[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-2203":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":32,"readmeContent":33,"aiSummary":34,"trendingCount":16,"starSnapshotCount":16,"syncStatus":35,"lastSyncTime":36,"discoverSource":37},2203,"openai-agents-python","openai\u002Fopenai-agents-python","openai","A lightweight, powerful framework for multi-agent workflows","https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002F",null,"Python",27083,4177,213,44,0,22,168,876,127,45,"MIT License",false,"main",true,[27,28,29,30,7,31],"agents","ai","framework","llm","python","2026-06-12 02:00:38","# OpenAI Agents SDK [![PyPI](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fopenai-agents?label=pypi%20package)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fopenai-agents\u002F)\n\nThe 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.\n\n\u003Cimg src=\"https:\u002F\u002Fcdn.openai.com\u002FAPI\u002Fdocs\u002Fimages\u002Forchestration.png\" alt=\"Image of the Agents Tracing UI\" style=\"max-height: 803px;\">\n\n> [!NOTE]\n> Looking for the JavaScript\u002FTypeScript version? Check out [Agents SDK JS\u002FTS](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-js).\n\n### Core concepts:\n\n1. [**Agents**](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Fagents): LLMs configured with instructions, tools, guardrails, and handoffs\n1. [**Sandbox Agents**](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Fsandbox_agents): Agents preconfigured to work with a container to perform work over long time horizons.\n1. **[Agents as tools](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Ftools\u002F#agents-as-tools) \u002F [Handoffs](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Fhandoffs\u002F)**: Delegating to other agents for specific tasks\n1. [**Tools**](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Ftools\u002F): Various Tools let agents take actions (functions, MCP, hosted tools)\n1. [**Guardrails**](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Fguardrails\u002F): Configurable safety checks for input and output validation\n1. [**Human in the loop**](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Fhuman_in_the_loop\u002F): Built-in mechanisms for involving humans across agent runs\n1. [**Sessions**](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Fsessions\u002F): Automatic conversation history management across agent runs\n1. [**Tracing**](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Ftracing\u002F): Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows\n1. [**Realtime Agents**](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Frealtime\u002Fquickstart\u002F): Build powerful voice agents with `gpt-realtime-1.5` and full agent features\n\nExplore the [examples](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python\u002Ftree\u002Fmain\u002Fexamples) directory to see the SDK in action, and read our [documentation](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002F) for more details.\n\n## Get started\n\nTo get started, set up your Python environment (Python 3.10 or newer required), and then install OpenAI Agents SDK package.\n\n### venv\n\n```bash\npython -m venv .venv\nsource .venv\u002Fbin\u002Factivate  # On Windows: .venv\\Scripts\\activate\npip install openai-agents\n```\n\nFor 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]'`.\n\n### uv\n\nIf you're familiar with [uv](https:\u002F\u002Fdocs.astral.sh\u002Fuv\u002F), installing the package would be even easier:\n\n```bash\nuv init\nuv add openai-agents\n```\n\nFor 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]'`.\n\n## Run your first Sandbox Agent\n\n[Sandbox Agents](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002Fsandbox_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.\n\n```python\nfrom agents import Runner\nfrom agents.run import RunConfig\nfrom agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig\nfrom agents.sandbox.entries import GitRepo\nfrom agents.sandbox.sandboxes import UnixLocalSandboxClient\n\nagent = SandboxAgent(\n    name=\"Workspace Assistant\",\n    instructions=\"Inspect the sandbox workspace before answering.\",\n    default_manifest=Manifest(\n        entries={\n            \"repo\": GitRepo(repo=\"openai\u002Fopenai-agents-python\", ref=\"main\"),\n        }\n    ),\n)\n\nresult = Runner.run_sync(\n    agent,\n    \"Inspect the repo README and summarize what this project does.\",\n    # Run this agent on the local filesystem\n    run_config=RunConfig(sandbox=SandboxRunConfig(client=UnixLocalSandboxClient())),\n)\nprint(result.final_output)\n\n# This project provides a Python SDK for building multi-agent workflows.\n```\n\n(_If running this, ensure you set the `OPENAI_API_KEY` environment variable_)\n\n(_For Jupyter notebook users, see [hello_world_jupyter.ipynb](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python\u002Fblob\u002Fmain\u002Fexamples\u002Fbasic\u002Fhello_world_jupyter.ipynb)_)\n\nExplore the [examples](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python\u002Ftree\u002Fmain\u002Fexamples) directory to see the SDK in action, and read our [documentation](https:\u002F\u002Fopenai.github.io\u002Fopenai-agents-python\u002F) for more details.\n\n## Acknowledgements\n\nWe'd like to acknowledge the excellent work of the open-source community, especially:\n\n- [Pydantic](https:\u002F\u002Fdocs.pydantic.dev\u002Flatest\u002F)\n- [Requests](https:\u002F\u002Fgithub.com\u002Fpsf\u002Frequests)\n- [MCP Python SDK](https:\u002F\u002Fgithub.com\u002Fmodelcontextprotocol\u002Fpython-sdk)\n- [Griffe](https:\u002F\u002Fgithub.com\u002Fmkdocstrings\u002Fgriffe)\n\nThis library has these optional dependencies:\n\n- [websockets](https:\u002F\u002Fgithub.com\u002Fpython-websockets\u002Fwebsockets)\n- [SQLAlchemy](https:\u002F\u002Fgithub.com\u002Fsqlalchemy\u002Fsqlalchemy)\n- [any-llm](https:\u002F\u002Fgithub.com\u002Fmozilla-ai\u002Fany-llm) and [LiteLLM](https:\u002F\u002Fgithub.com\u002FBerriAI\u002Flitellm)\n\nWe also rely on the following tools to manage the project:\n\n- [uv](https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fuv) and [ruff](https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fruff)\n- [mypy](https:\u002F\u002Fgithub.com\u002Fpython\u002Fmypy) and [Pyright](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fpyright)\n- [pytest](https:\u002F\u002Fgithub.com\u002Fpytest-dev\u002Fpytest) and [Coverage.py](https:\u002F\u002Fgithub.com\u002Fcoveragepy\u002Fcoveragepy)\n- [MkDocs](https:\u002F\u002Fgithub.com\u002Fsquidfunk\u002Fmkdocs-material)\n\nWe're committed to continuing to build the Agents SDK as an open source framework so others in the community can expand on our approach.\n","OpenAI Agents SDK 是一个轻量级且强大的多代理工作流构建框架。它支持OpenAI的响应和聊天完成API，同时也兼容超过100种其他大语言模型（LLM），具有高度的灵活性。该框架的核心功能包括配置有指令、工具、防护措施及交接能力的代理，以及能够在长时间跨度内执行工作的沙盒代理等。此外，还提供了自动会话历史管理、内置的工作流跟踪与调试等功能。适用于需要开发涉及多个AI代理协作的应用场景，如复杂的对话系统或任务自动化流程。",2,"2026-06-11 02:48:52","top_language"]