How to Use a Local LLM with Spring Boot and LangChain4j in 2026

This is a summary of the full tutorial published on howtostartprogramming.in.

TL;DR Plug a local LLM into a Spring Boot app in seconds with LangChain4j . The library abstracts model loading, prompt chaining, and response handling, letting you keep data on‑premise while writing idiomatic Spring code. Table of Contents TL;DR Introduction Prerequisites Core Concepts LangChain4j Abstractions Step‑by‑Step Integration 1. Add the required dependencies 2. Configure the LLM bean Full Example Project 1. Project Structure 2. pom.xml Common Mistakes & How to Avoid Them Production‑Ready Tips 1. Scale with Asynchronous Calls 2. Cache Expensive LLM Responses FAQ – Using a Local LLM with Spring Boot & LangChain4j (2026) 1. What license do I need for the LLM and LangChain4j? 2. Should I run inference on GPU or CPU? Key Takeaways Why go local with LangChain4j & Spring Boot? Best‑Prac