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Java AI Frameworks

A hands-on comparison of Spring AI and LangChain4j

Java AI Frameworks
This book is 100% completeLast updated on 2026-10-27
Nicolas Duminil

This is a hands-on, code-first book. Every example is real, tested, and runs against a local model with no cloud key required, except where a capability genuinely needs one. You get working Maven projects, not snippets, and a clear-eyed account of each stack's strengths, gaps, and surprises. The full source code is on GitHub under an open-source license What you'll learn:

  • Build a working AI service in three stacks and grade them with one shared test
  • Ground answers in your own documents with RAG, and tune what retrieval returns
  • Give a model memory and tools, and reach external systems over the Model Context Protocol
  • Add vision and audio, structured output, production observability, and security
  • Go from assistants to agents, and see where the JVM's agent story is heading

Who it's for: Java developers and architects choosing an AI stack, or already committed to one and curious what the alternatives do differently. Comfort with modern Java and either Spring Boot or Quarkus is all you need. No machine-learning background required.

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About

About

About the Book

Two frameworks. Three stacks. One consistent comparison.

Java has quietly become a first-class place to build AI applications. But which framework should you reach for, Spring AI or LangChain4j? And does it matter whether you run on Spring Boot or Quarkus?

Java AI Frameworks answers these questions the only way that really settles them: by building the same thing three time: with Spring AI on Spring Boot, with LangChain4j on Spring Boot and with LangChain4j on Quarkus. Every capability, from a first chat call to retrieval-augmented generation, tool calling, the Model Context Protocol, multimodal input, observability, security, and autonomous agents, is implemented on both stacks and on both runtimes and held to a single shared conformance test.

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Computers and ProgrammingLarge language modelsArtificial IntelligenceJava

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Author

About the Author

Nicolas Duminil

Nicolas Duminil is a "silver" software architect and developer with several decades of experience in Java enterprise grade applications and services design and development. He is the author of several books on Java and Cloud technology and contributes often with articles and posts on his web-site, where you can follow him: https://www.simplex-software.fr.

Contents

Table of Contents

Part 1: Foundations

  • 1. Meet the Java AI frameworks
  • 2. The smallest AI application
  • 3. Templates and structured output

Part 2: Grounding and quality

  • 4. Judging the answer
  • 5. Answering from your documents

Part 3: Memory, tools and agents

  • 6. Remembering the conversation
  • 7. Calling tools
  • 8. Tools over a protocol
  • 9. Audio and images

Part 4: Running it in production

  • 10. Watching the models work
  • 11. Securing the assistant

Part 5: Generative building blocks

  • 12. Generating structured text
  • 13. From assistants to agents

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