Vercel AI SDK

---

name: vercel-ai-sdk

description: Vercel AI SDK for building chat interfaces with streaming. Use when implementing useChat hook, handling tool calls, streaming responses, or building chat UI. Triggers on useChat, @ai-sdk/react, UIMessage, ChatStatus, streamText, toUIMessageStreamResponse, addToolOutput, onToolCall, sendMessage.

---

# Vercel AI SDK

The Vercel AI SDK provides React hooks and server utilities for building streaming chat interfaces with support for tool calls, file attachments, and multi-step reasoning.

Quick Reference

Basic useChat Setup

import { useChat } from '@ai-sdk/react';

const { messages, status, sendMessage, stop, regenerate } = useChat({
  id: 'chat-id',
  messages: initialMessages,
  onFinish: ({ message, messages, isAbort, isError }) => {
    console.log('Chat finished');
  },
  onError: (error) => {
    console.error('Chat error:', error);
  }
});

// Send a message
sendMessage({ text: 'Hello', metadata: { createdAt: Date.now() } });

// Send with files
sendMessage({
  text: 'Analyze this',
  files: fileList // FileList or FileUIPart[]
});

ChatStatus States

The `status` field indicates the current state of the chat:

Message Structure

Messages use the `UIMessage` type with a parts-based structure:

interface UIMessage {
  id: string;
  role: 'system' | 'user' | 'assistant';
  metadata?: unknown;
  parts: Array<UIMessagePart>; // text, file, tool-*, reasoning, etc.
}

Part types include:

Server-Side Streaming

import { streamText } from 'ai';
import { convertToModelMessages } from 'ai';

const result = streamText({
  model: openai('gpt-4'),
  messages: convertToModelMessages(uiMessages),
  tools: {
    getWeather: tool({
      description: 'Get weather',
      inputSchema: z.object({ city: z.string() }),
      execute: async ({ city }) => {
        return { temperature: 72, weather: 'sunny' };
      }
    })
  }
});

return result.toUIMessageStreamResponse({
  originalMessages: uiMessages,
  onFinish: ({ messages }) => {
    // Save to database
  }
});

Tool Handling Patterns

**Client-Side Tool Execution:**

const { addToolOutput } = useChat({
  onToolCall: async ({ toolCall }) => {
    if (toolCall.toolName === 'getLocation') {
      addToolOutput({
        tool: 'getLocation',
        toolCallId: toolCall.toolCallId,
        output: 'San Francisco'
      });
    }
  }
});

**Rendering Tool States:**

{message.parts.map(part => {
  if (part.type === 'tool-getWeather') {
    switch (part.state) {
      case 'input-streaming':
        return <pre>{JSON.stringify(part.input, null, 2)}</pre>;
      case 'input-available':
        return <div>Getting weather for {part.input.city}...</div>;
      case 'output-available':
        return <div>Weather: {part.output.weather}</div>;
      case 'output-error':
        return <div>Error: {part.errorText}</div>;
    }
  }
})}

Reference Files

Detailed documentation on specific aspects:

Common Patterns

Error Handling

const { error, clearError } = useChat({
  onError: (error) => {
    toast.error(error.message);
  }
});

// Clear error and reset to ready state
if (error) {
  clearError();
}

Message Regeneration

const { regenerate } = useChat();

// Regenerate last assistant message
await regenerate();

// Regenerate specific message
await regenerate({ messageId: 'msg-123' });

Custom Transport

import { DefaultChatTransport } from 'ai';

const { messages } = useChat({
  transport: new DefaultChatTransport({
    api: '/api/chat',
    prepareSendMessagesRequest: ({ id, messages, trigger, messageId }) => ({
      body: {
        chatId: id,
        lastMessage: messages[messages.length - 1],
        trigger,
        messageId
      }
    })
  })
});

Performance Optimization

// Throttle UI updates to reduce re-renders
const chat = useChat({
  experimental_throttle: 100 // Update max once per 100ms
});

Automatic Message Sending

import { lastAssistantMessageIsCompleteWithToolCalls } from 'ai';

const chat = useChat({
  sendAutomaticallyWhen: lastAssistantMessageIsCompleteWithToolCalls
  // Automatically resend when all tool calls have outputs
});

Type Safety

The SDK provides full type inference for tools and messages:

import { InferUITools, UIMessage } from 'ai';

const tools = {
  getWeather: tool({
    inputSchema: z.object({ city: z.string() }),
    execute: async ({ city }) => ({ weather: 'sunny' })
  })
};

type MyMessage = UIMessage<
  { createdAt: number }, // Metadata type
  UIDataTypes,
  InferUITools<typeof tools> // Tool types
>;

const { messages } = useChat<MyMessage>();

Key Concepts

Parts-Based Architecture

Messages use a parts array instead of a single content field. This allows:

Tool State Machine

Tool parts progress through states:

1. `input-streaming`: Tool input streaming (optional)

2. `input-available`: Tool input complete

3. `approval-requested`: Waiting for user approval (optional)

4. `approval-responded`: User approved/denied (optional)

5. `output-available`: Tool execution complete

6. `output-error`: Tool execution failed

7. `output-denied`: User denied approval

Streaming Protocol

The SDK uses Server-Sent Events (SSE) with UIMessageChunk types:

Client vs Server Tools

**Server-side tools** have an `execute` function and run on the API route.

**Client-side tools** omit `execute` and are handled via `onToolCall` and `addToolOutput`.

Best Practices

1. Always handle the `error` state and provide user feedback

2. Use `experimental_throttle` for high-frequency updates

3. Implement proper loading states based on `status`

4. Type your messages with custom metadata and tools

5. Use `sendAutomaticallyWhen` for multi-turn tool workflows

6. Handle all tool states in the UI for better UX

7. Use `stop()` to allow users to cancel long-running requests

8. Validate messages with `validateUIMessages` on the server