{"@context":"https://schema.org","@type":"BlogPosting","headline":"Hotel llms.txt Adoption Study 2026: 105,002 Properties Analyzed","description":"105,002 hotel websites scanned. Only 6.3% have a llms.txt file. US leads at 12.4%, France trails at 3.8%. Updated May 2026: Shopify now ships llms.txt by default on every store.","datePublished":"2026-03-21","dateModified":"2026-05-09","url":"https://nicolassitter.com/research/hotel-llms-txt-adoption-study-2026","category":"research","keywords":["llms.txt","hotel llms.txt","AI optimization","agent endpoints"],"articleSection":"Research","wordCount":6600,"readTime":"26 min","articleBody":"ResearchResearch / llms.txt StudyMarch 2026\n\n# Hotel llms.txt Adoption Study\n\nWe scanned 105,002 hotel websites for llms.txt files. **Only 6.3% have one** — and 7.3% of those misuse it as a robots.txt clone.\n\n105K\n\nHotels Scanned\n\n6.3%\n\nAdoption Rate\n\n12.4%\n\nUS Lead\n\n0.3%\n\nllms-full.txt\n\n[Summary](#executive-summary)[Adoption](#adoption-overview)[Generators](#generators)[Content](#content-types)[By Country](#by-country)[By Stars](#by-stars)[Schema Link](#schema-correlation)[File Sizes](#file-sizes)[FAQ](#faq)[Methodology](#methodology)\n\n## Update — May 2026\n\n**Shopify now ships `llms.txt`, `llms-full.txt`, and `agents.md` by default on every store**, and exposes them through a discovery sitemap at `/sitemap_agentic_discovery.xml`. Live example: [respire.co/sitemap\\_agentic\\_discovery.xml](https://respire.co/sitemap_agentic_discovery.xml) — the sitemap points to all three files plus a `.well-known/ucp` Universal Commerce Protocol endpoint.\n\n**This isn’t reflected in the adoption numbers below** — our crawl predates the rollout, and Shopify-hosted hotels are a minority of the sample. But if Shopify is doing it, the trajectory is clear: llms.txt adoption will stop being a marker of technical SEO maturity and start being whatever your platform ships by default. Worth tracking on the next crawl.\n\n## TL;DR\n\nWe fetched `/llms.txt` and `/llms-full.txt` from **105,002** hotel websites across 7 countries. Only **6.3% have a llms.txt file** (6,590 hotels) and just **0.3% serve a llms-full.txt** (265 hotels). The US leads adoption at 12.4%, while France trails at 3.8%. WordPress SEO plugins (AIOSEO, Yoast, Rank Math) drive 33.4% of all files, but **7.3% of llms.txt files misuse the format** as robots.txt-style access control rules. Hotels with llms.txt score **62% higher on schema.org quality** — suggesting it's a marker of technical SEO maturity.\n\n## Executive Summary\n\nThe `llms.txt` file is an emerging standard for websites to communicate their content structure to AI models. Unlike robots.txt (which tells bots what _not_ to crawl), llms.txt tells AI models what a site _is about_ — a curated index of pages with descriptions that helps LLMs understand and accurately represent a property.\n\nOur analysis of 105,002 hotel websites reveals extremely early adoption. At 6.3%, llms.txt is where robots.txt was in the early 2000s — a technical signal adopted by forward-thinking properties and platforms, but unknown to the vast majority. The companion file `llms-full.txt` (designed for detailed content) is even rarer at 0.3%, and every hotel with llms-full.txt also has llms.txt — the two-file approach has not gained traction.\n\nMost adoption is **plugin-driven, not strategic**. WordPress SEO plugins (AIOSEO, Yoast, Rank Math) account for 33.4% of all llms.txt files, often auto-generated with little curation. The best files — like rich hotel descriptions with room types, amenities, and policies — represent just 2.9% of the total. Meanwhile, 7.3% of files are outright misconfigurations: robots.txt-style access control rules served as llms.txt, providing zero value to AI models.\n\n93.7%\n\nNo llms.txt file\n\n42.9%\n\nFollow intended format\n\n7.3%\n\nMisuse as access control\n\n**The key finding:** llms.txt adoption is a proxy for technical SEO maturity, not an isolated decision. Hotels with llms.txt have 62% higher schema.org scores (22.4 vs 13.8) and 49% higher JSON-LD adoption (80.6% vs 54.2%). Both stem from the same investment in search optimization — whether driven by a web agency, CMS platform, or technically aware hotelier.\n\n## Adoption Overview\n\nHow does llms.txt adoption compare to robots.txt? (n=105,002 hotels)\n\n6.3%\n\nHave llms.txt\n\n6,590 hotels\n\n0.3%\n\nHave llms-full.txt\n\n265 hotels\n\n82.2%\n\nHave robots.txt\n\nFor comparison\n\n0\n\nOnly llms-full.txt\n\nAlways paired with llms.txt\n\nllms.txt vs robots.txt adoption rates\n\nllms.txt adoption overview\n\nMetric\n\nCount\n\n% of Reachable\n\nHas llms.txt\n\n6,590\n\n6.3%\n\nHas llms-full.txt\n\n265\n\n0.3%\n\nHas either\n\n6,590\n\n6.3%\n\nHas both\n\n265\n\n0.3%\n\n**llms-full.txt adoption is negligible.** Every hotel with llms-full.txt also has llms.txt — there are zero hotels with only the full variant. This suggests the standard's two-file approach hasn't gained traction; hotels treat llms.txt as a single deliverable.\n\n## Who Generates These Files?\n\nWordPress SEO plugins drive 33.4% of hotel llms.txt files. The majority (57.4%) are custom.\n\n57.4%\n\nCustom / Unknown\n\n3,784 files\n\n33.4%\n\nWordPress Plugins\n\nAIOSEO + Yoast + Rank Math\n\n4.2%\n\nBackhotelite\n\nSpain-specific platform\n\n2.1%\n\nComboCMS\n\nItaly-specific platform\n\nGenerator breakdown of hotel llms.txt files\n\nGenerator breakdown\n\nGenerator\n\nCount\n\n% of llms.txt Files\n\nCustom\n\n3,784\n\n57.4%\n\nAIOSEO\n\n1,197\n\n18.2%\n\nYoast SEO\n\n675\n\n10.2%\n\nBackhotelite\n\n280\n\n4.2%\n\nRank Math\n\n238\n\n3.6%\n\nrobots-style\n\n179\n\n2.7%\n\n**WordPress SEO plugins are the primary driver of identifiable adoption.** AIOSEO (19.6% combined free + pro), Yoast SEO (10.2%), and Rank Math (3.6%) together account for 33.4% of all llms.txt files. These plugins added llms.txt generation as a feature in late 2025/early 2026, making adoption as simple as toggling a setting. But auto-generated files need curation — many are generic site indexes, not curated hotel descriptions.\n\n#### Industry-specific platforms\n\nSpain\n\n**Backhotelite** (4.2%) — A hotel-specific CMS platform primarily used by Spanish properties. Accounts for 18.1% of Spanish llms.txt files.\n\nItaly\n\n**ComboCMS** (2.1%) — An Italian hotel CMS that auto-generates llms.txt. Accounts for 10.5% of Italian llms.txt files.\n\nMisuse\n\n**robots-style** (2.7%) — 179 files contain User-agent/Allow/Disallow directives instead of the intended llms.txt format. These provide zero value to LLMs.\n\n## What's Inside These Files?\n\nOnly 42.9% follow the intended spec. 7.3% misunderstand the format entirely.\n\nContent type distribution of hotel llms.txt files\n\nContent type breakdown\n\nContent Type\n\nCount\n\n%\n\nDescription\n\nSite Index\n\n2,826\n\n42.9%\n\nPage listings with URLs and descriptions\n\nOther\n\n2,013\n\n30.5%\n\nMixed or unstructured content\n\nSitemap Only\n\n933\n\n14.2%\n\nJust a sitemap reference, no page details\n\nAccess Control\n\n484\n\n7.3%\n\nUser-agent allow/disallow rules (misuse)\n\nHotel Description\n\n194\n\n2.9%\n\nDetailed property info, amenities, policies\n\nSummary\n\n91\n\n1.4%\n\nBrief business summary with contact info\n\n**7.3% of llms.txt files are robots.txt clones.** 484 hotels serve User-agent/Allow/Disallow rules as their llms.txt — a fundamental misunderstanding of the standard. These are primarily generated by Backhotelite, a Spanish hotel platform. While well-intentioned (explicitly allowing AI crawlers), this format doesn't help LLMs understand the site's content. The llms.txt spec is about _describing_ your content, not _controlling access_ to it.\n\n### The 2.9% doing it right: rich hotel descriptions\n\n194 hotels serve llms.txt files with detailed property information — room types, cancellation policies, amenities, and contact details. These are exactly what an AI concierge needs to recommend a property.\n\nThe sweet spot for content is 11-50 pages listed (39.1% of files), covering a typical hotel site's key pages: rooms, amenities, location, dining, events, contact, and blog posts. The median file lists 15 pages.\n\n## Adoption by Country\n\nThe US leads at 12.4%. France trails at 3.8% — consistent with its resistance to AI integration.\n\nllms.txt adoption rate by country\n\nFull country-level adoption data\n\nCountry\n\nReachable Hotels\n\nHas llms.txt\n\n% Adoption\n\nllms-full.txt %\n\nTop Generator\n\nUSA\n\n7,445\n\n924\n\n12.4%\n\n0.2%\n\nCustom\n\nSpain\n\n16,411\n\n1,418\n\n8.6%\n\n0.3%\n\nCustom\n\nNetherlands\n\n2,891\n\n237\n\n8.2%\n\n0.4%\n\nCustom\n\nUnited Kingdom\n\n10,547\n\n729\n\n6.9%\n\n0.3%\n\nCustom\n\nGermany\n\n22,268\n\n1,256\n\n5.6%\n\n0.2%\n\nCustom\n\nItaly\n\n27,319\n\n1,309\n\n4.8%\n\n0.3%\n\nCustom\n\n**The US leads at 12.4%** — 2x the adoption rate of France. American hotels are more likely to embrace AI visibility, and the US also has the highest \"custom\" rate (77.1%), suggesting tech-savvy hoteliers writing their own files rather than relying on CMS defaults.\n\n### The France Paradox: Blocks Most, Adopts Least\n\nFrance has the **highest AI blocking rate** (7.5% in our [robots.txt study](/research/hotel-robots-ai-blocking-study-2026)) AND the **lowest llms.txt adoption** (3.8%) among major markets. This is a consistent signal: the French hospitality industry is the most resistant to AI integration.\n\n7.5%\n\nFrance AI blocking rate\n\nHighest of 7 countries\n\n3.8%\n\nFrance llms.txt adoption\n\nLowest of 7 countries\n\n3.3x\n\nBlock-to-adopt ratio\n\nvs US at 0.2x\n\nFor comparison, the US has the **opposite pattern**: lowest blocking rate (2.1%) and highest llms.txt adoption (12.4%). The divergence suggests fundamentally different attitudes toward AI in the hospitality industry — with France leaning toward restriction and the US toward visibility.\n\n**Spain is surprisingly strong at 8.6%** — driven partly by Backhotelite (18.1% of Spanish llms.txt files), a hotel CMS platform that auto-generates the file. Platform decisions matter: a single CMS provider shipping llms.txt support can meaningfully move a country's adoption rate.\n\n## Adoption by Star Classification\n\n5-star hotels adopt at 2.5x the rate of 1-star properties.\n\nllms.txt adoption rate by star classification\n\nAdoption rates by star classification\n\nStars\n\nHotels\n\nHas llms.txt\n\n% Adoption\n\nllms-full.txt %\n\nAvg File Size\n\n5-star\n\n2,062\n\n225\n\n10.9%\n\n0.3%\n\n11.0 KB\n\n4-star\n\n16,548\n\n1,459\n\n8.8%\n\n0.2%\n\n17.9 KB\n\n3-star\n\n30,199\n\n1,812\n\n6%\n\n0.3%\n\n19.6 KB\n\n2-star\n\n10,222\n\n539\n\n5.3%\n\n0.3%\n\n10.9 KB\n\n1-star\n\n2,699\n\n118\n\n4.4%\n\n0.3%\n\n12.9 KB\n\nUnclassified\n\n43,272\n\n2,437\n\n5.6%\n\n0.3%\n\n10.9 KB\n\n**Clear correlation with hotel class.** 5-star hotels adopt llms.txt at 10.9% — 2.5x the rate of 1-star hotels (4.4%). This parallels our [schema.org findings](/research/hotel-schema-adoption-study-2026) where higher-rated properties invest more in technical SEO. Interesting size pattern: 3-star hotels have the largest average file size (20 KB), driven by WordPress sites with many blog posts and pages. 5-star hotels have smaller but more focused files (11 KB), suggesting curated content rather than full site dumps.\n\n## Schema.org Correlation\n\nHotels with llms.txt have 62% higher schema.org scores — it's a proxy for technical SEO maturity.\n\n22.4\n\nSchema Score (with)\n\n6,590 hotels\n\n13.8\n\nSchema Score (without)\n\n98,412 hotels\n\n80.6%\n\nJSON-LD (with)\n\nvs 54.2% without\n\n+62%\n\nScore Difference\n\nStrong correlation\n\nAverage schema.org score: with vs without llms.txt\n\n#### With llms.txt\n\nHotels6,590\n\nAvg Schema Score22.4\n\nJSON-LD Adoption80.6%\n\n#### Without llms.txt\n\nHotels98,412\n\nAvg Schema Score13.8\n\nJSON-LD Adoption54.2%\n\n**llms.txt adoption is a proxy for technical SEO maturity.** Hotels that care enough to implement llms.txt also tend to have better structured data. It's not that llms.txt _causes_ better schema — it's that both are symptoms of a hotel (or its web agency) that takes search optimization seriously. Use llms.txt presence as a quick diagnostic: if a hotel client doesn't have one, they likely have [schema.org gaps](/research/hotel-schema-adoption-study-2026) too.\n\n## File Size Distribution\n\nMedian: 3.3 KB (~15 pages). The long tail reaches 1 MB.\n\n3.3 KB\n\nMedian Size\n\nTypical 10-20 page listing\n\n15 KB\n\nAverage Size\n\nSkewed by large files\n\n1 MB\n\nMaximum Size\n\nLarge resort site dumps\n\n15\n\nMedian Pages\n\nAvg 38.8 pages\n\nllms.txt file size distribution\n\nFile size distribution\n\nSize Range\n\nCount\n\n% of Files\n\n1-100 B\n\n171\n\n2.6%\n\n101-500 B\n\n282\n\n4.3%\n\n501 B-1 KB\n\n820\n\n12.4%\n\n1-5 KB\n\n2,620\n\n39.8%\n\n5-10 KB\n\n1,208\n\n18.3%\n\n10-50 KB\n\n1,110\n\n16.8%\n\nPages listed per llms.txt file\n\nPages Listed\n\nCount\n\n% of Files\n\n0 pages\n\n1,398\n\n21.2%\n\n1-5 pages\n\n376\n\n5.7%\n\n6-10 pages\n\n994\n\n15.1%\n\n11-20 pages\n\n1,100\n\n16.7%\n\n21-50 pages\n\n1,473\n\n22.4%\n\n51-100 pages\n\n737\n\n11.2%\n\n**The median of 3.3 KB represents a typical 10-20 page listing with descriptions.** The long tail above 100 KB (188 files) consists of large resort/chain sites that list hundreds or thousands of pages with full descriptions. 21.2% of llms.txt files list zero pages — these are the access-control and minimal files that don't serve the intended purpose.\n\n## Frequently Asked Questions\n\n## Methodology\n\n### Data Collection\n\n-   **Source:** Global hotel index — 121,425 hotels from Google Maps\n-   **Reachable websites:** 105,002 (84.8% of total)\n-   **Files checked:** `/llms.txt` and `/llms-full.txt` per hotel\n-   7 countries: US (7.4K), ES (16.4K), NL (2.9K), UK (10.5K), DE (22.3K), IT (27.3K), FR (17.6K)\n-   Files fetched during March 2026 crawl window\n\n### Content Analysis\n\n-   **Generator detection:** Heuristic identification from file header comments (AIOSEO, Yoast, Rank Math, etc.)\n-   **Content classification:** Rule-based categorization into site-index, access-control, hotel-description, summary, sitemap-only, minimal\n-   **Language detection:** Keyword-frequency heuristics for English, German, Spanish, Italian, French\n-   **Structural analysis:** Section header extraction, URL counting, page listing enumeration\n\n105,002\n\nHotels Scanned\n\n84.8% of index\n\n6,590\n\nValid llms.txt Found\n\n6.3% adoption\n\n7\n\nCountries Covered\n\nUS, ES, NL, UK, DE, IT, FR\n\n## Continue Reading\n\nExplore more research on AI hotel search.\n\n[AI Hotel Landscape 2026](/research/ai-hotel-landscape-2026)\n\n[robots.txt AI Blocking Study](/research/hotel-robots-ai-blocking-study-2026)[Schema Adoption Study](/research/hotel-schema-adoption-study-2026)[Anatomy of ChatGPT Search](/research/anatomy-chatgpt-hotel-search-2026)[Google AI Mode Study](/research/google-ai-mode-hotel-study-2026)[All Research](/research)","author":{"@type":"Person","name":"Nicolas Sitter","url":"https://nicolassitter.com/about","sameAs":["https://www.linkedin.com/in/nicolassitternolleau/","https://github.com/Nicositter88","https://hotelrank.ai"]},"publisher":{"@type":"Person","name":"Nicolas Sitter","url":"https://nicolassitter.com"},"image":"https://nicolassitter.com/api/og/hotel-llms-txt-adoption-study-2026","mainEntityOfPage":{"@type":"WebPage","@id":"https://nicolassitter.com/research/hotel-llms-txt-adoption-study-2026"},"tags":["llms.txt","AI Optimization","Hotel Websites","Agent Endpoints"],"sameAs":["https://hotelrank.ai/research/hotel-llms-txt-adoption-study-2026"],"alternateFormat":{"html":"https://nicolassitter.com/research/hotel-llms-txt-adoption-study-2026","json":"https://nicolassitter.com/api/post/hotel-llms-txt-adoption-study-2026","rss":"https://nicolassitter.com/rss.xml"},"datasets":[{"name":"summary","contentUrl":"https://nicolassitter.com/data/hotel-llms-txt-adoption-study-2026/summary.csv","encodingFormat":"text/csv"}]}