Reader APISEO landing

URL to Markdown API for AI pipelines

A URL to Markdown API should do more than strip tags. It should remove navigation, keep useful structure, and return consistent metrics that make downstream agent workflows cheaper and easier to debug.

Primary use

Convert live pages into clean Markdown with stable token metrics and predictable output for AI systems.

Recommended flow

Fetch, clean, measure tokens, then hand consistent Markdown to agents or retrieval systems.

Next step

Use the Playground to compare raw HTML against optimized output before integrating the API.

What the API needs to preserve

Production ingestion needs stable headings, paragraphs, lists, tables and code blocks because those structures are what retrieval systems and agents actually rely on.

AIngestor returns Markdown that is easier to chunk, cache and diff than raw HTML, while still keeping the original document hierarchy intact.

Normalize public URLs and raw HTML into one output format.
Report tokens before and after conversion for budget visibility.
Keep useful links instead of flattening everything into plain text.

Recommended integration pattern

Start by sending canonical URLs through the API, cache the result by URL hash, and only refresh when content changes. This keeps agent browsing fast and predictable.

POST /api/v1/convert

curl -X POST https://aingestor.com/api/v1/convert \
  -H 'Content-Type: application/json' \
  -d '{"url":"https://example.com/docs"}'

FAQ

Why not pass raw HTML directly to the model?

Raw HTML wastes context on boilerplate and repeated layout chrome. Markdown preserves content structure with a far smaller token footprint.

Does the API keep useful links?

Yes. Useful in-content links stay in the Markdown output so agents and retrieval systems can preserve reference paths.

Internal links

Related technical paths

Open Playground

Agent access

Reader API for AI systems

Expose web content to agents and retrieval systems through a reader-style API that prioritizes clarity over browser markup.

Core positioning

AI ingestion infrastructure for websites

Normalize public web content into consistent, low-noise context that AI systems can index, retrieve and reason over.

Cost control

Reduce HTML token usage before RAG or agents

Shrink token spend by converting bloated HTML into compact Markdown before chunking, prompting or embedding.