This article provides a complete, practical walkthrough of the Filter Summarize Create Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   How to Automatically Generate AI-Ready llms.txt Files from Screaming Frog Crawls Using n8n
   
   Meta Description:
   Learn how to automate the creation of an llms.txt file using Screaming Frog exports and n8n. This no-code workflow helps you filter and structure web content for large language models (LLMs), ready for indexing or training.
   
   Keywords:
   llms.txt, n8n workflow, Screaming Frog, OpenAI, website crawling, LLM training, content discovery, SEO automation, AI-ready data, internal_html.csv, data preprocessing, intelligent content filtering
   
   Third-Party APIs Used:
   
   - OpenAI API (via n8n LangChain integration for optional AI-powered text classification)
   ---
   
   Article:
   
   Generating AI-Ready llms.txt Files from Screaming Frog Crawls Using n8n
   
   As large language models (LLMs) continue to redefine how we access and interact with information, making website content easily discoverable to these models has become increasingly important. One emerging standard in this realm is the llms.txt file—a centralized text file that lists high-value URLs from your website, helping LLMs identify and prioritize meaningful content.
   
   Manually creating and curating this file from websites’ internal pages can be time-consuming. But there's good news: using Screaming Frog and n8n (a powerful open-source workflow automation tool), you can fully automate this process from start to finish—without writing a single line of code.
   
   In this article, we’ll walk you through an n8n workflow specifically designed to transform your Screaming Frog website crawl into a polished, AI-ready llms.txt file.
   
   What is llms.txt?
   
   The llms.txt file is a new SEO format designed to help LLMs index the best, most useful content from your website. Each line typically includes a URL, its title, and optionally, a short meta description—formatted in markdown for easy parsing:
   
   ```
   - [Page Title](https://example.com/page): Meta description of content here
   ```
   
   Step 1: Crawl Your Website Using Screaming Frog
   
   Start by crawling the website with Screaming Frog. Once the crawl is complete, export the "internal_html.csv" file (or "internal_all.csv" if you want to include all internal links for additional filtering later).
   
   Step 2: Upload via n8n Form
   
   In this n8n workflow, the process begins with a form that collects three things:
   
   - The name of your website
   - A short description of the website (in the website's main language)
   - The Screaming Frog CSV export (ideally internal_html.csv)
   
   This user-friendly form is the entry point of the automated pipeline and allows content creators, marketers, and SEO managers to get started without technical friction.
   
   Step 3: Extract and Normalize Fields
   
   Once the form is submitted, the workflow extracts relevant data from the uploaded CSV file using the “Extract From File” node. The next step involves normalizing essential fields such as:
   
   - URL (from the "Address" column)
   - Title
   - Meta Description
   - HTTP Status Code
   - Indexability
   - Content Type
   - Word Count
   
   The workflow supports multiple languages (French, German, Spanish, Italian), intelligently detecting the appropriate column labels depending on Screaming Frog’s localized export.
   
   Step 4: Filter for Valuable Pages
   
   Filtering is a crucial step to ensure only meaningful pages are included. This workflow filters out pages that:
   
   - Don’t return a 200 (OK) status
   - Aren’t marked as indexable
   - Don’t have an HTML content type (e.g., avoid PDFs, images)
   
   Optional filters can be added based on word count, URL structure (path matching), or the presence of a meta description, ensuring a refined list of URLs that best represent your website content.
   
   Step 5 (Optional): AI-Powered Page Classification
   
   Though disabled by default, this workflow includes an optional AI classifier using OpenAI’s GPT model. It evaluates each page (based on its URL, title, meta description, and word count) to determine whether it offers "useful content" or “other content”.
   
   You can customize the classifier’s criteria and even use a loop node to process large websites efficiently without hitting token or timeout limits.
   
   Step 6: Format the llms.txt Rows
   
   Each remaining page is converted into a line that fits the llms.txt format. If a page has a meta description, it’s added as inline markdown:
   
   ```
   - [Great Blog Post](https://example.com/blog): A detailed exploration of our latest update.
   ```
   
   If the description is missing, it omits it gracefully:
   
   ```
   - [Great Blog Post](https://example.com/blog)
   ```
   
   Step 7: Build the Complete llms.txt File
   
   All formatted lines are concatenated and prefixed with the website’s title and description (entered in the form). This creates a complete, human- and machine-readable llms.txt file ready for downloading—or uploading to a designated location.
   
   Step 8: Download or Upload the File
   
   By default, the workflow ends with a "Convert to File" node, allowing you to download the llms.txt file directly from n8n’s interface. However, nodes like Google Drive, Dropbox, or OneDrive can easily be added to automatically store this file in the cloud for team collaboration or archiving.
   
   Why This Matters
   
   Having an llms.txt file ensures your most valuable content isn't lost in the noise when LLMs crawl your site. Combined with Screaming Frog’s detailed crawl and n8n’s automation capabilities, you gain an efficient, repeatable pipeline to keep this file fresh as your site evolves.
   
   Benefits:
   
   - Fully automated from form to file
   - Language agnostic and robust to Screaming Frog localization
   - Scalable to large websites
   - Optional OpenAI filtering for AI-powered content valuation
   - Outputs a clean, readable, markdown-friendly text file
   
   Conclusion
   
   Whether you're preparing content for fine-tuned AI training or simply aiming to improve how LLMs "see" your site, this n8n workflow streamlines the process end-to-end. With just a CSV export and a few clicks, you’ll have a polished llms.txt file in minutes—structured, filtered, and fine-tuned for semantic discovery.
   
   Try it in your automation pipeline and get one step closer to a truly AI-friendly website!
   
   — End —

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.