Earlier this year, I needed to add an AI assistant to a Spring Boot application I maintain. Nothing exotic -- just a chat interface that could answer questions about the system's documentation. I had two obvious choices: Spring AI or LangChain4j.
I assumed it would be a quick decision. Both frameworks are mature. Both support the major LLM providers. Both have Spring Boot integrations. How different could they be?
Six weeks and two prototypes later, I learned that the differences matter a lot more than the feature matrix suggests.
I am a Senior Software Engineer from Dhaka, Bangladesh. I work with Spring Boot every day. I also run Hermes Agent as my personal AI infrastructure. So I have a foot in both the enterprise Java world and the AI agent world. Here is what I found comparing these two frameworks head to head.
Spring AI is the official AI integration framework from the Spring team at Broadcom (formerly VMware). It reached version 2.0.0 in 2026, alongside Spring Boot 4. It covers the full range: chat, embeddings, image generation, audio transcription, text-to-speech, moderation, and -- the headline feature in 2.0 -- first-class MCP (Model Context Protocol) support. You can find it on start.spring.io alongside every other Spring starter.
LangChain4j started in early 2023 as a response to the Python-dominated AI landscape. Despite the name, it is not a Java port of LangChain. The maintainers emphasize this: it is built for Java, not ported to it. LangChain4j supports 20+ LLM providers and 30+ vector stores, and integrates with Spring Boot, Quarkus, Helidon, and Micronaut.
Both frameworks let you call LLMs, build RAG pipelines, call tools, and create agents. But they approach these tasks from fundamentally different angles.
This is the single biggest difference between the two, and it drives every other decision.
Spring AI is built for the Spring ecosystem. If you are already using Spring Boot, Spring AI slides in like any other Spring starter. You get auto-configuration, property-driven setup, the familiar dependency injection model, and tight integration with Spring's observability stack (Micrometer, Actuator). The API feels like it was designed by the same people who brought you RestTemplate and WebClient -- because it was.
Here is how you add Spring AI to a Spring Boot 4 project:
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>
Then in application.yaml:
spring:
ai:
openai:
api-key: ${OPENAI_API_KEY}
chat:
options:
model: gpt-4.1
And in your code:
@RestController
class ChatController {
private final ChatClient chatClient;
ChatController(ChatClient.Builder builder) {
this.chatClient = builder.build();
}
@PostMapping("/chat")
String chat(@RequestBody String message) {
return chatClient.prompt()
.user(message)
.call()
.content();
}
}
That is it. One dependency, a config property, a constructor injection, and you are calling an LLM.
LangChain4j is framework-agnostic. You can use it with Spring Boot, Quarkus, Helidon, Micronaut, or plain Java. It does not assume you are in a Spring context. This makes it more portable, but it also means you handle more wiring yourself when you use it with Spring Boot.
Here is the same chat endpoint with LangChain4j in Spring Boot:
@Configuration
class LangChain4jConfig {
@Bean
ChatLanguageModel chatModel() {
return OpenAiChatModel.builder()
.apiKey(System.getenv("OPENAI_API_KEY"))
.modelName("gpt-4.1")
.build();
}
}
@RestController
class ChatController {
private final ChatLanguageModel model;
ChatController(ChatLanguageModel model) {
this.model = model;
}
@PostMapping("/chat")
String chat(@RequestBody String message) {
return model.generate(message);
}
}
The code is not significantly longer, but it is more explicit. You build the model instance yourself rather than relying on auto-configuration.
Both frameworks cover the major providers. The practical difference is in the uncommon ones.
Spring AI 2.0 supports: OpenAI, Anthropic, Google Gemini, Amazon Bedrock, DeepSeek, Mistral AI, Ollama, Groq, Perplexity AI, NVIDIA, MiniMax, OCI Generative AI, Azure OpenAI, and Docker Model Runner. The full list is on the Spring AI reference docs.
LangChain4j supports: OpenAI, Anthropic, Google Gemini, Azure OpenAI, Amazon Bedrock, Ollama, Mistral AI, DeepSeek, Groq, Perplexity AI, HuggingFace, LocalAI, and about 10 more. The complete list is on their integrations page.
The difference is not in the big names -- both have those. It is in the long tail. LangChain4j has more community-contributed integrations, because its plugin-like architecture makes adding a new provider easier. Spring AI has fewer, but each one is maintained by the Spring team and follows a consistent API pattern.
Winner: Tie for mainstream providers. LangChain4j for esoteric ones.
Spring AI 2.0 introduced its fluent ChatClient API, which intentionally mirrors WebClient and RestClient. You chain .prompt()
, .user()
, .system()
, .call()
, and .entity()
calls. It is verbose but expressive -- you can see every part of the request at a glance.
LangChain4j instead pushes toward a declarative pattern called AI Services. You define an interface, annotate it, and LangChain4j generates the implementation at runtime:
interface Assistant {
String chat(@UserMessage String message);
}
// Usage
Assistant assistant = AiServices.create(Assistant.class, model);
String response = assistant.chat("Hello!");
This is closer to Spring Data JPA's repository pattern. You describe what you want, and the framework generates the implementation.
Both approaches work. I find the ChatClient API easier to debug because the flow is explicit. The AI Services pattern is cleaner for simple use cases but can be harder to trace when something goes wrong.
Both frameworks support RAG, but the approach differs.
Spring AI bundles an ETL framework for document ingestion. You can read documents, split them, embed them, and store them in a vector database using a pipeline:
var pipeline = DocumentReadConfig
.from("classpath:docs/")
.toVectorStore(pgvectorStore)
.splitter(TokenTextSplitter.builder().build())
.build();
pipeline.run();
Then at query time, you use the Advisors API to inject relevant context:
chatClient.prompt()
.user(question)
.advisors(assistant.createContextRetrievalAdvisor(pgvectorStore))
.call()
.content();
LangChain4j uses a ContentRetriever abstraction. You configure an embedding store and a retriever, then attach it to your AI Service:
var embeddingStore = new PgVectorEmbeddingStore(...);
var contentRetriever = new EmbeddingStoreContentRetriever(embeddingStore);
var assistant = AiServices.builder(Assistant.class)
.chatLanguageModel(model)
.contentRetriever(contentRetriever)
.build();
Both frameworks support the major vector stores: PostgreSQL with pgvector, Pinecone, Qdrant, Milvus, Chroma, Redis, Weaviate, MongoDB Atlas, Cassandra, Elasticsearch, and more.
The difference is in the ETL pipeline. Spring AI's DocumentReadConfig pipeline is more opinionated and structured. LangChain4j gives you more control over each step but requires more manual setup.
This is where the two frameworks diverge significantly in 2026.
Spring AI 2.0 has first-class MCP support with dedicated Boot Starters. You can run MCP servers and clients using STDIO, SSE, or Streamable-HTTP transports. There is even a MCP annotations system for exposing Spring Beans as MCP tools:
@McpServer
@Component
public class MathTools {
@McpTool(description = "Add two numbers")
public int add(@McpParam int a, @McpParam int b) {
return a + b;
}
}
Spring AI 2.0 treats MCP as a first-class integration point, not an afterthought. You configure MCP servers in application.yaml and they automatically load.
LangChain4j also supports MCP through its tool-calling and function-calling mechanism. But it does not have the same level of auto-configuration or the annotation-driven server model. You can connect to MCP servers, but you wire them up manually.
If MCP is core to your architecture, Spring AI 2.0 has a clear edge right now.
Pick Spring AI when:
The Spring AI docs are excellent. The reference guide covers every feature with runnable examples.
Pick LangChain4j when:
LangChain4j's getting started guide is at docs.langchain4j.dev.
After building two prototypes, I chose Spring AI for my documentation assistant. The reason was boring and practical: my project was already on Spring Boot 4, and the development speed from auto-configuration was noticeable. The Advisors API made RAG trivial to implement. The MCP annotations meant I could expose existing Spring Beans as tools without writing adapters.
But if I were starting a greenfield project on Quarkus, or building a library that needed to work across multiple Java frameworks, I would reach for LangChain4j without hesitation.
Both frameworks produce good applications. The wrong choice is not choosing between them -- it is trying to use both in the same project and duplicating abstractions.
Your framework -- Spring Boot 4? Pick Spring AI. Any other JVM framework? Pick LangChain4j.
Provider coverage -- Mainstream providers are well-supported by both. Esoteric or community-maintained providers lean toward LangChain4j.
MCP integration -- Spring AI 2.0 has first-class, annotation-driven MCP support with auto-configuration. LangChain4j requires manual wiring.
Observability -- Spring AI has built-in Micrometer and Actuator integration. LangChain4j expects you to wire your own monitoring.
Learning curve -- If you know Spring Boot, Spring AI feels natural. If you know Java without Spring, LangChain4j is simpler to start with.
Project portability -- LangChain4j is framework-agnostic (works with Quarkus, Helidon, Micronaut, plain Java). Spring AI ties you to the Spring ecosystem.
ETL for RAG -- Spring AI offers a structured document pipeline. LangChain4j gives you more flexible, manual control over each step.
I write about Java, Spring Boot, and AI every week. Subscribe -- it is free. No newsletter platform, no fluff. Just what I learn building real software in Dhaka.
Have you used either Spring AI or LangChain4j in production? What drove your choice? I would love to hear your experience in the comments.
The takeaway: Do not pick a framework by comparing feature lists. Pick the one whose philosophy matches your project. Spring AI rewards Spring Boot teams with speed and consistency. LangChain4j rewards any JVM team with flexibility and portability. Both are production-ready. Your context decides.