Open Source Python Software - Page 22
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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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1
OneForAll
OneForAll is a powerful subdomain collection tool
OneForAll is a comprehensive subdomain enumeration and reconnaissance tool primarily used in penetration testing and bug bounty workflows. Built in Python, it aggregates results from numerous DNS, certificate transparency, search engine, and threat intelligence sources to uncover hidden subdomains. The tool is particularly notable for incorporating many Chinese data sources that are often missed by Western-focused recon tools, increasing discovery coverage. It supports brute-force subdomain discovery, recursive scanning, and takeover detection to help identify potential attack surfaces. OneForAll also performs validation and enrichment of discovered domains, producing structured outputs for further analysis. Overall, it is a powerful asset discovery platform designed to maximize subdomain visibility during security assessments. -
2
OpenSnitch
OpenSnitch is a GNU/Linux port of the Little Snitch firewall
OpenSnitch is a GNU/Linux port of the Little Snitch firewall. You can launch the GUI from the icon or from the system menu. The daemon will start intercepting connections, prompting you to allow or deny them. If you don't apply an action, after 15 seconds (configurable) it'll apply the default action configured. When you open the GUI, you'll see all the connections and processes that the daemon has intercepted. Double click on a row to view the details of a process, rule, host or user. Once you know which are the common processes, IPs and hosts that your machine is connecting to, you can start creating permanent rules (Duration: always) to deny or allow them. You can also convert temporary rules to permanent by right-clicking on a temporary rule or by double-clicking on it, and then edit it. By default OpenSnitch UI listens on a local Unix socket in /tmp/osui.sock. -
3
Qwen-2.5-VL
Qwen2.5-VL is the multimodal large language model series
Qwen2.5 is a series of large language models developed by the Qwen team at Alibaba Cloud, designed to enhance natural language understanding and generation across multiple languages. The models are available in various sizes, including 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B parameters, catering to diverse computational requirements. Trained on a comprehensive dataset of up to 18 trillion tokens, Qwen2.5 models exhibit significant improvements in instruction following, long-text generation (exceeding 8,000 tokens), and structured data comprehension, such as tables and JSON formats. They support context lengths up to 128,000 tokens and offer multilingual capabilities in over 29 languages, including Chinese, English, French, Spanish, and more. The models are open-source under the Apache 2.0 license, with resources and documentation available on platforms like Hugging Face and ModelScope. -
4
ReClip
Download videos from almost any website
ReClip is a lightweight, self-hosted media downloader that provides a simple web-based interface for downloading videos and audio from a wide range of online platforms. Built around the yt-dlp engine, it supports over a thousand websites, including major platforms like YouTube, TikTok, and Instagram, allowing users to retrieve media content in various formats. The application emphasizes simplicity and minimalism, featuring a clean interface built with plain HTML, CSS, and JavaScript without requiring complex build steps or frameworks. Its backend is implemented in a compact Python Flask application, making it easy to deploy and customize. Users can paste multiple URLs at once, select output formats such as MP4 or MP3, and choose quality settings before downloading. The system also includes features like automatic URL deduplication and batch processing to improve usability. -
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5
Sideband
LXMF client for Android, Linux and macOS to communicate with people
Sideband is a privacy-focused, peer-to-peer mesh messaging protocol and platform that allows users to communicate directly over local networks or the internet without servers. It emphasizes metadata resistance and secure message delivery, working effectively in offline, low-bandwidth, or disaster recovery scenarios. Built-in Rust, Sideband supports encrypted group messaging, file transfers, and network self-discovery. It is ideal for use in community networks, crisis response, and decentralized communications infrastructure. -
6
SymPy
A computer algebra system written in pure Python
SymPy is an open source Python library for symbolic mathematics. Its goal is to become a full-featured computer algebra system (CAS) while maintaining the simplicity of its code. Written entirely in Python, SymPy is easy to use, comprehensible and easily extensible. It’s also very lightweight as it solely depends on mpmath, a pure Python library for arbitrary floating point arithmetic. SymPy has participated in every Google Summer of Code since 2007 and because of this has continuously improved. A lot more people have also contributed to it, and used it in their projects. Beyond being an interactive tool, it can be embedded in other apps and extended with custom functions. -
7
UFO³
Weaving the Digital Agent Galaxy
UFO is an open-source framework developed by Microsoft for building intelligent agents that automate interactions with graphical user interfaces on the Windows operating system. The system allows users to issue natural language instructions that are translated into automated actions across multiple desktop applications. Using a dual-agent architecture, the framework analyzes both visual interface elements and system control structures in order to understand how applications should be manipulated. This enables the agent to navigate complex software environments and perform tasks that normally require manual interaction. UFO integrates mechanisms for task decomposition, planning, and execution so that high-level user requests can be broken down into smaller steps performed by specialized agents. The framework can operate across multiple applications simultaneously, allowing workflows that span several programs to be automated seamlessly. -
8
clip-retrieval
Easily compute clip embeddings and build a clip retrieval system
clip-retrieval is an open-source toolkit designed to build large-scale semantic search systems for images and text by leveraging CLIP embeddings to enable multimodal retrieval. It allows developers to compute embeddings for both images and text efficiently and then index them for fast similarity search across massive datasets. The system is optimized for performance and scalability, capable of processing tens or even hundreds of millions of embeddings using GPU acceleration. It includes components for inference, indexing, filtering, and serving results through APIs, making it a complete pipeline for building production-ready retrieval systems. The framework also supports querying by image, text, or embedding, enabling flexible use cases such as reverse image search or multimodal content discovery. Additionally, it provides a simple frontend interface and backend services that can be deployed to expose search functionality to users. -
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ffmpeg-normalize
Audio Normalization for Python/ffmpeg
ffmpeg-normalize is a command-line utility designed to normalize audio levels in media files using FFmpeg, ensuring consistent volume across multiple tracks. It supports both EBU R128 loudness normalization and peak normalization methods, allowing users to choose the appropriate standard for their needs. The tool analyzes audio streams and applies adjustments to achieve target loudness levels without introducing distortion. It can process multiple files in batch mode, making it suitable for large media libraries or production workflows. ffmpeg-normalize also preserves metadata and supports a wide range of input and output formats. Its design emphasizes accuracy and compliance with broadcasting standards. Overall, it provides a reliable solution for achieving consistent audio quality in multimedia content. -
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10
gpt-oss
gpt-oss-120b and gpt-oss-20b are two open-weight language models
gpt-oss is OpenAI’s open-weight family of large language models designed for powerful reasoning, agentic workflows, and versatile developer use cases. The series includes two main models: gpt-oss-120b, a 117-billion parameter model optimized for general-purpose, high-reasoning tasks that can run on a single H100 GPU, and gpt-oss-20b, a lighter 21-billion parameter model ideal for low-latency or specialized applications on smaller hardware. Both models use a native MXFP4 quantization for efficient memory use and support OpenAI’s Harmony response format, enabling transparent full chain-of-thought reasoning and advanced tool integrations such as function calling, browsing, and Python code execution. The repository provides multiple reference implementations—including PyTorch, Triton, and Metal—for educational and experimental use, as well as example clients and tools like a terminal chat app and a Responses API server. -
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gpustat
A simple command-line utility for querying and monitoring GPU status
gpustat is a lightweight Python command-line utility designed to provide fast, human-readable monitoring of NVIDIA GPU status in real time. It serves as a simplified alternative to the more verbose nvidia-smi tool by presenting key GPU metrics in a compact, developer-friendly format. The utility retrieves data through NVIDIA’s NVML bindings and displays information such as temperature, utilization, memory usage, and running processes directly in the terminal. Because it is easy to install via pip and requires minimal configuration, gpustat is widely used in machine learning environments, research clusters, and shared GPU servers. The tool also supports watch mode for continuous monitoring and JSON output for integration into automation pipelines. Overall, gpustat focuses on speed, clarity, and scriptability, making it especially useful for engineers who need quick GPU visibility without heavy monitoring stacks. -
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RebornOS
Made For You - Made With You
RebornOS is an Arch Linux based distribution whose goal is to make Arch Linux more approachable to beginners. We stick close to Arch Linux, offer choices without much bloat, and we provide quite a bit more support and handholding than typical distributions so that beginners don't feel overwhelmed. -
13
ModbusPal - a Java MODBUS simulator
Modbus slave simulator
ModbusPal is a project to develop a PC-based Modbus simulator. Its goal is to reproduce a realistic environment, with many slaves and animated register values. Almost everything in ModbusPal can be customized and controlled by scripts. -
14
Equestria OS
An Arch Linux OS with 20+ custom GUI utilities & MLP theme customizer.
A user-friendly Linux distribution based on Arch Linux and KDE Plasma 6. Designed by a solo creator as a passion project, this lightweight operating system eliminates the need for terminal commands by offering over 20 unique, built-in graphical utilities for effortless computer management. Equestria OS makes your computer feel like home. It comes with a deeply integrated Proton Engine, allowing you to run your favorite Windows apps and games (.exe) with a simple double-click in secure, isolated sandboxes. You can instantly transform your desktop layout, colors, and icons inspired by My Little Pony characters using a built-in one-click theme editor. With smart tools for disk management, file relocation, and offline software installation, it provides everything you need out of the box. It is a stable, 100% bloatware-free system built for gamers, digital artists, and fandom fans alike. -
15
BerserkArch
A bleeding-edge, security-centric Arch-based Linux distribution.
BerserkArch is a security-focused, performance-tuned Linux operating system (OS) based on Arch Linux, designed for developers, hackers, and technical users. A bleeding-edge, security-centric Arch-based Linux distribution crafted for hackers, developers, and nerds alike. Following the Arch Linux philosophy, it is designed to be highly customizable, allowing users to build their environment with only the components they need, rather than having a lot of pre-installed software like some other security distributions (e.g., Kali Linux). As an Arch-based distribution, it benefits from the rolling release model, providing users with the latest software versions and kernel updates. BerserkArch is a dist "designed to make you powerful" for specific use cases like reverse-engineering binaries and automating exploits, rather than being an easy-to-use distribution for general beginners. -
16
AI Runner
Offline inference engine for art, real-time voice conversations
AI Runner is an offline inference engine designed to run a collection of AI workloads on your own machine, including image generation for art, real-time voice conversations, LLM-powered chatbots and automated workflows. It is implemented as a desktop-oriented Python application and emphasizes privacy and self-hosting, allowing users to work with text-to-speech, speech-to-text, text-to-image and multimodal models without sending data to external services. At the core of its LLM stack is a mode-based architecture with specialized “modes” such as Author, Code, Research, QA and General, and a workflow manager that automatically routes user requests to the right agent based on the task. The project has a strong focus on developer ergonomics, with thorough development guidelines, environment configuration using .env variables, and a clear structure for tests, tools and agents. -
17
AgentHandover
AgentHandover observes, learns and teaches agents with skills
AgentHandover is a Mac-focused system that observes how a user works and converts repeated workflows into reusable, self-improving skills for AI agents. It is designed for tools such as Claude Code, OpenClaw, Codex, Hermes, Cursor, Windsurf, and other MCP-compatible environments. Instead of asking users to manually write long prompts or static automation instructions, it records real actions, infers decision logic, and produces skills that include steps, strategy, guardrails, selection criteria, and writing style. The project supports both focused recording for specific tasks and passive discovery for workflows that appear repeatedly over time. It stores learned knowledge locally and uses feedback from later executions to improve confidence, add decision branches, and demote stale or failing skills. Its main value is helping agents learn how a person actually works, so recurring tasks can be handed off with more context, consistency, and trust. -
18
Anna’s Archive
Comprehensive search engine for books, papers, comics, magazines
Anna’s Archive is a large-scale open-source search engine and data aggregation platform designed to index and provide access to a vast collection of books, academic papers, comics, magazines, and other digital texts through a unified interface. The project includes all the infrastructure required to run a full instance locally or in production, combining web servers, databases, and search indexing systems into a scalable architecture. It relies heavily on technologies such as Elasticsearch for search functionality and MariaDB for structured data storage, enabling fast and efficient querying across massive datasets. The system is designed with redundancy and replication in mind, allowing distributed deployments and mirrored environments to handle high traffic and large data volumes. It also includes tooling for importing datasets, managing metadata, and maintaining structured archives using custom formats. -
19
AstronRPA
Agent-ready RPA suite with visual workflow automation tools engine
Astron RPA is an enterprise-grade robotic process automation platform designed to help organizations and developers build automated workflows for desktop and web applications. It provides a visual workflow designer that supports low-code and no-code development, allowing users to create automation processes through a drag-and-drop interface instead of writing extensive code. It enables automation of common desktop software and browser-based tasks, making it suitable for repetitive business operations and system integrations. Astron RPA includes a large library of reusable components that handle tasks such as user interface operations, data processing, and system interactions, allowing workflows to be assembled from modular building blocks. Astron RPA also integrates with intelligent agent systems so that automated processes and AI-driven workflows can work together in broader automation scenarios. -
20
AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run them in a sandbox environment, and analyze the results with statistical methods. The platform also uses multi-agent debate and automated peer review processes to refine research findings and improve paper quality. By combining literature discovery, experimentation, and writing automation, AutoResearchClaw aims to turn research ideas into conference-ready papers with minimal human intervention. -
21
ChatGPT Clone
ChatGPT interface with better UI
ChatGPT Clone demonstrates a ChatGPT-style conversational interface wired to large-language-model backends, packaged so developers can self-host and extend. The goal is to replicate the core chat UX—message history, streaming tokens, code blocks, and system prompts—while letting you plug in different provider APIs or local models. It showcases a clean separation between the web client and the message orchestration layer so you can experiment with prompts, roles, and memory strategies. The project is useful for prototyping assistants, documentation bots, and internal developer tools without committing to a specific vendor or UI framework. Configuration is kept simple so newcomers can get a working chat in minutes and then dial in features like authentication or multi-model routing. While it illustrates how to hook into third-party LLM endpoints, it is typically positioned as an educational, self-hosted starter that you should operate responsibly and within provider's terms of use. -
22
DouK-Downloader
TikTok releases/likes/compilations/live streams/videos/atlases/music
DouK-Downloader is a fully open-source data acquisition and media downloading tool designed to extract, collect, and download content from TikTok and its Chinese counterpart Douyin at scale. Built using Python and modern asynchronous networking libraries such as HTTPX, it enables batch downloading of videos, images, live streams, and metadata from accounts, playlists, and individual links. The software goes beyond simple downloading by offering comprehensive data collection features, including comments, user statistics, and trending data such as hot boards and search results. It supports automated workflows like incremental downloads, duplicate detection, and persistent storage of collected data in formats like CSV, XLSX, and SQLite, making it useful for both content archiving and analytics. The project also includes a Web UI interface and API support, allowing remote access, automation, and integration into larger systems or private servers. -
23
EmoLLM
Pre & Post-training & Dataset & Evaluation & Depoly & RAG
EmoLLM is an open-source family of large language models focused on mental health support and counseling-oriented interactions. The project is designed to help users through mental health conversations and has been fine-tuned from existing instruction-following LLMs rather than built as a base model from scratch. Its repository includes multiple model variants and training configurations spanning several underlying model families, including InternLM, Qwen, DeepSeek, Mixtral, LLaMA, and others, which shows that the initiative is structured as a broad ecosystem rather than a single release. The project also covers more than just model weights, with material for datasets, fine-tuning, evaluation, deployment, demos, RAG, and related subprojects such as its psychological digital assistant work. -
24
F1 Race Replay
An interactive Formula 1 race visualisation and data analysis tool
F1 Race Replay is an interactive replay viewer that lets users watch and analyze recorded Formula 1 race sessions with precise control over camera angles, timing, and telemetry overlay, offering a rich experience beyond standard broadcast replays. It ingests official timing and positional data, then renders vehicle movements through track maps and 3D visualizations so fans, analysts, and engineers can review strategy, overtakes, tire degradation effects, and pit stop impacts in detail. Users can scrub through time, jump between cars, and overlay performance graphs such as speed, sector times, and gap differentials to evaluate performance trends across laps. This deep dive capability turns passive viewing into active exploration, empowering enthusiasts and professionals to discover insights usually hidden in raw data. The viewer also supports annotations and bookmark capabilities so users can mark moments of interest for future review or comparison. -
25
Fashion-MNIST
A MNIST-like fashion product database
Fashion-MNIST is an open-source dataset created by Zalando Research that provides a standardized benchmark for image classification algorithms in machine learning. The dataset contains grayscale images of fashion products such as shirts, shoes, coats, and bags, each labeled according to its clothing category. It was designed as a direct replacement for the original MNIST handwritten digits dataset, maintaining the same structure and image size so that researchers could easily switch datasets without modifying their experimental pipelines. The dataset consists of 70,000 images in total, with 60,000 examples used for training and 10,000 reserved for testing. Each image has a resolution of 28 by 28 pixels and belongs to one of ten clothing classes, making it suitable for evaluating classification models. Because the dataset represents real-world objects rather than handwritten digits, it offers a more challenging benchmark for testing machine learning algorithms.
