What to paste
Paste the exact production robots.txt file and include the paths that matter to your business, such as article URLs, documentation pages, product pages, and account paths.
Audit existing files
Robots.txt AI Crawler Analyzer
Paste your robots.txt and test whether AI training bots, AI search crawlers, and user-triggered fetchers are allowed or blocked for important URL paths.
Input
AI crawler access result
This analyzer implements the common longest-match robots.txt rule pattern for quick planning. Use Google Search Console and server logs for production verification where available.
AI crawler access control
Practical notes for Robots.txt AI Crawler Analyzer
Robots.txt AI Crawler Analyzer is maintained for website owners, publishers, SaaS documentation teams, ecommerce operators, and SEO teams who need crawler access auditing. The goal is to help visitors complete a real task and leave with a robots.txt draft, llms.txt draft, crawler audit note, or crawler policy decision record, not only read a generic summary.
What to compare
Compare training crawler access against AI search crawler access. A site may intentionally block training while allowing AI search discovery and user-triggered retrieval.
What to save
Save the audit summary next to the policy decision. It is useful when a teammate later asks why a crawler was blocked or why AI answer visibility changed.
Before relying on this page
- Check one public path and one sensitive path.
- Look for duplicate user-agent groups that create confusing results.
- Test after deployment because CDN rewrites can serve a different robots.txt than the source file.
Field workflow
How to turn Robots.txt AI Crawler Analyzer into a maintainable crawler decision
A crawler policy is valuable only when someone can explain it later. Use the notes below to turn this page into a saved decision record instead of a one-time copied snippet.
1. Write the policy intent first
Before touching robots.txt, write one plain-language sentence: "We want normal search visibility, we want AI answer visibility for public pages, and we do not want training crawlers to collect licensed archives." If the intent is not clear, the file often becomes a long block list that nobody maintains. A short intent statement also helps you decide whether a future crawler belongs with training, search, user-triggered retrieval, or normal indexing.
2. Test representative URLs
Do not test only the homepage. Choose one article or documentation page, one product or pricing page, one sitemap URL, one login or account path, and one intentionally private path. The file should express different outcomes where the business logic is different. This matters because broad rules can accidentally block useful search pages while still failing to protect sensitive paths that need authentication.
3. Keep a change note
Save the date, the old rule, the new rule, and the reason for the change. If traffic drops, citations disappear, or a crawler starts hitting expensive paths, that note makes debugging much faster. A good note names the crawler role, the URL group affected, and the review owner who can change the policy later.
Operational checklist for this page
- Confirm whether Robots.txt AI Crawler Analyzer is being used to create a new file, review an existing file, or document a content strategy decision.
- Compare the result with live crawler documentation, because user-agent names, crawler roles, and AI search behavior change over time.
- Keep Googlebot and other ordinary search crawlers separate from AI-specific crawler groups unless the site intentionally wants lower search visibility.
- Use authentication, server access rules, or a WAF for private content. Robots.txt is a public preference file, not a security boundary.
- After publishing, open the live URL and the canonical domain variant. CDN, redirect, and www/non-www differences are common sources of policy mistakes.
This extra review layer is intentionally practical. It helps BotAccess Lab pages answer a real operational question, produce a durable artifact, and avoid the kind of thin, generic explanation that fails when a user has to make a production change.