Spring AI vs LangChain4j 2026: Java LLM Framework Guide

If you build backend services on the JVM, adding an LLM to your stack in 2026 comes down to two serious options. The Spring AI vs LangChain4j decision used to be about taste. After Spring AI 2.0 went GA in June 2026 with a hard Spring Boot 4 baseline, it has become a platform decision. This guide compares both frameworks on architecture, tool calling, RAG, agents, MCP support and migration cost, so your team can pick the right one the first time.

Spring AI vs LangChain4j at a Glance

Both projects reached 1.0 GA in 2025 and both are production-ready. They solve the same problems (chat models, embeddings, retrieval-augmented generation, tool calling and agents) but from different angles.

• Spring AI is the official Spring project. It is opinionated about composition: the ChatClient fluent API, an advisor chain, Spring beans as tools and Boot auto-configuration.
• LangChain4j is a community-driven, framework-agnostic library. It is opinionated about building blocks: declarative @AiService interfaces, pluggable memory, retrievers and a dedicated agentic module.

Architecture and Programming Model

Spring AI: ChatClient and the advisor chain

Spring AI 2.0 makes ChatClient the main user-facing API, with ChatModel as the lower-level building block. Every request flows through an ordered chain of advisors. The big 2.0 change is that the tool-calling loop is no longer hidden inside each model implementation. It now lives in the advisor chain as ToolCallingAdvisor, which means you can wrap, intercept or replace it.

String answer = chatClient.prompt()
    .system("You are a support assistant for order APIs.")
    .user(question)
    .tools(orderTools)
    .call()
    .content();

The same looping mechanism powers StructuredOutputValidationAdvisor, which retries when a model returns JSON that fails validation. There is also ToolSearchToolCallingAdvisor, which exposes large tool sets gradually instead of sending hundreds of tool schemas on every request.

LangChain4j: declarative AI services

LangChain4j’s standout feature is the AI Service: you declare a Java interface and the framework generates the implementation, wiring in memory, tools and retrieval.

interface SupportAgent {
    @SystemMessage("You are a support assistant for order APIs.")
    String chat(@MemoryId String userId, @UserMessage String question);
}

SupportAgent agent = AiServices.builder(SupportAgent.class)
    .chatModel(model)
    .tools(new OrderTools())
    .chatMemoryProvider(id -> MessageWindowChatMemory.withMaxMessages(20))
    .build();

Because it does not depend on Spring, the same code runs in a Quarkus microservice, a Micronaut function or a plain CLI tool.

Model Provider and Vector Store Support

This is where the two projects have moved apart. For 2.0, Spring AI deliberately trimmed its core to a focused set of chat providers: OpenAI (via the official SDK, which also covers OpenAI-compatible endpoints), Anthropic, Amazon Bedrock, Google GenAI, Mistral AI, DeepSeek and Ollama. Other integrations, such as OCI Generative AI and Azure Cosmos DB, are now maintained by the vendors themselves.

LangChain4j still has the broadest catalogue of model providers, embedding stores and document loaders in the Java ecosystem. If you need a niche vector database or a regional model provider, LangChain4j is more likely to have a ready-made module.

RAG, Memory and Agents

Retrieval-augmented generation

Both frameworks cover the standard RAG pipeline: document readers, splitters, embedding models, vector stores and retrievers. Spring AI plugs retrieval into the advisor chain (for example a question-answer advisor backed by a VectorStore). LangChain4j exposes a RetrievalAugmentor with query transformers, routers and content aggregators, which gives you finer control over advanced RAG patterns such as query expansion or multi-source routing.

Conversation memory

LangChain4j has per-user memory built in through @MemoryId. Spring AI provides a ChatMemory abstraction, and the community project spring-ai-session adds event-sourced memory with turn-aware compaction when the context window fills up.

Agentic workflows

LangChain4j currently leads here. Its langchain4j-agentic module offers sequential, parallel, loop and conditional workflows, a supervisor agent that plans dynamically, and an AgenticScope for sharing state between agents. Note that the module is still marked experimental, so expect API changes. Spring AI 2.0 provides the foundation (advisor loops and recursive advisors), while higher-level patterns such as Agent Skills live in the spring-ai-agent-utils community project.

MCP Support: Spring AI’s Biggest Advantage

The Model Context Protocol has become the standard way to connect LLMs to tools. The Spring team builds and maintains the official MCP Java SDK, and Spring AI 2.0 ships with SDK 2.0.0, which follows the 2025-11-25 specification. Turning a Spring service into an MCP server now takes a single annotation:

@Service
class OrderMcpTools {
    @McpTool(description = "Get the status of an order by ID")
    OrderStatus orderStatus(String orderId) { ... }
}

Streamable HTTP is now the default transport, replacing the deprecated SSE transport, and a stateless variant makes horizontal scaling easier. LangChain4j can consume MCP servers as a client and is adding A2A support, but if you plan to publish MCP tools from your Spring services, Spring AI is the natural choice.

Migration Cost: The Deciding Factor in 2026

For most enterprise teams this is the section that matters. Spring AI 2.0 requires Spring Boot 4 and Spring Framework 7, together with Jackson 3 and JSpecify null-safety. If your services still run on Boot 3.x, adopting Spring AI 2.x means doing the Boot 4 migration first. Spring AI 1.1 still works on Boot 3, but new features land on the 2.x line.

LangChain4j avoids this problem because it offers starters for both Boot 3.5+ and Boot 4. You can add AI features today and upgrade Boot on your own schedule.

Which Should You Choose?

• Choose Spring AI 2.0 if you are already on Spring Boot 4, want Micrometer and OpenTelemetry tracing without extra work, or plan to expose internal services as MCP servers.
• Choose LangChain4j if you are on Boot 3.x, Quarkus, Micronaut or plain Java, need the widest range of providers and vector stores, or want multi-agent workflows now.
• Use both carefully: some teams use LangChain4j for agent orchestration inside a Spring Boot app. It works, but you will have two abstractions for models and memory, so write down clear ownership rules.

Related reading on NewsifyAll: our guides to MCP servers, AI agent frameworks and LLM observability.

Frequently Asked Questions

Is Spring AI better than LangChain4j?

Neither is better in every case. Spring AI is the better fit for Spring Boot 4 teams that want native observability and MCP server support. LangChain4j is more flexible, runs on any Java framework and offers more integrations and agent patterns.

Does Spring AI 2.0 work with Spring Boot 3?

No. Spring AI 2.0 is built for Spring Boot 4.0/4.1 and Spring Framework 7. Teams on Boot 3.x should stay on Spring AI 1.1 or use LangChain4j, which supports both Boot generations.

Can LangChain4j be used with Spring Boot?

Yes. LangChain4j provides Spring Boot starters that auto-configure models and let you declare AI services as Spring beans. It also integrates with Quarkus and Micronaut.

Which Java framework is best for building MCP servers?

Spring AI. Its team maintains the official MCP Java SDK, and the @McpTool, @McpResource and @McpPrompt annotations let you expose Spring beans as MCP capabilities with very little code.

Conclusion

The Spring AI vs LangChain4j choice in 2026 depends mostly on your Spring Boot version. On Boot 4, Spring AI 2.0 gives you a clean advisor-based architecture, first-class MCP support and production observability. On anything else, LangChain4j gets you to production faster with more integrations and stronger agent tooling. Start with a small proof of concept, such as one RAG endpoint and one tool, in the framework that matches your platform, and measure latency, token cost and developer experience before you standardize.

Building AI features in Java? Subscribe to NewsifyAll for hands-on guides on LLM frameworks, RAG and AI agents, and share this comparison with your team before your next architecture review.

Sources: Spring AI 2.0.0 GA announcement (spring.io); LangChain4j Agents documentation.