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The State of AI Web Scraping in 2026 LAUNCH · AUG.27.2026 · Ritwik 75% of the web is dynamic. Basically database records presented in HTML. Built for humans. For Agents, this problem was solved by: RAG was the first generation (2024) And then, long-context LLMs took it to a completely new level (2025) Fast-forward to 2026, and we're basically dumping anything and everything into the context windows; taking Nvidia to the moon. This consists of claws, browser/computer-use, and agentic crawlers, and scrapers. These have made it easier to retrieve data, but the cost has stayed the same. Underneath, these still rely on long-context LLMs. And, here's where they fall short: Cost at scale: for large datasets, cost grows linearly on a per-page basis. Hallucinations: LLMs are prone to hallucinations Context pollution and prompt injection Let me now show you what we have built: Memoization-driven Knowledge and Data Retrieval It's an work-in-progress implementation for a continual learning harness focusing on Browser-use. Large-request correctness versus cost Scatter plot of correctness against average USD cost for Makra, Exa, and Firecrawl. Lower cost is toward the left. 400 pages across 20 sites Building applications on top of a memoization-engine like Makra solves all 3 of these problems: Lower cost at scale: as layouts are memoized, cost comes down to a vector query. Makra extracts data like a traditional scraper. So, hallucinations? Theoretically, none. Your agentic loop only has to deal with the DOM nodes it's concerned with: More signal, less noise. We have built an in-house browser harness (read-only for now) that does the following for you: Spins up a browser instance and sets up proxies. Extracts any structured or tabular data from the web for you. And you can do all this at 1/10th the cost of all the other previous generations. Try it out in: /playground