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AI context guide / 2026

Web Context API for AI Agents in 2026

Context quality matters more than raw page volume. A public page can contain the facts an agent needs alongside menus, scripts, and unrelated links. Normalize the readable layer and retain the canonical URL.

Quick answer

Turn one known public source into usable context.

Use extraction for a URL the agent already has, then use focused search when it needs another source. Markdown fits model reading; schema-v1 JSON fits application logic.

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

Available data

What you can extract

  • Readable Markdown for model context
  • Semantic JSON for systems
  • Canonical URL and page metadata
  • Optional focused sections for generic pages

AI workflows

Where normalized data helps

  • Chat assistants with live sources
  • Task-specific context assembly
  • RAG retrieval validation
  • Source-aware summaries

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

  • Only public URLs are accepted.
  • Context should be refreshed when freshness matters.
  • The service does not bypass access controls.

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.