OpenAI Agents SDK

Sweet spot: a team standardised on OpenAI, building agents, and wanting an official SDK with the least possible framework tax. The Agents SDK gives you exactly what you need — lightweight primitives, solid tracing, typed handoffs — and nothing you don't. Pair it with the Responses API and Realtime API and you have a tight OpenAI-native stack. Failure modes. If you rely on non-OpenAI providers as first-class citizens, the Agents SDK works but without the same feature polish — tracing is less rich, some features assume OpenAI semantics. For durable long-running workflows with branches, resumes, and scheduled runs, LangGraph is still deeper. And it is genuinely minimal — which means you will build your own evals, your own memory, your own observability around it as you scale. What to pilot. Rebuild one agent you currently run on LangChain or a custom framework using the OpenAI Agents SDK. Compare lines of code, trace quality, and readability. If the simpler framework is enough for your needs, commit — the operational savings of running fewer dependencies add up. If you find yourself re-adding LangGraph-shaped features, stay with LangGraph.

The OpenAI Agents SDK is the official Python framework for building agent applications, released by OpenAI as the successor to the earlier (and now-deprecated) Swarm experiment. It provides a small, focused set of primitives — Agents, Handoffs, Guardrails, and Sessions — that combine into arbitrarily complex workflows without imposing a heavy abstraction layer. Agents are lightweight objects with a model, instructions, and tools. Handoffs let one agent transfer a conversation to another (typed and structured, not free-form). Guardrails are input/output validation that short-circuit on policy violations. Sessions provide memory. Tracing is first-class — every agent run emits a structured trace that you can view in the OpenAI traces UI or export via OpenTelemetry. Unlike heavier frameworks, the Agents SDK is explicitly minimal: it does not try to be a universal orchestration layer. It is designed to work best with OpenAI and OpenAI-compatible models, and it plays cleanly with other OpenAI surfaces (Responses API, Realtime API, Assistants). MIT-licensed, officially supported, and rapidly gaining adoption as the canonical "official" Python agent framework for teams already on OpenAI.