Open Source Unix Shell Artificial Intelligence Software
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Unix Shell Artificial Intelligence Software
View 16159 business solutionsBrowse free open source Unix Shell Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source Unix Shell Artificial Intelligence Software by OS, license, language, programming language, and project status.
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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.
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Umbrel
A beautiful personal server OS for Raspberry Pi or any Linux distro
Run your personal server with a Bitcoin and Lightning node in your home, self-host open source apps like Nextcloud and Matrix to break away from big tech, and take full control of your data. For free. All our interactions on the internet today are mediated by a few companies who offer “free” services in exchange for storing our data on their servers to spy on us. Running a personal server fundamentally changes that. You and your family’s photos, videos, files, notes, passwords, everything, have nothing to do with someone else’s computer. They’re a part of your private life, and now they can all be stored by you, in your home, on your Umbrel. The Bitcoin network is made up of thousands of nodes that verify every single transaction in the blockchain. Some of them mine Bitcoin too, but unlike a mining node, running a non-mining node doesn’t require expensive hardware. Achieve unparalleled privacy by connecting your wallet directly to the Bitcoin node on your Umbrel. -
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CogVideo
Text and image to video generation: CogVideoX and CogVideo
CogVideo is an open-source family of advanced video generation models that can create videos from text, images, or existing video inputs. Built on large-scale Transformer and diffusion architectures, it enables multimodal generation across text-to-video, image-to-video, and video continuation tasks. The latest CogVideoX models offer higher resolution outputs, longer video durations, and improved controllability through prompt engineering. The project includes tools for inference, fine-tuning, and optimization, making it suitable for both research and production use. It supports efficient deployment on a range of GPUs, including consumer hardware with quantization techniques. Overall, CogVideo provides a powerful framework for generating high-quality AI videos and experimenting with cutting-edge multimodal AI systems. -
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SenseVoice
Multilingual speech recognition and audio understanding model
SenseVoice is a speech foundation model designed to perform multiple voice understanding tasks from audio input. It provides capabilities such as automatic speech recognition, spoken language identification, speech emotion recognition, and audio event detection within a single system. SenseVoice is trained on more than 400,000 hours of speech data and supports over 50 languages for multilingual recognition tasks. It is built to achieve high transcription accuracy while maintaining efficient inference performance. It includes different model variants optimized for either speed or accuracy, allowing developers to choose a configuration suitable for their use case. In addition to speech transcription, SenseVoice can detect emotional cues in speech and identify common sound events such as applause, laughter, or coughing. It also provides tools for running inference, exporting models to formats like ONNX or LibTorch, and deploying the system through APIs. -
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civitai
Open platform for sharing and discovering Stable Diffusion models
Civitai is an open source project that provides the codebase for a platform designed to share and manage generative AI models used for image generation. It focuses primarily on models compatible with Stable Diffusion and related technologies, allowing creators to upload, organize, and distribute custom AI models and related resources. These resources can include textual inversions, hypernetworks, aesthetic gradients, and variational autoencoders that modify or extend the capabilities of diffusion-based image generation systems. Civitai encourages collaboration by allowing users to publish their models, explore models created by others, and learn from the techniques used in the community. It also supports user accounts, model browsing, and interaction features that help creators showcase their work and receive feedback from other users. Developers can deploy the project to run their own instance of the platform and integrate with its available API to retrieve models. -
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AI File Sorter
Local AI file organization with categorization and rename suggestions
AI File Sorter is a cross-platform desktop application that uses AI (local LLMs run on your computer) to organize files and suggest meaningful file names based on real content, not just filenames or extensions. The app can analyze images locally and propose descriptive rename suggestions (for example, IMG_2048.jpg → clouds_over_lake.jpg). It can also analyze document text to improve categorization and renaming. Supported formats include PDF, DOCX, XLSX, PPTX, ODT, ODS, ODP, and common text files. For supported audio and video files, AI File Sorter can read embedded metadata (such as ID3, Vorbis, and MP4 tags) to suggest normalized names like year_artist_album_title.ext. AI analysis runs read-only, and all suggestions must be reviewed before being applied. AI File Sorter can run fully offline using local models like Mistral or LLaMA, so files and metadata stay on your device unless you configure a remote endpoint. -
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SwarmUI
Modular AI image and video generation web UI with extensible tools
SwarmUI is a modular web-based user interface designed for AI-driven image generation, with a strong focus on usability, performance, and extensibility. It serves as a unified environment for working with multiple AI models, including Stable Diffusion and newer image and video generation systems, allowing users to create and manage outputs through a browser interface. SwarmUI is built to accommodate both beginners and advanced users by offering a simple “Generate” interface alongside more advanced workflow tools that expose deeper configuration options. It integrates with underlying systems like node-based workflows, enabling flexible and customizable pipelines for complex generation tasks. SwarmUI also emphasizes scalability, originally inspired by the idea of coordinating multiple GPUs to work together for large batch or grid-based image generation. SwarmUI includes a variety of built-in tools such as image editing, prompt handling, and automation features. -
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NOFX
Open source AI trading OS for autonomous multi-model trading systems
NOFX is an open source AI-powered trading operating system designed to automate financial trading workflows using autonomous AI agents. It acts as an infrastructure layer that transforms market data into AI-driven trade decisions and execution. Instead of requiring users to manually configure machine learning models, data sources, and API integrations, the system allows AI components to perceive market conditions, select models, and perform trading actions automatically. It supports running multiple AI models simultaneously and allows them to compete or collaborate when making trading decisions. NOFX integrates trading infrastructure such as exchange connectivity, strategy management, and performance monitoring into a single environment. It also includes components for strategy development, backtesting, and real-time monitoring so traders and researchers can evaluate algorithmic trading approaches. -
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YuE
Open source AI model for generating full songs from lyrics prompts
YuE is an open source project that provides a foundation model designed for full-song music generation using artificial intelligence. It focuses on transforming text inputs such as lyrics and genre prompts into complete musical compositions that include both vocal and instrumental tracks. Unlike many shorter audio generators, the model is capable of producing songs that last several minutes while maintaining coherent musical structure and alignment with the provided lyrics. YuE introduces a family of models built on large language model architectures that process music generation as a sequence prediction task. YuE also incorporates techniques such as track-decoupled prediction and progressive conditioning to help manage complex audio signals and maintain consistency throughout long compositions. It includes inference scripts, prompt examples, evaluation tools, and training components that enable researchers and developers to experiment with AI-based music. -
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CodeGeeX
CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)
CodeGeeX is a large-scale multilingual code generation model with 13 billion parameters, trained on 850B tokens across more than 20 programming languages. Developed with MindSpore and later made PyTorch-compatible, it is capable of multilingual code generation, cross-lingual code translation, code completion, summarization, and explanation. It has been benchmarked on HumanEval-X, a multilingual program synthesis benchmark introduced alongside the model, and achieves state-of-the-art performance compared to other open models like InCoder and CodeGen. CodeGeeX also powers IDE plugins for VS Code and JetBrains, offering features like code completion, translation, debugging, and annotation. The model supports Ascend 910 and NVIDIA GPUs, with optimizations like quantization and FasterTransformer acceleration for faster inference. -
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Diplomacy Cicero
Code for Cicero, an AI agent that plays the game of Diplomacy
The project is the codebase for an AI agent named Cicero developed by Facebook Research. It is designed to play the board game Diplomacy by combining open-domain natural language negotiation with strategic planning. The repository includes training code, model checkpoints, and infrastructure for both language modelling (via the ParlAI framework) and reinforcement learning for strategy agents. It supports two variants: Cicero (which handles full “press” negotiation) and Diplodocus (a variant focused on no-press diplomacy) as described in the README. The codebase is implemented primarily in Python with performance-critical components in C++ (via pybind11 bindings) and is configured to run in a high‐GPU cluster environment. Configuration is managed via protobuf files to define tasks such as self-play, benchmark agent comparisons, and RL training. The project is now archived and read-only, reflecting that it is no longer actively developed but remains publicly available for research use. -
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StableSwarmUI
Multi-user UI for managing and running Stable Diffusion workflows tool
StableSwarmUI is a web-based interface designed to manage and coordinate Stable Diffusion image generation workflows in a multi-user environment. It focuses on enabling multiple users to interact with shared resources, making it suitable for collaborative or server-based deployments. It provides a centralized system where users can submit, monitor, and manage generation tasks through a browser interface. It abstracts much of the complexity involved in running diffusion models by offering a structured environment for handling prompts, outputs, and processing queues. StableSwarmUI is built to work alongside backend systems that execute the actual image generation, allowing separation between user interaction and compute workloads. It also emphasizes scalability, making it useful for setups where multiple jobs need to be processed efficiently. Overall, it serves as a coordination layer for Stable Diffusion usage rather than a standalone model implementation. -
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Everywhere
Context-aware desktop AI assistant that understands screen content
Everywhere is a context-aware desktop AI assistant designed to interact directly with the content displayed on a user’s screen. It distinguishes itself from traditional AI tools by eliminating the need for manual input methods such as copying text or taking screenshots, instead allowing users to invoke assistance instantly through a shortcut. It can analyze on-screen information in real time and provide contextual responses, making it useful for tasks like troubleshooting errors, summarizing articles, translating text, and refining written content. It integrates with multiple large language model providers and supports various tools, enabling flexible and extensible AI-powered workflows. Everywhere features a modern design with interactive elements such as markdown rendering, keyboard shortcuts, and voice input capabilities. Additionally, the project emphasizes seamless workflow integration by operating alongside existing applications rather than requiring users to switch. -
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Generative AI for Beginners (Version 3)
21 Lessons, Get Started Building with Generative AI
Generative AI for Beginners is a 21-lesson course by Microsoft Cloud Advocates that teaches the fundamentals of building generative AI applications in a practical, project-oriented way. Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. The course covers everything from model selection, prompt engineering, and chat/text/image app patterns to secure development practices and UX for AI. It also walks through modern application techniques such as function calling, RAG with vector databases, working with open source models, agents, fine-tuning, and using SLMs. Each lesson includes a short video, a written guide, runnable samples for Azure OpenAI, the GitHub Marketplace Model Catalog, and the OpenAI API, plus a “Keep Learning” section for deeper study. -
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Inbox Zero
AI assistant that automates email tasks to help achieve inbox zero
Inbox Zero is an open source AI-powered email assistant designed to help users manage and process their inbox more efficiently. It aims to reduce the time spent handling email by automatically organizing, prioritizing, and responding to messages using customizable automation rules and artificial intelligence. Users can define prompts or rule-based actions that guide how the assistant processes incoming messages, enabling automated workflows for sorting, replying, or handling routine communication. Inbox Zero is structured as a modern web application built with a monorepo architecture that contains multiple applications and shared packages, allowing modular development and easier maintenance. It integrates with email services and can automate actions such as scheduling tasks, generating replies, and managing follow-ups. Inbox Zero is designed to allow users to retain precise control over automation rules while still benefiting from AI-driven suggestions and analysis. -
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PasteMD
Paste Markdown and AI responses into Word Excel instantly fast
PasteMD is a lightweight desktop utility designed to streamline the process of transferring formatted content from the clipboard into office applications such as Word, WPS, and Excel. It primarily targets users who frequently copy content from AI chat tools or web pages and encounter formatting issues, especially with Markdown, tables, and LaTeX formulas. PasteMD operates from the system tray and monitors clipboard content, automatically converting Markdown or HTML into properly formatted documents using Pandoc. With a single global hotkey, users can paste structured content directly into the active application without manual cleanup or reformatting. It includes intelligent detection mechanisms that distinguish between Markdown tables, rich HTML content, and plain text, ensuring the correct output format is used for each target application. PasteMD also introduces extensible workflows that allow users to configure different paste behaviors. -
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Consistent Depth
We estimate dense, flicker-free, geometrically consistent depth
Consistent Depth is a research project developed by Facebook Research that presents an algorithm for reconstructing dense and geometrically consistent depth information for all pixels in a monocular video. The system builds upon traditional structure-from-motion (SfM) techniques to provide geometric constraints while integrating a convolutional neural network trained for single-image depth estimation. During inference, the model fine-tunes itself to align with the geometric constraints of a specific input video, ensuring stable and realistic depth maps even in less-constrained regions. This approach achieves improved geometric consistency and visual stability compared to prior monocular reconstruction methods. The project can process challenging hand-held video footage, including those with moderate dynamic motion, making it practical for real-world usage. -
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Kaldi
kaldi-asr/kaldi is the official location of the Kaldi project
Kaldi is an open source toolkit for speech recognition research. It provides a powerful framework for building state-of-the-art automatic speech recognition (ASR) systems, with support for deep neural networks, Gaussian mixture models, hidden Markov models, and other advanced techniques. The toolkit is widely used in both academia and industry due to its flexibility, extensibility, and strong community support. Kaldi is designed for researchers who need a highly customizable environment to experiment with new algorithms, as well as for practitioners who want robust, production-ready ASR pipelines. It includes extensive tools for data preparation, feature extraction, acoustic and language modeling, decoding, and evaluation. With its modular design, Kaldi allows users to adapt the system to a wide range of languages and domains. As one of the most influential projects in speech recognition, it has become a foundation for much of the modern work in ASR. -
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Replica Dataset
High-fidelity indoor 3D dataset for AI simulation and robotics
Replica Dataset is a high-quality 3D dataset of realistic indoor environments designed to advance research in computer vision, robotics, and embodied AI. Developed by Facebook Research (now Meta AI), it features accurate geometric reconstructions, high-resolution and high dynamic range textures, and comprehensive semantic annotations. Each environment contains detailed models of real-world spaces, including rooms, furniture, glass, and mirror surfaces. The dataset also provides semantic and instance segmentations, planar decomposition, and navigation meshes, making it highly suitable for simulation, visual perception, and autonomous navigation tasks. Replica integrates seamlessly with AI Habitat, Meta’s framework for embodied AI training, enabling large-scale agent simulation and photorealistic rendering for reinforcement learning and robotics. Researchers can use Replica’s ReplicaViewer to interactively explore the 3D scenes. -
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Xianyu Intelligent Monitor Bot
AI tool for real-time monitoring and analysis of Goofish listings
ai-goofish-monitor is an open source automation tool designed to monitor listings on the Goofish second-hand marketplace and analyze them using artificial intelligence. It combines browser automation with AI-based analysis to automatically search, collect, and evaluate newly posted items that match a user’s purchase criteria. It uses Playwright to simulate real user interactions with the marketplace, allowing the system to retrieve product data and track updates in near real time. ai-goofish-monitor can run multiple monitoring tasks simultaneously, each configured with specific keywords, price ranges, and filtering conditions. A built-in web management interface allows users to create tasks, review results, and manage monitoring rules without relying solely on command line tools. AI models analyze product descriptions, images, and seller information to determine whether a listing meets defined requirements and should be recommended to the user. -
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AIGCPanel
One-stop AI digital human system with video voice synthesis tools
AIGCPanel is an open source desktop application designed as a comprehensive, all-in-one platform for creating AI-powered digital humans and media content. It integrates multiple capabilities such as video synthesis, voice synthesis, and voice cloning into a unified interface, allowing users to generate realistic audiovisual outputs with minimal setup. AIGCPanel focuses heavily on simplifying the management of local AI models by providing streamlined workflows for importing, configuring, and running different models with minimal manual effort. It supports one-click model deployment, making it accessible even to beginners who may not be familiar with complex AI environments. AIGCPanel also includes tools for synchronizing lip movements with generated speech, enabling more realistic digital avatar videos. Built using modern desktop technologies, it delivers a cross-platform experience while maintaining a graphical interface for monitoring tasks and logs. -
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Bear Stone Smart Home
Custom Home Assistant configuration with automations and scripts setup
Bear Stone Smart Home contains a personalized configuration setup for Home Assistant, an open source home automation platform. It defines how various smart home devices, services, and integrations are organized and controlled within a single environment. It includes configuration files that manage entities such as lights, sensors, switches, and media devices, enabling centralized automation and monitoring. It demonstrates how to structure Home Assistant YAML files for scalability and maintainability in a real-world deployment. Bear Stone Smart Home also showcases custom automations and scripts designed to improve convenience, energy efficiency, and overall smart home behavior. Additionally, it may include examples of dashboards and user interface customization to enhance usability and visualization of home data. Overall, it serves as a practical reference for building and refining a tailored Home Assistant setup. -
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ChatGLM3
ChatGLM3 series: Open Bilingual Chat LLMs | Open Source Bilingual Chat
ChatGLM3 is ZhipuAI & Tsinghua KEG’s third-gen conversational model suite centered on the 6B-parameter ChatGLM3-6B. It keeps the series’ smooth dialog and low deployment cost while adding native tool use (function calling), a built-in code interpreter, and agent-style workflows. The family includes base and long-context variants (8K/32K/128K). The repo ships Python APIs, CLI and web demos (Gradio/Streamlit), an OpenAI-format API server, and a compact fine-tuning kit. Quantization (4/8-bit), CPU/MPS support, and accelerator backends (TensorRT-LLM, OpenVINO, chatglm.cpp) enable lightweight local or edge deployment. -
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CodeGeeX2
CodeGeeX2: A More Powerful Multilingual Code Generation Model
CodeGeeX2 is the second-generation multilingual code generation model from ZhipuAI, built upon the ChatGLM2-6B architecture and trained on 600B code tokens. Compared to the first generation, it delivers a significant boost in programming ability across multiple languages, outperforming even larger models like StarCoder-15B in some benchmarks despite having only 6B parameters. The model excels at code generation, translation, summarization, debugging, and comment generation, and it supports over 100 programming languages. With improved inference efficiency, quantization options, and multi-query/flash attention, CodeGeeX2 achieves faster generation speeds and lightweight deployment, requiring as little as 6GB GPU memory at INT4 precision. Its backend powers the CodeGeeX IDE plugins for VS Code, JetBrains, and other editors, offering developers interactive AI assistance with features like infilling and cross-file completion. -
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Desktop Commander MCP
AI-powered MCP server for desktop file and terminal automation
Desktop Commander MCP is an advanced Model Context Protocol server designed to extend AI assistants with direct control over a user’s local machine, including the file system and terminal. It integrates with clients like Claude Desktop to enable AI-driven workflows such as editing files, executing commands, and automating development tasks from a single conversational interface. Desktop Commander MCP builds on top of an MCP filesystem server and enhances it with powerful search, replace, and code editing capabilities tailored for real-world development environments. It allows users to run terminal commands with streaming output, manage long-running processes, and even execute code in memory without saving files. It also supports working with structured and document formats such as Excel, PDF, and DOCX, enabling AI to read, modify, and generate these files directly. -
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GLM-130B
GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
GLM-130B is an open bilingual (English and Chinese) dense language model with 130 billion parameters, released by the Tsinghua KEG Lab and collaborators as part of the General Language Model (GLM) series. It is designed for large-scale inference and supports both left-to-right generation and blank filling, making it versatile across NLP tasks. Trained on over 400 billion tokens (200B English, 200B Chinese), it achieves performance surpassing GPT-3 175B, OPT-175B, and BLOOM-176B on multiple benchmarks, while also showing significant improvements on Chinese datasets compared to other large models. The model supports efficient inference via INT8 and INT4 quantization, reducing hardware requirements from 8× A100 GPUs to as little as a single server with 4× RTX 3090s. Built on the SwissArmyTransformer (SAT) framework and compatible with DeepSpeed and FasterTransformer, it supports high-speed inference (up to 2.5× faster) and reproducible evaluation across 30+ benchmark tasks.
