Open Source Python Software - Page 64
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Python Software
Browse free open source Python Software and projects below. Use the toggles on the left to filter open source Python Software by OS, license, language, programming language, and project status.
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GitGot
Semi-automated tool for discovering exposed secrets in GitHub data
GitGot is an open source security tool designed to help users quickly search large amounts of public data on GitHub to identify potentially exposed secrets. It operates as a semi-automated, feedback-driven system that combines automated search capabilities with human guidance to refine results during investigation. GitGot leverages the GitHub Search API to perform queries across repositories, files, and gists, allowing security researchers and penetration testers to discover sensitive information that may have been unintentionally exposed in public code. During a search session, users review results and provide feedback that allows GitGot to filter out irrelevant or repetitive findings. This feedback is used to build blacklists that eliminate results based on repository names, file names, user names, or fuzzy matches of file content. The approach helps reduce noise while guiding the search process toward more relevant results. -
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GitSavvy
Full git and GitHub integration with Sublime Text
Sublime Text plugin providing probably all git has to offer. Sublime Text 2 is not supported. Also, GitSavvy takes advantage of modern features of Sublime Text (like annotations). For the best experience, use the latest Sublime Text dev build. The documentation is probably outdated. Yeah it's sad but you can contribute and I will eventually get onto it but every special view has help available, just press ?. GitSavvy requires Git versions at or greater than 2.18.0. basic Git functionality; init, add, commit, amend, checkout, pull, push, etc. Rebasing just from that "Repo History". Edit a commit, reword a commit, autosquash commits, apply a fixup, whatever... the [r] menu. git diff view, allowing user to stage, unstage and reset (discard) files, hunks or individual lines. GitHub-style blame view, showing hunk metadata and ability to view the commit that made the change. -
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Gitinspector
The statistical analysis tool for git repositories
Gitinspector is a statistical analysis tool for git repositories. The default analysis shows general statistics per author, which can be complemented with a timeline analysis that shows the workload and activity of each author. Under normal operation, it filters the results to only show statistics about a number of given extensions and by default only includes source files in the statistical analysis. This tool was originally written to help fetch repository statistics from student projects in the course Object-oriented Programming Project (TDA367/DIT211) at Chalmers University of Technology and Gothenburg University. Shows cumulative work by each author in history. Filters results by an extension (default: java,c,cc,cpp,h,hh,hpp,py,glsl,rb,js, SQL). Can display a statistical timeline analysis. Scans for all filetypes (by extension) found in the repository. Multi-threaded; uses multiple instances of git to speed up analysis when possible. -
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Gooey
Turn Python command line programs into a full GUI application
Gooey is a tool for transforming command line interfaces into beautiful desktop applications. It can be used as the frontend client for any language or program. Whether you've built your application in Java, Node, or Haskell, or you just want to put a pretty interface on an existing tool like FFMPEG, Gooey can be used to create a fast, practically free UI with just a little bit of Python (about 20 lines!). To show how this all fits together, and that it really works for anything, we're going to walk through building a graphical interface to one of my favorite tools of all time: FFMPEG. These steps apply to anything, though! You could swap out FFMPEG for a .jar you've written, or an arbitrary windows .exe, an OSX .app bundle, or anything on linux that's executable! In short, it will transform a "scary" terminal command line into an easy to use desktop application that you could hand over to users. -
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Google Gen AI SDK
Google Gen AI Python SDK provides an interface for developers
Google Gen AI Python SDK is Google’s official Python library for integrating Google generative AI models into Python applications. It supports both the Gemini Developer API and Google’s enterprise-oriented generative AI platform APIs, giving developers one SDK for consumer-facing and cloud-based Gemini workflows. The library provides a client-based interface for generating text, working with multimodal inputs, managing chats, handling files, using tools, and accessing model capabilities from Python code. It is intended to replace older Gemini Python SDK patterns with a more unified and actively maintained API surface. The project is useful for application developers, data scientists, AI engineers, and backend teams building Gemini-powered features. Its main value is providing a supported, production-ready Python interface for Google’s current generative AI ecosystem. -
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Gpt-Agreement-Payment
End-to-end protocol replay toolkit for ChatGPT Plus/Team/Pro sub
Gpt-Agreement-Payment is a research-oriented automation toolkit focused on subscription payment flows, protocol replay behavior, and anti-fraud mechanism analysis. It documents and implements controlled workflows around account setup, payment routing, runtime state storage, and operational monitoring. The project includes both command-line and web UI modes, with Docker and manual deployment options. It stores runtime output and logs in a local SQLite-backed structure for review and debugging. Because it touches payment systems, account workflows, and fraud controls, it should be treated strictly as a research and compliance-sensitive project, not as a general-purpose automation product. Its main value is documenting complex payment-flow behavior in a reproducible technical environment for authorized analysis. -
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GraalPy
A Python 3 implementation built on GraalVM
GraalPy is a high-performance implementation of the Python language for the JVM built on GraalVM. GraalPy is a Python 3.11 compliant runtime. It has first-class support for embedding in Java and can turn Python applications into fast, standalone binaries. GraalPy is ready for production running pure Python code and has experimental support for many popular native extension modules. -
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GramAddict
Completely free and open-source human-like Instagram bot
GramAddict is a fully open-source Instagram automation bot designed to simulate human-like interaction on Android devices using UI automation rather than direct API calls. It operates through ADB and UIAutomator2, meaning it interacts with the Instagram app as if it were a real user, reducing the risk of detection compared to API-based bots. The tool can automate a wide range of actions such as liking posts, following users, sending messages, and browsing content, all while introducing randomized delays and behaviors to mimic human activity. It supports both physical devices and emulators, making it flexible for different deployment environments. The project also includes advanced filtering and targeting capabilities, allowing users to define specific audiences based on hashtags, locations, or user attributes. Additionally, it provides reporting features via Telegram, giving users real-time feedback on bot performance. -
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Great Expectations
Always know what to expect from your data
Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams. Expectations are assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues. Expectations are a great start, but it takes more to get to production-ready data validation. Where are Expectations stored? How do they get updated? How do you securely connect to production data systems? How do you notify team members and triage when data validation fails? Great Expectations supports all of these use cases out of the box. Instead of building these components for yourself over weeks or months, you will be able to add production-ready validation to your pipeline in a day. -
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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Green Recorder
A simple screen recorder for Linux desktop
Green Recorder is a desktop screen recording application designed for Linux systems, providing a simple interface for capturing screen activity and audio. It supports recording in multiple formats by leveraging FFmpeg and other backend tools to encode output efficiently. The application allows users to record full screens or specific areas, making it suitable for tutorials and demonstrations. It includes options for selecting audio sources and controlling frame rates to balance quality and performance. green-recorder is designed to be lightweight and user-friendly, minimizing system overhead during recording sessions. It also supports Wayland and Xorg environments, ensuring compatibility across different Linux setups. Overall, it offers an accessible solution for screen recording with essential customization options. -
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Gym
Toolkit for developing and comparing reinforcement learning algorithms
Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. These environments have a shared interface, allowing you to write general algorithms. -
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HACS Integration
HACS gives you a UI to handle downloads of all your custom needs
HACS is an integration for Home Assistant that simplifies the management of custom components, themes, and other community-driven content. It provides a user-friendly interface within Home Assistant for browsing, installing, and updating custom add-ons, enhancing the customization and extensibility of Home Assistant setups. -
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HRM-Text
1B text generation model based on the HRM architecture
HRM-Text is a one-billion-parameter text generation model and pretraining framework based on the Hierarchical Reasoning Model architecture. It is designed to make foundation model pretraining more accessible by reducing compute and data requirements compared with traditional scaling-heavy approaches. The system combines hierarchical recurrent design, task-completion strengthening, and latent-space reasoning. Its training stack includes PrefixLM sequence packing, FlashAttention 3 kernels, PyTorch FSDP2, evaluation scripts, and checkpoint conversion tools. The repository supports reference pretraining runs for smaller and larger configurations, with Hopper-class GPUs expected for the attention path. It is useful for researchers and engineers exploring efficient language model pretraining, reasoning-focused architectures, and reproducible foundation model experiments. -
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HTTPX
A next generation HTTP client for Python
HTTPX is a fully featured HTTP client for Python 3, which provides sync and async APIs, and support for both HTTP/1.1 and HTTP/2. HTTPX should currently be considered in beta. A 1.0 release is expected to be issued sometime in 2021. International domains and URLs, keep-alive and connection pooling, sessions with cookie persistence, browser-style SSL verification. Basic/digest authentication, elegant key/value cookies, automatic decompression. Automatic content decoding, unicode response bodies, multipart file uploads, HTTP(S) proxy support. Connection timeouts, streaming downloads, .netrc support, and chunked requests. For more advanced topics, see the Advanced Usage section, the async support section, or the HTTP/2 section. The Developer Interface provides a comprehensive API reference. -
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Habitat-Lab
A modular high-level library to train embodied AI agents
Habitat-Lab is a modular high-level library for end-to-end development in embodied AI. It is designed to train agents to perform a wide variety of embodied AI tasks in indoor environments, as well as develop agents that can interact with humans in performing these tasks. Allowing users to train agents in a wide variety of single and multi-agent tasks (e.g. navigation, rearrangement, instruction following, question answering, human following), as well as define novel tasks. Configuring and instantiating a diverse set of embodied agents, including commercial robots and humanoids, specifying their sensors and capabilities. Providing algorithms for single and multi-agent training (via imitation or reinforcement learning, or no learning at all as in SensePlanAct pipelines), as well as tools to benchmark their performance on the defined tasks using standard metrics. -
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Harbor LLM
Run a full local LLM stack with one command using Docker
Harbor is an open source, containerized toolkit designed to simplify running local large language model (LLM) environments. It combines a CLI and companion app to launch backends, frontends, and supporting services with minimal setup. With a single command, users can start preconfigured tools like Ollama and Open WebUI, enabling chat, workflows, and integrations immediately. Harbor supports multiple inference engines, including llama.cpp and vLLM, and connects them seamlessly to user interfaces. It also includes tools for web retrieval, image generation, voice interaction, and workflow automation. Built on Docker, Harbor allows services to run in isolated containers while communicating over a local network. It is intended for local development and experimentation rather than production deployment, giving developers a flexible way to explore AI systems, test configurations, and manage complex LLM stacks without manual wiring or setup overhead. -
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Hasklig
A code font with monospaced ligatures
Programming languages are limited to relatively few characters. As a result, combined character operators surfaced quite early, such as the widely used arrow (->), comprised of a hyphen and greater sign. It looks like an arrow if you know the analogy and squint a bit. Composite glyphs are problematic in languages such as Haskell which utilize these complicated operators (=> -< >>= etc.) extensively. The readability of such complex code improves with pretty printing. Academic articles featuring Haskell code often use lhs2tex to achieve an appealing rendering, but it is of no use when programming. Hasklig solves the problem the way typographers have always solved ill-fitting characters which co-occur often, ligatures. The underlying code stays the same, only the representation changes. Not only can multi-character glyphs be rendered more vividly, other problematic things in monospaced fonts, such as spacing can be corrected. -
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Hello-Agents
Building an Intelligent Agent from Scratch
Hello Agents is an open educational project designed to teach developers how to understand, design, and build AI-native agents from the ground up through structured tutorials and practical examples. The project focuses on guiding learners beyond superficial framework usage toward deeper comprehension of agent architecture, reasoning loops, and real-world implementation patterns. It walks users through core concepts such as ReAct-style reasoning, tool usage, memory handling, and multi-step task execution, enabling hands-on experimentation with modern LLM-powered agent systems. The repository is structured as a progressive learning path, combining theory, exercises, and runnable code so users can incrementally build more capable agents. Its goal is to demystify agent engineering and help developers move from simple prompt scripts to robust autonomous systems. -
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HostHunter
OSINT reconnaissance tool for discovering hostnames from IP addresses
HostHunter is an open source reconnaissance tool designed to discover and extract hostnames associated with a large set of IPv4 or IPv6 addresses. It helps security professionals map IP addresses to virtual hostnames using a combination of OSINT data sources and active reconnaissance techniques. This approach enables users to identify hidden or additional services that may be hosted behind a single IP address. By correlating hostname information from certificates, APIs, HTTP headers, and other sources, the tool helps reveal the broader attack surface of an organization or infrastructure. HostHunter is commonly used in penetration testing, bug bounty reconnaissance, and security assessments where identifying virtual hosts is critical. HostHunter supports multiple output formats, making it easier to integrate the results into other security tools or workflows. -
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How to Train Your GPT
Build a modern LLM from scratch. Every line commented
How to Train Your GPT is an interactive textbook that teaches users how to build, train, and run a modern language model from scratch. It is written for learners with minimal machine-learning background, using simple explanations, commented code, and practical examples. The project covers the same broad family of architecture behind systems such as GPT-style models, LLaMA-style models, Claude-style systems, and Mistral-style models. It includes chapters and topic explainers on tokenizers, embeddings, attention, RoPE, RMSNorm, SwiGLU, KV cache, AdamW, mixed precision, training loops, and inference. The guide emphasizes writing every important component manually rather than only calling high-level APIs. Its purpose is to make the internals of language models understandable through runnable code and step-by-step explanations. -
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HunyuanVideo-I2V
A Customizable Image-to-Video Model based on HunyuanVideo
HunyuanVideo-I2V is a customizable image-to-video generation framework from Tencent Hunyuan, built on their HunyuanVideo foundation. It extends video generation so that given a static reference image plus an optional prompt, it generates a video sequence that preserves the reference image’s identity (especially in the first frame) and allows stylized effects via LoRA adapters. The repository includes pretrained weights, inference and sampling scripts, training code for LoRA effects, and support for parallel inference via xDiT. Resolution, video length, stability mode, flow shift, seed, CPU offload etc. Parallel inference support using xDiT for multi-GPU speedups. LoRA training / fine-tuning support to add special effects or customize generation. -
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HyperTools
A Python toolbox for gaining geometric insights
HyperTools is a library for visualizing and manipulating high-dimensional data in Python. It is built on top of matplotlib (for plotting), seaborn (for plot styling), and scikit-learn (for data manipulation). Functions for plotting high-dimensional datasets in 2/3D. Static and animated plots. Simple API for customizing plot styles. Set of powerful data manipulation tools including hyperalignment, k-means clustering, normalizing and more. Support for lists of Numpy arrays, Pandas dataframes, text or (mixed) lists. Applying topic models and other text vectorization methods to text data. HyperTools is designed to facilitate dimensionality reduction-based visual explorations of high-dimensional data. The basic pipeline is to feed in a high-dimensional dataset (or a series of high-dimensional datasets) and, in a single function call, reduce the dimensionality of the dataset(s) and create a plot. -
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IPRanges
Daily updated lists of cloud, bot, and service IP ranges
ipranges is an open source repository that provides continuously updated lists of IP address ranges associated with major cloud providers, search engine crawlers, and online services. ipranges collects IP ranges from publicly available sources and organizes them into structured files that can be easily used in security, networking, and automation workflows. It includes address ranges from providers such as Google Cloud, Amazon AWS, Microsoft, Oracle Cloud, and DigitalOcean, as well as well known service platforms like GitHub, Facebook, Twitter, and Telegram. It also tracks IP ranges used by search engine bots and automated agents including Googlebot, Bingbot, and OpenAI’s GPTBot. Lists are published in both IPv4 and IPv6 formats and are regularly updated through automated processes to keep the data current. In addition to provider specific lists, the project also offers merged and combined datasets that aggregate ranges from multiple sources into a single file. -
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IPython
Command shell for interactive computing in multiple languages
IPython provides a rich toolkit to help you make the most of using Python interactively. Comprehensive object introspection. IPython provides input history, persistent across sessions. Caching of output results during a session with automatically generated references. Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords. Extensible system of ‘magic’ commands for controlling the environment and performing many tasks related to IPython or the operating system. A rich configuration system with easy switching between different setups (simpler than changing $PYTHONSTARTUP environment variables every time). Session logging and reloading. Extensible syntax processing for special purpose situations. Access to the system shell with user-extensible alias system. Easily embeddable in other Python programs and GUIs. Integrated access to the pdb debugger and the Python profiler. -
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Image Quality Assessment
Convolutional Neural Networks to predict aesthetic quality of images
Image Quality Assessment is an open-source deep learning project that implements neural models for predicting the aesthetic and technical quality of digital images. The repository provides an implementation inspired by the NIMA (Neural Image Assessment) research approach, which uses convolutional neural networks trained on human-annotated datasets to estimate image quality scores. The goal of the project is to automatically evaluate images based on perceived quality factors such as composition, clarity, and visual appeal. Instead of relying on simple image statistics, the system learns patterns that correlate with human judgments about image aesthetics and technical quality. The repository includes code for training models, performing inference, and evaluating predicted scores against labeled datasets. It also provides utilities for image preprocessing and data management that help prepare datasets for training deep learning models.
