url2md4ai 2.1.0

Convert any URL into clean, token-efficient Markdown for LLMs. One thing, done well.

url2md4ai

Convert any URL into clean, token-efficient Markdown for LLMs.

uv tool install url2md4ai   # or: pip install url2md4ai
url2md4ai https://en.wikipedia.org/wiki/Markdown

Feed web pages to an LLM without paying for navigation menus, cookie banners, ads and scripts. url2md4ai fetches a page, extracts the main content and emits structured Markdown — headings, text, tables, lists, code blocks, image alt text — typically cutting raw page size by more than 90%.

---
title: "Markdown - Wikipedia"
source: https://en.wikipedia.org/wiki/Markdown
fetched: 2026-06-10
---

# Markdown

**Markdown** is a lightweight markup language for creating formatted text...

CLI

url2md4ai https://example.com/article            # markdown on stdout
url2md4ai --no-links https://example.com/article # drop link URLs, keep text (saves tokens)
url2md4ai --no-frontmatter https://example.com   # body only
url2md4ai --render https://example.com/spa       # force JavaScript rendering (js extra)
url2md4ai -v https://example.com                 # extraction strategy on stderr

MCP server (for agents)

Install with the mcp extra and register the stdio server in your MCP client (Claude Code, Claude Desktop, ...):

uv tool install "url2md4ai[mcp]"

{
  "mcpServers": {
    "url2md4ai": { "command": "url2md4ai-mcp" }
  }
}

The server exposes a single tool: url_to_markdown(url, include_links=true, render=false).

Python library

from url2md4ai import to_markdown, html_to_markdown

md = to_markdown("https://example.com/article")
md = to_markdown("https://example.com/article", include_links=False, timeout=30)

# already have the HTML? (cache, your own renderer, ...)
md = html_to_markdown(html, base_url="https://example.com/article")

Errors are explicit and typed: FetchError (network / HTTP status), UnsupportedContentError (PDF, images, JSON...), ExtractionError (empty or paywalled pages) — all subclasses of Url2md4aiError.

JavaScript pages

The core package uses static fetching only, which covers most articles, docs and blogs with zero browser overhead. For pages that only exist after client-side rendering, install the js extra:

pip install "url2md4ai[js]"
playwright install chromium

• With the extra installed, rendering kicks in automatically when static extraction finds nothing (render="auto", the default).
• Some pages serve partial static content and load the rest client-side (embedded job boards, widgets). No heuristic can detect that reliably, so force rendering when you know you need it: --render on the CLI, render="force" in Python, render=true on the MCP tool.
• Content-bearing iframes are included in the result; tracker frames, images, media and fonts are skipped.

How it works

1. Fetch — plain HTTP GET (httpx, redirects followed, 10 MB cap). Markdown and plain-text responses are returned as-is; PDFs and other binaries fail fast with a clear error.
2. Extract — trafilatura, precision-first. If precision mode finds too little it retries favoring recall, then reads the page's JSON-LD structured data (many JavaScript-heavy sites ship their content there for SEO), then optionally renders JavaScript, then falls back to whole-page text before giving up.
3. Post-process — whitespace normalization, decorative images without alt text dropped, optional link stripping, YAML frontmatter (title, source, fetched).

Philosophy

• One thing well: URL in, LLM-ready Markdown out. No crawling, no screenshots, no LLM calls.
• Token efficiency is the metric: every byte in the output should inform the model.
• Lean by default: 2 runtime dependencies; browsers and MCP are opt-in extras.

Development

uv sync --all-extras
uv run pytest
uv run ruff check && uv run ruff format --check

License

MIT