What Are ChatGPT Apps? A Complete Guide for 2026
Everything you need to know about ChatGPT apps — from the old plugin era to Custom GPTs to the new MCP-powered app directory. What they are, how they work, and how to build one.
ChatGPT apps are third-party tools that run inside ChatGPT. They let you book flights, search products, pull data from a CRM, generate design mockups, or do just about anything else — without leaving the conversation. OpenAI officially calls them "apps" as of December 2025, and they are built on MCP, the open protocol that is quickly becoming the standard for connecting AI to external services.
If you have used ChatGPT for a while, you have probably seen the ecosystem go through a few iterations. Plugins, Custom GPTs, Actions, connectors — it has been a confusing timeline. Apps are the current answer. They are the result of OpenAI adopting an open standard instead of building another proprietary system, and the shift matters more than the name suggests.
A brief history of ChatGPT's tool ecosystem
To understand where apps fit, it helps to see the full timeline. ChatGPT has gone through three distinct eras of third-party tool integration.
Era 1: Plugins (March 2023 - April 2024)
ChatGPT plugins were announced in March 2023 as a beta experiment to let ChatGPT call external APIs. The idea was simple — developers built API wrappers with OpenAPI manifests, and ChatGPT would call them during conversations.
It never really worked. The beta was limited to a small group of users. Discoverability was poor — most people never found the plugin directory. The quality was inconsistent, the proprietary manifest format locked developers to ChatGPT, and the whole system felt half-baked. It was an experiment that proved the concept had legs, but the execution was not there.
By early 2024, OpenAI quietly wound it down. New conversations could no longer use plugins after March 19, 2024, and the system was fully shut down on April 9, 2024. The beta lasted about a year before being replaced.
Era 2: Custom GPTs and Actions (November 2023 - present)
Before plugins were even deprecated, OpenAI launched Custom GPTs in November 2023. These were a different approach — instead of standalone tools, users could create custom versions of ChatGPT with specific instructions, knowledge files, and API connections called "Actions."
The GPT Store followed in January 2024, and adoption was fast. Over 3 million Custom GPTs were created in the first few months, though only about 159,000 were published publicly. Actions used OpenAPI schemas to connect to REST APIs, which worked well for structured integrations.
Custom GPTs are still around, and they are useful for simple configured experiences. But they have a ceiling. The UI is limited to text. The distribution is mediocre — finding a specific GPT in the store is hard. And every GPT is locked to ChatGPT. If you build a tool as a Custom GPT, it only works on OpenAI's platform.
Era 3: Apps via MCP (October 2025 - present)
At DevDay in October 2025, OpenAI announced native support for MCP — the Model Context Protocol that Anthropic had released as an open standard a year earlier. This was a big deal. Instead of building yet another proprietary integration system, OpenAI adopted the same protocol already used by Claude, Cursor, VS Code Copilot, and dozens of other AI clients.
By December 2025, OpenAI opened the app submission process and formally renamed "connectors" to "apps." The app directory went live at chatgpt.com/apps. Pilot partners included Booking.com, Canva, Coursera, Expedia, Figma, Spotify, and Zillow — big names that signaled OpenAI was serious about the ecosystem.
For the full comparison of plugins, GPTs, and apps, see ChatGPT Plugins vs Custom GPTs vs MCP Apps.
What makes apps different
Apps are not a rebrand of the old plugin beta. There are structural differences that matter.
Built on an open standard
The plugin beta and Custom GPTs used proprietary OpenAI specs. If you built for ChatGPT, your work only ran on ChatGPT. Apps are built on MCP, which is an open protocol. Build one MCP server and it works across ChatGPT, Claude, Cursor, VS Code Copilot, and 70+ other clients. That is a fundamentally different value proposition.
Rich UI rendering
The plugin beta returned plain text. Custom GPTs could format text but had no interactive UI. Apps can render structured content — cards, tables, forms, carousels, charts — directly inside the conversation. OpenAI supports three display modes:
- Inline — Content renders within the conversation flow, embedded between messages. Good for quick results like product cards or data tables.
- Panel — A side panel slides in alongside the conversation. Useful for detailed views, dashboards, or multi-step workflows where the user wants to see the tool output while continuing the chat.
- Full-screen — The app takes over the entire view. For immersive experiences like design tools, code editors, or complex data exploration.
| Mode | Where it appears | Best for |
|---|---|---|
| Inline | Within the conversation flow, between messages | Quick results — product cards, data tables, status updates |
| Panel | Side panel alongside the conversation | Detailed views — dashboards, multi-step workflows |
| Full-screen | Takes over the entire view | Immersive experiences — design tools, code editors, data exploration |
These rendering modes give developers real control over the user experience, which was never possible with the old plugin beta.
Massive distribution
ChatGPT has over 900 million weekly active users as of early 2026. Publishing an app to the directory means your tool is potentially discoverable by that entire user base. That is a distribution channel most SaaS products would spend years and millions of dollars to build.
Apps and Actions are mutually exclusive
One detail that catches people off guard: a single Custom GPT cannot use both Actions (the old OpenAPI-based system) and Apps (the new MCP-based system) at the same time. You pick one or the other. For new builds, MCP apps are the clear choice — they are more capable, more portable, and aligned with where the ecosystem is heading.
How ChatGPT apps work under the hood
If you want to understand the technical side, here is the flow.
The MCP connection
When a user adds an app to ChatGPT — either from the directory or by pasting a custom MCP URL — ChatGPT establishes a connection to the MCP server over Streamable HTTP. It sends an initialization request and the server responds with its capabilities: what tools it offers, what parameters they accept, and what kind of content they return.
sequenceDiagram participant U as User participant C as ChatGPT participant S as MCP Server participant A as Your API U->>C: "Find flights to Tokyo" C->>S: Initialize + discover tools S->>C: Available tools and schemas C->>S: tools/call search_flights S->>A: GET /flights?dest=Tokyo A->>S: Flight data (JSON) S->>C: Structured widget content C->>U: Rendered flight cards inline
This capability negotiation is automatic. ChatGPT reads the tool descriptions and schemas, and from that point on, it knows when to invoke which tool based on what the user asks. No manual configuration needed from the user's side.
Tool invocation
When a user's message matches a tool's purpose — say, "find me flights from Berlin to Tokyo next Friday" — ChatGPT sends a tools/call request to the MCP server with the parsed parameters. The server executes whatever logic it needs (calling APIs, querying databases, running computations) and returns structured content.
That content can be plain text, JSON data, or rich widget content with interactive elements. ChatGPT renders it according to the display mode the developer chose.
The user experience
From the user's perspective, it just works. They type naturally, and when ChatGPT determines a tool would help, it calls the tool and renders the result. There is no context switching, no opening another tab, no login flow. The AI client is the interface, and the app provides the capability.
For a step-by-step guide to connecting an MCP tool to ChatGPT, see How to Add MCP Tools to ChatGPT.
Who is building ChatGPT apps
The early ecosystem splits into two groups.
Pilot partners
The big names — Booking.com, Canva, Coursera, Expedia, Figma, Spotify, Zillow — are the first wave. These are companies with engineering teams who can build and maintain MCP servers from scratch. Their apps are polished, well-tested, and handle millions of requests. They set the quality bar for the directory.
Everyone else
And then there is the much larger group: product teams, entrepreneurs, small businesses, and individual developers who want to build apps but do not have the resources for a dedicated MCP engineering effort. Building a production MCP server from scratch requires protocol expertise, server development, widget rendering, infrastructure management, and multi-client testing. That is a lot of specialized work.
This is where visual builders come in. drio lets you build MCP apps visually — define tools, connect APIs, design widget responses, and deploy to ChatGPT and every other MCP client with one click. No server code, no infrastructure, no protocol spec to read. Your visual configuration compiles into a spec-compliant MCP server automatically.
If you want to try the visual approach, Build AI Apps Without Code walks through the full workflow.
How to build a ChatGPT app
There are two paths, and the right one depends on your team and timeline.
Path 1: Code-first with the MCP SDK
Use the official TypeScript SDK or Python SDK to build a server from scratch. Define your tools, implement the handlers, build widget responses, set up Streamable HTTP transport, deploy to a cloud provider, and submit to the directory.
This gives you full control but takes weeks and requires MCP protocol expertise. Best for teams with backend engineers who need custom behavior.
Path 2: Visual builder with drio
Open the drio builder, define your tool parameters, connect your APIs visually, pick widget primitives for the response, and hit deploy. You get a production MCP endpoint that works with ChatGPT, Claude, Cursor, and every other MCP client. The whole process takes minutes, not weeks.
For a hands-on walkthrough, see From Idea to AI App in 10 Minutes.
What you can build
The app directory is still early, but the use cases are already broad:
- E-commerce — Product search, personalized recommendations, cart management, order tracking. Users ask naturally and see interactive product cards.
- Travel — Flight and hotel search, itinerary planning, booking management. Expedia and Booking.com are already there.
- Productivity — Project management dashboards, CRM queries, document search, meeting schedulers. Anything a knowledge worker does in a separate tab.
- Data analysis — Connect to databases or analytics APIs and let users explore data through natural language. Charts and tables render inline.
- Creative tools — Figma and Canva are early adopters. Design generation, asset search, and creative workflows inside the chat.
- Education — Course recommendations, learning path generation, quiz tools. Coursera is a pilot partner.
If your tool connects to an API and returns structured data, it can be a ChatGPT app.
ChatGPT apps vs. Custom GPTs: which should you build?
Short answer: if you are starting something new, build an MCP app.
Custom GPTs still have their place for simple, text-only, ChatGPT-specific experiences — things like a custom writing assistant with specific instructions, or a knowledge-base bot with uploaded files. They are easy to create and require no external server.
But if your tool needs API connections, rich UI, interactive elements, or cross-platform compatibility, an MCP app is the better investment. You build once and deploy everywhere, instead of being locked into a single platform.
For the detailed comparison, see ChatGPT Plugins vs Custom GPTs vs MCP Apps.
Takeaways
- ChatGPT apps are third-party tools built on MCP that run inside ChatGPT conversations with rich, interactive UI.
- The timeline: Plugin beta (2023, never fully launched) to Custom GPTs (2023, still active) to MCP Apps (2025, the current standard).
- 900M+ weekly active users make the ChatGPT app directory one of the largest distribution channels for software tools.
- Built on MCP means apps work across ChatGPT, Claude, Cursor, and 70+ other AI clients — not just OpenAI's platform.
- Two ways to build: code-first with the MCP SDK, or visually with drio for a faster, no-code path.
The app directory is still early. The teams that build and ship now will have the same first-mover advantage that early App Store developers had in 2008.
