chat-sdk

To build a production-ready AI chatbot with Next.js App Router, use a unified scaffold that integrates server and client components, streaming responses, and persistence-backed chat histories. This approach provides App Router conventions, artifact registration, and database migrations out of the box.

Generative UI in AI chatbots works through interactive workspaces like code, image, sheet, and text artifacts that stream content and support in-browser execution. This allows chat interfaces to render rich, dynamic components directly instead of plain text responses.

Yes, you can use multiple LLM providers like OpenAI and Anthropic in a single chatbot application. The framework supports provider flexibility, allowing you to swap between xAI, Google, and others via an AI SDK Gateway or direct integrations without changing your chat interface logic.

The best way to deploy an AI chatbot to Vercel with PostgreSQL persistence is using a scaffold with built-in Drizzle ORM patterns, Vercel Blob storage for file uploads, and one-click deployment templates. You configure your DATABASE_URL, run migrations, and deploy.

No, you do not need to manage database migrations manually for a streaming chat application if you use a scaffold with built-in persistence patterns. The framework includes database migration workflows to set up your PostgreSQL conversation history storage automatically.

To add file uploads and secure authentication to an AI chatbot, leverage built-in patterns for Vercel Blob storage and secure auth flows. This allows your chat interface to accept file uploads and manage user sessions while streaming model responses.