Open Source Linux Artificial Intelligence Software - Page 38
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Artificial Intelligence Software for Linux
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labelme Image Polygonal Annotation
Image polygonal annotation with Python
Labelme is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface. Image annotation for polygon, rectangle, circle, line and point. Image flag annotation for classification and cleaning. Video annotation. (video annotation). GUI customization (predefined labels / flags, auto-saving, label validation, etc). Exporting VOC-format dataset for semantic/instance segmentation. (semantic segmentation, instance segmentation). Exporting COCO-format dataset for instance segmentation. (instance segmentation). The first time you run labelme, it will create a config file in ~/.labelmerc. You can edit this file and the changes will be applied the next time that you launch labelme. If you would prefer to use a config file from another location, you can specify this file with the --config flag. -
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llm
An ecosystem of Rust libraries for working with large language models
llm is an ecosystem of Rust libraries for working with large language models - it's built on top of the fast, efficient GGML library for machine learning. The primary entry point for developers is the llm crate, which wraps the llm-base and the supported model crates. Documentation for the released version is available on Docs.rs. For end-users, there is a CLI application, llm-cli, which provides a convenient interface for interacting with supported models. Text generation can be done as a one-off based on a prompt, or interactively, through REPL or chat modes. The CLI can also be used to serialize (print) decoded models, quantize GGML files, or compute the perplexity of a model. It can be downloaded from the latest GitHub release or by installing it from crates.io. -
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machine-learning-refined
Master the fundamentals of machine learning, deep learning
machine-learning-refined is an educational repository designed to help students and practitioners understand machine learning algorithms through intuitive explanations and interactive examples. The project accompanies a series of textbooks and teaching materials that focus on making machine learning concepts accessible through visual demonstrations and simple code implementations. Instead of presenting algorithms purely through mathematical derivations, the repository emphasizes geometric intuition, visualization, and step-by-step experimentation. It includes Jupyter notebooks and scripts that illustrate core machine learning topics such as regression, classification, optimization methods, and neural networks. These materials allow learners to see how algorithms behave during training and how different parameters affect model performance. -
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pyts
A Python package for time series classification
pyts is a Python package dedicated to time series classification. It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of several time series classification algorithms. The package comes up with many unit tests and continuous integration ensures new code integration and backward compatibility. The package is distributed under the 3-clause BSD license. -
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refinery
Open-source choice to scale, assess and maintain natural language data
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact. You are one of the people we've built refinery for. refinery helps you to build better NLP models in a data-centric approach. Semi-automate your labeling, find low-quality subsets in your training data, and monitor your data in one place. refinery doesn't get rid of manual labeling, but it makes sure that your valuable time is spent well. Also, the makers of refinery currently work on integrations to other labeling tools, such that you can easily switch between different choices. refinery is a multi-repository project, you can find all integrated services in the architecture below. The app builds on top of Hugging Face and spaCy to leverage pre-built language models for your NLP tasks, as well as qdrant for neural search. -
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robosuite
A Modular Simulation Framework and Benchmark for Robot Learning
Robosuite is a modular and extensible simulation framework for robotic manipulation tasks, built on top of MuJoCo. Developed by the ARISE Initiative, Robosuite offers a set of standardized benchmarks and customizable environments designed to advance research in robotic manipulation, control, and imitation learning. It emphasizes realistic simulations and ease of use for both single-task and multi-task learning. -
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video-shotcraft
AI video skill for Claude Code & Codex
Video Shotcraft is an AI agent skill for producing cinematic product videos with Claude Code, Codex, and Remotion. It guides an agent through visual direction, storyboarding, implementation, sound design, beat synchronization, and final quality review. The toolkit includes more than 100 shot recipe cards with timing, parameters, implementation notes, and common pitfalls. A searchable gallery provides 161 motion previews and corresponding Remotion examples with tuned easing and animation values. Its Ink Press template delivers a complete 1080p product promo with ten shots, transitions, title cards, camera moves, and cinematic sound effects. Reusable components support page capture, 2.5D movement, captions, flash cuts, digit rolls, and branded asset replacement. It is intended for web and desktop promos, feature demonstrations, launches, and brand films. -
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vocal-separate
An extremely simple tool for separating vocals and background music
vocal-separate is a simple but effective audio processing application that isolates vocals and instrumental tracks from music and video files using stem-based source separation models, enabling tasks such as karaoke creation, remixing, and music analysis. Built as a localized web-based tool, it runs entirely on the user’s machine without requiring an internet connection, emphasizing privacy and convenience for creative work. Users can drag and drop an audio or video file onto the interface to begin separation, choosing between two, four, or five stems, which allows isolating specific components like vocals, bass, drums, or piano depending on the chosen model. After processing, the tool outputs separate WAV files for each extracted stem, making it easy to export and use in audio editing or remix software. -
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x64dbg-MCP Server
x64dbg-MCP Server is a native MCP (Model Context Protocol) plugin
x64dbg-MCP Server is a native Model Context Protocol plugin that lets AI assistants control x64dbg through HTTP. It exposes debugger operations for loading programs, stepping execution, setting breakpoints, reading memory, inspecting registers, and analyzing modules. The toolset also covers threads, call stacks, symbols, references, pattern scans, PE structures, dumps, tracing, and process internals. Event callbacks let clients react to debugger state changes such as exceptions, pauses, DLL loads, and breakpoints. It supports both Streamable HTTP and SSE transports with JSON-RPC 2.0. Mandatory bearer authentication protects every request, and configuration changes can restart the server automatically. Written in Zig, it has no runtime dependencies and can build x32 and x64 plugins from one codebase. -
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zvt
Modular quant framework
For practical trading, a complex algorithm is fragile, a complex algorithm building on a complex facility is more fragile, complex algorithm building on a complex facility by a complex team is more and more fragile. zvt wants to provide a simple facility for building a straightforward algorithm. Technologies come and technologies go, but market insight is forever. Your world is built by core concepts inside you, so it’s you. zvt world is built by core concepts inside the market, so it’s zvt. The core concept of the system is visual, and the name of the interface corresponds to it one-to-one, so it is also uniform and extensible. You can write and run the strategy in your favorite ide, and then view its related targets, factor, signal and performance on the UI. Once you are familiar with the core concepts of the system, you can apply it to any target in the market. -
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Choco is not hosted on sourceforge anymore. Please now visit http://choco-solver.org/ !
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GnoppixNG
Gnoppix Linux
Gnoppix is a Linux distribution based on Debian Linux available in for amd64 and ARM architectures. Gnoppix is a great choice for users who want a lightweight and easy-to-use with security in mind. Gnoppix was first announced in June 2003. Currently we're working on a Gnoppix version for WSL, Mobile devices like smartphones and tablets as well. -
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Linux-Intelligent-Ocr-Solution
Easy-OCR solution and Tesseract trainer for GNU/Linux
Linux-intelligent-ocr-solution Lios is a free and open source software for converting print in to text using either scanner or a camera, It can also produce text out of scanned images from other sources such as Pdf, Image, Folder containing Images or screenshot. Program is given total accessibility for visually impaired. A Tesseract Trainer GUI is also shipped with this package. Forum : https://groups.google.com/forum/#!forum/lios Video Tutorial : https://www.youtube.com/playlist?list=PLn29o8rxtRe1zS1r2-yGm1DNMOZCgdU0i Tesseract Training Tutorial (beta) : https://www.youtube.com/watch?v=qLpCld4cdtk Source Code Github : https://github.com/Nalin-x-Linux/lios-3 Gitlab : https://gitlab.com/Nalin-x-Linux/lios-3 User guide is available in download page -
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chessPDFBrowser
Chess application whichs allows working with chess PDF books and PGNs.
Chess application which allows working with PDFs and PGNs. You can work with the chess games of the PDF and edit their tree of variants. Graphical environment. Standard PGN TAGs. PGN comments. Ocr like (Fen string detection from chess board position images). Connection to Uci chess engines (like stockfish). Position analysis, full game analysis. You can now play games against uci engines. pdf2pgn command line command included. Detailed documentation. Multilanguage currently support for English, Spanish and Catalan. Dark mode option. JDK-17 compatibility You will find more about it at this web sites: https://chesspdfbrowser.com?origin=sourceforge https://www.frojasg1.com:8443/downloads_web/web/html/chessPdfBrowser.html?origin=sourceforge -
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UnBBayes
Framework & GUI for Bayes Nets and other probabilistic models.
UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy. -
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500+ AI Agent Projects
The 500 AI Agents Projects is a curated collection of AI agent
500 AI Agents Projects is a curated repository that aggregates hundreds of real-world AI agent use cases across multiple industries, serving as a centralized knowledge hub for developers, researchers, and businesses. Rather than being a single application, it functions as a structured index linking to open-source implementations and conceptual examples of agent-based systems. The repository highlights how AI agents can be applied in domains such as healthcare, finance, education, and retail, offering inspiration and practical entry points for building solutions. It simplifies exploration by organizing diverse projects into a single accessible location, making it easier to discover trends and patterns in agentic AI development. The project encourages collaboration and contributions, positioning itself as a living resource that evolves with the ecosystem. Its value lies in breadth, discoverability, and practical orientation toward implementation. -
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ADK Go
Code-first Go toolkit for building, evaluating, and deploying AI agent
ADK-Go is an open source toolkit designed to help developers build, evaluate, and deploy sophisticated AI agents using the Go programming language. It is part of the Agent Development Kit ecosystem and follows a code-first approach that allows developers to define agent behavior, tools, and orchestration logic directly in Go code. ADK-Go applies traditional software engineering principles to agent development, making it easier to structure, test, and maintain complex agent-based systems. It supports building both simple task-oriented agents and more advanced multi-agent architectures that collaborate to perform workflows. It is designed to be modular and flexible, allowing developers to integrate custom tools, external services, or existing functionality into agent workflows. Although optimized for certain AI ecosystems, ADK-Go is model-agnostic and can work with different models and deployment environments. -
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AEA Framework
A framework for autonomous economic agent (AEA) development
agents-aea by Fetch.ai is a framework for building autonomous economic agents (AEAs) that can act independently, communicate, and transact on decentralized networks. It focuses on enabling AI-driven agents to participate in digital marketplaces and ecosystems. -
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AGiXT
AGiXT is a dynamic AI Automation Platform
AGiXT is a dynamic Artificial Intelligence Automation Platform engineered to orchestrate efficient AI instruction management and task execution across a multitude of providers. Our solution infuses adaptive memory handling with a broad spectrum of commands to enhance AI's understanding and responsiveness, leading to improved task completion. The platform's smart features, like Smart Instruct and Smart Chat, seamlessly integrate web search, planning strategies, and conversation continuity, transforming the interaction between users and AI. By leveraging a powerful plugin system that includes web browsing and command execution, AGiXT stands as a versatile bridge between AI models and users. With an expanding roster of AI providers, code evaluation capabilities, comprehensive chain management, and platform interoperability, AGiXT is consistently evolving to drive a multitude of applications, affirming its place at the forefront of AI technology. -
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AI Engineer Coach
Better agentic engineering
AI Engineering Coach is a Microsoft VS Code extension for analyzing AI coding assistant usage across local session logs. It is designed to help developers improve how they work with tools such as VS Code, Claude, Codex, OpenCode, GitHub Copilot for Xcode, and Copilot CLI. The extension reads local logs and turns them into dashboards, practice scores, trends, anti-pattern detection, and actionable feedback. It focuses on agentic engineering habits such as prompt quality, context management, review discipline, tool use, and session hygiene. The project is read-only and emphasizes that data stays on the user’s machine. Its main value is helping developers measure and improve their AI-assisted coding process instead of treating agent use as an untracked black box. -
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AICodeBot
AI-powered tool for developers, simplifying coding tasks
AICodeBot is a terminal-based coding assistant designed to make your coding life easier. Think of it as your AI version of a pair programmer. Perform code reviews, create helpful commit messages, debug problems, and help you think through building new features. A team member that accelerates the pace of development and helps you write better code. We've planned to build out multiple different interfaces for interacting with AICodeBot. To start, it's a command-line tool that you can install and run in your terminal and a GitHub Action for Code Reviews. This project was built before AI Coding Assistants were cool. As such, much of the functionality has been replicated in various IDEs. Where AICodeBot shines is a) it's in the terminal, not GUI, and b) it can be used in processes like GitHub actions. We're using AICodeBot to build AICodeBot, and it's upward spiraling all the time.️ We're looking for contributors to help us build it out. -
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AIMET
AIMET is a library that provides advanced quantization and compression
Qualcomm Innovation Center (QuIC) is at the forefront of enabling low-power inference at the edge through its pioneering model-efficiency research. QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware accelerators. Quantized inference is significantly faster than floating point inference. For example, models that we’ve run on the Qualcomm® Hexagon™ DSP rather than on the Qualcomm® Kryo™ CPU have resulted in a 5x to 15x speedup. Plus, an 8-bit model also has a 4x smaller memory footprint relative to a 32-bit model. However, often when quantizing a machine learning model (e.g., from 32-bit floating point to an 8-bit fixed point value), the model accuracy is sacrificed. -
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Agent Framework
Framework for building, orchestrating, and deploying AI agents
Microsoft Agent Framework is an open source framework designed to help developers build, orchestrate, and deploy AI agents and multi-agent systems. It provides a unified programming model that supports both Python and .NET implementations, allowing developers to create agent-driven applications in multiple programming environments. It includes tools and abstractions for constructing simple conversational agents as well as complex workflows where multiple agents collaborate to complete tasks. Microsoft Agent Framework supports graph-based orchestration that enables developers to connect agents, functions, and tools into structured workflows capable of handling multi-step processes. It also includes components such as agent sessions for managing state, context providers for maintaining memory, and middleware for intercepting and extending agent behavior. Developers can integrate external tools and services so that agents can execute actions beyond text generation. -
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Agent S
Agent S: an open agentic framework that uses computers like a human
Agent S is an open-source agentic framework designed to enable autonomous computer use through an Agent-Computer Interface (ACI). Built to operate graphical user interfaces like a human, it allows AI agents to perceive screens, reason about tasks, and execute actions across macOS, Windows, and Linux systems. The latest version, Agent S3, surpasses human-level performance on the OSWorld benchmark, demonstrating state-of-the-art results in complex multi-step computer tasks. Agent S combines powerful foundation models (such as GPT-5) with grounding models like UI-TARS to translate visual inputs into precise executable actions. It supports flexible deployment via CLI, SDK, or cloud, and integrates with multiple model providers including OpenAI, Anthropic, Gemini, Azure, and Hugging Face endpoints. With optional local code execution, reflection mechanisms, and compositional planning, Agent S provides a scalable and research-driven framework for building advanced computer-use agents.
