langchain4j-ai-services-patterns
Type-safe AI services in Java using interface-based patterns, annotations, and declarative configuration.
- Define AI capabilities as plain Java interfaces with
@SystemMessageand@UserMessageannotations, eliminating manual prompt construction and response parsing - Built-in memory management for multi-turn conversations with per-user or per-session isolation using
@MemoryIdand configurable chat memory providers - Tool integration enables AI services to call external functions and execute code through
@Toolannotations on methods - Supports structured output extraction, streaming responses, RAG patterns, and multi-agent systems with specialized personas and behaviors
LangChain4j AI Services Patterns
This skill provides guidance for building declarative AI Services with LangChain4j using interface-based patterns, annotations for system and user messages, memory management, tools integration, and advanced AI application patterns that abstract away low-level LLM interactions.
Overview
LangChain4j AI Services define AI functionality using Java interfaces with annotations, providing type-safe, declarative AI with minimal boilerplate.
When to Use
Use this skill when:
- Building declarative AI services with minimal boilerplate using Java interfaces
- Creating type-safe conversational AI with memory management
- Implementing AI agents with function/tool calling capabilities
- Designing AI services returning structured data (enums, POJOs, lists)
- Integrating RAG patterns declaratively
Instructions
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