Best Scrapers · Updated 2026-07-28
LLM-powered extraction tools ranked on schema accuracy, token cost and how well they survive layout changes.
AI scrapers replace brittle CSS selectors with a schema and a model. They shine on heterogeneous sources where hand-written parsers rot every month, and they are wasteful on stable, high-volume targets.
We scored each tool on field-level extraction accuracy against a labelled 500-page set, plus token cost per 1,000 pages.
Turns any site into clean, LLM-ready markdown with crawl, scrape, map and extract endpoints.
Why it ranks here: Cleanest LLM-ready markdown output and the most reliable crawl-to-schema pipeline.
Fast asynchronous open-source crawler built to feed retrieval pipelines with chunked, cleaned, LLM-ready content.
Why it ranks here: Best open-source option — no vendor lock-in, strong extraction strategies.
Prompt-defined extraction — describe the data you want and an LLM builds the scraping graph instead of you writing selectors.
Why it ranks here: Graph-based pipelines that handle multi-page extraction logic well.
Computer-vision page extraction plus a knowledge graph of billions of entities — no rules, no selectors, no maintenance.
Why it ranks here: Most accurate on article and product entities without any prompt engineering.
Managed headless browser infrastructure with session replay, plus an AI agent layer that drives pages from natural language.
Why it ranks here: Best managed browser infrastructure for AI agents that need real sessions.
Pass rate against Cloudflare, DataDome, PerimeterX and Akamai-protected pages.
Pages per minute at a fixed concurrency on identical hardware.
Docs quality, typed SDKs, retries, observability and local debugging.
How cleanly the tool accepts rotating residential or SOCKS5 upstreams.
Cost per 1,000 successful pages including retries and JS rendering.
Scores are recomputed whenever a vendor changes pricing, sourcing or infrastructure. This list was last verified on 2026-07-28. We earn commission from some links on this page; placement is decided from test data before any commercial terms are discussed.
Per page, no — tokens cost more than a CSS query. Over a quarter, yes, on sources whose layouts change often enough to keep an engineer busy.
Yes. The model parses content; it does not fetch it. Blocking happens at the network layer regardless of how you extract.
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