Spring AI vs LangChain4j: Which Java AI Framework Should You Choose in 2025?

Spring AI and LangChain4j are the two leading frameworks for integrating LLMs into Java applications. After using both in production enterprise projects, here is a direct comparison across architecture, developer experience, performance, and real-world use cases.

TL;DR: Which Framework Should You Choose?

Choose Spring AI if your team already runs Spring Boot and wants seamless integration with the Spring ecosystem — auto-configuration, Actuator observability, and familiar dependency injection patterns.

Choose LangChain4j if you need maximum flexibility, support for more LLM providers out of the box, or you're running Quarkus instead of Spring Boot.

Both frameworks reached 1.0 GA in May 2025, and both now support MCP (Model Context Protocol) for agent-tool interoperability.

Architecture Comparison

Feature	Spring AI 1.0	LangChain4j 1.0
Framework coupling	Deeply integrated with Spring Boot	Framework-agnostic (works with Spring, Quarkus, plain Java)
Configuration	Auto-configuration via application.yml	Programmatic builder pattern
DI approach	Spring beans, @Configuration	Works with any DI (CDI, Spring, manual)
Type safety	Strong (POJO mapping for structured outputs)	Strong (annotation-driven, compile-time checks)
Provider support	OpenAI, Anthropic, Google, Amazon, Ollama, Azure	15+ providers including all above + Mistral, Cohere, local models
Vector stores	pgvector, Pinecone, Milvus, Chroma, Redis	15+ stores including all above + Elasticsearch, Qdrant, Weaviate
Observability	Native Spring Boot Actuator + Micrometer	Manual integration required
ETL framework	Built-in document readers, transformers, writers	Document loaders, splitters with more formats
Memory	MessageChatMemoryAdvisor with compaction	ChatMemory with various stores
Enterprise backing	VMware/Broadcom (Spring team)	Microsoft + Red Hat (joint security audits)

Developer Experience

Spring AI: Convention Over Configuration

Spring AI follows the Spring philosophy. If you've built a Spring Boot app before, the learning curve is minimal:

@Bean
ChatClient chatClient(ChatClient.Builder builder) {
    return builder
        .defaultSystem("You are a helpful assistant")
        .build();
}

The auto-configuration detects your LLM provider from application.yml and wires everything automatically. Structured outputs map directly to Java records — no manual JSON parsing.

LangChain4j: Explicit and Flexible

LangChain4j favors explicit configuration with builder patterns. More verbose, but you see exactly what's happening:

ChatLanguageModel model = OpenAiChatModel.builder()
    .apiKey(System.getenv("OPENAI_API_KEY"))
    .modelName("gpt-4o")
    .build();

The annotation-driven @AiService interface is where LangChain4j shines — define your AI service as a Java interface, and the framework implements it:

@AiService
interface Assistant {
    @SystemMessage("You are a Java expert")
    String chat(@UserMessage String question);
}

RAG (Retrieval-Augmented Generation) Comparison

Both frameworks support full RAG pipelines, but with different ergonomics.

Spring AI provides a dedicated ETL framework with DocumentReader, DocumentTransformer, and DocumentWriter abstractions. The QuestionAnswerAdvisor wraps RAG into a single advisor pattern that plugs into the chat client.

LangChain4j offers more granular control with DocumentSplitter, EmbeddingStore, and ContentRetriever components. The EmbeddingStoreContentRetriever supports metadata filtering and score thresholds out of the box.

For hybrid search (keyword + semantic), both frameworks now support it, though LangChain4j had it earlier and offers more configuration options.

Production Performance

Based on benchmarks from enterprise deployments:

• Cold start: Spring AI adds ~200-400ms to Spring Boot startup. LangChain4j on Quarkus with GraalVM native image starts in under 100ms.
• Memory overhead: Spring AI inherits the Spring Boot memory footprint (~150-300MB). LangChain4j on Quarkus can run with 50-100MB.
• Throughput: Both frameworks add minimal overhead to LLM API calls — the bottleneck is always the provider API latency, not the framework.

Cost Optimization

Semantic caching — caching LLM responses for semantically similar queries — can reduce API costs by up to 73% according to VentureBeat analysis of production deployments.

• Spring AI: No built-in semantic cache. Can be implemented via custom ChatClient advisors.
• LangChain4j: No built-in semantic cache either. Both require custom implementation with vector similarity matching.

Both frameworks support streaming responses, which improves perceived latency for end users.

When to Use Each Framework

Choose Spring AI When:

• Your team already uses Spring Boot
• You value auto-configuration and convention over configuration
• You need native Actuator observability (metrics, health, tracing)
• You want the backing of the Spring team (Broadcom/VMware)
• You're building a standard enterprise application with predictable AI needs

Choose LangChain4j When:

• You need to support multiple LLM providers with easy switching
• You're running Quarkus or plain Java (no Spring dependency)
• You need the widest vector store compatibility
• You want more granular control over RAG pipelines
• You value Microsoft's enterprise support and Red Hat's security audits
• You need GraalVM native image for cloud-native deployments

Consider Both When:

• You want to prototype with LangChain4j's flexibility, then productionize with Spring AI's observability
• Different microservices have different requirements

The Quarkus Option

Teams running Quarkus should evaluate the Quarkus LangChain4j extension. It wraps LangChain4j with Quarkus-native features: CDI integration, zero-config setup for models, built-in metrics and tracing, and GraalVM native-image support. Red Hat actively maintains it, and Oracle has published guides on building agentic AI applications with this stack.

Bottom Line

Both frameworks are production-ready as of 2025. The choice comes down to your existing ecosystem:

• Spring Boot shop → Spring AI (seamless integration, familiar patterns)
• Quarkus/flexible stack → LangChain4j (wider provider support, lighter footprint)
• Need both → They can coexist in a microservices architecture

The good news: the Java ecosystem finally has world-class AI integration frameworks. The question is no longer whether Java can do enterprise AI — it's which framework fits your architecture best.

If you're unsure which framework fits your Java stack, an AI Integration Assessment can map your architecture to the right tooling in a structured way.