Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration.
SkillScore100Maintained
Automatic
◆AI Engineer pack
Last verified 8/21/2026
What it changes
Passed 22 of 22 test cases with the skill, and 19 of 22 without it.
Without skill86%
With skill100%
Checks it passed that the model alone missed
Sets wiringMode attribute to WiringMode.EXPLICIT on @AiService annotation
References LangChain4jSpringBootAutoConfiguration in application startup verification
Includes dependency dev.langchain4j:langchain4j-spring-boot-starter in Maven configuration
3 fixed19 passed either way0 still failing0 broke
Pass-rate lift
+14 pp
Tokens
+91%
Agent turns
0%
Response time
-36%
Model gemini-3.6-flashMeasured 8/21/2026v122 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 3
Prompt
To verify that LangChain4j Spring Boot integration has initialized properly upon application startup, which auto-configuration class name should be observed in the Spring Boot debug logs?
What was checked, without and with the skill
Checked
Without skill
With skill
References LangChain4jSpringBootAutoConfiguration in application startup verification
Missed
Passed
case-22 · gemini-3.6-flash · 8/21/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md299 lines4 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.
Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.