The State of AI Web Scraping in 2026

75% of the web is dynamic. Basically database records presented in HTML. Built for humans.

For Agents, this problem was solved by:

1. RAG was the first generation (2024)
2. 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:

3. Memoization-driven Knowledge and Data Retrieval

It's an work-in-progress implementation for a continual learning harness focusing on Browser-use.

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:

1. Spins up a browser instance and sets up proxies.
2. Extracts any structured or tabular data from the web for you.
3. And you can do all this at 1/10th the cost of all the other previous generations.

Try it out in: /playground