Open Source Python Libraries - Page 3
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Python Libraries
View 75 business solutionsBrowse free open source Python Libraries and projects below. Use the toggles on the left to filter open source Python Libraries by OS, license, language, programming language, and project status.
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pythondialog
Python module to build dialogs for terminal-based applications
This is a Python module for doing terminal-based user interaction. It wraps the dialog/Xdialog program, and provides a nice, object-oriented programming model. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. Finally, the Deep Learning book provides research directions covering theoretical topics including linear factor models, autoencoders, representation learning, structured probabilistic models, etc. -
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Docker SDK for Python
A Python library for the Docker Engine API
A Python library for the Docker Engine API. It lets you do anything the docker command does, but from within Python apps, run containers, manage containers, manage Swarms, etc. The latest stable version is available on PyPI. Either add docker to your requirements.txt file or install with pip. To communicate with the Docker daemon, you first need to instantiate a client. The easiest way to do that is by calling the function from_env(). It can also be configured manually by instantiating a DockerClient class. Run and manage containers on the server. You can also create more advanced networks with custom IPAM configurations. Get and list nodes in a swarm. Before you can use these methods, you first need to join or initialize a swarm. Manage plugins on the server. Both the main DockerClient and low-level APIClient can connect to the Docker daemon with TLS. -
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Hypothesis
The property-based testing library for Python
Hypothesis is a powerful library for property-based testing in Python. Instead of writing specific test cases, users define properties and Hypothesis generates random inputs to uncover edge cases and bugs. It integrates with unittest and pytest, shrinking failing examples to minimal reproducible cases. Widely adopted in production systems, Hypothesis boosts code reliability by exploring input spaces far beyond manually crafted tests. -
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Kornia
Open Source Differentiable Computer Vision Library
Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors. With Kornia we fill the gap between classical and deep computer vision that implements standard and advanced vision algorithms for AI. Our libraries and initiatives are always according to the community needs. -
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LLM CLI
Access large language models from the command-line
A CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine. -
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ModernGL
Modern OpenGL binding for Python
ModernGL is a Python wrapper over OpenGL, designed to simplify the creation of high-performance, modern graphics applications. It provides an intuitive API for rendering 2D and 3D graphics, making it accessible to both beginners and experienced developers. ModernGL is suitable for applications such as games, simulations, and data visualizations. -
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Ollama Python
Ollama Python library
ollama-python is an open-source Python SDK that wraps the Ollama CLI, allowing seamless interaction with local large language models (LLMs) managed by Ollama. Developers use it to load models, send prompts, manage sessions, and stream responses directly from Python code. It simplifies integration of Ollama-based models into applications, supporting synchronous and streaming modes. This tool is ideal for those building AI-driven apps with local model deployment. -
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Pants Build System
The Pants Build System
Pants 2 is a fast, scalable, user-friendly build system for codebases of all sizes. It's currently focused on Python, Go, Java, Scala, Kotlin, Shell, and Docker, with support for other languages and frameworks coming soon. A lot of effort has gone into making Pants easy to adopt, easy to use and easy to extend. We're super excited to bring Pants' distinctive features to Go, Java, Python, Scala, Kotlin, and Shell users. Pants requires very minimal BUILD file metadata/boilerplate. It uses a combination of static analysis and sensible defaults to infer most of that information on the fly. So your BUILD files can be very minimal — and even those can be generated and updated for you. Pants has out-of-the-box support for multiple dependency resolves and their corresponding lockfiles, so you can have hermetic, repeatable builds that are resilient to supply chain attacks, even in complex situations where you have multiple versions of the same dependencies in different parts of the codebase. -
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PyOpenCL
OpenCL integration for Python, plus shiny features
PyOpenCL is a Python wrapper for the OpenCL framework, providing seamless access to parallel computing on CPUs, GPUs, and other accelerators. It enables developers to harness the full power of heterogeneous computing directly from Python, combining Python’s ease of use with the performance benefits of OpenCL. PyOpenCL also includes convenient features for managing memory, compiling kernels, and interfacing with NumPy, making it a preferred choice in scientific computing, data analysis, and machine learning workflows that demand acceleration. -
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Transformers4Rec
Transformers4Rec is a flexible and efficient library
Transformers4Rec is an advanced recommendation system library that leverages Transformer models for sequential and session-based recommendations. The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, Hugging Face Transformers (HF). Transformers4Rec makes state-of-the-art transformer architectures available for RecSys researchers and industry practitioners. Traditional recommendation algorithms usually ignore the temporal dynamics and the sequence of interactions when trying to model user behavior. Generally, the next user interaction is related to the sequence of the user's previous choices. In some cases, it might be a repeated purchase or song play. User interests can also suffer from interest drift because preferences can change over time. Those challenges are addressed by the sequential recommendation task. -
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Writer Framework
No-code in the front, Python in the back. An open-source framework
Writer Framework is an open source platform designed to help developers build AI-powered applications by combining a visual interface builder with a Python-based backend architecture. It follows a hybrid approach where user interfaces are created using a drag-and-drop editor while business logic is implemented in Python, allowing teams to balance speed and flexibility without sacrificing control. The framework is particularly focused on AI use cases, enabling developers to integrate large language models, knowledge graphs, and custom machine learning workflows into user-facing applications. Its architecture enforces a clear separation of concerns between frontend and backend, which improves maintainability and scalability as applications grow in complexity. The system is designed to support rapid prototyping, enabling developers to iterate on UI and backend logic independently and deploy changes quickly. -
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django-viewflow
Reusable workflow library for Django
Viewflow is a lightweight reusable workflow library that helps to organize people collaboration business logic in Django applications. In conjunction with Django-material, they could be used as the framework to build ready-to-use business applications in minutes. Django web framework solves only technical problems related to the client-server interaction on top of the stateless HTTP protocol. Model-View-Template separation pattern helps to maintain simple CRUD-based logic. Viewflow is the library that offers an additional layer of Django web framework, allows explicitly specific people's workflow and extracts collaboration logic from Django views. Business process management and notation standard. It is a graphical notation readily understandable by all business stakeholders and software developers. Viewflow bridges the gap between a picture as the software specification and the working solution. -
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fpdf2
Simple PDF generation for Python
fpdf2 is a library for simple & fast PDF document generation in Python. It is a fork and the successor of PyFPDF. Compared with other PDF libraries, fpdf2 is fast, versatile, easy to learn and to extend (example). It is also entirely written in Python and has very few dependencies: Pillow, defusedxml, & fontTools. It is a fork and the successor of PyFPDF. -
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ipytest
Pytest in IPython notebooks
ipytest allows you to run Pytest in Jupyter notebooks. ipytest aims to give access to the full pytest experience and to make it easy to transfer tests out of notebooks into separate test files. -
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latexify
A library to generate LaTeX expression from Python code
latexify_py converts small, math-heavy pieces of Python code into human-readable LaTeX that mirrors the intent of the computation, not just its surface syntax. It parses Python functions and expressions into an abstract syntax tree (AST), applies symbolic rewrites for common mathematical constructs, and then emits LaTeX that compiles cleanly in standard environments. Typical use cases include turning analytical utilities—like probability mass functions, activation formulas, or recurrence relations—into equations suitable for papers, notebooks, and slide decks. The tool aims to preserve semantics such as exponentiation, summations, products, piecewise definitions, and function application while hiding Pythonic scaffolding. Users can control rendering details for names and operators so the output conforms to a project’s notation style. Because it works directly from Python source, it enables a tight authoring loop. -
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Antigravity Awesome Skills
The Ultimate Collection of 700+ Agentic Skills for Claude Code
Antigravity Awesome Skills is a playful yet practical repository that curates a set of clever, expressive, and sometimes whimsical AI agent skill templates designed to help users bootstrap agent behavior quickly. Rather than focusing on production-grade systems, it provides creative and high-impact skills that demonstrate how agents can be used to automate tasks, generate content, assist with daily operations, or integrate into larger workflows with minimal configuration. The project includes skill definitions, example prompts, and usage patterns that highlight how modular abilities can be assembled into functioning assistants. Because it aims to reduce cognitive overhead, many skills show how to structure intents, handle context, and orchestrate multi-step reasoning without deep technical complexity. It also serves as inspiration for users looking to prototype new use cases — from conversational helpers that answer questions to workflow automators that trigger actions. -
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DeepSeed
Deep learning optimization library making distributed training easy
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of arts, democratizing multi-billion-parameter model training such that many deep learning scientists can explore bigger and better models. Sparse attention of DeepSpeed powers an order-of-magnitude longer input sequence and obtains up to 6x faster execution comparing with dense transformers. -
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Django Notebook
Django + shell_plus + Jupyter notebooks made easy
Django + shell_plus + Jupyter notebooks made easy. A Jupyter notebook with access to objects from the Django ORM is a powerful tool to introspect data and run ad-hoc queries. Built-in integration with the imported objects from django-extensions shell_plus. Saves the state between sessions so you don't need to remember what you did. Inheritance diagrams on any object, including ORM models. -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a folder of images from the command line. It could even do real-time face recognition and blur faces on videos when used with other Python libraries. -
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Flama
Fire up your models with the flame
Flama is a python library which establishes a standard framework for development and deployment of APIs with special focus on machine learning (ML). The main aim of the framework is to make ridiculously simple the deployment of ML APIs, simplifying (when possible) the entire process to a single line of code. The library builds on Starlette, and provides an easy-to-learn philosophy to speed up the building of highly performant GraphQL, REST and ML APIs. Besides, it comprises an ideal solution for the development of asynchronous and production-ready services, offering automatic deployment for ML models. -
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GPT-Image2-Skill
GPT Image 2 prompt gallery, image prompt library, agentic skill
GPT-Image2-Skill is a prompt gallery, image prompt library, agent skill, and CLI for OpenAI image generation and editing workflows. It collects curated prompt examples with generated outputs so users can reuse strong visual patterns instead of starting from scratch. The project includes categories such as anime, gaming, cyberpunk, animation, character design, typography, illustration, watercolor, ink, pixel art, isometric scenes, product visuals, and food imagery. It can be installed as an agent skill for supported runtimes or used through a local CLI. The repository is designed for practical reuse, showing both how prompts are structured and how they can be executed. Its value is in combining inspiration, examples, and runnable tooling for repeatable AI image creation. -
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Graphene
GraphQL in Python Made Easy
Graphene is a Python library for building GraphQL APIs fast and easily, using a code-first approach. Instead of writing GraphQL Schema Definition Langauge (SDL), Python code is written to describe the data provided by your server. Graphene helps you use GraphQL effortlessly in Python, but what is GraphQL? GraphQL is a data query language developed internally by Facebook as an alternative to REST and ad-hoc webservice architectures. With Graphene you have all the tools you need to implement a GraphQL API in Python, with multiple integrations with different frameworks including Django, SQLAlchemy and Google App Engine. -
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Kami
Good content deserves good paper
Kami is a minimalistic productivity tool designed to help users organize tasks, notes, and daily workflows in a clean and distraction-free interface. It focuses on simplicity, enabling users to capture ideas quickly and manage them efficiently without unnecessary complexity. The application is built with a modern design philosophy that emphasizes clarity and usability. It supports lightweight task management and note-taking features for personal productivity. Kami is suitable for users who prefer streamlined tools over feature-heavy productivity suites. Its design encourages focus and reduces cognitive overload. The project reflects a trend toward minimalist digital tools for everyday organization. -
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NSync
nsync is a C library that exports various synchronization primitives
nsync is a portable C library that provides a collection of advanced synchronization primitives designed to facilitate safe and efficient multithreaded programming. It offers reader-writer locks, condition variables, run-once initialization, waitable counters, and waitable bits for coordination and cancellation between threads. Unlike traditional pthreads-based synchronization, nsync introduces conditional critical sections, allowing developers to wait for arbitrary conditions without explicit signaling or complex loop-based logic. This approach simplifies concurrency management and often improves readability and maintainability of multithreaded code. The library emphasizes efficiency, with locks and condition variables occupying minimal memory and supporting cancellation mechanisms through nsync_note objects rather than thread-level cancellation. Designed with portability and performance in mind, nsync can be compiled on Unix-like systems and Windows using a C90 compiler.
