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ai-sdk-5

ai-sdk-5

github.com/prowler-cloudDevelopment15.1k87 installsUpdated 2d ago
Repository

About ai-sdk-5

A reference skill for developers migrating to and building with AI SDK 5. It covers updated chat APIs, message structure, server routes, tool streaming, LangChain use, and error handling.

Key features

  1. 01AI SDK 4 to 5 migration
  2. 02React chat setup
  3. 03UIMessage parts structure
  4. 04Server-side streaming
  5. 05Tool-enabled generation
  6. 06Completion and error handling

Use cases

  1. 01Updating existing AI SDK chat code
  2. 02Building a React chat interface
  3. 03Streaming tool-enabled responses
  4. 04Connecting LangChain messages to UI streams

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/prowler-cloud/prowler --skill ai-sdk-5

After installing, type/ai-sdk-5in Claude Code to confirm it loaded.

Details

From the project README

Breaking Changes from AI SDK 4

// ❌ AI SDK 4 (OLD)
import { useChat } from "ai";
const { messages, handleSubmit, input, handleInputChange } = useChat({
  api: "/api/chat",
});

// ✅ AI SDK 5 (NEW)
import { useChat } from "@ai-sdk/react";
import { DefaultChatTransport } from "ai";

const { messages, sendMessage } = useChat({
  transport: new DefaultChatTransport({ api: "/api/chat" }),
});

Client Setup

import { useChat } from "@ai-sdk/react";
import { DefaultChatTransport } from "ai";
import { useState } from "react";

export function Chat() {
  const [input, setInput] = useState("");

  const { messages, sendMessage, isLoading, error } = useChat({
    transport: new DefaultChatTransport({ api: "/api/chat" }),
  });

  const handleSubmit = (e: React.FormEvent) => {
    e.preventDefault();
    if (!input.trim()) return;
    sendMessage({ text: input });
    setInput("");
  };

  return (
    <div>
      <div>
        {messages.map((message) => (
          <Message key={message.id} message={message} />
        ))}
      </div>

      <form onSubmit={handleSubmit}>
        <input
          value={input}
          onChange={(e) => setInput(e.target.value)}
          placeholder="Type a message..."
          disabled={isLoading}
        />
        <button type="submit" disabled={isLoading}>
          Send
        </button>
      </form>

      {error && <div>Error: {error.message}</div>}
    </div>
  );
}

UIMessage Structure (v5)

// ❌ Old: message.content was a string
// ✅ New: message.parts is an array

interface UIMessage {
  id: string;
  role: "user" | "assistant" | "system";
  parts: MessagePart[];
}

type MessagePart =
  | { type: "text"; text: string }
  | { type: "image"; image: string }
  | { type: "tool-call"; toolCallId: string; toolName: string; args: unknown }
  | { type: "tool-result"; toolCallId: string; result: unknown };

// Extract text from parts
function getMessageText(message: UIMessage): string {
  return message.parts
    .filter((part): part is { type: "text"; text: string } => part.type === "text")
    .map((part) => part.text)
    .join("");
}

// Render message
function Message({ message }: { message: UIMessage }) {
  return (
    <div className={message.role === "user" ? "user" : "assistant"}>
      {message.parts.map((part, index) => {
        if (part.type === "text") {
          return <p key={index}>{part.text}</p>;
        }
        if (part.type === "image") {
          return <img key={index} src={part.image} alt="" />;
        }
        return null;
      })}
    </div>
  );
}

Server-Side (Route Handler)

// app/api/chat/route.ts
import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";

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

  const result = await streamText({
    model: openai("gpt-4o"),
    messages,
    system: "You are a helpful assistant.",
  });

  return result.toDataStreamResponse();
}

With LangChain

// app/api/chat/route.ts
import { toUIMessageStream } from "@ai-sdk/langchain";
import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage, AIMessage } from "@langchain/core/messages";

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

  const model = new ChatOpenAI({
    modelName: "gpt-4o",
    streaming: true,
  });

  // Convert UI messages to LangChain format
  const langchainMessages = messages.map((m) => {
    const text = m.parts
      .filter((p) => p.type === "text")
      .map((p) => p.text)
      .join("");
    return m.role === "user"
      ? new HumanMessage(text)
      : new AIMessage(text);
  });

  const stream = await model.stream(langchainMessages);

  return toUIMessageStream(stream).toDataStreamResponse();
}

Streaming with Tools

import { openai } from "@ai-sdk/openai";
import { streamText, tool } from "ai";
import { z } from "zod";

const result = await streamText({
  model: openai("gpt-4o"),
  messages,
  tools: {
    getWeather: tool({
      description: "Get weather for a location",
      parameters: z.object({
        location: z.string().describe("City name"),
      }),
      execute: async ({ location }) => {
        // Fetch weather data
        return { temperature: 72, condition: "sunny" };
      },
    }),
  },
});

useCompletion (Text Generation)

import { useCompletion } from "@ai-sdk/react";
import { DefaultCompletionTransport } from "ai";

const { completion, complete, isLoading } = useCompletion({
  transport: new DefaultCompletionTransport({ api: "/api/complete" }),
});

// Trigger completion
await complete("Write a haiku about");

Error Handling

const { error, messages, sendMessage } = useChat({
  transport: new DefaultChatTransport({ api: "/api/chat" }),
  onError: (error) => {
    console.error("Chat error:", error);
    toast.error("Failed to send message");
  },
});

// Display error
{error && (
  <div className="error">
    {error.message}
    <button onClick={() => sendMessage({ text: lastInput })}>
      Retry
    </button>
  </div>
)}

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