Get started with the Gemini API using the Firebase AI Logic SDKs  |  Firebase AI Logic (original) (raw)

This guide shows you how to get started making calls to the Gemini APIdirectly from your app using the Firebase AI Logic client SDKs for your chosen platform.

Prerequisites

Swift

This guide assumes that you're familiar with using Xcode to develop apps for Apple platforms (like iOS).

Make sure that your development environment and Apple platforms app meet these requirements:

• Xcode 26.2 or higher
• Your app targets iOS 15 or higher, or macOS 12 or higher

Kotlin

This guide assumes that you're familiar with using Android Studio to develop apps for Android.

Make sure that your development environment and Android app meet these requirements:

• Android Studio (latest version)
• Your app targets API level 21 or higher

Java

This guide assumes that you're familiar with using Android Studio to develop apps for Android.

Make sure that your development environment and Android app meet these requirements:

• Android Studio (latest version)
• Your app targets API level 21 or higher

Web

This guide assumes that you're familiar with using JavaScript to develop web apps. This guide is framework-independent.

Make sure that your development environment and web app meet these requirements:

• (Optional) Node.js
• Modern web browser

Dart

This guide assumes that you're familiar with developing apps with Flutter.

Make sure that your development environment and Flutter app meet these requirements:

• Dart 3.2.0+

Unity

This guide assumes that you're familiar with developing games with Unity.

Make sure that your development environment and Unity game meet these requirements:

• Unity Editor 2021 LTS or newer

Check out helpful resources

Swift

Try out the quickstart app

Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if you don't have your own Apple platforms app. To use the quickstart app, you'll need toconnect it to a Firebase project.

Go to the quickstart app

Watch a video tutorial

This video demonstrates how to get started with Firebase AI Logic by building a real-world AI-powered meal planning app that generates recipes from a text prompt.

You can also download and explore the codebase for the app in the video.

View the codebase for the video's app

Kotlin

Try out the quickstart app

Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if you don't have your own Android app. To use the quickstart app, you'll need toconnect it to a Firebase project.

Go to the quickstart app

Watch a video tutorial

This video demonstrates how to get started with Firebase AI Logic by building a real-world AI-powered meal planning app that generates recipes from a text prompt.

You can also download and explore the codebase for the app in the video.

View the codebase for the video's app

Java

Try out the quickstart app

Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if you don't have your own Android app. To use the quickstart app, you'll need toconnect it to a Firebase project.

Go to the quickstart app

Watch a video tutorial

This video demonstrates how to get started with Firebase AI Logic by building a real-world AI-powered meal planning app that generates recipes from a text prompt.*

You can also download and explore the codebase for the app in the video.

View the codebase for the video's app

* This video and its app are in Kotlin, but they can still help Java developers understand the basics about how to get started withFirebase AI Logic.

Web

Try out the quickstart app

Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if you don't have your own web app. To use the quickstart app, you'll need toconnect it to a Firebase project.

Go to the quickstart app

Dart

Try out the quickstart app

Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if you don't have your own Flutter app. To use the quickstart app, you'll need toconnect it to a Firebase project.

Go to the quickstart app

Watch a video tutorial

This video demonstrates how to get started with Firebase AI Logic by building a real-world AI-powered meal planning app that generates recipes from a text prompt.

You can also download and explore the codebase for the app in the video.

View the codebase for the video's app

Unity

Try out the quickstart app

Use the quickstart app to try out the SDK quickly and see a complete implementation of various use cases. Or use the quickstart app if you don't have your own Unity game. To use the quickstart app, you'll need toconnect it to a Firebase project.

Go to the quickstart app

Step 1: Set up a Firebase project and connect your app

1. Sign into the Firebase console, and then select your Firebase project.
   Don't already have a Firebase project?
   If you don't already have a Firebase project, click the button to create a new Firebase project, and then use either of the following options:
   • Option 1: Create a wholly new Firebase project (and its underlyingGoogle Cloud project automatically) by entering a new project name in the first step of the workflow.
   • Option 2: "Add Firebase" to an existing Google Cloud project by clicking Add Firebase to Google Cloud project (at bottom of page). In the first step of the workflow, start entering the project name of the existing project, and then select the project from the displayed list.
     Complete the remaining steps of the on-screen workflow to create a Firebase project. Note that when prompted, you do not need to set upGoogle Analytics to use the Firebase AI Logic SDKs.
2. In the Firebase console, go to the Firebase AI Logic page.
3. Click Get started to launch a guided workflow that helps you set up therequired APIsand resources for your project.
4. Set up your project to use a "Gemini API" provider.
   **We recommend getting started using the Gemini Developer API.**At any point, you can alwaysset up the Vertex AI Gemini API(and its requirement for billing).
   For the Gemini Developer API, the console will enable the required APIs and create a Gemini API key in your project.
   Do not add this Gemini API key into your app's codebase. Learn more.
5. If prompted in the console's workflow, follow the on-screen instructions to register your app and connect it to Firebase.
6. Continue to the next step in this guide to add the SDK to your app.

Step 2: Add the SDK

With your Firebase project set up and your app connected to Firebase (see previous step), you can now add the Firebase AI Logic SDK to your app.

Swift

Use Swift Package Manager to install and manage Firebase dependencies.

The Firebase AI Logic library provides access to the APIs for interacting with Gemini models. The library is included as part of the Firebase SDK for Apple platforms (firebase-ios-sdk).

If you're already using Firebase, then make sure your Firebase package is v12.5.0 or later.

1. In Xcode, with your app project open, navigate toFile > Add Package Dependencies.
2. When prompted, add the Firebase Apple platforms SDK repository:

https://github.com/firebase/firebase-ios-sdk  

1. Select the latest SDK version.
2. Select the FirebaseAILogic library.

When finished, Xcode will automatically begin resolving and downloading your dependencies in the background.

Kotlin

The Firebase AI Logic SDK for Android (firebase-ai) provides access to the APIs for interacting with Gemini models.

In your module (app-level) Gradle file(like <project>/<app-module>/build.gradle.kts), add the dependency for the Firebase AI Logic library for Android. We recommend using theFirebase Android BoMto control library versioning.

dependencies { // ... other androidx dependencies

// Import the BoM for the Firebase platform implementation(platform("com.google.firebase:firebase-bom:34.12.0"))

// Add the dependency for the Firebase AI Logic library // When using the BoM, you don't specify versions in Firebase library dependencies implementation("com.google.firebase:firebase-ai") }

By using the Firebase Android BoM, your app will always use compatible versions of Firebase Android libraries.

(Alternative) Add Firebase library dependencies without using the BoM

If you choose not to use the Firebase BoM, you must specify each Firebase library version in its dependency line.

Note that if you use multiple Firebase libraries in your app, we strongly recommend using the BoM to manage library versions, which ensures that all versions are compatible.

dependencies { // Add the dependency for the Firebase AI Logic library // When NOT using the BoM, you must specify versions in Firebase library dependencies implementation("com.google.firebase:firebase-ai:17.11.0") }

Java

The Firebase AI Logic SDK for Android (firebase-ai) provides access to the APIs for interacting with Gemini models.

In your module (app-level) Gradle file(like <project>/<app-module>/build.gradle.kts), add the dependency for the Firebase AI Logic library for Android. We recommend using theFirebase Android BoMto control library versioning.

For Java, you need to add two additional libraries.

dependencies { // ... other androidx dependencies

// Import the BoM for the Firebase platform implementation(platform("com.google.firebase:firebase-bom:34.12.0"))

// Add the dependency for the Firebase AI Logic library // When using the BoM, you don't specify versions in Firebase library dependencies implementation("com.google.firebase:firebase-ai")

// Required for one-shot operations (to use ListenableFuture from Guava Android) implementation("com.google.guava:guava:31.0.1-android")

// Required for streaming operations (to use Publisher from Reactive Streams) implementation("org.reactivestreams:reactive-streams:1.0.4") }

By using the Firebase Android BoM, your app will always use compatible versions of Firebase Android libraries.

(Alternative) Add Firebase library dependencies without using the BoM

If you choose not to use the Firebase BoM, you must specify each Firebase library version in its dependency line.

Note that if you use multiple Firebase libraries in your app, we strongly recommend using the BoM to manage library versions, which ensures that all versions are compatible.

dependencies { // Add the dependency for the Firebase AI Logic library // When NOT using the BoM, you must specify versions in Firebase library dependencies implementation("com.google.firebase:firebase-ai:17.11.0") }

Web

The Firebase AI Logic library provides access to the APIs for interacting with Gemini models. The library is included as part of the Firebase JavaScript SDK for Web.

1. Install the Firebase JS SDK for Web using npm:

npm install firebase  

1. Initialize Firebase in your app:

import { initializeApp } from "firebase/app";  
// TODO(developer) Replace the following with your app's Firebase configuration  
// See: https://firebase.google.com/docs/web/learn-more#config-object  
const firebaseConfig = {  
  // ...  
};  
// Initialize FirebaseApp  
const firebaseApp = initializeApp(firebaseConfig);  

Dart

The Firebase AI Logic plugin for Flutter (firebase_ai) provides access to the APIs for interacting with Gemini models.

1. From your Flutter project directory, run the following command to install the core plugin and the Firebase AI Logic plugin:

flutter pub add firebase_core firebase_ai  

1. In your lib/main.dart file, import the Firebase core plugin, theFirebase AI Logic plugin, and the configuration file you generated earlier:

import 'package:firebase_core/firebase_core.dart';  
import 'package:firebase_ai/firebase_ai.dart';  
import 'firebase_options.dart';  

1. Also in your lib/main.dart file, initialize Firebase using theDefaultFirebaseOptions object exported by the configuration file:

await Firebase.initializeApp(  
  options: DefaultFirebaseOptions.currentPlatform,  
);  

1. Rebuild your Flutter application:

flutter run  

Unity

1. Download the Firebase Unity SDK, then extract the SDK somewhere convenient.
   The Firebase Unity SDK is not platform-specific.
2. In your open Unity project, navigate toAssets > Import Package > Custom Package.
3. From the extracted SDK, select the FirebaseAI package.
4. In the Import Unity Package window, click Import.
5. Back in the Firebase console, in the setup workflow, click Next.

Step 3: Initialize the service and create a model instance

Click your Gemini API provider to view provider-specific content and code on this page.

Before sending a prompt to a Gemini model, initialize the service for your chosen API provider and create aGenerativeModel instance.

Swift

import FirebaseAILogic

// Initialize the Gemini Developer API backend service
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case
let model = ai.generativeModel(modelName: "gemini-3-flash-preview")

Kotlin

// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
                        .generativeModel("gemini-3-flash-preview")

Java

// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
        .generativeModel("gemini-3-flash-preview");

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);

Web

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Gemini Developer API backend service
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(ai, { model: "gemini-3-flash-preview" });

Dart

import 'package:firebase_ai/firebase_ai.dart';
import 'package:firebase_core/firebase_core.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
final model =
      FirebaseAI.googleAI().generativeModel(model: 'gemini-3-flash-preview');

Unity

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case
var model = ai.GetGenerativeModel(modelName: "gemini-3-flash-preview");

Note that depending on the capability you're using, you might not always create a GenerativeModel instance. Tostream input and output using the Gemini Live API, create a LiveModel instance.

Also, after you finish this getting started guide, learn how to choose amodel for your use case and app.

Step 4: Send a prompt request to a model

You're now set up to send a prompt request to a Gemini model.

You can use generateContent() to generate text from a prompt that contains text:

Swift

import FirebaseAILogic

// Initialize the Gemini Developer API backend service
let ai = FirebaseAI.firebaseAI(backend: .googleAI())

// Create a `GenerativeModel` instance with a model that supports your use case
let model = ai.generativeModel(modelName: "gemini-3-flash-preview")

// Provide a prompt that contains text
let prompt = "Write a story about a magic backpack."

// To generate text output, call generateContent with the text input
let response = try await model.generateContent(prompt)
print(response.text ?? "No text in response.")

Kotlin

For Kotlin, the methods in this SDK are suspend functions and need to be called from a Coroutine scope.

// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
val model = Firebase.ai(backend = GenerativeBackend.googleAI())
                        .generativeModel("gemini-3-flash-preview")

// Provide a prompt that contains text
val prompt = "Write a story about a magic backpack."

// To generate text output, call generateContent with the text input
val response = model.generateContent(prompt)
print(response.text)

Java

For Java, the methods in this SDK return aListenableFuture.

// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())
        .generativeModel("gemini-3-flash-preview");

// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures model = GenerativeModelFutures.from(ai);

// Provide a prompt that contains text
Content prompt = new Content.Builder()
    .addText("Write a story about a magic backpack.")
    .build();

// To generate text output, call generateContent with the text input
ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        String resultText = result.getText();
        System.out.println(resultText);
    }

    @Override
    public void onFailure(Throwable t) {
        t.printStackTrace();
    }
}, executor);

Web

import { initializeApp } from "firebase/app";
import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai";

// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const firebaseConfig = {
  // ...
};

// Initialize FirebaseApp
const firebaseApp = initializeApp(firebaseConfig);

// Initialize the Gemini Developer API backend service
const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() });

// Create a `GenerativeModel` instance with a model that supports your use case
const model = getGenerativeModel(ai, { model: "gemini-3-flash-preview" });

// Wrap in an async function so you can use await
async function run() {
  // Provide a prompt that contains text
  const prompt = "Write a story about a magic backpack."

  // To generate text output, call generateContent with the text input
  const result = await model.generateContent(prompt);

  const response = result.response;
  const text = response.text();
  console.log(text);
}

run();

Dart

import 'package:firebase_ai/firebase_ai.dart';
import 'package:firebase_core/firebase_core.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
final model =
      FirebaseAI.googleAI().generativeModel(model: 'gemini-3-flash-preview');

// Provide a prompt that contains text
final prompt = [Content.text('Write a story about a magic backpack.')];

// To generate text output, call generateContent with the text input
final response = await model.generateContent(prompt);
print(response.text);

Unity

using Firebase;
using Firebase.AI;

// Initialize the Gemini Developer API backend service
var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI());

// Create a `GenerativeModel` instance with a model that supports your use case
var model = ai.GetGenerativeModel(modelName: "gemini-3-flash-preview");

// Provide a prompt that contains text
var prompt = "Write a story about a magic backpack.";

// To generate text output, call GenerateContentAsync with the text input
var response = await model.GenerateContentAsync(prompt);
UnityEngine.Debug.Log(response.Text ?? "No text in response.");

What else can you do?

Learn more about the supported models

Learn about themodels available for various use casesand theirquotas andpricing.

Try out other capabilities

• Learn more about generating text fromtext-only prompts, including how to stream the response.
• Generate text by prompting with various file types, likeimages,PDFs,video, andaudio.
• Build multi-turn conversations (chat).
• Generate structured output (like JSON) from both text and multimodal prompts.
• Generate and edit images from both text and multimodal prompts.
• Stream input and output (including audio) using the Gemini Live API.
• Use tools (like function calling and grounding with Google Search) to connect a Gemini model to other parts of your app and external systems and information.

Learn how to control content generation

• Understand prompt design, including best practices, strategies, and example prompts.
• Configure model parameters like temperature and maximum output tokens.
• Use safety settings to adjust the likelihood of getting responses that may be considered harmful.

You can also experiment with prompts and model configurations and even get a generated code snippet usingGoogle AI Studio.

Give feedback about your experience with Firebase AI Logic