What is llms.txt? The new standard for AI agents

For 30 years, the internet ran on a simple agreement called robots.txt. It was a “Do Not Enter” sign for clumsy search spiders.

But in 2025, we aren’t just dealing with spiders. We are dealing with readers.

AI agents—from ChatGPT’s crawler to autonomous research bots—don’t just want to index your links. They want to understand your content. And right now, the modern web is a nightmare for them. It is bloated with JavaScript, popups, cookie banners, and complex DOM structures that waste tokens and confuse context.

Enter llms.txt.

This emerging standard is the “Welcome Mat” for the AI age. It is a proposal to give AI agents exactly what they want: clean, structured, markdown-formatted context about your website.

If you want to survive the shift to Generative Engine Optimization (GEO), implementing an llms.txt file is no longer optional. It is the single highest-ROI technical change you can make today.

Table of contents

• The problem with HTML in the AI age
• What is llms.txt exactly?
• The anatomy of the file
• How to implement llms.txt
• Tools to generate llms.txt
• The business case for clean context
• Robots.txt vs LLMs.txt
• Monitoring agent behavior

The problem with HTML in the AI age

To understand why llms.txt is necessary, you have to understand how Large Language Models (LLMs) “read.”

When a traditional crawler (like Googlebot) visits your site, it looks for links and keywords. It ignores the visual noise.

When an AI agent (like a RAG system) visits your site, it is trying to ingest information. But the modern web is hostile to ingestion.

The “Token Tax” of the modern web:

1. Boilerplate: Headers, footers, and navbars are repeated on every page. An AI reading 10 pages reads your navbar 10 times. This wastes context window space (tokens) and money.
2. DOM Noise: <div>, <span>, class names, and scripts are gibberish to an LLM trying to find the answer to a question.
3. Visual vs. Semantic: A popup might visually obscure content, or a “Read More” button might hide it. AI struggles to “click” things.

The result? Hallucinations.

When an AI tries to scrape a JavaScript-heavy page, it often gets a fragmented mess of text. It has to fill in the blanks. That is when it makes things up about your pricing, your features, or your history.

llms.txt solves this by creating a dedicated API for knowledge.

What is llms.txt exactly?

The llms.txt proposal (popularized by Jeremy Howard and the AI community) is a convention for placing a file at the root of your domain (e.g., yourdomain.com/llms.txt).

It serves two purposes:

1. The Map: It tells AI agents where to find the “AI-ready” version of your website.
2. The Context: It provides a concise summary of who you are and what you do, injecting immediate context into the model’s prompt.

Think of it as a sitemap for robots that read.

Instead of forcing the AI to guess which pages are important, you explicitly list them. Instead of forcing it to parse HTML, you point it to clean Markdown files.

The anatomy of the file

The standard is simple. It typically lives at the root and points to a more comprehensive markdown file.

Example https://cloro.dev/llms.txt:

# cloro - AI Brand Monitoring Platform

> cloro is the leading platform for tracking brand visibility across Large Language Models (LLMs) like ChatGPT, Claude, and Perplexity.

## Key Pages

- [Pricing](https://cloro.dev/#pricing)

Key components:

• H1 Title: clearly states the entity name.
• Blockquote Summary: A “system prompt” for your brand. This is often the first thing the AI reads. Make it count.
• Links: Direct pointers to markdown (.md or .txt) versions of your most critical content.

How to implement llms.txt

Implementing this doesn’t require a full site redesign. You are essentially creating a “shadow site” of text files.

Step 1: Create your “Shadow Content”

You need to convert your key pages into Markdown. This removes the HTML noise.

Your pricing.html might be 50kb of code. Your pricing.md should be 2kb of text.

Example pricing.md:

# Pricing Plans

## Hobby Plan

- Cost: $29/month
- Features: 500 queries, Daily updates.

## Business Plan

- Cost: $99/month
- Features: 5,000 queries, Hourly updates.

Step 2: Consolidate into llms-full.txt

Many proposals suggest creating a single, massive text file (llms-full.txt) that contains all your core documentation concatenated together.

Why? Because RAG (Retrieval Augmented Generation) systems love fetching a single file. It reduces HTTP requests and ensures the AI gets the entire context in one go.

Step 3: Deploy the root file

Place the llms.txt file at your root directory. Ensure your server sends the correct text/markdown or text/plain headers.

Step 4: Advertise it

While widespread auto-discovery is still evolving, you can manually feed this URL to custom GPTs, Claude Projects, and other AI agents to instantly “train” them on your documentation.

Tools to generate llms.txt

If creating these files manually feels tedious, several tools have emerged to automate the process. These can crawl your site and generate the markdown structure for you.

• Keploy: A one-click generator that scans your URL and builds the file instantly. Great for simple sites.
• Writesonic: Offers a structured text generator specifically designed to enhance LLM training and inference.
• Gushwork: Provides more granular control, allowing you to select specific site areas to include or exclude.
• Fibr AI: Helps you create a file that specifically permissions bots like GPTBot and ClaudeBot.

Note: While these tools are excellent for getting started, we recommend manually reviewing the output to ensure your most critical “shadow content” is accurate.

The business case for clean context

Why spend engineering hours on this?

1. Reduced Hallucinations When you provide clean text, the “noise-to-signal” ratio drops to zero. The AI doesn’t get confused by your cookie banner and think you sell cookies. It reads your markdown and knows you sell software.

2. Improved “Citation Authority” Search engines like Perplexity utilize RAG. If their scraper can parse your content faster and cheaper than your competitor’s heavy React app, you get the citation.

3. Token Economy If an AI agent has a context window of 128k tokens, you don’t want to waste 50k of them on HTML boilerplate. By serving Markdown, you fit more of your valuable content into the model’s “brain” at once.

4. Future-Proofing OpenAI, Anthropic, and Google are actively looking for ways to reduce web scraping costs. It is highly probable that future crawlers will prioritize sites that offer an llms.txt simply because it saves them millions in compute costs.

Robots.txt vs LLMs.txt

It is crucial to understand that these two files serve different masters.

Feature	robots.txt	llms.txt
Audience	Crawlers (Googlebot)	Agents (ChatGPT, Claude)
Function	Exclusion (Do not go here)	Inclusion (Read this first)
Format	Rules & Disallow paths	Markdown & Links
Goal	Indexing control	Context injection
Parsing	Machine logic	Semantic understanding

Do not replace your robots.txt. You still need it to block sensitive admin pages. llms.txt is an additive layer for the semantic web.

Monitoring agent behavior

Once you implement llms.txt, how do you know if it’s working?

You need to track if AI agents are actually accessing these files and—more importantly—if they are using that data in their responses.

This is where cloro comes in.

By monitoring your brand mentions, you can correlate the deployment of your llms.txt file with an increase in citation accuracy.

The feedback loop:

1. Deploy llms.txt.
2. Wait 2 weeks.
3. Check cloro for mention quality.
4. If hallucinations persist, refine your markdown descriptions.

The internet is transitioning from a library of documents to a training set for minds. llms.txt is your way of ensuring your chapter in that training set is accurate, clean, and impossible to ignore.