DeepChat

Rather than building chatbot UI and logic from scratch, development teams can drop DeepChat into their existing stack and immediately connect it to popular AI backends including ChatGPT, Hugging Face, and custom API services, dramatically reducing the time to ship production-ready AI chat features.

The component's multimodal capabilities set it apart from basic chatbot widgets. Users can send and receive images, audio files, GIFs, spreadsheets, and other file types directly within the chat interface.

Text messages fully support Markdown rendering, enabling rich responses that include formatted code blocks, headers, lists, and links making DeepChat suitable for developer documentation, technical support, and knowledge base applications where structured information presentation is essential.

One of DeepChat's most technically distinctive features is its ability to host large language models entirely within the browser without any server-side infrastructure.

This browser-based LLM hosting capability enables completely private, offline AI interactions where no data is transmitted to external servers a critical requirement for applications handling sensitive personal or organizational information.

This approach also eliminates API costs for deployments where user privacy and data sovereignty take precedence.

Media capture capabilities extend DeepChat beyond text-based interactions into multimedia AI applications.