Open Source Python Libraries for BSD
Sort By:
Python Libraries for BSD
Browse free open source Python Libraries for BSD and projects below. Use the toggles on the left to filter open source Python Libraries for BSD by OS, license, language, programming language, and project status.
-
Earn up to 16% annual interest with Nexo.Generate interest, borrow against your crypto, and trade a range of cryptocurrencies — all in one platform. Geographic restrictions, eligibility, and terms apply.
-
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
1
*NOTE* Migrated to http://github.com/cracklib/cracklib Next generation version of libCrack password checking library. As of Oct 2008 (reflected in 2.8.15 code release), licensed under LGPL.
-
2
nature-skills
Skill that conforms to the academic expression and scientific research
nature-skills is an open-source collection of AI agent skills and structured workflows designed to enhance autonomous reasoning and tool usage in AI assistants. The project organizes reusable “skills” that agents can invoke for different operational contexts, helping AI systems perform specialized tasks more consistently and effectively. It appears to focus on modularity and interoperability, allowing skills to be combined, extended, or integrated into broader agent ecosystems. The repository is designed to support experimentation with multi-step reasoning, task automation, and contextual tool orchestration for agentic AI systems. By standardizing reusable capabilities, the project helps developers build more maintainable and extensible AI workflows. Overall, nature-skills contributes to the growing ecosystem of modular AI agent tooling and structured automation frameworks. -
3
Pandas TA
Python 3 Pandas Extension with 130+ Indicators
Technical Analysis Indicators - Pandas TA is an easy-to-use Python 3 Pandas Extension with 130+ Indicators. Pandas Technical Analysis (Pandas TA) is an easy-to-use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns. Many commonly used indicators are included, such as: Candle Pattern(cdl_pattern), Simple Moving Average (sma) Moving Average Convergence Divergence (macd), Hull Exponential Moving Average (hma), Bollinger Bands (bbands), On-Balance Volume (obv), Aroon & Aroon Oscillator (aroon), Squeeze (squeeze) and many more. -
4
Pillow
The friendly Python Imaging Library fork
If you've ever wondered or worried about the future of Python's Imaging Library, it's time to stop. Pillow is here to answer your concerns, and offer you more. Pillow is the friendly fork of the Python Imaging Library or PIL, a library that adds image processing capabilities to your Python interpreter. Why turn to Pillow? Aside from offering extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities, Pillow is setuptools compatible. While PIL is not officially over yet, with Pillow you can be assured of continuous integration testing, publicized development activity, and regular releases to the Python Package Index. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
-
5
iptv-api
IPTV live stream source automatic update tool
IPTV API is an automated platform for collecting, validating, testing, filtering, and publishing IPTV stream sources. It aggregates local files and subscriptions, then generates playable results as M3U, TXT, or API output. Users can customize channel templates, aliases, logos, EPG data, protocol preferences, geographic filters, providers, resolution, and speed requirements. The system measures latency, throughput, resolution, and frame rate while removing invalid, unavailable, or repetitive placeholder streams. Results can be categorized, cached, logged, analyzed, frozen, and restored as source quality changes. Deployment options include GitHub Actions workflows, a command-line interface, a graphical interface, and Docker images for several processor architectures. -
6
Goutoujunshi
A Codex dating strategist who first captures emotions
Goutoujunshi is a Codex relationship-advice skill that combines emotional support, structured relationship analysis, and concrete next-step planning. It separates known facts, assumptions, and unknowns before evaluating attraction, reciprocity, risk, opportunity cost, and longer-term options. The skill can analyze user summaries, exported conversations, and chat screenshots while keeping track of who said what. Its knowledge base draws from relationship psychology, personality research, communication, sexuality, family studies, law, sociology, philosophy, and breakup recovery. Advice can be turned into a sendable message, invitation plan, first-date outline, observation window, or practice conversation. It supports diverse sexual orientations, gender identities, relationship structures, and cultural contexts without assuming fixed gender roles. Optional local memory can preserve a concise relationship profile across tasks after explicit user consent. -
7
PyPDF
A pure-python PDF library capable of splitting, merging, cropping
pypdf is a pure Python library for working with PDF files, allowing developers to split, merge, rotate, encrypt, and extract content from PDFs. It’s an actively maintained fork of PyPDF2, improving performance, compatibility, and support for modern PDF standards. Suitable for both automation scripts and full-featured applications, pypdf handles PDFs without requiring external dependencies. -
8
python-zeroconf
A pure python implementation of multicast DNS service discovery
A pure python implementation of multicast DNS service discovery. -
9
Hack-Tools
Hack tools
hack-tools is a collection of various hacking tools and utilities. It serves as a comprehensive toolkit for penetration testers and cybersecurity enthusiasts, encompassing a wide range of functionalities. -
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.
-
10
Terraform Examples and Modules for GC
End-to-end modular samples and landing zones toolkit for Terraform
Terraform Examples and Modules for GC is a comprehensive infrastructure-as-code toolkit built on Terraform that enables organizations to design, deploy, and manage enterprise-grade Google Cloud environments using modular and reusable components. It provides a collection of end-to-end blueprints and composable modules that allow teams to implement standardized cloud architectures such as landing zones, networking configurations, and security frameworks. The project is designed to accelerate cloud adoption by offering opinionated yet flexible patterns aligned with Google Cloud best practices, helping organizations bootstrap their environments quickly while maintaining governance and scalability. It supports complex multi-project and multi-environment setups, making it suitable for large enterprises that require consistent infrastructure provisioning across teams. -
11
Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. Validate the functions that produce your data by automatically generating test cases for them. Integrate seamlessly with the Python ecosystem. Overcome the initial hurdle of defining a schema by inferring one from clean data, then refine it over time. Identify the critical points in your data pipeline, and validate data going in and out of them. Build confidence in the quality of your data by defining schemas for complex data objects. -
12
humanize
Python humanize functions
humanize is a Python utility library for converting machine-oriented numbers, dates, durations, and sizes into readable text. It can format large integers with separators or abbreviations such as millions and billions. Date and time helpers produce natural expressions such as relative times, human-readable dates, and approximate durations. Precise duration formatting can break intervals into days, hours, minutes, seconds, milliseconds, and smaller units. File sizes can be presented using decimal, binary, or GNU-style notation. The package also supports fractions, scientific notation, and extensive localization so applications can generate these human-friendly representations in many languages. Development of the project has since moved to the python-humanize organization. -
13
Google Fonts
Font files available from Google Fonts, and a public issue tracker
This is the central GitHub repository for Google Fonts, containing font binaries, metadata, and tools for uploading new typeface families. It serves as the staging area for fonts and follows stringent licensing structures. The top-level directories indicate the license of all files found within them. Subdirectories are named according to the family name of the fonts within. The /catalog subdirectory contains additional metadata, such as profile texts and portrait/avatar images of font designers, and this is open for contributions and corrections from anyone via GitHub. Since all the fonts available here are licensed with permission to redistribute, subject to the license terms, you can self-host using a variety of third-party projects. -
14
Plaso
Super timeline all the things
Plaso (Plaso Langar Að Safna Öllu), or "super timeline all the things," is a Python-based engine designed for automatic creation of timelines in digital forensic investigations. It processes various log files and artifacts to generate a chronological sequence of events, aiding analysts in understanding system activities. -
15
urllib3
Python HTTP library with thread-safe connection pooling
urllib3 is a powerful, user-friendly HTTP client for Python. Much of the Python ecosystem already uses urllib3 and you should too. Thread safety, connection pooling. Client-side TLS/SSL verification. File uploads with multipart encoding. Helpers for retrying requests and dealing with HTTP redirects. Support for gzip, deflate, brotli, and zstd encoding. Proxy support for HTTP and SOCKS. 100% test coverage. Professional support for urllib3 is available as part of the Tidelift Subscription. Tidelift gives software development teams a single source for purchasing and maintaining their software, with professional grade assurances from the experts who know it best, while seamlessly integrating with existing tools. -
16
VGGT-Ω
[CVPR 2026 Oral] VGGT Omega
VGGT-Omega is a Facebook Research computer vision project for feed-forward camera and depth reconstruction. It takes images as input and predicts camera parameters, depth maps, confidence values, and related scene tokens. The project is associated with 3D understanding workflows where models infer scene geometry without a traditional multi-stage reconstruction pipeline. It includes pretrained model variants with different resolutions and text-alignment capabilities, though checkpoint access may require approval. The repository also provides a Gradio demo that can visualize predicted cameras and depth-unprojected point clouds as a GLB scene. VGGT-Omega is best suited for researchers and developers working on 3D reconstruction, visual geometry, and image-based scene understanding. -
17
backtrader
Python Backtesting library for trading strategies
backtrader is a Python framework for developing, backtesting, and running trading strategies. Its Cerebro engine coordinates strategies, data feeds, brokers, indicators, analyzers, and execution. Developers can combine multiple strategies, instruments, and timeframes while resampling or replaying market data. The framework includes a large indicator library, custom indicator support, analyzers, position sizing, commissions, and trading calendars. Its broker simulation supports market, limit, stop, trailing, OCO, and bracket-style orders along with slippage and volume filling. Backtrader can also work with live data and brokerage integrations and provides integrated charting for strategy analysis. -
18
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. -
19
Facexlib
FaceXlib aims at providing ready-to-use face-related functions
facexlib is a PyTorch-based library providing ready-to-use face-related functions, including detection, alignment, recognition, and more. It integrates state-of-the-art open-source methods for various face processing tasks. -
20
PaperSpine
Motivation-driven skill for learning from strong academic papers
PaperSpine is a motivation-driven writing skill suite for academic papers, reports, reviews, and technical manuscripts. It is built for AI tools such as Codex, Claude Code, and OpenClaw, where the agent can follow structured writing workflows. The project asks the agent to study target formats and strong examples before drafting or revising. It emphasizes the central motivation of a paper, helping writers connect claims, structure, evidence, citations, and revisions into a coherent argument. The suite includes specialized skills for research, citation, rewriting, LaTeX, auditing, translation, humanization, and updates. It is best suited for users who need format-aware, evidence-aware academic writing support rather than generic text generation. -
21
Requests
A simple, yet elegant, HTTP library.
Requests is the de facto HTTP library for Python—simple, elegant, and human-friendly. It wraps urllib3 to provide intuitive methods for sending HTTP/1.1 requests, handling sessions, cookies, redirects, authentication, proxies, and more. -
22
lxml
The lxml XML toolkit for Python
A Python library for efficient XML and HTML processing, known for speed and compatibility. The lxml XML toolkit is a Pythonic binding for the C libraries libxml2 and libxslt. It is unique in that it combines the speed and XML feature completeness of these libraries with the simplicity of a native Python API, mostly compatible but superior to the well-known ElementTree API. The latest release works with all CPython versions from 3.6 to 3.12. See the introduction for more information about the background and goals of the lxml project. -
23
AudioCraft
Audiocraft is a library for audio processing and generation
AudioCraft is a PyTorch library for text-to-audio and text-to-music generation, packaging research models and tooling for training and inference. It includes MusicGen for music generation conditioned on text (and optionally melody) and AudioGen for text-conditioned sound effects and environmental audio. Both models operate over discrete audio tokens produced by a neural codec (EnCodec), which acts like a tokenizer for waveforms and enables efficient sequence modeling. The repo provides inference scripts, checkpoints, and simple Python APIs so you can generate clips from prompts or incorporate the models into applications. It also contains training code and recipes, so researchers can fine-tune on custom data or explore new objectives without building infrastructure from scratch. Example notebooks, CLI tools, and audio utilities help with prompt design, conditioning on reference audio, and post-processing to produce ready-to-share outputs. -
24
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. -
25
PyQuil
A Python library for quantum programming using Quil
PyQuil is a Python library for quantum programming using Quil, the quantum instruction language developed at Rigetti Computing. PyQuil serves three main functions. PyQuil has a ton of other features, which you can learn more about in the docs. However, you can also keep reading below to get started with running your first quantum program. Without installing anything, you can quickly get started with quantum programming by exploring our interactive Jupyter Notebook tutorials and examples. To run them in a preconfigured execution environment on Binder, click the "launch binder" badge at the top of the README or the link here! To learn more about the tutorials and how you can add your own, visit the rigetti/forest-tutorials repository. If you'd rather set everything up locally, or are interested in contributing to pyQuil, continue to the next section for instructions on installing pyQuil and the Forest SDK.
