Website to Markdown for RAG & LLMs
Pricing
Pay per usage
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
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2
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1
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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
| Field | Type | Description |
|---|---|---|
startUrls | array | Where the crawl starts (stays on the same domain) |
maxPages | integer | Max pages to crawl (default 50) |
includeUrlGlobs | array | Only crawl URLs matching these globs (optional) |
minTextLength | integer | Skip pages with less extracted text than this (default 100) |
proxyConfiguration | object | Optional 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