Website to Markdown for RAG & LLMs avatar

Website to Markdown for RAG & LLMs

Pricing

Pay per usage

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Website to Markdown for RAG & LLMs

Website to Markdown for RAG & LLMs

Crawls a website and converts every page into clean Markdown, ready for RAG pipelines, vector databases, and LLM context. Pay per page converted.

Pricing

Pay per usage

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Developer

Aaron Marxsen

Aaron Marxsen

Maintained by Community

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2

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1

Monthly active users

15 minutes ago

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Turn any website into clean, LLM-ready Markdown in one run. This Actor crawls a site, strips away navigation, footers, cookie banners, and scripts, and converts the real content of every page into Markdown — the ideal format for RAG pipelines, vector databases, fine-tuning datasets, and LLM context windows.

Why Markdown?

LLMs work dramatically better with clean Markdown than with raw HTML: fewer tokens, preserved structure (headings, lists, code blocks, links), no markup noise. This Actor does the cleanup for you at crawl time.

Features

  • 🕷️ Crawls an entire site or just a section (URL glob filters, e.g. https://example.com/docs/**)
  • 🧹 Removes navigation, headers, footers, sidebars, cookie banners, and scripts
  • 🎯 Prefers <main> / <article> content when the page provides it
  • 📝 GitHub-style Markdown: ATX headings, fenced code blocks
  • 🚫 Skips near-empty pages so your dataset stays clean
  • 💸 Pay per page converted — skipped pages cost nothing

Input

FieldTypeDescription
startUrlsarrayWhere the crawl starts (stays on the same domain)
maxPagesintegerMax pages to crawl (default 50)
includeUrlGlobsarrayOnly crawl URLs matching these globs (optional)
minTextLengthintegerSkip pages with less extracted text than this (default 100)
proxyConfigurationobjectOptional proxy settings

Output

One dataset item per page:

{
"url": "https://example.com/docs/getting-started",
"title": "Getting Started — Example",
"description": "Learn how to ...",
"markdown": "# Getting Started\n\nWelcome to ...",
"charCount": 5321,
"crawledAt": "2026-07-05T12:00:00.000Z"
}

Export as JSON/CSV from the Storage tab, or stream straight into your pipeline via the Apify API — perfect for feeding LangChain, LlamaIndex, or a vector DB loader.

Typical uses

  • Build a RAG knowledge base from your product docs
  • Keep an LLM assistant in sync with a changing website (run on a schedule)
  • Create clean text corpora for evaluation and fine-tuning
  • Migrate CMS content to Markdown