OpenAI Agents SDK vs LangGraph
The simplest on-ramp versus the most controllable framework. The right call depends on lock-in tolerance and how complex your agent gets.
The OpenAI Agents SDK and LangGraph sit at opposite ends of the simplicity-versus-control spectrum. The Agents SDK gives you a tiny API and the fastest start if you are building on OpenAI. LangGraph gives you full control over state and flow at the cost of a steeper learning curve.
API surface
The OpenAI Agents SDK is deliberately small: agents, tools, handoffs, and guardrails, and not much else to learn. You can read the whole API in a sitting. LangGraph is larger because it exposes the graph machinery, which is more to learn but also more to control.
Provider lock-in
This is the biggest dividing line. The Agents SDK works best with OpenAI models; you can reach others through a compatibility layer, but the smooth path is OpenAI-native. LangGraph is provider-agnostic and lets you mix OpenAI, Anthropic, Google, and open models freely. If avoiding lock-in matters to you, that weighs toward LangGraph.
State and persistence
LangGraph has first-class state persistence and checkpointing, so a run can pause and resume. The OpenAI Agents SDK leaves long-term state largely to you. For short request-response agents that never need to resume, the SDK's simplicity is a feature. For long-running or resumable agents, LangGraph's persistence is hard to beat.
How to choose
If you are committed to OpenAI and want the least friction, start with the OpenAI Agents SDK; you can always graduate later. If you expect provider flexibility, complex control flow, or resumable runs, start with LangGraph and accept the steeper curve. A reasonable path is to prototype with the Agents SDK and move to LangGraph if and when the requirements outgrow it.