Next.js tutorial

In this tutorial, you’ll build the Next.js UI for Bargain Chef and connect it to your existing Genkit backend. It uses two AI patterns Genkit simplifies: streaming structured output and tool calling.

What you’ll build

The user types what they’re craving, Gemini drafts a recipe, and the model calls a tool to look up mock grocery sale prices so it can prefer on-sale ingredients. The recipe streams into the UI incrementally, so users see progress before the full recipe is ready.

You can find the finished code on GitHub.

You’ll call the existing bargainChefFlow over HTTP and render the streamed recipe as fields arrive.

Prerequisites

• Node.js v20 or later
• npm
• Familiarity with Next.js and TypeScript

You should have already completed the Flask, FastAPI, or other backend tutorial. This tutorial picks up where those leave off and adds a Next.js UI to the bargainChefFlow you already built there. Make sure your backend is running and note the port it serves on, since you’ll need it later.

Set up the application

Create the Next.js project:

npx create-next-app@latest my-genkit-nextjs
cd my-genkit-nextjs

Install the Genkit web client, which lets the browser call your backend:

npm

npm install genkit

pnpm

pnpm add genkit

yarn

yarn add genkit

bun

bun add genkit

Your backend should already expose bargainChefFlow. For reference, here’s the shape the Next.js app expects:

// Input
{ craving: string }

// Streamed output (partial: fields fill in over time)
{
  title?: string;
  description?: string;
  servings?: number;
  ingredients?: { name: string; quantity: string; onSale: boolean }[];
  steps?: string[];
}

If your backend exposes a flow with a different name or shape, adjust the URL and the Recipe interface in the next section accordingly.

At this point, your Next.js app is ready to call the backend flow you already created.

Build the Next.js UI

The Next.js side calls the flow with streamFlow, then stores each partial recipe in React state so the component re-renders as fields arrive.

Update the page component

Replace the contents of src/app/page.tsx with the following. The url points to your backend, so adjust the port or route if your backend doesn’t run at http://localhost:8080/bargainChefFlow:

src/app/page.tsx

'use client';

import { useState } from 'react';
import { streamFlow } from 'genkit/beta/client';

interface RecipeIngredient {
  name?: string;
  quantity?: string;
  onSale?: boolean;
}

interface Recipe {
  title?: string;
  description?: string;
  servings?: number;
  ingredients?: RecipeIngredient[];
  steps?: string[];
}

// Point this at the URL where your bargainChefFlow is served
const FLOW_URL = 'http://localhost:8080/bargainChefFlow';

export default function Home() {
  const [craving, setCraving] = useState('something warm with chicken');
  const [recipe, setRecipe] = useState<Recipe | null>(null);
  const [isStreaming, setIsStreaming] = useState(false);

  async function generateRecipe(event: React.FormEvent) {
    event.preventDefault();
    if (!craving.trim()) return;
    setRecipe(null);
    setIsStreaming(true);
    try {
      const result = streamFlow({
        url: FLOW_URL,
        input: { craving },
      });
      for await (const partial of result.stream) {
        setRecipe(partial as Recipe);
      }
      await result.output;
    } catch (err) {
      console.error('Failed to generate recipe', err);
    } finally {
      setIsStreaming(false);
    }
  }

  return (
    <main>
      <h1>Bargain Chef</h1>
      <p className="tagline">
        Tell me what you feel like eating and I&apos;ll suggest a recipe built
        around today&apos;s grocery deals.
      </p>

      <form className="prompt" onSubmit={generateRecipe}>
        <input
          type="text"
          value={craving}
          onChange={(e) => setCraving(e.target.value)}
          name="craving"
          placeholder="What are you in the mood for?"
          disabled={isStreaming}
        />
        <button type="submit" disabled={isStreaming}>
          {isStreaming ? 'Cooking…' : 'Suggest a recipe'}
        </button>
      </form>

      {recipe && (
        <article>
          {recipe.title && <h2>{recipe.title}</h2>}
          {recipe.description && (
            <p className="description">{recipe.description}</p>
          )}
          {recipe.servings && (
            <p className="serves">
              <strong>Serves:</strong> {recipe.servings}
            </p>
          )}

          {recipe.ingredients && recipe.ingredients.length > 0 && (
            <>
              <h3>Ingredients</h3>
              <ul className="ingredients">
                {recipe.ingredients.map((ing, i) => (
                  <li key={i}>
                    {ing.quantity} {ing.name}
                    {ing.onSale && <span className="badge">on sale</span>}
                  </li>
                ))}
              </ul>
            </>
          )}

          {recipe.steps && recipe.steps.length > 0 && (
            <>
              <h3>Steps</h3>
              <ol className="steps">
                {recipe.steps.map((step, i) => (
                  <li key={i}>{step}</li>
                ))}
              </ol>
            </>
          )}
        </article>
      )}
    </main>
  );
}

Enable CORS on the backend

Your Next.js dev server runs on http://localhost:3000 and your Genkit backend runs on a different port, so the browser will block requests unless the backend explicitly allows that origin. If you haven't configured CORS on your backend yet, do that before running the Next.js app. See your backend framework's CORS documentation for the exact configuration.

streamFlow returns an object with two useful properties: stream, an async iterable of partial recipe objects, and output, a promise that resolves with the final validated recipe. Each chunk is the accumulated structured output so far, with fields such as title, ingredients, and steps filling in as the model generates them. The component stores each partial recipe in React state, so the page re-renders on every update.

Each recipe section is wrapped in a conditional so it only renders after that field arrives in the stream. The result is a UI that fills in progressively: title first, then description, then ingredients, then steps. Wrapping the input and button in a <form> lets the user submit by pressing Enter, and the onSubmit handler calls preventDefault() so the browser doesn’t reload the page before starting the streaming request.

Add styles

Replace the contents of src/app/globals.css with the following:

globals.css

body {
  font-family:
    -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue',
    Arial, sans-serif;
  color: #1a1a1a;
  background: #fafafa;
  min-height: 100vh;
  margin: 0;
  padding: 3rem 1.5rem;
}

main {
  max-width: 640px;
  margin: 0 auto;
}

h1 {
  font-size: 2rem;
  margin: 0 0 0.25rem;
  letter-spacing: -0.01em;
}

.tagline {
  color: #555;
  margin: 0 0 2rem;
}

.prompt {
  display: flex;
  gap: 0.5rem;
  margin-bottom: 2.5rem;
}

.prompt input {
  flex: 1;
  font: inherit;
  font-size: 1rem;
  padding: 0.75rem 1rem;
  border: 1px solid #d0d0d0;
  border-radius: 8px;
  background: #fff;
  transition:
    border-color 120ms ease,
    box-shadow 120ms ease;
}

.prompt input:focus {
  outline: none;
  border-color: #2563eb;
  box-shadow: 0 0 0 3px rgba(37, 99, 235, 0.15);
}

.prompt input:disabled {
  background: #f1f1f1;
  color: #888;
}

.prompt button {
  font: inherit;
  font-size: 1rem;
  font-weight: 500;
  padding: 0.75rem 1.25rem;
  border: 0;
  border-radius: 8px;
  background: #1a1a1a;
  color: #fff;
  cursor: pointer;
  transition: background 120ms ease;
  white-space: nowrap;
}

.prompt button:hover:not(:disabled) {
  background: #2563eb;
}

.prompt button:disabled {
  background: #999;
  cursor: not-allowed;
}

article {
  background: #fff;
  border: 1px solid #e5e5e5;
  border-radius: 12px;
  padding: 1.5rem 1.75rem;
}

article h2 {
  font-size: 1.5rem;
  margin: 0 0 0.5rem;
}

article h3 {
  font-size: 1rem;
  text-transform: uppercase;
  letter-spacing: 0.06em;
  color: #666;
  margin: 1.5rem 0 0.5rem;
}

.description {
  color: #444;
  margin: 0 0 1rem;
}

.serves {
  color: #555;
  margin: 0;
  font-size: 0.95rem;
}

.ingredients,
.steps {
  padding-left: 1.25rem;
  line-height: 1.6;
}

.ingredients li {
  margin-bottom: 0.25rem;
}

.steps li {
  margin-bottom: 0.5rem;
}

.badge {
  display: inline-block;
  margin-left: 0.4rem;
  padding: 0.05rem 0.5rem;
  font-size: 0.75rem;
  font-weight: 500;
  background: #e8f5e9;
  color: #2e7d32;
  border-radius: 999px;
}

@media (max-width: 480px) {
  .prompt {
    flex-direction: column;
  }
  .prompt button {
    width: 100%;
  }
}

Run the app

Start your Genkit backend in one terminal by following the run instructions in the backend tutorial you used. Then start the Next.js development server in another terminal:

npm

npm run dev

pnpm

pnpm run dev

yarn

yarn dev

bun

bun run dev

Open http://localhost:3000, enter a craving like something warm with chicken, and submit. The recipe streams in field by field: title first, then description, then ingredients (with “on sale” badges on the ones the model picked from the tool), then steps.

If the request fails, check the browser console first. The most common issue is a CORS error or a FLOW_URL that doesn’t match the backend route.

Test and inspect the app

The Genkit Developer UI is a local console for testing flows and inspecting traces. It records every tool call, model invocation, and streamed chunk, so you can see what the model called, what it received back, and how the recipe was assembled.

If your backend is running under genkit start, the Developer UI is already running at http://localhost:4000.

In the Developer UI:

• The Traces tab shows every invocation of bargainChefFlow, including requests from your Next.js app. Open one and you’ll see the getIngredientsOnSale tool call with the dayType the model chose, the model invocation, and each streamed chunk that the browser received.
• The Flows tab lets you run bargainChefFlow directly with custom input, which is useful for iterating on the prompt without round-tripping through the UI.

What you built

You now have a working Genkit app that streams structured output from Gemini into a Next.js UI incrementally, calls a tool during generation to ground the model’s response in mock sale-price data, validates input and output against schemas, and surfaces every step in a local trace UI.

Next steps

• Creating flows: Compose multi-step flows, branch on input, and chain model calls.
• Generating content: Swap Gemini for another provider, tune sampling parameters, and work with multimodal input.
• Deploy your app: Ship to Cloud Run, Vercel, Firebase, or your own infrastructure.
• Developer tools: Dig deeper into the Developer UI, tracing, and evaluation.