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firebase-ai-logic-basics

Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.

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价格未确认★ 434 GitHub Stars目录更新于 · 2026年9月5日agent-skill

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Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.

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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 -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 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.

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.

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.
    • 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.
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

| Language, | Gemini API | Context URL | : Framework, : provider : : : Platform : : : | :---------- | :--------- | :---------------------------------------------- | | Web Modular | Gemini | firebase://docs/ai-logic/get-started | : API : Developer : : : : API : : : : (Developer : : : : API) : : | iOS (Swift) | Gemini | ios_setup.md | : : Developer : : : : API : : | Flutter | Gemini | flutter_setup.md | : (Dart) : Developer : : : : API : :

[!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

查看来源 ↗
文件元数据
name: firebase-ai-logic-basics
description: Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.
version: 1.0.1
metadata:
  category: AiAndMachineLearning
查看原始文本
---
name: firebase-ai-logic-basics
description: Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.
version: 1.0.1
metadata:
  category: AiAndMachineLearning
---

# 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 -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 ailogic`

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

More info in
[Firebase AI Logic Getting Started](https://firebase.google.com/docs/ai-logic/get-started.md.txt)

## Core Capabilities

> [!WARNING] **CRITICAL: Use current model names:** Always check the
> [Firebase AI Logic Models documentation](https://firebase.google.com/docs/ai-logic/models.md.txt)
> 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.

### Generate Images with Nano Banana

> [!WARNING] **Use current Image model names:** Always check the
> [Firebase AI Logic Models documentation](https://firebase.google.com/docs/ai-logic/models.md.txt)
> 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](https://firebase.google.com/docs/ai-logic/hybrid-on-device-inference.md.txt).

## 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](https://firebase.google.com/docs/app-check/web/recaptcha-enterprise-provider.md.txt)
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.

##### 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.
    *   **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**.

##### 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](https://firebase.google.com/docs/ai-logic/change-model-name-remotely.md.txt)

> [!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

| Language,   | Gemini API | Context URL                                     |
: Framework,  : provider   :                                                 :
: Platform    :            :                                                 :
| :---------- | :--------- | :---------------------------------------------- |
| Web Modular | Gemini     | firebase://docs/ai-logic/get-started            |
: API         : Developer  :                                                 :
:             : API        :                                                 :
:             : (Developer :                                                 :
:             : API)       :                                                 :
| iOS (Swift) | Gemini     | [ios_setup.md](references/ios_setup.md)         |
:             : Developer  :                                                 :
:             : API        :                                                 :
| Flutter     | Gemini     | [flutter_setup.md](references/flutter_setup.md) |
: (Dart)      : Developer  :                                                 :
:             : API        :                                                 :

> [!WARNING] **CRITICAL: Use current model names:** Always check the
> [Firebase AI Logic Models documentation](https://firebase.google.com/docs/ai-logic/models.md.txt)
> 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](references/usage_patterns_web.md)
[iOS SDK code examples and usage patterns](references/ios_setup.md)
[Flutter SDK code examples and usage patterns](references/flutter_setup.md)

[Android (Kotlin) SDK usage patterns](references/usage_patterns_android.md)

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来源仓库
firebase/agent-skills
许可证
Apache-2.0
版本
1.0.1
最近 GitHub 推送
2026年9月2日
目录更新于
2026年9月5日
技能指令路径
skills/firebase-ai-logic-basics/SKILL.md @ a0b4e143f40c

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      "repository": "https://github.com/firebase/agent-skills/tree/main/skills/firebase-ai-logic-basics",
      "install": "npx skills add firebase/agent-skills --skill firebase-ai-logic-basics",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
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      "success_rate": null,
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      "risk_blocked": 0,
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      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
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      "agent-skill"
    ],
    "known_risks": [
      "The skill description focuses on web applications, but the content covers multiple platforms (Android, iOS, Flutter, Web). This is a minor inconsistency but not a functional issue.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: credential or environment access, external package install surface",
      "Permission surface: secrets or environment access, shell or command execution"
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  "agent_proven": {
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    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
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      "successfulOutcomes": 0,
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      "installAttempts": 0,
      "installSuccessRate": null,
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      "recentSuccessRate": null,
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      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 74,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "The skill description focuses on web applications, but the content covers multiple platforms (Android, iOS, Flutter, Web). This is a minor inconsistency but not a functional issue.",
      "The SKILL.md excerpt is truncated in the review, but the provided content is comprehensive and well-structured.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: credential or environment access, external package install surface",
      "Permission surface: secrets or environment access, shell or command execution"
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    "auto_install_policy": "block",
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    "human_review_required": true,
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  "quality": {
    "score": 70,
    "label": "Strong"
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    "track": "Design and creative production",
    "scenario": "Multimodal media",
    "maintenance": "1mo since push",
    "risk": "Needs review"
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      "slug": "projectdiscovery-nuclei",
      "name": "Nuclei",
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      "stars": 29159,
      "install_command": "",
      "trust_score": 91,
      "audit_score": 91
    }
  ],
  "do_not_use_when": [
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    "production agents without a repository review",
    "The skill description focuses on web applications, but the content covers multiple platforms (Android, iOS, Flutter, Web). This is a minor inconsistency but not a functional issue.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The SKILL.md excerpt is truncated in the review, but the provided content is comprehensive and well-structured.",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use firebase-ai-logic-basics in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
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      "Audit: 74/100 Needs review",
      "Safety: 26/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
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    "expected_agent_output": {
      "selected_skill": "firebase-firebase-ai-logic-basics (firebase-ai-logic-basics)",
      "install_command": "npx skills add firebase/agent-skills --skill firebase-ai-logic-basics",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
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  "outcome_feedback": {
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    "method": "POST",
    "requires_resolve_event_id": true,
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    "expected_outcomes": [
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      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
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    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "firebase-firebase-ai-logic-basics",
      "task": "Use firebase-ai-logic-basics in an agent workflow",
      "agent": "codex",
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      "task_success": true,
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      "error_type": null,
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      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
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  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/firebase-firebase-ai-logic-basics",
    "api": "https://www.openagentskill.com/api/agent/skills/firebase-firebase-ai-logic-basics",
    "audit": "https://www.openagentskill.com/skills/firebase-firebase-ai-logic-basics/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=firebase-firebase-ai-logic-basics&task=Use%20firebase-ai-logic-basics%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20firebase-ai-logic-basics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20firebase-ai-logic-basics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/firebase-firebase-ai-logic-basics/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/firebase-firebase-ai-logic-basics"
  }
}

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