Turn any URL into LLM-friendly text with a prefix

What it does

Reader is an open-source extraction engine that fetches web pages, PDFs, Microsoft Office files, and images, then distills them into markdown or plain text. It exposes two main endpoints: r.jina.ai for single URLs and s.jina.ai for web search, which automatically retrieves and cleans the top five results. Images get passed through a vision-language model for captioning, so even text-only downstream LLMs receive useful context.

The interesting bit

The entire interface is just a URL prefix, meaning any HTTP client can call it without SDKs or auth headers. Under the hood it intelligently picks between headless Chrome and a lightweight curl-impersonate fetch, and offers unusually fine-grained request headers—token budgets, CSS selectors, and six levels of response timing—to let you trade latency for completeness.

Key highlights

• Prepend r.jina.ai to any URL to receive markdown, HTML, text, screenshots, or semantically chunked output
• Search via s.jina.ai returns full content from the top five results rather than search-engine snippets alone
• Handles PDFs, Word, Excel, PowerPoint, and image captioning for text-only downstream models
• Deep controls via headers include x-max-tokens, x-target-selector, x-respond-timing, and markdown chunking strategies
• Open-source branch runs stateless by default with optional MinIO or S3-compatible bucket caching for self-hosted deployments

Caveats

• The open-source branch strips the MongoDB-backed SaaS storage layer, so it runs stateless (or with optional bucket caching) by default

Verdict

Teams building RAG pipelines or agents that ingest live web content will save a lot of scraping headaches. If you already run your own headless-browser farm and don’t need another upstream dependency, this won’t change your life.