Spring AI vs LangChain4j: Which Should You Choose?

If you're building an LLM application in Java, two frameworks dominate the conversation: Spring AI and LangChain4j. Both give you provider-agnostic model access, RAG building blocks, tool calling, and memory — so the choice isn't about features on paper. It's about how they fit your stack and how you like to build. Here's an honest comparison from someone who's shipped with both.

The 30-second answer

• Already on Spring Boot? Use Spring AI. It's built by the Spring team, auto-configures from your application.yml, and feels like the rest of your app.
• On Quarkus, plain Java, or want the richest agent/chain primitives? Use LangChain4j. It's framework-agnostic and ports LangChain's ideas to Java.

Both are production-viable. The rest of this post is the detail behind that answer.

Design philosophy

Spring AI is a first-class Spring project. It leans on Spring Boot auto-configuration, dependency injection, and a fluent ChatClient that mirrors the ergonomics of RestClient/WebClient. If you know Spring, it feels immediately familiar.

LangChain4j is a community project inspired by Python's LangChain. It's deliberately framework-neutral — it runs on Spring Boot, Quarkus, Micronaut, or plain Java — and offers a declarative AiServices interface plus lower-level building blocks (chains, tools, RAG components).

// Spring AI — fluent ChatClient
String answer = chatClient.prompt()
        .user("Summarise this ticket")
        .call()
        .content();

// LangChain4j — declarative AiServices
interface Assistant {
    String chat(String userMessage);
}
Assistant assistant = AiServices.create(Assistant.class, model);
String answer = assistant.chat("Summarise this ticket");

Provider support & portability

Both abstract over the major providers (OpenAI, Anthropic, Azure OpenAI, Vertex/Gemini, Bedrock, Ollama, and more) so you can swap models via configuration rather than code. In practice both are strong here; LangChain4j has historically added new providers quickly, while Spring AI keeps its provider modules tightly aligned with Spring Boot starters.

The app-building experience

This is where the difference is felt day to day:

• Spring AI's ChatClient is a fluent builder — set the system prompt, user message, options, advisors, and call or stream. It composes cleanly with Spring's DI and configuration.
• LangChain4j's AiServices lets you declare a Java interface and have the framework implement it, wiring in memory, tools, and RAG behind the scenes. It's concise and expressive for agent-style apps.

Neither is "better" — Spring AI feels more like idiomatic Spring; LangChain4j feels more like LangChain.

RAG, tools, and memory

Both cover the essentials:

• RAG: both provide document loaders, splitters, embedding models, and vector-store abstractions. Spring AI's QuestionAnswerAdvisor and VectorStore make retrieval-augmented prompts a few lines; LangChain4j offers an EmbeddingStore + retrieval chain approach.
• Tools: Spring AI uses @Tool-annotated methods wired into the ChatClient; LangChain4j uses @Tool on methods registered with AiServices. Both run the tool-call loop for you.
• Memory: both support conversational memory (windowed history), Spring AI via advisors, LangChain4j via ChatMemory.

Spring integration & observability

If you're deep in the Spring ecosystem, Spring AI wins on integration: native auto-config, Micrometer observability, Spring Boot Actuator, and consistent property-based configuration. LangChain4j has a Spring Boot starter too, but the integration is thinner by design because it stays framework-agnostic.

Ecosystem & maturity

Both projects move fast and have active communities. Spring AI benefits from the Spring team's backing and release discipline; LangChain4j benefits from a large surface area of integrations and quick provider additions. Pin your versions and read release notes — both are evolving quickly.

When to choose which

Choose Spring AI if:

• Your app is (or will be) a Spring Boot service.
• You value tight Spring integration, auto-config, and Micrometer observability.
• You want an API that feels like the rest of Spring.

Choose LangChain4j if:

• You're on Quarkus, Micronaut, or plain Java.
• You want the richest set of chain/agent/RAG primitives and rapid provider coverage.
• You prefer a declarative AiServices style.

FAQ

Can I use both in one project? Technically yes, but don't — you'd carry two overlapping abstractions. Pick one per service.

Which has better Anthropic/Claude support? Both support Claude well (directly and via Bedrock). Differences are minor; validate with your own prompts.

Is one faster at runtime? Latency is dominated by the model provider, not the framework. Both add negligible overhead.

I'm on Spring Boot but want LangChain-style agents — what then? Spring AI covers most agent needs now, but if you specifically want LangChain's chain/agent abstractions, LangChain4j's Spring Boot starter is a fine choice.

Both frameworks are solid; the right pick is mostly about your stack. On Spring Boot, Spring AI is the path of least resistance. Elsewhere — or for the richest agent primitives — LangChain4j shines.

New to Spring AI? Start with the Spring AI overview, then tool calling and streaming responses.

Choosing a framework for a production AI system and want a second opinion? Let's talk.