n---\n\n### OUTPUT FORMAT:\n\n# SEO Article Outline: [Insert Keyword]\n\n## Introduction\n- Brief overview of the topic\n- Importance of the keyword/topic\n\n## [Section 1 Title]\n- [Subtopic 1.1]\n- [Subtopic 1.2]\n\n## [Section 2 Title]\n- [Subtopic 2.1]\n- [Subtopic 2.2]\n\n...\n\n## Conclusion\n- Recap key sections\n- Reinforce keyword/topic importance\n\n---\n\n### IMPORTANT:\n- No hallucination. Stick strictly to the data in the 3 articles.\n- Do not introduce new facts or opinions.\n- Every section should clearly trace back to information present in the source material."
}
]
}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"retryOnFail": true,
"typeVersion": 1.8
},
{
"id": "b334649f-b35b-420c-be22-4895232c385f",
"name": "Get search results",
"type": "n8n-nodes-base.httpRequest",
"position": [
-1056,
-16
],
"parameters": {
"url": "https://serpapi.com/search",
"options": {},
"sendQuery": true,
"authentication": "predefinedCredentialType",
"queryParameters": {
"parameters": [
{
"name": "q",
"value": "={{ $json.Keyword }} -inurl:reddit.com -inurl:quora.com"
},
{
"name": "location",
"value": "United States"
}
]
},
"nodeCredentialType": "serpApi"
},
"credentials": {
"serpApi": {
"name": "<your credential>"
}
},
"retryOnFail": true,
"typeVersion": 4.2
},
{
"id": "6476facf-8f7c-4ef3-af4f-eea3e6124def",
"name": "Split out URLs",
"type": "n8n-nodes-base.splitOut",
"position": [
-336,
-16
],
"parameters": {
"options": {},
"fieldToSplitOut": "URLs"
},
"typeVersion": 1
},
{
"id": "1f8fbe1d-2a1b-43f7-a2e0-efa684ea41dc",
"name": "Clean up Text",
"type": "n8n-nodes-base.markdown",
"position": [
1136,
-32
],
"parameters": {
"html": "={{ $json.html }}",
"options": {},
"destinationKey": "markdown"
},
"typeVersion": 1
},
{
"id": "965b2a4f-bb84-4cfc-892a-a9782dbb522d",
"name": "Scrape Content",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueErrorOutput",
"maxTries": 5,
"position": [
32,
-16
],
"parameters": {
"url": "={{ $json.URLs }}",
"options": {
"batching": {
"batch": {
"batchSize": 5
}
}
},
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "User-Agent",
"value": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/0.0.0.0 Safari/537.36"
},
{
"name": "Accept",
"value": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8"
},
{
"name": "Accept-Encoding",
"value": "gzip, deflate, br"
},
{
"name": "Accept-Language",
"value": "en-US,en;q=0.5"
}
]
}
},
"retryOnFail": true,
"typeVersion": 4.2
},
{
"id": "d301d0b4-04fa-432b-ac2b-46fdb852a58f",
"name": "Limit",
"type": "n8n-nodes-base.limit",
"position": [
400,
-32
],
"parameters": {
"maxItems": 3
},
"typeVersion": 1
},
{
"id": "bc5e7c7e-6d6f-440a-85e9-4a4f13b6c377",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2000,
-368
],
"parameters": {
"color": 7,
"width": 416,
"height": 848,
"content": "## Overview\nUse this workflow to create SEO-friendly outlines from high-ranking Google articles. \n\nEnter a keyword, and the workflow scrapes the top results, scrapes the content, analyzes it with AI, and builds a MECE (mutually exclusive, collectively exhaustive) outline. It\u2019s useful for content creators and SEO specialists who want relevant, well-structured content.\n\n## How it works\n- Accepts a keyword submitted through a form\n- Uses the SerpAPI to get top Google results for a chosen country\n- Collects the top five URLs. We use five because we expect some to fail at the scraping stage\n- Scrapes each URL separately\n- Uses the first three articles to fit the AI model\u2019s context window\n- Extracts the main text from the page body\n- Converts HTML to Markdown for cleaner text\n- Combines the cleaned text into a single list for AI processing\n- Analyzes the content with an AI language model to find common topics and headings\n- Generates an SEO-focused outline based on the most frequent topics\n\n## Setup steps\n1. Sign up for a [SerpAPI account](https://serpapi.com/) (free tier available)\n2. Create an [OpenAI account](https://openai.com/api/) and get an API key\n3. Set up your credential within N8N\n4. Run the workflow and enter your keyword\n5. The workflow will generate an SEO-friendly outline for your content"
},
"typeVersion": 1
},
{
"id": "cac60180-c7cb-4c1b-82c8-65ef51c43dba",
"name": "On form submission",
"type": "n8n-nodes-base.formTrigger",
"position": [
-1440,
-16
],
"parameters": {
"options": {},
"formTitle": "SEO optimized outline generator",
"formFields": {
"values": [
{
"fieldLabel": "Keyword",
"placeholder": "Your keyword here",
"requiredField": true
}
]
},
"formDescription": "This workflow generates an SEO optimized outline based on what's already ranking"
},
"typeVersion": 2.3
},
{
"id": "c2f8b9ea-8401-4b15-90c7-c3cff105b028",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1552,
-368
],
"parameters": {
"color": 7,
"width": 336,
"height": 848,
"content": "## Form trigger\nAdd your keyword to the form and watch the magic happen :zap:"
},
"typeVersion": 1
},
{
"id": "5524c54e-6928-4683-8b96-245830a8161d",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1184,
-368
],
"parameters": {
"color": 7,
"width": 336,
"height": 848,
"content": "## Scrape Google using SerpAPI\nIn this Node we're scraping Google and excluding forums and UGC sites like Quora. If you have a big site you might want to exclude your own domain as well by adding `-site:yourdomain.com`"
},
"typeVersion": 1
},
{
"id": "22af269c-6324-4df9-80b0-522f719f1f8a",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-816,
-368
],
"parameters": {
"color": 7,
"width": 336,
"height": 848,
"content": "## Extract URLs from JSON\nSerpAPI gives us a lot of info we don't need. In this node we just clean up the results and only extract the URLs we'll use"
},
"typeVersion": 1
},
{
"id": "bdf80a10-0dd4-42ba-965e-00cf94928106",
"name": "Extract URLs from JSON",
"type": "n8n-nodes-base.set",
"position": [
-704,
-16
],
"parameters": {
"mode": "raw",
"options": {},
"jsonOutput": "={\n \"Keyword\": \"{{ $('On form submission').item.json.Keyword }}\",\n \"URLs\": [\n \"{{ $json.organic_results[0].link }}\",\n \"{{ $json.organic_results[1].link }}\",\n \"{{ $json.organic_results[2].link }}\",\n \"{{ $json.organic_results[3].link }}\",\n \"{{ $json.organic_results[4].link }}\"\n ]\n}\n"
},
"typeVersion": 3.4
},
{
"id": "a663bc54-0591-49c5-8bb4-f3f5b97b1418",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-448,
-368
],
"parameters": {
"color": 7,
"width": 336,
"height": 848,
"content": "## Split out URLs\nThe previous node gives us one list with all the URLs. To scrape them all, we split them out into 5 different items"
},
"typeVersion": 1
},
{
"id": "bfb5461d-ea2f-428b-b622-3ab89b7d23b1",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-80,
-368
],
"parameters": {
"color": 7,
"width": 336,
"height": 848,
"content": "## Scrape URLS\nIn this node we make a request to the different URLs we scraped. For now, we just get all the HTML on the page.\n\n:bangbang: IMPORTANT :bangbang:\nUse batching and keep the amount of urls you're scraping in one go to around 5. We don't want to hammer sites with a bunch of requests and we don't want to get our IP blocked for sending too many requests. Respect the sites, and use a proper user agent."
},
"typeVersion": 1
},
{
"id": "963457c6-4c5f-4a73-a960-e0963b60e96f",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
288,
-368
],
"parameters": {
"color": 7,
"width": 336,
"height": 848,
"content": "## Limit amount of articles\nDuring scraping, we expect some URLs to fail. This can be because the site is down, the site uses anti scraping measures, or there's just some glitch. \n\nIf we did manage to scrape all URLs, that's great, but we'll only be using 3. Otherwise it might not fit within the context window of the LLM"
},
"typeVersion": 1
},
{
"id": "7b212b7a-378b-40dd-8bf1-62ad070417cc",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
656,
-368
],
"parameters": {
"color": 7,
"width": 336,
"height": 848,
"content": "## Extract body content\nWe don't need all the HTML, we just need the content, so we extract the <body> from the HTML we just scraped. This removes all the stylesheets and JS scripts from the HTML"
},
"typeVersion": 1
},
{
"id": "fbff0de8-1556-40c4-9eda-acdf2d9fb830",
"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
1024,
-368
],
"parameters": {
"color": 7,
"width": 336,
"height": 848,
"content": "## Clean up text\nConverting our extracted HTML to markdown cleans up our extracted text by removing all the HTML tags and attributes. This again helps by reducing the amount of text we're passing onto the LLM\n"
},
"typeVersion": 1
},
{
"id": "0dce140c-8108-4d25-8e1f-0e9911b21fc1",
"name": "Extract body content",
"type": "n8n-nodes-base.html",
"position": [
768,
-32
],
"parameters": {
"options": {},
"operation": "extractHtmlContent",
"extractionValues": {
"values": [
{
"key": "html",
"cssSelector": "body",
"skipSelectors": "img, meta, a"
}
]
}
},
"typeVersion": 1.2
},
{
"id": "770a4308-2f09-45a6-a2c2-5f10275a53b9",
"name": "Sticky Note9",
"type": "n8n-nodes-base.stickyNote",
"position": [
1392,
-368
],
"parameters": {
"color": 7,
"width": 336,
"height": 848,
"content": "## Put all articles into one item\nWe take our different items containing the scraped content and put it all into one list. This just makes it easier to work with."
},
"typeVersion": 1
},
{
"id": "0b0b8316-ded3-4767-b33d-e9290ee577dc",
"name": "Put all articles into one item",
"type": "n8n-nodes-base.aggregate",
"position": [
1520,
-32
],
"parameters": {
"options": {},
"fieldsToAggregate": {
"fieldToAggregate": [
{
"fieldToAggregate": "markdown"
}
]
}
},
"typeVersion": 1
},
{
"id": "82bd491e-0ffe-4086-8ae5-4f6447be698b",
"name": "Sticky Note10",
"type": "n8n-nodes-base.stickyNote",
"position": [
1760,
-368
],
"parameters": {
"color": 7,
"width": 336,
"height": 848,
"content": "## Create outline using AI\nFinally we pass all the articles to an LLM and tell it to look for similarities between all articles.\n\nThis node has some logic to check if the list indeed contains three articles. Let's say 3 out of 5 articles failed to scrape, we just tell the AI to only use the two that succeeded."
},
"typeVersion": 1
},
{
"id": "8e928b9b-20ee-41ae-a54c-dae57ffa6446",
"name": "Do with the outline what you want",
"type": "n8n-nodes-base.noOp",
"position": [
2240,
-32
],
"parameters": {},
"typeVersion": 1
},
{
"id": "f0105a0d-d158-4237-a0d8-81cabee5c9d1",
"name": "Sticky Note11",
"type": "n8n-nodes-base.stickyNote",
"position": [
2128,
-368
],
"parameters": {
"color": 7,
"width": 336,
"height": 848,
"content": "## Continue the workflow...\nWrite the outline to a Google doc, add another LLM to write the content, enrich it with search data. The world is your oyster."
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "d3321dc0-f0cf-48df-ba9b-8a709eace218",
"connections": {
"Limit": {
"main": [
[
{
"node": "Extract body content",
"type": "main",
"index": 0
}
]
]
},
"Clean up Text": {
"main": [
[
{
"node": "Put all articles into one item",
"type": "main",
"index": 0
}
]
]
},
"Create outline": {
"main": [
[
{
"node": "Do with the outline what you want",
"type": "main",
"index": 0
}
]
]
},
"Scrape Content": {
"main": [
[
{
"node": "Limit",
"type": "main",
"index": 0
}
]
]
},
"Split out URLs": {
"main": [
[
{
"node": "Scrape Content",
"type": "main",
"index": 0
}
]
]
},
"Get search results": {
"main": [
[
{
"node": "Extract URLs from JSON",
"type": "main",
"index": 0
}
]
]
},
"On form submission": {
"main": [
[
{
"node": "Get search results",
"type": "main",
"index": 0
}
]
]
},
"Extract body content": {
"main": [
[
{
"node": "Clean up Text",
"type": "main",
"index": 0
}
]
]
},
"Extract URLs from JSON": {
"main": [
[
{
"node": "Split out URLs",
"type": "main",
"index": 0
}
]
]
},
"Put all articles into one item": {
"main": [
[
{
"node": "Create outline",
"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.
openAiApiserpApi
Pro
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About this workflow
Use this workflow to create SEO-friendly outlines based on articles that do well in Google.
Source: https://n8n.io/workflows/9056/ — original creator credit. Request a take-down →
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