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Get started with the Gemini API using the Firebase AI Logic SDKs Stay organized with collections
Save and categorize content based on your preferences.
This guide shows you how to get started making calls to the Gemini API
directly 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:
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 to connect 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 to connect 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 to connect 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 with Firebase 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 to connect 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 to connect 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 to connect it to a Firebase project
.
Go to the quickstart app
Step 1 : Set up a Firebase project and connect your app
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 underlying Google 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 up Google Analytics
to use the Firebase AI Logic
SDKs.
In the Firebase
console, go to AI Services > AI Logic
.
Click Get started to launch a guided workflow that helps you set up the required APIs
and resources for your project.
Set up your project to use a " Gemini API
" provider.
We recommend getting started using the Gemini Developer API
. At any point, you can always set 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.
If prompted in the console's workflow, follow the on-screen instructions to
register your app and connect it to Firebase.
Continue to the next step in this guide to add the SDK to your app.
Important: To help secure against abuse of the Gemini API
, it's critical
that you set up Firebase App Check
as early as possible.
You should enable and enforce App Check
before
you commit your app to a
publicly available source code control system, share your app, or make your app
publicly available.
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.
Note: Visit our installation guide
to learn
about the different ways you can add Firebase SDKs to your Apple project.
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.
In Xcode, with your app project open, navigate to File > Add Package Dependencies .
When prompted, add the Firebase Apple platforms SDK repository:
https://github.com/firebase/firebase-ios-sdk
Select the latest SDK version.
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 the Firebase Android BoM
to control library versioning.
dependencies
{
// ... other androidx dependencies
// Import the BoM
for the Firebase platform implementation
(
platform
(
"com.google.firebase:firebase-bom:34.14.1"
))
// 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:17.12.1"
)
}
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 the Firebase Android BoM
to 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:34.14.1"
))
// 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:17.12.1"
)
}
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.
Install the Firebase JS SDK for Web using npm:
npm install firebase
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.
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
In your lib/main.dart
file, import the Firebase core plugin, the Firebase 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'
;
Also in your lib/main.dart
file, initialize Firebase using the DefaultFirebaseOptions
object exported by the configuration file:
await
Firebase
.
initializeApp
(
options:
DefaultFirebaseOptions
.
currentPlatform
,
);
Rebuild your Flutter application:
flutter run
Unity
Download the Firebase
Unity
SDK
, then extract the SDK somewhere
convenient.
The Firebase
Unity
SDK is not platform-specific.
In your open Unity project, navigate to Assets > Import Package > Custom Package .
From the extracted SDK, select the FirebaseAI
package.
In the Import Unity Package
window, click Import .
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.
Gemini Developer API
Vertex AI Gemini API
Before sending a prompt to a Gemini
model,
initialize the service for your chosen API provider and create a GenerativeModel
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-3.5-flash"
)
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-3.5-flash"
)
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-3.5-flash"
);
// 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-3.5-flash"
});
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-3.5-flash'
);
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-3.5-flash"
);
Note that depending on the capability you're using, you might not always
create a GenerativeModel
instance . To stream input and output using the Gemini Live API
,
create a LiveModel
instance.
Also, after you finish this getting started guide, learn how to choose a model
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-3.5-flash"
)
// 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-3.5-flash"
)
// 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 a ListenableFuture
.
// 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-3.5-flash"
);
// 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-3.5-flash"
});
// 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-3.5-flash'
);
// 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-3.5-flash"
);
// 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."
);
The Gemini API
can also stream responses for faster
interactions, as well as handle multimodal prompts that
include content like images, video, audio, and PDFs. Later on this page, find
links to guides for various capabilities
of the Gemini API
. What else can you do?
Learn more about the supported models
Learn about the models available for various use cases
and their quotas
and pricing
. Try out other capabilities
Learn how to control content generation
You can also experiment with prompts and model configurations and even get a
generated code snippet using Google AI Studio
.
Give feedback
about your experience with Firebase AI Logic
Send feedback
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License
, and code samples are licensed under the Apache 2.0 License
. For details, see the Google Developers Site Policies
. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2026-06-12 UTC.
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