LLMs.txt: The New Standard for Optimizing Interaction Between Websites and AI

Since the advent of advanced language models (Large Language Models or LLMs) like ChatGPT, Claude, and Gemini, the web ecosystem has gradually adapted to facilitate interactions between these technologies and online content. In this rapidly evolving landscape, a new standard has emerged: the llms.txt file. This innovation promises to transform how artificial intelligence accesses, understands, and interprets website information. While robots.txt and sitemap.xml have long structured relationships between websites and search engines, the llms.txt file has established itself as a gateway specifically designed for AI. Let’s explore this emerging standard, how it works, and why its adoption could become crucial for optimizing website visibility in the artificial intelligence era.

What is the LLMs.txt File?

The llms.txt file is a proposed standard designed to help artificial intelligence models better understand and leverage website content. Unlike other standard web files (robots.txt or sitemap.xml), llms.txt is specifically designed to address the needs and constraints of large language models.

This file, written in Markdown, provides a structured and simplified view of a website’s content. It acts as a guide that directs AI to essential information by offering:

A structured overview of the site’s content
Clear and precise navigation paths
Context for understanding relationships between different content

The llms.txt file comes in two distinct forms:

The /llms.txt file: a simplified view of documentation navigation to help AI systems quickly understand a site’s structure
The /llms-full.txt file: a comprehensive file containing all documentation in one place

This standard addresses a major challenge for LLMs: their limitations in “context window” size, which prevent them from processing an entire website at once. By providing a clear and streamlined structure, llms.txt allows AI to efficiently retrieve relevant information without being overwhelmed by non-essential elements like navigation, advertisements, or JavaScript scripts.

Origin and Evolution of the LLMs.txt Standard

The llms.txt standard was proposed by Jeremy Howard, AI researcher and co-founder of fast.ai, in September 2024. This initiative was born from a simple observation: large language models face significant difficulties in effectively leveraging existing web content.

The origin of this proposal is based on several observations:

LLMs’ context windows are too limited to ingest entire websites
Converting complex HTML (with navigation, advertisements, JavaScript) into text usable by AI is difficult and imprecise
AI needs more concise and structured information than human readers

The FastHTML project was one of the first to adopt this proposal by integrating automatic generation of Markdown files for all its documents, making its content more accessible to artificial intelligence.

Since its initial proposal, the standard has gained popularity with adoption by several notable companies and projects including:

Anthropic (with Claude)
Cloudflare
ElevenLabs
Perplexity
LangChain and LangGraph

The evolution of this standard has been accompanied by the development of tools like llmstxt (by dotenv or Firecrawl) that facilitate the automatic generation of these files from existing sitemaps. The creation of the llmstxt.org website also marks an important step in the adoption of this standard, centralizing resources and listing sites that have implemented it.

Differences Between LLMs.txt, robots.txt, and sitemap.xml

Although these three files may appear similar, they serve fundamentally different purposes in the web ecosystem:

robots.txt:

Purpose: Control search engine crawler access to different parts of a site
Target audience: Indexing robots like Googlebot
Main function: Indicate which pages can or cannot be explored
Format: Plain text with specific directives (Allow, Disallow)
Does not provide: Context or help in understanding content

sitemap.xml:

Purpose: List all indexable pages on a site
Target audience: Search engines
Main function: Facilitate complete site indexation
Format: Structured XML
Does not provide: Context or hierarchical organization of information

llms.txt:

Purpose: Facilitate content understanding by AI
Target audience: Large language models and AI agents
Main function: Provide structure and context for content interpretation
Format: Hierarchical Markdown
Additionally provides: Summaries, relationships between content, and AI-adapted versions

The llms.txt file is not intended to replace robots.txt or sitemap.xml, but to complement them by specifically addressing the needs of artificial intelligence. While robots.txt focuses on access permissions and sitemap.xml on comprehensive indexing, llms.txt focuses on understanding and intelligent exploitation of content.

Structure and Syntax of an LLMs.txt File

An llms.txt file follows a precise Markdown structure, with mandatory and optional elements:

Basic Structure

# Site or Project Title

> Brief description of the site or project

Optional details about the project

## Section Name

- [Link Title](Link URL): Optional link description

## Optional

- [Link Title](Link URL): Optional link description

Mandatory Elements

An H1 title: The name of the project or site (the only truly mandatory section)
A blockquote: A concise summary of the project containing key information necessary to understand the rest of the file

Optional Elements

Markdown sections (paragraphs, lists, etc.): Detailed information about the project
Sections delimited by H2 headers: Containing “file lists” with URLs where additional details are available
“Optional” section: A special section indicating that the URLs provided can be ignored if a shorter context is needed

Link Format

Each list item must contain:

A mandatory Markdown link: [title](url)
Optionally followed by : and a description of the link

This hierarchical structure allows language models to easily navigate content and understand the relative importance of different information.

Files referenced in llms.txt are generally Markdown versions (with .md extension) of the original web pages, providing streamlined content that AI can easily leverage.

Advantages of the LLMs.txt File for AI and Websites

Implementing the llms.txt file offers numerous advantages for both artificial intelligence and website owners:

For AI Models

Enhanced Understanding: The structured format allows AI to quickly grasp a site’s essence and organization
Efficient Information Retrieval: Direct paths to relevant content reduce search time
Better Contextualization: Clear hierarchy and descriptions help establish relationships between different information
Circumventing Context Limitations: By providing a streamlined version of content, llms.txt allows AI to process more useful information
Optimized Format: Markdown is an ideal format for LLMs, easier to analyze than complex HTML

For Websites

Increased Visibility in AI Responses: A well-structured site with llms.txt is more likely to be correctly cited by AI assistants
Control Over Content Presentation: Owners can highlight information they consider essential
Reduced Misinterpretations: By guiding AI, the risk of content misunderstanding is limited
Adaptability to Various Sectors:
- Businesses: clear presentation of products and services
- Education: structured organization of educational resources
- Development: accessible technical documentation
Preparation for the Web’s Future: Anticipating the evolution of interactions between users and AI

This approach allows website owners to gain an advantage in optimizing their content for AI assistants, while improving the experience of users interacting with these systems.

How to Create and Implement an LLMs.txt File?

Creating and implementing an llms.txt file for your website can be done manually or using automated tools. Here are the steps to follow:

Manual Method

Create the File:
- Open a text editor (like Notepad++, Visual Studio Code, etc.)
- Write the content following the Markdown structure described above
- Start with your site’s title and a concise description
- Organize your content into relevant sections
Prepare Markdown Versions:
- For each important page on your site, create a streamlined Markdown version
- Place these Markdown files at the location indicated in your links
- A recommended convention is to use the same URL as the original page with the .md extension added
Publish Online:
- Save your file as llms.txt
- Place it at the root of your website (accessible via yoursite.com/llms.txt)
- If you also create a full version, name it llms-full.txt
Verify Accessibility:
- Test that your file is accessible by visiting its URL
- Check that links to Markdown versions work correctly

Using Automated Tools

Several tools have been developed to facilitate the creation of llms.txt files:

llmstxt by dotenv: An open-source command-line tool that generates an llms.txt file based on a site’s sitemap.xml
- Installation via pip: pip install llmstxt
- Usage: llmstxt generate --sitemap https://yoursite.com/sitemap.xml
Firecrawl: A service that analyzes your site and automatically generates an llms.txt file
- Access via API or web interface
- Ability to customize sections and structure
Mintlify: A documentation platform that natively integrates llms.txt generation
- Particularly useful for technical projects and APIs
CMS Integration: Some content management systems are beginning to offer plugins or extensions to automatically generate these files

Best Practices

Keep Updated: Regularly update your llms.txt file when your site structure changes
Be Selective: Only list truly important content in the main part
Use the “Optional” Section for secondary content
Provide Clear Descriptions for each link
Test with Different AI Models to verify that your content is correctly interpreted

By following these steps, you’ll make your site more accessible and understandable to artificial intelligence, improving its visibility in a web landscape increasingly dominated by AI interactions.

Real-World Examples of LLMs.txt Usage

Several organizations and projects have already adopted the llms.txt standard, each adapting it to their specific needs. Here are some concrete examples:

Cloudflare

Cloudflare uses llms.txt to structure its extensive technical documentation. Their implementation allows AI to easily access information about their various services and APIs. The main file directs to well-defined sections such as:

Product documentation
Implementation guides
API references
Developer resources

This organization allows AI models to precisely answer technical questions about Cloudflare services.

Anthropic (Claude)

Anthropic has implemented llms.txt for its prompt library and documentation. This approach is particularly interesting as it shows how an AI creator optimizes its own content for AI assistants. Their file includes:

Documentation on prompting best practices
Examples of effective prompts
Claude usage guides
Application examples

This implementation facilitates self-referencing and allows Claude to better understand how to interact with users.

LangChain and LangGraph

These projects in the AI ecosystem use llms.txt to make their documentation more accessible. The official LangChain site presents distinct versions for Python and JavaScript:

https://python.langchain.com/llms.txt
https://js.langchain.com/llms.txt

This approach allows developers using AI assistants to code more efficiently with these libraries.

FastHTML

The FastHTML project uses a concise llms.txt file with a clear structure:

# FastHTML

> FastHTML is a python library that combines Starlette, Uvicorn, HTMX, and FastTags to create server-rendered hypermedia applications.

Important notes:
- Although its API is inspired by FastAPI, it is not compatible with FastAPI syntax
- FastHTML is compatible with native JS web components and vanilla JS libraries

## Docs
- [FastHTML quick start](URL): An overview of FastHTML features
- [HTMX reference](URL): Description of all HTMX attributes

## Examples
- [Todo list application](URL): Detailed guide to a complete CRUD application

## Optional
- [Complete Starlette documentation](URL): Starlette documentation useful for FastHTML development

Waifu AI OS Project

A more complex use case with a particularly comprehensive llms-full.txt file including:

Code from various sub-projects
Detailed documentation
Research texts on topics such as tokenomics and quantum computing

This project shows how llms.txt can be used for sophisticated technical projects requiring in-depth understanding of multiple components.

Commercial Applications

E-commerce and business sites are also beginning to adopt this standard to:

Clearly present their products and services
Structure their privacy policy and terms of use
Organize their FAQs and customer support

These examples demonstrate the versatility of the llms.txt standard and its adaptability to different types of websites, whether technical, commercial, or informational.

Challenges and Limitations of the LLMs.txt Standard

Despite its advantages, the llms.txt standard faces several challenges and limitations that could affect its widespread adoption:

Technical Challenges

Maintenance: LLMs.txt files require regular updates to stay synchronized with site content, which can represent additional workload
Content Duplication: Creating Markdown versions of existing web pages creates redundancy that must be effectively managed
Limited Resources for Small Sites: Small structures may lack resources to implement and maintain these files
Loss of Visual and Interactive Elements: Conversion to Markdown eliminates visual and interactive elements that may be essential to understanding content

Conceptual Limitations

Lack of Automatic Discovery: Currently, most AI models do not automatically discover llms.txt files without explicit intervention
Unofficial Standard: It’s a proposal, not a standard officially recognized by an organization like the W3C
Absence of Validation: Unlike formats such as XML, validation tools do not yet exist to ensure file compliance
Difficulty Representing Complex Content: Certain types of content (interactive graphics, web applications, etc.) are difficult to effectively represent in Markdown

Ethical and Legal Issues

Copyright: Creating alternative versions of content raises questions about intellectual property and usage rights
Economic Model: How will site owners be compensated for the use of their data by commercial AI?
Information Control: Risk of manipulating AI perception by presenting a biased version of content
Protection of Sensitive Data: How to ensure sensitive information is not inadvertently exposed through these files?

Adoption Challenges

Web Practice Inertia: Web developers are accustomed to other standards and may be reluctant to adopt a new one
Need for Proof of Effectiveness: Without clear demonstration of benefits, adoption may remain limited
Implementation Fragmentation: Risk of seeing different interpretations of the standard emerge
Compatibility with Existing Technologies: How to integrate llms.txt into existing web development workflows?

For the llms.txt standard to reach its full potential, these challenges will need to be addressed by the web community and AI developers in the coming years.

Potential Impact on SEO and AI Access

The emergence of the llms.txt standard could have significant repercussions on referencing strategies and how AI interacts with web content.

Transformation of Traditional SEO

Emergence of “AI-SEO”: A new branch of search engine optimization specifically oriented toward AI optimization could develop
Evolution of Performance Metrics: Beyond Google rankings, frequency of citation in AI responses would become an indicator of visibility
Modification of Writing Practices: Content could be structured differently to appeal to both humans (classic sites) and AI (Markdown versions)
Complementarity with Classic SEO: Optimization practices for search engines and AI could mutually reinforce each other

Democratization of AI Access

Inclusion of More Diverse Sources: Smaller or niche sites could be better represented in AI responses thanks to a clear structure
Reduction of Information Bias: Better understanding of content could limit overrepresentation of dominant sources
Facilitated Access to Specialized Content: Technical or complex information would be more easily usable by AI
Improved Multilingualism: Standardized structure could facilitate understanding of content in different languages

Impacts on Content Creators

New Skills Required: Writers and web developers will need to acquire skills to optimize content for AI
Change in Distribution Strategies: Visibility via AI could become as important as visibility via search engines
Valuation of Structured Content: Well-organized and clearly structured content would be advantaged
Opportunities for Automated Tools: Development of solutions to automatically generate and maintain llms.txt files

Impacts on User Experience

More Precise AI Responses: Users would receive more accurate and relevant information when querying AI assistants
More Numerous and Accurate Citations: Sources would be better identified and cited
More Direct Access to Information: AI could more efficiently direct users to relevant sources
Mediation Between Users and Web Content: AI could become privileged intermediaries between users and websites

This new paradigm could profoundly modify the web ecosystem and how information circulates within it, with AI playing an increasingly central role in mediating between content and its users.

Future Perspectives for the LLMs.txt Standard

The llms.txt standard is just beginning, but its potential for evolution and adoption seems promising. Here are some future perspectives for this standard:

Possible Technical Evolutions

Native Integration in CMS: Platforms like WordPress, Drupal, or Shopify could integrate automatic generation of llms.txt
Official Standardization: Recognition by organizations like the W3C could establish llms.txt as an official web standard
Format Extensions: The standard could evolve to include additional metadata such as:
- Confidence indicators for information
- Last update dates
- Semantic relationships between content
Validation and Optimization Tools: Emergence of specialized tools to verify and improve llms.txt files

Industry Adoption

Automatic Discovery by AI: Major models could be trained to automatically search for llms.txt files
Adoption by Search Engines: Google, Bing, and others could use llms.txt as a signal for their own AI
Ecosystem of Tools and Services: Development of services specialized in creating and optimizing these files
Integration into Standard SEO Practices: Inclusion of llms.txt in SEO checklists and audit tools

Potential Innovations

Dynamic llms.txt: On-the-fly generation of files adapted to the context of the AI query
Bidirectional Interaction: AI could communicate their specific needs to sites via standardized protocols
Enhanced Version Beyond Markdown: Integration of structured elements like JSON-LD for specific data
Ecosystem of Related Services: Analysis platforms to measure the impact of llms.txt files on visibility in AI responses

Long-term Societal Impact

New Relationship Between Websites and AI: Transition from passive exploration to active and structured communication
Democratization of Information Access: Better representation of diverse sources in AI responses
Emergence of Ethical Standards: Development of best practices to ensure fair and factual representation
More Accessible Web: Markdown versions could also improve accessibility for people with disabilities

Economic Perspectives

New Attention Economy: Growing importance of being well-cited by AI
Innovative Compensation Models: Potential development of systems compensating content creators for the use of their data by AI
New Professions: Emergence of specialists in AI optimization, distinct from traditional SEO referrers
Competitive Advantage: Early adoption could constitute a significant advantage for innovative companies

The llms.txt standard potentially represents a fundamental evolution in how the web adapts to the artificial intelligence era. Although still emerging, it could become as crucial as robots.txt or sitemap.xml were for the search engine era, redefining the relationship between websites, AI, and end users.