An open-source AI chat interface that connects to various AI models, including OpenAI, Azure, and local models via Ollama.
Chatbot UI is an open-source web application that provides a customizable interface for chatting with various AI models. It solves the problem of fragmented AI tooling by offering a unified chat experience that supports multiple backends, including OpenAI, Azure OpenAI, and locally-run models via Ollama. Users can deploy it themselves to maintain full control over their data and infrastructure.
Developers, researchers, and AI enthusiasts who want a private, customizable chat interface for interacting with AI models without relying on proprietary platforms. It's ideal for those who need to integrate multiple AI backends or run models locally.
Developers choose Chatbot UI because it's fully open-source, self-hostable, and supports a wide range of AI models. Unlike closed-source alternatives, it offers complete data ownership, customization flexibility, and avoids vendor lock-in while providing a polished, user-friendly interface.
AI chat for any model.
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Supports OpenAI, Azure OpenAI, and local Ollama models, enabling seamless switching between cloud and local AI backends in a unified interface.
Self-hostable via Docker and Supabase gives users full control over chat history and data, avoiding reliance on proprietary platforms.
Uses Supabase's PostgreSQL for secure, scalable storage, overcoming browser storage limits and enabling multi-modal use cases.
Offers detailed guides for both local and cloud hosting with Vercel, allowing flexibility in infrastructure choices.
Requires installing Docker, Supabase CLI, and configuring multiple environment variables, making deployment non-trivial for beginners.
Relies on Supabase and Vercel for full functionality, which can introduce costs, maintenance overhead, and potential vendor lock-in.
The README directs setup issues to Discussions, and the contributing guide is pending, indicating gaps in comprehensive, step-by-step docs.
Mention of a big update and a legacy branch suggests frequent changes, potentially requiring users to handle breaking updates and migrations.