OpenAI Agents SDK
A lightweight, powerful framework for multi-agent workflows with tool use, handoffs, and guardrails.
What is this?
The OpenAI Agents SDK is a lightweight framework for building multi-agent workflows. Agents that use tools, maintain context, hand off to each other, and run guardrails on their inputs and outputs. It's the smallest useful abstraction over "call a model, do something, call it again" — and it's what a lot of production agent code ends up looking like once you strip the scaffolding.
What problem does it solve
The basic LLM loop is fine for chat. It falls apart the moment an agent needs to do real work: call an API, read a file, decide what to do next, pass half-finished work to another agent, and make sure none of it produces garbage.
You can write that yourself — everyone does, once — but you end up with the same 200 lines of boilerplate: tool registration, conversation state, handoff logic, guardrail checks. The SDK is that boilerplate, done well, with a small surface area.
How does it work
You define agents. An agent has a name, instructions, and a set of tools. You give it a task. It runs a loop: the model sees the conversation, decides to call a tool or respond, the tool executes, the result goes back in, repeat. Along the way it can hand off to another agent when the task crosses a domain boundary.
your task
│
▼
┌────────────────────┐
│ Agent │
│ instructions │
│ tools │
│ guardrails │
└────────┬───────────┘
│
model decides:
┌────┴────┐
▼ ▼
call tool respond
│ │
▼ │
result ─────┘
│
▼
handoff? ──yes──► another agent
│no
▼
final response
Guardrails run on the way in (validate the input) and on the way out (validate the output). If a guardrail trips, the agent gets feedback and tries again instead of returning something broken.
Repository Structure
openai/openai-agents-python/
├── src/ # core SDK code
├── examples/ # example implementations
├── tests/ # test suite
├── docs/ # documentation
└── pyproject.toml # package config
Key Features
- Agents with tools — define an agent with instructions and a set of functions it can call. The model decides when to use them.
- Handoffs — transfer a conversation to another agent mid-task. The new agent gets the full history and picks up where the last one stopped.
- Guardrails — validate inputs and outputs. A tripped guardrail feeds the failure back to the model so it can self-correct.
- Sessions and state — conversation state persists across turns, so you can pause and resume without losing context.
- Human-in-the-loop — pause execution for human review before a tool runs or a response is returned.
- Tracing — every run is traceable: what the model saw, what it decided, what the tools returned. Debugging agent code without this is misery.
What You'll Learn
- How to structure an agent loop that doesn't collapse under its own complexity
- How tool calling actually works end to end
- How to hand off between agents without losing context
- How to add guardrails that catch bad output before it reaches users
- How to trace and debug agent runs
See it in action
from agents import Agent, Runner
agent = Agent(
name="Assistant",
instructions="You are a helpful assistant.",
tools=[search_web, read_file],
)
result = Runner.run_sync(agent, "What's the weather in Tokyo?")
print(result.final_output)
The examples directory has working patterns for handoffs, guardrails, and human-in-the-loop. The docs cover the full API.
Want to learn more about this project?
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