Open Source Linux Artificial Intelligence Software - Page 16
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Artificial Intelligence Software for Linux
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Langflow
Low-code app builder for RAG and multi-agent AI applications
Langflow is a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database. -
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MLC LLM
Universal LLM Deployment Engine with ML Compilation
MLC LLM is a machine learning compiler and deployment framework designed to enable efficient execution of large language models across a wide range of hardware platforms. The project focuses on compiling models into optimized runtimes that can run natively on devices such as GPUs, mobile processors, browsers, and edge hardware. By leveraging machine learning compilation techniques, mlc-llm produces high-performance inference engines that maintain consistent APIs across platforms. The system supports deployment on environments including Linux, macOS, Windows, iOS, Android, and web browsers while utilizing different acceleration technologies such as CUDA, Vulkan, Metal, and WebGPU. It also provides OpenAI-compatible APIs that allow developers to integrate locally deployed models into existing AI applications without major code changes. -
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MNN
MNN is a blazing fast, lightweight deep learning framework
MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. In addition, MNN is also used on embedded devices, such as IoT. MNN Workbench could be downloaded from MNN's homepage, which provides pretrained models, visualized training tools, and one-click deployment of models to devices. Android platform, core so size is about 400KB, OpenCL so is about 400KB, Vulkan so is about 400KB. Supports hybrid computing on multiple devices. Currently supports CPU and GPU. -
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OpenShorts
Free & open source AI video platform
OpenShorts is an open-source, self-hosted AI video automation platform designed to generate, edit, and distribute short-form vertical content across social media platforms. It combines multiple tools into a single pipeline, including clip generation, AI-driven video creation, and YouTube optimization features. The system can transform long videos or uploaded files into short clips by detecting engaging moments, reframing content, and adding subtitles and visual effects. It also supports generating marketing videos using AI actors, voiceovers, and scripted narratives without requiring cameras or production resources. The platform integrates publishing capabilities, allowing users to distribute content directly to TikTok, Instagram, and YouTube. Its architecture uses modern technologies such as FastAPI, FFmpeg, and AI models for transcription, analysis, and rendering. Designed for creators and businesses, it automates the entire lifecycle of short-form video production. -
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PenEcho
Think with AI beyond the chat box
PenEcho is an open-source shared canvas that makes handwriting, equations, diagrams, and spatial context part of an AI conversation. Users can draw with pressure-sensitive ink, erase, pan, zoom, type formatted text, and arrange ideas across a large virtual workspace. The application sends focused visual regions to an API, Codex CLI, or Claude CLI and converts model responses into editable canvas commands. AI output can include text, formulas, plots, mixed drawings, erasures, and declarative animations. Sparse tile rendering keeps the 20,000-by-20,000 logical canvas efficient without allocating one enormous bitmap. Local snapshots, undo and redo, lasso selection, PNG export, and manual or automatic AI modes support iterative work. PenEcho is available as a desktop application or Node.js package and supports configurable models, reasoning levels, request retention, and plugins. -
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Pocket TTS
A TTS that fits in your CPU (and pocket)
Pocket TTS is a lightweight text-to-speech project designed to run efficiently on CPUs, targeting developers who want local speech generation without depending on GPUs or hosted web APIs. It is built to feel practical in everyday applications, where installation and usage should be as simple as adding a dependency and calling a function. The project focuses on keeping the runtime footprint manageable while still producing natural-sounding speech, which makes it attractive for offline tools, prototypes, and privacy-sensitive workflows. Because it is CPU-oriented, it fits well in server environments where GPU access is limited, in desktop apps, or in edge deployments where simplicity matters more than maximum throughput. It also emphasizes developer ergonomics, providing a straightforward API surface that can be integrated into pipelines, assistants, accessibility tools, or batch generation scripts. -
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Prem AI
Prem provides a unified environment to develop AI applications
An intuitive desktop application designed to effortlessly deploy and self-host Open-Source AI models without exposing sensitive data to third-party. Prem provides a unified environment to develop AI applications and deploy AI models on your infrastructure. Abstracting away all technical complexities for AI deployment and ushering in a new era of privacy-centric AI applications - users can finally retain control and ownership of their models. The AI services expose an HTTP API interface, standardized for their interface type. For example, all models of type Chat expose the OpenAI API for easy of integration of existing tools and AI app ecosystem. Each service we support it's published on the Prem Registry. -
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Qwen3-TTS
Qwen3-TTS is an open-source series of TTS models
Qwen3-TTS is an open-source text-to-speech (TTS) project built around the Qwen3 large language model family, focused on generating high-quality, natural-sounding speech from plain text input. It provides researchers and developers with tools to transform text into expressive, intelligible audio, supporting multiple languages and voice characteristics tuned for clarity and fluidity. The project includes pre-trained models and inference scripts that let users synthesize speech locally or integrate TTS into larger pipelines such as voice assistants, accessibility tools, or multimedia generation workflows. Because it’s part of the broader Qwen ecosystem, it benefits from the model’s understanding of linguistic nuances, enabling more accurate pronunciation, prosody, and contextual delivery than many traditional TTS systems. Developers can customize voice output parameters like speed, pitch, and volume, and combine the TTS stack with other AI components. -
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rtk
CLI proxy that reduces LLM token consumption
rtk is an open-source command-line proxy designed to optimize interactions between AI coding agents and the terminal by reducing unnecessary token consumption. When AI assistants execute shell commands during software development tasks, the resulting terminal output often contains large amounts of repetitive or irrelevant information that can overwhelm the model’s context window. RTK intercepts these command outputs and compresses them into concise summaries before sending them to the language model. This process helps maintain important information while removing redundant data such as boilerplate logs, long directory listings, or repetitive test outputs. By minimizing the amount of noise sent to the AI model, the tool improves reasoning quality and allows longer development sessions within the same context window. The system is implemented as a lightweight Rust binary that runs locally and integrates easily with common AI coding environments. -
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tickflow-stock-panel
Self-hosted, zero-operation A-share quantitative workbench
tickflow-stock-panel is a self-hosted quantitative stock workstation for China A-share screening, monitoring, and backtesting. It is aimed at individual retail investors and quant hobbyists who want a local research dashboard rather than a closed commercial terminal. The project uses TickFlow as a data source but is not an official TickFlow product. Its modules include a stock screener, indicator pipeline, backtesting engine, monitoring center, single-stock analysis, concept analysis, industry analysis, and post-market review. It also uses LLM capabilities for strategy generation, stock analysis, financial interpretation, and market recap workflows. The project is for learning and quantitative research only, and its backtests or AI outputs should not be treated as investment advice. -
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Hathi Download Helper
Download books from the hathitrust website in a fast and easy manner
2025-05-08 ====================== PLEASE NOTE ======================= Due to changes to the API of the hathirtust homepage, the HDH is no longer functional!! Please check the project Wiki for alternative methods. https://sourceforge.net/p/hathidownloadhelper/alternative/ ---------------------------------------------------------------------------------------------- Hathi Download Helper was a tool for downloading public domain books from hathitrust.org. E-Mail contact: hathidownloadhelper@hotmail.com -
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MOA - Massive Online Analysis
Big Data Stream Analytics Framework.
A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlier detection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning. -
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Accomplish
Accomplish is the open source Al coworker that lives on your desktop
Accomplish is an open-source AI desktop agent that automates everyday computer tasks directly on a user’s machine. It can handle file management, document creation, and browser-based workflows through natural language instructions. The system runs locally, ensuring that user data remains private and under full control. It supports integration with multiple AI providers or local models, giving users flexibility in how intelligence is powered. Accomplish emphasizes autonomy, allowing it to execute multi-step tasks without constant supervision. Its design focuses on replacing repetitive manual workflows with intelligent automation. Overall, it acts as a personal AI coworker embedded in the desktop environment. -
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Alpa
Training and serving large-scale neural networks
Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code. -
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ChatGPT API
Node.js client for the official ChatGPT API. 🔥
This package is a Node.js wrapper around ChatGPT by OpenAI. TS batteries included. ✨ The official OpenAI chat completions API has been released, and it is now the default for this package! 🔥 Note: We strongly recommend using ChatGPTAPI since it uses the officially supported API from OpenAI. We may remove support for ChatGPTUnofficialProxyAPI in a future release. 1. ChatGPTAPI - Uses the gpt-3.5-turbo-0301 model with the official OpenAI chat completions API (official, robust approach, but it's not free) 2. ChatGPTUnofficialProxyAPI - Uses an unofficial proxy server to access ChatGPT's backend API in a way that circumvents Cloudflare (uses the real ChatGPT and is pretty lightweight, but relies on a third-party server and is rate-limited) -
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FID score for PyTorch
Compute FID scores with PyTorch
This is a port of the official implementation of Fréchet Inception Distance to PyTorch. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. The weights and the model are exactly the same as in the official Tensorflow implementation, and were tested to give very similar results (e.g. .08 absolute error and 0.0009 relative error on LSUN, using ProGAN generated images). However, due to differences in the image interpolation implementation and library backends, FID results still differ slightly from the original implementation. In difference to the official implementation, you can choose to use a different feature layer of the Inception network instead of the default pool3 layer. -
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IndexTTS2
Industrial-level controllable zero-shot text-to-speech system
IndexTTS is a modern, zero-shot text-to-speech (TTS) system engineered to deliver high-quality, natural-sounding speech synthesis with few requirements and strong voice-cloning capabilities. It builds on state-of-the-art models such as XTTS and other modern neural TTS backbones, improving them with a conformer-based speech conditional encoder and upgrading the decoder to a high-quality vocoder (BigVGAN2), leading to clearer and more natural audio output. The system supports zero-shot voice cloning — meaning it can mimic a target speaker’s voice from a short reference sample — making it versatile for multi-voice uses. Compared to many open-source TTS tools, IndexTTS emphasizes efficiency and controllability: it offers faster inference, simpler training pipelines, and controllable speech parameters (like duration, pitch, and prosody), which is critical for production use. -
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MusicGPT
Generate music based on natural language prompts using LLMs
MusicGPT is an open-source application designed to generate music from natural language prompts using locally executed artificial intelligence models. The software allows users to run advanced music generation systems directly on their own devices without requiring heavy dependencies such as Python or full machine learning frameworks. Instead, it provides a lightweight environment capable of executing music generation models locally on CPUs or GPUs while maintaining strong performance across operating systems including Windows, macOS, and Linux. Users can describe a musical style, mood, or instrumentation using text prompts, and the system produces original audio samples based on those instructions. The application currently integrates with models such as MusicGen and is designed to support additional models transparently in the future. In addition to a command-line interface, the project includes a web-based interface that enables conversational interaction with the AI model. -
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Oumi
Everything you need to build state-of-the-art foundation models
Oumi is an open-source framework that provides everything needed to build state-of-the-art foundation models, end-to-end. It aims to simplify the development of large-scale machine-learning models. -
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Prompt Master
A Claude skill that writes the accurate prompts for any AI tool
Prompt Master is a repository focused on organizing, managing, and optimizing prompts for AI systems, particularly large language models. It provides a structured way to store and reuse prompts, making it easier to maintain consistency and improve performance across different applications. The project emphasizes clarity and organization, allowing users to categorize prompts by use case, domain, or functionality. It also supports experimentation, enabling users to refine prompts and compare results to achieve better outputs. The repository can be used as both a learning resource and a practical toolkit for developers working with AI systems. It reflects the growing importance of prompt engineering as a discipline in modern AI development. -
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Qwen-2.5-VL
Qwen2.5-VL is the multimodal large language model series
Qwen2.5 is a series of large language models developed by the Qwen team at Alibaba Cloud, designed to enhance natural language understanding and generation across multiple languages. The models are available in various sizes, including 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B parameters, catering to diverse computational requirements. Trained on a comprehensive dataset of up to 18 trillion tokens, Qwen2.5 models exhibit significant improvements in instruction following, long-text generation (exceeding 8,000 tokens), and structured data comprehension, such as tables and JSON formats. They support context lengths up to 128,000 tokens and offer multilingual capabilities in over 29 languages, including Chinese, English, French, Spanish, and more. The models are open-source under the Apache 2.0 license, with resources and documentation available on platforms like Hugging Face and ModelScope. -
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SAM 3
Code for running inference and finetuning with SAM 3 model
SAM 3 (Segment Anything Model 3) is a unified foundation model for promptable segmentation in both images and videos, capable of detecting, segmenting, and tracking objects. It accepts both text prompts (open-vocabulary concepts like “red car” or “goalkeeper in white”) and visual prompts (points, boxes, masks) and returns high-quality masks, boxes, and scores for the requested concepts. Compared with SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an open-vocabulary concept specified by a short phrase or exemplars, scaling to a vastly larger set of categories than traditional closed-set models. This capability is grounded in a new data engine that automatically annotated over four million unique concepts, producing a massive open-vocabulary segmentation dataset and enabling the model to achieve 75–80% of human performance on the SA-CO benchmark, which itself spans 270K unique concepts. -
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Telegram MCP
MCP server to work with Telegram through MTProto
An MCP server that bridges the Telegram API and AI assistants, enabling seamless interaction between AI applications and Telegram through MTProto. -
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Venom
Venom is the most complete javascript library for Whatsapp
Venom is a high-performance system developed with JavaScript to create a bot for WhatsApp, support for creating any interaction, such as customer service, media sending, sentence recognition based on artificial intelligence and all types of design architecture for WhatsApp. It's a high-performance alternative API to whatzapp, you can send, text messages, files, images, videos and more. Remember, the API was developed on a platform called RESTful Web services, providing interoperability between computer systems on the Internet. It uses a set of well-defined operations that apply to all information resources, HTTP itself defines a small set of operations, the most important being post, get, put and delete. Use it in your favorite language like PHP, Python, C# and others. as long as your language is supported with the HTTP protocol, you will save time and money. you don't need to know how Venom works, we have the complete API documentation, in a professional way! -
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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.
