open-webui: a feature-rich self-hosted AI platform for managing local and cloud LLMs with integrated RAG

What it solves

Open WebUI provides a self-hosted, user-friendly interface for interacting with Large Language Models (LLMs). It eliminates the need for complex setups by offering a centralized platform that can operate entirely offline, supporting both local model runners like Ollama and cloud-based OpenAI-compatible APIs.

How it works

It acts as a feature-rich frontend and orchestration layer that connects to various LLM providers (such as vLLM, GroqCloud, and Mistral). It includes a built-in inference engine for Retrieval Augmented Generation (RAG), allowing users to inject local documents or web search results into conversations. The platform is extensible via plugins (Filters, Actions, Pipes, Tools) and supports a wide range of vector databases for persistent memory and document storage.

Who it’s for

It is designed for individuals and teams who want a private, secure, and customizable AI interface that they can host on their own infrastructure, ranging from hobbyists using local GPUs to enterprises requiring RBAC, SSO, and LDAP integration.

Highlights

• Broad Integration: Supports Ollama and any OpenAI-compatible API.
• Local RAG: Built-in support for 9 vector databases and multiple content-extraction engines for document-based AI.
• Agentic Capabilities: Ability to wrap base models with custom instructions and tools to create specialized agents.
• Enterprise Ready: Includes granular Role-Based Access Control (RBAC), SSO, and horizontal scalability via Redis.
• Multimodal Support: Integrated image generation (DALL-E, ComfyUI, AUTOMATIC1111) and voice/video calling with various STT/TTS providers.
• Extensibility: Plugin system for custom tools, skills, and external service connections via MCP.