What are Tools in LangChain4j and how does tool calling work?

Tools (also called function calling) give LLMs the ability to invoke real Java methods during a conversation. Instead of answering entirely from its training knowledge, the model can recognize when a specific capability is needed — fetching live data, running calculations, calling APIs — and request that the application execute a registered tool and return the result to the model for incorporation into its final answer.

In LangChain4j, tools are defined by annotating Java methods with @Tool on a plain Java object. Parameters can be annotated with @P (or @ToolParam) to provide descriptions that help the model understand when and how to use them.

class WeatherTools {

    @Tool("Returns the current weather in a given city in Celsius")
    String currentWeather(@P("City name, e.g. 'London'") String city) {
        return weatherApiService.fetchCurrent(city); // real API call
    }

    @Tool("Returns the 5-day forecast for a city")
    String forecast(@P("City name") String city,
                    @P("Number of days 1-5") int days) {
        return weatherApiService.fetchForecast(city, days);
    }
}

// Register with AI Services
TravelAssistant assistant = AiServices.builder(TravelAssistant.class)
    .chatLanguageModel(model)
    .tools(new WeatherTools())
    .build();

The flow is: user sends a message → LLM decides a tool should be called → LangChain4j intercepts the tool-use response → executes the Java method → appends the result to the conversation → re-calls the LLM with the result → LLM generates the final answer. All of this happens transparently within the assistant.chat() call. The model may call tools multiple times before producing a final answer, and LangChain4j handles those multi-step loops automatically.