JD Open‑Sources JoyAgent‑JDGenie: A Product‑Grade Java Multi‑Agent AI Platform
JD Cloud has released JoyAgent‑JDGenie, the first fully product‑grade open‑source Java multi‑agent system that bundles front‑end, back‑end, framework, engine and core agents, supports major LLMs, offers layered architecture, Docker or manual deployment, and showcases demos such as PPT generation and sales analysis.
JoyAgent‑JDGenie Overview
JoyAgent‑JDGenie is an open‑source, product‑grade multi‑agent system written in Java. It provides the full stack—front‑end, back‑end, framework, engine, and core agents—100% open source, enabling developers to launch an enterprise‑level multi‑agent application in about five minutes.
Key Advantages
Complete product‑level source code, ready to use out of the box.
Cloud‑agnostic; can be deployed locally without binding to a specific cloud provider.
Supports multiple large language models, e.g., deepseekV3 and gpt4.1.
Comparison with Other Chinese AI Products
Unlike SDK‑only offerings such as SpringAI‑Alibaba and Coze, which focus on AI framework tooling for Java developers, JoyAgent‑JDGenie delivers a full product‑level solution that includes UI, backend services, and ready‑to‑run agents.
System Architecture
The system follows a layered design with collaborative multi‑agent interaction.
Model and Tools Layer – Provides foundational capabilities:
LLMs: deepseekV3, gpt4.1 for reasoning and generation.
NLP Tools: web search, browser use for information retrieval and interaction.
Report Tools: html tool for web page generation, ppt tool for PowerPoint creation.
Memory Layer – Persists essential information such as session history, task history, user profiles, and domain knowledge bases, enabling continuous learning and personalized interaction.
Agent Layer – Contains reasoning and planning logic, a library of base agents ( AgentBase), and mechanisms for multi‑agent collaboration.
Agent Application Layer – Exposes top‑level services to users, including DataAgent (data‑related tasks), Genie (general AI assistant), and AI‑assisted interview scenarios.
The architecture leverages multi‑level, multi‑modal reasoning, cross‑task workflow memory, and an automatic tool‑evolution mechanism that decomposes and recomposes tasks to improve efficiency.
Recent Additions
A DataAgent capability has been added, extending the system with data‑analysis functions.
Deployment Options
Two deployment methods are provided:
One‑click Docker installation.
Manual deployment following the provided guide.
Community feedback reports issues such as Docker build failures and runtime errors, indicating the need for further troubleshooting.
Demonstration Screenshots
Home page, Genie generating a PPT with market suggestions for Southeast Asian cross‑border e‑commerce, and Genie analyzing supermarket sales data are shown in the following images.
Repository
https://github.com/jd-opensource/joyagent-jdgenie
Signed-in readers can open the original source through BestHub's protected redirect.
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