Open Source Linux Artificial Intelligence Software - Page 45
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Artificial Intelligence Software for Linux
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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stable-diffusion-webui-forge
A Fork from Github repository of Illyasviel's Forge
This is for use by the StableProjectorz https://stableprojectorz.com Kept here, in case the file changes URL in his repo. The URL must remain the same, so that StableProjectorz installer can always download it. -
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Google2SRT
Download, save and convert multiple subtitles from YouTube videos
Google2SRT allows you to download, save and convert multiple subtitles and translations from YouTube and Google Video to SubRip (.srt) format, which is recognized by most video players. You can download XML subtitles or simply type video's URL, Google2SRT will do the rest. -
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Simplexity AI Desktop App
Simplexity AI Desktop app eases the process to access Perplexity AI
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sudobot
A free and open source Discord bot for moderation purposes.
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AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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AI as Workspace
An elegant AI chat client. Full-featured, lightweight
AI as Workspace, short for AI as Workspace, is an open-source AI client application that provides a unified interface for interacting with multiple large language models and AI tools within a single workspace environment. The platform is designed as a lightweight yet powerful desktop or web application that organizes AI interactions through structured workspaces. Instead of managing individual chat sessions separately, users can group conversations, artifacts, and tasks within customizable workspaces that support different projects or contexts. AIaW supports multiple AI providers and models through a flexible interface compatible with common API formats used by services such as OpenAI-style endpoints. The application also includes a plugin system that allows developers to extend the platform with additional capabilities such as automation tools, integrations, or custom AI utilities. -
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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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AWS IoT Arduino Yún SDK
SDK for connecting to AWS IoT from an Arduino Yún
The AWS-IoT-Arduino-Yún-SDK allows developers to connect their Arduino Yún compatible Board to AWS IoT. By connecting the device to the AWS IoT, users can securely work with the message broker, rules and the Thing Shadow provided by AWS IoT and with other AWS services like AWS Lambda, Amazon Kinesis, Amazon S3, etc. The AWS-IoT-Arduino-Yún-SDK consists of two parts, which take use of the resources of the two chips on Arduino Yún, one for native Arduino IDE API access and the other for functionality and connections to the AWS IoT built on top of AWS IoT Device SDK for Python. The AWS-IoT-Arduino-Yún-SDK provides APIs to let users publish messages to AWS IoT and subscribe to MQTT topics to receive messages transmitted by other devices or coming from the broker. This allows to interact with the standard MQTT PubSub functionality of AWS IoT. -
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AWS MCP Servers
Helping you get the most out of AWS, wherever you use MCP
AWS MCP Servers are a collection of remotely hosted, fully-managed Model Context Protocol (MCP) servers by AWS, providing AI applications with real-time access to AWS documentation, API references, best practices, and infrastructure-management capabilities via natural-language workflows. An MCP Server is a lightweight program that exposes specific capabilities through the standardized Model Context Protocol. Host applications (such as chatbots, IDEs, and other AI tools) have MCP clients that maintain 1:1 connections with MCP servers. Common MCP clients include agentic AI coding assistants (like Q Developer, Cline, Cursor, Windsurf) as well as chatbot applications like Claude Desktop, with more clients coming soon. MCP servers can access local data sources and remote services to provide additional context that improves the generated outputs from the models. -
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Agent Control
Centralized agent control plane for governing runtime agent behavior
Agent Control is a centralized control plane for governing AI agent behavior at runtime across different frameworks and deployment environments. It lets teams define controls once and apply them consistently to agents without rewriting the agent’s core code. The platform evaluates agent inputs and outputs against configurable policies to reduce risks such as prompt injection, unsafe responses, sensitive data exposure, and policy drift. It is designed for production environments where organizations need observability, enforcement, and governance around autonomous or semi-autonomous AI systems. The repository includes SDKs, a server, telemetry components, examples, and integrations for common agent frameworks. It is especially useful for teams building customer-facing, internal, or enterprise agents that need scalable runtime guardrails. -
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Agent Development Kit (ADK)
Open-source, code-first Python toolkit for building, evaluating, etc.
ADK (Android Device Key) Python is a reference implementation by Google for working with Android attestation keys in Python. It facilitates the integration of Android attestation features into backends or systems that require verification of device identity and integrity. This is especially important in high-security applications where verifying that a device is genuine and uncompromised is critical. ADK Python helps developers verify hardware-backed keys, work with JSON Web Tokens (JWT), and integrate with Android’s Key Attestation infrastructure. -
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AgentForge
Extensible AGI Framework
AgentForge is a framework for creating and deploying AI agents that can perform autonomous decision-making and task execution. It enables developers to define agent behaviors, train models, and integrate AI-powered automation into various applications. -
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AgentScope
Build and run agents you can see, understand and trust
AgentScope is a production-ready agent framework designed to help developers build, deploy, and scale intelligent agentic applications. It provides essential abstractions that evolve with advancing LLM capabilities, emphasizing reasoning, tool use, and flexible orchestration rather than rigid prompt constraints. With built-in support for ReAct agents, memory, planning, human-in-the-loop control, and real-time voice interaction, developers can create powerful agents in minutes. AgentScope integrates seamlessly with tools, long-term memory systems, MCP, A2A (Agent-to-Agent) protocols, and observability frameworks. It also supports reinforcement learning workflows for tuning agents and improving performance across complex tasks. Deployable locally, serverless in the cloud, or on Kubernetes with OpenTelemetry support, AgentScope is built for both experimentation and production environments. -
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AgentScope Java
Agent-Oriented Programming for Building LLM Applications
AgentScope Java is an agent-oriented programming framework that enables Java developers to build intelligent, LLM-powered applications using a dynamic reasoning-acting (ReAct) paradigm. It provides a comprehensive toolkit for creating autonomous agents that can plan, execute, and adjust complex workflows, making decisions about which tools to invoke and how to solve multi-step problems. The framework includes runtime controls such as safe interruption and graceful cancellation to manage agent execution robustly in production environments. It also supports human-in-the-loop intervention, allowing developers or users to inject guidance at any point during reasoning while preserving state and tool context. Built with enterprise needs in mind, AgentScope Java integrates into traditional Java stacks and provides structured abstractions for memory, workflows, and tool invocation. -
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Agentic Context Engine
Make your agents learn from experience
Agentic Context Engine (ACE) is an open-source framework designed to help AI agents improve their performance by learning from their own execution history. Instead of relying solely on model training or fine-tuning, the framework focuses on structured context engineering, allowing agents to accumulate knowledge from past successes and failures during task execution. The system treats context as a dynamic “playbook” that evolves over time through a process of generation, reflection, and curation, enabling agents to refine strategies across repeated tasks. In this workflow, one component generates solutions, another reflects on outcomes, and a third curates useful knowledge so it can be reused in future interactions. This architecture allows agents to gradually build persistent operational memory without requiring additional training datasets or model retraining. -
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Aim
An easy-to-use & supercharged open-source experiment tracker
Aim logs all your AI metadata (experiments, prompts, etc) enabling a UI to compare & observe them and SDK to query them programmatically. The Aim standard package comes with all integrations. If you'd like to modify the integration and make it custom, create a new integration package and share with others. Aim is an open-source, self-hosted AI Metadata tracking tool designed to handle 100,000s of tracked metadata sequences. The two most famous AI metadata applications are: experiment tracking and prompt engineering. Aim provides a performant and beautiful UI for exploring and comparing training runs, and prompt sessions. -
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AnyTrading
The most simple, flexible, and comprehensive OpenAI Gym trading
gym-anytrading is an OpenAI Gym-compatible environment designed for developing and testing reinforcement learning algorithms on trading strategies. It simulates trading environments for financial markets, including stocks and forex. -
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AudioGenerator
Generates a sound given: volume, frequency, duration
Generates a sound given: volume, frequency, duration! Download build.zip, unpack zip, and run the executable. -
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AudioMuse-AI
AudioMuse-AI is an Open Source Dockerized environment
AudioMuse-AI is an open-source system designed to automatically generate playlists and analyze music libraries using artificial intelligence and audio signal processing techniques. The platform runs locally in a Dockerized environment and performs detailed sonic analysis on audio files to understand characteristics such as tempo, mood, and acoustic similarity. By analyzing the underlying audio content rather than relying on external metadata services, the system can organize large personal music libraries and generate curated playlists for different moods or listening contexts. AudioMuse-AI integrates with several popular self-hosted music servers including Jellyfin, Navidrome, and Emby, allowing users to extend existing media servers with advanced AI-powered recommendation capabilities. The system uses machine learning and audio analysis tools such as Librosa and ONNX models to extract features directly from audio tracks. -
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Audiomentations
A Python library for audio data augmentation
A Python library for audio data augmentation. Inspired by albumentations. Useful for deep learning. Runs on CPU. Supports mono audio and multichannel audio. Can be integrated in training pipelines in e.g. Tensorflow/Keras or Pytorch. Has helped people get world-class results in Kaggle competitions. Is used by companies making next-generation audio products. Mix in another sound, e.g. a background noise. Useful if your original sound is clean and you want to simulate an environment where background noise is present. A folder of (background noise) sounds to be mixed in must be specified. These sounds should ideally be at least as long as the input sounds to be transformed. Otherwise, the background sound will be repeated, which may sound unnatural. Note that the gain of the added noise is relative to the amount of signal in the input. This implies that if the input is completely silent, no noise will be added. -
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AutoGPTQ
An easy-to-use LLMs quantization package with user-friendly apis
AutoGPTQ is an implementation of GPTQ (Quantized GPT) that optimizes large language models (LLMs) for faster inference by reducing their computational footprint while maintaining accuracy. -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing. Easily improve/tune your bespoke models and data pipelines, or customize AutoGluon for your use-case. AutoGluon is modularized into sub-modules specialized for tabular, text, or image data. You can reduce the number of dependencies required by solely installing a specific sub-module via: python3 -m pip install <submodule>. -
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Avalanche
End-to-End Library for Continual Learning based on PyTorch
Avalanche is an end-to-end Continual Learning library based on Pytorch, born within ContinualAI with the unique goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducible evaluation of continual learning algorithms. Avalanche can help Continual Learning researchers in several ways. This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major CL benchmarks (similar to what has been done for torchvision). Provides all the necessary utilities concerning model training. This includes simple and efficient ways of implementing new continual learning strategies as well as a set of pre-implemented CL baselines and state-of-the-art algorithms you will be able to use for comparison! Avalanche the first experiment of an End-to-end Library for reproducible continual learning research & development where you can find benchmarks, algorithms, etc. -
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Axolotl
Go ahead and axolotl questions
Axolotl is a powerful and flexible framework for fine-tuning large language models on custom datasets. Built for researchers and developers, Axolotl simplifies the process of adapting LLMs for specific tasks, including chat, code generation, and instruction following. It supports a wide variety of model architectures and offers out-of-the-box optimization strategies for efficient training. -
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Beads
A memory upgrade for your coding agent
Beads is an open-source project providing a distributed, structured memory system for AI coding agents, replacing ad-hoc text plans with a git-backed graph that represents tasks, dependencies, and progress in a persistent, queryable format. Instead of storing plans as unstructured Markdown or ephemeral notes, Beads organizes agent state, task artifacts, and relationships as nodes and edges in a version-controlled graph so that long-horizon projects don’t lose context or coherence as the agent proceeds. This approach helps coding agents — and human collaborators — track which tasks depend on others, what has been done, and where workflows branch or reunify without losing important data. By leveraging Git as the storage backbone, the project ensures that memory is persistent, diffable, and sharable, with the ability to roll back, branch, or merge memory states just like source code. -
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BertViz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism. The head view visualizes attention for one or more attention heads in the same layer. It is based on the excellent Tensor2Tensor visualization tool. The model view shows a bird's-eye view of attention across all layers and heads. The neuron view visualizes individual neurons in the query and key vectors and shows how they are used to compute attention.
