langchain4j-ai-services-patterns

The langchain4j-ai-services-patterns skill provides patterns for building declarative AI Services with LangChain4j in Java, solving the problem of verbose, low-level LLM integration by using type-safe, interface-based abstractions with minimal boilerplate. It targets Java developers building chatbots, AI agents, and conversational AI who want to define AI behavior through annotated interfaces rather than manual prompt plumbing.

The skill walks through a concrete workflow: defining an AI service interface, adding @SystemMessage and @UserMessage annotations for prompts, instantiating services with the AiServices builder or create method, configuring multi-turn memory with @MemoryId and a chat memory provider, integrating tools for function calling via the @Tool annotation, and validating and testing services with sample inputs and structured-output assertions. It supports structured return types such as enums, POJOs, and lists, and declarative RAG patterns. It ships a SKILL.md plus references/examples.md and references/references.md for expanded detail, and declares safe allowed-tools (Read, Write, Edit, Bash, Glob, Grep).

Use cases include building type-safe conversational AI with memory management, implementing AI agents with function or tool calling, designing services that return structured data, and integrating retrieval-augmented generation declaratively. It is aimed at Java backend engineers and AI application developers adopting LangChain4j who want maintainable, testable AI service definitions. The content is instructional pattern guidance and code examples with no destructive, network-exfiltration, or credential-stealing behavior.