Web Scraping / Crawling

AI-powered web scraping uses an LLM to extract structured data from web pages instead of hand-written CSS selectors, and an agent to drive the crawl itself — deciding which links to follow, handling pagination, and adapting when a page doesn’t look like expected. Traditional scrapers break the moment a site changes its markup; an agent reads the rendered content the way a person would and pulls out the fields you asked for regardless of the underlying HTML.

This matters because agents themselves run on data, and the web is where most of it lives: competitor pricing, job listings, product catalogs, research sources. Resilient extraction is also a gateway skill to browser automation more broadly — the same “observe page, decide action” loop powers agents that fill forms and complete checkout flows. Tools like Crawl4AI and Firecrawl exist precisely because LLM pipelines need clean, markdown-shaped web content at scale.

In practice, you’ll pair a fetching layer — HTTP requests for static pages, a headless browser like Playwright for JavaScript-heavy ones — with an extraction step that hands the page content to a model along with a target schema, getting validated JSON back. For crawling, the agent maintains a frontier of URLs and decides per page whether to extract, follow links, or skip. Keep costs sane by converting HTML to markdown before sending it to the model, caching aggressively, and respecting robots.txt and rate limits — an agent that hammers a site is still your outage to own.