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We Audited 25 SaaS Platforms for llms.txt: What GrowNexus Found

GrowNexus reported that 18 of 25 audited SaaS platforms published llms.txt. The result is a sample-specific finding, while v2’s Markdown index and link relations offer a practical approach to agent discovery.
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GrowNexus reported that 18 of 25 SaaS platforms in its audit published an llms.txt file. That 72% figure is a finding from one dated sample—not proof that SaaS adoption is widespread across the industry. The proposal offers a practical way to help AI agents navigate a site, but its value depends on clear content, useful links and discoverability.

What the audit reported

In an article published September 19, 2026, GrowNexus described an audit of 25 major SaaS platforms spanning developer tooling, CMS, infrastructure and productivity software. Its indexed article text reports these results:

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Measure GrowNexus-reported result
Platforms publishing llms.txt 18 of 25 (72%)
Adopters following the article’s described structure 12 of 18 (66.7%)
Adopters emitting an HTTP rel="describedby" header 2 of 18 (11.1%)
Adopters implementing an HTML <link rel="describedby"> 0 of 18 (0%)
Reported file sizes 2,088–210,736 bytes; median 16,299 bytes

These are GrowNexus’s reported counts and measurements, not independently recalculated results. The article’s indexed text names a dataset at github.com/grownexus/llms-txt-benchmark, but the platform-level rows and audit scripts were not available for independent review here. The accessible text also does not establish the platform list, crawl date, redirect rules, URL coverage or precise compliance criteria. The figures should therefore be read as results from the sample GrowNexus describes, not as a verified industry-wide adoption rate.

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GrowNexus also says files larger than 50 KB can consume more than 15,000 tokens before an agent’s first tool call. That is the publisher’s claim; without its conversion assumptions and measurement method, it should not be treated as a universal token estimate.

What llms.txt is—and what it is not

llms.txt is a proposal for a Markdown index that points language-model agents toward useful information on a website. Jeremy Howard, author of the proposal, writes: “We propose adding a /llms.txt markdown file to websites to provide LLM-friendly content.” The proposal is titled “The /llms.txt file, v2,” was published September 3, 2024 and modified August 10, 2026.

The file is meant to provide concise context and links to fuller material. It is not an established enforcement standard, a replacement for robots.txt access instructions, or a complete inventory like sitemap.xml. It can help an agent find relevant documentation; it does not itself guarantee that an agent will use or follow that content.

Core format

The proposal calls for a Markdown file named llms.txt at a site root or a relevant subpath. Its required element is an H1 naming the project or site. A short blockquote summary, explanatory text and H2 sections containing lists of Markdown links are optional conventions. The proposal says agents may omit optional material when context is limited, so keep the most useful orientation and links easy to identify.

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How v2 makes the file discoverable

An index is useful only if agents can find it and reach relevant content. Version 2 recommends standard link relations in either HTML or HTTP response headers:

  • rel="alternate" type="text/markdown" identifies a Markdown version of a page.
  • rel="describedby" points to the llms.txt file covering that page.

The proposal allows these relations to appear in HTML <link> elements or HTTP Link: response headers. Markdown twins may append .md to an existing URL or replace its extension with .md. A file placed at a subpath describes URLs beneath that path; where multiple files apply, agents should use the most specific one. The intended flow is for agents to inspect the index, follow relevant links and use content written in an LLM-friendly form.

What the percentages can—and cannot—tell you

The 18-of-25 result suggests that GrowNexus found many files within its chosen set of platforms. It does not establish how common llms.txt is across all SaaS companies, nor does a published file alone demonstrate that the file is complete, discoverable or useful to agents.

A separate GeoSalience crawl, performed May 19, 2026, reported files at 37 of 100 apex domains overall and at 14 of 20 top SaaS domains (70%). Its method checked only https://<brand>.com/llms.txt. A file hosted only on a docs subdomain or deeper path would count as absent under that scope; GeoSalience notes that its apex-only check missed documentation surfaces including Anthropic and Mintlify. Those results do not confirm or contradict GrowNexus’s percentages: the dates, samples and URL scopes differ, and the GrowNexus audit rules are not fully available in the indexed text.

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For two adoption studies to be comparable, they need aligned definitions and collection rules. At minimum, check:

  • When the crawl ran and which sites qualified for the sample.
  • Whether checks covered only apex domains or also documentation subdomains and deeper paths.
  • How redirects, successful responses and content types were handled.
  • What counted as structural compliance.
  • Whether discoverability was checked in HTML, HTTP headers or both.

Until those details are known, report an audit’s date, sample and scope alongside its percentage rather than presenting the result as a universal adoption benchmark.

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How an engineering team can approach implementation

For an API or documentation site, treat llms.txt as a small, maintained navigation layer—not a dump of every page. A practical implementation follows the proposal’s purpose:

  1. Choose the scope. Put the file at the site root if it covers the whole site, or at a documentation or API subpath if it describes only that section. Make the scope clear in the links and use the most specific applicable file for narrower documentation areas.
  2. Write the index in Markdown. Start with an H1 naming the product or project. Add a concise summary and organize high-value links under descriptive H2 headings. Prioritize API references, authentication and quick-start guides, versioning information, and other material an agent needs to orient itself; link to maintained pages rather than duplicating them.
  3. Expose discovery signals. Where appropriate, use rel="describedby" to identify the covering index and rel="alternate" type="text/markdown" for Markdown page twins. The proposal supports HTML link elements and HTTP Link headers; use the mechanism your serving stack can maintain reliably.
  4. Keep destinations useful. Ensure links resolve to current, relevant documentation. If you publish Markdown twins, follow a consistent URL convention, such as appending .md or replacing a page extension with .md.
  5. Recheck after documentation changes. Treat the index and its discovery links as part of documentation maintenance. A stale index can direct agents toward obsolete or irrelevant material.

The proposal describes a navigation convention, not a ranking or access-control mechanism. Publishing the file should complement—not replace—your existing access policies, documentation architecture and sitemap.

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