LLMS.txt Adoption Tracker
8.7% of the top 1,000 websites publish an llms.txt file, as of June 2026.
Last updated: 2026-06-29 · Source list: Tranco June 2026 (2026-06-23)
Adoption Over Time
We re-scan the top 1,000 every month and keep every data point, so this line only grows over time.
llms.txt vs. llms-full.txt
Some sites publish the short index file (llms.txt), some add the full-content file
(llms-full.txt), and a few publish both.
llms.txtllms-full.txt 72 publish only llms.txt · 0 publish only llms-full.txt
Adoption by Category
Where adoption is concentrated among the top 1,000, by site category.
Notable Adopters
High-ranking sites that already publish an llms.txt file.
Methodology
Transparent, automated, and re-run every month.
Each month we take the Tranco top 1,000 domains (a research-grade ranking that
averages several traffic sources) and request both /llms.txt and
/llms-full.txt over HTTPS with a clearly identified crawler. The top 1,000 and top
10,000 views are derived from the same crawl. The list used for this run was published June 2026 (2026-06-23).
A site is counted as an adopter only when it returns a real text file (HTTP 200
with plain-text content). HTML pages, empty files, and soft-404s do not count —
those are recorded as “no file”. When a site can’t be reached (timeout, DNS/TLS failure, or it
blocks our crawler with a 401/403/429/5xx), we record it as unknown rather than
guessing. To keep unknowns to a genuine minimum we try both the apex and the
www host and retry transient failures with a longer timeout before giving up. This run
reached a verdict on 549 of 1,000 sites;
451 were unknown (most are infrastructure, CDN, or API domains that don’t serve a website at the root).
The headline rate is deliberately the conservative measure: confirmed adopters as a share of all sites in the sample (87 ÷ 1,000 = 8.7%) — unknowns stay in the denominator rather than being dropped. We report it this way (instead of adopters ÷ reachable sites) so the number can never be quietly inflated by counting an unreachable domain out of the sample, and so every month stays directly comparable on the same fixed base. For reference, among only the 549 sites we could reach this run, 15.8% publish an llms.txt file. Every monthly result is stored permanently, so the trend above is an immutable time series, and large month-over-month swings are flagged for human review before publishing.
Want to get this right?
Read our guide to writing an llms.txt file that AI engines actually use.
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