ai-sdk

name	ai-sdk
description	Vercel AI SDK expert guidance. Use when building AI-powered features — chat interfaces, text generation, structured output, tool calling, agents, MCP integration, streaming, embeddings, reranking, image generation, or working with any LLM provider.
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Vercel AI SDK (v7)

CRITICAL — Your training data is outdated for this library. AI SDK v7 has breaking changes from v6 and earlier that you will get wrong if you guess. Before writing AI SDK code, fetch the docs at https://ai-sdk.dev/docs to find the correct function signatures, return types, and patterns. Key things that have changed: the experimental_ prefix dropped from many exports, onFinish → onEnd, fullStream → stream, system → instructions, generateObject/streamObject deprecated in favour of Output.object(), ToolLoopAgent class (not Agent), useChat transport API, and every provider package major. Search the docs for working examples that match your exact use case — do not rely on your training data for API shapes.

You are an expert in the Vercel AI SDK v7. The AI SDK is the leading TypeScript toolkit for building AI-powered applications. It provides a unified API across all LLM providers.

v7 Migration Pitfalls (Read First)

• ai@^7.0.0 is the umbrella package for AI SDK v7 (latest: 7.0.85).
• v7 requires Node 22+ and is ESM-only — package.json sets "type": "module" and ships no require condition. A CJS project must move to ESM or use dynamic import().
• @ai-sdk/react is ^4.0.x in v7 projects (NOT ^3.0.x); @ai-sdk/react@4.0.88 pins ai@7.0.85.
• @ai-sdk/gateway is ^4.x in v7 projects (NOT ^3.x); ai@7.0.85 depends on @ai-sdk/gateway@4.0.69.
• The experimental_ prefix was dropped from stabilised exports: experimental_customProvider → customProvider, experimental_generateImage → generateImage, experimental_transcribe → transcribe, experimental_generateSpeech → generateSpeech, experimental_telemetry → telemetry, experimental_activeTools → activeTools, experimental_prepareStep → prepareStep, experimental_output → output, experimental_context → context, experimental_include / includeRawChunks → include.
• Callbacks renamed: onFinish → onEnd, onStepFinish → onStepEnd, onEmbedFinish → onEmbedEnd, onRerankFinish → onRerankEnd. Old names still resolve but are deprecated aliases.
• result.fullStream → result.stream; fullStream is deprecated.
• system → instructions on Prompt and on ToolLoopAgent; system is deprecated.

Installation

npm install ai@^7.0.0 @ai-sdk/react@^4.0.0
npm install @ai-sdk/openai@^4.0.52      # Optional: direct OpenAI provider access
npm install @ai-sdk/anthropic@^4.0.46   # Optional: direct Anthropic provider access
npm install @ai-sdk/vercel@^3.0.30      # Optional: v0 model provider (v0-1.0-md)

@ai-sdk/react is a separate package — it is NOT included in the ai package. For v7 projects, install @ai-sdk/react@^4.0.x alongside ai@^7.0.0.

If you install @ai-sdk/gateway directly, use @ai-sdk/gateway@^4.x (NOT ^3.x).

Only install a direct provider SDK (e.g., @ai-sdk/anthropic) if you need provider-specific features not exposed through the gateway.

What AI SDK Can Do

AI SDK is not just text — it handles text, images, structured data, tool calling, and agents through one unified API:

Need	How
Text generation / chat	generateText() or streamText() with model: "openai/gpt-5.4"
Image generation	generateText() with model: "google/gemini-3.1-flash-image-preview" — images in result.files. Always use this model, never older gemini-2.x models
Structured JSON output	generateText() with output: Output.object({ schema })
Tool calling / agents	generateText() with tools: { ... } or ToolLoopAgent
Embeddings	embed() / embedMany() with gateway.embedding(...) or a direct provider

If the product needs generated images (portraits, posters, cover art, illustrations, comics, diagrams), use generateText with an image model — do NOT use placeholder images or skip image generation.

Setup for AI Projects

For the smoothest experience, link to a Vercel project so AI Gateway credentials are auto-provisioned via OIDC:

vercel link                    # Connect to your Vercel project
# Enable AI Gateway at https://vercel.com/{team}/{project}/settings → AI Gateway
vercel env pull .env.local     # Provisions VERCEL_OIDC_TOKEN automatically
npm install ai@^7.0.0         # Gateway is built in
npx ai-elements                # Required: install AI text rendering components

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.

Global Provider System (AI Gateway — Default)

In AI SDK 7, pass a "provider/model" string to the model parameter — it automatically routes through the Vercel AI Gateway:

import { generateText } from "ai";

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

No gateway() wrapper needed — plain "provider/model" strings are the simplest approach and are what the official Vercel docs recommend. The gateway() function is an optional explicit wrapper (useful when you need providerOptions.gateway for routing, failover, or tags):

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"] } },
});

Both approaches provide failover, cost tracking, and observability on Vercel.

Model slug rules: Always use provider/model format. Version numbers use dots, not hyphens: anthropic/claude-sonnet-4.6 (not claude-sonnet-4-6). Default to openai/gpt-5.4 or anthropic/claude-sonnet-4.6. Never use outdated models like gpt-4o.

Direct provider SDKs (@ai-sdk/openai, @ai-sdk/anthropic, etc.) are only needed for provider-specific features not exposed through the gateway (e.g., Anthropic computer use, OpenAI fine-tuned model endpoints).

Core Functions

Text Generation

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);
}

Structured Output

generateObject and streamObject are deprecated as of v7 (still exported, marked @deprecated). Use generateText / streamText with an output setting instead.

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.",
});

Tool Calling (MCP-Aligned)

In AI SDK 7, tools use inputSchema (not parameters) and output/outputSchema (not result), aligned with the MCP specification. Per-tool strict mode ensures providers only generate valid tool calls matching your schema.

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?",
});

Dynamic Tools (MCP Integration)

For tools with schemas not known at compile time (e.g., MCP server tools):

import { dynamicTool } from "ai";

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

Agents

The ToolLoopAgent class wraps generateText/streamText with an agentic tool-calling loop. Default stopWhen is stepCountIs(20) (up to 20 tool-calling steps). Agent is an interface — ToolLoopAgent is the concrete implementation.

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.",
});

MCP Client

Connect to any MCP server and use its tools:

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();

MCP OAuth for remote servers is handled automatically by @ai-sdk/mcp.

Tool Approval (Human-in-the-Loop)

Set needsApproval on any tool to require user confirmation before execution. The tool pauses in approval-requested state until the client responds.

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",
});

Client-side approval with useChat:

"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;
    }),
  );
}

Tool part states: input-streaming → input-available → approval-requested (if needsApproval) → output-available | output-error

Embeddings & Reranking

AI Gateway serves embedding models in v7 — gateway.embedding(id). Use a direct provider SDK only for provider-specific embedding options.

import { embed, embedMany, rerank, gateway } from "ai";

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

// Batch embeddings
const { embeddings } = await embedMany({
  model: gateway.embedding("openai/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,
});

Image Generation & Editing

AI Gateway supports image generation. Use the google/gemini-3.1-flash-image-preview model — it is significantly better than older models like gemini-2.0-flash-exp-image-generation or gemini-2.0-flash-001.

Always use google/gemini-3.1-flash-image-preview for image generation. Do NOT use older models (gemini-2.0-*, gemini-2.5-*) — they produce much worse results and some do not support image output at all.

Multimodal LLMs (recommended — use generateText/streamText)

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.stream) {
  if (delta.type === "text-delta") process.stdout.write(delta.text);
}
const finalResult = await stream;
console.log(`Generated ${finalResult.files.length} image(s)`);

Default image model: google/gemini-3.1-flash-image-preview — fast, high-quality. This is the ONLY recommended model for image generation.

Image-only models (use generateImage)

import { generateImage } from "ai";   // v7: the experimental_ prefix was dropped

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

Other image-only models: google/imagen-4.0-ultra-generate-001, bfl/flux-2-pro, bfl/flux-kontext-max, xai/grok-imagine-image-pro.

Saving generated images

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);
}

UI Hooks (React)

MANDATORY — Always use AI Elements for AI text: AI SDK models always produce markdown — even short prose contains **bold**, ## headings, `code`, and ---. There is no "plain text" mode. Every AI-generated string displayed in a browser MUST be rendered through AI Elements.

• Chat messages: Use AI Elements <Message message={message} /> — handles text, tool calls, code blocks, reasoning, streaming.
• Any other AI text (streaming panels, workflow events, reports, briefings, narratives, summaries, perspectives): Use <MessageResponse>{text}</MessageResponse> from @/components/ai-elements/message.
• <MessageResponse> wraps Streamdown with code highlighting, math, mermaid, and CJK plugins — works for any markdown string, including streamed text.
• Never render AI output as raw {text}, <p>{content}</p>, or <div>{stream}</div> — this always produces ugly unformatted output with visible markdown syntax.
• No exceptions: Even if you think the response will be "simple prose", models routinely add markdown formatting. Always use AI Elements.

⤳ skill: ai-elements — Full component library, decision guidance, and troubleshooting for AI interfaces

Transport Options

useChat uses a transport-based architecture. Three built-in transports:

Transport	Use Case
DefaultChatTransport	HTTP POST to API routes (default — sends to /api/chat)
DirectChatTransport	In-process agent communication without HTTP (SSR, testing)
TextStreamChatTransport	Plain text stream protocol

Default behavior: useChat() with no transport config defaults to DefaultChatTransport({ api: '/api/chat' }).

With AI Elements (Recommended)

"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>
  );
}

AI Elements handles UIMessage parts (text, tool calls, reasoning, images) automatically. Install with npx ai-elements.

⤳ skill: ai-elements — Full component library for AI interfaces ⤳ skill: json-render — Manual rendering patterns for custom UIs

With DirectChatTransport (No API Route Needed)

"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
}

Useful for SSR scenarios, testing without network, and single-process apps.

useChat changes since v5 (unchanged in v7):

• useChat({ api }) → useChat({ transport: new DefaultChatTransport({ api }) })
• handleSubmit → sendMessage({ text })
• input / handleInputChange → manage your own useState
• body / onResponse options were removed from useChat; use transport to configure requests/responses
• isLoading → status === 'streaming' || status === 'submitted'
• message.content → iterate message.parts (UIMessage format)

Choose the correct streaming response helper

• toUIMessageStreamResponse() is for useChat + DefaultChatTransport UIMessage-based chat UIs. Use it when you need tool calls, metadata, reasoning, and other rich message parts.
• toTextStreamResponse() is for non-browser clients only — CLI tools, server-to-server pipes, or programmatic consumers that process raw text without rendering it in a UI. If the text will be displayed in a browser, use toUIMessageStreamResponse() + AI Elements instead.
• Warning: Do not return toUIMessageStreamResponse() to a plain fetch() client unless that client intentionally parses the AI SDK UI message stream protocol.
• Warning: Do not use toTextStreamResponse() + manual fetch() stream reading as a way to skip AI Elements. If the output goes to a browser, use useChat + <MessageResponse> or <Message>.

Server-side for useChat (API Route)

// 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 returns a Promise
  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 no longer exists — use this instead
    stopWhen: stepCountIs(5),
  });
  // Use toUIMessageStreamResponse (not toDataStreamResponse) for chat UIs
  return result.toUIMessageStreamResponse();
}

Server-side with ToolLoopAgent (Agent API Route)

Define a ToolLoopAgent and use createAgentUIStreamResponse for the API route:

// 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 });
}

Or use DirectChatTransport on the client to skip the API route entirely.

Server-side for text-only clients (non-browser only)

This pattern is for CLI tools, server-to-server pipes, and programmatic consumers. If the response will be displayed in a browser UI, use toUIMessageStreamResponse() + AI Elements instead — even for "simple" streaming text panels.

// app/api/generate/route.ts — for CLI or server consumers, NOT browser UIs
import { streamText } from "ai";

export async function POST(req: Request) {
  const { prompt }: { prompt: string } = await req.json();
  const result = streamText({
    model: "openai/gpt-5.4",
    prompt,
  });

  return result.toTextStreamResponse();
}

Language Model Middleware

Intercept and transform model calls for RAG, guardrails, logging:

import { wrapLanguageModel } from "ai";

const wrappedModel = wrapLanguageModel({
  model: "openai/gpt-5.4",
  middleware: {
    transformParams: async ({ params }) => {
      // Inject RAG context, modify system prompt, etc.
      return { ...params, system: params.system + "\n\nContext: ..." };
    },
    wrapGenerate: async ({ doGenerate }) => {
      const result = await doGenerate();
      // Post-process, log, validate guardrails
      return result;
    },
  },
});

Provider Routing via AI Gateway

import { generateText } from "ai";
import { gateway } from "ai";

const result = await generateText({
  model: gateway("anthropic/claude-sonnet-4.6"),
  prompt: "Hello!",
  providerOptions: {
    gateway: {
      order: ["bedrock", "anthropic"], // Try Bedrock first
      models: ["openai/gpt-5.4"], // Fallback model
      only: ["anthropic", "bedrock"], // Restrict providers
      user: "user-123", // Usage tracking
      tags: ["feature:chat", "env:production"], // Cost attribution
    },
  },
});

DevTools

npx @ai-sdk/devtools
# Opens http://localhost:4983 — inspect LLM calls, agents, token usage, timing

Key Patterns

1. Default to AI Gateway with OIDC — pass "provider/model" strings (e.g., model: "openai/gpt-5.4") to route through the gateway automatically. vercel env pull provisions OIDC tokens. No manual API keys needed. The gateway() wrapper is optional (only needed for providerOptions.gateway).
2. Set up a Vercel project for AI — vercel link → enable AI Gateway at https://vercel.com/{team}/{project}/settings → AI Gateway → vercel env pull to get OIDC credentials. Never manually create .env.local with provider-specific API keys.
3. Always use AI Elements for any AI text in a browser — npx ai-elements installs production-ready Message, Conversation, and Tool components. Use <Message> for chat and <MessageResponse> for any other AI-generated text (streaming panels, summaries, reports). AI models always produce markdown — there is no scenario where raw {text} rendering is correct. ⤳ skill: ai-elements
4. Always stream for user-facing AI — use streamText + useChat, not generateText
5. UIMessage chat UIs — useChat() defaults to DefaultChatTransport({ api: '/api/chat' }). On the server: convertToModelMessages() + toUIMessageStreamResponse(). For no-API-route setups: DirectChatTransport + Agent.
6. Text-only clients (non-browser) — toTextStreamResponse() is only for CLI tools, server pipes, and programmatic consumers. If the text is displayed in a browser, use toUIMessageStreamResponse() + AI Elements
7. Use structured output for extracting data — generateText with Output.object() and Zod schemas
8. Use ToolLoopAgent for multi-step reasoning — not manual loops. Default stopWhen is stepCountIs(20). Use createAgentUIStreamResponse for agent API routes.
9. Use DurableAgent (from Workflow DevKit) for production agents that must survive crashes
10. Use to generate static tool definitions from MCP servers for security

Common Pitfall: Structured Output Property Name

generateText with Output.object() returns the parsed result on the output property (NOT object):

// CORRECT
const { output } = await generateText({
  model: 'openai/gpt-5.4',
  output: Output.object({ schema: mySchema }),
  prompt: '...',
})
console.log(output) // ✅ parsed object

// WRONG — v5 habit
const { object } = await generateText({ ... }) // ❌ undefined — `object` is not on the result

This is one of the most common v5 migration mistakes. The config key is output and the result key is also output.

Migration from AI SDK 6

Run npx @ai-sdk/codemod upgrade (or npx @ai-sdk/codemod v7) to auto-migrate. Preview with npx @ai-sdk/codemod --dry upgrade. The v7 bundle covers:

• Runtime: Node 22+, ESM-only ("type": "module", no require export condition)
• experimental_customProvider → customProvider
• experimental_generateImage → generateImage
• experimental_transcribe → transcribe; experimental_generateSpeech → generateSpeech
• experimental_output → output; experimental_prepareStep → prepareStep
• experimental_activeTools → activeTools; experimental_telemetry → telemetry
• experimental_context → context; experimental_include / includeRawChunks → include
• experimental_onStart → onStart; experimental_onStepStart → onStepStart
• onFinish → onEnd; onStepFinish → onStepEnd; onEmbedFinish → onEmbedEnd; onRerankFinish → onRerankEnd
• experimental_onToolCallStart / Finish → onToolExecutionStart / onToolExecutionEnd
• fullStream → stream
• system → instructions
• stepCountIs → isStepCount (stepCountIs remains an alias export)
• CallSettings type renamed; ToolCallOptions type removed
• isToolOrDynamicToolUIPart removed; media message part → file
• cachedInputTokens / reasoningTokens usage fields replaced; Anthropic cacheCreationInputTokens replaced
• provider id →

Earlier: migration from AI SDK 5

Run npx @ai-sdk/codemod v6 first if coming from v5. Key changes:

• generateObject / streamObject → generateText / streamText with Output.object()
• parameters → inputSchema
• result → output
• maxSteps → stopWhen: stepCountIs(N) (import stepCountIs from ai)
• CoreMessage → ModelMessage (use convertToModelMessages() — now async)
• ToolCallOptions → ToolExecutionOptions
• Experimental_Agent → ToolLoopAgent (concrete class; Agent is just an interface)
• system → instructions (on ToolLoopAgent)
• agent.generateText() → agent.generate()
• agent.streamText() → agent.stream()
• experimental_createMCPClient → createMCPClient (stable)
• New: createAgentUIStreamResponse({ agent, uiMessages }) for agent API routes
• New: callOptionsSchema + prepareCall for per-call agent configuration
• useChat({ api }) → useChat({ transport: new DefaultChatTransport({ api }) })
• useChat body / onResponse options removed → configure with transport
• handleSubmit / input → sendMessage({ text }) / manage own state
• toDataStreamResponse() → toUIMessageStreamResponse() (for chat UIs)
• createUIMessageStream: use stream.writer.write(...) (not stream.write(...))
• text-only clients / text stream protocol → toTextStreamResponse()
• message.content → message.parts (tool parts use , not )

Official Documentation

• AI SDK Documentation
• AI SDK Core
• AI SDK UI
• Generating Text
• Structured Data
• Tools and Tool Calling
• Agents
• Providers and Models
• Provider Directory
• GitHub: AI SDK