MCP on JAVAPRO International

Bring AI into your Jakarta EE apps with LangChain4J-CDI (formerly SmallRye-LLM)

Wed, 25 Feb 2026 07:00:01 +0000

Goal: This article will demonstrate how to add AI features to a Jakarta EE / MicroProfile application using LangChain4J‑CDI, with simple to implement examples that runs on Payara, WildFly, Open Liberty, Helidon, Quarkus or any CDI 4.x compatible runtime.

What is LangChain4J-CDI?

Langchain4J is a Java library that simplifies the integration of AI and LLMs easier, and with their feature of AI services it provides a declarative and type-safe API for developers to define interfaces that represent AI services, abstracting away the complexities of direct LLM communication

Building MCP Tools (for AI Agents) using Spring AI

Wed, 07 Jan 2026 07:00:02 +0000

Introduction

The innovation of AI Agents and Agentic AI systems has revolutionized the adoption of Generative AI. Agents empower systems to autonomously achieve goals by performing tasks. Tools are the key building blocks for AI agents that provide them with superior decision-making and implementation capabilities. Model Context Protocol (MCP) protocol has standardized the approach of building tools enabling faster, seamless integration. In this paper, we are going to talk about MCP, the underlying architecture. We will also provide a simple code example of building MCP tools in Java using Spring AI and integrating it with an AI Agent.

Build AI Apps and Agents in Java: Hands-On with LangChain4j

Wed, 23 Apr 2025 07:00:02 +0000

LangChain4j is a java library to make interactions with AI models and LLMs in Java easy, with unified APIs that wrap different models. It also provides tools to let you build more complex use cases on top of basic functionality. At this 2nd anniversary of LangChain4j, we are thrilled to give you a walkthrough of the functionality that enables you to build basic and advanced AI-powered apps in Java.