API design guide / 2026

Why Cache-First APIs Work Well for AI Data in 2026

AI systems often repeat retrieval: a user revisits a chat, a tool retries after formatting, or an evaluation runs the same prompt. Stable cache keys make those repetitions a predictable performance benefit.

Quick answer

Use identical requests to make repeated agent work cheaper and faster.

A cache-first API gives a repeated successful GET the same result without repeating upstream extraction work, helping retries, previews, and evaluation runs.

curl --get 'https://extractor.sh/api/extract' \
  --data-urlencode 'url=https://example.com/' \
  --data-urlencode 'format=json'

Available data

What you can extract

Versioned edge-cache behavior
Endpoint-specific success TTLs
Free successful cache hits
Cache-hit response headers

AI workflows

Where normalized data helps

Agent retries
Shared research sessions
Evaluation suites
Fast chat result rendering

AI-ready output

Markdown for models. JSON for systems.

Raw HTML consumes tokens on navigation, scripts, styling, and interface labels. Clean Markdown keeps the readable hierarchy for LLM prompts and RAG chunks. Normalized JSON is better when your application needs an explicit semantic type, source, author, publication date, media, attributes, and collection items.

Always retain the canonical URL from the response. AI-generated summaries should remain traceable to the public source, especially when the underlying page can change.

Boundaries

Public data only

Errors are not cached as success.
Changing a parameter changes cache identity.
Freshness-sensitive products must disclose refresh behavior.

extractor.sh does not bypass CAPTCHAs, login walls, paywalls, access controls, or regional restrictions. Review the source’s terms and applicable law before collecting or reusing data.