If you build for the JVM, the "which AI framework" question has exactly two serious answers, and the internet will tell you the difference is maturity or breadth. It isn't. Spring AI and LangChain4j reached 1.0 in the same week of May 2025 — LangChain4j tagged 1.0.0 on the 14th, Spring AI announced GA on the 20th. Six days apart. Neither is the scrappy upstart; both have since marched well past 1.0. So drop the "one's newer" framing before it misleads you.

Drop the feature-checklist framing too, because that fight is also over. Both ship a unified API across ~20 model providers, tool/function calling via a @Tool annotation, retrieval-augmented generation, the Model Context Protocol, structured output that maps JSON to Java types, streaming, and Micrometer-based observability. Both are Apache-2.0 and both want Java 17. If you make a capability matrix, almost every cell will be a checkmark in both columns.

When two frameworks have converged on features and shipped 1.0 the same week, the differentiator isn't what they do. It's what they assume about your application.

The actual axis: where your app's gravity sits

The honest difference is a philosophy you can read straight off their taglines. Spring AI calls itself "an Application Framework for AI Engineering" and exists to "apply Spring ecosystem design principles" to AI. LangChain4j's first line is "built for Java, not ported to it." Those aren't marketing fluff; they describe two different bets about where your code lives.

**Spring AI assumes the Spring container is your application.** Its ergonomics — auto-configuration, Spring Boot starters, ChatClient, and Advisors that are just Spring beans in an interceptor chain — are spectacular if you're already inside Spring Boot. You wire a model with a starter and a property file; an Advisor that does RAG or chat memory drops into the same bean graph as your repositories and controllers. The framework removes glue you would otherwise hand-write, because it already owns your dependency injection, your config, and (through Actuator + Micrometer) your metrics.

LangChain4j refuses to own any of that. Its high-level abstraction, AiServices, is a declarative interface — you define a Java interface with @SystemMessage/@UserMessage annotations and it hands you a proxy that implements it, the same trick Spring Data JPA and Retrofit use. Crucially, that mechanism doesn't need a container. LangChain4j runs in plain Java, and it ships dedicated integrations for Quarkus (quarkus-langchain4j) and Micronaut and Spring Boot. It's the neutral substrate; you bring the runtime.

Why "I use Spring Boot" doesn't end the argument

Here's the part the easy version of this comparison gets wrong. The reflex is: on Spring Boot → Spring AI; otherwise → LangChain4j. But LangChain4j has a first-party Spring Boot integration. So even the Spring shop has a real choice, and it's a choice about coupling, not compatibility.

Pick Spring AI when you want to live inside Spring's abstractions — when "an Advisor is a bean" and "the model is auto-configured from properties" is exactly the leverage you're buying, and you have no plan to ever run this code outside Spring. Pick LangChain4j when you want a portable core: business logic expressed as AiServices interfaces that would lift, more or less intact, to Quarkus or a plain-Java service if your platform team changes its mind. One optimizes for depth inside an ecosystem; the other optimizes for escape velocity from any single one.

The non-Spring case is simpler. If your stack is Quarkus or Micronaut or no framework at all, Spring AI's greatest strength — its total fluency in the Spring container — becomes a tax: you'd be importing an opinion about application structure you don't share. LangChain4j was built for precisely that world, and it's where its agnosticism stops being a slogan and starts being the reason.

A couple of real tiebreakers

When ecosystem fit genuinely doesn't decide it, two smaller facts can:

Neither is a knockout. The license is the same, the Java baseline is the same, the headline capabilities are the same. That's the whole point: stop scoring features and answer one question — do you want your AI code to be a citizen of Spring, or a guest that can leave? That answer picks the framework.

For the language-level version of this decision, see Python vs TypeScript for AI agents; for the framework split one ecosystem over, LangChain vs LangGraph and the JS-side Mastra vs Vercel AI SDK vs LangGraph.js.