Client-side Gemini integration for web apps with multimodal inference, streaming, and on-device hybrid execution.
Works with
Supports text-only and multimodal inputs (images, audio, video, PDFs); files over 20 MB route through Cloud Storage
Includes chat sessions with automatic history, streaming responses for real-time display, and structured JSON output enforcement
Offers hybrid on-device inference via Gemini Nano in Chrome, with automatic fallback to cloud execution
Requires App Check for
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfirebase-ai-logicExecute the skills CLI command in your project's root directory to begin installation:
Fetches firebase-ai-logic from firebase/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate firebase-ai-logic. Access via /firebase-ai-logic in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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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:
Use the Gemini Developer API as a default, and only Vertex AI Gemini API if the application requires it.
The library is part of the standard Firebase Web SDK.
npm install -g 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 # Choose AI logic
This will automatically enable the Gemini Developer API in the Firebase console.
More info in Firebase AI Logic Getting Started
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.
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.
Recommended: The developer must enable Firebase App Check to prevent unauthorized clients from using their API quota. see App-check recaptcha enterprise.
Consider that you do not need to hardcode model names (e.g., gemini-flash-lite-latest). Use Firebase Remote Config to update model versions dynamically without deploying new client code. See Changing model names remotely
| Language, Framework, Platform | Gemini API provider | Context URL |
|---|---|---|
| Web Modular API | Gemini Developer API (Developer API) | firebase://docs/ai-logic/get-started |
Always use the most recent version of Gemini (gemini-flash-latest) unless another model is requested by the docs or the user. DO NOT USE gemini-1.5-flash
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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I recommend firebase-ai-logic for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
firebase-ai-logic has been reliable in day-to-day use. Documentation quality is above average for community skills.
firebase-ai-logic reduced setup friction for our internal harness; good balance of opinion and flexibility.
firebase-ai-logic has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for firebase-ai-logic matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: firebase-ai-logic is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: firebase-ai-logic is focused, and the summary matches what you get after install.
Keeps context tight: firebase-ai-logic is the kind of skill you can hand to a new teammate without a long onboarding doc.
firebase-ai-logic fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for firebase-ai-logic matched our evaluation — installs cleanly and behaves as described in the markdown.
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