videopython 0.31.3

Minimal video generation and processing library.

videopython

Minimal, LLM-friendly Python library for programmatic video editing, processing, and AI video workflows.

Full documentation: videopython.com

Disclaimer: This project started as a hand-written hobby project, but most of the code is now produced by LLM agents. Humans still drive direction, approve changes, and own design decisions.

Installation

1. Install FFmpeg

# macOS
brew install ffmpeg

# Ubuntu / Debian
sudo apt-get install ffmpeg

# Windows (Chocolatey)
choco install ffmpeg

2. Install videopython

pip install videopython          # core video/audio editing
pip install "videopython[ai]"    # + local AI features (GPU recommended)

Python >=3.10, <3.14. AI features run locally - no cloud API keys required, but model weights are downloaded on first use.

Quick Start

Imperative editing

Every editing primitive is an Operation subclass — a Pydantic model whose fields ARE the JSON wire format. Apply one to a Video:

from videopython.base import Video, CutSeconds, Resize, Fade

video = Video.from_path("raw.mp4")
video = CutSeconds(start=10, end=25).apply(video)
video = Resize(width=1080, height=1920).apply(video)
video = Fade(mode="in", duration=0.5).apply(video)
video.save("output.mp4")

Concatenate clips with + (must share fps + dimensions):

combined = video_a + video_b

JSON editing plans

Define multi-segment edits as JSON — the format LLM-driven workflows generate against. VideoEdit.json_schema() returns the schema:

from videopython.editing import VideoEdit

plan = {
    "segments": [{
        "source": "raw.mp4",
        "start": 10.0,
        "end": 20.0,
        "operations": [
            {"op": "resize", "width": 1080, "height": 1920},
            {"op": "color_adjust", "saturation": 1.15, "contrast": 1.05},
            {"op": "fade", "mode": "in", "duration": 0.5,
             "window": {"stop": 0.5}},
        ],
    }],
}

edit = VideoEdit.from_dict(plan)
edit.validate()                  # dry-run via metadata, no frames loaded
edit.run_to_file("output.mp4")   # stream to disk, ~constant memory

run_to_file() pipes ffmpeg decode → per-frame effects → ffmpeg encode, so memory stays bounded even for hour-long sources. Use edit.run() instead if you want the result back in memory as a Video.

AI generation

from videopython.ai import TextToImage, ImageToVideo, TextToSpeech
from videopython.base import Resize

image = TextToImage().generate_image("A cinematic mountain sunrise")
video = ImageToVideo().generate_video(image=image)
audio = TextToSpeech().generate_audio("Welcome to videopython.")

video = Resize(width=1080, height=1920).apply(video)
video.add_audio(audio).save("ai_video.mp4")

LLM & AI Agent Integration

The library is built for LLM-driven editing. Two surfaces matter:

1. Plan schema for tool / structured-output calls. VideoEdit.json_schema() returns a JSON Schema covering segments, post_operations, and a discriminated union over every registered Operation. Drop it into any LLM API:

from videopython.editing import VideoEdit

schema = VideoEdit.json_schema()
# Anthropic: tools=[{"name": "edit", "input_schema": schema}]
# OpenAI:    tools=[{"type": "function",
#                    "function": {"name": "edit", "parameters": schema}}]

Validate the LLM's output without touching the filesystem, then run it:

edit = VideoEdit.from_dict(plan)
edit.validate()                  # catches bad ops, time ranges, fps mismatches
edit.run_to_file("output.mp4")

2. Operation discovery for agent loops. Every registered op exposes its own Pydantic schema, so an agent can introspect what's available without hardcoded lists:

from videopython.base import Operation, OpCategory

for op_id, cls in Operation.registry().items():
    print(f"{op_id}: {(cls.__doc__ or '').splitlines()[0]}")

schema = Operation.get("color_adjust").model_json_schema()  # per-op schema

Field constraints (minimum, maximum, enum, exclusiveMinimum, nullability) flow through to the schema, so LLMs that support constrained generation produce valid parameters on the first try.

For ops that need side-channel data (e.g. silence_removal and add_subtitles need a Transcription), pass it via context:

edit.run(context={"transcription": my_transcription})

Docs: Editing Plans | Operations | LLM Integration Guide

Features

videopython.base - core editing (no AI dependencies)

Area	Highlights
Video I/O	Video, VideoMetadata, FrameIterator - load, save, inspect
Operation foundation	Operation, Effect, TimeRange, OpCategory - Pydantic base + auto-registry + discriminated-union schema
Editing plans	VideoEdit, SegmentConfig - JSON/LLM-friendly multi-segment plans with JSON Schema generation, dry-run validation, and streaming run_to_file
Transforms	Cut (time/frame), resize, crop, FPS resampling, speed change, reverse, freeze frame, silence removal
Effects	Blur, zoom, color grading, vignette, Ken Burns, image overlay, fade, text overlay, volume adjust
Audio	Load/save, overlay, concat, normalize, time-stretch, silence detection, segment classification
Text	Transcription data classes, TranscriptionOverlay for subtitle rendering
Scene detection	Histogram-based scene boundaries (detect, detect_streaming, detect_parallel)

API docs: Core | Video | Audio | Editing Plans | Operations | Transforms | Effects | Text

videopython.ai - local AI features (install with [ai])

Area	Highlights
Generation	TextToVideo, ImageToVideo, TextToImage, TextToSpeech, TextToMusic
Understanding	AudioToText (transcription), AudioClassifier, SceneVLM (structured visual scene description), FaceTracker (per-shot face tracks)
Scene detection	SemanticSceneDetector (neural scene boundaries)
Video analysis	VideoAnalyzer - full-pipeline analysis combining multiple AI capabilities
Transforms	FaceTrackingCrop
Dubbing	VideoDubber - voice cloning and revoicing with timing sync

API docs: Generation | Understanding | Transforms | Dubbing

Examples

• Social Media Clip
• AI-Generated Video
• Auto-Subtitles
• Processing Large Videos

Development

See DEVELOPMENT.md for local setup, testing, and contribution workflow.