ruoyi-ai and ragent
The frameworks are competitors, as both aim to provide comprehensive AI application development capabilities, with B specifically focusing on RAG within the Spring Boot ecosystem, while A offers a broader enterprise-grade AI application development framework that also includes high-precision retrieval optimization relevant to RAG.
About ruoyi-ai
ageerle/ruoyi-ai
面向企业级市场的一站式AI应用开发框架，支持多厂商大模型统一接入与管理，具备安全可控的企业知识库与高精度检索优化能力，提供可视化流程编排、自主决策智能体与多智能体协同调度，兼容主流 Agent Skill 协议，同时支持微信生态扩展，帮助企业与开发者零门槛快速构建安全、高效、可落地的AI智能体应用与行业解决方案。
Built on Spring Boot 4.0, Spring AI 2.0, and Langchain4j, the platform integrates multiple vector databases (Milvus, Weaviate, Qdrant) for RAG-based knowledge retrieval and supports MCP protocol tools alongside custom Skills. Multi-agent coordination uses a Supervisor pattern with multiple decision models, while SSE streaming and WebSocket enable real-time workflow execution with document parsing (PDF, Word, Excel) and image analysis capabilities.
About ragent
nageoffer/ragent
RAG综合智能体 - 基于Spring Boot的智能文档处理与检索系统，集成向量数据库，拥有智能问答、知识库、会话记忆、深度思考等功能
Implements a production-grade RAG pipeline with parallel multi-channel retrieval (semantic + keyword-based), model routing across multiple LLM providers with automatic failover, and document ingestion via composable node-based ETL. Integrates Milvus for vector search, RocketMQ for async processing, and supports MCP tool invocation alongside knowledge retrieval, with full request tracing across intent recognition, query rewriting, and response generation stages.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work