Langchain4j Spring Boot Integration

This brings LangChain4j into Spring Boot with auto-configuration and declarative AI services through interface annotations. You define an interface with @AiService, add message templates, and Spring's dependency injection handles the rest. It covers the full stack: configuring model beans through application.properties, setting up chat memory with @MemoryId, wiring RAG pipelines with Spring Data embedding stores, and exposing tools as @Component beans. The property-based configuration is clean, but you'll likely need manual bean definitions once you're juggling multiple providers or custom retry logic. Good fit if you're already deep in the Spring ecosystem and want AI services that feel like any other autowired bean.

skills.sh installs: 3.9K
GitHub stars: 369
last updated: Aug 21, 2026

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Install

$ npx -y skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-spring-boot-integration --agent claude-code

Installs into .claude/skills of the current project.

Files

LangChain4j Spring Boot Integration

Integrate LangChain4j with Spring Boot using declarative AI Services, auto-configuration, and Spring Boot starters. Configure AI model beans, set up chat memory, implement RAG pipelines with Spring Data, and build production-ready AI applications.

When to Use

Use this skill when:

Integrating LangChain4j into existing Spring Boot applications
Building AI-powered microservices with Spring Boot
Configuring AI model beans with @Bean annotations
Setting up auto-configuration for AI models and services
Creating declarative AI Services with Spring dependency injection
Implementing RAG systems with Spring Data integrations
Setting up chat memory with Spring context management
Configuring multiple AI providers (OpenAI, Azure, Ollama, Anthropic)
Building production-ready AI applications with Spring Boot

Overview

LangChain4j Spring Boot integration provides declarative AI Services through Spring Boot starters, enabling automatic configuration of AI components based on properties. Combine Spring dependency injection with LangChain4j's AI capabilities using interface-based definitions with annotations.