Spring AI vs LangChain4j: Which Should Your Java Team Use?

This question comes up in every Java team's first AI planning meeting, and it usually consumes more time than it deserves. Here is the short version, followed by the reasoning.

If your application is a Spring Boot application, start with Spring AI. If it is not, or if you want the declarative style, start with LangChain4j. Both are stable at 1.x. Neither choice is a trap, and the migration cost between them is days, not months.

Now the detail, because "it depends" is only useful if you know what it depends on.

What they have in common#

More than the comparison posts suggest. Both give you:

• A provider-neutral chat model abstraction, so switching from OpenAI to Anthropic is a dependency and a config change
• Embedding models and vector store abstractions
• Tool calling driven by annotations
• Chat memory with bounded windows
• Retrieval components for RAG
• Structured output derived from your Java types

If you built the same feature twice, once in each, the business logic would look remarkably similar. Prompts are text you own; both libraries put the provider behind an interface. The thing people fear — lock-in — is largely absent from both.

Where they genuinely differ#

Configuration and startup#

Spring AI is auto-configured. Add the starter, set spring.ai.openai.api-key, inject ChatClient.Builder. The configuration lives in application.yml alongside your datasource and your Kafka settings, which means it participates in profiles, config server and everything else your platform already does.

LangChain4j is builder-first:

ChatModel model = OpenAiChatModel.builder()
        .apiKey(System.getenv("OPENAI_API_KEY"))
        .modelName("gpt-4o-mini")
        .build();

Explicit, and it works anywhere. There are Spring Boot and Quarkus starters if you want auto-configuration, but the plain builders are the primary path.

Verdict: matters more than it sounds. Configuration that follows your existing conventions is configuration your platform team already knows how to operate.

Programming style#

Spring AI is fluent-imperative:

String answer = chatClient.prompt().system(role).user(question).call().content();

LangChain4j is declarative:

interface Assistant {
    @SystemMessage("You are a Java expert.")
    String chat(String question);
}

The declarative style is genuinely pleasant. The prompt sits next to the method that uses it, so it gets reviewed when the method changes — which is more than can be said for a prompt in a constant three classes away. The trade is a layer of indirection when you need to do something the annotations do not cover.

Verdict: taste, mostly. Teams that like Spring Data repositories tend to like AiServices.

Observability#

Spring AI emits Micrometer metrics and observations without extra work. Token counts, latencies and tracing spans land in the same Prometheus and Grafana setup as the rest of your service.

LangChain4j has a listener API you wire yourself, or you integrate LangFuse.

Verdict: the clearest practical difference, and the one that should carry the most weight. Token cost visibility is not a nice-to-have — it is how you find out that one endpoint is responsible for 80% of your bill.

Integration breadth#

LangChain4j has more, particularly document loaders and parsers. If you need to ingest from SharePoint, Confluence, S3 and a dozen file formats, it will probably have it already.

Spring AI's coverage of the mainstream — the major providers, the major vector stores — is complete, and it is growing.

Verdict: check your specific integration before deciding. This is the one criterion that can settle the question outright.

Testing#

Both mock cleanly, because both put the model behind an interface. Spring AI gets a small edge from @MockBean and the Spring Boot test slices your team already uses.

The harder testing problem — that model output is non-deterministic — is identical in both, and neither solves it for you. See testing AI applications.

The criteria that should not decide it#

GitHub stars. Both are healthy. Neither is going away.

Feature checklists. Every comparison table is out of date within a quarter. Check the current docs for the specific thing you need.

"Which is more like Python LangChain?" Neither, deliberately. If you are porting a Python prototype, expect to redesign rather than translate — and that is usually an improvement, because the Python original was probably prototype-shaped.

Performance. Both spend more than 99% of a request waiting on the model. The library is not your bottleneck.

Using both#

This is more common than either project's documentation implies, and it is fine. A pattern we have seen work well:

• Spring AI in the request path — it is already wired into your metrics, security and config
• LangChain4j in the ingestion pipeline — for its document loaders

They are ordinary libraries with no shared global state. Nothing prevents this, and pretending you must pick one framework for everything is a habit from a different era of Java.

How to actually decide, in an afternoon#

Do not run a three-week evaluation. Do this instead:

1. Pick the most awkward thing you need to build — not the simplest. The hello-world is identical in both and tells you nothing.
2. Build it in both, for two hours each. Two hours is enough to hit the first real friction.
3. Ask your team which code they would rather maintain.
4. Ship it.

The decision is smaller than the meeting about it. Both libraries are good, the abstractions are similar enough that the concepts transfer, and the second one is much easier to learn than the first.

Fuller treatment of each: Introduction to the Spring AI Framework and LangChain4j Introduction & Architecture.