LangChain4j vs Spring AI: Which Java AI Framework Is Right for Your Project?

Design Philosophy

Spring AI

Spring AI aims to bring AI development into the Spring Boot experience, applying Spring principles of portability, modularity, and dependency injection. Open‑source repository: https://github.com/spring-projects/spring-ai. The core idea is to promote POJO as the building block for AI applications.

LangChain4j

LangChain4j is the official Java implementation of Python’s LangChain, created to fill the gap of a native LLM library for Java and Kotlin. Repository: https://github.com/langchain4j/langchain4j. Its goal is to provide a complete LLM toolchain without requiring a Python bridge.

Core Features

Spring AI implementation

Dependency configuration (Maven):

<dependency>
  <groupId>org.springframework.ai</groupId>
  <artifactId>spring-ai-starter</artifactId>
  <version>2.0.0-M2</version>
</dependency>

Configuration file (application.yml):

spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
    chat:
      model: gpt-4

Tool class example:

@Component
public class OrderTools {
  @Autowired
  private OrderService orderService;

  @Tool(description = "根据订单号查询订单状态")
  public String getOrderStatus(String orderId) {
    Order order = orderService.findByOrderId(orderId);
    if (order == null) {
      return "未找到订单";
    }
    return String.format("订单状态：%s，下单时间：%s", order.getStatus(), order.getCreateTime());
  }
}

@Service
public class CustomerService {
  @Autowired
  private ChatClient chatClient;

  public String handleQuery(String userMessage) {
    return chatClient.prompt()
        .user(userMessage)
        .tools(new OrderTools())
        .call()
        .content();
  }
}

Spring AI 2.0.0‑M2 adds null‑safety APIs and JSpecify compliance, enabling compile‑time null‑pointer detection.

LangChain4j implementation

Dependency configuration (Maven):

<dependency>
  <groupId>dev.langchain4j</groupId>
  <artifactId>langchain4j-core</artifactId>
  <version>1.11.0</version>
</dependency>

<dependency>
  <groupId>dev.langchain4j</groupId>
  <artifactId>langchain4j-open-ai</artifactId>
  <version>1.11.0</version>
</dependency>

Tool class and usage example:

public class CustomerServiceAgent {
  public static class OrderTools {
    @Tool("根据订单号查询订单状态")
    public String getOrderStatus(@Tool("订单号") String orderId) {
      // 查询订单逻辑
      return "订单状态：已发货";
    }
  }

  public static void main(String[] args) {
    ChatModel model = OpenAiChatModel.builder()
        .apiKey(System.getenv("OPENAI_API_KEY"))
        .modelName("gpt-4")
        .build();

    Assistant assistant = AiServices.builder(Assistant.class)
        .chatLanguageModel(model)
        .tools(new OrderTools())
        .build();

    String answer = assistant.chat("帮我查一下订单123456的状态");
    System.out.println(answer);
  }
}

interface Assistant {
  String chat(String userMessage);
}

The AiServices abstraction supports system messages, user messages, chat memory, RAG, tool calls, and multiple return types (String, POJO, enum, TokenStream).

Code‑style comparison

Spring AI leverages Spring’s dependency injection and auto‑configuration, resulting in less boilerplate for developers already familiar with Spring.

LangChain4j provides richer configuration via AiServices but requires manual wiring of components.

Feature Richness

Model support : LangChain4j supports 30+ LLMs (OpenAI, Claude, Gemini, Qwen, etc.) and 20+ vector stores. Spring AI supports fewer models and stores but is actively expanding.

RAG capability : LangChain4j 1.11.0 introduces PgVector hybrid retrieval (vector + keyword search with RRF ranking). Spring AI provides basic RAG.

Agent capabilities : LangChain4j offers chains, multi‑agent collaboration, and advanced memory strategies; Spring AI supports tool calls with more basic agent features.

Streaming and function calling are supported by both frameworks via the @Tool annotation.

Ecosystem Integration

Spring AI integrates seamlessly with Spring Boot, Spring Cloud, Actuator, and other Spring projects, making adoption near‑zero cost for existing Spring stacks.

Alibaba extends Spring AI with Spring AI Alibaba, adding multi‑agent orchestration, AgentScope integration, and deep ties to Alibaba Cloud services.

LangChain4j is framework‑agnostic; it provides a Spring Boot starter but also supports Quarkus, Micronaut, and other JVM frameworks.

Community Maturity & Learning Curve

LangChain4j started in early 2023 and has a more aggressive feature iteration cadence.

Spring AI, backed by the Spring team, shows rapid growth; version 2.0.0‑M2 contains 94 changes (36 improvements, 16 bug fixes).

Learning curve: Spring AI is shallow for developers familiar with Spring Boot (potentially half‑day onboarding). LangChain4j requires understanding of chains, tools, memory, and agents.

Selection Guide

When to choose Spring AI

Project is built on Spring Boot/Cloud and minimal integration effort is desired.

Rapid prototyping of standard AI use‑cases such as chatbots or content generation.

Enterprise requirements for monitoring, security, and configuration management.

Team already proficient with the Spring ecosystem.

Example of a quick RAG service with Spring AI:

@Service
public class RagService {
  @Autowired
  private VectorStore vectorStore;
  @Autowired
  private ChatClient chatClient;

  public String query(String question) {
    List<Document> documents = vectorStore.similaritySearch(question);
    String prompt = "基于以下资料回答问题：%s
问题：%s".formatted(documents, question);
    return chatClient.prompt(prompt).call().content();
  }
}

When to choose LangChain4j

Complex AI workflows, autonomous agents, and multi‑tool orchestration are required.

Project uses non‑Spring stacks such as Quarkus or Micronaut.

Deep customization or low‑level control is needed.

Familiarity with Python LangChain design patterns.

Example of a multi‑tool agent with LangChain4j:

public class ResearchAgent {
  interface Researcher {
    @SystemMessage("你是一个研究助手，可以联网搜索、查阅文档、总结信息")
    String research(@UserMessage String query);
  }

  public static void main(String[] args) {
    ChatModel model = OpenAiChatModel.builder()
        .apiKey(System.getenv("OPENAI_API_KEY"))
        .modelName("gpt-4")
        .build();

    Researcher researcher = AiServices.builder(Researcher.class)
        .chatLanguageModel(model)
        .tools(new WebSearchTool(), new DocumentReaderTool(), new SummaryTool())
        .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
        .build();

    String result = researcher.research("Spring AI和LangChain4j的优缺点对比");
    System.out.println(result);
  }
}

Mixed‑use scenario

Use Spring AI for model invocation and infrastructure management, while leveraging LangChain4j for complex chains and agents. Both can coexist in the same project.

Future Outlook

Spring AI 2.0 will strengthen null‑safety APIs, add MCP protocol support, and integrate more vector‑store providers.

LangChain4j 1.11.0 adds hybrid retrieval, streaming agents, and multimodal support.

Spring AI Alibaba will bring graph workflow orchestration and AgentScope integration for enterprise scenarios.

In the next 12 months the functional gap will narrow, but the underlying design philosophies will remain distinct.

Conclusion

There is no absolute winner between LangChain4j and Spring AI; the choice depends on project context. Spring AI provides a "official meal" for Spring‑centric teams needing quick, standard AI features. LangChain4j offers a "buffet" for teams that require advanced agents, deep customization, or framework‑agnostic solutions. A pragmatic approach is to start with Spring AI for rapid validation and introduce LangChain4j when complex workflows emerge, allowing both frameworks to complement each other.