openai/openai-agents-python Source: https://github.com/openai/openai-agents-python # OpenAI Agents SDK PyPI (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. > [!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 that enable agents to 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-1.5 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 the 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 is 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 Sandbox Agent Sandbox Agents (https://openai.github.io/openai-agents-python/sandbox_agents) are new in version 0.14.0. A sandbox agent is an agent that uses a computer environment to perform real work with a filesystem, in an environment you configure and control. Sandbox agents are useful when the agent needs to inspect files, run commands, apply patches, or maintain 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 this agent on the local filesystem run_config=RunConfig(sandbox=SandboxRunConfig(client=UnixLocalSandboxClient())), ) print(result.final_output) # This project provides a Python SDK for building multi-agent workflows. (If running this, ensure you set the OPENAI_API_KEY environment variable) (For Jupyter notebook users, see hello_world_jupyter.ipynb (https://github.com/openai/openai-agents-python/blob/main/examples/basic/hello_world_jupyter.ipynb)) 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.