The State of AI Web Scraping in 2026

Much of the web is basically database records presented as HTML. It's primarily built for humans.

For AI, 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, we are basically dumping anything and everything into the context windows.

This consists of agentic browsers, crawlers, and scrapers. These have made it easier to retrieve data for various applications, but the cost has stayed the same. Underneath, they 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

So, let us introduce:

3. Memoization-driven Knowledge Retrieval

A memoization engine like Makra solves all 3 problems:

• Once layouts are memoized, cost comes down to a vector query.
• We extract data like a traditional scraper. So, hallucinations? Theoretically, none.
• Your loop only deals with the DOM nodes it's concerned with. More signal, less noise.

Makra is a Web Memoization-Engine. You can think of it as a very granular Agentic Web-scrapers. But with this, as usage grows, the cost of parsing the HTML decreases.

We have built an in-house browser harness (read-only for now) that can interact with a browser at an ever-decreasing compute cost. And today we are releasing a public beta.

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