LangChain4j Tool & Function Calling Patterns

LangChain4j Tool & Function Calling Patterns

Provides patterns for annotating methods as tools, configuring tool executors, registering tools with AI services, validating parameters, and handling tool execution errors in LangChain4j applications.

Overview

LangChain4j uses the @Tool annotation to expose Java methods as callable functions for AI agents. The AiServices builder registers tools with a chat model, enabling LLMs to perform actions beyond text generation: database queries, API calls, calculations, and business system integrations. Parameters use @P for descriptions that guide the LLM.

When to Use

• Building AI agents that call external tools (weather, stocks, database queries)
• Defining function specifications for LLM tool use (@Tool, @P annotations)
• Registering and managing tool sets with AiServices.builder().tools()
• Handling tool execution errors, timeouts, and hallucinated tool names
• Implementing context-aware tools that inject user state via @ToolMemoryId
• Configuring dynamic tool providers for large or conditional tool sets

Instructions

1. Annotate Methods with @Tool

Define a tool class with methods annotated @Tool. Provide a description as the first parameter. Use @P for each parameter description.

public class WeatherTools {
    private final WeatherService weatherService;

    public WeatherTools(WeatherService weatherService) {
        this.weatherService = weatherService;
    }

    @Tool("Get current weather for a city")
    public String getWeather(
            @P("City name") String city,
            @P("Temperature unit: celsius or fahrenheit") String unit) {
        return weatherService.getWeather(city, unit);
    }
}