Webclaw

Turn any website into LLM-ready data

24 followers

Turn any website into LLM-ready data

24 followers

Webclaw turns websites into clean markdown, JSON, structured data, and LLM-ready content. Use it to scrape pages, crawl docs, extract fields, summarize, diff changes, and feed reliable web context into AI agents, RAG pipelines, CLI workflows, SDKs, and MCP clients. Designed for developers building with Claude Code, Cursor, LangChain, LlamaIndex, and custom agents.

Free

Launch tags:Productivity•API•Developer Tools

Launch Team

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Maker

📌

Hey Product Hunt, I’m Massi, founder of webclaw. I built webclaw because I kept running into the same problem while building AI agents: the web is useful, but the raw input is terrible. Most pages are full of navigation, cookie banners, scripts, duplicated layout text, missing rendered content, and random HTML noise. If you pass that directly into an LLM or RAG pipeline, you waste tokens and get worse answers. Webclaw turns websites into clean, usable context: - scrape a URL into markdown, JSON, text, or LLM-ready output - crawl docs, blogs, and websites - map a site before crawling - extract structured data with schemas - summarize pages - diff content changes - extract brand identity from websites - use it from the API, CLI, SDKs, or MCP server The main users I’m building for are developers working on AI agents, RAG pipelines, Claude Code/Cursor workflows, LangChain/LlamaIndex apps, and web data extraction systems. The product is intentionally technical. No “AI magic” wrapper. The goal is simple: give agents and apps cleaner web data so they can do useful work. I’d love feedback on: 1. Which output format matters most for your workflow: markdown, JSON, structured extraction, or something else? 2. What is the hardest website or docs site you’ve tried to ingest into an agent/RAG pipeline? 3. If you use Firecrawl, Apify, Jina Reader, Crawl4AI, or Browserless today, what made you choose it? Try it here: https://webclaw.io Docs: https://webclaw.io/docs Happy to answer anything about the API, MCP server, CLI, SDKs, or the extraction pipeline.

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4mo ago

mailX by mailwarm

Feeding raw HTML into an LLM context window is the ultimate token-waster, and trying to pull documentation from modern, Cloudflare-protected sites using basic fetch requests is an absolute headache. Webclaw addresses both of these developer pain points perfectly. Stripping out DOM noise down to clean markdown—while natively handling TLS fingerprinting—makes it an essential bridge for agentic workflows.

Integrating this as a local-first MCP server means tools like Cursor and Claude Code can dynamically grab up-to-date web context without forcing developers to manually copy-paste documentation or spin up a fragile custom scraper. It’s a massive quality-of-life upgrade for anyone building RAG pipelines or running autonomous CLI agents!

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3mo ago

Maker

@manal_essalek1 If you want to try there is the OSS version on our github to start to feed your LLM model with real data :)

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3mo ago

Mailwarm

Interesting! congrats on your launch :))

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3mo ago

Maker

@naimz Thanks, let me know if you have any questions or feedback

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3mo ago

Forum Threads

p/webclaw

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4mo ago

What do you use to turn websites into clean LLM context?

I m launching webclaw here because I kept hitting the same problem while building agents and RAG workflows: getting a page is easy, but getting clean context from that page is not.

Raw HTML usually brings too much noise: nav, footers, cookie banners, duplicated layout text, scripts, missing JS-rendered content, and inconsistent structure.

webclaw is my attempt at solving that layer: scrape/crawl/map websites and return clean markdown, JSON, structured extraction, summaries, diffs, and MCP/CLI-friendly output.

I m curious how people here handle this today.

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