We opened a call for community project proposals earlier this year, offering small grants to support Mojo and MAX libraries forming the foundation of an ecosystem. We heard from a wide range of contributors and received many more strong proposals than we are able to support.
Here are the projects we’re supporting right now:
noeira
Denis Laboureyras (@denislabs)
Created in early 2026, noeira is an open-source reinforcement learning (RL) framework written entirely in Mojo. The project currently supports 40+ RL algorithms, GPU-accelerated deep RL, custom 2D/3D physics engines, native arcade game engines and SDL3 rendering.
Born of frustration with performance cliffs in the Python reinforcement learning stack, the long-term goal of this project is to provide a fully end-to-end RL framework where environments, agents and training all run on the GPU in pure Mojo.
In the near term, the project aims to publish rigorous benchmarks against established RL stacks (Gymnasium, Stable Baselines3, PyTorch) as a way of quantifying performance gains obtained by taking a pure Mojo approach.
Watch Denis’ community meeting presentation here.
bajo
Jean-Gabriel Simard (@jgsimard)
A parallel physics simulator, bajo is designed to run thousands of RL/robotics training environments simultaneously on a single GPU. The project has already shipped a state of the art GPU sorting implementation (Morton encoding + BVH construction) as its compute foundation.
The six-month plan is to package that sorting layer, implement performant parallel collision detection and rigid-body dynamics. This will then be scaled to a working multi-thousand-environment prototype and benchmarked against NVIDIA’s Warp and Madrona.
Watch Jean-Gabriel’s community meeting presentation here.
Fast Fourier Transform
Martin Vuyk Loperena (@martinvuyk)
A Fast Fourier Transform (FFT) implementation that's already approaching state of the art performance on both CPU and GPU, competitive with FFTW and cuFFT respectively. Started at a hackathon in mid-2025, the project has been continuously evolving since then.
Near term goals include adding half-sized transform support (hC2R, R2hC) and pushing for outright performance wins on the R2R, C2R and R2C transforms already supported. If the remaining gap can be closed, MAX's cuFFT dependency could be completely removed.
Catch the November community meeting for Martin’s presentation on his FFT implementation.
EmberJson
Brian Grenier (@bgreni)
EmberJson is the most widely used JSON parsing library in Mojo today. The package leans heavily on Mojo’s reflection capabilities and supports both structured and unstructured parsing.
The roadmap points toward a general serialization framework in the spirit of Rust's Serde, plus continued work on Python interoperability. Early benchmarks already show it out-pacing Python's built-in JSON handling despite interop overhead.
floki
Mikhail Tavarez (@thatstoasty)
floki is an easy to use Mojo HTTP client built on libcurl bindings. The project already supports many of the features users of Python’s requests module know and love. Development is now focused on providing ergonomic access to advanced libcurl configuration and simplifying the implementation of the underlying bindings.
Watch Mikhail’s community meeting presentation here.
momanim
Josiah Laivin (@josiahls)
momanim is a 2D animation framework for Mojo inspired by Manim. The project is aimed at using CPU and GPU acceleration to dramatically reduce rendering times and targets an API close enough to Manim's that LLM-assisted Python-to-Mojo conversion is realistic. The project is in its infancy: basic image/video APIs and shape rendering are already in place and a 3D rendering API is next on the roadmap.
Tune into the October community meeting to hear from Josiah about momanim.
The shape of things to come
Taken together, this cohort is a snapshot of where the Modular community is headed as Mojo and MAX continue to develop: foundational infrastructure and exciting applications. We’re thankful to everyone in the community for pushing the platform forward and we can’t wait to see what you’ll do next.
