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  2. πŸŽ₯ Multimedia Process
  3. Ffmpeg Render Pro
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Ffmpeg Render Pro

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Parallel video rendering tools: detect GPU encoders, render, color grade, merge audio, concat.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "ffmpeg-render-pro": {
      "command": "npx",
      "args": [
        "-y",
        "ffmpeg-render-pro"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸŽ₯ More in Multimedia Process

Documentation Overview

ffmpeg-render-pro

ffmpeg-render-pro

npm version License: MIT Platform: Cross-platform Node.js MCP Server

Render video from code, in parallel. You write one function that paints a frame; ffmpeg-render-pro splits the frame range across worker threads, encodes one MP4 segment per worker, joins the segments with stream copy (no re-encode), and shows a live dashboard in your browser while it runs. It also detects GPU encoders, grades color, merges audio, and ships as a CLI, a Node library, an MCP server for AI agents, and a Claude Code skill.

Built by Beeswax Pat. Free and open source.

Start here

Three commands. You need Node.js 18 or newer and ffmpeg on your PATH.

bash
# 1. Prove the setup works: a 5 second test render. The dashboard opens in your browser.
npx ffmpeg-render-pro benchmark

# 2. Write a starter worker script into the current folder.
npx ffmpeg-render-pro init my-worker.js

# 3. Render it. Output lands in output.mp4.
npx ffmpeg-render-pro render my-worker.js --duration=5

Open my-worker.js. The only function you need to change is renderFrame(frameNum, buffer): fill the buffer with your pixels (B, G, R, A, one row after another) and everything else is already done. Derive any randomness from the seed it receives and parallel output stays identical to a sequential render.

Install it globally if you would rather not type npx:

Terminal
npm install -g ffmpeg-render-pro

Using an ffmpeg that is not on PATH

bash
FFMPEG_RENDER_PRO_FFMPEG=/opt/ffmpeg/bin/ffmpeg     # ffmpeg binary
FFMPEG_RENDER_PRO_FFPROBE=/opt/ffmpeg/bin/ffprobe   # optional; the sibling ffprobe is found automatically
FFMPEG_RENDER_PRO_CACHE_DIR=~/.ffmpeg-render-pro    # optional; where GPU probe results are cached

The variables are read at call time, so a long-running process such as the MCP server picks up changes without a restart.

CLI

bash
ffmpeg-render-pro init [my-worker.js]   # write the starter worker (--force overwrites)
ffmpeg-render-pro benchmark             # 5 second test render with the bundled worker
ffmpeg-render-pro render <worker.js>    # render with your worker
ffmpeg-render-pro info                  # cores, RAM, recommended workers, ffmpeg version, GPU
ffmpeg-render-pro detect-gpu            # probe hardware encoders (--cpu / --gpu force a mode)
ffmpeg-render-pro version

Render and benchmark flags: --width=1920 --height=1080 (must be even), --fps=60, --duration=60 (fractions allowed), --output=out.mp4, --workers=N, --max-workers=8, --seed=42, --title="...", --crf=20 (0-51, lower is higher quality), --encoder-preset=fast (any x264 preset). Dashboard flags: --no-dashboard, --no-open, --port=8080, --linger-ms=30000 (0 exits as soon as the render finishes). Run ffmpeg-render-pro with no arguments for the full list.

An unknown flag warns and continues. A value that does not parse, such as --fps=abc, exits 1 instead of rendering at the default.

Installed binaries: ffmpeg-render-pro (this CLI) and ffmpeg-render-pro-mcp (the MCP server). The older ffmpeg-render-mcp name still works so existing MCP configs never break.

How a render works

  1. renderParallel checks ffmpeg, validates the resolution, and picks a worker count from your CPU cores and RAM (never more workers than frames).
  2. It starts the dashboard server on 127.0.0.1 and opens your browser.
  3. Each worker thread runs your script with a frame range in workerData, pipes raw BGRA frames into its own ffmpeg process, and writes one MP4 segment.
  4. Segments are joined with the concat demuxer and -c copy, which takes seconds regardless of length.
  5. Temp files are removed. A failed worker's range is retried once before the render fails.

Your worker

A worker is a Node script that runs in a worker_threads thread. init gives you one where only renderFrame needs editing; examples/basic-worker.js in the installed package is a larger reference with a particle system and seeded RNG.

Fields the renderer injects through workerData:

FieldMeaning
width, height, fpsFrame size and rate
seedDerive every random value from this
startFrame, endFrameRender exactly [startFrame, endFrame)
segmentPathWrite this worker's MP4 here
workerIdInclude it in every message you post
totalFrames, durationWhole-video totals, for global effects such as a progress bar
anything in renderParallel({ workerData })Your own extra keys (the bundled workers honor codecArgs)

Messages the worker posts with parentPort.postMessage:

MessageWhenFields
{ type: 'progress' }periodicallyworkerId, pct, fps, frame, eta
{ type: 'fast-forward-start' }optional, before replaying state to reach startFrameworkerId, frames
{ type: 'done' }once, after the segment is fully writtenworkerId
{ type: 'error' }on failure, never followed by doneworkerId, error

Every worker must encode with the same codec, resolution, framerate, and pixel format, because the segments are stream-copied together.

Library

server.ts
const {
  renderParallel,       // the render engine
  createEncoder,        // pipe raw frames into ffmpeg with backpressure
  detectGPU,            // hardware encoder discovery, cached 7 days
  getConfig,            // worker count and codec choice for a resolution
  computeTotalFrames,   // float-safe fps x duration
  concatSegments,       // stream-copy join (validates inputs by default)
  colorGrade,           // presets or a custom -vf chain
  mergeAudio,           // add a soundtrack without re-encoding video
  startDashboard,       // the local progress server
  ProgressTracker,      // per-worker progress plus dashboard JSON
  saveCheckpoint, loadCheckpoint, restoreCheckpoint, generateCheckpoints,
  getEncoderIO,         // encoder recipe as { inputArgs, filter, outputArgs }
  getCodecArgs,         // the same recipe as one flat array
  ffmpegBin, ffprobeBin // resolved binaries, env-var aware
} = require('ffmpeg-render-pro');

renderParallel(options)

server.ts
const controller = new AbortController();

const result = await renderParallel({
  workerScript: './my-worker.js',   // required
  outputPath: './output.mp4',       // required
  width: 1920, height: 1080,        // even numbers, up to 7680x4320
  fps: 60, duration: 60,
  seed: 42,
  title: 'My Render',               // shown in the dashboard
  workerCount: undefined,           // exact count; omit to auto-detect
  maxWorkers: 8,                    // cap for auto-detect
  dashboard: true, autoOpen: true, dashboardPort: 8080,
  dashboardLingerMs: 0,             // 0 resolves as soon as the render ends (the CLI keeps it up 30s)
  quiet: false,                     // true keeps stdout byte-clean; status goes to stderr
  signal: controller.signal,        // abort() stops workers and removes temp files
  workerData: {},                   // extra keys for your worker
});
// result: { outputPath, elapsed, totalFrames, avgFps }

Abort rejects with an error whose name is 'AbortError'. In library use set dashboardLingerMs: 0 so the call returns without holding the process open. Set FFMPEG_RENDER_PRO_DEBUG=1 for full stack traces from the CLI.

Post-processing

js
// Color grade with a preset (noir, warm, cool, cinematic, vintage) or a custom -vf chain
await colorGrade({ inputPath: 'raw.mp4', outputPath: 'graded.mp4', preset: 'cinematic' });
await colorGrade({ inputPath: 'raw.mp4', outputPath: 'graded.mp4', filter: 'eq=contrast=1.08:saturation=0.9', crf: 18 });
await colorGrade({ inputPath: 'final.mp4', outputPath: 'graded.mp4', preset: 'noir', keepAudio: true }); // default strips audio

// Merge audio: video is stream-copied, audio becomes AAC. loop and normalize (loudnorm) are optional.
await mergeAudio({ videoPath: 'graded.mp4', audioPath: 'track.mp3', outputPath: 'final.mp4', bitrate: 320, loop: true, normalize: true });

// Join same-codec, same-size videos with stream copy. Inputs are probed with ffprobe first; pass { validate: false } to skip.
await concatSegments(['part-000.mp4', 'part-001.mp4'], 'joined.mp4');

colorGrade accepts any encoder name in codec; encoders that need their own filter (VA-API) get it merged into the grade chain automatically.

Checkpoints for long renders

For multi-hour renders, snapshot your simulation state every N frames once, so each worker replays only the frames since the nearest snapshot instead of starting from frame 0.

server.ts
generateCheckpoints({ systems, totalFrames: 432000, fps: 60, checkpointDir: './.checkpoints', interval: 60000 });

// inside a worker
const cp = loadCheckpoint('./.checkpoints', startFrame);
if (cp) {
  const resumeFrame = restoreCheckpoint(cp, systems);
  // fast-forward from resumeFrame to startFrame, then render
}

systems is an object of named modules with getState(), setState(), and update(dt). A checkpoint labeled frame F holds exactly F updates. _frame and _timestamp are reserved keys.

MCP server (for AI agents)

Seven tools over stdio, usable from Claude Code, Claude Desktop, or any MCP client.

Read the full README β†’View source on GitHub β†’

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Frequently Asked Questions about Ffmpeg Render Pro

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "ffmpeg-render-pro": { "command": "npx", "args": ["-y", "ffmpeg-render-pro"] } }

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Technical Specs & Signals

CategoryπŸŽ₯Multimedia Process
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
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27Quality signal: Emerging Β· 27/100How this signal is calculated β–Ύ
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Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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