Open Source Unix Shell Artificial Intelligence Software
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Unix Shell Artificial Intelligence Software
View 16733 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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Cog
Package and deploy machine learning models using Docker containers
Cog is an open source tool designed to package machine learning models into standardized, production-ready containers. It simplifies the process of deploying models by automatically generating Docker images based on a simple configuration file, eliminating the need to manually write complex Dockerfiles. Developers can define the runtime environment, dependencies, and Python versions required for their models, allowing Cog to build a consistent container environment that follows best practices. Cog also resolves compatibility issues between frameworks and GPU libraries by automatically selecting compatible combinations of CUDA, cuDNN, and machine learning frameworks such as PyTorch or TensorFlow. Cog automatically generates a RESTful HTTP API for running predictions, enabling models to be accessed programmatically through a built-in prediction server. -
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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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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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Qodo Cover
AI tool that generates tests to improve code coverage quickly
Qodo Cover is an open source developer tool designed to automate the creation of unit tests using generative AI, helping teams improve code coverage with minimal manual effort. It operates as a command-line interface and can also be integrated into continuous integration workflows, making it adaptable to different development environments. It analyzes an existing codebase, identifies gaps in test coverage, and generates new tests that target uncovered or weakly tested areas. It follows an iterative workflow where generated tests are executed, validated, and refined to ensure they contribute meaningful coverage improvements. Internally, Qodo Cover uses a modular architecture that includes components for prompt generation, AI interaction, coverage analysis, and test validation. It supports scanning entire repositories to automatically detect test files, gather relevant context, and extend test suites accordingly. -
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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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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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Preswald
Python tool for browser-based interactive data apps in one file
Preswald is an open source Python-based framework and static-site generator designed for building interactive data applications that run entirely in the browser. It packages application logic, data processing, and user interface components into a single self-contained output, enabling easy sharing and deployment without requiring local dependencies. Preswald leverages a WebAssembly runtime along with technologies like Pyodide and DuckDB to execute Python code directly in the browser environment. This approach allows developers to create dashboards, reports, notebooks, and data tools that are portable, fast, and capable of running offline. Preswald emphasizes a code-first workflow where users define applications entirely in Python while using built-in UI components such as tables, charts, and forms. It also includes a reactive execution model that only recomputes necessary parts of the app, improving performance and responsiveness. -
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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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TaskingAI
Open platform for building, deploying, and managing LLM agents
TaskingAI is an open source platform designed to simplify the development and deployment of applications powered by large language models. It follows a Backend as a Service approach, allowing developers to separate AI logic from frontend product development while maintaining a structured and scalable workflow. TaskingAI integrates hundreds of language models from multiple providers into a unified system, enabling developers to switch models or combine capabilities without major reconfiguration. It includes a modular architecture that supports components such as assistants, tools, retrieval systems, and conversation management, all accessible through a consistent interface. TaskingAI also provides a built-in user interface for managing projects, testing workflows, and configuring AI agents without needing to rely entirely on code. It supports advanced techniques like retrieval-augmented generation and plugin-based extensions, allowing developers to enhance agent capabilities. -
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OpenAI Harmony
Renderer for the harmony response format to be used with gpt-oss
Harmony is a response format developed by OpenAI for use with the gpt-oss model series. It defines a structured way for language models to produce outputs, including regular text, reasoning traces, tool calls, and structured data. By mimicking the OpenAI Responses API, Harmony provides developers with a familiar interface while enabling more advanced capabilities such as multiple output channels, instruction hierarchies, and tool namespaces. The format is essential for ensuring gpt-oss models operate correctly, as they are trained to rely on this structure for generating and organizing their responses. For users accessing gpt-oss through third-party providers like HuggingFace, Ollama, or vLLM, Harmony formatting is handled automatically, but developers building custom inference setups must implement it directly. With its flexible design, Harmony serves as the foundation for creating more interpretable, controlled, and extensible interactions with open-weight language models. -
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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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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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GitDiagram
AI tool that converts GitHub repositories into interactive diagrams
GitDiagram is an open source web application designed to help developers quickly understand the structure and architecture of GitHub repositories by automatically generating interactive diagrams. It analyzes repository metadata such as the file tree and project documentation to build a visual representation of how different components of a project relate to one another. It uses an AI-powered pipeline to interpret repository structure and transform that information into system design diagrams rendered with Mermaid visualization. These diagrams provide a high-level overview of a codebase, making it easier for developers to explore unfamiliar projects or understand large and complex repositories. Users can interact with the generated diagrams by clicking components to navigate directly to related files or directories within the repository. GitDiagram combines a modern web frontend with a backend service that processes repository data and generates diagrams dynamically. -
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Mito
AI-powered Jupyter spreadsheet that converts workflows into Python
Mito is an open source set of Jupyter extensions designed to speed up Python workflows and data analysis. It combines a spreadsheet-style interface with AI-assisted coding, allowing users to explore, clean, and transform data without switching tools. Mito includes a context-aware AI assistant that helps generate code, debug errors, and guide workflows directly inside Jupyter. Its spreadsheet layer supports familiar functions such as filters, pivot tables, and formulas, while automatically converting every action into production-ready Python code. This removes the need to manually translate spreadsheet logic into scripts. Mito also integrates with tools like Streamlit and Dash, enabling users to embed interactive spreadsheet functionality into apps with minimal setup. Built for analysts, developers, and teams, it simplifies automation, reduces repetitive tasks, and accelerates the transition from data exploration to reusable code. -
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Claude-Flow
The leading agent orchestration platform for Claude
Claude-Flow v2 Alpha is an advanced AI orchestration and automation framework designed for enterprise-grade, large-scale AI-driven development. It enables developers to coordinate multiple specialized AI agents in real time through a hive-mind architecture, combining swarm intelligence, neural reasoning, and a powerful set of 87 Modular Control Protocol (MCP) tools. The platform supports both quick swarm tasks and persistent multi-agent sessions known as hives, facilitating distributed AI collaboration with persistent contextual memory. At its core, Claude-Flow integrates Dynamic Agent Architecture (DAA) for self-organizing agent management, neural pattern recognition accelerated by WebAssembly SIMD, and a SQLite-based memory system for context retention and knowledge persistence across tasks. It automates development workflows via pre- and post-operation hooks, providing seamless coordination, code formatting, validation, and performance optimization. -
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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. -
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WeKnora
LLM framework for document understanding and semantic retrieval
WeKnora is an open source framework developed for deep document understanding and semantic information retrieval using large language models. It focuses on analyzing complex and heterogeneous documents by combining multiple processing stages such as multimodal document parsing, vector indexing, and intelligent retrieval. It follows the Retrieval-Augmented Generation (RAG) paradigm, where relevant document segments are retrieved and used by language models to generate accurate, context-aware responses. This approach enables the system to provide more reliable answers by grounding model reasoning in the content of uploaded documents. WeKnora is designed with a modular architecture that separates components for document processing, search strategies, and model inference, allowing developers to customize or extend different parts of the pipeline. It supports knowledge base management and conversational question answering built on top of structured and unstructured documents. -
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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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DeepAnalyze
Autonomous LLM agent for end-to-end data science workflows
DeepAnalyze is an open source project that introduces an agentic large language model designed to perform autonomous data science tasks from start to finish. It is built to handle the entire data science pipeline, including data preparation, analysis, modeling, visualization, and report generation without requiring continuous human guidance. DeepAnalyze is capable of conducting open-ended data research across multiple data formats such as structured tables, semi-structured files, and unstructured text, enabling flexible and comprehensive analysis workflows. It integrates execution-based reasoning by generating and running code as part of its analysis process, allowing it to iteratively refine results and produce more accurate outputs. DeepAnalyze provides multiple interaction interfaces, including a web-based UI, a command-line interface, and a Jupyter-style notebook environment for interactive workflows. -
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Every Code
Local AI coding agent CLI with multi-agent orchestration tools
Every Code (often referred to simply as Code) is a fast, local AI-powered coding agent designed to run directly in the terminal environment. It is a community-driven fork of the Codex CLI, with a strong emphasis on improving real-world developer ergonomics and workflows. Every Code enhances the traditional coding assistant model by introducing multi-agent orchestration, allowing multiple AI agents to collaborate, compare solutions, and refine outputs in parallel. It supports integration with various AI providers, enabling users to route tasks across different models depending on their needs. Every Code also includes browser integration and automation capabilities, extending its usefulness beyond simple code generation into more complex development tasks. Customization is a key focus, with support for theming, configurable settings, and reasoning controls that allow developers to fine-tune how the agent behaves. -
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LoLLMs WEBUI
Local AI WebUI for running and managing large language models offlineA
lollms-webui is a locally hosted web interface designed to run and manage large language models without relying on external services. It provides users with a centralized environment to interact with multiple AI models, making it suitable for experimentation, development, and personal use. lollms-webui emphasizes offline capability, allowing users to maintain privacy and control over their data while still accessing advanced AI features. It integrates model management tools that help users download, configure, and switch between different language models with ease. It is built to be user-friendly while still offering advanced customization options for power users who want deeper control over model behavior. Additionally, it supports extensibility through plugins or modular components, enabling users to expand functionality as needed. Overall, it serves as a flexible platform for running AI locally with a focus on usability and adaptability. -
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Microsandbox
Secure local-first microVM sandbox for running untrusted code fast
Microsandbox is an open source platform designed to securely execute untrusted code in isolated environments using lightweight virtualization techniques. It focuses on combining strong security guarantees with fast startup times by leveraging hardware-level microVM isolation instead of relying solely on traditional containers or full virtual machines. It aims to solve the common tradeoffs between speed, isolation, and control that developers encounter when running untrusted workloads. It provides a local-first and self-hosted approach, allowing users to maintain full ownership of their execution environment without depending on external cloud services. Microsandbox is particularly geared toward AI agent workflows, offering integrations that enable automated systems to safely run generated code and commands. It also supports standard container images, making it compatible with existing development ecosystems and tooling. -
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MobileLLM
MobileLLM Optimizing Sub-billion Parameter Language Models
MobileLLM is a lightweight large language model (LLM) framework developed by Facebook Research, optimized for on-device deployment where computational and memory efficiency are critical. Introduced in the ICML 2024 paper “MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases”, it focuses on delivering strong reasoning and generalization capabilities in models under one billion parameters. The framework integrates several architectural innovations—SwiGLU activation, deep and thin network design, embedding sharing, and grouped-query attention (GQA)—to achieve a superior trade-off between model size, inference speed, and accuracy. MobileLLM demonstrates remarkable performance, with the 125M and 350M variants outperforming previous state-of-the-art models of the same scale by up to 4.3% on zero-shot commonsense reasoning tasks. -
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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.
