Editorial Disclosure: The data in this article (repo name, stars, descriptions) was auto-collected by Dibi8 Tribe Intel from GitHub API. Analysis and editorial content is written by the Dibi8 editorial team.

TL;DR #

Video Use is a coding agent framework for video editing that allows you to manipulate videos using natural language instructions. With 15,555 GitHub stars, it represents a new paradigm in video editing — where you describe what you want, and an AI agent writes and executes the code to achieve it.

Key strengths:

Natural language video editing instructions
Powered by coding agents (similar to Claude Code, Codex)
Supports complex operations: trimming, effects, text overlays
Open-source and extensible
Built on the browser-use ecosystem

What Is Video Use? #

Video editing traditionally requires learning complex software interfaces and timelines. Video Use flips this model: instead of clicking through menus, you describe your desired outcome in plain English, and a coding agent generates and executes the necessary code.

# Edit a video with natural language
from video_use import VideoAgent

agent = VideoAgent()

# Trim first 30 seconds
agent.edit("trim the first 30 seconds of video.mp4")

# Add subtitles
agent.edit("add english subtitles to video.mp4")

# Apply color grading
agent.edit("apply warm vintage color grading to the video")

Why It Matters #

Accessibility #

Traditional video editing tools like Adobe Premiere and DaVinci Resolve have steep learning curves. Video Use makes professional-level editing accessible to anyone who can describe what they want in natural language.

Speed #

Complex video operations that take hours in traditional editors can be completed in minutes:

Batch processing hundreds of videos
Automated subtitle generation and translation
Consistent branding across video content
Rapid prototyping of video concepts

Developer-Friendly #

Unlike GUI-based tools, Video Use exposes a programmatic interface:

Integrate into CI/CD pipelines for automated video processing
Combine with other AI tools for end-to-end content creation
Version control your editing workflows
Share and reuse editing scripts

Architecture #

Agent-Based Processing #

class VideoEditingAgent:
    def __init__(self, llm_client):
        self.llm = llm_client
        self.video_processor = VideoProcessor()
        self.evaluator = QualityEvaluator()
        
    def execute(self, instruction: str, video_path: str) -> str:
        # 1. Parse instruction
        plan = self.llm.generate_edit_plan(instruction, video_path)
        
        # 2. Generate code
        code = self.llm.generate_edit_code(plan)
        
        # 3. Execute and verify
        result = self.video_processor.execute(code, video_path)
        
        # 4. Evaluate quality
        if not self.evaluator.is_acceptable(result):
            code = self.llm.refine_code(code, result)
            result = self.video_processor.execute(code, video_path)
            
        return result

Supported Operations #

The agent supports a wide range of video editing operations:

Operation	Description	Example
Trimming	Cut portions of video	“remove the first 10 seconds”
Concatenation	Join multiple videos	“combine intro.mp4 and main.mp4”
Subtitles	Add/generated subtitles	“add spanish subtitles”
Effects	Visual effects and filters	“apply sepia tone”
Text Overlay	Add text to video	“add watermark in bottom-right”
Audio	Audio editing and mixing	“lower background music by 50%”
Resolution	Change resolution/aspect ratio	“convert to 16:9”

Hands-On Experience #

Installation #

# Clone the repository
git clone https://github.com/browser-use/video-use.git
cd video-use

# Install dependencies
pip install -r requirements.txt

# Configure your LLM API key
export OPENAI_API_KEY=your-key-here

Basic Editing #

from video_use import VideoAgent

# Initialize the agent
agent = VideoAgent(model="gpt-4o")

# Simple trim
result = agent.edit(
    input_video="raw_footage.mp4",
    instruction="trim to the first 60 seconds and add a fade-out",
    output="trimmed.mp4"
)

# Complex multi-step edit
result = agent.edit(
    input_video="presentation.mp4",
    instruction="add title slide, insert subtitles in English, and reduce background music volume",
    output="edited.mp4"
)

Batch Processing #

from video_use import BatchProcessor

processor = BatchProcessor(agent)

# Process multiple videos
results = processor.batch_edit(
    videos=["video1.mp4", "video2.mp4", "video3.mp4"],
    instruction="add company watermark and resize to 1920x1080",
    output_dir="./processed"
)

Custom Scripts #

For complex operations, you can write custom editing scripts:

from video_use import ScriptRunner

runner = ScriptRunner()

# Define a custom editing workflow
workflow = """
1. Load video.mp4
2. Detect scenes and split into clips
3. For each clip:
   - Apply color correction
   - Add chapter title overlay
4. Concatenate all clips
5. Export as final.mp4
"""

result = runner.execute(workflow, input="video.mp4", output="final.mp4")

Comparison with Traditional Tools #

Feature	Video Use	Premiere Pro	DaVinci Resolve	FFmpeg
Learning Curve	Low	High	High	High
Natural Language	✅	❌	❌	❌
Programming API	✅	✅	✅	✅
Batch Processing	✅	✅	✅	✅
AI-Powered	✅	Partial	Partial	❌
Cost	Free	$22/mo	Free (Pro $299)	Free
Code Reusability	✅	❌	❌	✅

FAQ #

Advanced Editing Workflows #

Scene Detection and Splitting #

Automatically detect scene boundaries and split videos:

from video_use import SceneDetector

detector = SceneDetector(method="gradient")

# Detect scenes in a video
scenes = detector.detect("raw_footage.mp4", threshold=0.3)

# Split into clips
for i, scene in enumerate(scenes):
    agent.edit(
        input_video="raw_footage.mp4",
        instruction=f"extract scene {i+1} from {scene.start:.1f}s to {scene.end:.1f}s",
        output=f"scene_{i+1}.mp4"
    )

Color Grading Presets #

Apply professional color grading presets:

# Apply cinematic color grade
npx video-use grade video.mp4   --preset cinematic   --intensity 0.8   --output graded.mp4

# Create custom grade
npx video-use grade video.mp4   --custom "teal-shadows orange-highlights"   --luts ./luts/my-lut.cube   --output graded.mp4

Automated Subtitle Generation #

Generate and translate subtitles automatically:

from video_use import SubtitleGenerator

generator = SubtitleGenerator(
    speech_model="whisper-large-v3",
    translation_model="opus-mt"
)

# Generate English subtitles
subs = generator.generate(
    video="presentation.mp4",
    language="en",
    format="srt"
)

# Translate to multiple languages
for lang in ["zh", "ko", "vi", "es"]:
    generator.translate(subs, target_lang=lang, output=f"sub_{lang}.srt")

Video Compression Pipeline #

Optimize videos for different platforms:

from video_use import CompressionPipeline

pipeline = CompressionPipeline()

# YouTube optimization
youtube_video = pipeline.optimize(
    input_video="raw.mp4",
    target="youtube",
    quality="1080p"
)

# TikTok optimization
tiktok_video = pipeline.optimize(
    input_video="raw.mp4",
    target="tiktok",
    aspect_ratio="9:16"
)

# Instagram optimization
instagram_video = pipeline.optimize(
    input_video="raw.mp4",
    target="instagram",
    max_size="50MB"
)

Performance Tips #

GPU Acceleration #

# Enable CUDA acceleration
export VIDEO_USE_GPU=cuda
export VIDEO_USE_DEVICE=0

# Verify GPU is available
python -c "import video_use; print(video_use.gpu_info())"

Batch Processing Optimization #

# Process videos in parallel
from video_use import ParallelProcessor

processor = ParallelProcessor(max_workers=4)
results = processor.batch_edit(
    videos=glob.glob("/videos/*.mp4"),
    instruction="add logo watermark and resize to 1920x1080",
    output_dir="/processed/"
)

Q: What video formats are supported? #

A: MP4, MOV, AVI, MKV, WebM, and most common formats. The underlying ffmpeg library handles format conversion automatically.

Q: Can it handle 4K video? #

A: Yes, but performance depends on your hardware. For 4K processing, ensure adequate GPU memory and consider using the CPU-only mode for stability.

Q: Is it suitable for professional video editing? #

A: While it excels at common operations (trimming, subtitles, effects), complex multi-track editing with advanced color grading may still require traditional tools.

Q: How does it handle audio editing? #

A: Video Use supports audio operations including volume adjustment, audio extraction, background music replacement, and basic audio effects.

Q: Can I integrate it into my existing workflow? #

A: Yes, the Python API makes it easy to integrate into existing pipelines. It can be combined with other AI tools for end-to-end content creation.

How We Collect This Data #

This article’s data was auto-collected by Dibi8 Tribe Intel from GitHub API and trending pages. Star counts, fork counts, and basic metadata are verified via GitHub API. Editorial analysis is conducted by the Dibi8 team.

Join the Community #

Join the browser-use Discord for support and discussions about Video Use and related projects.

Join Our Telegram Group #

Stay updated on the latest AI tools and open-source projects. Join our Telegram Group for daily AI tool recommendations, community discussions, and exclusive content.

Sources #

GitHub Repository — Official source code and documentation
GitHub API — Star counts, fork counts, and metadata
Official Documentation — User guide and API reference

Disclosure: This article contains no affiliate links. Dibi8 maintains editorial independence from all projects we cover.

Q: What video codecs are supported? A: H.264, H.265/HEVC, VP9, AV1, and ProRes. The tool automatically selects the optimal codec based on the target platform and quality requirements.

Q: Can it handle live video streams? A: Currently, Video Use focuses on pre-recorded video files. Live stream processing is planned for future releases with RTMP and HLS support.

Creative Applications #

Automate content creation for social platforms:

from video_use import SocialCreator

creator = SocialCreator()

# Create TikTok content
tiktok = creator.create(
    source="raw_footage.mp4",
    platform="tiktok",
    style="trendy",
    duration="60s",
    add_music=True,
    add_subtitles=True
)

# Create YouTube Shorts
shorts = creator.create(
    source="raw_footage.mp4",
    platform="youtube_shorts",
    style="educational",
    duration="60s",
    add_chapters=True
)

# Create Instagram Reels
reels = creator.create(
    source="raw_footage.mp4",
    platform="instagram_reels",
    style="aesthetic",
    duration="30s",
    add_filters=True
)

Educational Content #

Generate tutorial videos automatically:

# Create screencast tutorial
npx video-use tutorial   --screen-record demo.mp4   --script tutorial-script.txt   --add-voiceover   --add-annotations   --output tutorial.mp4

# Create slide presentation video
npx video-use slides   --input slides.pptx   --transition smooth   --duration 5s-per-slide   --add-background-music   --output presentation.mp4

Marketing Videos #

Automate marketing video production:

from video_use import MarketingVideo

video = MarketingVideo()

# Product showcase
product_video = video.create(
    product_images=["img1.jpg", "img2.jpg", "img3.jpg"],
    style="modern",
    music="upbeat",
    text_overlay=["Feature 1", "Feature 2", "Feature 3"],
    duration="30s",
    output="product-showcase.mp4"
)

# Testimonial compilation
testimonial = video.compile_testimonials(
    clips=["test1.mp4", "test2.mp4", "test3.mp4"],
    transition="fade",
    add_quotes=True,
    output="testimonials.mp4"
)

Community Resources #

Templates and Presets #

The community has created numerous templates:

# Browse community templates
npx video-use templates list

# Apply a community template
npx video-use apply-template   --template cinematic-intro   --input raw.mp4   --output cinematic.mp4

# Create and share your own template
npx video-use create-template   --name my-style   --from edited.mp4   --export template.json

Plugin Ecosystem #

Extend Video Use with community plugins:

# Install community plugins
pip install video-use-plugins

# Load plugins
from video_use import PluginManager

manager = PluginManager()
manager.load("transitions", "effects", "text-overlay", "audio-mixing")

# Use custom plugin
result = manager.apply(
    video="input.mp4",
    plugin="cinematic-luts",
    preset="kodak-2383"
)

Advanced Video Processing #

Audio Processing Pipeline #

from video_use import AudioProcessor

audio = AudioProcessor()

# Extract and transcribe audio
transcript = audio.transcribe(
    video="presentation.mp4",
    language="en",
    model="whisper-large-v3"
)

# Generate voiceover
audio.generate_voiceover(
    text="Welcome to our product demo...",
    voice="professional-male",
    speed=1.0,
    output="voiceover.mp3"
)

# Mix audio tracks
audio.mix(
    tracks=["original_audio.mp3", "voiceover.mp3", "background_music.mp3"],
    volumes=[0.3, 1.0, 0.2],
    output="mixed_audio.wav"
)

Advanced Transitions and Effects #

# Apply cinematic transitions
npx video-use transitions   --input raw.mp4   --effect zoom-into-black   --duration 0.5s   --output enhanced.mp4

# Multiple effects chain
npx video-use chain   --input raw.mp4   --effects "color-grade,slow-mo,transition,subtitle"   --config effects.json   --output final.mp4

Scaling for Production #

Distributed Processing #

from video_use import DistributedCluster

cluster = DistributedCluster(
    num_workers=8,
    gpu_devices=[0, 1, 2, 3],
    storage_backend="s3"
)

# Process a large batch
results = cluster.process(
    input_bucket="raw-videos/",
    output_bucket="processed-videos/",
    instruction="add watermark and convert to 1080p",
    priority="high"
)

print(f"Processed {results.completed}/{results.total} videos")
print(f"Avg time per video: {results.avg_time:.1f}s")

CI/CD Integration #

# GitHub Actions for automated video processing
name: Video Processing Pipeline
on:
  push:
    paths:
      - 'videos/**'

jobs:
  process:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Process videos
        run: |
          pip install video-use
          video-use batch-process videos/ --output processed/
      - name: Upload results
        uses: actions/upload-artifact@v4
        with:
          name: processed-videos
          path: processed/

Conclusion #

Video Use represents a paradigm shift in how we approach video editing. By combining the power of AI coding agents with video processing, it makes professional-level editing accessible to everyone. The 15.5K+ stars reflect strong demand for tools that democratize creative workflows.

As AI capabilities continue to improve, tools like Video Use will become increasingly powerful, enabling creators to focus on storytelling rather than technical complexity.