Open Source Large Language Model (LLM) Tools

DeepSeek is a state-of-the-art AI assistant designed to provide intelligent, conversational support across diverse tasks. It leverages advanced natural language processing and machine learning to answer questions, generate creative content, solve complex problems, and offer...

An open-source AI chatbot for tasks like code writing, email drafting in a privacy-friendly, ad-free interface, often requiring no sign-up.

Facilitates local deployment of Llama 3, Code Llama, and other language models, enabling customization and offline AI development. Perfect for creating personalized AI chatbots and writing tools.

An ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.

Jan is an open source alternative to ChatGPT that runs 100% offline on your computer.

Fully private LLM chatbot that runs entirely with a browser with no server needed. Supports Mistral and LLama 3.

A multi-user ChatGPT for any LLMs and vector database. Unlimited documents, messages, and storage in one privacy-focused app. Now available as a desktop application!.

LibreChat is a free, open source AI chat platform. This Web UI offers vast customization, supporting numerous AI providers, services, and integrations. Serves all AI Conversations in one place with a familiar interface, innovative enhancements, for as many users as you need.

Alpaca is an Ollama client where you can manage and chat with multiple models, Alpaca provides an easy and begginer friendly way of interacting with local AI.

Open-source AI powered answer engine.

Alibaba Cloud's general-purpose AI models.

Cherry Studio is a desktop client that supports for multiple LLM providers, available on Windows, Mac, and Linux.

A modern and easy-to-use client for Ollama.

Mellum-4b-base is JetBrains' first open-source large language model (LLM) optimized for code-related tasks.

As a fully open language model, Apertus allows researchers, professionals and enthusiasts to build upon the model and adapt it to their specific needs, as well as to inspect any part of the training process.

As part of Meta’s commitment to open science, today we are publicly releasing Llama (Large Language Model Meta AI), a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI.

Autoregressive models that unify multimodal tasks, surpassing specialized models with visual path decoupling, autoregressive integration, and flexible design.

The simplest way to use private LLMs: Works fully offline and private when you don’t have internet. Runs models on-device optimized for Apple silicon.

Khoj is an open-source AI second brain that learns from your notes (Obsidian, EMACS), documents, and has access to the internet. It can replace your search engine, help you with reading papers, and get you transparent, fast answers.

Open R1 is a community-driven, open-source initiative aimed at replicating the advanced AI capabilities of DeepSeek-R1 through transparent methodologies.

The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a...

Maid is a cross-platform Flutter app for interfacing with GGUF / llama.cpp models locally, and with Ollama and OpenAI models remotely.

Ask questions to your documents without an internet connection, using the power of LLMs. 100% private, no data leaves your execution environment at any point. You can ingest documents and ask questions without an internet connection!

Empowering LLM researchers and hobbyists with seamless control over self-hosted models. Connect remotely, customize prompts, manage chats, and fine-tune configurations. All in one intuitive app.

AgentGPT allows you to configure and deploy Autonomous AI agents. Name your own custom AI and have it embark on any goal imaginable. It will attempt to reach the goal by thinking of tasks to do, executing them, and learning from the results.