Awesome Open Source AI
Curated open-source artificial intelligence models, libraries, infrastructure, and developer tools.
Contributing
Contents
About this list
Awesome Open Source AI is a curated list of open-source projects for people building with AI.
The goal is to help readers find useful models, libraries, tools, infrastructure, datasets, and learning resources without sorting through a directory dump.
Projects do not need a minimum number of GitHub stars to be included. Stars can be useful context, but they are only one signal. A smaller project may belong here if it is useful, well-maintained, technically interesting, clearly documented, or important to a specific part of the AI ecosystem.
Good entries should have a clear reason to exist. They should help people build, study, run, evaluate, or understand AI systems.
1. Core Frameworks & Libraries
Core libraries and frameworks used to build, train, and run AI and machine learning systems.
Deep Learning Frameworks
- PyTorch - Dynamic computation graphs, Pythonic API, dominant in research and production. The current standard for most frontier AI work.
- TensorFlow - End-to-end platform with excellent production deployment, TPU support, and large-scale serving tools.
- JAX - High-performance numerical computing with composable transformations (JIT, vmap, grad). Rising favorite for research and scientific ML.
+ Flax 
- dm-haiku - JAX-based neural network library from Google DeepMind. Elegant functional API with state management, widely used in DeepMind's research. Apache 2.0 licensed.
- Equinox - Elegant easy-to-use neural networks and scientific computing in JAX. Callable PyTrees with filtered transformations, seamless interoperability with the JAX ecosystem. Apache 2.0 licensed.
- Diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable ODE/SDE/CDE solvers for scientific machine learning and neural differential equations. Apache 2.0 licensed.
- vit-pytorch - Comprehensive Vision Transformer (ViT) implementations in PyTorch. Reference implementations of all major vision transformer variants including ViT, DeiT, Swin, and more. MIT licensed.
- NumPyro - Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation. Bayesian modeling and inference at scale.
High-Performance Compute Libraries
- oneDNN - oneAPI Deep Neural Network Library. Cross-platform performance library of basic building blocks for deep learning, optimized for Intel CPUs, GPUs, and Arm architectures. Apache 2.0 licensed.
- ONNX - Open standard for machine learning interoperability. Open Neural Network Exchange provides an open ecosystem that empowers AI developers to choose the right tools as their project evolves. Apache 2.0 licensed.
- IREE - Retargetable MLIR-based machine learning compiler and runtime toolkit. Lowers ML models to unified IR that scales from datacenter to mobile and edge deployments. Apache 2.0 licensed.
Rust ML Frameworks
- Burn - Next-generation deep learning framework in Rust. Backend-agnostic with CPU, GPU, WebAssembly support.
- Candle (Hugging Face) - Minimalist ML framework for Rust. PyTorch-like API with focus on performance and simplicity.
- linfa - Comprehensive Rust ML toolkit with classical algorithms. scikit-learn equivalent for Rust with clustering, regression, and preprocessing.
Julia ML Frameworks
- Flux.jl - 100% pure-Julia ML stack with lightweight abstractions on top of native GPU and AD support. Elegant, hackable, and fully integrated with Julia's scientific computing ecosystem.
- MLJ.jl - Comprehensive Julia machine learning framework providing a unified interface to 200+ models with meta-algorithms for selection, tuning, and evaluation. MIT licensed.
- ModelingToolkit.jl - High-performance symbolic-numeric modeling framework for scientific machine learning. Automatically generates fast functions for model components like Jacobians and Hessians with automatic sparsification and parallelization. MIT licensed.
NLP & Transformers
- spaCy (Explosion AI) - Industrial-strength natural language processing with 75+ languages, transformer pipelines, and production-grade NER, parsing, and text classification.
- Transformers (Hugging Face) - The de facto standard library for pretrained NLP models. 1M+ models, 250,000+ downloads/day. BERT, GPT, Llama, Qwen, and hundreds more.
- sentence-transformers - Classic library for sentence and image embeddings.
- tokenizers (Hugging Face) - Fast state-of-the-art tokenizers for training and inference.
- fairseq2 - FAIR Sequence Modeling Toolkit 2. Complete rewrite of fairseq with modern PyTorch APIs, native support for LLM training (70B+ models), vLLM integration, and first-party recipes for instruction finetuning and preference optimization. MIT licensed.
- LibreTranslate - Self-hosted machine translation API powered by the Argos Translate engine. AGPL-3.0 licensed.
Data Processing & Manipulation
- Pandas - The gold standard for data analysis and manipulation in Python.
- [Polars](https:
