We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers.LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that:- Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise.- LLMs return malformed JSON more often than you'd expect, especially with nested arrays and complex schemas. One bad bracket and your pipeline crashes.- Relative URLs, markdown-escaped links, tracking parameters — the "small" URL issues compound fast when you're processing thousands of pages.- You end up writing the same boilerplate: HTML cleanup → markdown conversion → LLM call → JSON parsing → error recovery → schema validation. Over and over.We got tired of rebuilding this stack for every project, so we extracted it into a library.Lightfeed Extractor is a TypeScript library that handles the full pipeline from raw HTML to validated, structured data:- Converts HTML to LLM-ready markdown with main content extraction (strips nav, headers, footers), optional image inclusion, and URL cleaning- Works with any LangChain-compatible LLM (OpenAI, Gemini, Claude, Ollama, etc.)- Uses Zod schemas for type-safe extraction with real validation- Recovers partial data from malformed LLM output instead of failing entirely — if 19 out of 20 products parsed correctly, you get those 19- Built-in browser automation via Playwright (local, serverless, or remote) with anti-bot patches- Pairs with our browser agent (@lightfeed/browser-agent) for AI-driven page navigation before extractionWe use this ourselves in production at Lightfeed, and it's been solid enough that we decided to open-source it.GitHub: https://github.com/lightfeed/extractor npm: npm install @lightfeed/extractor Apache 2.0 licensed.Happy to answer questions or hear feedback.Comments URL: https://news.ycombinator.com/item?id=47526486Points: 8# Comments: 0