20 Open-Source AI Projects You Should Contribute to in 2025
The open-source AI ecosystem is booming in 2025, with new libraries, tools, and models appearing all the time.
Contributing to AI projects on GitHub can help you learn, build your resume, and advance the field. In this post, we list 20 active open-source AI projects – across machine learning, deep learning, NLP, computer vision, and more – that have welcoming communities and frequent updates. Each project below includes a brief description and why it’s worth contributing to. Whether you’re a beginner or an advanced developer, you’ll find projects that match your skills. Let’s dive into some of the best AI projects for 2025 that you can explore and contribute to!
Foundational AI Frameworks
TensorFlow – A comprehensive open-source platform for machine learning and AI. TensorFlow provides a flexible collection of tools and libraries to build and train models, and it is backed by Google Brain. It supports deployment anywhere (cloud, browsers, mobile, etc.) and has many variants like TensorFlow Lite and TensorFlow.js. TensorFlow has a huge community of contributors and many beginner-friendly tutorials. It’s a great project to contribute to if you want to help improve a widely used AI toolkit.
PyTorch – A popular open-source deep learning framework developed by Meta (Facebook). PyTorch offers a Pythonic interface for tensor computation and neural networks, with eager execution that’s intuitive for research and production. It is extendable (with libraries for interpretability, graph neural networks, etc.) and supports seamless distributed training. PyTorch has a very active GitHub repo and many contributors worldwide. Contributing to PyTorch helps advance a core tool that powers much of modern AI research and applications.
Keras – A user-friendly neural network API that runs on top of TensorFlow (and other backends). Keras allows for easy and fast prototyping of deep learning models, supporting convolutional and recurrent networks on both CPUs and GPUs. Because of its simple interface, Keras is widely used by beginners learning deep learning. The Keras codebase (now integrated with TensorFlow) is open for contributions, so you can help improve its ease of use or add new features.
Scikit-learn – A staple open-source machine learning library in Python. Scikit-learn provides simple, efficient tools for data mining and analysis, including classifiers, regressors, clustering, preprocessing, and more. It supports both supervised and unsupervised learning and is designed to interoperate with the Python ecosystem (NumPy, SciPy, pandas). Scikit-learn is beginner-friendly and has a large community of users. Its clear documentation and code make it an excellent project for newcomers to contribute to.
JAX – A newer open-source library for high-performance machine learning and numerical computing. JAX brings together NumPy-like array computing with automatic differentiation and just-in-time (JIT) compilation via Google’s XLA compiler. It’s developed by Google with contributions from Nvidia and others. JAX is designed for fast, parallel computation on CPU, GPU, and TPU, and it works well with existing ML frameworks. Contributing to JAX lets you work on cutting-edge compiler and autodiff technology for AI.
Natural Language Processing (NLP)
Hugging Face Transformers – A library of state-of-the-art pre-trained models (BERT, GPT, T5, etc.) for NLP and beyond. Hugging Face’s Transformers lets you perform tasks like text classification, translation, summarization, and more using models that work in TensorFlow or PyTorch. It has become the go-to hub for NLP models (“the GitHub of open-source models”). The repo is very active with thousands of contributors. By joining, you can help improve documentation, add new model features, or enhance training pipelines.
spaCy – An industry-standard open-source library for advanced NLP in Python. SpaCy is designed for large-scale information extraction and text processing, with blazing-fast performance thanks to Cython implementation. It provides pretrained models for many languages, and components for tokenization, POS tagging, NER, parsing, and more. SpaCy’s ecosystem includes plugins and integrations. With its huge community and practical focus, contributing to spaCy is rewarding – you might help build new language models, improve accuracy, or add cutting-edge features (like new Transformer-based pipelines).
Rasa – An open-source framework for building conversational AI chatbots and voice assistants. Rasa provides a flexible pipeline for natural language understanding and dialogue management. It is fully programmable, so you have complete control over your assistant’s behaviors. Rasa has an active developer community (over 750 contributors) and supports many languages. Contributing to Rasa is a great way to get into conversational AI; you could help by improving its ML models, adding integrations, or expanding documentation.
Computer Vision
OpenCV – A leading open-source computer vision and machine learning library. OpenCV offers thousands of optimized algorithms for image and video analysis, such as face detection, object recognition, motion tracking, and 3D reconstruction. It is cross-platform and supports GPU acceleration for real-time applications like video analytics or autonomous systems. OpenCV has an enormous user base and many stars on GitHub. By contributing, you can work on popular computer-vision code and help improve algorithms that power many industries (robotics, healthcare imaging, security, etc.).
Ultralytics YOLO (You Only Look Once) – An open-source project providing state-of-the-art real-time object detection models. Ultralytics maintains up-to-date YOLO models and code that excel at tasks like object detection, tracking, segmentation, and pose estimation. The models are fast and easy to use via a simple CLI or Python API. Contributing to YOLO (for example, by adding new model architectures, improving training code, or writing tutorials) helps keep one of the most popular vision repositories cutting-edge. The Ultralytics community is active and welcomes new developers.
Detectron2 – Facebook AI Research’s next-generation library for object detection and segmentation. Detectron2 provides modular, flexible implementations of state-of-the-art algorithms (e.g., Mask R-CNN, RetinaNet, etc.). It’s written in PyTorch and is the successor to the original Detectron. Detectron2 supports features like panoptic segmentation and DensePose, and it’s used in research and production at Meta and elsewhere. With over 32k stars on GitHub, Detectron2 has an active user base. Contributors can work on new models, improve performance, or extend it to new tasks in computer vision.
AI Infrastructure & Tools
ONNX (Open Neural Network Exchange) – An open-source AI ecosystem for model interoperability. ONNX defines a standard format for neural network models, so you can train in one framework (PyTorch, TensorFlow, etc.) and run in another with no hassle. This promotes reusability of models across tools. ONNX is backed by a community led by Facebook and Microsoft. Contributing to ONNX means helping the AI community by making models more portable – for example, by adding support for new operators or improving framework converters.
Ray – A flexible, high-performance platform for building distributed applications, including AI/ML workloads. Ray (developed at UC Berkeley) lets you scale Python code from a laptop to a cluster with minimal changes. It is already “a backbone technology for OpenAI and other hyperscalers” thanks to its ability to do distributed training and inference at scale. Ray is ideal for parallel and reinforcement learning projects. If you contribute to Ray, you’ll be working on core infrastructure that can speed up AI training and make large-scale experiments easier.
Kubeflow – An open-source toolkit for deploying machine learning workflows on Kubernetes. Kubeflow provides modular, Kubernetes-native components to support each stage of the AI lifecycle (training, serving, pipelines, etc.). You can use individual projects (like Pipelines or Katib) independently or deploy the full reference platform. Kubeflow focuses on portability and scalability of ML workloads. Contributing to Kubeflow means helping teams run AI in production, for example by improving pipeline tooling, adding support for new Kubernetes features, or writing examples.
DVC (Data Version Control) – An open-source tool to manage data and model versioning in ML projects. DVC extends Git to track large datasets, ML models, and pipelines, making experiments reproducible. It integrates with cloud storage to handle big files. By using DVC, teams can share ML projects without losing track of data changes. Contributing to DVC (on GitHub) is a good way to help develop ML tooling; you could add new storage options, improve the CLI/UI, or make experiment tracking more robust.
MLflow – An open-source platform for managing the end-to-end machine learning lifecycle. MLflow, originated by Databricks, provides experiment tracking, model packaging, deployment, and a model registry. Its goal is to keep each phase of an ML project “manageable, traceable, and reproducible”. MLflow has a large community and is framework-agnostic (works with any ML library). Contributors to MLflow can help improve its tracking UI, add new serving backends, or integrate with popular cloud services.
Other Popular AI Projects
OpenAI Gym – A standard toolkit for developing and comparing reinforcement learning (RL) algorithms. Gym provides a simple, unified interface to a variety of environments (games, control tasks, etc.) for RL agents. It’s been an industry standard since its release.
Note: development has moved to the Gymnasium project, but many users still rely on Gym’s API. Contributing to Gym/Gymnasium (or new RL libraries like Stable Baselines3) lets you impact the RL community. You might improve environments, add new benchmarks, or help with documentation and tutorials.
PyTorch Lightning – A lightweight framework on top of PyTorch that abstracts away boilerplate code. Lightning handles the engineering (training loops, logging, checkpointing) so researchers can focus on model development. It is beginner-friendly and makes PyTorch training more organized. With an active developer team and users, Lightning is open to contributions in areas like new callbacks, features for distributed training, or better integration with cloud platforms.
XGBoost – A widely-used open-source library for gradient boosting on decision trees. XGBoost is highly efficient, portable, and works across many environments (single machine, clusters, Python, R, etc.). It’s known for its speed and accuracy on tabular data. Many data scientists use XGBoost for modeling. Contributing to XGBoost (on its GitHub repo) could involve optimizing performance, adding new features, or improving documentation. It’s a great way to get involved in an ML project with a very large user base.
Hugging Face Diffusers – A library for state-of-the-art diffusion models (image, audio, even molecule generation). Diffusers makes it easy to use pretrained diffusion pipelines or train your own generative models. It focuses on usability and has become the go-to toolbox for diffusion research. Contributing to Diffusers lets you shape the future of generative AI – for example, by adding new models (like Stable Diffusion variants), improving speed or memory usage, and expanding tutorials. The Hugging Face community is very welcoming, especially for cutting-edge AI.
Each of these projects has an active community on GitHub or related forums. They are good entry points for contributors: you can fix bugs, add new features, improve docs, or help others learn. As open-source AI grows, contributing to these AI GitHub repos not only helps you learn, but also advances tools that many developers and researchers rely on.
Pick a project that interests you, check its issue tracker, and start contributing!
Sources: Descriptions are based on official project docs and industry articles, The cited sources provide more details on each project.
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