Open Source Linux Artificial Intelligence Software - Page 46
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Artificial Intelligence Software for Linux
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BettaFish
Public opinion analysis system
BettaFish is an open-source, multi-agent public opinion analysis system built to automate the collection, deep analysis, and reporting of social media data at scale through conversational queries. It uses a modular architecture of specialized agents that collaborate to crawl mainstream platforms, extract multimodal content like text and short video, and synthesize insights through both statistical and large language model techniques. With a design that lets users pose questions in natural language and receive structured reports, charts, and visualizations, the system aims to break information cocoons and provide comprehensive views of trends and public sentiment. Unlike simpler analytics tools, BettaFish employs agent collaboration and a “forum” style internal mechanism to combine diverse model outputs, making the analysis richer and more robust. It also integrates multimodal processing, enabling it to parse images and video alongside text. -
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BitNet
BitNet: Scaling 1-bit Transformers for Large Language Models
BitNet is a machine learning research implementation that explores extremely low-precision neural network architectures designed to dramatically reduce the computational cost of large language models. The project implements the BitNet architecture described in research on scaling transformer models using extremely low-bit quantization techniques. In this approach, neural network weights are quantized to approximately one bit per parameter, allowing models to operate with far lower memory usage than traditional 16-bit or 32-bit neural networks. The architecture introduces specialized layers such as BitLinear, which replace standard linear projections in transformer networks with quantized operations. By limiting weight precision while maintaining efficient scaling and normalization strategies, the architecture aims to retain competitive performance while significantly reducing hardware requirements. -
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Burn
Burn is a new comprehensive dynamic Deep Learning Framework
Burn is a new comprehensive dynamic Deep Learning Framework from Tracel AI built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. Burn emphasizes performance, flexibility, and portability for both training and inference. Developed in Rust, it is designed to empower machine learning engineers and researchers across industry and academia. -
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CUTLASS
CUDA Templates for Linear Algebra Subroutines
CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS and cuDNN. CUTLASS decomposes these "moving parts" into reusable, modular software components abstracted by C++ template classes. These thread-wide, warp-wide, block-wide, and device-wide primitives can be specialized and tuned via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies their use as building blocks within custom kernels and applications. To support a wide variety of applications, CUTLASS provides extensive support for mixed-precision computations, providing specialized data-movement and multiply-accumulate abstractions for half-precision floating point (FP16), BFloat16 (BF16), Tensor Float 32 (TF32), etc. -
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Cangjie Skill
Distill high-value content like books, long videos, podcasts, and more
cangjie-skill is a workflow for converting books and other long-form knowledge into executable AI skill packs. Its goal is structured reuse rather than producing another summary or set of reading notes. The seven-stage RIA-TV++ pipeline analyzes the full source, extracts candidate frameworks, verifies them, constructs skill modules, links related ideas, pressure-tests behavior, and prepares delivery files. Each accepted skill records supporting material, a reconstructed explanation, examples, trigger situations, executable steps, and limitations. Strict verification rejects generic or weak ideas that lack independent evidence, predictive value, or meaningful uniqueness. The generated package can include an overview, glossary, reference graph, long-form digest, individual skill files, and test prompts. Completed skills can be installed for compatible coding agents such as Claude Code and Cursor. -
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Chainer
A flexible deep learning framework
Chainer is a Python-based deep learning framework. It provides automatic differentiation APIs based on dynamic computational graphs as well as high-level APIs for neural networks. -
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ChatGPT Telegram Bot
A Telegram bot that integrates with OpenAI's official ChatGPT APIs
A Telegram bot that integrates with OpenAI's official ChatGPT, DALL·E and Whisper APIs to provide answers. Ready to use with minimal configuration required. -
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ChatGPT-bot
Run your own GPTChat Telegram bot, with a single command
Go CLI to fuels a Telegram bot that lets you interact with ChatGPT, a large language model trained by OpenAI. -
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Chatbot UI
AI chat for any model
Chatbot UI is an open-source, full-featured chat interface for AI models that aims to lower the barrier for anyone wanting to run their own AI-powered chatbot — whether using remote LLM APIs or local/self-hosted models. It bundles a modern frontend (Next.js + TypeScript + Tailwind) with a backend (Supabase by default) for persistent storage of chats, history, and user settings, replacing earlier local-storage-only approaches. The project supports multiple models/providers (e.g. OpenAI, local models via Ollama), and by switching env-vars you can self-host the whole stack or deploy in the cloud (e.g. via Vercel + Supabase) for personal or shared use. With version 2.0, the maintainers redesigned parts of the UI/UX, improved backend compatibility, and enhanced mobile-layout responsiveness — reflecting active maintenance and responsiveness to user feedback. -
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Chinese-LLaMA-Alpaca-3
Chinese Llama-3 LLMs) developed from Meta Llama 3
Chinese-LLaMA-Alpaca-3 is an open-source project that provides Mandarin-focused large language models based on Meta’s LLaMA-3 architecture, with both foundational and instruction-tuned variants to support high-quality Chinese natural language understanding and generation. It extends the original LLaMA models with expanded Chinese vocabularies and additional pretraining on Chinese corpora to improve semantic encoding and decoding specifically for Chinese text. Alongside the base models, the project also releases Chinese Alpaca models that are fine-tuned on instruction datasets so they behave more like conversational and instruction-following AI assistants. It includes scripts and tooling that let researchers or developers run training, fine-tuning, quantization, and deployment on local machines (CPU or GPU), making experimentation and testing accessible without requiring large clusters. -
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Chitu
High-performance inference framework for large language models
Chitu is a high-performance inference engine designed to deploy and run large language models efficiently in production environments. The framework focuses on improving efficiency, flexibility, and scalability for organizations that need to run LLM inference workloads across different hardware platforms. It supports heterogeneous computing environments, including CPUs, GPUs, and various specialized AI accelerators, allowing models to run across a wide range of infrastructure configurations. Chitu is designed to scale from small single-machine deployments to large distributed clusters that handle high volumes of concurrent inference requests. The system also includes performance optimizations for large models, including support for quantized formats and efficient computation operators that reduce memory usage and latency. Its architecture aims to support enterprise adoption by ensuring stable long-term operation under production workloads. -
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Chrome DevTools MCP
Chrome DevTools for coding agents
chrome-devtools-mcp is an MCP server that connects AI agents to the Chrome DevTools Protocol so they can inspect pages, record traces, read console/network data, and modify the live browser state under user control. It makes a running Chrome instance visible to MCP clients, enabling agents to debug websites end-to-end—launching Chrome, navigating, profiling, and collecting artifacts in a structured way. The repository spells out environment requirements and cautions that exposing a live browser to agents grants powerful access, so sensitive data should be handled carefully. Beyond static inspection, it exposes operational tools like starting a performance trace that an agent can later analyze to propose optimizations. The server is intended to slot into MCP-capable assistants and IDEs, giving them reliable, typed tools and resource endpoints rather than ad-hoc automation. Documentation from the Chrome team explains how the server augments agents with real debugging capabilities. -
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Claude for Financial Services
Reference agents, skills, and data for the financial-services
Claude for Financial Services is an open-source collection of AI agents, plugins, and workflow templates designed to transform Claude into a specialized assistant for financial services professionals. The project targets domains such as investment banking, equity research, private equity, and wealth management by providing reusable prompts, structured workflows, and domain-specific analytical skills. It supports deployment either as Claude Cowork plugins or through the Claude Managed Agents API, allowing organizations to integrate the same logic into internal systems and automation pipelines. The repository includes tools for competitive analysis, financial modeling, market research, data-pack generation, and strategic synthesis. Its architecture emphasizes modularity, enabling firms to customize workflows and extend functionality for proprietary use cases. Overall, the project serves as a foundation for building AI-enhanced financial research and decision-support systems. -
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Claw3D
Claw3D is an open source 3D engine built on OpenClaw
Claw3D is an experimental open-source platform that combines elements of 3D simulation, developer tooling, and AI orchestration by creating an interactive virtual workspace where AI agents can be visualized as active participants in a shared environment. It is designed as a 3D “virtual office” where users can observe, manage, and interact with multiple AI agents performing tasks such as coding, reviewing pull requests, and coordinating workflows in real time. Instead of relying on traditional dashboards or logs, Claw3D introduces a spatial interface that allows users to navigate through a simulated office and watch agents collaborate, effectively turning abstract processes into tangible visual interactions. The system supports task assignment, progress tracking, and communication between agents, creating a representation of autonomous or semi-autonomous workflows. It can be self-hosted, giving users full control over deployment, customization, and scaling of their AI workspace. -
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ClawRouter
Smart LLM router
ClawRouter is a flexible networking and routing framework designed to support AI-oriented distributed systems and agent ecosystems by managing how messages, requests, and responses are routed between components. It provides a programmable router abstraction that can handle complex traffic patterns, enabling dynamic message forwarding, load balancing, and custom routing logic based on content, context, or policy rules. Because distributed AI systems often involve many services, agents, and runtime components interacting with each other and with external APIs, ClawRouter helps ensure that communication paths remain clear, efficient, and adaptable as systems scale. The framework supports plugin-based extensions so developers can define custom protocols, transformation hooks, and monitoring handlers without modifying core routing logic. It also offers operational features like health checking, metrics reporting, and failure handling that make production deployments more reliable. -
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ClawTeam
ClawTeam: Agent Swarm Intelligence (One Command → Full Automation)
ClawTeam is an advanced multi-agent orchestration framework that enables AI agents to form collaborative swarms capable of solving complex tasks autonomously. Instead of relying on a single agent, the system allows a leader agent to spawn and coordinate multiple specialized sub-agents, each responsible for different aspects of a problem. These agents communicate, share insights, and dynamically adapt their strategies based on real-time feedback, creating a form of collective intelligence. The framework supports a wide range of use cases, including software development, machine learning research, financial analysis, and content production. It is designed to work with various AI tools and command-line agents, making it highly flexible and extensible. ClawTeam also includes monitoring tools such as dashboards and tmux-based views to observe agent activity and progress. -
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Cloud Annotations
A fast, easy and collaborative open source image annotation tool
Learn computer vision & AI by building real-world applications. Learn to build and train computer vision models—then show off your skills in an interactive web application. Build impressive applications and learn coveted skills. The examples below were created by the Skills Network Team—right here in CV Studio. Create your own project dataset by uploading images and videos. Coming soon, you'll be able to use a pre-compiled dataset so you can hit the ground running. Creating image annotations for your project is easy inside CV Studio. For classification projects, just select and label your images. For object detection, use the integrated tool to highlight target elements in your images. Train your model using the image annotations from the previous step. Practice using cutting-edge tools like Jupyter Notebook, Watson Machine Learning, Elyra, and more. -
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CodeBurn
See where your AI coding tokens go
CodeBurn is a security-focused tool designed to evaluate and stress-test codebases using adversarial techniques, often leveraging AI to identify vulnerabilities and weaknesses. It simulates attack scenarios against code to uncover potential security risks, helping developers proactively identify issues before they reach production. The system is designed to integrate into development workflows, allowing continuous testing as code evolves. It emphasizes automation, enabling large-scale analysis without requiring manual inspection of every component. Codeburn also provides insights and reports that help developers understand the nature and severity of detected vulnerabilities. Its approach aligns with modern DevSecOps practices, where security is embedded throughout the development lifecycle. Overall, Codeburn acts as an automated adversarial testing layer that strengthens application security. -
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CodeCursor
An extension for using Cursor in Visual Studio Code
Cursor is an AI code editor based on OpenAI GPT models. You can write, edit and chat about your code with it. At this time, Cursor is only provided as a dedicated app, and the team currently has no plans to develop extensions for other editors or IDEs. -
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CodeLlama
Inference code for CodeLlama models
Code Llama is a family of Llama-based code models optimized for programming tasks such as code generation, completion, and repair, with variants specialized for base coding, Python, and instruction following. The repo documents the sizes and capabilities (e.g., 7B, 13B, 34B) and highlights features like infilling and large input context to support real IDE workflows. It targets both general software synthesis and language-specific productivity, offering strong performance among open models at release time. Typical usage includes prompt-driven generation, function or class completion, and zero-shot adherence to natural-language instructions about code changes. The ecosystem provides multiple distributions (e.g., HF format) so developers can integrate with standard toolchains and serving stacks. As part of the broader Llama effort, Code Llama complements instruction-tuned chat models by focusing on code-centric tasks and editor integrations. -
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Codebuff
Generate code from the terminal!
Codebuff is an open-source AI coding assistant that helps developers modify and improve their codebases using natural language instructions. Instead of relying on a single model, it orchestrates multiple specialized agents that collaborate to understand, plan, edit, and review code changes. This multi-agent approach enables more accurate edits, better context awareness, and fewer errors across complex projects. Codebuff operates primarily through a CLI, allowing developers to interact with their code directly from the terminal. It also offers an SDK for integrating agent-based coding workflows into applications and development pipelines. With support for multiple AI models and customizable agents, Codebuff provides a flexible and powerful alternative to traditional coding assistants. -
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Codex plugin for Claude Code
Use Codex from Claude Code to review code or delegate tasks
Codex plugin for Claude Code is an integration layer that connects OpenAI Codex-style capabilities with agent-based coding environments, enabling seamless execution of coding tasks through structured plugins. The project is designed to extend the functionality of coding agents by allowing them to delegate tasks to Codex or similar models in a controlled and modular way. It likely provides abstractions for handling code generation, editing, and analysis while maintaining consistency across workflows. The system emphasizes interoperability, allowing developers to plug Codex capabilities into broader agent ecosystems without rewriting core logic. It may also include mechanisms for managing execution context, permissions, and tool access, ensuring that generated code can be safely applied. This makes it particularly useful for complex development pipelines where multiple agents or tools need to collaborate. -
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Compose
A machine learning tool for automated prediction engineering
Compose is a machine learning tool for automated prediction engineering. It allows you to structure prediction problems and generate labels for supervised learning. An end user defines an outcome of interest by writing a labeling function, then runs a search to automatically extract training examples from historical data. Its result is then provided to Featuretools for automated feature engineering and subsequently to EvalML for automated machine learning. Prediction problems are structured by using a label maker and a labeling function. The label maker automatically extracts data along the time index to generate labels. The process starts by setting the first cutoff time after the minimum amount of data. Then subsequent cutoff times are spaced apart using gaps. Starting from each cutoff time, a window determines the amount of data, also referred to as a data slice, to pass into a labeling function. -
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ContextGem
ContextGem: Effortless LLM extraction from documents
ContextGem is an open-source framework designed to simplify the extraction of structured data and insights from documents using large language models (LLMs). It provides a flexible, intuitive API that minimizes boilerplate code, enabling developers to build complex extraction workflows efficiently. ContextGem supports various document formats and integrates with multiple LLM providers, making it a versatile tool for tasks like contract analysis, anomaly detection, and information retrieval. -
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Copilot for Obsidian
AI assistant plugin that brings chat, search, and agents to Obsidian
Obsidian Copilot is an open source plugin that integrates AI-powered assistance directly into the Obsidian note-taking environment. It enables users to interact with their notes through conversational chat, allowing them to ask questions, summarize information, and generate new content using large language models. It works inside a user’s vault and can analyze notes, documents, and other referenced materials to provide context-aware responses. It supports multiple AI providers and models, giving users the flexibility to choose external APIs or run compatible local models depending on their setup. Obsidian Copilot emphasizes user ownership of data by keeping notes in the local Obsidian vault while allowing the AI to process them as context when requested. Additional capabilities include agent-based workflows, semantic note discovery, and commands that can manipulate or generate text directly within notes.
