AI & ML

The most popular Deep Learning framework created by Google.

A framework that lets you easily use pre-trained transformer models for NLP, vision, and audio tasks.

Building applications with LLMs through composability.

An adaptive AI agent framework that grows with you.

Tensors and Dynamic neural networks in Python with strong GPU acceleration.

A general-purpose automatic speech recognition model trained on 680k hours of multilingual and multitask supervised data.

A collection of tools and datasets for Chinese NLP.

A high-throughput and memory-efficient inference and serving engine for LLMs.

The most popular Python library for Machine Learning with extensive documentation and community support.

A high-level deep learning library with support for JAX, TensorFlow, and PyTorch backends.

A library for faster LLM fine-tuning and training with reduced memory usage.

A multi-agents LLM financial trading framework.

A programming framework for building agentic AI applications.

An intelligent memory layer for AI agents enabling personalized interactions.

A framework for orchestrating role-playing autonomous AI agents for collaborative task solving.

A data framework for your LLM application.

A family of open-source voice AI models from Microsoft for text-to-speech and long-form speech recognition.

Apache Spark's scalable Machine Learning library for distributed computing.

MindsDB is an open source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using standard queries.

A library for high-performance numerical computing with automatic differentiation and JIT compilation.

The most popular Chinese text segmentation library.

A framework for programming, not prompting, language models.

A library for industrial-strength natural language processing in Python and Cython.

A library that provides pre-trained diffusion models for generating and editing images, audio, and video.

Deep learning framework to train, deploy, and ship AI products Lightning fast.

Ready-to-use OCR with 40+ languages supported.

A scalable, portable, and distributed gradient boosting library.

A high-performance serving framework for large language models and multimodal models.

OpenAI's framework for building and managing AI agents.

A pretrained foundation model from Google Research for time-series forecasting.

A fast, distributed, high performance gradient boosting framework.

A tokenizer-free text-to-speech foundation model for multilingual speech generation and voice cloning.

A Python agent framework for building generative AI applications with structured schemas.

Topic Modeling for Humans.

A leading platform for building Python programs to work with human language data.

Approximate Nearest Neighbors in C++/Python optimized for memory usage.

PyTorch implementations of Stable Baselines (deep) reinforcement learning algorithms.

A library for extracting structured data from LLMs, powered by Pydantic.

Open Source Differentiable Computer Vision Library for PyTorch.

A fast, scalable, high performance gradient boosting on decision trees library.

The Stanford NLP Group's official Python library, supporting 60+ languages.

Open Source Fast Scalable Machine Learning Platform.

A scikit for building and analyzing recommender systems.

A wrapper for Google Tesseract OCR.

Open Source Computer Vision Library.

Run and fine-tune large language models on Apple Silicon with MLX.

Python-friendly security skills for auditing, testing, and safer backend development. Also skills-curated.

An open-source AgentOS for multi-agent orchestration and building agentic AI systems.

A fast Python implementation of collaborative filtering for implicit datasets.

A Python library for probabilistic graphical models and Bayesian networks.

sklearn compatible API with the widest toolset for feature engineering and selection.

Open-source, local-first memory for any tool-capable LLM agent.

A lightweight, hook-first Python framework for channel-native agents that live alongside people.

Python-focused engineering skills for code review, debugging, and backend workflows.

Synthetic tabular data generation using GANs, Diffusion Models, and LLMs.

Django backend agent skills for Django, DRF, Celery, and Django-specific code review.