robots.txt used to be a mostly search-engine conversation. Today the same file can influence whether automated AI crawlers can train on your content, whether search-oriented AI systems can discover it, and whether some platforms can retrieve it when a user asks a question.

The problem is that those are different use cases. A rule written to stop model training can accidentally block a search crawler. A wildcard rule written years ago for an old staging setup can catch new bots nobody considered at the time. And a team can assume that allowing one OpenAI or Anthropic bot means allowing all of them when each bot actually has its own purpose.

That is why the useful question is not simply Do we block AI? The useful question is Which crawler is this, what does it do, and do we want that specific behavior?

AI access makes more sense when you separate crawlers into three practical groups.

• Training crawlers collect public web content that may be used to improve or train future models.
• Search crawlers discover and index pages so an AI product can surface or cite them in search-style answers.
• User-triggered fetchers request a page because a person explicitly asked an AI assistant to retrieve or use it.

Those categories are not interchangeable. Blocking training can be a deliberate content-policy decision. Blocking a search crawler can reduce visibility in that product's search experience. User-triggered fetchers are different again, and some providers note that robots.txt may not apply to those user-initiated requests in the same way it applies to automated crawling.

OpenAI currently documents three distinct crawler or user-agent roles that matter to site owners.

• GPTBot is used to crawl content that may be used to improve and train OpenAI's generative AI foundation models. Blocking GPTBot is a training-data preference.
• OAI-SearchBot is used for ChatGPT Search. If a site opts out of OAI-SearchBot, its pages are not eligible to surface normally in ChatGPT search answers, although navigational links may still appear.
• ChatGPT-User is used for certain user-initiated actions. It is not the crawler that controls ChatGPT Search visibility, and OpenAI notes that robots.txt rules may not apply to these user-triggered requests.

This distinction matters because an old rule that blocks every OpenAI-related user agent is much broader than a rule that only opts out of model training.

Anthropic now makes the same separation explicit.

• ClaudeBot is used to collect web content that may contribute to model development and training.
• Claude-SearchBot crawls and analyzes online content to improve search result quality for Claude users.
• Claude-User is used when a person asks Claude to access content from the web as part of a user-directed request.

That means a blanket block on every Claude user agent can affect more than model training. If the business wants to opt out of training but still wants content discoverable in Claude search experiences, those rules should be separated deliberately.

Perplexity documents PerplexityBot as a crawler designed to surface and link websites in Perplexity search results. It is not described as a crawler for foundation-model training. Perplexity also uses Perplexity-User for user-initiated requests.

For a site that wants to appear as a source in Perplexity, blocking PerplexityBot is directly at odds with that goal. Perplexity also recommends verifying crawler identity with its published IP ranges rather than trusting a user-agent string alone, which is a useful reminder that robots.txt and bot verification solve different problems.

Google is the place where older AI-crawler advice is most likely to cause confusion.

Google Search features such as AI Overviews and AI Mode are part of Google Search. Google says access for those search features is controlled through Googlebot and the same Search crawling and preview controls used elsewhere in Google Search.

Google-Extended is different. It is used for Gemini apps and certain other Google AI systems. Blocking Google-Extended does not remove a page from normal Google Search rankings, but it is not the switch for appearing or not appearing in AI Overviews. If your goal is to manage Google Search AI visibility, Googlebot access and Search preview controls are the relevant layer.

The most common accidental block is usually not a carefully written rule for a named AI crawler. It is a broad wildcard rule.

A User-agent: * group can apply to bots that do not have a more specific matching group. If that wildcard group disallows an important path - or the entire site - newly introduced crawlers can inherit the restriction even though nobody intentionally made a decision about them.

This is especially common after migrations, staging launches and security changes. A rule that made sense for a temporary environment survives into production, or an ecommerce platform adds broad restrictions that nobody reviews when new AI crawlers appear.

The tester is useful because it resolves those rules crawler by crawler instead of relying on a quick visual scan of the file.

The tool above does not crawl your website and it does not contact the AI companies. It analyzes the robots.txt content you paste into the browser and evaluates which rules apply to the supported user agents.

That distinction is important. A robots.txt tester can answer questions such as:

• Is this crawler explicitly allowed or disallowed?
• Is it inheriting a wildcard rule?
• Does a more specific user-agent group change the outcome?
• Is the entire site blocked with Disallow: /?
• Are different AI crawlers being treated differently, intentionally or accidentally?

It cannot confirm that a provider actually crawled your site, whether your content was indexed, or whether an AI answer will cite you. Access is a prerequisite, not a visibility guarantee.

robots.txt is a published instruction file. Compliant crawlers read it and follow the rules they support, but the file does not physically prevent a request from reaching your server.

If the requirement is this system must not access this content, use controls that actually enforce access: authentication, server rules, CDN or WAF policies, rate limits, or IP-based controls where appropriate.

This also means bot identity matters. A request can claim almost any user-agent string. For high-confidence enforcement or allowlisting, use the provider's published IP information or other verification guidance when available.

1. Open the live robots.txt file. Visit yourdomain.com/robots.txt and copy the complete file.
2. Run the tester. Look for explicit blocks and inherited wildcard rules.
3. Classify each crawler by purpose. Training, search and user-triggered retrieval are different decisions.
4. Compare the result to the business goal. A publisher may want to block training while an ecommerce brand may prioritize discoverability. There is no universal rule.
5. Change only what you intend to change. Avoid broad copy-paste blocks that affect unrelated crawlers.
6. Test again after editing. One missing slash or overlapping group can change the result.
7. Document the decision. Add comments or internal documentation explaining why each crawler is allowed or blocked.
8. Review periodically. Bot names, purposes and provider guidance change over time.

• Treating every AI bot as a training crawler. Search crawlers and user fetchers may serve completely different functions.
• Assuming one provider has one bot. OpenAI and Anthropic both document multiple user agents for separate use cases.
• Using Google-Extended as an AI Overview switch. Google Search AI features are governed through Googlebot and Search controls.
• Blocking a wildcard and forgetting about it. New bots can inherit old restrictions.
• Confusing crawl access with citation. Allowing a bot does not guarantee indexing, retrieval or a mention in an answer.
• Using robots.txt as a firewall. It expresses crawler preferences; it does not enforce security.

Allowing the right crawler only means the page can potentially be retrieved. It says nothing about whether the content is easy to understand, extract or cite.

After access, the next layers are familiar SEO fundamentals: important pages should be internally linked, the page should clearly answer the query it targets, entities and authorship should be understandable, and structured data should accurately reflect visible content.

That is why crawler testing pairs naturally with the AEO Checker, Schema Markup Generator, XML Sitemap Checker and llms.txt Generator. Access comes first, but access alone is not an AI visibility strategy.

Retest whenever the file changes, after a migration, after a CDN or WAF change, and whenever a major AI provider introduces a new crawler or changes the documented purpose of an existing one.

For an actively managed site, a lightweight quarterly review is sensible even when nobody remembers changing robots.txt. The file may be generated by the CMS, modified by an app, replaced during a theme or platform migration, or affected by infrastructure rules outside the SEO team's normal workflow.

The goal is not to allow every bot. The goal is to make sure the current behavior matches a current decision.