How to Build an AI Code Review Agent with LangChain4j and Spring Boot

Automated code review is one of the most practical applications of AI in software development. In this guide, you’ll learn how to build an AI-powered code review agent using LangChain4j and Spring Boot that integrates with GitHub to automatically review pull requests.

Why Build an AI Code Review Agent?

Manual code reviews are time-consuming and inconsistent. Studies show that developers spend up to 6 hours per week on code reviews. An AI agent can:

LangChain4j vs Spring AI: Which Framework Should Java Developers Choose in 2026?

Java developers building AI applications face a critical choice: LangChain4j or Spring AI? Both frameworks enable LLM integration, but they take fundamentally different approaches. After building production applications with both, here’s an honest comparison to help you decide.

Quick Answer

• Choose LangChain4j if you want maximum flexibility, mature Agent/RAG support, and don’t want to be locked into the Spring ecosystem.
• Choose Spring AI if you’re already deep in the Spring ecosystem and want tight integration with Spring Boot auto-configuration.

For most new AI projects in 2026, LangChain4j is the safer bet. Here’s why.

Spring Boot AI Integration: Complete Guide to Adding LLM Capabilities to Your Application

Adding AI capabilities to your Spring Boot application doesn’t require rebuilding from scratch. In this comprehensive guide, you’ll learn how to enhance an existing Spring Boot application with LLM-powered features using LangChain4j.

What You’ll Build

A customer support assistant that can:

• Answer questions about your product using documentation (RAG)
• Process natural language commands via tool calling
• Maintain conversation context across requests

Step 1: Add Dependencies

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15

<dependencies>
    <!-- LangChain4j with OpenAI -->
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
        <version>0.36.2</version>
    </dependency>
    
    <!-- For RAG with PgVector -->
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j-pgvector</artifactId>
        <version>0.36.2</version>
    </dependency>
</dependencies>

Step 2: Configure LLM Connection

 1
 2
 3
 4
 5
 6
 7
 8
 9
10

# application.yml
langchain4j:
  open-ai:
    chat-model:
      model-name: gpt-4o-mini
      temperature: 0.3
      max-tokens: 2000
      timeout: 30s
      log-requests: true
      log-responses: true

For cost optimization, use gpt-4o-mini for most tasks and gpt-4o only for complex reasoning.