#Spring AI
34 related articles
Spring Boot + LLM: Ship AI Features Without Building a Python Service
Integrate LLM features directly into your existing Java API with Spring Boot and Spring AI for ticket auto-classification: structured output, enum guardrails, deadlines, concurrency budgets, retry pitfalls, fallback, and a launch checklist.
Spring AI 2.0's Five Major Updates: How Java Backends Can Break Into Agent Development
A breakdown of Spring AI 2.0's five core updates: mandatory Spring Boot 4 baseline, Tools parsing moved up to Chat Client Advisor, AgentUtils library, MCP switching to Streamable HTTP, and Tool Search on-demand loading. Includes positioning analysis and a learning path for Java backends breaking into Agent development.
Farewell to Hallucinated Footnotes: Native Claude Citations with Spring AI and Java 26
Implement Claude native citations with Spring AI and Java 26. Use document blocks, citation_delta streaming events, and char_location offsets to build auditable enterprise RAG citation chains.
What Exactly Is the Difference Between MCP and Tool Calling?
Is MCP the same as Tool Calling? This article breaks down their essential difference: Tool Calling lets AI request function execution, while MCP provides a standardized communication protocol. With Spring AI scenarios, it explains when to use MCP and when you don't need it.
Spring AI in Practice: A Full-Chain Optimization Guide to the 10 Major Pain Points of RAG Knowledge Bases
Based on a Spring AI hands-on course, this guide systematically explains the 10 major pain points in RAG knowledge base development: chunking strategy, Top-K optimization, cross-vector retrieval, permission security, and production deployment, helping Java developers quickly master core AI application development skills.
DeepSeek + Spring AI in Practice: Building an Offline AI Family Doctor App from Scratch
A practical tutorial on building an offline AI family doctor app with DeepSeek + Spring AI: covers Ollama local deployment, Spring Boot 3 integration, role customization, and conversation persistence.
Spring AI Practical Guide: Building a RAG + MCP Intelligent Engine with Java
A practical Spring AI guide for Java developers: integrate RAG knowledge bases, real-time web search, and MCP tools to build enterprise-grade AI applications.
Spring AI 2.0 in Practice: From Chatbots to Agent Development — A Complete Guide
A practical guide to Spring AI 2.0: from ChatClient, RAG, and Tools to native Agent development. Covers Agent Utils inspired by Claude Code, including Ask User Question, Skills, task planning, and long-term memory for Java developers.
From Copilot to Agentic AI: A Practical Guide to Enterprise Autonomous Agent Development
From Copilot to Agentic AI: explore multi-agent collaboration, ReAct autonomous reasoning, and Agent Skills — plus a practical Spring AI Alibaba course breakdown.
AI Agent for Java Developers: A Practical Guide to Spring AI Alibaba Framework
A practical guide for Java developers on AI Agents: covering core concepts, Workflow vs. Agentic paradigms, and how to build production-ready Agents with Spring AI Alibaba Framework.
Getting Started with Spring AI: A Large Language Model Integration Framework for Java Developers
Spring AI is a Java framework for LLM integration, similar to Python's LangChain. Learn its core features: structured output, Chat Memory, RAG, Tool Calling, and MCP protocol.
Spring AI in Practice: RAG, MCP, and Agents for Java Developers
A hands-on Spring AI course for Java developers covering LLM integration, prompt engineering, RAG, MCP, and Agents with three real-world projects.
TutorialsHow AI Coding Skills Work: From Function Call to Sub-Agent Implementation
Deep dive into how AI coding Skills work technically, from Function Call to MCP to Skills as sub-agents with on-demand loading, implemented via Spring AI Alibaba.
TutorialsHow AI Coding Skills Work: From Function Call to Sub-Agent Implementation
Deep dive into how AI coding Skills work: from Function Call to MCP to Skills as sub-agents with on-demand loading, implemented via Spring AI Alibaba.
TutorialsSpring AI Alibaba Agent Framework: Three-Layer Architecture and Learning Path
Deep dive into Spring AI Alibaba Agent Framework's three-layer architecture: Spring AI foundation, Graph framework, and Agent Framework, with a recommended learning path for Java developers.
TutorialsComplete Learning Roadmap for Java Developers Transitioning to AI Engineering
A detailed five-phase learning roadmap for Java developers transitioning to AI engineering, covering Spring AI, LangChain4j, RAG core technology, and Agent development.
TutorialsSpring AI + MCP Protocol in Practice: Tool Calling, Security Authentication, and Enterprise Deployment
Deep dive into Spring AI and MCP protocol integration, covering tool calling, OAuth security, horizontal scaling, and context optimization for enterprise AI Agent services.
Deep DivesDeep Dive into Cursor Skills: From Function Call to Practice
Deep dive into Cursor Skills' underlying principles, from Function Call and MCP protocol to Workflow Agent, with Spring AI Alibaba practical demo for any LLM.
TutorialsSpring AI Alibaba MCP Integration in Practice: Full Workflow from Server Setup to Client Invocation
Learn how Java developers can build MCP Server and Client using Spring AI Alibaba, define tools with @Tool annotations, and integrate with AI clients like Trae for LLM-powered business data access.
TutorialsSpring AI Agent Utils: A Java Agent Toolkit Reverse-Engineered from Claude Code's Core Features
Deep dive into Spring AI Agent Utils toolkit covering Skill modules, Ask a User Question, To Do Write, Auto Memory, and multi-Agent orchestration — empowering Java developers to build powerful AI Agents.