Open WebUI: An Open, Extensible, and Usable Interface for AI Interaction
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911190407446528 |
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| author | Baek, Jaeryang Hussain, Ayana Liu, Danny Vincent, Nicholas Kim, Lawrence H. |
| author_facet | Baek, Jaeryang Hussain, Ayana Liu, Danny Vincent, Nicholas Kim, Lawrence H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02546 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Open WebUI: An Open, Extensible, and Usable Interface for AI Interaction Baek, Jaeryang Hussain, Ayana Liu, Danny Vincent, Nicholas Kim, Lawrence H. Human-Computer Interaction 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. |
| title | Open WebUI: An Open, Extensible, and Usable Interface for AI Interaction |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.02546 |