Java Devs Finally Have a Choice
Large language models are no longer “nice-to-have” addons in Java apps—they’re becoming core features that power chatbots, copilots, smart search, and automation flows. The big question for Java developers in 2025 is simple: when integrating LLMs, should you bet on Spring AI or LangChain4j?spring+3
In this post, you will see what each framework is best at, where they struggle, and real code snippets to help you decide which one fits your next Java project.baeldung+1
What Is Spring AI?
Spring AI is the official Spring ecosystem framework for integrating AI and LLMs into Spring Boot applications using familiar Spring patterns like auto-configuration, dependency injection, and portable service abstractions. It gives you high-level clients such as ChatClient and EmbeddingClient so you can swap providers (OpenAI, Azure OpenAI, Hugging Face, and more) with minimal code changes.javacodegeeks+3
If you already live in the Spring Boot world, Spring AI feels “native”: configuration via application properties, starter dependencies, observability, and security hooks integrate nicely with the rest of your stack.spring+1
What Is LangChain4j?
LangChain4j is an open-source Java library that focuses on making LLM integration easy and modular for any Java application, not just Spring Boot. It provides a unified API over many LLM providers and vector stores (OpenAI, Gemini, Pinecone, Milvus, pgvector, and more) plus a rich toolbox for agents, RAG pipelines, memory, and function calling.github+2
Because LangChain4j does not require Spring, you can use it in Quarkus, Micronaut, plain Java, or even CLI tools, making it attractive for smaller services and framework-agnostic architectures.elastic+1
Design Philosophy: Convention vs Composition
Spring AI embraces “convention over configuration” and leans heavily on Spring Boot’s auto-configuration model. You declaratively configure providers and then inject high-level clients, letting Spring manage most of the wiring for you.infoq+3
LangChain4j takes a more explicit, building-block approach, where you compose chains, tools, retrievers, and memories yourself, giving you fine-grained control over the LLM pipeline. Recent real-world comparisons show Spring AI shines in typical enterprise Spring Boot apps, while LangChain4j often feels lighter and more flexible for custom pipelines.dev+3
Quick Start: Simple Chat Example
Here is a conceptual comparison of a simple “chat completion” in both frameworks, assuming you have added the right Maven dependencies and configured keys via environment variables or application properties.baeldung+1
Spring AI style (pseudo-style example):
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Define a ChatClient bean and inject it into your service.
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Call a high-level method like chatClient.generate(prompt) to get responses.
LangChain4j style (pseudo-style example):
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Construct an LLM object with your provider configuration.
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Build a chain or use a helper method to send prompts and handle responses.
Both approaches reduce boilerplate when talking to LLM APIs, but Spring AI hides more behind Spring Boot magic, while LangChain4j exposes more explicit objects and chains.layer5+1
RAG and Agents: Who Does What Better?
LangChain4j has focused heavily on advanced patterns like Retrieval-Augmented Generation (RAG), agents, and tool calling since its early releases, offering ready-made components for ingestion, retrievers, and vector stores. For complex agentic workflows, community resources and integrations (e.g., with Elastic, MongoDB, and Quarkus) make it easy to build production-grade pipelines.javapro+3
Spring AI has been rapidly adding patterns like advisors and LLM-as-a-judge, plus integrations for multi-provider setups and evaluation flows, which makes it strong for enterprise-y Spring Boot apps that need governance and structured evaluation. If your main scenario is “Spring Boot app + RAG + observability + security,” Spring AI is quickly becoming a very compelling default.geeksforgeeks+3
Performance and Resource Usage
Independent benchmarks and community articles indicate that LangChain4j is often slightly leaner and faster in basic scenarios like chat and streaming, with lower memory overhead, especially when not running inside a heavy Spring Boot context. However, when you are already running Spring Boot for the rest of your app, the incremental overhead of Spring AI is minimal and may be outweighed by the benefits of native integration.youtubelayer5+2
For serverless or microservices where cold start and memory are critical, combining LangChain4j with lightweight runtimes like Quarkus or native images can be a powerful choice. For monoliths or larger microservices already on Spring Boot, Spring AI’s tight integration with the Spring ecosystem can simplify deployment, monitoring, and scaling.developers.redhat+3
Example: Building a Java RAG Service
A typical RAG flow in LangChain4j would involve configuring an embeddings model, a vector store, a document loader, and a retriever, then wiring them into a chain that takes user queries and context documents. This level of explicit composition gives you freedom to swap MongoDB, Elastic, or Pinecone, and to tweak retrieval logic in detail.mongodb+3
In Spring AI, you would configure your embedding provider and vector store via Spring properties, then use Spring-managed beans to orchestrate retrieval and generation, potentially with advisors for response evaluation. This works especially well when you already rely on Spring Data, Spring Security, and Actuator for metrics and tracing.javacodegeeks+3
When Spring AI Is the Better Choice
Spring AI is generally the better framework when:
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You are already using Spring Boot for your REST APIs, data access, and security.spring+1
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You want “Spring-native” configuration, monitoring, and dependency injection for your AI services.infoq+1
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Your organization values standardized frameworks and long-term support in the Spring ecosystem.geeksforgeeks+1
In these cases, Spring AI minimizes stack fragmentation and lets your team reuse existing Spring expertise to ship AI features faster.layer5+1
When LangChain4j Is the Better Choice
LangChain4j is usually the better fit when:
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You need framework-agnostic LLM tooling for plain Java, Quarkus, Micronaut, or CLI tools.github+1
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You want advanced agent/RAG tooling with fine-grained control over each step of the pipeline.javapro+1
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You care about lightweight performance, cold starts, or running in non-Spring environments.dev+1
If you are building experimental AI services, side projects, or high-performance microservices, LangChain4j keeps your options open without forcing you into the Spring ecosystem.youtubemongodb
Final Verdict: “Best” Depends on Your Stack
There is no single winner—instead, “best” depends entirely on your existing stack and priorities as a Java developer. If your world is already built on Spring Boot, Spring AI is the natural, low-friction choice that keeps everything under one well-known framework.linkedin+3
If you want maximum flexibility, framework independence, and a rich toolbox for agents and RAG, LangChain4j is hard to beat in 2025. The smartest strategy is to pick the one that aligns with your architecture today, while keeping an eye on how both ecosystems evolve—because the Java AI landscape is moving very fast.github+2youtube
- https://spring.io/projects/spring-ai
- https://layer5.io/blog/docker/spring-ai-streamlining-local-llm-integration-for-java-developers
- https://github.com/langchain4j/langchain4j
- https://www.geeksforgeeks.org/advance-java/introduction-to-spring-ai-1/
- https://www.baeldung.com/spring-ai
- https://dev.to/superorange0707/springai-vs-langchain4j-the-real-world-llm-battle-for-java-devs-128f
- https://www.javacodegeeks.com/managing-multiple-llm-integrations-with-spring-ai.html
- https://spring.io/blog/2025/11/10/spring-ai-llm-as-judge-blog-post
- https://www.baeldung.com/java-langchain-basics
- https://javapro.io/2025/04/23/build-ai-apps-and-agents-in-java-hands-on-with-langchain4j/
- https://www.elastic.co/search-labs/blog/langchain4j-llm-integration-introduction
- https://developers.redhat.com/articles/2024/02/07/how-use-llms-java-langchain4j-and-quarkus
- https://www.infoq.com/presentations/spring-ai-framework/
- https://www.linkedin.com/pulse/leading-java-ai-frameworks-langchain4j-vs-spring-custom-miliari-gz7je
- https://www.mongodb.com/company/blog/product-release-announcements/ai-powered-java-applications-with-mongodb-langchain4j
- https://www.youtube.com/watch?v=w35WrPfZYxA
- https://www.blogger.com/u/2/blog/post/edit/6595824266791213416/3317282117741799597
- https://spring.io/blog/2025/01/21/spring-ai-agentic-patterns
- https://github.com/ThomasVitale/llm-apps-java-spring-ai
- https://www.reddit.com/r/SpringBoot/comments/1mvc78r/need_help_in_deciding_to_use_spring_ai_vs/
- https://www.youtube.com/watch?v=lkMhqEyjfXs
Dear Reader,
In the fast-evolving landscape of Artificial Intelligence, integrating Large Language Models (LLMs) into applications has become a priority for many developers. Java, a stalwart in the programming world, offers robust frameworks to facilitate this integration. Among these, Spring AI and LangChain4j stand out as leading contenders. This article dives deep into both frameworks to help you decide which is best suited for your Java application.
Overview of Spring AI
Spring AI, an extension of the well-known Spring ecosystem, provides a comprehensive solution for integrating AI capabilities into Java applications. Leveraging Spring Boot's simplicity and scalability, Spring AI allows developers to seamlessly incorporate AI models, including LLMs, with minimal configuration.
Key Features
- Seamless Spring Boot Integration: Utilizes the familiar Spring Boot setup, making it easy for developers already versed in the Spring ecosystem.
- Extensive Model Support: Supports a wide array of AI models and libraries.
- Robust Configuration Management: Offers extensive configuration options, utilizing Spring's powerful configuration management capabilities.
Practical Code Example
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@SpringBootApplication
public class SpringAiApplication {
public static void main(String[] args) {
SpringApplication.run(SpringAiApplication.class, args);
}
@Bean
public LlmService llmService() {
return new LlmServiceImpl();
}
}
@Configuration
class LlmConfiguration {
@Bean
public LlmModel llmModel() {
return new LlmModel("path/to/your/model");
}
}
Real-World Use Cases
- Customer Support Automation: Utilize LLMs for automated customer service chatbots.
- Content Generation: Automatically generate reports or articles based on data inputs.
Overview of LangChain4j
LangChain4j is a relatively new framework focused on providing intuitive tools for language model integration into Java applications. It emphasizes simplicity and ease of use, making it accessible even for those without extensive AI experience.
Key Features
- Lightweight and Fast: Minimal overhead, designed for efficiency.
- Intuitive API: Offers an easy-to-use API that abstracts much of the complexity.
- Strong Community Support: Growing community with active contributions and support.
Practical Code Example
import langchain4j.LangChain;
import langchain4j.models.LlmModel;
import langchain4j.services.LlmService;
public class LangChain4jExample {
public static void main(String[] args) {
LangChain langChain = new LangChain();
LlmModel model = langChain.loadModel("path/to/your/model");
LlmService service = langChain.createService(model);
String response = service.query("What is the weather like today?");
System.out.println(response);
}
}
Real-World Use Cases
- Interactive Educational Tools: Build applications that use LLMs to provide interactive learning experiences.
- Data Analysis Assistants: Enhance data analysis tools with natural language processing capabilities.
Key Features Comparison
Performance and Scalability
Both Spring AI and LangChain4j are designed to handle high-performance tasks, but their approach differs. Spring AI leverages the Spring ecosystem's scalability, providing robust performance for enterprise-grade applications. LangChain4j, being lightweight, excels in scenarios where quick deployment and low latency are crucial.
Ease of Integration
Spring AI offers a smooth integration path for existing Spring Boot projects, making it an ideal choice if you're already using the Spring framework. LangChain4j stands out for its simplicity and ease of use, particularly beneficial for new projects or developers new to AI.
Community and Support
Spring AI benefits from the extensive Spring community and resources, offering strong support and a wealth of documentation. LangChain4j, while newer, has a rapidly growing user base and active community, providing ample support through forums and collaborative platforms.
Use Cases and Suitability
When deciding between Spring AI and LangChain4j, consider your project's specific needs. Spring AI is well-suited for projects that require deep integration with existing Spring applications, while LangChain4j is perfect for lightweight, quick-to-deploy solutions.
- Enterprise Applications: Opt for Spring AI for its robust infrastructure.
- Startups and Prototypes: Choose LangChain4j for its speed and simplicity.
Conclusion: Choosing the Right Framework
Selecting between Spring AI and LangChain4j depends on your project's requirements and your familiarity with the frameworks. Spring AI is a natural fit for those already embedded in the Spring ecosystem, offering extensive support and scalability. LangChain4j provides a compelling option for those seeking simplicity and speed in deploying LLMs.
Future Outlook
Both frameworks are poised for growth as AI technology continues to advance. Spring AI will likely expand its feature set, while LangChain4j will continue to refine its user-friendly approach. Keeping an eye on community developments and updates will ensure you make the most out of these powerful tools.
Whether you choose Spring AI or LangChain4j, integrating LLMs into your Java application can transform how you build and interact with software, opening new avenues for innovation and efficiency.
For more insights and updates, visit our blog at thinkwithjava.blogspot.com.
Best Regards,
Your Java AI Enthusiast Team
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