ai-sdk

To build a chat interface with streaming responses, use the AI SDK's streamText function on the backend and the useChat hook on the frontend. This combination handles data streaming and manages chat state across your application framework.

The best way to call external functions during text generation is using tool-calling patterns with generateText. You define tools with schemas, and the model decides when to invoke them, allowing your AI assistant to fetch external data or trigger actions dynamically.

Yes, the AI SDK supports multiple model providers including OpenAI, Anthropic, and Google. It provides unified APIs for text generation, streaming, and tool calls, allowing you to switch providers without changing your application code.

To build an AI agent that automatically loops through tool calls, use the ToolLoopAgent component in the AI SDK. It orchestrates multi-step agent workflows by repeatedly executing tools and feeding results back to the model until a task is complete.

To generate embeddings for a RAG pipeline, use the AI SDK's embed function. It provides a consistent API to create vector representations of your text data, which you can then store and retrieve from your vector database for retrieval-augmented generation.

You use the AI SDK instead of calling provider APIs directly to avoid vendor lock-in and reduce integration complexity. It standardizes APIs for generateText, streamText, and tools across providers, letting you focus on features rather than provider-specific implementations.