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

AI coding assistants like Cursor and GitHub Copilot need to read your API documentation to generate working integration code. The problem? These tools can't extract useful information from HTML documentation pages. They hit token limits parsing navigation menus, JavaScript, and visual styling before reaching your endpoint descriptions. When your API documentation isn't accessible to AI tools, developers get incorrect code suggestions and slower integration. The llms.txt format solves this by providing a markdown-based version of your docs that AI systems can parse within their token budgets. It strips away everything except the information needed for code generation.

TLDR:

• llms.txt provides a compact overview with links to full documentation, while llms-full.txt embeds complete content directly in the file without requiring external fetches
• AI agents need explicit parameter descriptions and error schemas to generate working code
• Structure your content for AI consumption by including only technical reference material and excluding marketing copy, decorative elements, and other content that adds no value for code generation
• Tools like Fern auto-generate both llms.txt and llms-full.txt from your API spec as part of the docs build process

What is llms.txt and why does it matter for API documentation?

llms.txt is a plain-text, markdown-based format designed specifically for AI consumption. It serves a similar role to a sitemap: instead of helping search engines crawl your site, it helps coding assistants locate and understand your API's endpoints, authentication methods, and response schemas.

The format strips away CSS, navigation elements, and JavaScript, leaving clean structured text that AI systems can parse efficiently within their token budgets. For API documentation specifically, this means coding assistants can spend their context window on your endpoint descriptions, parameter types, and error schemas rather than on page chrome.

Developers increasingly rely on AI assistants to write integration code, and that trend is accelerating. If your docs aren't accessible to these tools, you're adding friction to every developer's onboarding. llms.txt removes that friction.

Understanding the llms.txt specification and format

The llms.txt specification uses a structured markdown format optimized for machine readability. Each file starts with an H1 header containing your project name, followed by a blockquote summarizing your API in one or two sentences.

Documentation is organized into sections using H2 headers for major topics like authentication or API endpoints. Within each section, provide essential details in plain markdown with links to the corresponding full documentation pages. These links allow AI assistants to reference the condensed llms.txt content while following URLs for deeper context when needed.

Keep descriptions focused on what developers need to integrate your API — parameter types, authentication requirements, error handling. Avoid marketing language or context that doesn't serve code generation. Use markdown code blocks for examples that illustrate usage patterns and clarify how your API behaves in practice.

llms.txt vs. llms-full.txt: choosing the right approach

The specification defines two file variants that serve different AI consumption patterns. llms.txt provides a compact overview with links to full documentation. llms-full.txt embeds complete content directly in the file without requiring external fetches.

llms.txt works best for larger documentation sites where including everything would exceed token limits. AI assistants read the summary, identify relevant sections, and follow links for detailed information.

llms-full.txt suits APIs with concise documentation that fits entirely within AI context windows. The file contains complete endpoint descriptions, authentication details, and code examples without requiring link traversal.

Most API providers should implement both files. Serve llms.txt for AI assistants optimizing for token usage and llms-full.txt for agents that prefer self-contained context. Tools like Fern generate both variants automatically from the same source specification, eliminating manual maintenance.

Best practices for generating and maintaining llms.txt files

Regenerate llms.txt after major changes like new authentication methods, endpoint additions, or breaking changes. Outdated authentication flows or removed endpoints cause AI tools to suggest non-functional code, so breaking changes need immediate regeneration. Minor updates — typo fixes, clarified descriptions, small parameter changes — can wait for scheduled regeneration cycles.

Test AI tool parsing by loading your llms.txt file into Cursor or Claude and asking specific questions about your API. Request code for authenticating with your API, calling a specific endpoint, or handling a particular error response. If the AI tool references outdated information or can't locate documented features, adjust your file structure. Token count matters less than whether AI assistants can extract correct information quickly.

Monitor which sections AI tools reference most frequently by tracking link clicks from llms.txt to your full documentation pages. High-traffic sections deserve more detail in the llms.txt summary, while rarely-accessed content can rely on brief descriptions with links. This usage data reveals whether your information hierarchy matches how developers actually integrate your API. Tools like Fern provide analytics for llms.txt usage, including traffic breakdowns by LLM provider and page-level metrics showing bot versus human visitors.

API versioning adds complexity to llms.txt maintenance. When releasing a new API version, you can either maintain separate llms.txt files per version or include all versions in a single file. Separate files prevent token bloat but require AI tools to know which version a developer is targeting. A unified file with version-specific sections works better when most endpoints remain stable across versions. If your documentation tooling supports versioned docs, generating separate llms.txt files per version gives you the most control over what each AI assistant sees.

Optimizing OpenAPI specifications for AI agent discovery

OpenAPI specifications serve as machine-readable contracts that AI agents can parse directly to identify endpoints, methods, parameters, and response schemas. While llms.txt helps with general documentation discovery, a well-structured OpenAPI file gives agents structured access to your API's capabilities without relying on natural language interpretation.

Parameter descriptions matter more for AI agents than for human developers. A human can infer that a userId parameter expects a user identifier, but AI tools need explicit descriptions stating format requirements, whether IDs are UUIDs or integers, and any validation rules. Be thorough with every parameter — what seems obvious to a human reader is ambiguous to an AI agent.

Include realistic example responses in your OpenAPI schema. AI coding assistants use these examples to generate response parsing code. Generic placeholder values like "string" or 123 produce less accurate code suggestions than responses that reflect your actual API output. Some documentation tools, including Fern, can generate realistic examples automatically, but even manually written examples significantly improve code generation accuracy.

Document all error responses with status codes and schemas. When AI tools know your API returns a 429 with retry-after headers, they can generate proper error handling code that respects rate limits. Error schemas are easy to overlook, but they're some of the highest-value content for AI-assisted code generation.

How Fern automatically generates and optimizes llms.txt for your API docs

Fern generates both llms.txt and llms-full.txt files automatically when building documentation sites. The generated files typically reduce token consumption by over 90% compared to AI tools parsing full HTML pages.

The generation process pulls from the same API specification that creates your SDK code and API reference. When an endpoint or authentication method is updated in your OpenAPI file or Fern Definition, the llms.txt files update automatically during the next documentation build. This eliminates drift between your human-readable docs and AI-optimized files.

Both files are available at any level of your documentation hierarchy — not just the site root. You can access /docs/llms.txt, /docs/ai-features/llms-full.txt, or any other section path, letting AI tools request only the portion of your documentation relevant to a specific task.

Query parameters provide additional control over AI-accessible content. Append ?lang=python to filter code examples to a specific SDK language, or use ?excludeSpec=true to remove OpenAPI and AsyncAPI specifications and focus AI tools on conceptual guides rather than raw schemas. These parameters can be combined: ?lang=python&excludeSpec=true.

Custom markdown tags control content visibility between human readers and AI agents. Wrap content in <llms-only> tags to include information exclusively in llms.txt files — useful for adding technical context like implementation details or architecture notes that would clutter the visual documentation. Use <llms-ignore> tags to exclude marketing CTAs, promotional content, or navigation hints that add no value for AI parsing. To exclude entire pages from llms.txt output, add noindex: true to the page's frontmatter — this removes the page from both AI-optimized files and site navigation while keeping it accessible by direct URL.

Final thoughts on llms.txt adoption for APIs

Developers are already writing integration code inside AI coding assistants. llms.txt ensures those tools have clean, structured access to your API documentation rather than struggling to parse HTML pages. When llms.txt is generated automatically from your API specification, your AI-optimized documentation stays current with every API change — and developers get accurate code suggestions on their first try instead of dealing with hallucinated endpoints.

Want to see automated llms.txt generation in action? Book a demo with the Fern team.

FAQ

How do I generate an llms.txt file for my API documentation?

The most reliable approach is to integrate llms.txt generation into your documentation build pipeline rather than writing the file manually. If you use Fern, llms.txt and llms-full.txt files are generated automatically from your API specification when building your documentation site, typically reducing token consumption by over 90% compared to HTML pages.

What's the difference between llms.txt and llms-full.txt?

llms.txt provides a compact overview with links to full documentation, making it ideal for larger documentation sites where including everything would exceed token limits. llms-full.txt embeds complete content directly in the file without requiring external fetches, which works best for APIs with concise documentation that fits entirely within AI context windows.

When should I use MCP instead of llms.txt for my API?

Use llms.txt when helping developers write better integration code faster through AI-assisted development tools like Cursor and GitHub Copilot. Choose MCP when AI agents should interact with your API autonomously without human developers writing code, as it requires building and maintaining a server that implements the protocol specification.

How often should I update my llms.txt file?

Update your llms.txt file whenever you publish documentation changes, and integrate generation into your build pipeline so updates happen automatically. AI coding assistants cache these files, so stale versions can persist for days or weeks, leading to incorrect code suggestions if your documentation has changed.

What information should I include in my OpenAPI specification for AI agents?

Include explicit parameter descriptions stating format requirements and validation rules, realistic example responses rather than generic placeholders, and all error responses with status codes and schemas. AI agents need this level of detail to generate accurate code — unlike human developers, they can't infer missing information from naming conventions or context.