OpenAI Agents SDK vs
computer agents
This is not just a feature comparison. It is an architectural decision between a framework for building agent logic and a platform for running persistent cloud agents in production.
Last reviewed
March 9, 2026
Core framing
Framework versus infrastructure: orchestration primitives on one side, managed runtime and persistence on the other.
Best for
Teams deciding whether to build more of the stack themselves or start from a hosted cloud execution platform.
OpenAI Agents SDK
Code-first framework for building agentic applications with tools, handoffs, and traces.
- Strong orchestration primitives for agent logic
- Official TypeScript and Python SDKs
- Good fit if you want to keep more of the stack inside your own codebase
- OpenAI also offers hosted tools elsewhere, but the SDK itself is not a managed persistent workspace platform
- Cloud execution infrastructure still needs to be assembled when that is part of the requirement
Computer Agents
Managed cloud execution platform for persistent agent workflows.
- Hosted runtime and persistent workspaces built in
- Schedules, triggers, files, and environments as first-class product features
- Better fit for long-lived operational workflows
- SDKs and APIs on top of managed execution rather than instead of it
- Lower burden when agents need to keep running after the first demo
Why the infrastructure versus framework framing matters
Teams often compare these products too literally. The more useful question is which layer of the stack you are actually trying to buy.
Infrastructure, not only primitives
Computer Agents is designed as a managed execution layer. You are not just getting agent orchestration helpers, you are getting the runtime those agents actually live in.
Persistent workspaces
The main architectural difference is persistence. Workspaces, files, and runtime state remain available across runs, which matters immediately for long-lived workflows.
Workflow execution built in
Schedules, triggers, and repeatable operational workflows are part of the product model rather than an extra orchestration layer you add later.
Framework-friendly developer surface
You still get SDKs and APIs, but on top of a hosted platform. That changes how much infrastructure your team needs to own.
Better fit for recurring jobs
Recurring research, monitoring, reporting, and file-based automation are easier when the runtime and the workspace are already durable.
Lower operational burden
Teams can spend more time on agent logic and less time stitching together executors, storage, secrets, scheduling, and runtime lifecycle.
Side-by-side comparison
This table focuses on architecture and workflow operations, where the real differences appear fastest.
Buy the layer you actually need
If you want more control and you are comfortable assembling runtime, persistence, and scheduling yourself, a framework can be the right choice. If you want hosted execution, durable workspaces, and repeatable cloud workflows out of the box, infrastructure is the better purchase.
Frequently asked questions
Is this page saying the OpenAI Agents SDK is weak?
No. The OpenAI Agents SDK is a capable framework for building agents with tools, handoffs, and traces. The distinction is that it is primarily a framework, while Computer Agents is a managed execution platform.
What does infrastructure vs framework mean here?
Framework means you get developer primitives to assemble agent behavior in your own stack. Infrastructure means the platform also provides the hosted runtime, persistence, environments, and workflow execution layer those agents run on.
Does OpenAI offer hosted agent products too?
Yes. OpenAI now offers hosted tools and Agent Builder in adjacent parts of the platform. This page is specifically comparing the OpenAI Agents SDK itself against a managed cloud agent platform.
When should I choose Computer Agents over the OpenAI Agents SDK?
Choose Computer Agents when your agent needs hosted execution, persistent workspaces, native schedules or triggers, and lower operational overhead for real workflow automation.