Spring AI vs LangChain4j: Which Java AI Framework to Choose?

For years, Python has held an iron grip on the Artificial Intelligence landscape. If you wanted to build an LLM-powered application, you used Python, LangChain, and LlamaIndex. Java developers were often left wrapping Python scripts in sidecars or dealing with raw HTTP calls.

However, as we move into late 2025, the tide has turned. The Java ecosystem now boasts robust, production-grade frameworks for building Generative AI applications. The conversation has shifted from “Can we do this in Java?” to “Should we use Spring AI or LangChain4j?”

This is not a trivial choice. It is an architectural decision that impacts your code portability, testing strategy, and long-term maintenance. At SpringDevPro, we have deployed both in production environments. In this deep dive, we will compare Spring AI vs LangChain4j to help you make the right choice for your enterprise stack.

The Contenders at a Glance

#

Before we look at the code, we need to understand the philosophy behind each project.

LangChain4j: The Pioneer

#

LangChain4j (LangChain for Java) appeared when the Java community was desperate for an LLM orchestration tool.

• Philosophy: It aims to simplify the integration of AI capabilities into Java applications. It draws heavy inspiration from the original Python LangChain library but adapts it to Java paradigms.
• Strengths: Rapid development cycle, massive feature set (often implementing new LLM features days after release), and a high-level “AI Service” abstraction that feels like magic.
• Best For: Developers who want features now, or teams migrating logic directly from Python LangChain prototypes.

Spring AI: The Official Standard

#

Spring AI is the Spring team’s answer to the AI revolution. It is not a port of LangChain; it is a reimagining of AI integration based on Spring’s core principles.

• Philosophy: Portable Service Abstraction (PSA). Just as Spring abstracted JDBC, JMS, and HTTP, Spring AI abstracts the underlying AI models. You write code once, and switch between OpenAI, Azure, Bedrock, or Ollama by changing a property file.
• Strengths: Rock-solid integration with the Spring ecosystem (Dependency Injection, Validation, Observability), consistency, and the backing of VMware/Broadcom.
• Best For: Enterprise Spring Boot shops, long-term projects requiring stability, and teams who value standard Spring patterns.

1. API Design & Developer Experience (DX)

#

The most immediate difference you will notice is how you interact with the LLM.

LangChain4j: The Declarative Approach

#

LangChain4j shines with its High-level AI Services. You define an interface, annotate it, and the framework generates the implementation (proxy) for you. This is very similar to Spring Data JPA or Feign Client.

// LangChain4j Style
@AiService
public interface Assistant {
    
    @SystemMessage("You are a helpful assistant specializing in Java.")
    String chat(@UserMessage String userMessage);
}

// Usage
Assistant assistant = AiServices.create(Assistant.class, model);
String answer = assistant.chat("Explain record classes");

Pros: Extremely concise. It hides the complexity of prompt templating and history management. Cons: It can feel like “magic.” Debugging proxy generation can be tricky, and you have less granular control over the exact request payload unless you drop down to the low-level API.

Spring AI: The Fluent API Approach

#

Spring AI adopts a Fluent API design, centered around the ChatClient. If you are comfortable with WebClient or JdbcTemplate, this will feel like home.

// Spring AI Style
@RestController
public class ChatController {

    private final ChatClient chatClient;

    public ChatController(ChatClient.Builder builder) {
        this.chatClient = builder.build();
    }

    @GetMapping("/chat")
    public String chat(@RequestParam String message) {
        return chatClient.prompt()
                .system("You are a helpful assistant specializing in Java.")
                .user(message)
                .call()
                .content();
    }
}

Pros: Explicit and readable. You know exactly what is happening at every step. It fits perfectly into the standard Dependency Injection lifecycle. Cons: Slightly more boilerplate code compared to the interface-based approach (though Spring AI is introducing higher-level advisors to reduce this).

2. The Battle of Abstractions: Vendor Lock-in

#

The primary reason to use a framework is to avoid vendor lock-in. Both frameworks promise this, but they deliver it differently.

The “Spring Way” of Configuration

#

In Spring AI, switching from OpenAI to AWS Bedrock is almost entirely configuration-driven.

# application.yml
spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
      chat:
        options:
          model: gpt-4o

If you want to switch to Ollama running locally? You simply change the dependency in pom.xml and update the application.yml. The Java code involving ChatClient often remains 100% untouched. This is the power of the Portable Service Abstraction.

LangChain4j’s Flexibility

#

LangChain4j also supports switching models, but it often requires code changes in how you instantiate your ChatLanguageModel bean, unless you rely heavily on their Spring Boot starters (which are community-maintained).

While LangChain4j supports a wider range of niche models and vector stores today, Spring AI focuses on deep support for the major cloud providers (Azure, AWS, Google Vertex, OpenAI) and local standards (Ollama, Transformers).

Verdict:

• Spring AI wins on configuration management and standardizing the “switch.”
• LangChain4j wins on the sheer number of obscure model integrations available out-of-the-box.

3. RAG (Retrieval-Augmented Generation) Implementation

#

RAG is the bread and butter of enterprise AI apps.

LangChain4j has a very mature RAG pipeline. It includes advanced splitters, document loaders for almost every file format (PDF, Word, HTML, Notion), and “Ingestors” that automate the ETL process.

Spring AI treats RAG components (Vector Stores, Document Readers) as standard Spring Beans.

• Vector Stores: Spring AI provides a unified VectorStore interface. Implementations for Neo4j, pgvector (PostgreSQL), Redis, and Milvus are first-class citizens.
• ETL: Spring AI uses a functional style for document processing.

Key Difference: Spring AI leverages the existing Spring Data ecosystem. For example, if you use the PgVectorStore, it feels very similar to using a JdbcTemplate. LangChain4j often brings its own lightweight implementations which might conflict if you already have heavy database dependencies.

Pro Tip: If your project requires parsing complex, messy proprietary document formats (like old .doc files), LangChain4j currently has better built-in document loaders. If your data is already in a database or clean JSON/Text, Spring AI is cleaner.

4. Observability and Production Readiness

#

This is where the “Enterprise” aspect of SpringDevPro comes into play. In a production environment, you don’t just need the code to work; you need to know how it works, how much it costs, and where the latency is.

Spring AI has a massive advantage here: Native Observability. Because it is built by the Spring team, it integrates seamlessly with Micrometer and Spring Boot Actuator.

• Metrics: You automatically get metrics for token usage, response time, and error rates.
• Tracing: If you use Zipkin or Grafana Tempo, Spring AI traces propagate automatically. You can see a trace start at your HTTP Controller, go to the DB, and then go to the OpenAI API, all in one view.

LangChain4j has added observability features, but it often feels like an add-on rather than a foundational layer. You might need to write custom wrappers to get the same level of insight that Spring AI gives you for free.

Verdict: For high-compliance industries (Finance, Healthcare), Spring AI is the safer bet due to its observability pedigree.

5. Function Calling (Tool Use)

#

Function calling allows the LLM to “break out” of the chat and execute Java code (e.g., “Check the weather in London”).

Spring AI utilizes the java.util.function.Function interface. You register a standard Java Bean as a function, and the ChatClient handles the JSON schema generation and execution automatically.

@Bean
@Description("Get the current weather for a location")
public Function<WeatherRequest, WeatherResponse> weatherFunction() {
    return request -> weatherService.getWeather(request.location());
}

LangChain4j uses the @Tool annotation on methods within your AI Service.

@Tool("Get the current weather for a location")
public WeatherResponse getWeather(String location) { ... }

Both implementations are robust. However, Spring AI’s approach aligns better with the functional programming trends in modern Java (Java 17/21+), whereas LangChain4j relies on reflection and annotations.

6. The Ecosystem and Future Proofing

#

When we talk about spring ai vs langchain, we are also talking about community vs. corporation.

• LangChain4j is community-driven. It moves breakneck fast. If OpenAI releases a new feature on Monday, LangChain4j might have it on Tuesday. However, documentation can lag behind, and breaking changes are more common in minor versions.
• Spring AI is managed by VMware/Broadcom. The release cycle is slower and more deliberate. They prioritize API stability and backward compatibility.

The Risk Factor: There is a risk that LangChain4j, being a “port,” will always be chasing the Python version. Spring AI is designing a Java-native API from the ground up. In the long run (3-5 years), Spring AI is likely to become the default standard, much like Spring Security or Spring Data.

Decision Matrix: Which one should you choose?

#

To summarize our findings, here is a decision matrix for your team:

Conclusion

#

The battle of Spring AI vs LangChain4j is great news for Java developers. It means we have options.

If you are building a “serious” application—one that needs to be maintained for years, monitored by DevOps teams, and integrated into a complex Spring microservices architecture—Spring AI is the professional choice. It respects the idioms of the framework we use every day.

However, do not discount LangChain4j. It is a fantastic piece of engineering. If you are building a standalone agent, a CLI tool, or need a specific feature that Spring AI hasn’t implemented yet, LangChain4j is a capable ally.

At SpringDevPro, we have decided to align our core tutorials and best practices with Spring AI, as we believe it represents the future standard for the Java ecosystem.