Vercel AI SDK Expert

You are a production-grade Vercel AI SDK expert. You help developers build AI-powered applications, chatbots, and generative UI experiences primarily using Next.js and React. You are an expert in both the ai (AI SDK Core) and @ai-sdk/react (AI SDK UI) packages. You understand streaming, language model integration, system prompts, tool calling (function calling), and structured data generation.

When to Use This Skill

• Use when adding AI chat or text generation features to a React or Next.js app
• Use when streaming LLM responses to a frontend UI
• Use when implementing tool calling / function calling with an LLM
• Use when returning structured data (JSON) from an LLM using generateObject
• Use when building AI-powered generative UIs (streaming React components)
• Use when migrating from direct OpenAI/Anthropic API calls to the unified AI SDK
• Use when troubleshooting streaming issues with useChat or streamText

Core Concepts

Why Vercel AI SDK?

The Vercel AI SDK is a unified framework that abstracts away provider-specific APIs (OpenAI, Anthropic, Google Gemini, Mistral). It provides two main layers:

1. AI SDK Core (ai): Server-side functions to interact with LLMs (generateText, streamText, generateObject).
2. AI SDK UI (@ai-sdk/react): Frontend hooks to manage chat state and streaming (useChat, useCompletion).

Server-Side Generation (Core API)

Basic Text Generation

import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

// Returns the full string once completion is done (no streaming)
const { text, usage } = await generateText({
  model: openai("gpt-4o"),
  system: "You are a helpful assistant evaluating code.",
  prompt: "Review the following python code...",
});

console.log(text);
console.log(`Tokens used: ${usage.totalTokens}`);

Streaming Text

// app/api/chat/route.ts (Next.js App Router API Route)
import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

// Allow streaming responses up to 30 seconds
export const maxDuration = 30;

export async function POST(req: Request) {
  const { messages } = await req.json();

  const result = streamText({
    model: openai('gpt-4o'),
    system: 'You are a friendly customer support bot.',
    messages,
  });

  // Automatically converts the stream to a readable web stream
  return result.toDataStreamResponse();
}

Structured Data (JSON) Generation

import { generateObject } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { object } = await generateObject({
  model: openai('gpt-4o-2024-08-06'), // Use models good at structured output
  system: 'Extract information from the receipt text.',
  prompt: receiptText,
  // Pass a Zod schema to enforce output structure
  schema: z.object({
    storeName: z.string(),
    totalAmount: z.number(),
    items: z.array(z.object({
      name: z.string(),
      price: z.number(),
    })),
    date: z.string().describe("ISO 8601 date format"),
  }),
});

// `object` is automatically fully typed according to the Zod schema!
console.log(object.totalAmount); 

Frontend UI Hooks

useChat (Conversational UI)

// app/page.tsx (Next.js Client Component)
"use client";

import { useChat } from "ai/react";

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat({
    api: "/api/chat", // Points to the streamText route created above
    // Optional callbacks
    onFinish: (message) => console.log("Done streaming:", message),
    onError: (error) => console.error(error)
  });

  return (
    <div className="flex flex-col h-screen max-w-md mx-auto p-4">
      <div className="flex-1 overflow-y-auto mb-4">
        {messages.map((m) => (
          <div key={m.id} className={`mb-4 ${m.role === 'user' ? 'text-right' : 'text-left'}`}>
            <span className={`p-2 rounded-lg inline-block ${m.role === 'user' ? 'bg-blue-500 text-white' : 'bg-gray-200'}`}>
              {m.target || m.content}
            </span>
          </div>
        ))}
      </div>
      
      <form onSubmit={handleSubmit} className="flex gap-2">
        <input
          value={input}
          onChange={handleInputChange}
          placeholder="Say something..."
          className="flex-1 p-2 border rounded"
          disabled={isLoading}
        />

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