Open WebUI

What is Open WebUI?

Open WebUI is a self-hosted platform that acts as a unified front-end for large language models, letting users connect to Ollama, OpenAI-compatible APIs, or Open Responses providers from one interface. Instead of juggling separate apps for chat, document search, image generation, and code execution, Open WebUI brings conversations, knowledge bases, tools, and models into a single workspace that can run entirely offline. It is designed for teams and individuals who want full control over their data, infrastructure, and model choices without depending on a single vendor.

Key Features

• Multi-Provider Chat

  Connect to Ollama, OpenAI, Anthropic, or any OpenAI-compatible API, and run multiple models side-by-side in the same conversation for comparison.

• Knowledge & RAG

  Build knowledge bases from uploaded documents using hybrid vector search, cross-encoder reranking, and multiple extraction engines like Tika and Docling.

• Custom Agents & Model Presets

  Wrap any base model with system prompts, bound tools, and knowledge sources to create specialized agents such as reviewers or summarizers.

• Open Terminal

  Give the AI access to a real or containerized computing environment where it can write, execute, and debug code directly from chat.

• Extensibility via Tools & MCP

  Extend functionality with Python tools, pipelines, OpenAPI servers, or native Model Context Protocol support to add custom capabilities.

• Enterprise-Grade Access Control

  Manage multi-user environments with role-based access, SSO/OIDC/LDAP integration, SCIM provisioning, and granular per-model permissions.

Use Cases

• Self-Hosted AI Deployment : Organizations run their own private AI stack on Docker, Kubernetes, or bare metal instead of relying on third-party cloud AI services.
• Team Knowledge Assistants : Teams upload internal documents and let the AI autonomously search and synthesize answers from a shared knowledge base.
• Developer Code Workflows : Developers use Open Terminal to have the AI write, run, and iterate on code within an isolated environment.
• Collaborative AI Channels : Teams tag different models into shared channels to draft, critique, and refine work together in real time.
• Custom Agent Building : Product and support teams create task-specific agents, like meeting summarizers or code reviewers, using bound tools and instructions.

FAQs