Open Source Python Libraries - Page 4
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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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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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PyQt-SiliconUI
A powerful and artistic UI library based on PyQt5
PyQt-SiliconUI is an open source UI framework built on top of PyQt5 that focuses on delivering visually elegant and highly customizable desktop application interfaces. It is designed as a comprehensive collection of UI components, layouts, and utilities that allow developers to build rich graphical user interfaces in Python with a modern and artistic aesthetic. The library includes a wide range of refactored widgets such as buttons, containers, editors, menus, sliders, and progress bars, all structured to work seamlessly with Qt’s layout system. It also provides core modules for animations, event handling, and custom painting, enabling developers to create smooth, interactive desktop experiences beyond standard PyQt capabilities. A key aspect of the project is its ongoing refactoring effort, which aims to modernize components, improve performance, and replace older implementations with more stable and maintainable versions. -
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PyTensor
Python library for defining and optimizing mathematical expressions
PyTensor is a fork of Aesara, a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays. PyTensor is based on Theano, which has been powering large-scale computationally intensive scientific investigations since 2007. A hackable, pure-Python codebase. Extensible graph framework is suitable for rapid development of custom operators and symbolic optimizations. Implements an extensible graph transpilation framework that currently provides compilation via C, JAX, and Numba. Based on one of the most widely-used Python tensor libraries: Theano. -
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SVoice (Speech Voice Separation)
We provide a PyTorch implementation of the paper Voice Separation
SVoice is a PyTorch-based implementation of Facebook Research’s study on speaker voice separation as described in the paper “Voice Separation with an Unknown Number of Multiple Speakers.” This project presents a deep learning framework capable of separating mixed audio sequences where several people speak simultaneously, without prior knowledge of how many speakers are present. The model employs gated neural networks with recurrent processing blocks that disentangle voices over multiple computational steps, while maintaining speaker consistency across output channels. Separate models are trained for different speaker counts, and the largest-capacity model dynamically determines the actual number of speakers in a mixture. The repository includes all necessary scripts for training, dataset preparation, distributed training, evaluation, and audio separation. -
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Shumai
Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun
Shumai is an experimental differentiable tensor library for TypeScript and JavaScript, developed by Facebook Research. It provides a high-performance framework for numerical computing and machine learning within modern JavaScript runtimes. Built on Bun and Flashlight, with ArrayFire as its numerical backend, Shumai brings GPU-accelerated tensor operations, automatic differentiation, and scientific computing tools directly to JavaScript developers. It allows seamless integration of machine learning, deep learning, and custom differentiable programs into web-based or server-side environments without relying on Python frameworks. The library supports matrix operations, gradient computation, and tensor conversions with intuitive APIs and near-native speed, thanks to Bun’s low-overhead FFI bindings. It can automatically leverage GPU acceleration on Linux (via CUDA) and CPU computation on macOS. -
Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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Strawberry GraphQL
A GraphQL library for Python that leverages type annotations
Python GraphQL library based on dataclasses. Strawberry's friendly API allows to create GraphQL API rather quickly, the debug server makes it easy to quickly test and debug. Django and ASGI support allow having your API deployed in production in a matter of minutes. The quick start method provides a server and CLI to get going quickly. Strawberry comes with a mypy plugin that enables statically type-checking your GraphQL schema. A Django view is provided for adding a GraphQL endpoint to your application. To support graphql Subscriptions over WebSockets you need to provide a WebSocket enabled server. Create a GraphQL schema defining a User type and a single query field user that will return a hardcoded user. -
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Tile Kernels
A kernel library written in tilelang
Tile Kernels is a DeepSeek kernel library written with TileLang for high-performance AI and machine-learning workloads. It contains specialized kernels for areas such as mixture-of-experts routing, quantization, batched transpose operations, Engram gating, and Manifold HyperConnection components. The project includes both optimized kernel implementations and PyTorch reference versions for comparison and validation. It is aimed at developers and researchers who work close to model internals and need efficient low-level building blocks. TileKernels also includes testing and benchmarking utilities to help evaluate correctness and performance. Its main value is providing reusable TileLang-based kernels for experimental and production-adjacent deep-learning systems. -
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UCO3D
Uncommon Objects in 3D dataset
uCO3D is a large-scale 3D vision dataset and toolkit centered on turn-table videos of everyday objects drawn from the LVIS taxonomy. It provides about 170,000 full videos per object instance rather than still frames, along with per-video annotations including object masks, calibrated camera poses, and multiple flavors of point clouds. Each sequence also ships with a precomputed 3D Gaussian Splat reconstruction, enabling fast, differentiable rendering workflows and modern implicit/point-based modeling experiments. The repository includes automated downloaders with checksum verification, fine-grained controls to fetch only selected modalities or super-categories, and a lightweight Python API for loading frames, geometry, and splats on demand. Metadata is indexed in SQLite for quick queries at scale, and helper builders handle alignment, undistortion, frame extraction from videos, and cropping around the object. -
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Watchdog
Python library and shell utilities to monitor filesystem events
Python API library and shell utilities to monitor file system events. A simple program that uses watchdog to monitor directories specified as command-line arguments and logs events generated. Watchdog comes with an optional utility script called watchmedo. Please type watchmedo --help at the shell prompt to know more about this tool. You can use the shell-command subcommand to execute shell commands in response to events. watchmedo can read tricks.yaml files and execute tricks within them in response to file system events. Tricks are actually event handlers that subclass watchdog.tricks.Trick and are written by plugin authors. Trick classes are augmented with a few additional features that regular event handlers don't need. The directory containing the tricks.yaml file will be monitored. Each trick class is initialized with its corresponding keys in the tricks.yaml file as arguments and events are fed to an instance of this class as they arrive. -
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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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any-llm
Communicate with an LLM provider using a single interface
any-llm is a unified SDK and platform developed by Mozilla AI that allows developers to interact with multiple large language model providers through a single, consistent interface. Instead of rewriting code for each provider, developers can switch between services like OpenAI, Anthropic, Mistral, and Ollama simply by changing configuration parameters. The project is designed to be framework-agnostic, making it easy to integrate into scripts, applications, or production systems without being tied to a specific stack. It includes a core SDK for direct usage as well as an optional gateway layer that adds enterprise features such as budget management, API key control, usage analytics, and multi-tenant support. The gateway exposes an OpenAI-compatible API, allowing existing tools and workflows to connect without modification. The system emphasizes developer experience with clear error handling, strong typing, and minimal configuration overhead. -
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blinker-py
Blinker python library for hardware. Works with Raspberry Pi
blinker-py is a Python library for connecting hardware projects to the Blinker IoT platform. It is designed for Raspberry Pi, Banana Pi, Linux devices, and similar single-board or embedded systems. The library helps developers control hardware through the Blinker mobile app, where users can build graphical interfaces with drag-and-drop widgets. It supports common IoT communication patterns such as Wi-Fi, MQTT, WebSocket, and BLE-related platform workflows. The project is useful for makers, students, and hardware developers who want a faster way to connect sensors, switches, relays, and small automation projects to a mobile control panel. It is best suited for lightweight IoT prototyping rather than large industrial deployments. -
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dateutil
Useful extensions to the standard Python datetime features
The dateutil module provides powerful extensions to the standard date time module, available in Python. dateutil can be installed from PyPI using pip (note that the package name is different from the importable name). -
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plotly.py
The interactive graphing library for Python
plotly.py is a browser-based, open source graphing library for Python that lets you create beautiful, interactive, publication-quality graphs. Built on top of plotly.js, it is a high-level, declarative charting library that ships with more than 30 chart types. Everything from statistical charts and scientific charts, through to maps, 3D graphs and animations, plotly.py lets you create them all. Graphs made with plotly.py can be viewed in Jupyter notebooks, standalone HTML files, or hosted online using Chart Studio Cloud. -
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python-zeroconf
A pure python implementation of multicast DNS service discovery
A pure python implementation of multicast DNS service discovery. -
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zpdf
Zero-copy PDF text extraction library written in Zig
zpdf is a high-performance PDF text extraction library written in Zig that focuses on speed, low overhead, and modern parsing techniques. It leans heavily on memory-mapped file reading and zero-copy patterns where possible, so it can scan large PDFs without repeatedly copying data around in memory. The library supports streaming extraction using efficient arena allocation, making it well suited for workloads that need to process big documents quickly or in batches. It implements multiple PDF decompression filters and handles common font encoding pathways, which are essential for turning raw PDF content streams into readable text. It also understands both classic cross-reference tables and newer cross-reference streams, including PDF 1.5+ features, and it offers configurable strict vs permissive error handling depending on whether you prioritize correctness or robustness. -
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Alfred-Workflow
Full-featured library for writing Alfred 3 & 4 workflows
Alfred-Workflow is a Python helper library for Alfred 2, 3 and 4 workflow authors, developed and hosted on GitHub. Alfred workflows typically take user input, fetch data from the Web or elsewhere, filter them and display results to the user. Alfred-Workflow takes care of a lot of the details for you, allowing you to concentrate your efforts on your workflow’s functionality. Alfred-Workflow supports macOS 10.7+ (Python 2.7). Easily launch background tasks (daemons) to keep your workflow responsive. Check for and install new workflow versions using GitHub releases. Post notifications with Notification Center (10.8+ only) Error handling and logging for easier development and support. “Magic” arguments to help development, debugging and management of the workflow. -
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Ansible Examples
A few starter examples of ansible playbooks, to show features
This repository collects practical, real-world examples of using Ansible to automate infrastructure, deployments, and configurations. Each directory demonstrates a specific use case—ranging from setting up web servers, load balancers, and databases to orchestrating multi-tier applications in cloud environments. The examples highlight common Ansible practices such as organizing inventories, writing reusable playbooks, using roles, and handling variables and templates. They’re designed to be adapted directly into your own infrastructure or to serve as reference blueprints when learning how to structure automation projects. Whether you’re managing a handful of servers or deploying at scale, this repo provides starting points that illustrate how Ansible can streamline repetitive operational tasks. -
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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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Awesome Fraud Detection Research Papers
A curated list of data mining papers about fraud detection
A curated list of data mining papers about fraud detection from several conferences. -
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BayesianOptimization
A Python implementation of global optimization with gaussian processes
BayesianOptimization is a Python library that helps find the maximum (or minimum) of expensive or unknown objective functions using Bayesian optimization. This technique is especially useful for hyperparameter tuning in machine learning, where evaluating the objective function is costly. The library provides an easy-to-use API for defining bounds and optimizing over parameter spaces using probabilistic models like Gaussian Processes. -
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Best-of Web Development with Python
A ranked list of awesome python libraries for web development
This curated list contains 570 awesome open-source projects with a total of 2.4M stars grouped into 26 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from Github and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! A ranked list of awesome python libraries for web development. Updated weekly. -
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Claude Code Projects Index
An index of my Claude Code related repos
Claude Code Projects Index is a curated directory of projects, tools, and resources built around Claude Code and related AI development ecosystems. It functions as a centralized index that helps developers discover useful repositories, workflows, and integrations. The project is organized to make navigation easy, grouping resources by categories such as tooling, frameworks, and use cases. It is particularly valuable for developers exploring the Claude ecosystem and looking for inspiration or best practices. The repository is continuously updated, reflecting the evolving landscape of AI-assisted development. It also serves as a knowledge-sharing platform, highlighting innovative approaches and implementations. Overall, it acts as a discovery hub that accelerates learning and adoption of AI development tools. -
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Claude Quickstarts
A collection of projects for building deployable applications
Claude Quickstarts is a curated collection of starter projects and templates that help developers quickly begin building applications with the Claude API, making it easier to leverage Anthropic’s Claude models for real use cases. Each quickstart provides a foundational codebase with preconfigured settings and examples for common deployment scenarios, so developers can focus on customizing functionality instead of bootstrapping infrastructure. The repository includes demos, sample integrations, and instructions to get environments running with minimal setup while handling authentication, API calls, and error handling best practices. Because it’s designed as a learning and prototyping resource, Claude Quickstarts supports exploration of interactive applications, backend services, and workflows that benefit from large language model capabilities. -
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Copy Fail - CVE-2026-31431
epository that demonstrates and analyzes a Linux kernel vulnerability
Copy Fail - CVE-2026-31431 is a proof-of-concept repository that demonstrates and analyzes a specific Linux kernel vulnerability identified as CVE-2026-31431. The project provides experimental scripts and documentation to reproduce and study the exploit in controlled environments. It is designed for security researchers and engineers who want to understand the mechanics of the vulnerability. The repository includes tested configurations across multiple Linux distributions and kernel versions. It emphasizes reproducibility and technical clarity in demonstrating the issue. The project serves as both a research tool and an educational resource for vulnerability analysis. Overall, it contributes to the study of system-level security flaws and mitigation strategies. -
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Courses (Anthropic)
Anthropic's educational courses
Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured outputs, and evaluation. Assignments emphasize realistic tasks such as building small utilities, testing prompts against edge cases, and measuring quality so you learn to ship things that work. The materials are written for developers but remain friendly to newcomers, with clear setup instructions and minimal boilerplate. Because the repo is live and maintained, lessons are updated as the SDK and models evolve, and issues are used to track fixes, clarifications, and new modules.
