Optimizing API docs for AI agents: a complete guide to llms.txt (February 2026)

AI coding assistants like Cursor and GitHub Copilot struggle with parsing traditional HTML API documentation due to token limits, often resulting in inaccurate code suggestions and slower integration. The llms.txt format, a markdown-based approach, offers a solution by providing a streamlined version of API documentation that AI tools can efficiently parse, focusing on endpoint descriptions, parameter details, and error schemas while excluding non-essential elements like CSS and JavaScript. This format serves a role similar to a sitemap, aiding AI in quickly locating and understanding key API components to generate precise integration code. The llms.txt and llms-full.txt variants cater to different documentation needs: the former provides a concise overview with links for detailed exploration while the latter includes comprehensive content directly. Tools like Fern automate the generation of both file types, ensuring they remain up-to-date with any API changes, thereby enhancing the accuracy and efficiency of AI-assisted code generation. Effective llms.txt maintenance involves regular updates following significant API changes and leveraging analytics to refine content based on developer usage patterns, ultimately facilitating smoother AI-driven development processes.