Get started with Genkit

This guide shows you how to get started with Genkit in your preferred language and test it in the Developer UI.

Prerequisites

Before you begin, make sure your environment meets these requirements:

Node.js v20 or later
npm

This guide assumes you’re already familiar with building Node.js applications.

Go 1.24 or later (Download and install)

This guide assumes you’re already familiar with building Go applications.

Python 3.10 or later (Download and install)

Set up your project

Create a new Node.js project and configure TypeScript:

mkdir my-genkit-app
cd my-genkit-app
npm init -y

# Set up your source directory
mkdir src
touch src/index.ts

# Install and configure TypeScript
npm install -D typescript tsx
npx tsc --init

This sets up your project structure and a TypeScript entry point at src/index.ts.

Initialize a new Go project directory:

mkdir genkit-intro && cd genkit-intro

go mod init example/genkit-intro

Create a main.go file for your application entry point.

Create a new project directory and set up a virtual environment:

mkdir genkit-intro && cd genkit-intro

(Recommended) Create a Python virtual environment:

python3 -m venv .

Activate the virtual environment if necessary:

source bin/activate  # for bash

Install Genkit packages

First, install the Genkit CLI. This gives you access to local developer tools, including the Developer UI:

curl -sL cli.genkit.dev | bash

First, install the Genkit CLI globally. This gives you access to local developer tools, including the Developer UI:

npm install -g genkit-cli

Then, add the following packages to your project:

npm install genkit @genkit-ai/google-genai

genkit provides Genkit core capabilities.
@genkit-ai/google-genai provides access to the Google AI Gemini models.

Then, install the Genkit package for Go:

go get github.com/firebase/genkit/go

This provides Genkit core capabilities and access to Google AI Gemini models.

Then, install the required Python packages:

pip3 install genkit
pip3 install genkit-plugin-google-genai

Or create a requirements.txt file:

genkit
genkit-plugin-google-genai

and run:

pip3 install -r requirements.txt

Configure your model API key

Genkit can work with multiple model providers. This guide uses the Gemini API, which offers a generous free tier and doesn’t require a credit card to get started.

To use it, you’ll need an API key from Google AI Studio:

Get a Gemini API Key

Once you have a key, set the GEMINI_API_KEY environment variable:

export GEMINI_API_KEY=<your API key>

Create your first application

A flow is a special Genkit function with built-in observability, type safety, and tooling integration.

Update src/index.ts with the following:

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

// Initialize Genkit with the Google AI plugin
const ai = genkit({
  plugins: [googleAI()],
  model: googleAI.model('gemini-2.5-flash', {
    temperature: 0.8,
  }),
});

// Define input schema
const RecipeInputSchema = z.object({
  ingredient: z.string().describe('Main ingredient or cuisine type'),
  dietaryRestrictions: z.string().optional().describe('Any dietary restrictions'),
});

// Define output schema
const RecipeSchema = z.object({
  title: z.string(),
  description: z.string(),
  prepTime: z.string(),
  cookTime: z.string(),
  servings: z.number(),
  ingredients: z.array(z.string()),
  instructions: z.array(z.string()),
  tips: z.array(z.string()).optional(),
});

// Define a recipe generator flow
export const recipeGeneratorFlow = ai.defineFlow(
  {
    name: 'recipeGeneratorFlow',
    inputSchema: RecipeInputSchema,
    outputSchema: RecipeSchema,
  },
  async (input) => {
    // Create a prompt based on the input
    const prompt = `Create a recipe with the following requirements:
      Main ingredient: ${input.ingredient}
      Dietary restrictions: ${input.dietaryRestrictions || 'none'}`;

    // Generate structured recipe data using the same schema
    const { output } = await ai.generate({
      prompt,
      output: { schema: RecipeSchema },
    });

    if (!output) throw new Error('Failed to generate recipe');

    return output;
  },
);

// Run the flow
async function main() {
  const recipe = await recipeGeneratorFlow({
    ingredient: 'avocado',
    dietaryRestrictions: 'vegetarian',
  });

  console.log(recipe);
}

main().catch(console.error);

This code sample:

Defines reusable input and output schemas with Zod
Configures the gemini-2.5-flash model with temperature settings
Defines a Genkit flow to generate a structured recipe based on your input
Runs the flow with a sample input and prints the result

Create a main.go file with the following sample code:

package main

import (
    "context"
    "encoding/json"
    "fmt"
    "log"

    "github.com/firebase/genkit/go/ai"
    "github.com/firebase/genkit/go/genkit"
    "github.com/firebase/genkit/go/plugins/googlegenai"
)

// Define input schema
type RecipeInput struct {
    Ingredient           string `json:"ingredient" jsonschema:"description=Main ingredient or cuisine type"`
    DietaryRestrictions  string `json:"dietaryRestrictions,omitempty" jsonschema:"description=Any dietary restrictions"`
}

// Define output schema
type Recipe struct {
    Title        string   `json:"title"`
    Description  string   `json:"description"`
    PrepTime     string   `json:"prepTime"`
    CookTime     string   `json:"cookTime"`
    Servings     int      `json:"servings"`
    Ingredients  []string `json:"ingredients"`
    Instructions []string `json:"instructions"`
    Tips         []string `json:"tips,omitempty"`
}

func main() {
    ctx := context.Background()

    // Initialize Genkit with the Google AI plugin
    g := genkit.Init(ctx,
        genkit.WithPlugins(&googlegenai.GoogleAI{}),
        genkit.WithDefaultModel("googleai/gemini-2.5-flash"),
    )

    // Define a recipe generator flow
    recipeGeneratorFlow := genkit.DefineFlow(g, "recipeGeneratorFlow", func(ctx context.Context, input *RecipeInput) (*Recipe, error) {
        // Create a prompt based on the input
        dietaryRestrictions := input.DietaryRestrictions
        if dietaryRestrictions == "" {
            dietaryRestrictions = "none"
        }

        prompt := fmt.Sprintf(`Create a recipe with the following requirements:
            Main ingredient: %s
            Dietary restrictions: %s`, input.Ingredient, dietaryRestrictions)

        // Generate structured recipe data using the same schema
        recipe, _, err := genkit.GenerateData[Recipe](ctx, g,
            ai.WithPrompt(prompt),
        )
        if err != nil {
            return nil, fmt.Errorf("failed to generate recipe: %w", err)
        }

        return recipe, nil
    })

    // Run the flow
    recipe, err := recipeGeneratorFlow.Run(ctx, &RecipeInput{
        Ingredient:          "avocado",
        DietaryRestrictions: "vegetarian",
    })
    if err != nil {
        log.Fatalf("could not generate recipe: %v", err)
    }

    // Print the structured recipe
    recipeJSON, _ := json.MarshalIndent(recipe, "", "  ")
    fmt.Println(string(recipeJSON))
}

This code sample:

Defines reusable input and output schemas using Go structs with JSON schema tags
Configures the gemini-2.5-flash model as the default
Defines a Genkit flow to generate a structured recipe based on your input
Runs the flow with a sample input and prints the structured result

Create a main.py file:

import json
from typing import Optional
from pydantic import BaseModel, Field
from genkit.ai import Genkit
from genkit.plugins.google_genai import GoogleAI

# Initialize Genkit with the Google AI plugin
ai = Genkit(
    plugins=[GoogleAI()],
    model='googleai/gemini-2.5-flash',
)

# Define input schema
class RecipeInput(BaseModel):
    ingredient: str = Field(description='Main ingredient or cuisine type')
    dietary_restrictions: Optional[str] = Field(default=None, description='Any dietary restrictions')

# Define output schema
class Recipe(BaseModel):
    title: str
    description: str
    prep_time: str
    cook_time: str
    servings: int
    ingredients: list[str]
    instructions: list[str]

# Define a recipe generator flow
@ai.flow()
async def recipe_generator_flow(input_data: RecipeInput) -> Recipe:
    # Create a prompt based on the input
    dietary_restrictions = input_data.dietary_restrictions or 'none'

    prompt = f"""Create a recipe with the following requirements:
        Main ingredient: {input_data.ingredient}
        Dietary restrictions: {dietary_restrictions}"""

    # Generate structured recipe data using the same schema
    result = await ai.generate(
        prompt=prompt,
        output_schema=Recipe,
    )

    if not result.output:
        raise ValueError('Failed to generate recipe')

    return result.output

async def main() -> None:
    # Run the flow
    recipe = await recipe_generator_flow(RecipeInput(
        ingredient='avocado',
        dietary_restrictions='vegetarian'
    ))

    # Print the structured recipe
    print(json.dumps(recipe, indent=2))

ai.run_main(main())

This code sample:

Defines reusable input and output schemas with Pydantic
Configures the gemini-2.5-flash model as the default
Defines a Genkit flow to generate a structured recipe based on your input
Runs the flow with a sample input and prints the structured result

Why use flows?

Type-safe inputs and outputs: Define clear schemas for your data
Integrates with the Developer UI: Test and debug flows visually
Easy deployment as APIs: Deploy flows as HTTP endpoints
Built-in tracing and observability: Monitor performance and debug issues

Run your application

Run your application to see it in action:

npx tsx src/index.ts

You should see a structured recipe output in your console.

Run your application to see it in action:

go run .

You should see a structured recipe output in JSON format.

Run your app (Genkit apps are just regular Python applications):

python3 main.py

You should see a structured recipe output in JSON format.

Test in the Developer UI

The Developer UI is a local tool for testing and inspecting Genkit components, like flows, with a visual interface.

Start the Developer UI

The Genkit CLI is required to run the Developer UI. If you followed the installation steps above, you already have it installed.

Run the following command from your project root:

genkit start -- npx tsx --watch src/index.ts

This starts your app and launches the Developer UI at http://localhost:4000 by default.

Optional: Add an npm script

To make starting the Developer UI easier, add the following to your package.json scripts:

"scripts": {
  "genkit:ui": "genkit start -- npx tsx --watch src/index.ts"
}

Then run it with:

npm run genkit:ui

Run the following command from your project root:

genkit start -- go run .

This starts your app and launches the Developer UI at http://localhost:4000 by default.

To inspect your app with Genkit Dev UI, run:

genkit start -- python3 main.py

The command will print the Dev UI URL:

Genkit Developer UI: http://localhost:4000

Run and inspect flows

In the Developer UI:

Select your recipe generator flow from the list of flows:

- recipeGeneratorFlow

- recipe_generator_flow
Enter sample input:

{
  "ingredient": "avocado",
  "dietaryRestrictions": "vegetarian"
}

{
  "ingredient": "avocado",
  "dietary_restrictions": "vegetarian"
}

Click Run

You’ll see the generated recipe as structured output, along with a visual trace of the AI generation process for debugging and optimization.

Next steps

Now that you’ve created and tested your first Genkit application, explore more features to build powerful AI-driven applications:

Developer tools: Set up your local workflow with the Genkit CLI and Dev UI.
Generating content: Use Genkit’s unified generation API to work with multimodal and structured output across supported models.
Creating flows: Learn about streaming flows, schema customization, deployment options, and more.
Tool calling: Enable your AI models to interact with external systems and APIs.
Managing prompts with Dotprompt: Define flexible prompt templates using .prompt files or code.