What a ChatGPT App is and what it is NOT

1. Introduction

From a typical developer’s perspective, this all feels somewhat familiar already: there’s a new buzzword — ChatGPT App — there’s an API, there’s an SDK, a couple more acronyms, and it seems like it’s “yet another way to call an AI model.”

The problem is that without a clear mental model, people start conflating very different things: the old ChatGPT plugins, the Assistants API, Custom GPTs, and the new ChatGPT Apps. As a result, someone expects to “click it together” in the UI in an evening like a Custom GPT, but ends up needing to run an MCP server, write a Next.js widget, and think about the Store. And vice versa: someone writes a complex backend when a simple Custom GPT or a basic API wrapper would have sufficed.

So let’s start with what the new ChatGPT App is (and neatly sort everything), and what it is not.

A brief “historical” evolution of LLM integrations

Before we jump into definitions, it helps to see the evolution of approaches. This lets you go beyond memorising new terms to understanding where the idea of an App inside ChatGPT came from.

First, there was the classic “API wrapper”. You ran your own web app or a bot and, somewhere on the backend, called the OpenAI API: send a prompt, get a text response, show it to the user. All logic, UI, auth, and billing were on your side. ChatGPT as a product was not involved at all.

Then came ChatGPT Plugins.

This was the first attempt to embed external services into the ChatGPT interface itself. A plugin was described via OpenAPI, ChatGPT could call its endpoints, and you responded with JSON that the model paraphrased back to the user. The plugin had no custom UI — at most, Markdown that the model could show in chat. Today that system is considered legacy.

Next came Custom GPTs (MyGPTs).

This is a no‑code builder for “your version of ChatGPT”: you configure prompts, attach files, and sometimes add Custom Actions to HTTP APIs. Everything lives in the ChatGPT interface, but the UI is strictly standard, and integrations are limited by what Actions can do.

And finally, a step change — the latest ChatGPT Apps. Here you get a rich UI (a widget inside the chat), a standard protocol for working with your data and backend logic (MCP), and a distinct place in the ecosystem: Dev Mode, the Store, permissions, built‑in payments, and so on.

At the time of writing this course, Google Play had 4 million apps, the Apple App Store had 2 million, and ChatGPT had only 5. Not 5 million — just only 5(!) apps. And ChatGPT has 800 million weekly active users. It has never been easier to get into the top 100 apps and earn a few million.

If I’ve piqued your interest, let’s dig into the main question: what exactly is this new ChatGPT App?

2. A concise definition of a ChatGPT App

There are several marketing explanations on the internet, but since we’re all engineers here, I’ll translate it into plain English.

A ChatGPT App is a web application that runs inside ChatGPT and has its own UI widget. The app provides ChatGPT with a set of tools (functions) and data, and it’s registered in the app catalogue (the App Store). The app combines a conversational interface (chat) with a graphical UI and backend logic, communicating with the ChatGPT platform via standardised protocols like MCP.

And here is what that gives us:

First, it “runs inside the ChatGPT interface.” The user never leaves https://chatgpt.com/ or the mobile app; your app’s UI is embedded into this interface as a widget. Your widget appears directly in the chat stream.

Second, it “has its own UI widget.” It’s not just text the model prints in chat. You can render React components: cards, lists, forms, maps, players — all the joys of frontend life. Technically, it’s a regular Next.js app running in a sandbox and talking to ChatGPT via window.openai and the Apps SDK.

Third, it “provides ChatGPT with a set of tools (functions) and data.” You run an MCP server on your backend and register your App’s MCP tools — you describe the actions your service can perform: catalogue search, booking, data analysis, report generation. ChatGPT sees these tools as functions with a JSON Schema and may call them at its discretion.

Fourth, it “is registered in the app catalogue.” An App has a name, icon, descriptions, categories, permissions, versions, and monetisation. It’s not a quick “hacky script,” but a full‑fledged App in the ChatGPT ecosystem.

A very important mindset shift: you are not writing a “bot” that decides everything on its own. You describe your interface and capabilities (UI + tools), and ChatGPT decides for itself when to use them and how to weave them into the dialogue. You only partially control the entire scenario.

There’s a huge upside for you — ChatGPT proactively offers users to install your app and decides on its own when to launch it. In other words, your user acquisition cost is $0. A million installs of your app will cost you $0. At least, if you’re among the first.

Launching your own ChatGPT app in 2025 is like buying Bitcoin at $1. The choice is yours.

3. Anatomy of a ChatGPT App: UI, tools, and context

To avoid confusion later, let’s break an App down into three major components that will keep coming up throughout the course.

The first component is the UI layer. This is your widget, typically written in React/Next.js using the Apps SDK. It renders inside ChatGPT, showing gift lists, booking forms, charts — any other visual elements. It runs in a sandbox: it can’t tamper with the global DOM, can’t freely access the internet, and operates within a constrained window.

The second component is tools, resources, and prompts. At the protocol level, this is an MCP server with declared capabilities: tools (actions), resources (data), and prompts (templates). Tools are defined by JSON Schemas, and the model sees them as functions to call when appropriate. In later modules, we’ll look closely at how callTool actually works, but for now remember: tools are your App’s hands and eyes in the real world.

The third component is the usage context. This is everything you use to describe your App to the model and to users: the system prompt, tool descriptions, permissions, target audience, categories in the Store. These metadata determine when GPT will suggest the App, which requests are relevant, and what actions are allowed.

A bit later, when we walk through the sample app GiftGenius, you’ll see all three layers in action: the UI widget with gift cards and a clarification wizard; the selection and checkout tools on the MCP/backend; and the context — system instructions, descriptions, permissions, and Store categories.

4. Comparing “apps” in the ChatGPT ecosystem

Now that we have a shared understanding and the anatomy of an App, let’s step back and compare it to its “relatives.” This will help you keep Apps, plugins, the Assistants API, and the plain OpenAI API separate in your mind. Below is a table to keep these entities distinct.

A good analogy, explicitly emphasised in the official docs: the Assistants API is when you take the “GPT brain” and embed it into your product, and a ChatGPT App is the opposite — when you bring your product into the ChatGPT interface.

5. What a ChatGPT App is NOT

Let’s go through popular misconceptions. This is important so we don’t design an App as something it isn’t.

ChatGPT App ≠ just a Next.js site

A frontend developer’s intuition is to see an App as “just another SPA,” except instead of / you have “some strange window inside ChatGPT.” That’s partially true, but there’s a critical difference: you don’t live on your domain and you don’t control the entire UI — you’re renting a small piece of the ChatGPT interface. You can’t rewrite navigation, slap your banner over everything, or “hack” the environment.

In this course, we’ll treat the widget as an isolated component, not a full site: it has strict limits on network, DOM, and resources, and the heavy lifting goes to the backend/MCP. We’ll cover the sandbox in detail in the final lecture of this level; for now, just remember it’s not “just another Next.js hosting.”

For clarity — a code example. Here’s what a classic “API wrapper” around OpenAI in your Next.js app looks like — this is not a ChatGPT App:

// app/api/chat/route.ts — your site's regular backend, not an App
import OpenAI from "openai";
import { NextRequest, NextResponse } from "next/server";

const client = new OpenAI();

export async function POST(req: NextRequest) {
  const { message } = await req.json();
  const response = await client.responses.create({
    model: "gpt-5.2",
    input: [{ role: "user", content: [{ type: "text", text: message }] }],
  });
  return NextResponse.json({ reply: response.output[0].content[0].text });
}

A user of this app interacts with your backend, not with ChatGPT. All UI and session logic is yours. This is a great option for AI features inside your own product, but it is not a ChatGPT App.

ChatGPT App ≠ the old ChatGPT Plugin

We’ll have to send the word “plugin” to the museum of 2023 and use it only to refer to the old system. Plugins gave ChatGPT the ability to call your HTTP endpoints via an OpenAPI spec, but they didn’t allow a rich UI: at most you could return Markdown that the model showed in chat.

New Apps, in contrast to plugins, can render React widgets, work via MCP, have permissions, and participate in financial scenarios. Thinking of them as “plugins 2.0” is an oversimplification that will bite you as soon as you start designing the UI and tools.

ChatGPT App ≠ Assistants API

The Assistants API solves a different problem: how to give your product (website, mobile app, internal tool) a smart GPT‑based assistant. Everything lives “on your side,” you control the UI, and GPT is a backend service you talk to via API.

With a ChatGPT App, it’s the other way around. The UI and core user experience belong to ChatGPT, and you “move in” with your application. The user doesn’t see your domain; they see your App’s name and icon inside ChatGPT, and tokens are typically paid by the user via their ChatGPT subscription.

In short: the Assistants API is GPT inside your product; a ChatGPT App is your product inside ChatGPT.

ChatGPT App ≠ just a Custom GPT

Custom GPTs are a great tool for a quick start: assemble a prompt, attach a couple of files — and you have a “personal assistant.” But its UI is standard, without widgets, and integrations via Custom Actions are rather limited; there’s no full Apps SDK or MCP layer.

A ChatGPT App is a pro‑code story. You write a widget (usually in Next.js), run an MCP server, set up authentication, permissions, and payments. The flexibility is much higher, but so is the responsibility: for security, UX, and passing review when registering the app.

A practical strategy I can recommend for a business: use a Custom GPT as a quick marketing entry (a simple assistant in the GPT Store), while in parallel developing a full App on the Apps SDK for serious scenarios and future monetisation.

ChatGPT App ≠ “just another bot”

Finally, an important psychological point. A ChatGPT App is not “just another chatbot.” It’s a product with a lifecycle: there’s Dev Mode, review, versions, constraints, analytics, and payment flows. Thinking of it as a “demo bot” is a reliable way to underestimate the effort, fail the real launch, and miss your millions.

6. Types of ChatGPT apps

To better understand what you’re building, it helps to have a rough typology of ChatGPT Apps. In this course we’ll talk about four primary focuses and use short English labels: UI-heavy, tool-first, commerce-oriented, and data/analytics.

• First type — UI‑heavy or UI‑first apps. The primary value is the visual interface: wizards, configurators, complex forms, a canvas. Examples: selecting insurance with a dozen parameters, a design configurator, data visualisation.
• Second type — Tool‑first apps. The main thing here is not the UI but the tools. The App provides a powerful set of functions for the model, and ChatGPT handles most of the user experience itself, inserting textual explanations and sometimes minimal UI. Example: an App that gives GPT access to a company’s internal knowledge base — the model decides when and how to call search and how to explain results to the user.
• Third type — Commerce‑oriented apps. The centre of gravity is sales, subscriptions, bookings. The App is integrated with the Agentic Commerce Protocol (ACP), can place orders, work with a cart and Instant Checkout, and link orders to users.
• Fourth type — Data/analytics apps. They focus on connecting data sources and analytics: reports, BI dashboards, log and metric analysis, working with uploaded files.

The same idea can be implemented in different styles. For example, gift selection can be purely Tool‑first (the model crafts the explanations and the App only returns JSON with ideas) or UI‑heavy (a rich widget with filters, product cards, and comparisons).

7. Our practice project: GiftGenius

It’s useful to anchor everything we do in the course to a single “end‑to‑end” app. So as we go, we’ll build our own app: GiftGenius — an app for selecting gifts and placing orders through ChatGPT. We’ll keep coming back to it.

From the typology perspective, GiftGenius is primarily a commerce‑oriented App with UI‑heavy elements. A user writes in ChatGPT something like: “I need a gift for an IT friend, budget 50–70 dollars,” the AI model decides to invoke GiftGenius, the App shows a widget with clarifying questions, a gift selection, and ultimately checkout via ACP.

To start thinking in TypeScript terms right away, we can sketch a minimal domain model that will accompany us:

// gift-types.ts — simplified GiftGenius domain model
export type GiftIdea = {
  id: string;
  title: string;
  priceUsd: number;
  tags: string[];      // recipient interests
  occasion: string;    // occasion: birthday, wedding, etc.
};

For now, it’s just a type, not tied to any SDK. But as we progress, you’ll see how such domain models seep into MCP tools, the UI widget, and even the commerce layer.

8. How users experience a ChatGPT App inside a conversation

Although “user flow” will be the main topic of the third lecture, it’s important to outline the big picture now so you understand why the UI is needed and how an App appears in the dialogue.

The user talks to ChatGPT as usual: sends messages, asks questions, requests help. In turn, ChatGPT decides on each turn what to do: answer by itself, call a tool, show or update your App’s widget, suggest using the App if it’s relevant to the context.

For example, the user writes: “I need an anniversary gift, budget up to 100 dollars; my husband loves board games.” The model sees it has the GiftGenius App that can pick gifts using such criteria. It can proceed in two ways:

1. First, suggest the user try GiftGenius, saying something like: “I can connect the GiftGenius App to pick some options. Launch it?”
2. Or immediately call the App’s tools and display the widget with prefilled fields, showing the selection.

All this happens without your direct if user_said_gift then call_app(). You describe the App’s capabilities, and the model learns to use them. That’s why clear descriptions, constraints, and thoughtful UX are so important — otherwise GPT will either overuse your App or, conversely, never touch it.

For clarity, here is a diagram:

flowchart TD
  U[User in ChatGPT] -->|message| G[GPT model]
  G -->|decision: use the App?| A[Your ChatGPT App]
  A -->|widget| W[UI in chat]
  A -->|tools/MCP| B[Your backend / MCP]
  B --> A --> G --> U

We’ll discuss in detail how GPT decides to call an App when we cover tools and the system prompt, but it’s already useful to remember: this is cooperation, not imperative control.

9. Mini exercise: your App idea

To keep the material from staying abstract, it makes sense to think up an idea for your own App that you’ll mentally develop alongside GiftGenius.

Try to state in one sentence what your app does inside ChatGPT. For example: “The App helps developers estimate task complexity and break them into subtasks,” or “The App plans travel routes factoring in weather and budget.”

Next, honestly answer two questions. First, which type from our typology is it closer to: UI‑heavy, tool‑first, commerce‑oriented, or data/analytics? Second, is it really a ChatGPT App or, in essence, just a bot in your site or another Custom GPT? If all you need is to call the OpenAI API from your backend a bit more conveniently, you might not need a full App at all.

These mini reviews are a good way to save yourself a couple of months building the wrong product.

10. Common misconceptions about a ChatGPT App

Mistake #1: Calling everything a “plugin.”
The plugin system was a historical stage of 2023. The new generation of integrations is Apps on the Apps SDK + MCP. If you keep clinging to the term “plugin,” it’s easy to underestimate the role of the UI, the sandbox, the Store, and the entire product lifecycle. In this course, we use “plugin” only for the old system; “App” always means the new generation.

Mistake #2: Expecting full control over GPT.
Sometimes developers come in thinking “I’ll write an App and the model will strictly do what I tell it.” The ChatGPT ecosystem works differently: you describe your capabilities and intent, but the model decides when to call tools, when to show the widget, and when to answer with text. If you design the App like a classic SPA with a rigid script, you’re in for disappointment.

Mistake #3: Confusing a ChatGPT App with the Assistants API.
A very common situation: someone wants “a bot in their product,” but out of habit looks toward the Apps SDK, whereas it would be much simpler and more logical to use the Assistants API. They then spend energy on a widget in ChatGPT that their users don’t need. The right distinction is simple: if users go to your site or app, think Assistants API; if you want to come to users inside ChatGPT, think ChatGPT App.

Mistake #4: Treating the App as “just another frontend” without accounting for the sandbox.
When a developer tries to use the Apps SDK as a regular Next.js frontend and ignores sandbox constraints (limited access to network, DOM, resources), they quickly find “nothing works like on my site.” Accept upfront that the widget is an isolated component and that heavy integrations and secret storage must move to the backend/MCP.

Mistake #5: Overestimating Custom GPT and underestimating the Apps SDK (or vice versa).
Custom GPTs and Apps aren’t “either/or” but different levels of maturity. Often the right strategy is to use both: a Custom GPT for quick entry and marketing, and an App for a serious product with rich UI and commerce. When a developer expects Apps SDK-level capabilities from a Custom GPT, or drags the Apps SDK into a use case where a Custom GPT would suffice, they only make their life harder.