Open WebUI
Extensible, offline-capable web interface for LLM interactions
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Actively maintained
Last commit 27 Jul 2026.
Open WebUI is a web-based, extensible AI interface that provides a unified GUI for interacting with local and cloud LLMs. It supports multiple LLM runners and OpenAI-compatible APIs, built-in RAG, artifact storage, and collaboration features.
Key Features
- Multi-runner support (Ollama and OpenAI-compatible endpoints) and built-in inference integrations for flexible model selection
- Local Retrieval-Augmented Generation (RAG) with support for multiple vector databases and content extractors
- Image generation and editing integrations with local and remote engines; prompt-based editing workflows
- Granular role-based access control (RBAC), user groups, and enterprise provisioning (SCIM, LDAP/AD, SSO integrations)
- Persistent artifact/key-value storage for journals, leaderboards, and shared session data
- Progressive Web App (PWA) experience, responsive UI, and multi-device support
- Native Python function-calling tools (BYOF) and a web-based code editor for tool/workspace development
- Docker/Kubernetes deployment options, prebuilt image tags for CPU/GPU and Ollama bundles
- Production observability with OpenTelemetry traces, metrics and Redis-backed session management
Use Cases
- Teams wanting a central, auditable chat interface to query multiple LLMs and manage permissions
- Knowledge workers and developers using local RAG pipelines to query private document collections securely
- Experimentation and model comparison workflows combining multiple models, image tools, and custom functions
Limitations and Considerations
- Advanced features (model inference, heavy image generation) require external runners or GPU resources; performance depends on the chosen backend
- Some enterprise integrations and optional storage backends require additional configuration and credentials
- Desktop app is experimental; recommended production deployment paths are Docker, Docker Compose or Kubernetes
Open WebUI is positioned as a flexible interface layer for LLM workflows, emphasizing provider-agnostic integration, RAG, and enterprise features. It is suited for teams that need a full-featured, customizable web UI for local and cloud model workflows.
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