A self-hosted, extensible AI platform with a user-friendly web interface supporting Ollama, OpenAI API, and offline RAG.
Open WebUI is a self-hosted, extensible web interface for interacting with large language models (LLMs) locally or via compatible APIs. It provides a feature-rich alternative to cloud-based AI platforms, with built-in RAG, multi-modal capabilities, and enterprise-grade security. The platform is designed to operate entirely offline, giving users full control over their data and AI interactions.
Developers, data scientists, and organizations seeking a private, self-hosted AI interface for local LLM inference, RAG applications, and enterprise AI deployments with granular access control.
It offers a comprehensive, privacy-focused AI platform that combines ease of use with powerful extensibility, supporting a wide range of LLM backends, vector databases, and enterprise integrations out of the box.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
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Seamlessly connects with Ollama, OpenAI-compatible APIs, and services like LMStudio and GroqCloud, allowing flexible backend choices without vendor lock-in.
Offers Retrieval Augmented Generation with support for 9 vector databases (e.g., ChromaDB, PGVector) and multiple content extraction engines, enabling powerful offline document analysis.
Includes LDAP/Active Directory integration, SCIM 2.0 provisioning, and OAuth/SSO, making it suitable for secure, scalable organizational deployments.
Supports the Pipelines Plugin Framework for adding custom Python logic, such as function calling or rate limiting, enhancing adaptability for advanced workflows.
The README details multiple Docker commands and troubleshooting steps (e.g., network issues with --network=host), indicating a steep learning curve for non-Docker users or those unfamiliar with networking.
The Open WebUI License requires preserving the 'Open WebUI' branding, which can be a drawback for commercial projects seeking a completely white-labeled solution.
With features like multi-modal capabilities, horizontal scaling via Redis, and extensive RAG support, it may consume significant system resources, making it overkill for simple chat applications.