Open Source Linux Artificial Intelligence Software - Page 36
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Artificial Intelligence Software for Linux
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Open Autonomy
A framework for the creation of autonomous agent services
Open Autonomy is a framework that enables the development of autonomous economic agents (AEAs) capable of operating independently in various economic contexts. -
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OpenFang
Open-source Agent Operating System
OpenFang is an open-source agent operating system designed to orchestrate autonomous AI agents and workflows in a structured, production-oriented environment. Written primarily in Rust, the project focuses on building a high-performance runtime where multiple specialized agents can collaborate to complete complex computational or development tasks. It aims to move beyond simple chat-based agents by providing infrastructure for persistent agent memory, task coordination, and scalable execution. The system is positioned as a foundation for building advanced AI tooling, particularly in environments that require tight integration with GPU workflows and modern AI pipelines. OpenFang emphasizes modularity and extensibility so developers can plug in custom agents, tools, or execution backends. Overall, the project represents an emerging class of “agent OS” platforms that treat AI agents as first-class computational actors rather than isolated scripts. -
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OpenMythos
A theoretical reconstruction of the Claude Mythos architecture
OpenMythos is an experimental, open-source implementation that attempts to reconstruct a hypothesized architecture behind advanced language models using a design called a Recurrent-Depth Transformer. The project explores the idea that instead of stacking hundreds of unique transformer layers, a smaller set of layers can be reused iteratively during inference to achieve deeper reasoning without increasing parameter count. It divides computation into three main stages, including a pre-processing phase, a looped recurrent reasoning block, and a final output refinement stage, creating a structured pipeline for inference. The architecture incorporates advanced techniques such as mixture-of-experts routing, adaptive computation time, and multiple attention mechanisms to dynamically allocate compute where needed. It is highly configurable through a centralized configuration system, allowing experimentation with different architectural parameters such as loop depth, attention type. -
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Ornith-1.0
Ornith-1.0 is a self-improving open-source models for agentic coding
Ornith-1 is an open-source family of agentic coding models from DeepReinforce AI. It is designed for coding agents that need to solve software engineering tasks through iterative tool use and solution rollouts. The project presents 9B dense, 31B dense, 35B mixture-of-experts, and 397B mixture-of-experts variants. These models are post-trained on top of Gemma 4 and Qwen 3.5 foundations. Its training approach uses reinforcement learning to optimize both the solution and the scaffold that guides the solution process. The repository emphasizes benchmark performance on Terminal-Bench, SWE-bench, NL2Repo, OpenClaw, and SWE Atlas while keeping the project MIT licensed and globally accessible. -
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PHP Telegram Bot Api
Native PHP Wrapper for Telegram BOT API
An extended native php wrapper for Telegram Bot API without requirements. Supports all methods and types of responses. Bots are special Telegram accounts designed to handle messages automatically. Users can interact with bots by sending them command messages in private or group chats. The Bot API is an HTTP-based interface created for developers keen on building bots for Telegram. -
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Porcupine
On-device wake word detection powered by deep learning
Build always-listening yet private voice applications. Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening voice-enabled applications. It is using deep neural networks trained in real-world environments. Compact and computationally-efficient. It is perfect for IoT. Cross-platform. Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RT. Raspberry Pi, NVIDIA Jetson Nano, and BeagleBone. Android and iOS. Chrome, Safari, Firefox, and Edge. Linux (x86_64), macOS (x86_64, arm64), and Windows (x86_64). Scalable. It can detect multiple always-listening voice commands with no added runtime footprint. Self-service. Developers can train custom wake word models using Picovoice Console. Porcupine is the right product if you need to detect one or a few static (always-listening) voice commands. If you want to create voice experiences similar to Alexa or Google, see the Picovoice platform. -
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Pwnagotchi
Deep Reinforcement learning instrumenting bettercap for WiFi pwning
Pwnagotchi is an A2C-based “AI” powered by bettercap and running on a Raspberry Pi Zero W that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of handshake supported by hashcat, including full and half WPA handshakes as well as PMKIDs. Instead of merely playing Super Mario or Atari games like most reinforcement learning based “AI” (yawn), Pwnagotchi tunes its own parameters over time to get better at pwning WiFi things in the real world environments you expose it to. To give hackers an excuse to learn about reinforcement learning and WiFi networking, and have a reason to get out for more walks. -
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PyGPT
Open source personal AI Assistant for Linux, Windows and Mac
PyGPT is a desktop application that allows you to talk to OpenAI's LLM models such as GPT4 and GPT3 using your own computer and OpenAI API. It allows you to talk in chat mode and in completion mode, as well as generate images using DALL-E 2. PyGPT also adds access to the Internet for GPT via Google Custom Search API and Wikipedia API and includes voice synthesis using Microsoft Azure Text-to-Speech API. Moreover, the application has implemented context memory support, context storage, history of contexts, which can be restored at any time and e.g. continue the conversation from point in history, and also has a convenient and intuitive system of presets that allows you to quickly and pleasantly create and manage your prompts. Plugins support is also available. -
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PyResParser
A simple resume parser used for extracting information from resumes
PyResParser is a simple resume parser that extracts information from resumes, aiding in the automation of resume-processing tasks. -
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PyTorch Implementation of SDE Solvers
Differentiable SDE solvers with GPU support and efficient sensitivity
This library provides stochastic differential equation (SDE) solvers with GPU support and efficient backpropagation. examples/demo.ipynb gives a short guide on how to solve SDEs, including subtle points such as fixing the randomness in the solver and the choice of noise types. examples/latent_sde.py learns a latent stochastic differential equation, as in Section 5 of [1]. The example fits an SDE to data, whilst regularizing it to be like an Ornstein-Uhlenbeck prior process. The model can be loosely viewed as a variational autoencoder with its prior and approximate posterior being SDEs. The program outputs figures to the path specified by <TRAIN_DIR>. Training should stabilize after 500 iterations with the default hyperparameters. examples/sde_gan.py learns an SDE as a GAN, as in [2], [3]. The example trains an SDE as the generator of a GAN, whilst using a neural CDE [4] as the discriminator. -
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Pyrogram
Elegant, modern and asynchronous Telegram MTProto API framework
Pyrogram is a modern, elegant and asynchronous MTProto API framework. It enables you to easily interact with the main Telegram API through a user account (custom client) or a bot identity (bot API alternative) using Python. Ready: Install Pyrogram with pip and start building your applications right away. Easy: Makes the Telegram API simple and intuitive, while still allowing advanced usages. Elegant: Low-level details are abstracted and re-presented in a more convenient way. Fast: Boosted up by TgCrypto, a high-performance cryptography library written in C. Type-hinted: Types and methods are all type-hinted, enabling excellent editor support. Async: Fully asynchronous (also usable synchronously if wanted, for convenience). Powerful: Full access to Telegram's API to execute any official client action and more. -
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Python Outlier Detection
A Python toolbox for scalable outlier detection
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as outlier detection or anomaly detection. PyOD includes more than 30 detection algorithms, from classical LOF (SIGMOD 2000) to the latest COPOD (ICDM 2020) and SUOD (MLSys 2021). Since 2017, PyOD [AZNL19] has been successfully used in numerous academic researches and commercial products [AZHC+21, AZNHL19]. PyOD has multiple neural network-based models, e.g., AutoEncoders, which are implemented in both PyTorch and Tensorflow. PyOD contains multiple models that also exist in scikit-learn. It is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework. A benchmark is supplied for select algorithms to provide an overview of the implemented models. In total, 17 benchmark datasets are used for comparison, which can be downloaded at ODDS. -
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Qwen3.6
Qwen3.6 is the large language model series developed by Qwen team
The Qwen3.6 project is an open-source large language model series developed by Alibaba’s Qwen team, designed to deliver high-performance AI capabilities with a strong emphasis on real-world usability and developer productivity. It builds upon the advancements introduced in Qwen3.5, focusing on improving stability, responsiveness, and practical application in coding and agent-based workflows. The repository serves as a central hub for documentation, community discussion, and access to the latest model releases, rather than a standalone application. One of its defining goals is to enhance “agentic coding,” enabling the model to reason across entire codebases, handle multi-step development tasks, and assist with complex software engineering workflows. The architecture incorporates modern techniques such as mixture-of-experts and hybrid attention mechanisms, allowing it to scale efficiently while maintaining strong performance. -
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README-AI
README file generator, powered by AI
README-AI is an automated documentation generator that creates structured README files for GitHub repositories using AI-powered analysis. -
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Ragas
Supercharge Your LLM Application Evaluations
Objective metrics, intelligent test generation, and data-driven insights for LLM apps. Ragas is your ultimate toolkit for evaluating and optimizing Large Language Model (LLM) applications. Say goodbye to time-consuming, subjective assessments and hello to data-driven, efficient evaluation workflows. Don't have a test dataset ready? We also do production-aligned test set generation. -
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Rasa
Open source machine learning framework to automate text conversations
Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual assistants on Facebook Messenger, Slack, Google Hangouts, Webex Teams, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram, and Twilio or on your own custom conversational channels. Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forths. In order for a human to have a meaningful exchange with a contextual assistant, the assistant needs to be able to use context to build on things that were previously discussed. Rasa enables you to build assistants that can do this in a scalable way. Rasa uses Poetry for packaging and dependency management. If you want to build it from the source, you have to install Poetry first. By default, Poetry will try to use the currently activated Python version to create the virtual environment for the current project automatically. -
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SageAttention
NeurIPS2025 Spotlight] Quantized Attention
SageAttention is an open-source optimization library designed to accelerate the attention mechanism used in transformer-based neural networks. Since attention operations are often the most computationally expensive component of modern AI models, SageAttention introduces quantization techniques that significantly reduce computational overhead while preserving model accuracy. The system achieves this by using low-precision numerical formats such as INT4, FP8, or INT8 to represent key matrices within the attention computation. These optimizations allow models to perform matrix operations faster and consume less memory during inference. SageAttention is designed to function as a plug-and-play replacement for standard attention implementations, enabling developers to accelerate existing models without modifying their architecture. -
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Shire
Empower Your Dev Ecosystem with AI Agents
Shire is an AI-driven development ecosystem that empowers developers with AI agents to automate coding tasks, enhance productivity, and elevate code quality. The concept of Shire has its roots in AutoDev, a subproject of UnitMesh. Within AutoDev, we envisioned an AI-driven integrated development environment for developers, which included Shire’s predecessor, DevIns. DevIns was designed to empower users to create custom AI agents tailored to their own IDEs, thus forging a personalized AI-powered development realm. -
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SimpleEnglish
Agent skill: make LLMs write docs in ASD-STE100
SimpleEnglish is a portable agent skill that makes language models write technical documentation in ASD-STE100 Simplified Technical English. It replaces vague, promotional, and overly complex AI prose with short, direct, testable statements. The instructions enforce controlled practices such as active voice, simple tenses, consistent terminology, limited sentence length, and one instruction per sentence. It is designed for documentation, error messages, runbooks, incident reports, release notes, prompts, and translation preparation rather than marketing copy. The repository includes a complete skill, reusable system prompts, examples, evaluations, and a compact version for limited context budgets. It works with Agent Skills-compatible tools such as Claude Code, Cursor, Codex, Copilot, Gemini CLI, Goose, and OpenCode. Users without native skill support can paste the provided prompt into chatbot instructions or project configuration files. -
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Skills For Real Engineers
Skills for Real Engineers. Straight from my .claude directory
Skills For Real Engineers is a curated collection of modular AI “skills” designed to improve how developers interact with coding agents by enforcing structured engineering workflows. Each skill is a small, focused instruction set that guides an AI through tasks such as planning, refactoring, testing, or architectural analysis. Instead of relying on vague prompts, the system encodes repeatable processes that ensure consistent and higher-quality outputs. The repository includes tools for converting conversations into product requirements, breaking plans into actionable issues, and stress-testing ideas through structured questioning. It emphasizes disciplined thinking before coding, encouraging developers to fully explore design decisions. Skills can be installed individually and integrated into agent environments, making them highly composable. Overall, the project transforms AI from a reactive assistant into a process-driven engineering collaborator. -
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Sprite Fusion Pixel Snapper
A tool to snap pixels to a perfect grid
Sprite Fusion Pixel Snapper is a utility designed to eliminate sub-pixel rendering issues that often arise in pixel art, UI icons, and 2D sprite graphics when displayed on screens with high DPI or during motion animations. The tool works by adjusting sprite rendering coordinates and texture sampling so that every pixel aligns cleanly to the screen’s pixel grid, avoiding blurring, distortion, or unintended smoothing artifacts. This is especially important in pixel art games, retro-styled interactive media, or precise UI designs where crisp edges and predictable alignment are essential. SpriteFusion Pixel Snapper integrates with popular game engines and rendering pipelines to ensure that assets remain sharp across a broad range of resolutions and aspect ratios without requiring manual fiddling from artists or developers. It includes options for snapping modes, filtering overrides, and automatic correction detection that can be applied on export or at runtime. -
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Stable Diffusion v 2.1 web UI
Lightweight Stable Diffusion v 2.1 web UI: txt2img, img2img, depth2img
Lightweight Stable Diffusion v 2.1 web UI: txt2img, img2img, depth2img, in paint and upscale4x. Gradio app for Stable Diffusion 2 by Stability AI. It uses Hugging Face Diffusers implementation. Currently supported pipelines are text-to-image, image-to-image, inpainting, upscaling and depth-to-image. -
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Step1X-Edit
A SOTA open-source image editing model
Step1X-Edit is a state-of-the-art open-source image editing model/framework that uses a multimodal large language model (LLM) together with a diffusion-based image decoder to let users edit images simply via natural-language instructions plus a reference image. You supply an existing image and a textual command — e.g. “add a ruby pendant on the girl’s neck” or “make the background a sunset over mountains” — and the model interprets the instruction, computes a latent embedding combining the image content and user intent, then decodes a new image implementing the edit. The model targets general-purpose editing: from object addition/removal, style changes, recoloring, retouching, background replacement, to complex transformations like changing lighting, mood, or art style. The authors trained it on a large curated dataset and benchmarked it on a newly introduced evaluation suite, showing that Step1X-Edit significantly outperforms previous open-source baselines. -
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Story Flicks
Generate high-definition story short videos with one click using AI
Story Flicks is another open-source project in the AI-assisted video generation / editing space, focused on creating short, story-style videos from script or prompt inputs. It aims to let users generate high-definition short movies or video stories with minimal manual effort, using AI models under the hood to assemble visuals, timing, and possibly narration or subtitles. For creators who want to produce narrative short-form content — whether for social media, storytelling, or prototyping video ideas — story-flicks offers a lightweight, code-backed alternative to complex video editing suites. Because the project is open and modifiable, developers can customize the generation pipeline: adjust story structure, alter rendering parameters, tweak video quality or resolution, or integrate with other AI models (e.g. for audio, voice-over, or image-to-video). It’s especially useful as a starting template or experimentation ground for developers building automated content-creation tools. -
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SuperAGI
A dev-first open source autonomous AI agent framework
An open-source autonomous AI framework to enable you to develop and deploy useful autonomous agents quickly & reliably. Join a community of developers constantly contributing to make SuperAGI better. Access your agents through a graphical user interface. Interact with agents by giving them input, permissions, etc. Agents typically learn and improve their performance over time with feedback loops. Run multiple agents simultaneously to improve efficiency and productivity. Connect to multiple Vector DBs to enhance your agent’s performance. Each agent is unique, use different models of your choice. Get insights into your agent’s performance and optimize accordingly. Control token usage to manage costs effectively. Enable your agents to learn and adapt by storing their memory. Get notified when agents get stuck in the loop, and provide proactive resolution. Read and store files generated by Agents.
