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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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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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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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. -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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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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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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libtmux
Python API / wrapper for tmux
libtmux is a typed Python library that provides a wrapper for interacting programmatically with tmux, a terminal multiplexer. You can use it to manage tmux servers, sessions, windows, and panes. Additionally, libtmux powers tmuxp, a tmux workspace manager. libtmux builds upon tmux’s target and formats to create an object mapping to traverse, inspect and interact with live tmux sessions. -
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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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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. -
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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FSP - File Service Protocol Suite
UDP File transfer protocol
FSP - File Service Protocol. FSP is lightweight UDP based protocol for transferring files. It is designed for anonymous transfers over unreliable networks. -
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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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Claude Code Plugins Directory
Official, Anthropic-managed directory of high quality Claude Plugins
Claude Code Plugins Directory repository provides a collection of plugins intended to extend Claude’s capabilities by turning the model into a specialized assistant tailored to specific workflows, teams, or organizational needs. These plugins define how Claude should access tools, retrieve data, and execute structured tasks so that outputs become more consistent and production-ready. The project emphasizes customizable automation by allowing developers to encode preferred workflows, domain knowledge, and operational rules directly into plugin configurations. It is built to work with Claude Cowork and Claude Code environments, enabling teams to standardize how AI assistance behaves across different use cases. By exposing slash commands and workflow logic, the repository helps organizations operationalize AI in real business contexts rather than relying on generic prompting. -
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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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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. -
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CuPy
A NumPy-compatible array library accelerated by CUDA
CuPy is an open source implementation of NumPy-compatible multi-dimensional array accelerated with NVIDIA CUDA. It consists of cupy.ndarray, a core multi-dimensional array class and many functions on it. CuPy offers GPU accelerated computing with Python, using CUDA-related libraries to fully utilize the GPU architecture. According to benchmarks, it can even speed up some operations by more than 100X. CuPy is highly compatible with NumPy, serving as a drop-in replacement in most cases. CuPy is very easy to install through pip or through precompiled binary packages called wheels for recommended environments. It also makes writing a custom CUDA kernel very easy, requiring only a small code snippet of C++. -
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DGL
Python package built to ease deep learning on graph
Build your models with PyTorch, TensorFlow or Apache MXNet. Fast and memory-efficient message passing primitives for training Graph Neural Networks. Scale to giant graphs via multi-GPU acceleration and distributed training infrastructure. DGL empowers a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, DGL-LifeSci for bioinformatics and cheminformatics, and many others. We are keen to bringing graphs closer to deep learning researchers. We want to make it easy to implement graph neural networks model family. We also want to make the combination of graph based modules and tensor based modules (PyTorch or MXNet) as smooth as possible. DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks. -
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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
DeepPavlov makes it easy for beginners and experts to create dialogue systems. The best place to start is with user-friendly tutorials. They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual assistants development. It has comprehensive and flexible tools that let developers and NLP researchers create production-ready conversational skills and complex multi-skill conversational assistants. Use BERT and other state-of-the-art deep learning models to solve classification, NER, Q&A and other NLP tasks. DeepPavlov Agent allows building industrial solutions with multi-skill integration via API services. -
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Differentiable Neural Computer
A TensorFlow implementation of the Differentiable Neural Computer
The Differentiable Neural Computer (DNC), developed by Google DeepMind, is a neural network architecture augmented with dynamic external memory, enabling it to learn algorithms and solve complex reasoning tasks. Published in Nature in 2016 under the paper “Hybrid computing using a neural network with dynamic external memory,” the DNC combines the pattern recognition power of neural networks with a memory module that can be written to and read from in a differentiable way. This allows the model to learn how to store and retrieve information across long time horizons, much like a traditional computer. The architecture consists of modular components including an access module for managing memory operations, a controller (often an LSTM or feedforward network) for issuing read/write commands, and submodules for temporal linkage and memory allocation tracking. -
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Flask-SocketIO
Socket.IO integration for Flask applications
Flask-SocketIO is an extension for the Flask web framework that enables real-time bi-directional communication between clients and servers using WebSockets or long-polling fallbacks, making it possible to build interactive applications like chat systems, live dashboards, and collaborative tools. It abstracts the complexities of asynchronous sockets by providing a familiar Flask-style API where developers can define event handlers that trigger on client messages, broadcast to connected users, and manage namespaces and rooms. The extension supports multiple asynchronous workers through integrations with popular async servers like eventlet or gevent, allowing scalable handling of concurrent connections. It also includes features such as session and user tracking across socket connections, JSON message support, and simple decorators to bind events to handler functions. -
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Google Cloud Vision API examples
Sample code for Google Cloud Vision
The cloud-vision repository is a sample code collection for the Google Cloud Vision API that shows developers how to implement image analysis tasks across a wide range of languages and platforms. It contains examples organized by language and environment, including Go, Java, Node.js, PHP, Python, Ruby, .NET, Android, iOS, and even a Chrome extension, which makes it especially valuable as a cross-platform learning resource. The repository demonstrates concrete image understanding use cases, such as landmark detection and mobile photo analysis with label and face detection, so developers can see how Vision API outputs are consumed in real interfaces and workflows. Although the repository has been marked as deprecated in favor of language-specific repositories for new work, it still serves as a broad reference hub for legacy examples and multi-language implementation patterns. -
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Helium
Lighter web automation with Python
Helium is a Python library built on top of Selenium to make browser automation more intuitive and human-friendly. It replaces verbose boilerplate code with natural language-like API calls such as click("Login") or write("hello", into="Name"). Helium manages browser setup, waits, and teardown, enabling quick development of scripts for testing, scraping, or task automation without requiring deep Selenium knowledge. -
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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.
