Open WebUI is an open-source, self-hosted AI interface designed to simplify interaction with large language models (LLMs) through a clean, intuitive web-based UI. It supports multiple LLM backends including Ollama and OpenAI-compatible APIs, enabling users to run AI models locally or connect to cloud services without vendor lock-in. Built for developers, researchers, and teams seeking full control over their AI infrastructure, Open WebUI provides enterprise-grade features like role-based access control, multi-user environments, and cloud storage integration—all while operating entirely offline when desired. Its extensible architecture allows for plugin-based enhancements and seamless integration with vector databases, voice systems, and external tools.
The platform is optimized for both individual use and team deployments, offering persistent data storage, multilingual support, and progressive web app (PWA) capabilities for mobile access. With built-in RAG, image generation, web browsing, and voice/video chat features, Open WebUI transforms local LLMs into a full-featured AI workspace that rivals commercial platforms while maintaining data privacy and open-source transparency.
What You Get
- Ollama & OpenAI API Integration - Connect to local Ollama models or remote OpenAI-compatible endpoints (LMStudio, GroqCloud, Mistral, OpenRouter) by setting OLLAMA_BASE_URL or OPENAI_API_KEY environment variables.
- Built-in RAG with 9 Vector Databases - Load documents directly into chat using # command; supports ChromaDB, PGVector, Qdrant, Milvus, Elasticsearch, OpenSearch, Pinecone, S3Vector, and Oracle 23ai with Tika, Docling, and Mistral OCR for content extraction.
- Web Search Integration - Perform real-time web searches via 15+ providers (SearXNG, Brave, Google PSE, Tavily, Perplexity, Bing, etc.) and inject results into conversations for up-to-date responses.
- Model Builder & Custom Agents - Create and manage Ollama models directly from the UI, define custom AI agents, and import models via Open WebUI Community integration.
- Native Python Function Calling (BYOF) - Write and execute pure Python functions within the UI’s code editor to extend LLM capabilities without external APIs.
- Persistent Artifact Storage - Use built-in key-value storage API to save journals, trackers, leaderboards, and shared data across sessions with personal or team scopes.
- Enterprise Authentication - Integrate with LDAP/Active Directory, SCIM 2.0 for automated user provisioning, and SSO via OAuth providers like Okta, Azure AD, and Google Workspace.
- Multi-Model Conversations - Simultaneously interact with multiple LLMs to compare outputs and leverage each model’s strengths in a single interface.
- Image Generation & Editing - Generate and edit images using DALL-E, Gemini, ComfyUI (local), or AUTOMATIC1111 (local) directly from the chat interface.
- Voice & Video Call Support - Enable hands-free interaction using local or cloud STT/TTs engines: Whisper, OpenAI, Deepgram, Azure STT, and ElevenLabs, Azure, OpenAI TTS.
- PWA & Mobile Support - Install as a native-like app on mobile devices with offline access via Progressive Web App functionality.
- Flexible Storage Backends - Choose between SQLite (with encryption), PostgreSQL, or cloud storage (S3, Google Cloud Storage, Azure Blob) for scalable deployments.
- OpenTelemetry Observability - Built-in tracing, metrics, and logging integration to monitor performance with existing tools like Prometheus or Grafana.
- Horizontal Scalability - Redis-backed session management and WebSocket support for multi-node deployments behind load balancers.
- Plugin Framework (Pipelines) - Extend functionality with custom Python plugins for rate limiting, Langfuse usage tracking, LibreTranslate translation, and toxic message filtering.
- Cloud File Integration - Import documents directly from Google Drive or OneDrive/SharePoint without downloading files manually.
Common Use Cases
- Building a private AI assistant for enterprise teams - Companies use Open WebUI to deploy secure, internally hosted LLMs with RBAC and SCIM 2.0 integration so employees can query internal docs via RAG without exposing data to third parties.
- Creating a research lab’s AI documentation hub - Researchers load PDFs, papers, and reports into RAG-enabled Open WebUI to ask natural language questions across hundreds of documents using ChromaDB or PGVector for fast retrieval.
- Problem: Need offline LLM access with voice input → Solution: Open WebUI + Ollama on a local server - Users in low-connectivity environments run the :ollama Docker image with Whisper STT to create a voice-controlled AI assistant that works without internet.
- DevOps teams managing multi-cloud LLM deployments - Teams use Open WebUI’s Docker/Kubernetes support with S3 storage and Redis to scale LLM interfaces across AWS, GCP, and on-prem clusters with centralized auth via LDAP.
- Educators building AI-powered learning tools - Professors deploy Open WebUI with custom Python functions to grade student essays or generate practice questions from uploaded syllabi, all hosted on a campus server.
- Developers prototyping LLM apps with RAG - Engineers test retrieval-augmented workflows using local Ollama models and ChromaDB before deploying to production, leveraging the built-in document upload and # command system.
Under The Hood
Open WebUI is a self-hosted, open-source AI interface that enables users to interact with local and remote language models through a modern web-based UI. It combines a Python-powered backend API with a Svelte-based frontend, offering extensibility and enterprise-grade features such as authentication and role-based access control.
Architecture
The system adopts a monolithic yet modular architecture with clear separation between frontend and backend components.
- The backend follows a layered structure with dedicated modules for database, authentication, and API routing.
- The frontend is built using Svelte and TypeScript, supporting component-based UI development.
- Middleware patterns are used for handling cross-cutting concerns like authentication and request validation.
- External services such as Ollama and AI models are integrated via configurable endpoints and environment settings.
Tech Stack
The project leverages a modern full-stack tech stack designed for flexibility and scalability.
- The backend is built with Python, FastAPI, and SQLAlchemy, ensuring efficient API handling and data persistence.
- The frontend utilizes Svelte and TypeScript for a responsive, type-safe user interface.
- Key dependencies include Ollama for local LLM inference, Pydantic for data validation, and MSAL-Browser for OAuth support.
- Development tools like Vite, Tailwind CSS, and Docker streamline building, styling, and deployment.
Code Quality
The codebase reflects a mature development approach with structured testing and consistent patterns.
- A comprehensive suite of integration and end-to-end tests is implemented using Cypress and Playwright.
- Linting and formatting are enforced through ESLint, Prettier, and PyLint to maintain code consistency.
- Error handling and data validation are well-defined across modules and API endpoints.
What Makes It Unique
Open WebUI distinguishes itself through its extensible architecture and enterprise-ready features.
- A modular API design supports plugin systems and customizable AI workflows through pipeline configurations.
- An advanced authentication framework includes LDAP support and role-based access control for secure deployment.
- Custom tools, functions, and model integrations are enabled through a well-defined middleware and configuration layer.
- The platform unifies technical backend capabilities with an intuitive admin dashboard and user-facing interface.