browser-use/video-use

Edit videos with coding agents

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Summary Information

Updated 16 minutes ago
Added to GitGenius on July 2nd, 2026
Created on April 12th, 2026
Open Issues & Pull Requests: 75 (+0)
Number of forks: 2,620
Total Stargazers: 21,051 (+3)
Total Subscribers: 133 (-1)

Repository Insights (GitGenius)

Median issue/PR response: 3.6 days
Mean response time: 10.5 days
90th percentile: 32.4 days
Tracked items: 14

How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 4% of issues opened in the past year have been closed.

Charts & Analytics

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Issue Activity (beta)

Open issues: 18
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 30 days
Stale 30+ days: 12
Stale 90+ days: 2

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

Video-use is a Python-based video editing tool that leverages Claude Code and other AI agents to automate video post-production workflows. Rather than requiring users to manually edit footage through traditional video editing interfaces, the system allows users to drop raw video files into a folder and communicate editing requests through natural language conversation with an AI agent, which then produces a finished MP4 file. The tool is designed to handle diverse content types including talking heads, montages, tutorials, travel videos, and interviews without relying on presets or menu-driven workflows.

The core functionality encompasses several automated editing capabilities. The system cuts out filler words such as "umm" and "uh" along with false starts and dead space between takes. It applies automatic color grading to every segment with options for warm cinematic looks, neutral punch, or custom FFmpeg processing chains. Audio fades of 30 milliseconds are inserted at every cut to eliminate audible pops. The tool can burn subtitles into videos with customizable styling, defaulting to two-word uppercase chunks. It generates animation overlays through integration with HyperFrames, Remotion, Manim, or PIL, with animations spawned in parallel through sub-agents. The system self-evaluates rendered output at every cut boundary before presenting results to users, and maintains session memory in a project.md file so editing work can resume across multiple sessions.

The technical approach distinguishes video-use through its text-first architecture rather than frame-dumping. This design avoids the computational inefficiency of processing 30,000 frames multiplied by 1,500 tokens per frame, instead focusing the AI's analysis on structured text data supplemented by targeted visual references.

The editing pipeline operates through a structured workflow: the agent inventories source files, proposes an editing strategy for user approval, executes cuts at word boundaries and silence gaps, self-evaluates the rendered output at cut boundaries to catch visual jumps and audio artifacts, and persists all session data. All outputs are organized in an edit subdirectory alongside source files, keeping the skill directory clean.

The tool can be deployed through Browser Use Cloud for immediate access, or installed manually with setup handled through a prompt pasted into Claude Code or compatible agents. For persistent editing workflows, the system supports deployment through Browser Use Box for always-on operation from personal VPS or Telegram integration. The design philosophy emphasizes text plus on-demand visuals, audio-primary editing decisions, explicit strategy confirmation before execution, content-agnostic analysis, and strict production correctness rules while allowing artistic freedom in other aspects.