If you are a Java developer building AI-powered applications in 2026, you have two serious framework options: Spring AI and LangChain4j. Both let you integrate large language models into your Java applications. Both have grown substantially over the past year. And both will handle the majority of real-world use cases.
The question is not which one is "better" — it is which one fits your team, your existing stack, and the type of application you are building.
This guide compares them directly, with Java code examples.
What Spring AI Is
Spring AI is Pivotal's official framework for AI integration in the Spring ecosystem. It follows the same opinionated, convention-over-configuration philosophy that Spring Boot developers already know. If you have ever used Spring Data JPA or Spring Security, Spring AI feels immediately familiar — auto-configuration, dependency injection, abstraction layers, and first-class Spring Boot starter support.
Spring AI's core abstraction is the ChatClient interface. You inject it, call it, and Spring handles the rest. Provider-specific configuration happens in application.properties. Switching from OpenAI to Anthropic is a dependency swap and a few property changes — your application code stays the same.
The framework also ships with:
- A
VectorStoreabstraction backed by PgVector, Chroma, Redis, and others - Template-based prompt management with
PromptTemplate - Advisors for RAG, memory, and logging
- Structured output binding via Jackson
- Function/tool calling support
Spring AI hit its 1.0 GA milestone in May 2024 and has been in active development since. It is now the path of least resistance for any team already invested in the Spring ecosystem.
What LangChain4j Is
LangChain4j is a Java port of the Python LangChain library, built specifically for Java developers who want the composition patterns that LangChain is known for — chains, agents, memory, tools — without switching languages.
LangChain4j takes a more explicit, composable approach. You build pipelines by chaining components together, and the API surface is closer to how LangChain Python works. Developers coming from the Python AI world and moving into Java codebases often find LangChain4j easier to reason about because the mental model matches what they already know.
Key capabilities:
- AI services via annotated interfaces (similar to Spring Data repositories)
- Built-in RAG pipeline with
EmbeddingStore,ContentRetriever, andAugmentationStrategy - Agent support with tools via
@Toolannotations - Memory implementations (in-memory, window-based)
- Streaming support across major providers
- Quarkus and Spring Boot integration
LangChain4j is mature, widely used, and well-documented. It does not depend on Spring and works equally well in Quarkus, Micronaut, or plain Java applications.
Spring Boot Integration: How Each Fits
This is where the two frameworks diverge most significantly.
Spring AI is built as a Spring Boot starter. Everything is auto-configured:
// application.properties
spring.ai.anthropic.api-key=${ANTHROPIC_API_KEY}
spring.ai.anthropic.chat.model=claude-sonnet-4-6
// Your service — zero boilerplate
@Service
public class SupportService {
private final ChatClient chatClient;
public SupportService(ChatClient.Builder builder) {
this.chatClient = builder.build();
}
public String answer(String question) {
return chatClient.prompt()
.user(question)
.call()
.content();
}
}
That is the entire integration. Spring manages the bean lifecycle, configuration binding, and HTTP client pooling. Observability hooks (Micrometer), retry logic, and connection pooling all come from the Spring ecosystem — you configure them the same way you configure any other Spring component.
LangChain4j with Spring Boot requires an explicit integration layer, but it is not cumbersome:
@Configuration
public class AiConfig {
@Bean
public ChatLanguageModel chatModel() {
return AnthropicChatModel.builder()
.apiKey(System.getenv("ANTHROPIC_API_KEY"))
.modelName("claude-sonnet-4-6")
.build();
}
@Bean
public Assistant assistant(ChatLanguageModel chatModel) {
return AiServices.builder(Assistant.class)
.chatLanguageModel(chatModel)
.build();
}
}
// AI Service interface
public interface Assistant {
String answer(String question);
}
// Inject and use
@Service
public class SupportService {
private final Assistant assistant;
public SupportService(Assistant assistant) {
this.assistant = assistant;
}
public String answer(String question) {
return assistant.answer(question);
}
}
LangChain4j's AiServices pattern — defining AI behavior through annotated interfaces — is one of its best features. It feels like Spring Data repositories, and Java developers pick it up quickly.
Comparing RAG Implementation
Building a RAG pipeline is a common requirement. Here is how each framework approaches it.
Spring AI RAG:
@Configuration
public class RagConfig {
@Bean
public QuestionAnswerAdvisor ragAdvisor(VectorStore vectorStore) {
return new QuestionAnswerAdvisor(vectorStore,
SearchRequest.defaults().withTopK(4));
}
}
@Service
public class DocumentQAService {
private final ChatClient chatClient;
private final QuestionAnswerAdvisor ragAdvisor;
public String askAboutDocuments(String question) {
return chatClient.prompt()
.user(question)
.advisors(ragAdvisor)
.call()
.content();
}
}
LangChain4j RAG:
@Configuration
public class RagConfig {
@Bean
public ContentRetriever contentRetriever(EmbeddingStore<TextSegment> embeddingStore,
EmbeddingModel embeddingModel) {
return EmbeddingStoreContentRetriever.builder()
.embeddingStore(embeddingStore)
.embeddingModel(embeddingModel)
.maxResults(4)
.build();
}
@Bean
public Assistant ragAssistant(ChatLanguageModel model,
ContentRetriever retriever) {
return AiServices.builder(Assistant.class)
.chatLanguageModel(model)
.contentRetriever(retriever)
.build();
}
}
Both are clean. Spring AI's Advisor pattern makes it easy to compose multiple behaviors (RAG + memory + logging) in a single call. LangChain4j's explicit pipeline gives you more visibility into each step.
Tool Calling: Agents in Both Frameworks
Both frameworks support tool calling, which is the foundation for building agents.
Spring AI function calling:
@Bean
@Description("Get the current price for a product SKU")
public Function<PriceRequest, PriceResponse> getProductPrice(InventoryService inventory) {
return request -> new PriceResponse(inventory.getPrice(request.sku()));
}
// Spring AI detects @Description-annotated Function beans automatically
LangChain4j @Tool annotation:
@Component
public class InventoryTools {
private final InventoryService inventory;
@Tool("Get the current price for a product SKU")
public double getProductPrice(String sku) {
return inventory.getPrice(sku);
}
}
// Pass to AiServices builder
AiServices.builder(Agent.class)
.chatLanguageModel(model)
.tools(inventoryTools)
.build();
LangChain4j's @Tool approach is more ergonomic. You annotate regular methods and pass the component to the service builder. Spring AI requires explicit Function bean definitions, which is more verbose but integrates cleanly with Spring's bean registry.
Maturity and Ecosystem
| | Spring AI | LangChain4j | |---|---|---| | First release | 2023 | 2023 | | GA / stable release | 1.0 GA (May 2024) | 0.35+ (stable, not 1.0) | | Spring Boot integration | Native (first-class) | Via starter (good) | | Quarkus support | No | Yes | | Provider support | OpenAI, Anthropic, Azure, Bedrock, Ollama, Mistral | Same + more | | Vector stores | PgVector, Chroma, Redis, Pinecone, Weaviate | Same set | | GitHub stars (Apr 2026) | ~5k | ~5k | | Backing | Spring / Broadcom | Community / JetBrains sponsorship |
Both are production-ready. Spring AI has the institutional weight of the Spring team behind it. LangChain4j has a more active open-source community and ships features faster.
When to Choose Spring AI
Choose Spring AI when:
- Your team lives in Spring Boot and wants AI features that feel like the rest of the application
- You need Micrometer metrics and Spring Actuator health checks on your AI calls out of the box
- You want to follow Pivotal's roadmap and get long-term maintenance guarantees
- Your app already uses Spring Data — the
VectorStoreabstraction mirrors Spring Data patterns exactly - You are building an enterprise application where consistency with existing Spring conventions matters
When to Choose LangChain4j
Choose LangChain4j when:
- You are not using Spring Boot (Quarkus, Micronaut, plain Java)
- Your team comes from Python LangChain and wants a familiar mental model in Java
- You want more composability and explicit control over the RAG pipeline
- You are building an agent-heavy application and want LangChain4j's more mature agent patterns
- You want faster iteration — LangChain4j ships features and provider support faster than Spring AI
The Practical Answer
For most Java teams: Spring AI if you are Spring Boot, LangChain4j otherwise.
If your application is already a Spring Boot application — and most Java enterprise applications are — Spring AI is the right default. The zero-configuration integration, familiar patterns, and Spring ecosystem compatibility will save significant time. You do not have to learn a new wiring model.
If you are not on Spring Boot, or if you want the richer composability that LangChain4j provides, it is an excellent choice. The @Tool annotations and AiServices interface pattern are genuinely elegant Java API design.
The good news: both frameworks use the same underlying concepts. What you learn about RAG, tool calling, embeddings, and agent loops in one framework transfers directly to the other. The framework is the wiring — understanding the concepts is what actually matters.
Learn the Concepts That Work Across Both
Understanding how LLM frameworks actually work — chunking, embedding, retrieval strategies, tool call routing, agent loop design — is more valuable than mastering any single framework's API.
Phase 5 of the Agentic AI course at MindloomHQ covers AI frameworks in depth, including how to evaluate framework choices, build RAG systems from scratch, and design agent architectures. The lessons use both Python and Java examples, and every lesson includes full working implementations.
Phases 0 and 1 are completely free. No credit card required.