LangChain4j vs Spring AI — which Java AI framework should you use?

By now you know Spring AI well. But LangChain4j has been around longer, has a large community, and you will encounter it in job postings, open-source projects, and conference talks. Understanding both — and being able to choose between them — is part of being a complete AI engineer.

Table of contents

The 30-second summary

Spring AI is the Spring Framework team’s approach: opinionated, deeply integrated with the Spring ecosystem, auto-configured, annotation-driven. If your project is already on Spring Boot, Spring AI integrates seamlessly and the learning curve is minimal.

LangChain4j is inspired by Python’s LangChain, built as a standalone library. It does not depend on Spring but provides a Spring Boot starter for those who want it. It has a richer set of pre-built components (more vector store integrations, more ready-made patterns) and a larger international community.

Both are production-ready. The choice is mostly about ecosystem fit, not capability.

Concept mapping

The concepts are nearly identical. The APIs look different, but if you understand one framework you can learn the other quickly.

Code comparison: a simple chat call

Spring AI:

String answer = chatClient.prompt()
        .system("You are a support assistant.")
        .user(question)
        .call()
        .content();

LangChain4j:

// Using ChatLanguageModel directly
ChatLanguageModel model = OpenAiChatModel.builder()
        .apiKey(apiKey)
        .modelName("gpt-4o-mini")
        .build();

String answer = model.generate(question);

// Or using AiServices (more idiomatic LangChain4j)
interface SupportAssistant {
    @SystemMessage("You are a support assistant.")
    String chat(String question);
}

SupportAssistant assistant = AiServices.create(SupportAssistant.class, model);
String answer = assistant.chat(question);

LangChain4j’s AiServices is a distinctive feature — it turns a Java interface into an AI-backed implementation automatically. Spring AI has no direct equivalent; ChatClient is the nearest analogue.

Code comparison: structured output

Spring AI:

record Classification(String category, int confidence) {}

Classification result = chatClient.prompt()
        .user("Classify: " + message)
        .call()
        .entity(Classification.class);

LangChain4j:

interface Classifier {
    Classification classify(String message);
}

record Classification(String category, int confidence) {}

Classifier classifier = AiServices.create(Classifier.class, model);
Classification result = classifier.classify(message);

LangChain4j’s approach is more Java-idiomatic (an interface with a return type). Spring AI’s .entity() is more explicit about where the structured output happens. Both generate the JSON schema and parse the response automatically.

Code comparison: RAG

Spring AI:

// Wire the advisor to ChatClient — RAG is automatic on every call
@Bean
ChatClient chatClient(ChatClient.Builder builder, VectorStore vectorStore) {
    return builder
            .defaultAdvisors(new QuestionAnswerAdvisor(vectorStore))
            .build();
}

LangChain4j:

// Wire the retriever to AiServices — RAG is automatic on every call
EmbeddingStore<TextSegment> embeddingStore = // PgVector, Chroma, etc.
EmbeddingModel embeddingModel = // OpenAI, Ollama, etc.

ContentRetriever retriever = EmbeddingStoreContentRetriever.builder()
        .embeddingStore(embeddingStore)
        .embeddingModel(embeddingModel)
        .maxResults(5)
        .minScore(0.7)
        .build();

interface SupportAssistant {
    String chat(String question);
}

SupportAssistant assistant = AiServices.builder(SupportAssistant.class)
        .chatLanguageModel(model)
        .contentRetriever(retriever)
        .build();

Functionally identical results. Spring AI’s is slightly less verbose for Spring Boot users because VectorStore and ChatClient.Builder are auto-configured.

Where each framework leads

Spring AI has an advantage when:

• You are already on Spring Boot — auto-configuration means almost no setup boilerplate
• Your team knows Spring idioms — advisors, beans, profiles, @Value
• You want tight integration with Spring Security, Spring Data, Spring Actuator
• You prefer the fluent ChatClient API over interface proxies

LangChain4j has an advantage when:

• You are not on Spring Boot (plain Java, Jakarta EE, Quarkus)
• You want the AiServices interface pattern — it produces very clean, testable code
• You need a vector store or model that Spring AI does not yet support
• You are coming from Python LangChain and want conceptual familiarity
• You want a larger pool of tutorials and community examples (LangChain4j’s community is larger in absolute numbers)

Tip: Both frameworks provide Spring Boot starters, so "Spring Boot project" is not a deciding factor — either works. The real differentiator is team familiarity and the AiServices interface pattern vs. the ChatClient fluent API. Try both on a small prototype and pick whichever your team finds more readable.

Maturity and ecosystem

Both frameworks have broad provider support. LangChain4j has slightly more integrations in total; Spring AI adds new ones quickly.

Interoperability

You can use both in the same project — though it is unusual. A more common pattern is using Spring AI for the main application and LangChain4j for a specific component it supports better (or vice versa).

Since both frameworks ultimately call the same underlying LLM APIs, there is no runtime incompatibility.

The decision guide

Is your project on Spring Boot?
  Yes → Use Spring AI (auto-configuration, familiar patterns)

Do you need the AiServices interface pattern specifically?
  Yes → Use LangChain4j

Are you coming from Python LangChain and want conceptual familiarity?
  Yes → Use LangChain4j

Do you need a specific vector store or model only one supports?
  → Use whichever supports it

None of the above?
  → Use Spring AI for Spring Boot projects, LangChain4j otherwise

Note: This course covers Spring AI, but the concepts — embeddings, RAG, agents, memory, chunking — apply equally to LangChain4j. If you ever need to switch, the translation is mostly API surface changes. The mental models carry over completely.

References

• LangChain4j documentation
• Spring AI reference documentation
• LangChain4j GitHub