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AI Crawler Policy Generator

Generate robots.txt rules for AI training crawlers, AI search crawlers, assistant fetchers, sitemap discovery, and llms.txt references without blocking classic search by accident.

Last reviewed: June 11, 2026

About this tool

Build a robots.txt draft that separates AI training control from AI search discovery, keeps private paths closed, preserves sitemap references, and documents llms.txt discovery for crawlers that support it.

AI Crawler Policy Generator helps site owners make deliberate choices about AI training crawlers, AI search crawlers, assistant fetchers, and classic search crawlers. It is built for the current search landscape where blocking the wrong bot can protect training preferences but also reduce visibility in AI-powered discovery surfaces.

  • Generates purpose-specific robots.txt groups for AI training bots, AI search bots, and optional user-initiated assistant fetchers.
  • Adds sitemap and llms.txt discovery references so allowed crawlers can find high-value pages instead of thin or private paths.
  • Flags broad blocking choices that may reduce AI search visibility or accidentally hide useful public pages.

How to use AI Crawler Policy

Choose a policy mode, enter the canonical site URL, list public paths that should remain discoverable, and list private paths that should never be crawled. Review the generated robots.txt draft, compare it against official crawler documentation, and test it before replacing the production robots.txt file.

When this tool is useful

  • Before publishing a new robots.txt policy after an AI search or AdSense quality reset.
  • When deciding whether to allow AI search crawlers while blocking model-training crawlers.
  • After adding llms.txt, sitemap-index.xml, trust pages, or new high-value SEO tools.

Practical tips

  • Do not block all AI crawlers unless you accept reduced visibility in assistant search experiences.
  • Keep private paths protected by authentication; robots.txt is not access control.
  • List sitemap and llms.txt references so allowed crawlers can find curated, high-value pages.

Examples you can test

These examples show the kind of real input and reviewed output this tool is designed to support. Use them as a starting point before pasting your own production content, then compare the output with the destination system that will use the result. The goal is not only to produce a value, but to make the input assumptions, output format, and review step clear enough that the result can be trusted in a real workflow.

Block model training while keeping AI search discovery

Example input

Mode: Block training, allow search
Protected paths: /api/, /admin/, /private/
Discovery: /sitemap-index.xml and /llms.txt

Expected output

Training bots such as GPTBot, Google-Extended, and ClaudeBot are blocked, while AI search bot groups keep public pages and discovery files available.

This is often the most balanced policy for a site that wants search visibility without broadly allowing training crawlers.

Avoid accidental AI search opt-out

Example input

Mode: Block listed AI bots
Public tools: /tools/
Goal: improve AI search visibility

Expected output

Warning: broad blocking conflicts with the stated visibility goal. Use the training-only blocking mode instead.

Crawler names are increasingly purpose-specific, so a single broad block can create unintended discovery loss.

Validation checklist

Run through these checks before copying the result into a CMS, codebase, spreadsheet, campaign, support ticket, or production document. Small formatting differences, unit assumptions, hidden whitespace, and platform-specific rules are common sources of mistakes in quick browser tools, so the final review should happen in the same context where the output will be used.

  • Confirm every protected path is also protected server-side if the content is sensitive.
  • Verify that sitemap and llms.txt URLs return 200 before publishing the policy.
  • Check official crawler documentation for current user-agent tokens before deployment.
  • Use Search Console and server logs after deployment to confirm important pages remain crawlable.

Why people use this tool

A low-value site often has unclear crawl policy, thin discovery files, or broad blocks that hide useful pages while exposing weak ones. Separating training, search, and user-initiated assistant access creates a more intentional technical SEO surface and pairs well with sitemap and llms.txt cleanup.

Related search intents

ai crawler policy generator, robots.txt ai crawler, gptbot robots.txt generator, google extended robots txt, oai searchbot robots txt.

Frequently asked questions

Why separate AI training crawlers from AI search crawlers?

Some crawler tokens are meant for model training control while others affect AI search, assistant retrieval, or user-initiated fetches. Separating them helps protect content use preferences without accidentally reducing discovery.

Does blocking Google-Extended remove my site from Google Search?

Google documents Google-Extended as a standalone product token for Gemini-related training and grounding controls, and says it does not affect inclusion or ranking in Google Search.

Should I block OAI-SearchBot?

Only if you intentionally want to opt out of OpenAI search crawling. OpenAI documents OAI-SearchBot as the crawler to use for Search opt outs and automatic crawl management.

Are robots.txt rules a security mechanism?

No. robots.txt is a voluntary crawler directive. Keep private, account, admin, draft, and API content protected with authentication or server-side access controls.

Is Content-Signal a standard robots.txt directive?

No. The generator treats Content-Signal as an optional experimental note. Keep explicit User-agent rules as the primary crawler policy.

Review and privacy notes

Utiloom reviews tool pages for practical examples, validation checks, browser-side processing notes, and clear limitations before they are promoted in search. Read more about the editorial approach on the About page, check data handling in the Privacy Policy, or contact us if a tool needs correction.

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