mlx-mdx

mlx-markdown

mlx-markdown modern take for something like turndown to easily convert documents, websites into Markdown that's loved by LLMs. mlx-markdown is powered by small MLX models that can run on any Apple Sillicon device and its flexible enough to swap out for other MLX models or even Pytorch models. Everything runs locally, so you can customize prompts, swap in different models, and adapt the flow for your publishing targets. Contributions and experiments are welcome—the CLI is intentionally small and hackable.

This will not give you cloud level performances but it gets you maybe 70-80% of the way there for quick use cases.

The resulting Markdown stays LLM-ready—headings, tables, and figure takeaways are explicit—so you can feed it straight into retrieval pipelines or even wire mlx-mdx up as an MCP tool to auto-parse documents on demand. This is particularly useful as not all codign agents can read or understand PDFs or deal with raw HTML.

Before:

After: graph LR A[NVIDIA GPU VRAM] --> B[Scenario 1: Both CUDA and Mac devices have enough memory] A --> C[Scenario 2: CUDA device has insufficient memory, but Mac device has enough memory] A --> D[Scenario 3: Mac device has enough memory but quite near the capacity while CUDA device has insufficient memory] B --> E[Apple Silicon Unified Memory] C --> E D --> E

The graph in a PDF becomes structured text that mermaid can render.

Key Capabilities

• crawl: Render websites with Playwright, isolate the readable article, and rewrite it with mlx-community/jinaai-ReaderLM-v2.
• document: Transcribe PDFs, page images, and photo scans with mlx-community/Nanonets-OCR2-3B-4bit.
• Outputs stay portable: assets are downloaded, validated, and relinked alongside YAML-front-matter Markdown.

Requirements

• macOS on Apple Silicon (MLX requirement).
• Python 3.12 (recommended).
• uv 0.4+ for packaging and tooling.
• Playwright Chromium binaries for the crawl subcommand (install once with uvx --from playwright python -m playwright install chromium).

Quick Start

Choose the path that fits how you want to run the CLI.

If you're not on uv yet - you're missing out big.

curl -LsSf https://astral.sh/uv/install.sh | sh

Run without installing with uvx

uvx --from git+https://github.com/FluidInference/mlx-mdx.git@v0.0.4 mlx-mdx --help

uvx (an alias for uv tool run) clones the repository into uv's cache, builds it, and launches the mlx-mdx entry point—handy for trying the pipelines without installing anything permanently.

Install as a uv tool

uv tool install --from git+https://github.com/FluidInference/mlx-mdx.git@v0.0.4 mlx-mdx

uv tool run mlx-mdx -- crawl https://ml-explore.github.io/mlx/build/html/index.html --output output/mlx-docs --verbose

uv tool run mlx-mdx -- document examples/2501.14925v2.pdf --output output/mlx-docs --verbose

uv tool run ensures the tool executes inside the managed environment even if your shell PATH is unaware of ~/.local/bin. Swap crawl for document to run the OCR pipeline.

MCP integrations

mlx-mdx can participate in MCP workflows two ways:

• One-shot CLI streaming — run the existing CLI with --mcp to emit Markdown on STDOUT.
• Persistent MCP server — launch the bundled mlx-mdx-mcp command so clients can discover the crawl and document tools without extra arguments.

Option 1: Stream with the CLI

uv tool run mlx-mdx -- crawl "{{url}}" --mcp --wait 2.0
uv tool run mlx-mdx -- document /path/to/file-or-folder --mcp --verbose

Configure your MCP client to execute the appropriate command (replace crawl with document for the OCR pipeline) and it will receive the generated Markdown over stdout.

Option 2: Run the MCP server

uv run mlx-mdx-mcp

(This runs the server from the current checkout; once a release includes the new entry point, you can also uv tool install the package and call uv tool run mlx-mdx-mcp.)

The server keeps memory usage low by loading MLX models only while a request is active. It exposes two tools:

• crawl(url) — render a URL with Playwright and rewrite it as Markdown using default settings.
• document(path) — transcribe PDFs, standalone images, or directories of page captures using default settings.

Each client below supports registering a local stdio command as a custom MCP server. Point the command at uv run --project /ABS/PATH/TO/mlx-mdx mlx-mdx-mcp (swap in uv tool run mlx-mdx-mcp once a release ships) and both tools will appear automatically. For custom parameters (alternative models, longer timeouts, etc.), keep using the CLI streaming mode from option 1.

• Codex CLI — in ~/.codex/config.toml:

  [mcp_servers.mlx_mdx]
  command = "uv"
  args = ["run", "--project", "/ABS/PATH/TO/mlx-mdx", "mlx-mdx-mcp"]
  

• Claude Code — extend ~/Library/Application Support/Claude/claude_desktop_config.json:

  "mcpServers": {
    "mlx-mdx": {
      "command": "uv",
      "args": ["run", "--project", "/ABS/PATH/TO/mlx-mdx", "mlx-mdx-mcp"]
    }
  }
  

• Cursor — edit ~/.cursor/mcp.json:

  {
    "mcpServers": {
      "mlx-mdx": {
        "command": "uv",
        "args": ["run", "--project", "/ABS/PATH/TO/mlx-mdx", "mlx-mdx-mcp"]
      }
    }
  }
  

• Zed — add a custom server in ~/.config/zed/settings.json (see Zed’s MCP guide):

  {
    "context_servers": {
      "mlx-mdx": {
        "source": "custom",
        "command": "uv",
        "args": ["run", "--project", "/ABS/PATH/TO/mlx-mdx", "mlx-mdx-mcp"]
      }
    }
  }
  

• OpenCode (Factory CLI) — update ~/.factory/mcp.json:

  {
    "mcpServers": {
      "mlx-mdx": {
        "command": "uv",
        "args": ["run", "--project", "/ABS/PATH/TO/mlx-mdx", "mlx-mdx-mcp"]
      }
    }
  }
  

Add a second entry pointing at the CLI streaming command if you need fine-grained control (custom tokens, timeouts, etc.) alongside the persistent server. Replace /ABS/PATH/TO/mlx-mdx with the absolute path to this repository. Once a packaged release is available, you can switch the args back to "tool", "run", "mlx-mdx-mcp".

Usage

The CLI exposes two focused subcommands. For backward compatibility, calling mlx-mdx <url> still routes to crawl.

Crawl websites

uv tool run mlx-mdx -- crawl https://example.com --output output/example --verbose

Key options:

• --output — destination directory for Markdown and images (default: ./output).
• --wait — seconds to wait after Playwright reports the page is idle (default: 1.0).
• --timeout — navigation timeout in seconds (default: 30).
• --model — MLX model identifier (default: mlx-community/jinaai-ReaderLM-v2).
• --max-html-chars / --max-text-chars — trim limits before passing content to the model.
• --verbose — emit detailed logging, including readability decisions.

OCR documents or images

uv tool run mlx-mdx -- document examples/2501.14925v2.pdf --output output/docs --verbose

Accepts PDFs, standalone images, or directories of page images. Each input becomes output/documents/<slug>/index.md.

The VLM also looks at embedded charts and figures. Captions such as “Figure 4 …” are followed by a short Figure insight: summary so the Markdown captures the visual takeaway even when the image is absent.

Useful flags:

• --model — VLM identifier (default: mlx-community/Nanonets-OCR2-3B-4bit).
• --max-tokens — limit Markdown length per page (default: 2048).
• --temperature — sampling temperature (default: 0.0).
• --pdf-dpi — render PDFs at this DPI before OCR (default: 200).
• --max-image-side — clamp the longest edge of page images (default: 2048).
• --system-prompt — override the OCR system instructions.
• --verbose — emit per-page timings once transcription completes.

Outputs

Website crawls produce:

<output>/<domain>/<slug>/
  ├─ index.md          # Markdown with YAML front matter
  └─ images/           # Downloaded images (if any)

Document transcription produces:

<output>/documents/<slug>/
  └─ index.md          # Markdown assembled from per-page OCR

Examples

Browse examples/ for sample outputs. For a larger knowledge base that uses these pipelines, see möbius and adapt the prompts or publishing recipes for your own content.

Work from a local checkout

1. uv sync — creates .venv/ with the runtime dependencies.
2. uv run python -m playwright install chromium — downloads the browser used by crawl.
3. uv run mlx-mdx crawl https://ml-explore.github.io/mlx/build/html/index.html --output output/mlx-docs --verbose
4. (Optional) uv tool run ty check --python .venv/bin/python — mirror the CI static type check.

Use uv run for development tasks inside the synced virtual environment.

Pipeline Overview

crawl input (URL)
  │
  ▼
Playwright renders the page in Chromium (handles client-side rendering)
  │
  ▼
Readability extracts the main article HTML
  │
  ▼
ReaderLM (MLX) rewrites the content as clean Markdown (no YAML in generation)
  │
  ▼
Image pipeline downloads, validates, and relinks assets
  │
  ▼
Outputs saved to <output>/<domain>/<slug>/index.md (with optional images/)

document input (PDFs, images, or directories)
  │
  ▼
Optional PDF rendering with pypdfium2
  │
  ▼
Nanonets OCR (MLX VLM) transcribes each page to structured Markdown
  │
  ▼
Markdown composer stitches pages with metadata and downloads referenced assets
  │
  ▼
Outputs saved to <output>/documents/<slug>/index.md

Operational Notes

• First runs of new models download weights from Hugging Face; subsequent runs reuse the cache.
• Only common web image formats under 10 MB are saved; files smaller than 512 bytes are skipped.
• Remove the output directory manually (rm -rf output) to clear prior runs.
• The crawler only processes URLs you provide—it does not follow links or recurse through sites.
• Document OCR relies on mlx-vlm. PDF rendering uses pypdfium2; if it is missing, reinstall with extras or provide page images directly.

Disclaimer

Please follow each site's terms of service. You are responsible for respecting rate limits and avoiding bans.