--- title: "openai-agents-python" type: "tool" slug: "openai-openai-agents-python" canonical_url: "https://www.graphcanon.com/tools/openai-openai-agents-python" github_url: "https://github.com/openai/openai-agents-python" homepage_url: "https://openai.github.io/openai-agents-python/" stars: 27709 forks: 4268 primary_language: "Python" license: "MIT" categories: ["developer-tools", "ai-agents"] tags: ["agents", "llm", "ai", "python", "harness", "openai", "framework"] updated_at: "2026-07-07T18:22:42.302263+00:00" --- # openai-agents-python > A lightweight yet powerful framework for building multi-agent workflows The OpenAI Agents SDK is a Python package designed to facilitate the creation of multi-agent systems. It supports various large language models (LLMs) and offers features like agent handoffs, guardrails, human-in-the-loop mechanisms, automated session management, and tracing of workflow execution. ## Facts - Repository: https://github.com/openai/openai-agents-python - Homepage: https://openai.github.io/openai-agents-python/ - Stars: 27,709 · Forks: 4,268 · Open issues: 65 · Watchers: 219 - Primary language: Python - License: MIT - Last pushed: 2026-07-07T06:08:15+00:00 ## Categories - [Developer Tools](/categories/developer-tools.md) - [AI Agents](/categories/ai-agents.md) ## Tags agents, llm, ai, python, harness, openai, framework ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [hermes-agent](/tools/nousresearch-hermes-agent.md) - The self-improving AI agent built by Nous Research (★ 210,880) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,659) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,019) - [JavaGuide](/tools/snailclimb-javaguide.md) - Snailclimb/JavaGuide: 面试 & 后端通用面试指南，覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发 (★ 156,863) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,298) - [dify](/tools/langgenius-dify.md) - Production-ready platform for agentic workflow development (★ 148,070) ## README (excerpt) ```text # OpenAI Agents SDK 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. Image of the Agents Tracing UI > [!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` 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 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 carry 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.", ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/openai-openai-agents-python`](/api/graphcanon/tools/openai-openai-agents-python) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_