What to include
Include only stable, public, useful URLs: product docs, API references, pricing explanations, changelogs, support pages, and policy pages that help an assistant understand the site.
Agent-friendly site summary
LLMs.txt Generator
Create a concise Markdown file for AI agents and assistants. Use it to point models toward the pages that best explain your product, docs, policies, or public resources.
Site details
Generated llms.txt
llms-full.txt starter
AI crawler access control
Practical notes for LLMs.txt Generator
LLMs.txt Generator for AI Agents and Search Assistants is maintained for website owners, publishers, SaaS documentation teams, ecommerce operators, and SEO teams who need llms.txt information architecture. 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 exclude
Do not include private dashboards, thin tag pages, account pages, internal search pages, or pages that are not intended to become canonical entry points.
Maintenance rhythm
Review the file after major product launches, documentation reorganizations, or migration from one CMS structure to another.
Before relying on this page
- Use short descriptions that explain why a URL matters.
- Keep the file human-readable Markdown.
- Link from the homepage or documentation footer when the audience includes AI agents and technical users.
Field workflow
How to turn LLMs.txt Generator 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 LLMs.txt Generator 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.