Dione is described as 'Makes installing complex applications as simple as clicking a button — no terminal or technical knowledge needed. For developers, Dione offers a zero-friction way to distribute apps using just a JSON file. App installation has never been this effortless' and is an app. There are more than 10 alternatives to Dione for a variety of platforms, including Mac, Windows, Linux, Android and Docker apps. The best Dione alternative is Ollama, which is both free and Open Source. Other great apps like Dione are AnythingLLM, LM Studio, local.ai and Backyard AI.
Supports local deployment of Llama 3, Code Llama, and other language models, enabling users to customize and create personalized models. Ideal for AI development, it offers flexibility for offline AI needs and integrates AI writing and chatbot tools in local setups.
Open-source privacy-focused chatbot platform allowing unlimited document uploads, multi-user and single-user modes, AI-powered chat with documents, vector database and LLM compatibility, customizable interface, message history, secure desktop deployment, and full user controls.
Enables running large language models entirely offline on desktop with integrated chat UI, OpenAI-compatible server, HuggingFace downloads, model discovery, support for multiple LLM formats and architectures, local privacy, GPU/CPU operation, and quick setup.
Run open-source LLMs on your computer. You can download and make custom characters for your models. Works offline. The new name is Back yard though it is the same.
Shinkai is a two click install App that allows you to create Local AI agents in 5 minutes or less using a simple UI. Supports: MCPs, Remote and Local AI, Crypto and Payments.
A local device focused AI assistant built in Rust — persistent memory, autonomous tasks, ~27MB binary. Inspired by and compatible with OpenClaw.
QVAC converts local AI into a high-quality experience that sits in your pocket. All the value you’re used to seeing in other AI assistants, minus the lack of privacy.
Run AI models locally on your machine with node.js bindings for llama.cpp. Enforce a JSON schema on the model output on the generation level.
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