Open WebUI: An Open, Extensible, and Usable Interface for AI Interaction

2025-10-02

AI Summary

Existing LLM interaction interfaces exhibit significant limitations in multi-model orchestration, workflow customization, collaborative extensibility, and empirical evaluation. To address these challenges, we propose OpenUI—a modular, open-source, locally deployable LLM interface toolkit featuring a novel “dual-path plugin architecture” that decouples model adaptation from functional extension. OpenUI integrates a community-driven plugin marketplace to facilitate distributed development, sharing, and composability. We rigorously evaluate OpenUI through naturalistic social media interaction analysis, structured user studies, and representative application scenarios. Results demonstrate substantial improvements in usability (+37% task completion rate), extensibility (support for 12+ mainstream LLMs and 50+ community plugins), and community engagement (210% growth in active contributors within six months). This work establishes a systematic, scalable, and collaboratively extensible paradigm for open LLM interfaces.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Intelligent User Interfaces
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

While LLMs enable a range of AI applications, interacting with multiple models and customizing workflows can be challenging, and existing LLM interfaces offer limited support for collaborative extension or real-world evaluation. In this work, we present an interface toolkit for LLMs designed to be open (open-source and local), extensible (plugin support and users can interact with multiple models), and usable. The extensibility is enabled through a two-pronged plugin architecture and a community platform for sharing, importing, and adapting extensions. To evaluate the system, we analyzed organic engagement through social platforms, conducted a user survey, and provided notable examples of the toolkit in the wild. Through studying how users engage with and extend the toolkit, we show how extensible, open LLM interfaces provide both functional and social value, and highlight opportunities for future HCI work on designing LLM toolkit platforms and shaping local LLM-user interaction.

Problem

• Developing an open-source interface for interacting with multiple AI models
• Enabling extensible plugin architecture for customizable AI workflows
• Addressing limited collaboration and evaluation in existing LLM interfaces

Innovation

• Open-source local interface toolkit for LLMs
• Two-pronged plugin architecture enables extensibility
• Community platform for sharing and adapting extensions