a multi-agent RAG application with Spring AI Alibaba

An enterprise-level AI Love Master Agent based on Spring Boot 3, Spring AI, RAG, Tool Calling, and MCP, offering emotional guidance with multi-round conversations, memory persistence, and RAG retrieval. Using the ReAct pattern, it autonomously calls tools for tasks like web search, downloads, and PDF plan generation.

Built a modular Spring Boot AI integration layer using Spring AI to connect enterprise microservices with multiple LLMs (OpenAI). Implemented a RAG pipeline with Qdrant for context-aware responses and added agent-style tool calling so LLMs can trigger Java backend functions for multi-step workflows.

A demo on how to build multi agent rag system using spring ai

Intelligent OnCall Agent System — RAG Q&A + AIOps multi-agent alert analysis, built with Spring Boot + Spring AI + Milvus

A multi-agent AI platform built with Spring AI, featuring RAG, interview simulation, and chat tools.

Enterprise AI Agent Platform | RAG-powered Q&A + AIOps Automation | 2 Spring Boot 3 + Spring AI + Milvus + DashScope | 3 Intelligent chat, multi-agent collaboration, auto-diagnosis & reporting

This is a simple implementation of an AI agent in the Spring ecosystem using LangChain4j. The agent answers questions about me, my projects, my education, and my knowledge using Agentic Multi-Modal RAG, AI Agents with Spring, and LangChain4j

Exploring Spring AI 2.0 capabilities: RAG pipeline with intelligent agents, query routing, answer evaluation, and multi-format document ingestion using pgvector

AI Deep Research platform built with Spring Boot, FastAPI, Milvus and Redis, featuring hybrid RAG, multi-agent orchestration and SSE streaming reports.

Real-time multi-agent transaction pipeline with PII sanitization, semantic classification (pgvector RAG), and graph-based fraud detection (Neo4j GraphRAG). Built with Spring Boot, Spring AI, Kafka, and Resilience4j.

A fully local, multi-agent Retrieval-Augmented Generation (RAG) system built with Spring Boot 4 and Java 25. It features explicit agent orchestration, SSE-based agent observability, document versioning, and re-embedding strategies.

An intelligent multi-agent marketplace system integrating LangGraph, FastAPI, and Spring Boot microservices. Includes RAG-based retrieval, role-based authentication, and autonomous agents for inventory, order processing, and user interaction.

A Spring Boot project demonstrating AI Agents using LangChain4j, including ReAct agents, multi-tool orchestration, chat memory, and RAG (Retrieval-Augmented Generation) with OpenAI. Shows how LLMs reason, call tools, retrieve documents via embeddings, and generate grounded responses.

A multi-agent Spring AI application provides a conversational assistant that remembers past chats, retrieves and summarizes knowledge using RAG, executes real actions through tools and MCP, delegates tasks to specialized agents via an orchestrator, and supports monitoring with logs and metrics for better observability and system reliability.

Calmara is a multimodal AI agent for campus mental health. Supports text, voice, image, video inputs with emotion fusion. Features Agentic RAG for retrieval, multi-step reasoning, and intent classification (CHAT/CONSULT/RISK). Built on Spring Boot 3.2 with Qwen2.5-7B LLM, offering SSE streaming, MCP integration, and JWT security.