diffusionstudio/lottie

Generate production-ready Lottie animations with Claude Code or Codex

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Summary Information

Updated 22 seconds ago
Added to GitGenius on September 12th, 2026
Created on June 4th, 2026
Open Issues & Pull Requests: 0 (+0)
GitHub issues: Enabled
Number of forks: 291
Total Stargazers: 5,455 (+0)
Total Subscribers: 11 (+0)

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Detailed Description

Lottie is a framework for generating production-ready Lottie animations through AI coding agents like Claude Code or Codex.

The tool solves the problem of creating complex vector animations by enabling developers to describe animations in natural language, which coding agents then translate into Lottie JSON files. The framework provides a workspace with a built-in player that displays animations as scenes within projects, automatically loading from a standard directory structure. As the agent edits animation files, the player updates in real time, allowing developers to inspect, scrub, and refine the output interactively. The approach emphasizes grounding prompts with concrete assets like SVGs or screenshots, using motion design terminology for timing and movement, and thinking in terms of camera operations to achieve professional results.

Developers should choose this tool if they want to generate animations programmatically without hand-coding Lottie JSON or learning animation software. It suits projects where animations need to be created at scale or integrated into development workflows. The generated animations can be used directly as Lottie JSON files or imported into After Effects for further refinement. The tool supports deployment across multiple platforms including web, React Native via Skia, iOS Swift, Android Kotlin, and Flutter.

The project maintains an active codebase with regular updates to both the framework and documentation. Development includes multilingual support, with documentation available in English and Traditional Chinese. The tool is actively refined based on practical usage patterns, as evidenced by the detailed prompt guide that captures lessons learned from generating animations with AI agents. The project provides concrete examples of generated animations and maintains clear guidance on how to structure prompts for better results.