OpenAI Agents SDK TypeScript

OpenAI Agents SDK

Build text and voice agents with a small set of primitives.

Text Agent
Voice Agent

import { Agent, run } from '@openai/agents';

const agent = new Agent({
  name: 'Assistant',
  instructions: 'You are a helpful assistant.',
});

const result = await run(
  agent,
  'Write a haiku about recursion in programming.',
);

console.log(result.finalOutput);

import { RealtimeAgent, RealtimeSession } from '@openai/agents/realtime';

const agent = new RealtimeAgent({
  name: 'Assistant',
  instructions: 'You are a helpful assistant.',
});

// Automatically connects your microphone and audio output in the browser via WebRTC.
const session = new RealtimeSession(agent);
await session.connect({
  apiKey: '<client-api-key>',
});

Overview

The OpenAI Agents SDK for TypeScript enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It’s a production-ready upgrade of our previous experimentation for agents, Swarm, that’s also available in Python. The Agents SDK has a very small set of primitives:

Agents, which are LLMs equipped with instructions and tools
Agents as tools / Handoffs, which allow agents to delegate to other agents for specific tasks
Guardrails, which enable the inputs to agents to be validated

In combination with TypeScript, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real-world applications without a steep learning curve. In addition, the SDK comes with built-in tracing that lets you visualize and debug your agentic flows, as well as evaluate them and even fine-tune models for your application.

Why use the Agents SDK

The SDK has two driving design principles:

Enough features to be worth using, but few enough primitives to make it quick to learn.
Works great out of the box, but you can customize exactly what happens.

Here are the main features of the SDK:

Agent loop: A built-in agent loop that handles tool invocation, sends results back to the LLM, and continues until the task is complete.
TypeScript-first: Orchestrate and chain agents using native TypeScript language features, without needing to learn new abstractions.
Agents as tools / Handoffs: A powerful mechanism for coordinating and delegating work across multiple agents.
Guardrails: Run input validation and safety checks in parallel with agent execution, and fail fast when checks do not pass.
Function tools: Turn any TypeScript function into a tool with automatic schema generation and Zod-powered validation.
MCP server tool calling: Built-in MCP server tool integration that works the same way as function tools.
Sessions: A persistent memory layer for maintaining working context within an agent loop.
Human in the loop: Built-in mechanisms for involving humans across agent runs.
Tracing: Built-in tracing for visualizing, debugging, and monitoring workflows, with support for the OpenAI suite of evaluation, fine-tuning, and distillation tools.
Realtime Agents: Build powerful voice agents with features such as automatic interruption detection, context management, guardrails, and more.

Installation

npm install @openai/agents zod

The SDK requires Zod v4; installing zod via npm will fetch the latest v4 release.

Choose your starting point

Most first-time users only need one of these entry points:

Start with	Use it when	Notes
`@openai/agents`	You are building most text or voice applications.	Recommended default. It includes the OpenAI provider setup and exposes voice APIs under `@openai/agents/realtime`.
`@openai/agents-realtime`	You only need the standalone Realtime package.	Useful for browser-only voice apps or when you want a narrower package boundary.
Lower-level packages (`@openai/agents-core`, `@openai/agents-openai`, `@openai/agents-extensions`)	You need lower-level composition, custom provider wiring, or specific integrations.	Most new users can ignore these until they have a concrete need.

Hello world example

import { Agent, run } from '@openai/agents';

const agent = new Agent({
  name: 'Assistant',
  instructions: 'You are a helpful assistant',
});

const result = await run(
  agent,
  'Write a haiku about recursion in programming.',
);
console.log(result.finalOutput);

// Code within the code,
// Functions calling themselves,
// Infinite loop's dance.

(If running this, ensure you set the OPENAI_API_KEY environment variable)

export OPENAI_API_KEY=sk-...

Start here

Pick one path first, get it working end to end, then come back for the deeper guides.

Quickstart Build your first text-based agent and learn the core SDK workflow.

Voice Agents Quickstart Start with the Realtime voice path when you are building spoken interactions.

Choose your path

Use this table when you know the job you want to do, but not which page explains it.

Goal	Start here
Build the first text agent and see one complete run	Quickstart
Add function tools, hosted tools, or agents as tools	Tools
Decide between handoffs and manager-style orchestration	Agent orchestration
Keep memory across turns	Running agents and Sessions
Use OpenAI models, websocket transport, or non-OpenAI providers	Models
Review outputs, run items, interruptions, and resume state	Results
Build a low-latency voice agent	Voice Agents Quickstart