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An Open Source Machine Learning Framework for Everyone

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Open Source Computer Vision Library

Deep Learning for humans

Ultralytics YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking

Streamlit — A faster way to build and share data apps.

Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search

Apache Spark - A unified analytics engine for large-scale data processing

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

Making large AI models cheaper, faster and more accessible

A toolkit for developing and comparing reinforcement learning algorithms.

结巴中文分词

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

Caffe: a fast open framework for deep learning.

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

💫 Industrial-strength Natural Language Processing (NLP) in Python

Visualizer for neural network, deep learning and machine learning models

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

The fastai deep learning library

MLX: An array framework for Apple silicon

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

Data Apps & Dashboards for Python. No JavaScript Required.

Graph Neural Network Library for PyTorch

Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, DeepSeek, and more. Simple declarative configs with command line and CI/CD integration. Used by OpenAI and Anthropic.

Minimalist ML framework for Rust

Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.