Building AI-Powered Applications with Java: A Comprehensive Guide

Java has evolved into a robust platform for artificial intelligence development, offering a rich ecosystem of frameworks and libraries that enable developers to create everything from simple chatbots to sophisticated agentic systems. With strong support through tools like Spring AI and LangChain4j, as well as direct integrations with major large language model (LLM) providers, Java developers can now harness the power of AI without leaving their familiar environment.

This article provides a practical overview of the key areas in Java AI development, covering foundational concepts, Spring AI's capabilities, retrieval-augmented generation (RAG), the Model Context Protocol (MCP), AI agents, and deep learning libraries. Each section includes links to detailed tutorials for hands-on learning.

Getting Started with AI in Java

To begin building AI applications in Java, you need to understand the core frameworks and how to connect to LLM APIs. Start with Spring AI, an abstraction layer that simplifies integration with OpenAI, Anthropic, and other providers. Alternatively, LangChain4j offers a Java-native implementation of the popular LangChain concepts. For direct API access, the OpenAI API Java Client provides a low-level interface. A quick way to get started is by using Spring Boot with ChatGPT APIs.

• Introduction to Spring AI
• Introduction to LangChain4j
• OpenAI API Java Client
• Using OpenAI ChatGPT APIs in Spring Boot

Building with Spring AI

Spring AI provides a cohesive set of tools for common AI tasks. Its ChatClient Fluent API offers a builder-style pattern for constructing prompts and handling responses. Chat Memory enables conversations with context retention, while Structured Output allows you to parse LLM responses into Java objects. The Advisors framework lets you inject cross-cutting concerns like logging or moderation. You can also integrate specific models, such as Anthropic's Claude, DeepSeek, or local models via Ollama. For quality assurance, use Spring AI Evaluators to test LLM responses.

Key Tutorials:

• ChatClient Fluent API
• Chat Memory
• Structured Output
• Spring AI Advisors
• Using Claude Models
• DeepSeek Chatbot
• Ollama Chatbot
• LLM Response Evaluators

RAG and Vector Search

Retrieval-augmented generation (RAG) enhances LLM outputs by grounding them in external data. Implementing RAG in Java involves vector stores and embedding models. Start with the Embeddings Model API in Spring AI, then build a full RAG application using Redis or MongoDB as the vector store. LangChain4j with MongoDB Atlas offers another path. For PostgreSQL users, PGVector enables semantic search. ChromaDB provides a lightweight vector store, and Semantic Caching speeds up repeated queries.

Key Tutorials:

• Embeddings Model API
• RAG with Redis and Spring AI
• Langchain4j and MongoDB Atlas
• Semantic Search with PGVector
• Spring AI with ChromaDB
• RAG with MongoDB and Spring AI
• Semantic Caching

Model Context Protocol (MCP)

The Model Context Protocol standardizes how AI models interact with tools and data sources. The MCP Java SDK provides a foundation, and Spring AI has first-class support for MCP. Security is critical, so tutorials cover MCP Authorization with OAuth2 and securing MCP Servers with Spring Security.

Key Tutorials:

• Introduction to MCP Java SDK
• MCP with Spring AI
• MCP Authorization with OAuth2
• Securing MCP Servers with OAuth2

AI Agents

Autonomous agents represent the next frontier. In Java, you can create agents using the Embabel Agent Framework or Google Agent Development Kit (ADK). Practical applications include Text-to-SQL generation with Spring AI and building a full AI Assistant with Spring AI.

Key Tutorials:

• Creating an AI Agent with Embabel
• Building Agents with Google ADK
• Text-to-SQL with Spring AI
• AI Assistant with Spring AI

Deep Learning and ML Libraries

For custom models and deep learning, Java offers several libraries. The Deep Java Library (DJL) provides a unified API for training and inference. Deeplearning4j is a mature deep learning library for JVM. Jlama is a newer, lightweight option for LLM inference. An overview of AI libraries in Java completes the picture.

Key Tutorials:

• Overview of AI Libraries in Java
• Deep Java Library
• Deeplearning4j
• Introduction to Jlama

This comprehensive guide covers the essential components for building AI-powered applications in Java. Each linked tutorial provides detailed steps and code examples to help you implement these techniques in your own projects. Whether you're creating a simple chatbot or a complex agentic system, Java's AI ecosystem has the tools you need.