Authority AI Editorial
OpenAI Agents SDK: handoffs, guardrails and tracing
The OpenAI Agents SDK is a Python framework for building software where more than one AI agent works on a task, and each agent can hand the task to another agent when it hits the edge of what it knows how to do. It is aimed at developers who already write Python and want a small, production-grade layer for agent orchestration rather than a full application platform. The SDK works with OpenAI models by default but is not locked to them: it can route to other providers through adapters, so a team is not forced to standardize on one vendor to use it.
What it does
The SDK is built around a short list of primitives instead of a large framework surface. An agent is an LLM configured with instructions, a set of tools, and optionally guardrails and other agents it can hand off to. Tools are plain functions, calls into Model Context Protocol servers, or hosted actions the model can invoke directly. A handoff lets one agent pass a running task to another agent that is better suited to it, so a general assistant can delegate a billing question to a billing agent without the developer writing custom routing logic. Guardrails run validation on what goes into an agent and what comes out of it, so a project can enforce constraints such as blocking a category of request or checking a reply before it reaches a user. Sessions store conversation history automatically across turns, with backends for SQLite, SQLAlchemy-supported databases, Redis, and MongoDB, so a developer does not have to hand-roll memory.
Beyond these core primitives, the documentation lays out several agent variants aimed at different workloads. Standard agents cover ordinary text-based tasks. Sandbox agents are preconfigured for container-based work that can run over an extended period, which suits coding-style tasks that touch a real repository rather than a single reply. Realtime agents add voice capability built on OpenAI's realtime models, with automatic interruption handling so a spoken exchange feels like a conversation rather than a queue of turns. Voice pipelines chain speech-to-text, the agent loop, and text-to-speech together for applications that need a full spoken interface rather than raw text in and out.
How you use it
A project starts with pip install openai-agents, then defines one or more agents in code: instructions as a string, tools as decorated functions or MCP connections, and guardrails as validation functions attached to the agent. Handoffs are declared by listing which other agents an agent may transfer to. From there the SDK's run loop handles the back-and-forth: calling the model, executing any tool calls it requests, checking guardrails, and following a handoff if the agent triggers one. The same pattern extends to sandbox agents, which run in an isolated workspace with file and repository access for longer-running coding-style tasks, and to realtime and voice agents, which combine speech-to-text and text-to-speech around the same agent loop for voice applications.
What to know before adopting it
The SDK is MIT licensed and requires Python 3.10 or later, with optional extras for voice support and Redis-backed sessions. It is the direct successor to Swarm, an earlier experimental framework OpenAI published for exploring lightweight multi-agent patterns. Swarm's own repository now states plainly that it has been replaced by the Agents SDK and recommends migrating any production use to it, so a team evaluating this space should treat Swarm as retired rather than as a lighter alternative. Tracing is built in: every agent run, tool call, and handoff can be visualized for debugging, and the same traces feed evaluation and fine-tuning workflows. Because models, sessions, and tracing are all pluggable, the cost model is whatever the underlying model provider and storage backend charge; the SDK itself adds no separate fee.
Adopting it also means deciding on a memory strategy up front, since sessions are not automatic until a backend is chosen, and picking between a handoff-based design and a sandbox-agent design for any long-running task. The documentation frames this as a decision tree: simple text workflows lean on handoffs between narrowly scoped agents, while tasks that need sustained file or repository access lean on sandbox agents instead. Getting that choice right early avoids restructuring an agent's boundaries later once tools and guardrails are already attached to it.
Where it sits next to similar tools
Inside the OpenAI ecosystem, this SDK is the layer above single-agent chat completions and below a full application: it assumes a developer wants explicit control over agent boundaries rather than one large prompt doing everything. Compared to broader open-source agent frameworks that bring their own extension marketplaces or desktop apps, the Agents SDK stays close to plain Python and a small set of primitives, which makes it easier to read but means a team supplies its own tool catalog rather than pulling from a large shared library. Anyone writing an agent's methods by hand can shorten that step: the AI Ultimate Skill Collection packages 3,029 ready-made skills in 12 packs, each a written procedure your agent follows step by step.