Google Gemini Vercel AI SDK Cheatsheet

Gemini models are accessible using the AI SDK by Vercel. This guide helps you get started with the AI SDK and Gemini, and provides code snippets for most of the important features.

For more information, see the following resources:

• AI SDK docs
• AI SDK Google Generative AI docs

Install the AI SDK and the Google Generative AI integration:

npm install ai
npm install @ai-sdk/google
// pnpm: pnpm add ai @ai-sdk/google
// yarn: yarn add ai @ai-sdk/google

Set the GOOGLE_GENERATIVE_AI_API_KEY environment variable with your API key. A free API key can be obtained at Google AI Studio.

# MacOS/Linux
export GOOGLE_GENERATIVE_AI_API_KEY="YOUR_API_KEY_HERE"

# Powershell
setx GOOGLE_GENERATIVE_AI_API_KEY "YOUR_API_KEY_HERE"

Here's a basic example that takes a single text input:

import { generateText } from 'ai';
import { google } from '@ai-sdk/google';

const model = google('gemini-2.0-flash');

const { text } = await generateText({
  model: model,
  prompt: 'Why is the sky blue?',
  // system: 'You are a friendly assistant!',
  // temperature: 0.7,
});

console.log(text);

Here's a basic streaming example:

import { streamText } from 'ai';
import { google } from '@ai-sdk/google';

const model = google('gemini-2.0-flash');

const { textStream }  = streamText({
  model: model,
  prompt: 'Why is the sky blue?',
});

for await (const textPart of textStream) {
  console.log(textPart);
}

You can use thinking models with support for thinking budgets and thought summaries:

import { generateText } from 'ai';
import { google } from '@ai-sdk/google';
import { GoogleGenerativeAIProviderOptions } from '@ai-sdk/google';

const model = google('gemini-2.5-flash-preview-05-20');

const response = await generateText({
  model: model,
  prompt: 'What is the sum of the first 10 prime numbers?',
  // optional thinking config:
  providerOptions: {
    google: {
      thinkingConfig: {
        thinkingBudget: 2024,  //  [0, 24576] for 2.5 Flash, 0 = disable thinking
        includeThoughts: true
      },
    } satisfies GoogleGenerativeAIProviderOptions,
  },
});

console.log(response.text);

// Log the reasoning summary
console.log("Reasoning");
console.log(response.reasoning);

You can configure Search grounding with Google Search:

import { generateText } from 'ai';
import { google } from '@ai-sdk/google';

const model = google('gemini-2.5-flash-preview-05-20', { useSearchGrounding: true });

const { text, sources, providerMetadata } = await generateText({
  model: model,
  prompt: 'Who won the Super Bowl in 2025?',
});

console.log(text);

console.log("Sources:")
console.log(sources);

console.log("Metadata:")
console.log(providerMetadata?.google.groundingMetadata);

The AI SDK supports function calling:

import { z } from 'zod';
import { generateText, tool } from 'ai';
import { google } from '@ai-sdk/google';

const model = google('gemini-2.5-flash-preview-05-20');

const result = await generateText({
  model: model,
  prompt: 'What is the weather in San Francisco?',
  tools: {
    weather: tool({
      description: 'Get the weather in a location',
      parameters: z.object({
        location: z.string().describe('The location to get the weather for'),
      }),
      // execute: An optional async function that is called with the arguments from the tool call.
      execute: async ({ location }) => ({
        location,
        temperature: 72 + Math.floor(Math.random() * 21) - 10,
      }),
    }),
  },
  maxSteps: 5, // Optional, enables multi step calling
});

console.log(result.text)

// Inspect the different messages, this will contain messages from the different steps
// here:
// Step 1 with tool-call and tool-result messages
// Step 2 with the final generated text based on the tool result
for (const message of result.response.messages) {
    console.log(message.content);
}

See the AI SDK tool calling guide for further resources.

The AI SDK supports file inputs, e.g. PDF files:

import { generateText } from 'ai';
import { google } from '@ai-sdk/google';
import { readFile } from 'fs/promises';  // npm install @types/node

const model = google('gemini-2.0-flash');

const { text }  = await generateText({
  model: model,
  messages: [
    {
      role: 'user',
      content: [
        {
          type: 'text',
          text: 'Extract the date and price from the invoice',
        },
        {
          type: 'file',
          data: await readFile('./invoice.pdf'),
          mimeType: 'application/pdf',
        },
      ],
    },
  ],
});

console.log(text);

The AI SDK supports image inputs:

import { generateText } from 'ai';
import { google } from '@ai-sdk/google';
import { readFile } from 'fs/promises';  // npm install @types/node

const model = google('gemini-2.0-flash');

const { text }  = await generateText({
  model: model,
  messages: [
    {
      role: 'user',
      content: [
        {
          type: 'text',
          text: 'List all items from the picture',
        },
        {
          type: 'image',
          image: await readFile('./veggies.jpeg'),
          mimeType: 'image/jpeg',
        },
      ],
    },
  ],
});

console.log(text);

The AI SDK supports structured outputs:

import { generateObject } from 'ai';
import { z } from 'zod';
import { google } from '@ai-sdk/google';
import { readFile } from 'fs/promises';

const model = google('gemini-2.0-flash');

const { object } = await generateObject({
  model: model,
  schema: z.object({
    date: z.string(),
    total_gross_worth: z.number(),
    invoice_number: z.string()
  }),
  messages: [
      {
        role: 'user',
        content: [
          {
            type: 'text',
            text: 'Extract the structured data from the following PDF file',
          },
          {
            type: 'file',
            data: await readFile('./invoice.pdf'),
            mimeType: 'application/pdf',
          },
        ],
      },
    ],
});

console.log(object)

See the AI SDK structured data guide for further resources.

For further resources about building with the Gemini API, see

• Gemini API docs