LLMs.txtHub
Helping developers make their docs AI-accessible
Overview
The largest directory of websites implementing the llms.txt standard — a simple protocol for making documentation accessible to AI assistants. Includes guides, validators, and tools to help developers adopt the standard.
The Problem
AI assistants struggle with most documentation
Large Language Models can't efficiently consume traditional documentation. They need structured, concise summaries optimized for their context windows. The llms.txt standard solves this, but adoption requires awareness and tooling.
- AI assistants can't navigate complex documentation sites
- No standardized way to make docs AI-friendly
- Developers unaware of the llms.txt standard
- No central resource to discover AI-ready documentation
- Lack of tooling to validate and generate llms.txt files
The Solution
The central hub for AI-ready documentation
Website Directory
Browse 910+ websites that implement llms.txt. Filter by category, search by name, and discover how others structure their AI-ready docs.
Implementation Guides
Step-by-step tutorials for adding llms.txt to your project. Covers different frameworks, hosting platforms, and use cases.
Validator Tool
Check if your llms.txt file follows the specification. Get actionable feedback on improvements.
Generator
Automatically generate llms.txt files from your existing documentation. Supports multiple documentation platforms.
Submit Your Site
Add your website to the directory. Community-driven growth keeps the directory comprehensive and up-to-date.
Technical Implementation
Production-grade architecture
Technology Stack
- Next.js 15
- React 19
- Tailwind CSS 4
- Framer Motion
- Content Collections
- MDX
- Clerk
- Vercel Postgres
- Upstash Redis
- Fuse.js
- Jest
- Playwright
Scalable Monorepo
Turborepo-powered architecture with shared packages for design system, analytics, caching, and utilities. Clean separation between web app and tooling.
Real-time Validation
API endpoints that fetch and validate llms.txt files in real-time. Rate limiting and caching ensure reliable performance.
Automated Content Pipeline
Scripts to generate and update website entries. Automated favicon fetching, metadata extraction, and validation.
Comprehensive Testing
Unit tests, integration tests, and E2E tests with Playwright. High coverage for critical paths.
Results
The go-to resource for llms.txt
Recognition
- Featured in AI/ML newsletters
- Recommended by documentation platform teams
- Referenced in llms.txt specification discussions
Impact
Accelerating AI-friendly documentation adoption
- Raised awareness of llms.txt standard across developer community
- Provided free tooling that lowered adoption barriers
- Created reference implementations for common platforms
- Built community around documentation accessibility
What I Learned
- 01Directories need great search and filtering to be useful
- 02Community submissions require careful validation workflows
- 03Performance matters when fetching external resources
- 04Documentation projects benefit from dogfooding (using llms.txt ourselves)
Explore LLMs.txt Hub
Discover AI-ready documentation or submit your site