Build Hour: Apps in ChatGPT

Learn how to design, build, and enhance real-time, multi-player applications within ChatGPT using the Apps SDK and Codex. This guide covers the core architecture, an AI-first development workflow, and best practices for creating valuable user experiences.

January 23, 2026 5 min read

Build Hour: Apps in ChatGPT — source: https://www.youtube.com/watch?v=mFG-4vUJ0kI

The paradigm for application development is shifting, and ChatGPT Apps offer a new surface for creating interactive, model-aware experiences. The core of this platform is a combination of a powerful protocol, a flexible UI framework, and an AI-first development process that can dramatically accelerate creation.

The ChatGPT App Framework: MCP and Web Components

An App in ChatGPT is built upon the Multi-modal Capabilities Protocol (MCP), an open protocol that enables AI clients like ChatGPT to interact with external tools, actions, and resources. The fundamental flow is as follows:

A user’s prompt signals an intent that a specific app can handle.
ChatGPT makes a tool call to the app’s MCP Server. This server can be a simple backend that pulls data from a database, generates information on the fly, or connects to complex external services.
The MCP Server processes the request and responds with a tool output. This output can optionally include a URI for a web component.
ChatGPT then renders this web component—an HTML file with CSS and JavaScript—within the chat interface, creating an interactive UI.

The web component is not isolated; it communicates with the host environment through the window.openAI JavaScript API. This allows the UI to:

React to context and update its state.
Receive tool results directly.
Call other tools to perform subsequent operations.
Control its presentation, such as entering full-screen or picture-in-picture mode.

This architecture enables a powerful synergy between the conversational abilities of the LLM, the logic and data access of your backend (MCP Server), and a rich, interactive user interface (Web Component).

A New Development Paradigm: Building with Codex

While the traditional development process of reading docs, downloading sample code, and coding manually is still valid, an AI-first approach using tools like Codex offers a significantly faster path from idea to implementation.

By leveraging the OpenAI Docs MCP Server, you can provide Codex with the complete context of the Apps SDK and all related documentation. The process is straightforward:

Install Codex: Begin by installing the Codex CLI (npm install -g @openai/codex).
Add the Docs MCP Server: Register the OpenAI documentation as a source of context for Codex with a single command.
Instruct Codex: In an agents.md file, instruct Codex to always use the OpenAI Developer Docs MCP server for tasks related to the OpenAI platform.
Prompt and Build: With the setup complete, you can ask Codex to build an application from a natural language prompt. For example, “Create me a ChatGPT app with a simple UI and MCP server to play a ping-pong game.”

Codex will then call the Docs MCP server, reference the quickstart guides and SDK documentation, and scaffold the entire project, including the server files, the HTML for the web component, and the necessary configuration. This transforms a multi-hour setup process into a task of a few minutes.

Live Demo: A Real-time, Multi-player Ping-Pong App

The power of this platform was demonstrated with a real-time, multi-player ping-pong game built inside ChatGPT. The initial version was scaffolded by Codex in minutes. A more advanced version showcased several key capabilities:

Real-time Interaction: Two players in separate ChatGPT sessions were able to join the same game and play against each other in real time. The MCP server managed the game state, synchronizing the paddle and ball positions between the two clients.
Lobby System: The app supported creating a match and joining with a unique code, managed through interactions with the MCP server.
Model-Enhanced Experience: This is where the app truly shines. After a match, a user can ask for “post-game stats and analysis.” The app makes another tool call, sending the game data (score, rally length, etc.) to its MCP server, which then passes this structured data back to the model. The model analyzes the data and provides personalized feedback and tips for improvement directly in the chat, demonstrating a seamless loop between the interactive UI, the backend data, and the LLM’s analytical power.

To test and develop such apps locally, developers must enable Developer Mode in ChatGPT settings, which allows for “sideloading” an app running on a local server, typically exposed to the web via a tunneling service like ngrok or Cloudflare.

Best Practices for Building Great Apps

To create a valuable and compelling app, consider the following principles:

Extract, Don’t Port: Instead of trying to replicate your entire existing web application, distill its core value into a concise, delightful experience tailored for a conversational interface.
Design for Conversational Entry: Acknowledge that users will interact with your app through natural language, which can be imprecise. Your MCP server should be robust enough to handle ambiguity, either by taking a best-guess action or by prompting the model to ask for clarification.
Provide Unique Value: A great app helps a user know something new (e.g., AllTrails surfacing specific trail data), do something more efficiently (e.g., Adobe Express creating a flyer), or show something in a more engaging way (e.g., a map view).
Monetization: Monetization can be implemented through a standard authentication flow where users log in to your service, or by using an external checkout link. The window.openAI API supports a redirect_url to seamlessly bring the user back into the ChatGPT experience after completing a transaction on your site.