Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.
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Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.
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.
Version history
It supports the two Gemini API providers:
Use the Gemini Developer API as a default, and only Agent Platform Gemini API (formerly branded Vertex AI) if the application requires it.
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
[!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-proorgemini-2.0-flashor other older models that are shutdown.
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.
Maintain history automatically using startChat.
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.
[!WARNING] Use current Image model names: Always check the Firebase AI Logic Models documentation for the currently supported image generation (Nano Banana) model names.
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.
Enforce a specific JSON schema for the response.
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.
[!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.
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.
self.FIREBASE_APPCHECK_DEBUG_TOKEN = true; before
initializing App Check.DebugAppCheckProviderFactory.getInstance().AppCheckDebugProviderFactory().AppCheck debug token: "123a4567-b89c-12d3-e456-789012345678"APP_CHECK_DEBUG_TOKEN).self.FIREBASE_APPCHECK_DEBUG_TOKEN = process.env.APP_CHECK_DEBUG_TOKEN).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 ailogicto provision the service.flutterfire configureONLY handles client configuration and does NOT enable the AI service, leading toPERMISSION_DENIEDerrors.
[!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-proorgemini-2.0-flashor other older models that are shutdown.
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
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