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:
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.
MyBatis is the most popular ORM framework in the Java ecosystem, powering millions of applications. But its flexibility comes with a dangerous gotcha: ${} vs #{} syntax. One is safe, the other is not — and the difference is a single character.
Here are 10 MyBatis SQL injection patterns that slip past human reviewers but an AI code review agent catches instantly.
The Core Problem: ${} vs #{}
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<!-- SAFE: #{} uses PreparedStatement parameter binding --><selectid="findById"resultType="User"> SELECT * FROM users WHERE id = #{id}
</select><!-- VULNERABLE: ${} directly interpolates the string --><selectid="findById"resultType="User"> SELECT * FROM users WHERE id = ${id}
</select>
The difference: #{id} generates WHERE id = ? with parameter binding, while ${id} generates WHERE id = 1 OR 1=1 with direct string interpolation.