Ff Toolkit

ff-toolkit

FFmpeg operations as LLM-callable tools.

**Stop hand-writing FFmpeg subprocess calls and JSON tool schemas.** `ff-toolkit` gives you 5 production-ready media operations, dual-format LLM schemas (OpenAI + Anthropic), and an MCP server — all in one `pip install`.

Real-World Use Cases

**"My agent pipeline needs to process uploaded videos"** — Give your agent `openai_tools()` or `anthropic_tools()` and let it decide how to clip, transcode, or extract audio. The `dispatch()` function handles execution.

**"I need to batch-extract 16kHz WAV for ASR"** — One line: `extract_audio("video.mp4", "out.wav", codec="pcm_s16le", sample_rate=16000, channels=1)`

**"I want FFmpeg tools in Claude Desktop / Cursor"** — Add the MCP server config (3 lines of JSON) and Claude can edit your videos directly.

**"I want FFmpeg tools in DeepSeek Harness"** — `dsh plugin add dsh-ffkit` installs the native plugin from [integrations/deepseek-harness](integrations/deepseek-harness/).

**"I'm tired of writing the same FFmpeg commands"** — Use the CLI: `ffkit clip input.mp4 output.mp4 --start 00:01:00 --duration 30`

60-Second Quick Start

# Install (requires FFmpeg on PATH)
pip install ff-toolkit

# Verify it works — no API keys needed
ffkit probe some_video.mp4

# Or run the full demo with a generated test video
python -m ff_kit.examples.local

Python API

from ff_kit import clip, extract_audio, merge, transcode

# Trim seconds 60-90
clip("raw.mp4", "highlight.mp4", start="00:01:00", duration="30")

# Extract 16kHz mono audio for Whisper/Paraformer
extract_audio("raw.mp4", "speech.wav", codec="pcm_s16le", sample_rate=16000, channels=1)

# Concatenate intro + main + outro
merge(["intro.mp4", "main.mp4", "outro.mp4"], "final.mp4")

# Compress to 720p WebM for web delivery
transcode("raw.mp4", "web.web