jd-opensource/OxyGent

最近记录的来源活动: 2026年5月15日 08:17

SkillsMP 收录数据更新: 2026年11月2日 18:02

已收集 skills: 2

GitHub 星标: 2,073
GitHub Forks: 277

用 AI 助手安装

复制这段 Prompt，发给你正在使用的 AI 助手。

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/jd-opensource/OxyGent.

仓库 README

仓库 README 来源：README.md

查看 README 源文件

展开完整 README

[English](./README.md) | [中文](./README_zh.md) <p align="center"> <a href="https://github.com/jd-opensource/OxyGent/pulls"> <img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square" alt="PRs Welcome"> </a> <a href="https://github.com/jd-opensource/OxyGent/blob/v4/LICENSE"> <img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="license"/> </a> <a href="https://pypi.org/project/oxygent/"> <img src="https://img.shields.io/pypi/v/oxygent.svg?logo=pypi&logoColor=white" alt="pip"/> </a> <a href="https://arxiv.org/abs/2604.25602"> <img src="https://img.shields.io/badge/Paper-ACL%202026-orange" alt="paper"/> </a> <html> <h2 align="center"> <img src="https://storage.jd.com/ai-gateway-routing/prod_data/oxygent_github_images/banner.jpg" width="1256"/> </h2> <h3 align="center"> An advanced Python framework that empowers developers to quickly build production-ready intelligent systems. </h3> <h3 align="center"> Visit our website: <a href="http://oxygent.jd.com">OxyGent</a> ｜Open Source: <a href="https://github.com/jd-opensource/OxyGent">Python</a> or <a href="https://github.com/jd-opensource/JDOxyGent4J">Java</a> </h3> </html> ## 1. Project Overview **OxyGent** is an open-source framework that unifies tools, models, and agents into modular Oxy. Empowering developers with transparent, end-to-end pipelines, OxyGent makes building, running, and evolving multi-agent systems seamless and infinitely extensible. ## 2. Core Features 🏎️ **Efficient Development** - OxyGent is a modular multi-agent framework that lets you build, deploy, and evolve AI teams with unprecedented efficiency. Its standardized Oxy components snap together like LEGO bricks, enabling rapid assembly of agents while supporting hot-swapping and cross-scenario reuse - all through clean Python interfaces without messy configs. 🤝 **Intelligent Collaboration** - The framework supercharges collaboration with dynamic planning paradigms, where agents intelligently decompose tasks, negotiate solutions, and adapt to changes in real-time. Unlike rigid workflow systems, OxyGent's agents handle emergent challenges naturally while maintaining full auditability of every decision. 🕸️ **Elastic Architecture** - Under the hood, an elastic architecture supports any agent topology- from simple ReAct to complex hybrid planning patterns. Automated dependency mapping and visual debugging tools make it easy to optimize performance across distributed systems. 🔁 **Continuous Evolution** - Every interaction becomes a learning opportunity - thanks to built-in evaluation engines that auto-generate training data. Your agents continuously improve through knowledge feedback loops while maintaining full transparency. 📈 **Scalability** - Scaling follows Metcalfe's Law- OxyGent's distributed scheduler enables linear cost growth while delivering exponential gains in collaborative intelligence. The system effortlessly handles domain-wide optimization and real-time decision making at any scale. The latest version of OxyGent (July 15, 2025) in the [GAIA](https://huggingface.co/spaces/gaia-benchmark/leaderboard) get 59.14 points, and current top opensource system OWL gets 60.8 points. ![](https://storage.jd.com/ai-gateway-routing/prod_data/oxygent_github_images/points.png) ## 3. Framework Core Classes ![](docs/images/structure.png) ## 4. Feature Highlight **For Developers**: Focus on business logic without reinventing the wheel. **For Enterprises**: Replace siloed AI systems with a unified framework, reducing communication overhead. **For Users**: Experience seamless teamwork from an intelligent agent ecosystem. ## 5. Quick Start ### Step 1: Create and activate a python environment - Method 1: conda ```bash conda create -n oxy_env python==3.10 conda activate oxy_env ``` - Method 2: uv ```bash curl -LsSf https://astral.sh/uv/install.sh | sh uv python install 3.10 uv venv .venv --python 3.10 source .venv/bin/activate ``` ### Step 2: Install the required python package - Method 1: conda ```bash pip install oxygent ``` - Method 2: uv ```bash uv pip install oxygent ``` - Method 3: set develop environment ```bash git clone https://github.com/jd-opensource/OxyGent.git cd OxyGent pip install -r requirements.txt # or in uv brew install coreutils # maybe essential ``` ### Step 3: Node.js Environment (if using MCP) - Download and install **[Node.js](https://nodejs.org)** ### Step 4: Write a sample python script - demo.py ```python import os from oxygent import MAS, Config, oxy, preset_tools Config.set_agent_llm_model("default_llm") oxy_space = [ oxy.HttpLLM( name="default_llm", api_key=os.getenv("DEFAULT_LLM_API_KEY"), base_url=os.getenv("DEFAULT_LLM_BASE_URL"), model_name=os.getenv("DEFAULT_LLM_MODEL_NAME"), ), preset_tools.time_tools, oxy.ReActAgent( name="time_agent", desc="A tool that can query the time", tools=["time_tools"], ), preset_tools.file_tools, oxy.ReActAgent( name="file_agent", desc="A tool that can operate the file system", tools=["file_tools"], ), preset_tools.math_tools, oxy.ReActAgent( name="math_agent", desc="A tool that can perform mathematical calculations.", tools=["math_tools"], ), oxy.ReActAgent( is_master=True, name="master_agent", sub_agents=["time_agent", "file_agent", "math_agent"], ), ] async def main(): async with MAS(oxy_space=oxy_space) as mas: await mas.start_web_service( first_query="What time is it now? Please save it into time.txt." ) if __name__ == "__main__": import asyncio asyncio.run(main()) ``` ### Step 5: Set Environment Variables - Method 1: Declare in terminal ```bash export DEFAULT_LLM_API_KEY="your_api_key" export DEFAULT_LLM_BASE_URL="your_base_url" export DEFAULT_LLM_MODEL_NAME="your_model_name" ``` - Method 2: Create a .env file ```bash DEFAULT_LLM_API_KEY="your_api_key" DEFAULT_LLM_BASE_URL="your_base_url" DEFAULT_LLM_MODEL_NAME="your_model_name" ``` ### Step 6: Run the example - Start the multi-agent system ```bash python demo.py ``` ### Step 7: View the output - ![](https://storage.jd.com/ai-gateway-routing/prod_data/oxygent_github_images/vision.png) ## 6. Contributing There are several ways you can contribute to OxyGent: 1. Reporting Issues (Bugs & Errors) 2. Suggesting Enhancements 3. Improving Documentation - Fork the repository - Add your view in document - Send your pull request 4. Writing Code - Fork the repository - Create a new branch - Add your feature or improvement - Send your pull request We appreciate all kinds of contributions! 🎉🎉🎉 If you have problems about development, please check our document: **[Document](http://oxygent.jd.com/docs/)** ## 7. Community & Support If you encounter any issues along the way, you are welcomed to submit reproducible steps and log snippets in the project's Issues area, or contact the OxyGent Core team directly via your internal Slack. Welcome to contact us: <div align="center"> <img src="https://pfst.cf2.poecdn.net/base/image/b1e96084336a823af7835f4fe418ff49da6379570f0c32898de1ffe50304d564?w=1760&h=2085&pmaid=425510216" alt="contact" width="50%" height="50%"> </div> ## 8. About the Contributors Thanks to all the following [developers](https://github.com/jd-opensource/OxyGent/graphs/contributors) who have contributed to OxyGent. <a href="https://github.com/jd-opensource/OxyGent/graphs/contributors"> <img src="https://contrib.rocks/image?repo=jd-opensource/OxyGent" /> </a> ## FAQ ### General Questions **What is OxyGent?** OxyGent is an open-source framework that unifies tools, models, and agents into modular Oxy components. It empowers developers to build, run, and evolve multi-agent systems with transparent, end-to-end pipelines. **How is OxyGent different from other multi-agent frameworks?** OxyGent uses a unique "Oxy" abstraction that standardizes agent components, enabling hot-swapping and cross-scenario reuse. Unlike rigid workflow systems, OxyGent supports dynamic planning paradigms where agents intelligently decompose tasks and adapt to changes in real-time. **What is the Oxy abstraction?** Oxy is a standardized component model that snaps together like LEGO bricks. Each Oxy encapsulates a specific capability (tool, model, or agent) with clean Python interfaces, enabling rapid assembly and evolution of AI teams. ### Setup & Configuration **What are the system requirements?** - Python 3.10+ - pip package manager - API key from an LLM provider (OpenAI, Anthropic, etc.) **How do I install OxyGent?** ```bash pip install oxygent ``` **Can I use OxyGent with local models?** Yes! OxyGent supports any LLM provider that exposes a compatible API. Configure your local model via environment variables (`DEFAULT_LLM_BASE_URL`, `DEFAULT_LLM_MODEL_NAME`). ### Agent Development **How do I create a custom Oxy?** Create a new class that inherits from the base Oxy class and implement the required methods. See the [documentation](http://oxygent.jd.com/docs/) for detailed guides. **What planning paradigms does OxyGent support?** OxyGent supports multiple planning paradigms: - **ReAct**: Reasoning + Acting pattern for simple tasks - **Dynamic Planning**: Agents intelligently decompose tasks and negotiate solutions - **Hybrid Planning**: Combines multiple paradigms for complex scenarios **How does OxyGent handle agent collaboration?** OxyGent's elastic architecture supports any agent topology. Agents communicate through standardized interfaces, with automated dependency mapping and visual debugging tools for optimization. ### Deployment **How do I deploy OxyGent applications?** OxyGent applications can be deployed as standard Python applications: - **Local Development**: Run `python demo.py` for local testing - **Production**: Use the distributed scheduler for scaling across multiple nodes - **Docker**: Containerize your OxyGent application for consistent deployment **Is OxyGent suitable for enterprise use?** Yes! OxyGent is designed for enterprise scenarios with features like: - Distributed scheduling for horizontal scaling - Full auditability of every decision - Built-in evaluation engines for continuous improvement - Support for domain-wide optimization and real-time decision making ### Troubleshooting **Common Issues:** - **Import Errors**: Ensure Python 3.10+ is installed and dependencies are up to date - **API Key Errors**: Verify your LLM provider API key is correctly configured - **Agent Communication Issues**: Check that all Oxy components are properly registered **Where can I get help?** - [Official Documentation](http://oxygent.jd.com/docs/) - [GitHub Issues](https://github.com/jd-opensource/OxyGent/issues) - Internal Slack (for JD.com employees) ## 9. License [Apache License]( ./LICENSE.md) #### OxyGent is provided by Oxygen JD.com #### Thanks for your Contributions!

正在加载仓库 README…

这个仓库中的 skills

软件开发工程师

1 个职业分类 · 已分类 100%

skill

职业分类

描述

更新

oxygent-framework-explorer

软件开发工程师

Explore and understand the OxyGent framework structure, components, and implementation patterns.

原文语言：英语

2026-05-15

weather

软件开发工程师

Retrieve real-time weather data from public weather services without requiring API authentication.

原文语言：英语

2026-05-15

已展示 2 / 2 个已收集 Skill。