AI SDK Core

Use AI SDK Core to generate text/structured output, call tools, and connect to MCP servers with consistent APIs across providers.

Quick Start

pnpm add ai @ai-sdk/openai zod@^4.3.5

import { generateText } from 'ai';

const { text } = await generateText({
  model: 'openai/gpt-4o',
  prompt: 'Explain quantum computing in one paragraph.',
});

Function Selection

Core Patterns

Generate Text

import { generateText } from 'ai';

const { text, usage } = await generateText({
  model: 'anthropic/claude-sonnet-4.5',
  system: 'You are a helpful assistant.',
  prompt: 'What is the capital of France?',
});

Stream Text

import { streamText } from 'ai';

const result = streamText({
  model: 'openai/gpt-4o',
  prompt: 'Write a short story.',
});

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

Generate Structured Data

import { generateObject } from 'ai';
import { z } from 'zod';

const { object } = await generateObject({
  model: 'openai/gpt-4o',
  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 (Typed)

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

const { text, toolCalls } = await generateText({
  model: 'openai/gpt-4o',
  tools: {
    weather: tool({
      description: 'Get weather for a location',
      inputSchema: z.object({ location: z.string() }),
      execute: async ({ location }) => ({ temperature: 72, condition: 'sunny' }),
    }),
  },
  prompt: 'What is the weather in San Francisco?',
});

Dynamic Tools (Runtime Schemas)

import { dynamicTool } from 'ai';
import { z } from 'zod';

const customTool = dynamicTool({
  description: 'Execute a custom function',
  inputSchema: z.object({}),
  execute: async input => ({ ok: true, input }),
});

Multi-Step Tool Execution

import { generateText, stepCountIs } from 'ai';

const { steps } = await generateText({
  model: 'openai/gpt-4o',
  tools: { search, analyze, summarize },
  stopWhen: stepCountIs(5),
  prompt: 'Research and summarize AI developments.',
});

Tooling Checklist

• Use tool() for typed inputs and dynamicTool() for unknown schemas.
• Use needsApproval for sensitive actions (tool-approval-request/response flow).
• Use stopWhen with stepCountIs/hasToolCall for multi-step loops.
• Use prepareStep for per-step controls (model swap, toolChoice, activeTools, prompt compression).
• Use experimental_context when tools need app-specific context.
• Use inputExamples and strict to improve tool call reliability.

MCP Integration (Model Context Protocol)

• Use createMCPClient() to load MCP tools, resources, and prompts.
• Prefer HTTP transport for production; use Experimental_StdioMCPTransport only for local Node.js servers.
• Close MCP clients after use (try/finally or onFinish).

See references/mcp-integration.md for transports, schema definition, outputSchema typing, and elicitation.

Reference Files

Error Handling

import { generateText, AI_APICallError } from 'ai';

try {
  await generateText({ model: 'openai/gpt-4o', prompt: 'Hello' });
} catch (error) {
  if (error instanceof AI_APICallError) {
    console.error('API Error:', error.message);
  }
}

Provider Setup

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

const { text } = await generateText({
  model: openai('gpt-4o'),
  prompt: 'Hello!',
});

Version Guidance

• Use AI SDK v6+ with matching provider packages.
• Pin major versions in package.json to avoid breaking changes.