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AI-readable web index

The web,
rebuilt for AI.

Lyrenth continuously indexes the open web and returns every page as a clean, structured AIDocument: the signal your agents need, without the markup they don't.

Free tier: 2,000 AIDocuments / month. No card required.
POST
https://
from the cached index · site not contacted2,722 tokens · 89.0% saved
Web indexing - Wikipedia
en.wikipedia.org/wiki/Web_indexing

From Wikipedia, the free encyclopedia Methods for indexing the Internet **Web indexing**, or **Internet indexing**, comprises methods for indexing the contents of a [website](https://en.wikipedia.org/wiki/Website) or of the [Internet](https://en.wikipedia.org/wiki/Internet) as a whole. Individual websites or [intranet

lang entype Article1,556 words8 min readhas JSON-LD
Tokens saved
89.0%
24,7342,722
AIDocument tokens
2,722
what the model reads
Origin fetches
0
shared, cross-caller cache
Served from
cached index
instant · fresh copy on demand
AI-readable pages indexed
1,505,699,515
Indexed domains
139,200,563
Pages audited
1503.8M
Recrawl cadence
24h to 90d · by plan
The transform

Signal,
not markup.

Every crawl of raw HTML drags in navs, ads, cookie walls, scripts, and trackers. Models pay, in tokens, latency, and dollars, to parse junk before reaching a single useful sentence.

An indexed page is different: rendered, cleaned, normalized, and rewritten into one canonical document a model reads in one pass. Lyrenth removes the noise once, for everyone.

up to 8x less
cost & latency per page
Tokens to read one pagesignal · 9%
Raw HTML page~14,800 tok
Actual signal~1,330 tok
Lyrenth AIDocument~1,840 tok
The adapter layer

An index,
not another scraper.

Every read resolves against the shared index, not the origin. When a thousand agents request the same URL, the origin sees one fetch: everyone else is served from cache in milliseconds. When freshness matters, force_refresh re-indexes on demand.

01 · Origins
one crawl →
02 · The standing index
→ every read
03 · Readers
crawl
crawl
crawl
Lyrenthindexing · live
AIDocuments in the index
POST/v1/aidocumentHIT

One canonical AIDocument per URL. Written once, read by everyone: shared infrastructure, not a per-customer scrape.

Freshness, per request
cache_firstforce_refreshyou choose
Autonomous agents
per-url · mcp
AI assistants
retrieve · cite
AI search & discovery
search · rank
RAG systems
embed · index
Model labs
corpus · bulk
Enterprise & research
pipeline · api

The open web, crawled on our schedule. Readers ask for the latest; Lyrenth fetches and serves it from the index.

A standing index sits between the two. Rendered, cleaned, normalized, and held.

Every reader resolves against the index and is answered in milliseconds.

01 · Origins
crawl
crawl

The open web, crawled on our schedule. Readers ask for the latest; Lyrenth fetches and serves it from the index.

one crawl
02 · The standing index
Lyrenthindexing · live
AIDocuments in the index
POST/v1/aidocumentHIT

One canonical AIDocument per URL. Written once, read by everyone: shared infrastructure, not a per-customer scrape.

Freshness, per request
cache_firstforce_refreshyou choose

A standing index sits between the two. Rendered, cleaned, normalized, and held.

every read
03 · Readers
Autonomous agents
per-url · mcp
AI assistants
retrieve · cite
AI search & discovery
search · rank
RAG systems
embed · index
Model labs
corpus · bulk
Enterprise & research
pipeline · api

Every reader resolves against the index and is answered in milliseconds.

Is notRaw crawlingScraping-as-a-serviceA normal search engineAnother data broker
IsThe AI-readable web index
One request, one document

A stable contract
for every URL.

Send any public URL, get one AIDocument back: the same grouped JSON envelope every time, versioned forever. Validate against the public JSON Schema.

schema · version, refsource · crawl tracecache · truthidentity · title, langcontent · markdownstructure · headings, linkssignals · qualityeconomics · tokens, cost
POST/v1/aidocumentresolve any URL
GET/v1/document?url=…index lookup
POST/v1/submitqueue for indexing
GET/v1/statspublic counter
GET/aidocument.schema.jsonthe contract
Quickstart: first call in a minute
Built for

Every system that
reads the web.

If it consumes web data, it runs better on clean AIDocuments than on raw HTML.

01Autonomous agentsBrowse and act with structured pages instead of burning context on markup.PER-URL · MCP
02AI assistantsAnswer with fresh, normalized web content and clean citations.RETRIEVE · CITE
03AI search & discoveryBuild on a corpus already structured for ranking and recall.SEARCH · RANK
04RAG systemsSkip the scrape-and-clean pipeline: embed AIDocuments directly.EMBED · INDEX
05Model labsGround, retrieve, and evaluate on a clean, deduplicated web corpus.CORPUS · BULK
06Enterprise & researchMonitor, extract, and analyze the live web as structured data.PIPELINE · API
Real data, from the production API

What famous pages cost,
raw vs indexed.

Every row is a real page read through the index, with token economics reported by the API itself. Reproducible with one call.

PageRaw HTML tokensAIDocument tokensSmallerSaved
Stripe API reference307,9022,000154x99.4%
Vercel Functions docs237,3902,149110x99.1%
Cloudflare Workers docs94,9631,37769x98.5%
GitHub REST API quickstart88,6144,87618x94.5%
Kubernetes Pods concepts131,9777,47518x94.3%
Measured 2026-07-05 · cost basis $3.00 / 1M input tokensAll 10 benchmarks
Get started

Read the web like a machine.

Get an API key and pull your first AIDocument in under a minute. Web-scale, structured, and live.