For the past few months we’ve been building dotlottie-rs, a new Lottie and dotLottie player that aims to run everywhere with smooth, high frame rate rendering and guarantee visual and feature support consistency across a large number of platforms and device types. It is lightweight, has low resource requirements and is performant.

It is MIT-licensed and is available at: https://github.com/LottieFiles/dotlottie-rs

The player is written in Rust and uses a minimal number of external dependencies. We utilize uniffi-rs to generate FFI bindings for Kotlin, Swift, and WASM, which are then used in our platform native distributions for Android, iOS and Web while maintaining a consistent API and experience across them. We also provide distributions for React and Vue to make it easy to adopt in many existing web projects. The player is also ideal for use in backend systems and pipelines for high performance server-side rendering of Lottie/dotLottie, and can be used easily in NodeJS projects.

The player is named dotlottie-rs because, apart from the first class Lottie support, we aim to have first class support for dotLottie (https://www.dotlottie.io), a superset of Lottie we developed that builds on Lottie to add enhanced features like multi-animation support, improved resource bundling, theming, state machines and interactivity (latter two are currently in development).

Under the hood, the player uses the open-source, lightweight, high performance ThorVG library (https://www.thorvg.org/) for vector graphics and Lottie rendering, supporting software, OpenGL, and WebGPU (currently in beta) rasterization backends. We are working towards landing complete support of the Lottie format spec (https://lottie.github.io/) as soon as possible.

We are starting to test and deploy it across our platform and hope it helps achieve similar improvements in performance and support as we are seeing!

There’s a few demos: Rust project: https://github.com/LottieFiles/dotlottie-rs/tree/main/demo-p... Web: https://github.com/LottieFiles/dotlottie-web?tab=readme-ov-f...

Would love to hear your thoughts and feedback :)