Chatbot UI is an open-source AI chat application built with TypeScript that enables users to interact with various AI models through a web-based interface. The project is maintained by Mckay Wrigley and provides both a self-hosted option and an official hosted version at chatbotui.com for users who prefer not to manage their own deployment.
The application is classified across multiple UI and UX-focused categories including web chat application, conversation flow, responsive design, reactive design, and dialogue system management. It leverages React for its component architecture and emphasizes interactive design patterns for message rendering and interaction flow. The frontend is built as a modern web application with careful attention to user experience and interface design.
The repository recently underwent a major version update to 2.0, with the previous 1.0 codebase preserved on a legacy branch. According to the README, the creator is actively working on significant improvements including simpler deployment processes, better backend compatibility, and improved mobile layouts. The project maintains an official hosted version to lower barriers to entry for users who want to use the application without managing infrastructure.
For local development, Chatbot UI uses Supabase as its backend database solution, replacing previous browser storage approaches due to security concerns and storage limitations. The migration to Supabase was chosen because it is open-source, uses Postgres, offers a free tier, and supports multi-modal use cases. The setup process requires Docker for running Supabase locally and includes detailed configuration steps for environment variables and SQL migrations. The application also supports optional integration with Ollama for running local AI models.
Deployment options include both local quickstart and hosted cloud deployment. For hosted instances, the project integrates with Vercel for frontend deployment and Supabase for backend services. The setup documentation covers obtaining necessary API credentials, configuring authentication providers, and deploying both database and frontend components. The application supports multiple AI model providers through environment variable configuration, including OpenAI, Azure OpenAI, and local Ollama models.
Community engagement is structured through GitHub Discussions rather than Issues, with the maintainer explicitly restricting the Issues section to actual codebase bugs. This separation aims to reduce noise from feature requests and deployment-related questions, directing users toward the Discussions tab for help and idea sharing. The project encourages community participation and offers sponsorship options for those who find the application useful.
