LLM-Powered Web Scraping: A How-To for 2025

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Traditional scraping? Regex, XPath, brittle selectors.

LLM-powered scraping? Smarter, more adaptive, and surprisingly fun.

I’ve been experimenting with GPT-based scraping pipelines. Here’s what I learned—and how you can set one up without losing your mind.

🏗️ The architecture

✅ HTTP client: Requests, Playwright, or Puppeteer for raw page fetching
✅ LLM agent: Parses HTML into structured data using OpenAI or Claude
✅ Postprocessing layer: Validates, normalizes, stores to Postgres

Simple version:

html = get_page(url)
extracted = call_llm(f"Extract {fields} from this HTML: {html}")
save_to_db(extracted)

🧠 Why use an LLM?

Because scrapers always break.

With an LLM in the loop, you can prompt it with semantic goals instead of brittle selectors:

“Extract product name, price, availability from this ecommerce page.”

Boom. Works even if the HTML structure changes a bit.

⚠️ Caveats

• Tokens can get expensive for big pages
• Works best on reasonably clean pages
• Sometimes needs a fallback regex layer for edge cases

Takeaway: LLMs won’t replace scraping libraries—but they’ll be your best friend when data formats drift.