OpenAI Agents SDK

What it does

OpenAI Agents SDK provides a compact set of primitives for configuring agents with instructions, tools, guardrails, sessions, and handoffs. It includes tracing and supports both single-agent and multi-agent designs.

Best for

• Python applications that need explicit agent handoffs
• Workflows requiring guardrails and built-in tracing
• Teams that want a small framework rather than a large orchestration platform

Before you use it

Treat tool permissions, output validation, and retry behavior as application concerns. Start with narrow tools and observable runs before allowing an agent to make external changes.

Core design model

The SDK keeps the main abstractions small: an agent combines instructions, a model, and tools; a runner manages execution; handoffs transfer work to another agent; guardrails inspect inputs or outputs; sessions retain conversation state; and tracing records the run. This makes it approachable for applications that need more structure than one model call without adopting a full workflow platform.

Start with one agent and ordinary Python functions as tools. Add a handoff only when a specialist needs a distinct instruction set or tool boundary. If the order of steps is known in advance, keep that order in application code rather than asking the model to plan it. Deterministic code is easier to test, cheaper to run, and clearer during failures.

Production checks

Define typed inputs and outputs for every tool, validate model-produced arguments again inside the function, and make write operations idempotent where possible. Put approval gates in front of payments, messages, permission changes, deletions, and other high-impact actions. Cap turns and retries so a failed tool or circular handoff cannot consume an unbounded budget.

Use tracing during development, but decide which content may be retained before enabling it on sensitive workloads. Redact secrets and personal data, attach a stable request identifier, and monitor task success, tool errors, latency, token use, and human interventions. Build evaluation cases from real failure modes rather than relying on a successful demo conversation.

When to choose it

OpenAI Agents SDK fits Python teams that want first-class tools, guardrails, handoffs, sessions, MCP integration, and tracing with a relatively compact API. Choose LangGraph when explicit graph state, durable checkpoints, and complex pause/resume behavior are central. Consider Microsoft Agent Framework for a Python/.NET estate or Microsoft-hosted enterprise integration. For a fixed sequence of model calls, normal application code may remain the best framework.