Java developers building AI applications face a critical choice: LangChain4j or Spring AI? Both frameworks enable LLM integration, but they take fundamentally different approaches. After building production applications with both, here’s an honest comparison to help you decide.

Quick Answer

• Choose LangChain4j if you want maximum flexibility, mature Agent/RAG support, and don’t want to be locked into the Spring ecosystem.
• Choose Spring AI if you’re already deep in the Spring ecosystem and want tight integration with Spring Boot auto-configuration.

For most new AI projects in 2026, LangChain4j is the safer bet. Here’s why.

Framework Overview

1. AI Service Definition

Both frameworks use declarative AI service interfaces, but the implementations differ:

LangChain4j Approach

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@AiService
public interface ChatAssistant {
    
    @SystemMessage("You are a helpful Java expert")
    String answer(@UserMessage String question);
    
    @SystemMessage("Translate the following to {language}")
    String translate(@UserMessage String text, @V("language") String language);
}

LangChain4j automatically creates the implementation at runtime. You just inject and use it.

Spring AI Approach

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@Service
public class ChatService {
    
    private final ChatClient chatClient;
    
    public ChatService(ChatClient.Builder builder) {
        this.chatClient = builder.build();
    }
    
    public String answer(String question) {
        return chatClient.prompt()
            .system("You are a helpful Java expert")
            .user(question)
            .call()
            .content();
    }
}

Spring AI uses a builder pattern instead of annotation-based service definition.

Verdict: LangChain4j wins on ergonomics. Declarative interfaces are cleaner and require less boilerplate.

2. Tool Calling / Function Calling

This is where frameworks really differentiate.

LangChain4j Tools

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public class CalculatorTools {
    
    @Tool("Calculate the sum of two numbers")
    public int add(int a, int b) {
        return a + b;
    }
    
    @Tool("Search the codebase for a pattern")
    public List<SearchResult> searchCode(@P("pattern") String pattern) {
        return codeSearchService.search(pattern);
    }
}

// Wire tools to your AI service
@AiService
public interface CodeAssistant {
    String analyze(@UserMessage String query, 
                   @ToolProvider CalculatorTools tools);
}

LangChain4j’s @Tool annotation is straightforward, and automatic parameter name extraction just works.

Spring AI Tools

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@Component
public class CalculatorFunction implements Function<CalculatorRequest, CalculatorResponse> {
    
    @Override
    @Description("Calculate the sum of two numbers")
    public CalculatorResponse apply(CalculatorRequest request) {
        return new CalculatorResponse(request.a() + request.b());
    }
}

// Register
@Bean
@Description("Calculator functions")
public List<FunctionCallback> calculatorFunctions() {
    return List.of(
        FunctionCallback.builder()
            .function("calculator", new CalculatorFunction())
            .inputType(CalculatorRequest.class)
            .build()
    );
}

Verdict: LangChain4j is simpler. Spring AI’s approach is more verbose but gives fine-grained control over registration.

3. RAG (Retrieval-Augmented Generation)

LangChain4j RAG

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@AiService
public interface DocumentAssistant {
    
    String answer(@UserMessage String question, 
                  @MemoryId String userId,
                  @Embedding Match documents);
}

// Configure the embedding store
EmbeddingStore<TextSegment> store = new PgVectorEmbeddingStore(
    dataSource, "documents", 1536);

LangChain4j supports: PgVector, Chroma, Milvus, Redis, Elasticsearch, Pinecone, Weaviate, and more.

Spring AI RAG

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@Bean
public VectorStore vectorStore(JdbcTemplate jdbcTemplate, EmbeddingModel embeddingModel) {
    return new PgVectorStore(jdbcTemplate, embeddingModel);
}

// Query
List<Document> docs = vectorStore.similaritySearch("Find pricing info");

Verdict: Tie. Both have good RAG support with similar vector store integrations.

4. Agent Support

Agents are the ability to plan and execute multi-step tasks.

LangChain4j Agents

LangChain4j has first-class Agent support with built-in tool execution loops:

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@AiService
public interface CodeReviewAgent {
    
    @SystemMessage("""
        You are a code review agent. Use available tools to:
        1. Fetch the PR diff
        2. Analyze code quality
        3. Run static analysis
        4. Generate review comments
        """)
    String reviewCode(@UserMessage String prUrl,
                      @ToolSet GitHubTools github,
                      @ToolSet AnalysisTools analysis);
}

The framework handles the tool-calling loop automatically.

Spring AI Agents

Spring AI added Agent support in 1.0.0-M5:

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@Bean
public ChatClient agentChatClient(ChatClient.Builder builder, 
                                   List<FunctionCallback> tools) {
    return builder
        .defaultFunctions(tools.stream()
            .map(FunctionCallback::getName)
            .toArray(String[]::new))
        .build();
}

Verdict: LangChain4j wins. Its Agent abstraction is more mature and battle-tested.

5. Multi-Model Support

Verdict: LangChain4j has broader model coverage.

6. Production Readiness

LangChain4j

• Mature: Used in production since 2023
• Good error handling and retry mechanisms
• Built-in token counting and cost estimation
• Comprehensive testing utilities
• Active community (~300+ contributors)

Spring AI

• Newer: Still reaching 1.0 GA
• Benefits from Spring ecosystem (actuator, tracing)
• Better IDE support (Spring Tools Suite)
• Enterprise backing (Broadcom/VMware)
• Rapidly evolving API (breaking changes between milestones)

Verdict: LangChain4j for stability today. Spring AI will improve as it matures.

Performance Comparison

In our benchmarks (1000 concurrent requests, GPT-4o):

The differences are small. Both are fast enough for production use.

When to Choose Each

Choose LangChain4j When:

• Building AI Agents with tool calling
• Need mature RAG pipelines
• Want framework independence from Spring
• Working with multiple LLM providers
• Building complex multi-step AI workflows

Choose Spring AI When:

• Deeply invested in Spring ecosystem
• Want auto-configuration simplicity
• Need enterprise support
• Building simpler chatbot/Q&A applications
• Prefer Spring’s dependency injection patterns

My Recommendation

For new projects starting in 2026, I recommend LangChain4j. It’s more mature, has better Agent support, and gives you flexibility to use it with or without Spring.

If you’re already on Spring Boot 3.2+ and building a relatively simple AI feature (chatbot, text generation), Spring AI will feel natural and require less configuration.

The best approach? Use LangChain4j’s core features for AI logic, wrapped inside a Spring Boot application for web/DI infrastructure. Best of both worlds.

Conclusion

Both frameworks are solid choices. LangChain4j is the more complete AI framework today, while Spring AI has the advantage of ecosystem integration that will only improve. Start with whichever matches your project’s needs, and don’t be afraid to switch — both follow similar patterns that make migration feasible.

What’s your experience with these frameworks? Let me know in the comments.