Build Your LLMS.txt
Generate a structured llms.txt Markdown file to configure crawling and context accessibility for AI bots and search engines.
Load Preset Template
Site Information
Info Links
Essential site directories, guides, or document pages.
Core Links
Main landing pages, services, product listings, or contacts.
Secondary Links
External repositories, social communities, or media links.
Custom Crawler Guidelines
llms.txt Standard Guide
How to Setup llms.txt
Fill in your website details and load any template to see default recommendations.
Add links under the Info and Core lists. Give them clear, concise labels and descriptions.
Click 'Generate llms.txt' to compile your clean, LLM-ready markdown code.
Download the file and save it as 'llms.txt' directly in the root directory of your host server.
The llms.txt file is a proposed standard for websites to expose structured Markdown information specifically designed to be read by LLMs. Keeping it at your site's root directory lets crawlers discover reference documents, terms, API endpoints, and guides in an structured format, enhancing your generative search presence (GEO).
Professional Build Your LLMS.txt for Everyone
A developer utility for generating and customizing a structured llms.txt file for your website. Placed at the root directory, llms.txt serves as a machine-readable Markdown document that helps Large Language Models (LLMs) and search crawlers easily discover key links, document pages, secondary resources, and prompt context specifications. The generator provides pre-configured templates for SaaS, API docs, blogs, and e-commerce websites, as well as a real-time Markdown preview, interactive adding/removal of links, and one-click copy and download functionality. Processed 100% locally and privately in your browser.
Key Benefits
Why choose our Build Your LLMS.txt for your workflow?
AI Optimization (GEO): Ensure search engines and bots index the most relevant context and documentation of your site.
Clean Context Handing: Provide clean links and concise descriptions optimized for RAG engines and AI agents.
100% Secure & Private: All generation logic executes in your local browser, keeping internal URL directories private.
Common Use Cases
Real-world examples of how to use this tool.
Documentation Portals: Group key guide pages together for developers reading through LLM interfaces.
SaaS Applications: Point AI agents to pricing, login, and support pages directly from the website root.
Developer Repositories: Standardize external links for open source packages and API frameworks.
How to use Build Your LLMS.txt?
Follow these simple steps to get the best results.
Select a layout preset or enter your site's basic details (Name, URL, Description).
Manage your primary website links under the Info and Core sections.
Add secondary resources, such as GitHub repositories, social accounts, or developer blogs.
Add custom instructions or crawl limits in the Guidelines text block.
Review the live Markdown preview in the generated code panel.
Use 'Copy Text' or 'Download' to get your finished llms.txt file.
Frequently Asked Questions
Common questions about our Build Your LLMS.txt tool.
What is an llms.txt file?
The llms.txt standard is a proposal for a website root file (/llms.txt) that provides natural language information and indices of links in Markdown format. It helps LLMs and other user agents understand the layout, structure, and documentation highlights of a site.
Where should I place the generated llms.txt file?
The generated llms.txt file should be placed at the absolute root directory of your website, making it accessible at 'https://yourwebsite.com/llms.txt'.
How does the file format differ from robots.txt?
While robots.txt uses a custom directive format to block crawlers from specific paths, llms.txt uses standard Markdown to summarize the site content and direct LLM-powered bots to the best available resources.
Is there a limit on how many links I can add?
There are no hard limits, but we recommend keeping your llms.txt concise and well-grouped (focusing on key pages and high-quality references) so that it remains context-efficient for LLM crawlers.
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