Firebase Genkit

When to use this skill

• AI workflow orchestration: Building multi-step AI pipelines with type-safe inputs/outputs
• Flow-based APIs: Wrapping LLM calls into deployable HTTP endpoints
• Tool calling / agents: Equipping models with custom tools and implementing agentic loops
• RAG pipelines: Retrieval-augmented generation with vector databases (Pinecone, pgvector, Firestore, Chroma, etc.)
• Multi-agent systems: Coordinating multiple specialized AI agents
• Streaming responses: Real-time token-by-token output for chat or long-form content
• Firebase/Cloud Run deployment: Deploying AI functions to Google Cloud
• Prompt management: Managing prompts as versioned .prompt files with Dotprompt

Installation & Setup

Step 1: Install the Genkit CLI

# npm (recommended for JavaScript/TypeScript)
npm install -g genkit-cli

# macOS/Linux binary
curl -sL cli.genkit.dev | bash

Step 2: Create a TypeScript project

mkdir my-genkit-app && cd my-genkit-app
npm init -y
npm pkg set type=module
npm install -D typescript tsx
npx tsc --init
mkdir src && touch src/index.ts

Step 3: Install Genkit core and a model plugin

# Core + Google AI (Gemini) — free tier, no credit card required
npm install genkit @genkit-ai/google-genai

# Or: Vertex AI (requires GCP project)
npm install genkit @genkit-ai/vertexai

# Or: OpenAI
npm install genkit genkitx-openai

# Or: Anthropic (Claude)
npm install genkit genkitx-anthropic

# Or: Ollama (local models)
npm install genkit genkitx-ollama

Step 4: Configure API Key

# Google AI (Gemini)
export GEMINI_API_KEY=your_key_here

# OpenAI
export OPENAI_API_KEY=your_key_here

# Anthropic
export ANTHROPIC_API_KEY=your_key_here

Core Concepts

Initializing Genkit

import { googleAI } from '@genkit-ai/google-genai';
import { genkit } from 'genkit';

const ai = genkit({
  plugins: [googleAI()],
  model: googleAI.model('gemini-2.5-flash'), // default model
});

Defining Flows

Flows are the core primitive: type-safe, observable, deployable AI functions.

import { genkit, z } from 'genkit';
import { googleAI } from '@genkit-ai/google-genai';

const ai = genkit({ plugins: [googleAI()] });

// Input/output schemas with Zod
const SummaryInputSchema = z.object({
  text: z.string().describe('Text to summarize'),
  maxWords: z.number().optional().default(100),
});

const SummaryOutputSchema = z.object({
  summary: z.string(),
  keyPoints: z.array(z.string()),
});

export const summarizeFlow = ai.defineFlow(
  {
    name: 'summarizeFlow',
    inputSchema: SummaryInputSchema,
    outputSchema: SummaryOutputSchema,
  },
  async ({ text, maxWords }) => {
    const { output } = await ai.generate({
      model: googleAI.model('gemini-2.5-flash'),
      prompt: `Summarize the following text in at most ${maxWords} words and extract key points:\n\n${text}`,
      output: { schema: SummaryOutputSchema },
    });

    if (!output) throw new Error('No output generated');
    return output;
  }
);

// Call the flow
const result = await summarizeFlow({
  text: 'Long article content here...',
  maxWords: 50,
});
console.log(result.summary);

Generating Content

// Simple text generation
const { text } = await ai.generate({
  model: googleAI.model('gemini-2.5-flash'),
  prompt: 'Explain quantum computing in one sentence.',
});

// Structured output
const { output } = await ai.generate({
  prompt: 'List 3 programming languages with their use cases',
  output: {
    schema: z.object({
      languages: z.array(z.object({
        name: z.string(),
        useCase: z.string(),
      })),
    }),
  },
});

// With system prompt
const { text: response } = await ai.generate({
  system: 'You are a senior TypeScript engineer. Be concise.',
  prompt: 'What is the difference between interface and type in TypeScript?',
});

// Multimodal (image + text)
const { text: description } = await ai.generate({
  prompt: [
    { text: 'What is in this image?' },
    { media: { url: 'https://example.com/image.jpg', contentType: 'image/jpeg' } },
  ],
});

Streaming Flows

export