OxyGent - JD Retail's open-source multi-agent collaboration framework

What is OxyGent?

OxyGent is an open-source multi-agent collaboration framework developed by the JD Retail Oxygen team. It helps developers efficiently assemble multi-agent systems. OxyGent abstracts tools, models, and agents into pluggable modules (Oxy), allowing for flexible combination like building blocks, and boasts extreme scalability and end-to-end decision traceability. Based on a team-based collaborative interface, developers can assign tasks as if communicating with real teammates, with agents automatically responding and providing feedback. OxyGent supports agent cognition visualization, performance profiling, and continuous evolution, helping developers quickly build, deploy, and optimize multi-agent systems.

OxyGent's main functions

• Team collaboration interfaceBased on a team chat-like interface, developers can assign tasks as if communicating with teammates, and the intelligent agent automatically responds and provides feedback, achieving efficient collaboration.
• Intelligent agent cognitive visualizationFrom high-level policies to atomic operations, it provides full visualization of the agent's reasoning path, supports auditing and traceability, and ensures decision-making transparency.
• Multi-agent system performance analysisBuilt-in production-grade time tracking function, which visualizes the time consumption of intelligent agents in real time, helping developers quickly locate performance bottlenecks.
• Exploring the many possibilities of intelligent agentsIt supports replaying decision branches, resampling alternative inference paths, and large-scale stress testing to explore the various possibilities of intelligent agents.
• Continuous evolution and self-optimizationIt has a built-in evaluation engine that supports agents to continuously evolve themselves through knowledge feedback mechanisms and optimize the decision-making process.

OxyGent's technical principles

• Modular architectureOxyGent abstracts tools, models, and agents into pluggable modules (Oxy), allowing for flexible combination like building blocks. Each module can be developed and tested independently, supporting hot-swapping and cross-scenario reuse.
• Dynamic Programming ParadigmIntelligent agents can intelligently decompose tasks, negotiate solutions, and adapt to changes in real time. Based on the dynamic programming paradigm, the intelligent agent automatically adjusts its behavior according to task requirements, achieving efficient collaboration.
• End-to-end decision tracingEach decision step automatically generates a traceable decision graph, similar to Git version control. Auditing and backtracking are supported to ensure the transparency of the agent's behavior.
• Distributed architectureThe underlying architecture uses a flexible structure, supporting arbitrary agent topologies. It optimizes the performance of distributed systems based on automatic dependency mapping and visual debugging.

OxyGent's project address

• Project official websitehttps://oxygent.jd.com/
• GitHub repositoryhttps://github.com/jd-opensource/OxyGent

Application scenarios of OxyGent

• Automated officeBased on intelligent agents to allocate and manage tasks, it automates office processes such as document processing, email classification, and scheduling, thereby improving work efficiency.
• Document processingIntelligent body can automatically generate document content, assist in editing, or perform document review and proofreading to ensure document quality.
• Code generation and optimizationThe intelligent agent generates code snippets or complete programs based on requirements, or analyzes code performance and provides optimization suggestions to improve development efficiency.
• Intelligent Customer ServiceIntelligent agents can automatically answer customer questions and provide immediate support, or multiple intelligent agents can collaborate to handle complex problems, thereby improving customer satisfaction.
• Data Analysis and Decision SupportIntelligent agents automatically collect and analyze data to provide decision support, or make predictions and models based on historical data.