Firebase AI Logic Basics

Overview

Firebase AI Logic is a product of Firebase that allows developers to add gen AI to their mobile and web apps using client-side SDKs. You can call Gemini models directly from your app without managing a dedicated backend. Firebase AI Logic, which was previously known as "Vertex AI for Firebase", represents the evolution of Google's AI integration platform for mobile and web developers.

It supports the two Gemini API providers:

• Gemini Developer API: It has a free tier ideal for prototyping, and pay-as-you-go for production
• Agent Platform Gemini API (formerly branded Vertex AI): Ideal for scale with enterprise-grade production readiness, requires Blaze plan

Use the Gemini Developer API as a default, and only Agent Platform Gemini API (formerly branded Vertex AI) if the application requires it.

Setup & Initialization

Prerequisites

• Before starting, ensure you have Node.js 16+ and npm installed. Install them if they aren’t already available.
• Identify the platform the user is interested in building on prior to starting: Android, iOS, Flutter or Web.
• If their platform is unsupported, Direct the user to Firebase Docs to learn how to set up AI Logic for their application (share this link with the user https://firebase.google.com/docs/ai-logic/get-started)

Installation

The library is part of the standard Firebase Web SDK.

npm install firebase@latest

If you're in a firebase directory (with a firebase.json) the currently selected project will be marked with "current" using this command:

npx -y firebase-tools@latest projects:list

Ensure there's at least one app associated with the current project

npx -y firebase-tools@latest apps:list

Initialize AI logic SDK with the init command

npx -y firebase-tools@latest init ailogic

This will automatically enable the Gemini Developer API in the Firebase console.

More info in Firebase AI Logic Getting Started

Core Capabilities

[!WARNING] CRITICAL: Use current model names: Always check the Firebase AI Logic Models documentation for the currently supported model names. Do NOT use gemini-2.0-pro or gemini-2.0-flash or other older models that are shutdown.

Text-Only Generation

Multimodal (Text + Images/Audio/Video/PDF input)

Firebase AI Logic allows Gemini models to analyze image files directly from your app. This enables features like creating captions, answering questions about images, detecting objects, and categorizing images. Beyond images, Gemini can analyze other media types like audio, video, and PDFs by passing them as inline data with their MIME type. For files larger than 20 megabytes (which can cause HTTP 413 errors as inline data), store them in Cloud Storage for Firebase and pass their URLs to the Gemini Developer API.

Chat Session (Multi-turn)

Maintain history automatically using startChat.

Streaming Responses

To improve the user experience by showing partial results as they arrive (like a typing effect), use generateContentStream instead of generateContent for faster display of results.

Text-to-Speech (TTS) Generation

Generate spoken audio directly on client devices without a custom speech backend. Firebase AI Logic supports speech synthesis using dedicated Gemini TTS models:

• Supported Models: gemini-3.8-flash-tts and gemini-3.1-flash-tts-preview
• Capabilities:
  • Single-speaker voice persona selection (voiceName) and multi-speaker dialogues (up to 2 distinct speakers) via SpeechConfig / MultiSpeakerVoiceConfig
  • Direct audio responses via responseModalities: [.audio] / ["AUDIO"]
  • Streaming speech responses with generateContentStream for low-latency playback
  • Audio directives ([Audio Profile: ...], [Scene: ...], [Director's Note: ...]) and emotional tags ([whispers], [laughs], [slowly])
  • Client-side decoding of 24 kHz 16-bit linear PCM (audio/l16) via AVAudioEngine (iOS) / AudioTrack (Android) or WAV container format (audio/x-wav) via AVAudioPlayer (iOS) / MediaPlayer (Android)

See the Text-to-Speech (TTS) Generation Guide for full implementation details, streaming patterns, and copy-paste code snippets for iOS (Swift), Android (Kotlin), and Web.

Generate Images with Nano Banana

[!WARNING] Use current Image model names: Always check the Firebase AI Logic Models documentation for the currently supported image generation (Nano Banana) model names.

• Requires an upgraded Blaze pay-as-you-go billing plan.

Search Grounding with the built in googleSearch tool

Supported Platforms and Frameworks

Supported Platforms and Frameworks include Kotlin and Java for Android, Swift for iOS, JavaScript for web apps, Dart for Flutter, and C Sharp for Unity.

Advanced Features

Structured Output (JSON)

Enforce a specific JSON schema for the response.

On-Device AI (Hybrid)

Hybrid on-device inference for web apps, where the Firebase Javascript SDK automatically checks for Gemini Nano's availability (after installation) and switches between on-device or cloud-hosted prompt execution. This requires specific steps to enable model usage in the Chrome browser, more info in the hybrid-on-device-inference documentation.

Security & Production

App Check

[!WARNING] Critical Safety Requirement: In order to use AI Logic safely, you MUST set up App Check on your app. This prevents unauthorized clients from using your API quota and accessing your backend resources.

See App Check with reCAPTCHA Enterprise for setup instructions.

App Check Debug Tokens for Local Development & CI/CD

Because App Check attestation providers (like Play Integrity or DeviceCheck) reject emulators, simulators, or CI environments, you must use App Check Debug Tokens during development and testing to bypass standard attestation.

[!WARNING] CRITICAL: Never Hardcode or Commit Debug Tokens App Check debug tokens allow clients to bypass attestation and access backend resources without a genuine device. Treat them as private secrets. Never commit debug tokens to version control or hardcode raw token strings in client code (Web, Android, iOS, or Flutter). Always inject them through local environment variables, gitignored local configurations, or CI secrets. If a token is compromised, revoke it immediately in the Firebase Console.

Local Development (Auto-Generated)

1. Configure your code's App Check provider to use the debug factory:
   • Web: Set self.FIREBASE_APPCHECK_DEBUG_TOKEN = true; before initializing App Check (generates a token in the browser console; do not hardcode secret strings in client code).
   • Android: Install DebugAppCheckProviderFactory.getInstance().
   • iOS: Set provider factory to AppCheckDebugProviderFactory().
2. Run your app in the emulator/localhost.
3. Look at your runtime debugger console / Logcat logs for the generated UUID:
   • Example: AppCheck debug token: "123a4567-b89c-12d3-e456-789012345678"
4. Register this token in the Firebase Console under Security > App Check > Apps > Manage debug tokens.

💡 iOS Tip (Prevent Debug Token Churn): On iOS, simulator resets or fresh installs erase NSUserDefaults, causing the SDK to generate a new debug token UUID each time and invalidating tokens registered in the Firebase console. To persist a stable debug token without hardcoding secrets, set the AppCheckDebugToken environment variable in your unshared Xcode Scheme (Edit Scheme -> Run -> Arguments -> Environment Variables -> Add AppCheckDebugToken = <YOUR_DEBUG_TOKEN>). The iOS SDK automatically reads this environment variable at runtime. If setting it programmatically, load it dynamically from a gitignored local file rather than hardcoding the token literal into source code.

💡 Web Tip (Prevent Debug Token Churn): Clearing site data, using incognito/private windows, or switching browsers generates a new debug token when self.FIREBASE_APPCHECK_DEBUG_TOKEN = true is used. To persist a stable debug token without leaking secrets into client bundles, load it from a gitignored local environment file (e.g., using process.env.NEXT_PUBLIC_APP_CHECK_DEBUG_TOKEN for Next.js or import.meta.env.VITE_APPCHECK_DEBUG_TOKEN for Vite). Never hardcode literal token strings into JavaScript or TypeScript source files.

💡 Android Tip (Prevent Debug Token Churn): Emulator resets or clearing app storage erase SharedPreferences, causing DebugAppCheckProviderFactory to print a new debug token in Logcat. To keep a stable debug token across test runs and builds without committing it to git, store it in gitignored local.properties and inject it in build.gradle.kts if non-empty: if (appCheckDebugToken.isNotEmpty()) { testInstrumentationRunnerArguments["firebaseAppCheckDebugSecret"] = appCheckDebugToken }.

CI/CD Pipelines (Pre-Provisioned)

1. Generate and register a new debug token in the Firebase Console under Security > App Check > Apps > Manage debug tokens.
2. Add this token string as an encrypted secret in your CI system (e.g. APP_CHECK_DEBUG_TOKEN).
3. Configure your build to pass this secret as an environment variable to the SDK during test execution (e.g. self.FIREBASE_APPCHECK_DEBUG_TOKEN = process.env.APP_CHECK_DEBUG_TOKEN).

Remote Config

Consider that you do not need to hardcode model names (e.g., a specific model version string). Use Firebase Remote Config to update model versions dynamically without deploying new client code. See Changing model names remotely

[!WARNING] CRITICAL: Backend Provisioning Required For all platforms (Flutter, Android, iOS, Web), you MUST run npx firebase-tools init ailogic to provision the service. flutterfire configure ONLY handles client configuration and does NOT enable the AI service, leading to PERMISSION_DENIED errors.

Initialization Code References

• Web Modular API
  • Provider: Gemini Developer API
  • Reference: usage_patterns_web.md
• Android (Kotlin)
  • Provider: Gemini Developer API
  • Reference: usage_patterns_android.md
• iOS (Swift)
  • Provider: Gemini Developer API
  • Reference: ios_setup.md
• Flutter (Dart)
  • Provider: Gemini Developer API
  • Reference: flutter_setup.md

[!WARNING] CRITICAL: Use current model names: Always check the Firebase AI Logic Models documentation for the currently supported model names. Do NOT use gemini-2.0-pro or gemini-2.0-flash or other older models that are shutdown.

References

Web SDK code examples and usage patterns iOS SDK code examples and usage patterns Flutter SDK code examples and usage patterns

Android (Kotlin) SDK usage patterns Client-side Text-to-Speech (TTS) generation