A founder’s test of whether a “cold” Claude agent could discover and use a SaaS site’s llms.txt ended with a failed fetch, followed later by success after changes to the site’s discoverability. That is a useful example of the difference between publishing a file and getting an agent to find it—but it is one account, not proof that Claude generally requires indexing or that llms.txt improves AI search visibility.
What llms.txt is—and what it is meant to do
llms.txt is a proposal for a short Markdown guide that points language-model agents to useful information on a website. The proposal describes it as a way to give agents a concise map and links to fuller material: “We propose adding a /llms.txt markdown file to websites to provide LLM-friendly content.” The proposal, version 2, frames the file as a practical response to the difficulty of turning some websites’ HTML into clean, usable text. That is the proposal’s rationale, not a guarantee that a particular AI product will use the file.
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A file can live at the site root or under a path such as /docs/llms.txt. A path-scoped file applies to URLs beneath that path; the proposal recommends that agents use the most specific applicable file. Its suggested structure is deliberately simple:
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- A level-one heading naming the project or site.
- An optional blockquote summarizing it.
- Optional explanatory text.
- Level-two headings grouping lists of linked resources.
The intended workflow is an index, not an exhaustive dump: an agent finds a relevant link in the guide, then follows it for detail. That means the usefulness of the file depends on the quality and upkeep of its links.
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What happened in the reported Claude test
A founder of the vacation-rental discovery SaaS CielStay described setting up an llms.txt with API documentation. The hoped-for sequence was straightforward: “Agent reads llms.txt → calls /api/search → returns results.” In the founder’s account, a cold Claude attempt could not fetch the file even though the URL returned HTTP 200. The founder attributed the problem to the domain’s absence from search results, first directed Claude to indexed third-party listings, and later found Claude could locate CielStay as a secondary reference.
The founder says the team then added the file to its sitemap, linked it from a crawlable page, and added a <link> element. The account reports a later successful discovery, but it does not isolate which change mattered. It also does not provide a controlled comparison or independently verify how Claude chose its retrieval tools. Read it as a practical anecdote about discoverability, not a reproducible rule about Claude. The founder’s account uses “cold Claude agent” to describe the initial test.
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Does publishing llms.txt make Claude or AI search cite a site?
There is no demonstrated basis here for saying that publishing the file increases citations in AI search answers. A 2026 review of usage evidence reports an Ahrefs analysis of 137,210 domains in which 97% of llms.txt files received zero requests in May 2026. That figure is specific to the reported analysis and month; it does not establish whether a model used a file internally without making a request visible to the measurement, nor does it predict every site’s experience. The review also says that, in its 2026 publication context, no provider had stated in writing that its search product reads llms.txt.
The same review distinguishes signals that are easy to mistake for proof: a provider hosting a file or a tool checking whether one exists does not establish that a live consumer answer fetched and relied on it. Separately, Anthropic’s Claude 3.7 Sonnet system card says its general-purpose crawler follows robots.txt instructions when obtaining public web pages. That describes crawler policy; it does not confirm that Claude’s interactive retrieval reads llms.txt.
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How llms.txt differs from robots.txt
These files have different jobs. The proposal describes robots.txt as a way to communicate access preferences to automated tools, while llms.txt supplies contextual guidance and links for agents. One is not a substitute for the other. Publishing an llms.txt file does not grant access that a crawler is otherwise disallowed from taking, and it does not compel an agent to visit the URLs it lists.
What a useful implementation looks like
If a site chooses to publish the file, the proposal favors a compact, maintainable index rather than copying all site content into one document. For documentation, it also recommends offering clean Markdown versions of relevant pages and identifying those versions with standard link relations: rel="alternate" for a Markdown version and rel="describedby" for the applicable llms.txt. These can be expressed as HTML <link> elements or HTTP Link: headers. These are format recommendations from the proposal, not evidence that a given AI service honors them.
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- Keep the file concise and group links by meaningful topic.
- Link to authoritative, current pages rather than duplicating details that may go stale.
- Use a path-scoped file when its contents apply to a particular section; follow the proposal’s specificity principle for overlapping files.
- Check that linked pages are reachable and contain the information the guide promises.
- If publishing Markdown alternatives, keep them aligned with their HTML counterparts.
How to test whether Claude actually uses it
A useful test separates direct access from discovery. Record the exact Claude product and mode, the date, the prompt, whether you supplied the file URL, any visible tool actions, the HTTP response, and the outcome. Then compare attempts without changing other conditions:
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- Cold start: Ask Claude to find and use the site’s documentation without giving it the
llms.txtURL. - Direct URL: Repeat with the exact file URL in the prompt. This checks whether supplying the address changes the outcome.
- Ordinary page: Provide a crawlable documentation page instead, so the file is not the only route to the information.
- Trace the result: Note whether Claude reports opening the file, what information it uses, and whether it reaches the linked resource needed to answer the question.
A successful answer alone does not show that the file caused it; the agent may have used another page or source. A failed cold attempt alone does not show that the format is unsupported; it may reflect discovery, access, or tool-routing conditions. A comparison can clarify what happened in that particular setup, but it should not be generalized into a claim about all Claude sessions or AI search products.
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