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How OpenAI Crawls and Indexes Your Website

How OpenAI crawls, indexes, and trains on your content—and how to prepare for all three

Apr 28

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Thenuka Karunaratne

How OpenAI Crawls and Indexes Your Website

The architecture of online visibility is being rewritten.

For decades, search visibility meant optimizing for Google’s index: ensuring crawlability, earning backlinks, and ranking in blue links. As large language models (LLMs) become default interfaces for information retrieval, a new layer of discoverability is emerging—one that blends training data, indexed sources, and real-time retrieval.

OpenAI is at the forefront of this shift. With three separate bots operating across its platform, visibility today means understanding the distinct roles each one plays:‍‍

‍GPTBot crawls content for model training.‍
OAI-SearchBot indexes content for search results inside ChatGPT.‍
ChatGPT-User accesses content on demand during user-initiated browsing or plug-in activity.

Each bot has different rules, capabilities, and strategic implications. To ensure your site is visible in this new environment, you need to consider how your content feeds, shapes, and surfaces within a trillion-token ecosystem.

1. Training: Inclusion in OpenAI’s foundational models

GPTBot is OpenAI’s crawler for model training. It collects publicly available data to expand the model’s understanding of the world, improving its ability to generate accurate, comprehensive responses across topics.

By default, GPTBot respects your site’s robots.txt file. If your goal is inclusion, ensure it has access:

User-agent: GPTBot Allow: /

If you prefer to exclude your site from training, use:

User-agent: GPTBot Disallow: /

You can also allow or disallow specific directories. For example:

User-agent: GPTBot Allow: /docs/ Disallow: /checkout/

Note: Blocking GPTBot only affects future training runs. If your content was previously ingested, it remains part of the model.

Being included in GPTBot’s crawl isn’t just about visibility. It shapes how your brand is represented in outputs. We’ve seen tools and frameworks earn default mentions in generated answers, without ever ranking in Google, because they were well-represented in training data.

2. Indexing: Real-time visibility in ChatGPT search

OAI-SearchBot supports ChatGPT’s live search capabilities, including inline citations and real-time answers. This bot builds and maintains an internal index that supplements the model’s knowledge with up-to-date web data.

This is where source attribution happens. For example, when ChatGPT returns a cited paragraph with a clickable link, that’s OAI-SearchBot at work.

Like GPTBot, it can be allowed or disallowed independently:

User-agent: OAI-SearchBot Allow: /

Updates to robots.txt are typically honored within 24 hours.

Optimizing for OAI-SearchBot requires attention to:

Clear, scannable content structure
High-authority backlinks and mentions
Fast, renderable pages with minimal JavaScript blockers

3. Browsing: On-demand retrieval via user interaction

ChatGPT-User is triggered when a user asks a Custom GPT to fetch content, uses a plug-in, or interacts with external web tools inside ChatGPT. While this isn’t a crawler in the traditional sense, it functions like a browser agent for LLM users.

You can control access in robots.txt just like the others:

User-agent: ChatGPT-User Allow: /

This type of access powers functionality like ChatGPT’s web browsing tool, or integrations that pull real-time product specs, documentation, or support content.

Allowing this bot ensures your site can respond to direct user-driven requests within the ChatGPT interface.

Structuring content for LLMs

Whether content is trained on, indexed, or retrieved in real time, visibility isn’t just about access. It’s about interpretability.

OpenAI’s systems parse full page renders—including HTML, JavaScript, images (via OCR), and transcripts. However, LLMs are selective. They prioritize content that is:

Structured: Use schema markup (FAQ, Article, Product) to clarify intent.
Explanatory: Clarity, conciseness, and coherence matter more than stylistic flair.
Distinctive: Avoid thin, templated, or derivative content. Training models value novelty and specificity.

We often use internal tools to audit how well content is likely to embed in LLMs.

What you should know about llms.txt

A new file is gaining traction in AI indexing: llms.txt.

Similar to how robots.txt and sitemap.xml became standard for guiding traditional web crawlers, llms.txt is designed to support AI systems—specifically, large language models (LLMs)—in navigating and understanding website content at inference time.

llms.txt is a markdown-formatted file placed at the root of your domain (e.g., yourdomain.com/llms.txt). It provides a structured overview of your site’s purpose and most relevant machine-readable pages. Its goal is to help LLMs quickly determine:

What your website is about
Where to find authoritative, structured resources (like API docs, guides, or reference material)
Which sections can be skipped if context limits are tight

Think of it as a curated, human- and AI-readable table of contents—optimized not for humans browsing your homepage, but for machines trying to load meaningful context fast.

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

While llms.txt is a high-level index of your documentation with links and short descriptions, llms-full.txt contains the full content of those pages in a single file. This can be helpful for tools or IDEs that support Retrieval-Augmented Generation (RAG), enabling them to load the entire knowledge base into memory.

Use llms.txt for navigation.
Use llms-full.txt for compact, full-page reference (if your site is small enough to fit into an LLM’s context window).

Most companies publish both.

Who supports llms.txt?

Even though llms.txt is not yet an official standard (unlike robots.txt or sitemap.xml), adoption is growing quickly.

Organizations like Anthropic, Cloudflare, Cursor, Perplexity, LangChain, ElevenLabs, and others have already implemented it. Directories like llms.txt hub and llmstxt.cloud track hundreds of live implementations across AI, dev tools, and infrastructure platforms.

It’s worth noting: OpenAI, Claude, and Perplexity have not officially stated that they use llms.txt files in production. Today, it’s a proactive optimization, making the trajectory clear.

Why bother if it’s not official?

The file llms.txt itself may not yet be universally recognized, but its benefits are grounded in how LLMs work. These models operate within limited context windows and rely heavily on structured content to make accurate inferences. Even without official support, a well-crafted llms.txt file can:

Improve reasoning performance in tools like Cursor and Claude Code
Make your site easier to use in RAG pipelines
Boost interpretability in prompt-based retrieval (e.g., sharing links in ChatGPT or Claude)

And perhaps most importantly: LLMs still rely on traditional signals like Google rankings to determine what to cite.

A recent study by Grow & Convert found that brands ranking on the first page of Google were mentioned by AI tools like ChatGPT and Perplexity up to 77% of the time for bottom-funnel queries. Ranking in the top 3 boosted that likelihood to 82%.

TL;DR: Google SEO still matters—a lot. And files like sitemap.xml and robots.txt remain critical.

So, should you use llms.txt?

Yes, if you want to future-proof your AI visibility and improve how your content is used in LLM-powered interfaces. The key is to use it in addition to, not instead of, foundational SEO practices.

Best practices:

Keep your sitemap.xml up to date. This is still essential for crawlability and discoverability.
Add a clear, well-structured llms.txt. Follow the markdown spec with H1 title, summary, sections, and prioritized links.
Include an llms-full.txt if your documentation is concise enough to fit in LLM contexts (or you’re using an IDE or RAG pipeline).
Use generation tools like Firecrawl, dotenvx’s CLI, or Mintlify to build your file based on existing site content or your sitemap.

A well-crafted llms.txt might include:

# Acme AI - Tools for programmatic document summarization and retrieval ## Docs - [API Quickstart](/api/quickstart.md) - Setup guide and authentication - [Use Cases](/guides/use-cases.md) - Common implementation scenarios - [Error Reference](/reference/errors.md) - Troubleshooting codes and tips ## Optional - [Company Blog](/blog/company-history.md) - Brand story and milestones

Files like llms.txt won’t replace traditional SEO, but they do fill a gap that sitemap.xml and robots.txt can’t. One helps you get crawled. The other helps you get understood.

As LLMs become the front door to the internet, helping them parse your content faster and smarter will only become more important.

Closing the loop: Prepare for every layer

Visibility in OpenAI isn’t about blue links. It’s about presence in the model’s worldview.

Your content is now a training input, a citation source, and an interactive response layer. It informs the answers given to millions of users across enterprise tools, developer environments, and general search.

Consider this: when users ask ChatGPT for "the best open-source web analytics platforms," the model isn’t just retrieving links. It’s synthesizing. If your brand has been seen, cited, and structured well enough to be included, you become part of the answer.

At daydream, we help companies prepare their content for the new era of AI discovery. From crawlability to LLM-specific formatting, our team ensures your site is both accessible and influential in model-generated answers. If you’re ready to optimize for how people actually search today, let’s talk.