What is the GPT Apps SDK?
The Apps SDK is OpenAI’s framework for building apps that appear and run directly in ChatGPT conversations. Developers can start building and testing now (preview). Apps combine conversational turns with rich UI, and are built on the Model Context Protocol (MCP)—an open standard that connects ChatGPT to external tools and data. OpenAI states the SDK is open source so apps can run anywhere that adopts the standard.
Why it matters for business: your service can meet customers in the chat—with native UI and account linking—rather than pushing them out to a website or separate bot. OpenAI highlights reach to “over 800 million ChatGPT users” and first-party distribution (app suggestions, directory coming soon).
When to choose Apps SDK vs. other OpenAI build paths
- Apps SDK (this article): Best when you want a ChatGPT-native product surface with UI, OAuth, and MCP tools—discoverable in ChatGPT and testable in Developer Mode
- AgentKit & Agents SDK: Use when you’re building agentic workflows (multi-step, multi-agent, guardrails, evaluations) primarily for your own product or API surfaces. Apps SDK can coexist—e.g., an app in ChatGPT that talks to your backend agent via API.
How an Apps SDK build works (architecture at a glance)
- ChatGPT client renders your app’s UI components in the conversation.
- MCP server (your backend) exposes typed tools (JSON Schema) for the model to call; returns structured results and component metadata for the UI.
- Your systems (APIs, CRM, commerce, databases) are invoked safely via the MCP server with explicit scopes and auditability.
A pragmatic delivery plan (from discovery to launch)
1) Scope the use case and success criteria
Start with a specific, high-value task (booking, quoting, troubleshooting, configurators). OpenAI’s “Plan → Research use cases” guidance aligns with prioritizing tasks where chat + UI beats a web form. Define success metrics (conversion, AHT reduction, CSAT uplift).
2) Model the tools (contract-first)
Design MCP tools with clear JSON Schemas for inputs/outputs (and mark any write actions). Keep them small and composable (e.g., search_inventory, calculate_quote, create_order). The ChatGPT model will call these tools with structured args during the conversation.
3) Design the in-chat UI
Use the Apps SDK’s design guidelines so components feel native (lists, detail panes, maps, forms, confirmation steps). Treat chat as the primary nav and components as focused, context-aware surfaces.
4) Build the MCP server and custom UX
Stand up your MCP server (Streamable HTTP recommended). Implement list/call tools and return component metadata for the ChatGPT UI to render. Add auth (OAuth 2.1 + PKCE) and state persistence where needed.
5) Test like you mean it (Developer Mode + MCP Inspector)
- MCP Inspector for local debugging of tool schemas, calls, and component rendering.
- ChatGPT Developer Mode to validate discovery (“does the model pick my tool?”), UI behavior on web and mobile, and auth/permissions flows end-to-end.
OpenAI’s testing guide provides a regression checklist before launch.
6) Ship with security & privacy by design
Follow OpenAI’s Security & Privacy guidance: least-privilege scopes, clear consent for linking accounts or write actions, input validation (assume prompt injection attempts), PII redaction in logs, sandboxed widgets with strict CSP.
7) Prepare for distribution and monetization
OpenAI plans to accept app submissions later this year, launch a directory, and share monetization details (including support for the Agentic Commerce Protocol for instant checkout). Ensure your app meets the App Developer Guidelines for listing and potential featuring.
What you can build (proven patterns)
Guided commerce (catalog search → configuration → checkout), with upcoming ACP support.
Customer support flows (account lookup, knowledge retrieval, in-chat forms) with safe write actions (ticket creation, refunds requiring confirmation).
B2B assistants (quoting, pricing, lead enrichment) that authenticate into CRMs/ERPs via OAuth and MCP tools
Governance & readiness checklist (exec view)
- Policy fit: App aligns with OpenAI usage policies and is appropriate for all audiences (age-gating for mature content is not yet general in Apps).
- Privacy: Published policy; data minimization; clear retention; user transparency at install/connect.
- Security: Least privilege, write-action confirmations, input validation, CSP compliance, dependency patching.
- Quality gates: Golden prompts, discovery precision, tool schema tests, mobile layout checks, regression checklist.
Example implementation timeline (indicative)
- Week 1: Discovery, tool/contracts, UX sketches (guided by Apps SDK design guidelines).
- Weeks 2–3: MCP server + UI build; OAuth; initial end-to-end run in Developer Mode.
- Week 4: Security hardening, evals, and regression pass; go-live on allowlisted orgs; prep for directory submission when available.
(Timelines vary with scope and integrations; use as a planning scaffold, not a guarantee.)