Open Source Linux Artificial Intelligence Software - Page 12
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Artificial Intelligence Software for Linux
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Chatbox
The Ultimate AI Copilot on Your Desktop
Chatbox is a cross-platform desktop AI client designed to give you a fast, polished, and private way to work with modern language models. It runs locally on Windows, macOS, and Linux, keeping your conversations and data stored on your own device. Chatbox acts as a unified interface for popular LLMs like ChatGPT, Claude, Gemini, and local models via Ollama, making it easy to switch providers without changing tools. Built with an ergonomic UI, it’s optimized for long sessions, prompt experimentation, and everyday productivity. The app supports rich formatting, streaming responses, and advanced prompting to help you get clearer, more useful outputs. For individuals and teams alike, Chatbox serves as a powerful desktop copilot that blends simplicity with flexibility. -
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DeepChat
A smart assistant that connects powerful AI to your personal world
DeepChat is an open‑source, multi‑model AI chat platform featuring a unified interface for cloud and local language models, enriched with tool‑calling capabilities, search enhancements, privacy protection, and extensive model support. DeepChat is a powerful open-source AI chat platform providing a unified interface for interacting with various large language models. Whether you're using cloud APIs like OpenAI, Gemini, Anthropic, or locally deployed Ollama models, DeepChat delivers a smooth user experience. As a cross-platform AI assistant application, DeepChat not only supports basic chat functionality but also offers advanced features such as search enhancement, tool calling, and multimodal interaction, making AI capabilities more accessible and efficient. -
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MMDeploy
OpenMMLab Model Deployment Framework
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN frameworks. Please read getting_started for the basic usage of MMDeploy. -
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MetaMCP
MCP Aggregator, Orchestrator, Middleware, Gateway in one docker
MetaMCP is an all-in-one MCP aggregator, orchestrator, and middleware that merges many MCP servers into a single, policy-aware server you can point any MCP client at. It’s built to centralize discovery, apply middlewares (e.g., auth, rate limits, transforms), and present a unified catalog of tools and resources from diverse backends. The repository and site describe it as “the one MCP to manage all your MCPs,” with diagrams and examples showing how to compose fleets behind a single endpoint. It ships Dockerized for quick deployment and emphasizes dynamic aggregation so teams can register or remove servers without restarting clients. The org maintains related repos and a GUI app for cloud and self-hosted setups, with a note that the cloud demo is outdated while the open-source v2 evolves. Overall, MetaMCP aims to simplify multi-server MCP operations for individuals and organizations. -
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Open ChatGPT Atlas
Open Source and Free Alternative to ChatGPT Atlas
Open ChatGPT Atlas is an open-source toolkit and interface for working with OpenAI’s ChatGPT models in a more extensible, adaptable, and composable way than standard web UIs allow. It provides an architecture where developers and power users can manage state, tool integrations, and multi-turn workflows with more control, enabling custom UIs, automation layers, and advanced routing logic. Unlike a fixed chat app, Atlas is designed as a foundation that can be extended with plugins, external APIs, and custom logic to support domain-specific assistants, agent-like behaviors, and multi-task workflows. The project includes utilities for session management to preserve context across interactions, structured interfaces for streaming responses, and integration layers that let you plug in third-party tools and services. It often forms the basis for research, prototyping, or production systems where the default ChatGPT interface isn’t flexible enough. -
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Remotion
Make videos programmatically with React
Remotion is a cutting-edge library that lets developers create real videos programmatically using React components, transforming familiar UI paradigms into a flexible, code-driven video production workflow. Instead of traditional timeline editors, Remotion leverages HTML, CSS, and JavaScript to define video frames, animations, and transitions, which means developers can use states, props, loops, and component hierarchies to automate complex motion graphics. Because it integrates with the React ecosystem, Remotion fits naturally into modern front-end stacks and tooling, and can produce dynamic content like personalized videos, dashboards, and data-driven animations with the same code used to build interactive web apps. The framework supports exporting to standard video formats, audio synchronization, frame callbacks, and powerful tooling for previewing and debugging, so teams can iterate quickly and reliably. -
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SD.Next
All-in-one WebUI for AI generative image and video creation
SD.Next is an all-in-one web user interface for generative image creation that expands beyond basic Stable Diffusion workflows to cover broader image and video generation, captioning, and processing tasks. It is designed as a power-user environment where model management, generation features, and workflow controls are centralized in a single UI rather than spread across separate scripts and utilities. The project emphasizes broad model support and includes mechanisms for discovering, downloading, and configuring models through integrated tooling, lowering the setup burden for experimentation. It also provides documentation and an ecosystem of guides that help users move from basic generation to more advanced usage patterns, including API-based automation. SD.Next is built to run across common desktop platforms and focuses on practicality: install, generate, iterate, and automate with minimal friction. -
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Speech Note
Speech Note Linux app. Note taking, reading and translating
Speech Note is a Linux desktop and Sailfish OS application for taking, reading, and translating notes with integrated offline speech technology. It combines speech-to-text, text-to-speech, and machine translation in a single interface, allowing users to dictate notes, listen back to them, and translate them without ever sending data to the cloud. All processing is done locally, which means audio, text, and translations never leave the device, emphasizing strong privacy guarantees. The application supports multiple STT engines such as Coqui STT (DeepSpeech fork), Vosk, whisper.cpp, Faster Whisper, and april-asr, giving users flexibility in accuracy, speed, and hardware requirements. For text-to-speech, it can plug into a wide range of engines including espeak-ng, MBROLA, Piper, RHVoice, Coqui TTS, Mimic 3, WhisperSpeech, Kokoro, Parler-TTS, F5-TTS, and even classic S.A.M., making it highly customizable in terms of voices and languages. -
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Telegram SMS
An SMS-forwarding Robot Running on Your Android Device
With the power of Telegram SMS, your multi-phone life is much easier than before. Receiving and sending SMS, relaying APP notifications, monitoring battery status. All stuff can be done with a single Telegram bot. You can use the bot in both private chat and group chat, in case you have more than 2 Android phones, or sharing the bot with other people. Telegram SMS connects with Telegram's bot API server directly, no 3rd-party services involved. -
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X-UI
Lightweight Xray panel with multi-protocol and multi-user
The xray panel supports single-end multi-user and multi-protocol. Monitor and manage your agency services conveniently and quickly through free Telegram bot. -
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llama.cpp
LLM inference in C/C++
llama.cpp is a high-performance C and C++ project for running large language models locally and in the cloud with minimal setup. It is built around efficient inference, broad hardware support, and the GGUF model format. The project supports many model families and has become a major foundation for local AI tools, model serving, and embedded inference workflows. It provides command-line tools, a server mode with an OpenAI-compatible API style, model conversion utilities, and extensive backend acceleration options. llama.cpp runs on CPUs and GPUs, with support for Apple silicon, x86, RISC-V, CUDA, HIP, Vulkan, SYCL, Metal, and hybrid CPU-GPU execution. Its main value is making practical LLM inference accessible across consumer machines, servers, and specialized deployment environments. -
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llama.cpp Python Bindings
Python bindings for llama.cpp
llama-cpp-python provides Python bindings for llama.cpp, enabling the integration of LLaMA (Large Language Model Meta AI) language models into Python applications. This facilitates the use of LLaMA's capabilities in natural language processing tasks within Python environments. -
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ChatMCP
ChatMCP is an AI chat client implementing the Model Context Protocol
ChatMCP is a cross‑platform AI chat client that implements the Model Context Protocol (MCP) to provide unified chat experiences across environments—including desktop, mobile, and web—with synchronization and protocol support tailored for MCP. You can install MCP Server from MCP Server Market, MCP Server Market is a collection of MCP Server, you can use it to chat with different data. Tested on major distributions: Ubuntu, Fedora, Arch Linux, openSUSE. Improved Experience: Latest versions include better dark theme support, unified data storage following XDG Base Directory Specification, and an optimized UI layout for Linux desktop environments is planned. -
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Claude Code Bridge
Real-time multi-AI collaboration: Claude, Codex & Gemini
Claude Code Bridge is an open-source command-line tool designed to enable real-time collaboration between multiple AI coding assistants within a unified development environment. The system allows developers to coordinate interactions between models such as Claude, Codex, and Gemini so that they can work together on programming tasks. By maintaining persistent shared context between these models, the tool reduces redundant prompts and minimizes token usage while allowing each AI system to contribute specialized capabilities. The architecture functions as a unified launcher that manages communication between multiple AI providers and coordinates their responses within the same development session. Developers can run the tool in terminal environments and integrate it with terminal multiplexers such as tmux or advanced terminal emulators. -
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DeepSeek-V3.2-Exp
An experimental version of DeepSeek model
DeepSeek-V3.2-Exp is an experimental release of the DeepSeek model family, intended as a stepping stone toward the next generation architecture. The key innovation in this version is DeepSeek Sparse Attention (DSA), a sparse attention mechanism that aims to optimize training and inference efficiency in long-context settings without degrading output quality. According to the authors, they aligned the training setup of V3.2-Exp with V3.1-Terminus so that benchmark results remain largely comparable, even though the internal attention mechanism changes. In public evaluations across a variety of reasoning, code, and question-answering benchmarks (e.g. MMLU, LiveCodeBench, AIME, Codeforces, etc.), V3.2-Exp shows performance very close to or in some cases matching that of V3.1-Terminus. The repository includes tools and kernels to support the new sparse architecture—for instance, CUDA kernels, logit indexers, and open-source modules like FlashMLA and DeepGEMM are invoked for performance. -
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Dexter
An autonomous agent for deep financial research
Dexter is an autonomous agent tailored for deep financial research: you pose complex financial questions (for example, about a company’s revenue growth or financial ratios) and Dexter breaks them down into structured research tasks, fetches relevant real-time data (e.g. income statements, cash flows), performs analysis, and returns data-backed answers. It uses a multi-agent architecture with components such as a planning agent (to decompose queries), an action agent (to run tasks & fetch data), and self-validation mechanisms: after getting results, Dexter checks its own outputs and refines them until it is confident about its answer. This means it's more than a simple script — it’s a research assistant that loops through analysis steps until convergence. -
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LLaMA-Factory
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
LLaMA-Factory is a fine-tuning and training framework for Meta's LLaMA language models. It enables researchers and developers to train and customize LLaMA models efficiently using advanced optimization techniques. -
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Label Studio
Label Studio is a multi-type data labeling and annotation tool
The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models. The frontend part of Label Studio app lies in the frontend/ folder and written in React JSX. Multi-user labeling sign up and login, when you create an annotation it's tied to your account. Configurable label formats let you customize the visual interface to meet your specific labeling needs. Support for multiple data types including images, audio, text, HTML, time-series, and video. -
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MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
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OpenCV
Open Source Computer Vision Library
OpenCV (Open Source Computer Vision Library) is a comprehensive open-source library for computer vision, machine learning, and image processing. It enables developers to build real-time vision applications ranging from facial recognition to object tracking. OpenCV supports a wide range of programming languages including C++, Python, and Java, and is optimized for both CPU and GPU operations. -
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OpenWhip
Optimize interaction with AI coding assistants
OpenWhip is a desktop utility built as a cross-platform Node.js application that humorously gamifies interaction with AI coding assistants by simulating a “whip” tool to interrupt and motivate them during long or stalled operations. The application runs as a lightweight system tray program and overlays a visual whip animation on the screen when activated, creating an interactive and slightly absurd interface for user engagement. Its core functionality is surprisingly practical beneath the joke: when triggered, it sends a keyboard interrupt signal (Ctrl+C) to halt the current AI process, effectively giving developers a quick way to stop unresponsive or slow-running tasks. In addition to the interrupt, the tool also delivers randomized “encouraging” or humorous messages, blending utility with personality and making the experience more entertaining. -
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Qwen3
Qwen3 is the large language model series developed by Qwen team
Qwen3 is a cutting-edge large language model (LLM) series developed by the Qwen team at Alibaba Cloud. The latest updated version, Qwen3-235B-A22B-Instruct-2507, features significant improvements in instruction-following, reasoning, knowledge coverage, and long-context understanding up to 256K tokens. It delivers higher quality and more helpful text generation across multiple languages and domains, including mathematics, coding, science, and tool usage. Various quantized versions, tools/pipelines provided for inference using quantized formats (e.g. GGUF, etc.). Coverage for many languages in training and usage, alignment with human preferences in open-ended tasks, etc. -
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Telegram File Stream Bot
A telegram bot that will give instant stream links for telegram files
A Telegram bot to generate direct link for your Telegram files. -
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VideoCaptioner
AI-powered tool for generating, optimizing, and translating subtitles
VideoCaptioner is an open source AI-powered subtitle processing tool designed to simplify the workflow of creating subtitles for videos. It integrates speech recognition, language processing, and translation technologies to automatically generate and refine subtitles from video or audio sources. VideoCaptioner uses speech-to-text engines such as Whisper variants to transcribe spoken content and convert it into subtitle text with accurate timestamps. After transcription, large language models are used to intelligently restructure subtitles into natural sentences, correct wording, and improve readability for viewers. It can also translate subtitles into other languages while preserving the original timing, making it suitable for multilingual video publishing and accessibility. In addition to generating subtitles, it supports editing, formatting, and embedding subtitles into videos as either hard or soft subtitles. -
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reacTIVision is a computer vision framework for the fast and robust tracking of markers attached on physical objects, and the creation of multi-touch surfaces. It was designed for the rapid development of table-based tangible user interfaces.
