# OpenAI Agents SDK [![PyPI](https://img.shields.io/pypi/v/openai-agents?label=pypi%20package)](https://pypi.org/project/openai-agents/)

The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs.

<img src="https://cdn.openai.com/API/docs/images/orchestration.png" alt="Image of the Agents Tracing UI" style="max-height: 803px;">

> [!NOTE]
> Looking for the JavaScript/TypeScript version? Check out [Agents SDK JS/TS](https://github.com/openai/openai-agents-js).

### Core concepts:

1. [**Agents**](https://openai.github.io/openai-agents-python/agents): LLMs configured with instructions, tools, guardrails, and handoffs
1. [**Sandbox Agents**](https://openai.github.io/openai-agents-python/sandbox_agents): Agents preconfigured to work with a container to perform work over long time horizons.
1. **[Agents as tools](https://openai.github.io/openai-agents-python/tools/#agents-as-tools) / [Handoffs](https://openai.github.io/openai-agents-python/handoffs/)**: Delegating to other agents for specific tasks
1. [**Tools**](https://openai.github.io/openai-agents-python/tools/): Various Tools let agents take actions (functions, MCP, hosted tools)
1. [**Guardrails**](https://openai.github.io/openai-agents-python/guardrails/): Configurable safety checks for input and output validation
1. [**Human in the loop**](https://openai.github.io/openai-agents-python/human_in_the_loop/): Built-in mechanisms for involving humans across agent runs
1. [**Sessions**](https://openai.github.io/openai-agents-python/sessions/): Automatic conversation history management across agent runs
1. [**Tracing**](https://openai.github.io/openai-agents-python/tracing/): Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows
1. [**Realtime Agents**](https://openai.github.io/openai-agents-python/realtime/quickstart/): Build powerful voice agents with `gpt-realtime-2.1` and full agent features

Explore the [examples](https://github.com/openai/openai-agents-python/tree/main/examples) directory to see the SDK in action, and read our [documentation](https://openai.github.io/openai-agents-python/) for more details.

## Get started

To get started, set up your Python environment (Python 3.10 or newer required), and then install OpenAI Agents SDK package.

### venv

```bash
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install openai-agents
```

For voice support, install with the optional `voice` group: `pip install 'openai-agents[voice]'`. For Redis session support, install with the optional `redis` group: `pip install 'openai-agents[redis]'`.

### uv

If you're familiar with [uv](https://docs.astral.sh/uv/), installing the package would be even easier:

```bash
uv init
uv add openai-agents
```

For voice support, install with the optional `voice` group: `uv add 'openai-agents[voice]'`. For Redis session support, install with the optional `redis` group: `uv add 'openai-agents[redis]'`.

## Run your first agents

The SDK supports three primary ways to run agents. Set the `OPENAI_API_KEY` environment variable before running any of these examples.

### Run a sandbox agent

Use a [`SandboxAgent`](https://openai.github.io/openai-agents-python/sandbox_agents) when the agent needs to inspect files, run commands, apply patches, or preserve workspace state across longer tasks.

```python
from agents import Runner
from agents.run import RunConfig
from agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig
from agents.sandbox.entries import GitRepo
from agents.sandbox.sandboxes import UnixLocalSandboxClient

agent = SandboxAgent(
    name="Workspace Assistant",
    instructions="Inspect the sandbox workspace before answering.",
    default_manifest=Manifest(entries={"repo": GitRepo(repo="openai/openai-agents-python", ref="main")}),
)

result = Runner.run_sync(
    agent,
    "Inspect the repo README and summarize what this project does.",
    run_config=RunConfig(sandbox=SandboxRunConfig(client=UnixLocalSandboxClient())),
)
print(result.final_output)
```

### Run a text agent

Use a text `Agent` for workflows that do not need a persistent realtime connection or a sandbox workspace.

```python
from agents import Agent, Runner

agent = Agent(name="Assistant", instructions="You are a helpful assistant")

result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")
print(result.final_output)

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

(_For Jupyter notebook users, see [hello_world_jupyter.ipynb](https://github.com/openai/openai-agents-python/blob/main/examples/basic/hello_world_jupyter.ipynb)_)

### Run a realtime agent

Use a [`RealtimeAgent`](https://openai.github.io/openai-agents-python/realtime/quickstart/) for low-latency, server-side voice and multimodal experiences over WebSocket.

```python
import asyncio
from agents.realtime import RealtimeAgent, RealtimeRunner

async def main() -> None:
    agent = RealtimeAgent(name="Assistant", instructions="You are a helpful voice assistant. Keep responses short.")
    runner = RealtimeRunner(starting_agent=agent)
    session = await runner.run()

    async with session:
        await session.send_message("Say hello in one short sentence.")
        async for event in session:
            if event.type == "audio":
                # Forward or play event.audio.data.
                pass
            elif event.type == "history_added":
                print(event.item)
            elif event.type == "agent_end":
                break

if __name__ == "__main__":
    asyncio.run(main())
```

Explore the [examples](https://github.com/openai/openai-agents-python/tree/main/examples) directory to see the SDK in action, and read our [documentation](https://openai.github.io/openai-agents-python/) for more details.

## Acknowledgements

We'd like to acknowledge the excellent work of the open-source community, especially:

- [Pydantic](https://docs.pydantic.dev/latest/)
- [Requests](https://github.com/psf/requests)
- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)
- [Griffe](https://github.com/mkdocstrings/griffe)

This library has these optional dependencies:

- [websockets](https://github.com/python-websockets/websockets)
- [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy)
- [any-llm](https://github.com/mozilla-ai/any-llm) and [LiteLLM](https://github.com/BerriAI/litellm)

We also rely on the following tools to manage the project:

- [uv](https://github.com/astral-sh/uv) and [ruff](https://github.com/astral-sh/ruff)
- [mypy](https://github.com/python/mypy) and [Pyright](https://github.com/microsoft/pyright)
- [pytest](https://github.com/pytest-dev/pytest) and [Coverage.py](https://github.com/coveragepy/coveragepy)
- [MkDocs](https://github.com/squidfunk/mkdocs-material)

We're committed to continuing to build the Agents SDK as an open source framework so others in the community can expand on our approach.
