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Generate & Upload llms.txt for websites GEO optimization 🕸️🌐 with ScrapegraphAI

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Created by: Davide Boizza || n3witalia
Davide Boizza

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Last update 18 hours ago

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This workflow automatically generates an llms.txt file (following the llmstxt.org specification) for any given website. It uses ScrapegraphAI to crawl and scrape pages, an OpenAI chat model to process content, and finally uploads the generated file via FTP.

Key Advantages

1. ✅ Automated llms.txt Generation

The workflow fully automates the creation of a compliant llms.txt file, eliminating the need for manual documentation and reducing maintenance time.

2. ✅ AI-Powered Website Understanding

Using OpenAI and ScrapeGraphAI, the system intelligently analyzes:

  • Website structure
  • Internal pages
  • Titles and descriptions
  • Content relevance
  • Logical page categorization

This produces a high-quality output specifically optimized for AI systems and LLM indexing.

3. ✅ Dynamic Internal Link Discovery

The crawler automatically extracts all internal links from the website, making the workflow scalable for:

  • Small business websites
  • Large corporate websites
  • Ecommerce stores
  • Blogs and documentation portals

4. ✅ Intelligent Content Categorization

Pages are automatically grouped into meaningful sections such as:

  • Main Pages
  • Services
  • Products
  • Portfolio
  • Blog
  • Company
  • Contact
  • Legal / Optional pages

This improves readability and machine interpretability.

5. ✅ Multilingual Support

The workflow preserves the original language of the website content, ensuring consistency and localization for international projects.

6. ✅Fully Automated Publishing

After generation, the workflow converts the output into a .txt file and uploads it directly to an FTP server or CDN, enabling instant deployment without manual intervention.

7. ✅ Reduced Manual Work*

The entire process — from crawling to publishing — is automated inside n8n, significantly reducing operational effort for SEO teams, developers, and AI optimization workflows.

8. ✅ AI & SEO Optimization

The generated llms.txt file helps:

  • AI crawlers better understand the website
  • Improve AI discoverability
  • Structure content for LLM consumption
  • Support future AI search indexing strategies

9. ✅ Modular and Scalable Architecture

The workflow is built with reusable components:

  • Crawler module
  • Status monitoring
  • AI analysis agent
  • Scraper tool
  • Binary conversion
  • FTP deployment

This makes it easy to extend, customize, or integrate into larger automation systems.

Ideal Use Cases

  • AI-ready website optimization
  • Automated SEO infrastructure
  • LLM indexing preparation
  • Agency website automation
  • Large-scale multi-site management
  • Documentation platforms
  • AI search visibility enhancement

How it works

The process begins when the workflow is manually triggered. It then:

  1. Starts a crawl of the specified domain using ScrapegraphAI’s smartcrawler. The crawler extracts all internal links from the domain (acting like a sitemap generator).
  2. Waits for the crawl to complete (configurable wait time, default 20 units).
  3. Checks the crawler’s status – if the crawl is still processing, the workflow waits again; if successful, it proceeds.
  4. Extracts the discovered internal links and passes them to an AI agent.
  5. Uses an AI agent (with OpenAI GPT) that:
    • Receives the list of internal URLs.
    • Uses a Scraper tool (via ScrapegraphAI) to scrape each URL’s content.
    • Follows a strict prompt to:
      • Analyze the homepage (title, description, language).
      • Extract concise descriptions for each internal page.
      • Group pages into logical sections (Main pages, Services, Portfolio, Contact, Optional, etc.).
      • Generate a clean Markdown file (llms.txt) following the official spec.
  6. Converts the Markdown output into a binary file (llms.txt).
  7. Uploads the file to an FTP server (configured for BunnyCDN or any FTP storage).
  8. Ends the workflow once the upload is complete.

The AI agent is explicitly forbidden from inventing content – it must call the Scraper tool for every URL before describing it. The output is pure Markdown, starting with #.

Setup steps

To use this workflow in n8n, follow these steps:

1. Prerequisites

  • An n8n instance (self-hosted or cloud).
  • A ScrapegraphAI account with API access.
  • An OpenAI account with API key (model used: gpt-5.4-mini – note: this may be a custom/typo; usual models are gpt-4o-mini or gpt-4).
  • An FTP server (traditional FTP, or SFTP if modified).

2. Configure credentials in n8n

Go to Credentials in n8n and add:

  • ScrapegraphAI API

    • Name: ScrapegraphAI account
    • API Key: your ScrapegraphAI API key

  • OpenAI API

    • Name: OpenAi account (Eure)
    • API Key: your OpenAI API key

  • FTP

    • Name: FTP BunnyCDN
    • Host, Port, Username, Password (or SSH key) for your FTP server

3. Modify the domain

In the Set domain node, change the your_domain to your target domain (e.g., example.com).
Do not include https:// – only the domain name.

4. Adjust wait time (optional)

In the Wait node, change the amount (default 20) to a higher value if the target site is large or slow to crawl.

5. Update FTP upload path

In the Upload to FTP node, update the path field.
Currently it is:
=/YOUR_PATH/{{$binary.data.fileName}}
Change YOUR_PATH to the actual remote directory (e.g., /public_html/).
The file will be saved as llms.txt.

6. (Optional) Modify the AI prompt

The prompt inside the LLMS.txt Agent node can be adapted for:

  • Different section names
  • Different output structure
  • Different languages
  • Exclusion of certain URL patterns

7. Activate and execute

  • Save the workflow.
  • Toggle Active to enable manual execution.
  • Click ‘Execute workflow’ on the Manual Trigger node.
  • Monitor execution – the workflow will wait for the crawl, then process all pages, and upload the final file.

8. Verify

Check your FTP server for the generated llms.txt.
Test it by opening in a text editor – it should be pure Markdown starting with # Site name.

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