npm install ai@^6.0.0 @ai-sdk/react@^3.0.0
npm install @ai-sdk/openai@^3.0.41      # Optional: required for embeddings
npm install @ai-sdk/anthropic@^3.0.58   # Optional: direct Anthropic provider access
npm install @ai-sdk/vercel@^2.0.37      # Optional: v0 model provider (v0-1.0-md)

vercel link                    # Connect to your Vercel project

This gives you AI Gateway access with OIDC authentication, cost tracking, failover, and observability — no manual API keys needed.

**OIDC is the default auth**: `vercel env pull` provisions a `VERCEL_OIDC_TOKEN` (short-lived JWT, ~24h). The `@ai-sdk/gateway` reads it automatically via `@vercel/oidc`. On Vercel deployments, tokens auto-refresh. For local dev, re-run `vercel env pull` when the token expires. No `AI_GATEWAY_API_KEY` or provider-specific keys needed.

import { generateText } from "ai";

const { text } = await generateText({
  model: "openai/gpt-5.4", // plain string — routes through AI Gateway automatically
  prompt: "Hello!",
});

import { gateway } from "ai";

// Explicit gateway() — only needed for advanced providerOptions
const { text } = await generateText({
  model: gateway("openai/gpt-5.4"),
  providerOptions: { gateway: { order: ["openai", "azure-openai"] } },
});

import { generateText, streamText } from "ai";

// Non-streaming
const { text } = await generateText({
  model: "openai/gpt-5.4",
  prompt: "Explain quantum computing in simple terms.",
});

// Streaming
const result = streamText({
  model: "openai/gpt-5.4",
  prompt: "Write a poem about coding.",
});

for await (const chunk of result.textStream) {
  process.stdout.write(chunk);
}

import { generateText, Output } from "ai";
import { z } from "zod";

const { output } = await generateText({
  model: "openai/gpt-5.4",
  output: Output.object({
    schema: z.object({
      recipe: z.object({
        name: z.string(),
        ingredients: z.array(
          z.object({
            name: z.string(),
            amount: z.string(),
          }),
        ),
        steps: z.array(z.string()),
      }),
    }),
  }),
  prompt: "Generate a recipe for chocolate chip cookies.",
});

import { generateText, tool } from "ai";
import { z } from "zod";

const result = await generateText({
  model: "openai/gpt-5.4",
  tools: {
    weather: tool({
      description: "Get the weather for a location",
      inputSchema: z.object({
        city: z.string().describe("The city name"),
      }),
      outputSchema: z.object({
        temperature: z.number(),
        condition: z.string(),
      }),
      strict: true, // Providers generate only schema-valid tool calls
      execute: async ({ city }) => {
        const data = await fetchWeather(city);
        return { temperature: data.temp, condition: data.condition };
      },
    }),
  },
  prompt: "What is the weather in San Francisco?",
});

import { dynamicTool } from "ai";

const tools = {
  unknownTool: dynamicTool({
    description: "A tool discovered at runtime",
    execute: async (input) => {
      // Handle dynamically
      return { result: "done" };
    },
  }),
};

import { ToolLoopAgent, stepCountIs, hasToolCall } from "ai";

const agent = new ToolLoopAgent({
  model: "anthropic/claude-sonnet-4.6",
  tools: { weather, search, calculator, finalAnswer },
  instructions: "You are a helpful assistant.",
  // Default: stepCountIs(20). Override to stop on a terminal tool or custom logic:
  stopWhen: hasToolCall("finalAnswer"),
  prepareStep: (context) => ({
    // Customize each step — swap models, compress messages, limit tools
    toolChoice: context.steps.length > 5 ? "none" : "auto",
  }),
});

const { text } = await agent.generate({
  prompt:
    "Research the weather in Tokyo and calculate the average temperature this week.",
});

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

const mcpClient = await createMCPClient({
  transport: {
    type: "sse",
    url: "https://my-mcp-server.com/sse",
  },
});

const tools = await mcpClient.tools();

const result = await generateText({
  model: "openai/gpt-5.4",
  tools,
  prompt: "Use the available tools to help the user.",
});

await mcpClient.close();

import { streamText, tool } from "ai";
import { z } from "zod";

const result = streamText({
  model: "openai/gpt-5.4",
  tools: {
    deleteUser: tool({
      description: "Delete a user account",
      inputSchema: z.object({ userId: z.string() }),
      needsApproval: true, // Always require approval
      execute: async ({ userId }) => {
        await db.users.delete(userId);
        return { deleted: true };
      },
    }),
    processPayment: tool({
      description: "Process a payment",
      inputSchema: z.object({ amount: z.number(), recipient: z.string() }),
      // Conditional: only approve large amounts
      needsApproval: async ({ amount }) => amount > 1000,
      execute: async ({ amount, recipient }) => {
        return await processPayment(amount, recipient);
      },
    }),
  },
  prompt: "Delete user 123",
});

"use client";
import { useChat } from "@ai-sdk/react";

function Chat() {
  const { messages, addToolApprovalResponse } = useChat();

  return messages.map((m) =>
    m.parts?.map((part, i) => {
      // Tool parts in approval-requested state need user action
      if (part.type.startsWith("tool-") && part.approval?.state === "approval-requested") {
        return (
          <div key={i}>
            <p>Tool wants to run: {JSON.stringify(part.args)}</p>
            <button onClick={() => addToolApprovalResponse({ id: part.approval.id, approved: true })}>
              Approve
            </button>
            <button onClick={() => addToolApprovalResponse({ id: part.approval.id, approved: false })}>
              Deny
            </button>
          </div>
        );
      }
      return null;
    }),
  );
}

import { embed, embedMany, rerank } from "ai";
import { openai } from "@ai-sdk/openai";

// Single embedding
const { embedding } = await embed({
  model: openai.embedding("text-embedding-3-small"),
  value: "The quick brown fox",
});

// Batch embeddings
const { embeddings } = await embedMany({
  model: openai.embedding("text-embedding-3-small"),
  values: ["text 1", "text 2", "text 3"],
});

// Rerank search results by relevance
const { results } = await rerank({
  model: cohere.reranker("rerank-v3.5"),
  query: "What is quantum computing?",
  documents: searchResults,
});

import { generateText, streamText } from "ai";

// generateText — images returned in result.files
const result = await generateText({
  model: "google/gemini-3.1-flash-image-preview",
  prompt: "A futuristic cityscape at sunset",
});
const imageFiles = result.files.filter((f) => f.mediaType?.startsWith("image/"));

// Convert to data URL for display
const imageFile = imageFiles[0];
const dataUrl = `data:${imageFile.mediaType};base64,${Buffer.from(imageFile.data).toString("base64")}`;

// streamText — stream text, then access images after completion
const stream = streamText({
  model: "google/gemini-3.1-flash-image-preview",
  prompt: "A futuristic cityscape at sunset",
});
for await (const delta of stream.fullStream) {
  if (delta.type === "text-delta") process.stdout.write(delta.text);
}
const finalResult = await stream;
console.log(`Generated ${finalResult.files.length} image(s)`);

import { experimental_generateImage as generateImage } from "ai";

const { images } = await generateImage({
  model: "google/imagen-4.0-generate-001",
  prompt: "A futuristic cityscape at sunset",
  aspectRatio: "16:9",
});

import fs from "node:fs";

// From multimodal LLMs (result.files)
for (const [i, file] of imageFiles.entries()) {
  const ext = file.mediaType?.split("/")[1] || "png";
  await fs.promises.writeFile(`output-${i}.${ext}`, file.uint8Array);
}

// From image-only models (result.images)
for (const [i, image] of images.entries()) {
  const buffer = Buffer.from(image.base64, "base64");
  await fs.promises.writeFile(`output-${i}.png`, buffer);
}

"use client";
import { useChat } from "@ai-sdk/react";
import { Conversation } from "@/components/ai-elements/conversation";
import { Message } from "@/components/ai-elements/message";

function Chat() {
  // No transport needed — defaults to DefaultChatTransport({ api: '/api/chat' })
  const { messages, sendMessage, status } = useChat();

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

"use client";
import { useChat } from "@ai-sdk/react";
import { DirectChatTransport } from "ai";
import { myAgent } from "@/lib/agent"; // a ToolLoopAgent instance

function Chat() {
  const { messages, sendMessage, status } = useChat({
    transport: new DirectChatTransport({ agent: myAgent }),
  });
  // Same UI as above — no /api/chat route required
}

// app/api/chat/route.ts
import { streamText, convertToModelMessages, stepCountIs } from "ai";
import type { UIMessage } from "ai";

export async function POST(req: Request) {
  const { messages }: { messages: UIMessage[] } = await req.json();
  // IMPORTANT: convertToModelMessages is async in v6
  const modelMessages = await convertToModelMessages(messages);
  const result = streamText({
    model: "openai/gpt-5.4",
    messages: modelMessages,
    tools: {
      /* your tools */
    },
    // IMPORTANT: use stopWhen with stepCountIs for multi-step tool calling
    // maxSteps was removed in v6 — use this instead
    stopWhen: stepCountIs(5),
  });
  // Use toUIMessageStreamResponse (not toDataStreamResponse) for chat UIs
  return result.toUIMessageStreamResponse();
}

// lib/agent.ts
import { ToolLoopAgent, stepCountIs } from "ai";

export const myAgent = new ToolLoopAgent({
  model: "openai/gpt-5.4",
  instructions: "You are a helpful assistant.",
  tools: { /* your tools */ },
  stopWhen: stepCountIs(5),
});

// app/api/chat/route.ts — agent API route
import { createAgentUIStreamResponse } from "ai";
import { myAgent } from "@/lib/agent";

export async function POST(req: Request) {
  const { messages } = await req.json();
  return createAgentUIStreamResponse({ agent: myAgent, uiMessages: messages });
}