Choosing Between LangChain4j and Spring AI for Java Projects: A Practical Comparison

In recent months the author has been asked many times in a technical group: "Which Java AI framework should I use, LangChain4j or Spring AI?" The article notes that 2025 is a pivotal year for Java AI frameworks, with Spring AI 1.0 GA released in May 2025 and LangChain4j reaching version 1.11.0 with hybrid retrieval support, while Alibaba also offers AgentScope‑Java and Spring AI Alibaba.

Design Philosophy

Spring AI

Spring AI aims to make AI development as natural as writing Spring Boot code, applying Spring’s principles of portability, modularity, and dependency injection to AI. Its core goal is to let developers use POJOs as building blocks. The project is open‑source at https://github.com/spring-projects/spring-ai and follows a Spring‑style configuration and auto‑configuration.

LangChain4j

LangChain4j is the official Java implementation of Python’s LangChain, created to fill the gap of a mature LLM library in the Java ecosystem. Its repository is https://github.com/langchain4j/langchain4j. It targets Java/Kotlin developers, providing a complete LLM toolchain without requiring a Python bridge.

Core Features

The article presents a concrete use case: building an intelligent customer‑service assistant that can query order status. It shows Spring AI’s Maven dependency and YAML configuration, then defines a tool class with @Tool and a service that calls ChatClient. Spring AI 2.0.0‑M2 adds null‑safety APIs and JSpecify compliance.

For LangChain4j, the Maven coordinates are dev.langchain4j:langchain4j-core:1.11.0 and dev.langchain4j:langchain4j-open-ai:1.11.0. The code demonstrates defining a tool with @Tool, building a chat model with OpenAI, and using

AiServices.builder(...).chatLanguageModel(model).tools(new OrderTools()).build()

to create an agent.

Feature Richness

Model support : LangChain4j supports 30+ LLMs (OpenAI, Claude, Gemini, Qwen, etc.) and 20+ vector stores; Spring AI supports fewer models and stores.

RAG capability : LangChain4j 1.11.0 introduces PgVector hybrid retrieval, combining vector and keyword search with RRF ranking; Spring AI offers basic RAG.

Agent capabilities : LangChain4j provides chain, multi‑agent collaboration, and advanced memory strategies, while Spring AI mainly supports tool calls.

Ecosystem Integration

Spring AI integrates tightly with the Spring ecosystem (Boot, Cloud, Actuator, etc.), making it almost zero‑cost for existing Spring projects. Alibaba’s Spring AI Alibaba adds enterprise features, model gateways, and agent orchestration.

LangChain4j, while offering a Spring Boot starter, emphasizes framework neutrality and can be used with Quarkus, Micronaut, or other Java stacks.

Community Maturity & Learning Curve

LangChain4j has a longer history and more aggressive feature releases, whereas Spring AI, backed by the Spring team, shows rapid growth with 94 changes in the 2.0.0‑M2 release.

Spring AI’s learning curve is shallow for developers familiar with Spring Boot—often a half‑day to get started. LangChain4j requires understanding concepts like Chain, Tool, Memory, and Agent, resulting in a steeper curve but offering greater flexibility.

Selection Guide

When to Choose Spring AI

Project is based on Spring Boot/Cloud and you want minimal integration effort.

Rapid prototyping of standard AI use cases (chatbots, content generation).

Enterprise requirements for monitoring, security, and configuration management.

Team already proficient with the Spring ecosystem.

When to Choose LangChain4j

Complex AI workflows requiring autonomous agents and multi‑tool collaboration.

Non‑Spring stacks such as Quarkus or Micronaut.

Need for deep customization or low‑level control.

Familiarity with Python LangChain patterns.

A hybrid approach is also suggested: use Spring AI for model management and infrastructure, and LangChain4j for sophisticated chains and agents.

Future Outlook

Spring AI 2.0 will enhance null‑safety, MCP protocol support, and vector store integrations.

LangChain4j 1.11.0 adds hybrid retrieval, streaming, and multimodal agent support.

Spring AI Alibaba introduces Graph workflow orchestration and AgentScope integration.

Over the next year the functional gap will narrow, but the fundamental design philosophies will remain distinct.

Conclusion

There is no universally "better" framework; the choice depends on project context. Spring AI is the "official meal" for Spring‑centric teams needing quick, standard AI features, while LangChain4j is the "buffet" for teams requiring advanced, customizable AI agents and cross‑framework compatibility. A pragmatic strategy is to start with Spring AI for rapid validation and introduce LangChain4j when complex workflows emerge.