genkit

Build production-ready AI workflows using Firebase Genkit. Use when creating flows, tool-calling agents, RAG pipelines, multi-agent systems, or deploying AI to Firebase/Cloud Run. Supports TypeScript, Go, and Python with Gemini, OpenAI, Anthropic, Ollama, and Vertex AI plugins.

Install with Tessl CLI

npx tessl i github:supercent-io/skills-template --skill genkit

What are skills?

1.13x

Quality

88%

Does it follow best practices?

Impact

92%

1.13x

Average score across 3 eval scenarios

SKILL.md

Review

Evals

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 const streamingFlow = ai.defineFlow(
  {
    name: 'streamingFlow',
    inputSchema: z.object({ topic: z.string() }),
    streamSchema: z.string(),        // type of each chunk
    outputSchema: z.object({ full: z.string() }),
  },
  async ({ topic }, { sendChunk }) => {
    const { stream, response } = ai.generateStream({
      prompt: `Write a detailed essay about ${topic}.`,
    });

    for await (const chunk of stream) {
      sendChunk(chunk.text);         // stream each token to client
    }

    const { text } = await response;
    return { full: text };
  }
);

// Client-side consumption
const stream = streamingFlow.stream({ topic: 'AI ethics' });
for await (const chunk of stream.stream) {
  process.stdout.write(chunk);
}
const finalOutput = await stream.output;

Tool Calling (Agents)

import { z } from 'genkit';

// Define tools
const getWeatherTool = ai.defineTool(
  {
    name: 'getWeather',
    description: 'Get current weather for a city',
    inputSchema: z.object({ city: z.string() }),
    outputSchema: z.object({ temp: z.number(), condition: z.string() }),
  },
  async ({ city }) => {
    // Call real weather API
    return { temp: 22, condition: 'sunny' };
  }
);

const searchWebTool = ai.defineTool(
  {
    name: 'searchWeb',
    description: 'Search the web for information',
    inputSchema: z.object({ query: z.string() }),
    outputSchema: z.string(),
  },
  async ({ query }) => {
    // Call search API
    return `Search results for: ${query}`;
  }
);

// Agent flow with tools
export const agentFlow = ai.defineFlow(
  {
    name: 'agentFlow',
    inputSchema: z.object({ question: z.string() }),
    outputSchema: z.string(),
  },
  async ({ question }) => {
    const { text } = await ai.generate({
      prompt: question,
      tools: [getWeatherTool, searchWebTool],
      returnToolRequests: false, // auto-execute tools
    });
    return text;
  }
);

Prompts with Dotprompt

Manage prompts as versioned .prompt files:

# src/prompts/summarize.prompt
---
model: googleai/gemini-2.5-flash
input:
  schema:
    text: string
    style?: string
output:
  schema:
    summary: string
    sentiment: string
---
Summarize the following text in a {{style, default: "professional"}} tone:

{{text}}

Return JSON with summary and sentiment (positive/negative/neutral).

// Load and use dotprompt
const summarizePrompt = ai.prompt('summarize');
const { output } = await summarizePrompt({
  text: 'Article content here...',
  style: 'casual',
});

RAG — Retrieval-Augmented Generation

import { devLocalVectorstore } from '@genkit-ai/dev-local-vectorstore';
import { textEmbedding004 } from '@genkit-ai/google-genai';

const ai = genkit({
  plugins: [
    googleAI(),
    devLocalVectorstore([{
      indexName: 'documents',
      embedder: textEmbedding004,
    }]),
  ],
});

// Index documents
await ai.index({
  indexer: devLocalVectorstoreIndexer('documents'),
  docs: [
    { content: [{ text: 'Document 1 content...' }], metadata: { source: 'doc1' } },
    { content: [{ text: 'Document 2 content...' }], metadata: { source: 'doc2' } },
  ],
});

// RAG flow
export const ragFlow = ai.defineFlow(
  {
    name: 'ragFlow',
    inputSchema: z.object({ question: z.string() }),
    outputSchema: z.string(),
  },
  async ({ question }) => {
    // Retrieve relevant documents
    const docs = await ai.retrieve({
      retriever: devLocalVectorstoreRetriever('documents'),
      query: question,
      options: { k: 3 },
    });

    // Generate answer grounded in retrieved docs
    const { text } = await ai.generate({
      system: 'Answer questions using only the provided context.',
      prompt: question,
      docs,
    });

    return text;
  }
);

Chat Sessions

export const chatFlow = ai.defineFlow(
  {
    name: 'chatFlow',
    inputSchema: z.object({ message: z.string(), sessionId: z.string() }),
    outputSchema: z.string(),
  },
  async ({ message, sessionId }) => {
    const session = ai.loadSession(sessionId) ?? ai.createSession({ sessionId });
    const chat = session.chat({
      system: 'You are a helpful assistant.',
    });

    const { text } = await chat.send(message);
    return text;
  }
);

Multi-Agent Systems

// Specialist agents
const researchAgent = ai.defineFlow(
  { name: 'researchAgent', inputSchema: z.string(), outputSchema: z.string() },
  async (query) => {
    const { text } = await ai.generate({
      system: 'You are a research expert. Gather facts and cite sources.',
      prompt: query,
      tools: [searchWebTool],
    });
    return text;
  }
);

const writerAgent = ai.defineFlow(
  { name: 'writerAgent', inputSchema: z.string(), outputSchema: z.string() },
  async (brief) => {
    const { text } = await ai.generate({
      system: 'You are a professional writer. Write clear, engaging content.',
      prompt: brief,
    });
    return text;
  }
);

// Orchestrator delegates to specialists
export const contentPipelineFlow = ai.defineFlow(
  {
    name: 'contentPipelineFlow',
    inputSchema: z.object({ topic: z.string() }),
    outputSchema: z.string(),
  },
  async ({ topic }) => {
    const research = await researchAgent(`Research: ${topic}`);
    const article = await writerAgent(`Write an article based on: ${research}`);
    return article;
  }
);

Developer Tools

CLI Commands

# Start Developer UI + connect to your app
genkit start -- npx tsx --watch src/index.ts
genkit start -o -- npx tsx src/index.ts    # auto-open browser

# Run a specific flow from CLI
genkit flow:run summarizeFlow '{"text": "Hello world", "maxWords": 10}'

# Run with streaming output
genkit flow:run streamingFlow '{"topic": "AI"}' -s

# Evaluate a flow
genkit eval:flow ragFlow --input eval-inputs.json

# View all commands
genkit --help

# Disable analytics telemetry
genkit config set analyticsOptOut true

Developer UI

The Developer UI runs at http://localhost:4000 and provides:

Flow runner: Execute flows with custom JSON inputs
Trace inspector: Visualize each step (generate, embed, retrieve, tool calls)
Prompt playground: Test prompts interactively
Model tester: Compare outputs across different models
Evaluator: Run evaluation datasets against flows

# Add npm script for convenience
# package.json
"scripts": {
  "genkit:dev": "genkit start -- npx tsx --watch src/index.ts"
}

npm run genkit:dev

Deployment

Firebase Cloud Functions

import { onCallGenkit } from 'firebase-functions/https';
import { defineSecret } from 'firebase-functions/params';

const apiKey = defineSecret('GOOGLE_AI_API_KEY');

export const summarize = onCallGenkit(
  { secrets: [apiKey] },
  summarizeFlow
);

firebase deploy --only functions

Express.js Server

import express from 'express';
import { expressHandler } from 'genkit/express';

const app = express();
app.use(express.json());

app.post('/summarize', expressHandler(summarizeFlow));
app.post('/chat', expressHandler(chatFlow));

app.listen(3000, () => console.log('Server running on port 3000'));

Cloud Run

# Build and deploy
gcloud run deploy genkit-app \
  --source . \
  --region us-central1 \
  --set-env-vars GEMINI_API_KEY=$GEMINI_API_KEY

Supported Plugins

Model Providers

Plugin	Package	Models
Google AI	`@genkit-ai/google-genai`	Gemini 2.5 Flash/Pro
Vertex AI	`@genkit-ai/vertexai`	Gemini, Imagen, Claude
OpenAI	`genkitx-openai`	GPT-4o, o1, etc.
Anthropic	`genkitx-anthropic`	Claude 3.5/3
AWS Bedrock	`genkitx-aws-bedrock`	Claude, Titan, etc.
Ollama	`genkitx-ollama`	Local models
DeepSeek	`genkitx-deepseek`	DeepSeek-R1
xAI (Grok)	`genkitx-xai`	Grok models

Vector Databases

Plugin	Package
Dev Local (testing)	`@genkit-ai/dev-local-vectorstore`
Pinecone	`genkitx-pinecone`
pgvector	`genkitx-pgvector`
Chroma	`genkitx-chroma`
Cloud Firestore	`@genkit-ai/firebase`
LanceDB	`genkitx-lancedb`

Best Practices

Always define input/output schemas — Use Zod objects for Dev UI labeled fields and API safety
Use flows for all AI logic — Even simple calls; flows give you tracing and deployment for free
Store API keys in environment variables — Never hardcode; use Firebase Secrets for production
Use ai.run() to trace custom steps — Wrap non-Genkit code in ai.run() for trace visibility
Stream long-form content — Use defineFlow with streamSchema + sendChunk for better UX
Separate concerns with agents — Specialized subflows > one monolithic flow
Use Dotprompt for team prompts — .prompt files enable versioning, review, and reuse

Constraints

Must Do

Define schemas for all flow inputs and outputs
Handle null output from generate() — throw meaningful errors
Set GENKIT_ENV=dev when running flows separately from the dev server
Use onCallGenkit (not raw Cloud Functions) when deploying to Firebase

Must Not Do

Never hardcode API keys in source code
Do not use generate() outside a flow if you need tracing/observability
Do not call genkit start without a command — always pass -- <your-run-command>
Avoid blocking the event loop in tool handlers — use async/await

References

Examples

Example 1: Minimal Flow

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

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

export const helloFlow = ai.defineFlow(
  {
    name: 'helloFlow',
    inputSchema: z.object({ name: z.string() }),
    outputSchema: z.string(),
  },
  async ({ name }) => {
    const { text } = await ai.generate(`Say hello to ${name} in a creative way.`);
    return text;
  }
);

// Run it
const greeting = await helloFlow({ name: 'World' });
console.log(greeting);

Example 2: Full RAG + Agent Pipeline

import { googleAI, textEmbedding004 } from '@genkit-ai/google-genai';
import { devLocalVectorstore } from '@genkit-ai/dev-local-vectorstore';
import { genkit, z } from 'genkit';

const ai = genkit({
  plugins: [
    googleAI(),
    devLocalVectorstore([{ indexName: 'kb', embedder: textEmbedding004 }]),
  ],
});

// Index knowledge base documents
const indexKnowledgeBase = ai.defineFlow(
  { name: 'indexKB', inputSchema: z.array(z.string()) },
  async (texts) => {
    await ai.index({
      indexer: devLocalVectorstoreIndexer('kb'),
      docs: texts.map(text => ({ content: [{ text }] })),
    });
  }
);

// Answer questions using RAG
export const answerFlow = ai.defineFlow(
  {
    name: 'answerFlow',
    inputSchema: z.object({ question: z.string() }),
    outputSchema: z.object({ answer: z.string(), sources: z.number() }),
  },
  async ({ question }) => {
    const docs = await ai.retrieve({
      retriever: devLocalVectorstoreRetriever('kb'),
      query: question,
      options: { k: 5 },
    });

    const { text } = await ai.generate({
      system: 'Answer only from the provided context. If unsure, say so.',
      prompt: question,
      docs,
    });

    return { answer: text, sources: docs.length };
  }
);

Example 3: Multi-Model Comparison

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

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

export const compareModelsFlow = ai.defineFlow(
  {
    name: 'compareModelsFlow',
    inputSchema: z.object({ prompt: z.string() }),
    outputSchema: z.object({ gemini: z.string(), gpt4o: z.string() }),
  },
  async ({ prompt }) => {
    const [geminiResult, gptResult] = await Promise.all([
      ai.generate({ model: googleAI.model('gemini-2.5-flash'), prompt }),
      ai.generate({ model: 'openai/gpt-4o', prompt }),
    ]);

    return {
      gemini: geminiResult.text,
      gpt4o: gptResult.text,
    };
  }
);

Repository: supercent-io/skills-template
Commit: 2311988
Last updated: about 19 hours ago
Created: about 19 hours ago

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