Freshness guide / 2026

Real-Time Web Context for LLMs in 2026

“Real time” is a product decision. News, markets, and changing public pages require different refresh expectations. A clear cache policy saves cost and latency while preserving a path to request a current source.

Quick answer

Choose a freshness policy before you call a model.

Use current search or finance requests for time-sensitive questions, reuse successful cacheable responses for repeated work, and disclose when a source may be delayed.

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

Available data

What you can extract

Cacheable public responses
Endpoint-specific TTLs
Delayed-market-data disclosure
Current web and news discovery

AI workflows

Where normalized data helps

Current-events assistants
Market-aware chat
Change-sensitive research
Cost-aware agent loops

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

Cache hits can contain earlier public data.
No response is automatically live.
The API does not provide monitoring or push alerts.

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.