Spring AI 2.0 vs LangChain4j: Which Should You Choose?

In Java enterprise applications that need to integrate large language models (LLMs), two main approaches exist: embedding the model natively into the Spring ecosystem with Spring AI , or using the framework‑agnostic library LangChain4j . Both aim to reduce the cost of connecting models, RAG pipelines, tools, and agents, but they differ in philosophy, version baseline, and integration style.

Product positioning : Spring AI 2.0 is positioned as a first‑class layer inside the Spring stack, unifying abstractions for chat, embeddings, vector stores, and tools via Advisors. It aligns with Spring Boot 4.x, Spring Framework 7, and Jakarta EE 11, following the Spring release train. LangChain4j describes itself as an independent Java library that offers a complete toolbox—from ChatModel to high‑level AI services, RAG pipelines, and Agent —with first‑class integrations for Quarkus, Spring Boot, Helidon, and Micronaut, but without being a sub‑project of any framework.

Technical stack and version strategy : Spring AI binds tightly to the Spring Boot version you use, requiring migration guides when upgrading. LangChain4j requires JDK 17+ and evolves on its own SemVer, allowing gradual adoption in non‑Spring projects.

Architecture layers : Both frameworks share a layered model → application orchestration (Advisors or AiService) → side‑car capabilities (memory, RAG, tools) → infrastructure (vector stores, object storage, cache). Spring AI emphasizes ChatClient plus Advisors as cross‑cutting middleware, similar to Spring interceptors. LangChain4j centers on AiService, pipelines, and agents, which can run on plain Java SE, Quarkus, or any Spring Boot version.

Programming model and API style : Spring AI uses declarative configuration, bean lifecycle, and automatic wiring, making it natural for engineers familiar with Spring. LangChain4j relies on interface‑plus‑annotation definitions (e.g., AiService) that map natural‑language capabilities directly to Java interfaces, which can be easier to mock in unit tests.

RAG, vector stores, and tool calling : Both treat RAG as a core scenario, handling chunking, embedding, storage, retrieval, and re‑ranking. LangChain4j showcases many vector‑store implementations and retrieval strategies, while Spring AI abstracts vector stores and adds Advisors to the ChatClient pipeline, integrating with common enterprise anchors such as S3 or Bedrock Knowledge Base. Tool calling is supported by both, but Spring AI can inject tools directly in a @Bean configuration, whereas LangChain4j requires manual wiring in non‑Spring environments.

Observability and safety : Spring AI inherits Spring Actuator, Micrometer, and built‑in null‑safety (JSpecify, optional NullAway). LangChain4j relies on the host framework’s observability stack (Micrometer, OpenTelemetry), adding an extra step for developers.

Learning curve and community : Developers already proficient with Spring experience a short ramp‑up for Spring AI, while those comfortable with pure Java interfaces may prefer LangChain4j’s AiService style. Both projects provide authoritative English documentation and active open‑source communities.

Selection guidance : The article proposes a decision matrix using two axes—Spring version/ops habits and team’s Java/framework expertise. For new projects locked to Spring Boot 4, Spring AI aligns best; for legacy Spring 2.x/3.x projects or non‑Spring stacks, LangChain4j can be introduced incrementally. A concise decision tree recommends eliminating clearly mismatched options, then running a two‑week POC that evaluates RAG quality, latency, cost, observability, security, compliance, and maintainability.

Conclusion : Spring AI 2.0 and LangChain4j are not mutually exclusive choices. Spring AI embeds AI capabilities into an existing Spring lifecycle, while LangChain4j offers a framework‑neutral library that can be carried across JVM projects. The safest practice is to conduct a small‑scale POC, score the same checklist, and record the decision in an ADR, ensuring clear abstraction boundaries for long‑term evolution.