browser-use/video-usePublic

Edit videos with coding agents

AI summary: An open-source tool that enables AI coding agents to autonomously edit and compile video footage.

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19.9K
+110 today
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Open PRs
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PythonMITCreated Apr 12, 2026Last push 1mo ago+1.8K stars this week+1.9K this month

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since Apr 12, 2026
05K10K15KApr 2026May 2026Jun 2026Aug 2026
19.9K stars as of Aug 7, 2026, tracked back to Apr 12, 2026. Historical curve reconstructed from public GitHub event archives, calibrated to the current total.

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16 commits in the last yearLessMore

Signals and awards

derived from tracked data
  • Widely adopted

    19,912 stars

  • Breakout launch

    19,912 stars in 118 days

  • Rising fast

    +1,756 stars this week

  • Permissive license

    MIT

  • Continuous integration

    Automated checks passing

  • Repeat trending

    4 trending appearances

What video-use does

video-use acts as an interface that allows AI agents, specifically Claude Code, to perform complex video editing tasks autonomously. By dropping raw footage into a designated folder and providing a natural language prompt, the tool orchestrates the necessary underlying scripts (like FFmpeg) to trim, splice, and compile a final MP4 file. It removes the need for complex, manual timeline editing in heavy GUI applications. The system is entirely prompt-driven, handling everything from talking-head cuts to dynamic montages without requiring the user to navigate presets or menus.

Designed for content creators, marketers, and developers looking to automate tedious video editing tasks. Requires familiarity with running AI coding agents and basic command-line environments.

  • Natural language editing: Translates conversational prompts into precise video trimming and compilation commands.
  • Autonomous workflow orchestration: Integrates with Claude Code to write and execute the necessary scripts to process the video.
  • Format agnostic processing: Handles various types of content including interviews, travel montages, and tutorials natively.
  • Zero-GUI operation: Runs entirely in the background, eliminating the need to learn complex timeline-based editing software.
  • Open-source transparency: Relies on standard underlying tools like FFmpeg, ensuring users can audit or modify the exact commands run.

Where teams use it

Automating talking-head edits

Content creators drop a long raw take into the folder and tell the AI to 'remove all silence and compile the best takes'.

Rapid montage generation

Social media managers prompt the tool to stitch together various b-roll clips to the beat of an audio track.

Repurposing long-form content

Marketers instruct the agent to extract three 30-second highlight clips from a 2-hour webinar recording.

Programmatic video pipelines

Developers integrate the tool into a backend workflow to automatically compile daily video reports from raw source files.

Getting started: Clone the repo, place your footage in the input folder, and run your agent with the provided prompt.

README

main branch

video-use

video-use

Introducing video-use — edit videos with Claude Code. 100% open source.

Drop raw footage in a folder, chat with Claude Code, get final.mp4 back. Works for any content — talking heads, montages, tutorials, travel, interviews — without presets or menus.

Try video-use in Browser Use Cloud.

What it does

  • Cuts out filler words (umm, uh, false starts) and dead space between takes
  • Auto color grades every segment (warm cinematic, neutral punch, or any custom ffmpeg chain)
  • 30ms audio fades at every cut so you never hear a pop
  • Burns subtitles in your style — 2-word UPPERCASE chunks by default, fully customizable
  • Generates animation overlays via HyperFrames, Remotion, Manim, or PIL — spawned in parallel sub-agents, one per animation
  • Self-evaluates the rendered output at every cut boundary before showing you anything
  • Persists session memory in project.md so next week's session picks up where you left off

Setup prompt

Paste into Claude Code, Codex, Hermes, Openclaw, or any agent with shell access:

Set up https://github.com/browser-use/video-use for me.

Read install.md first to install this repo, wire up ffmpeg, register the skill with whichever agent you're running under, and set up the ElevenLabs API key — ask me to paste it when you need it. Then read SKILL.md for daily usage, and always read helpers/ because that's where the editing scripts live. After install, don't transcribe anything on your own — just tell me it's ready and wait for me to drop footage into a folder.

The agent handles the clone, dependencies, skill registration, and prompts you once for your ElevenLabs API key (grab one at elevenlabs.io/app/settings/api-keys).

Then point your agent at a folder of raw takes:

cd /path/to/your/videos
claude    # or codex, hermes, etc.

For always-on editing from your own VPS or Telegram, run the agent through Browser Use Box. Watch the 15-second demo.

And in the session:

edit these into a launch video

It inventories the sources, proposes a strategy, waits for your OK, then produces edit/final.mp4 next to your sources. All outputs live in <videos_dir>/edit/ — the skill directory stays clean.

Manual install

If you'd rather do it by hand:

# 1. Clone and symlink into your agent's skills directory
git clone https://github.com/browser-use/video-use ~/Developer/video-use
ln -sfn ~/Developer/video-use ~/.claude/skills/video-use        # Claude Code
# ln -sfn ~/Developer/video-use ~/.codex/skills/video-use       # Codex

# 2. Install deps
cd ~/Developer/video-use
uv sync                         # or: pip install -e .
brew install ffmpeg             # required
brew install yt-dlp             # optional, for downloading online sources

# 3. Add your ElevenLabs API key
cp .env.example .env
$EDITOR .env                    # ELEVENLABS_API_KEY=...

How it works

The LLM never watches the video. It reads it — through two layers that together give it everything it needs to cut with word-boundary precision.

timeline_view composite — filmstrip + speaker track + waveform + word labels + silence-gap cut candidates

Layer 1 — Audio transcript (always loaded). One ElevenLabs Scribe call per source gives word-level timestamps, speaker diarization, and audio events ((laughter), (applause), (sigh)). All takes pack into a single ~12KB takes_packed.md — the LLM's primary reading view.

## C0103  (duration: 43.0s, 8 phrases)
  [002.52-005.36] S0 Ninety percent of what a web agent does is completely wasted.
  [006.08-006.74] S0 We fixed this.

Layer 2 — Visual composite (on demand). timeline_view produces a filmstrip + waveform + word labels PNG for any time range. Called only at decision points — ambiguous pauses, retake comparisons, cut-point sanity checks.

Naive approach: 30,000 frames × 1,500 tokens = 45M tokens of noise. Video Use: 12KB text + a handful of PNGs.

Same idea as browser-use giving an LLM a structured DOM instead of a screenshot — but for video.

Pipeline

Transcribe ──> Pack ──> LLM Reasons ──> EDL ──> Render ──> Self-Eval
                                                              │
                                                              └─ issue? fix + re-render (max 3)

The self-eval loop runs timeline_view on the rendered output at every cut boundary — catches visual jumps, audio pops, hidden subtitles. You see the preview only after it passes.

Design principles

  1. Text + on-demand visuals. No frame-dumping. The transcript is the surface.
  2. Audio is primary, visuals follow. Cuts come from speech boundaries and silence gaps.
  3. Ask → confirm → execute → self-eval → persist. Never touch the cut without strategy approval.
  4. Zero assumptions about content type. Look, ask, then edit.
  5. 12 hard rules, artistic freedom elsewhere. Production-correctness is non-negotiable. Taste isn't.

See SKILL.md for the full production rules and editing craft.

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16 commits in the last 52 weeks.

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DateListRankStars gained
Aug 6, 2026daily#13+320
Aug 5, 2026daily#13+320
Jul 3, 2026daily#18+11
Jun 29, 2026daily#25+10