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Ffmpeg Render Pro

Parallel Node.js video rendering with FFmpeg, GPU detection, color grading, audio merging, and segment concatenation.

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Install Ffmpeg Render Pro

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "ffmpeg-render-pro": {
      "command": "npx",
      "args": [
        "-y",
        "ffmpeg-render-pro"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx -y ffmpeg-render-pro

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 7d ago.

Overview

The ffmpeg-render-pro MCP server gives AI agents access to a Node.js and FFmpeg-based video rendering workflow. It renders frame-producing worker scripts in parallel, encodes per-worker MP4 segments, and joins them without re-encoding. It also supports GPU encoder detection, color grading, audio merging, and video concatenation. Reach for it when generated video must be rendered from code on Windows, macOS, or Linux.

Use cases

  • Render generated frames into MP4 video
  • Detect available GPU encoders before rendering
  • Apply color presets or custom FFmpeg filters
  • Merge a soundtrack into rendered video
  • Concatenate matching MP4 segments

Key features

  • Parallel frame rendering with Node.js worker threads
  • GPU encoder detection with cached results
  • Browser-based local progress dashboard
  • Color grading with presets or custom filters
  • Audio merging with optional looping and normalization
  • Stream-copy concatenation of matching video segments

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

6/6 checks healthy over the last 42d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 21h ago via OSV.dev · ffmpeg-render-pro (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars3Stargazers on the source repository.
npm downloads264Package downloads in the last 30 days.
Last commit1mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity2 viewsConfig copies, upvotes, and views on AllMCPs.

How Ffmpeg Render Pro works

What ffmpeg-render-pro does

The ffmpeg-render-pro MCP server connects an AI-agent workflow to a local FFmpeg installation and a Node.js rendering library. Its core use case is rendering video from a worker script that fills raw BGRA pixel buffers one frame at a time. The renderer divides the frame range across Node.js worker threads, creates one MP4 segment per worker, and combines those segments with FFmpeg's concat demuxer using stream copy.

The package is also available as a command-line application and a Node.js library. Alongside rendering, it provides GPU encoder discovery, configurable encoding recipes, color grading, soundtrack insertion, segment joining, checkpoint helpers, and a local browser dashboard for progress.

How it works

A worker receives rendering data through workerData, including dimensions, frame rate, seed, its assigned frame range, segment path, worker ID, and whole-video totals. The worker writes frames to its own FFmpeg process. Every segment must use matching codec, dimensions, frame rate, and pixel format so the final join can use stream copy rather than another video encode.

The renderer chooses a worker count from available CPU cores and memory unless an exact count or maximum is supplied. It never creates more workers than there are frames. A failed worker range is retried once. Temporary segment files are removed after completion, and an AbortController can stop the operation and trigger cleanup.

The optional dashboard runs on 127.0.0.1, can open in a browser, and reports per-worker progress, frame rate, and estimated completion. Library callers can set the dashboard linger time to zero so the render call returns without keeping the process open.

Setup and configuration

Install Node.js 18 or newer and make ffmpeg available on PATH. A five-second benchmark can verify the installation, while init creates a starter worker and render runs it. The package can be invoked with npx or installed globally with npm. The installed command names are ffmpeg-render-pro for the CLI and ffmpeg-render-pro-mcp for the MCP server; the older ffmpeg-render-mcp name remains accepted.

Set FFMPEG_RENDER_PRO_FFMPEG when FFmpeg is installed outside PATH. FFMPEG_RENDER_PRO_FFPROBE can point to a separate ffprobe binary, although a sibling binary is detected automatically. FFMPEG_RENDER_PRO_CACHE_DIR changes the location of cached GPU probe results. These values are read when called, so a long-running MCP process can see changes without restarting.

Tools and capabilities

The ffmpeg-render-pro MCP server is suited to workflows that need to:

  • Render code-generated frames with configurable width, height, frame rate, duration, seed, worker count, output path, and encoder settings.
  • Detect available hardware encoders and cache the result for seven days.
  • Select encoder input, filter, and output arguments through library helpers.
  • Apply built-in color presets such as noir, warm, cool, cinematic, and vintage, or provide a custom FFmpeg video filter chain.
  • Add an AAC soundtrack while stream-copying the video; optional looping and loudness normalization are supported.
  • Concatenate same-codec, same-size videos after optional ffprobe validation.
  • Track worker progress and expose dashboard JSON during a render.

The CLI also includes info, detect-gpu, version, and benchmark commands. Render settings include CRF, encoder preset, dashboard behavior, output filename, and worker limits.

Limitations and notes

FFmpeg must be installed and resolvable either through PATH or the documented environment variables. Width and height must be even, with supported output dimensions documented up to 7680 by 4320. Parallel output is deterministic only when random values are derived from the supplied seed.

Workers must keep their encoding parameters consistent because segments are joined without re-encoding. Color grading normally produces a new encoded video and strips audio unless keepAudio is enabled. The README identifies Claude Code as a supported skill environment, but does not document compatibility with specific editor clients such as Cursor.

Read the full README View source on GitHub

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Frequently Asked Questions about Ffmpeg Render Pro

ffmpeg-render-pro is an MCP server that connects AI agents to local FFmpeg and Node.js video rendering workflows. Its main capabilities include parallel rendering from worker scripts, GPU encoder detection, color grading, audio merging, and concatenating matching MP4 segments without re-encoding.

Install it with npm or run it through npx; Node.js 18 or newer and FFmpeg are required. Use `npm install -g ffmpeg-render-pro` for a global install, or use the package's `ffmpeg-render-pro-mcp` executable in an MCP configuration.

Create a worker with `npx ffmpeg-render-pro init my-worker.js`, then run `npx ffmpeg-render-pro render my-worker.js --duration=5`. Edit the worker's `renderFrame` function to write BGRA pixels into the supplied buffer.

Yes, set `FFMPEG_RENDER_PRO_FFMPEG` to the binary path. You can also set `FFMPEG_RENDER_PRO_FFPROBE` for a separate probe binary and `FFMPEG_RENDER_PRO_CACHE_DIR` for GPU probe cache files.

It identifies the package as an MCP server and separately mentions a Claude Code skill, but provides no Cursor-specific setup or support details.

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