ffmpeg-llm
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ffmpeg-llmImport an MP4 file and add it to the timeline"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mnehmos.ffmpeg-llm.mcp
FFmpeg-based video editing engine exposed as an MCP (Model Context Protocol) server with OpenRouter LLM autopilot.
You don't need a video editor — you need a video editing engine with MCP tool exposure.
What is this?
An MCP server that gives AI agents structured control over FFmpeg. Instead of writing raw FFmpeg commands, agents call tools like clip_add, clip_trim, preview_segment, and render_full. The server maintains a JSON timeline as the source of truth and generates optimized FFmpeg filter graphs at render time.
47 tools across 8 categories: Project Management, Asset Management, Timeline Editing, Chapters, Preview & Render, Analysis, Chess Content Pipeline, and LLM Autopilot.
Related MCP server: ffmpeg-mcp
Features
Structured timeline editing — Tracks, clips, filters, chapters as JSON
FFmpeg filter graph generation — Timeline → optimized
-filter_complexcommandsPreview engine — Quick low-res previews before committing to full renders
Audio/scene analysis — Silence detection, scene changes, audio levels via FFmpeg
Chess content pipeline — Game boundary detection, overlays, YouTube export
LLM autopilot — OpenRouter vision models analyze video, suggest edits, identify highlights
Background rendering — Long exports run async with progress tracking
Undo/redo — Full operation history
Prerequisites
Node.js >= 18
FFmpeg >= 4.0 (in PATH)
ffprobe >= 4.0 (in PATH)
Installation
git clone https://github.com/Mnehmos/mnehmos.ffmpeg-llm.mcp.git
cd mnehmos.ffmpeg-llm.mcp
npm install
npm run buildUsage
As MCP Server
Add to your MCP client configuration:
{
"mcpServers": {
"ffmpeg-llm": {
"command": "node",
"args": ["path/to/mnehmos.ffmpeg-llm.mcp/dist/index.js"],
"env": {
"OPENROUTER_API_KEY": "your-key-here"
}
}
}
}Quick Start
1. project_create({ name: "My Video", workDir: "/path/to/project" })
2. asset_import({ projectId: "...", filePath: "/path/to/recording.mp4" })
3. clip_add({ projectId: "...", assetId: "...", trackId: "...", timelineStart: 0 })
4. clip_trim({ clipId: "...", sourceStart: 30, sourceEnd: 120 })
5. preview_segment({ projectId: "...", start: 0, end: 10 })
6. render_full({ projectId: "...", outputPath: "/path/to/output.mp4" })Development
npm run dev # Run with tsx
npm run test # Run tests
npm run test:watch # Watch mode
npm run test:coverage # With coverage
npm run preflight # typecheck + lint + testArchitecture
See DESIGN.md for full architecture documentation.
License
MIT
This server cannot be deployed
Maintenance
Related MCP Connectors
MCP server for Clipkit — gives AI agents a video toolbox via the Clipkit schema.
FFmpeg as a service for AI agents: typed video editing tools, async jobs, downloadable outputs.
A real timeline video editor for AI agents: journaled edits, FFmpeg/MLT rendering, exports
AI video editor for agents and humans: timeline, captions, color, audio and generation as MCP tools.
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