How do you test LangChain4j AI Services without making real LLM API calls?

Testing AI Services without hitting real LLM endpoints is essential for fast, cost-free, deterministic unit tests. LangChain4j supports this through mock model implementations and the AiServices builder accepting any ChatLanguageModel — including test doubles you create yourself.

The most direct approach is to implement a simple mock that returns predetermined responses:

// Simple lambda mock
ChatLanguageModel mockModel = (messages, toolSpecifications) ->
    new AiMessage("The capital of France is Paris.");

GeographyAssistant assistant = AiServices.builder(GeographyAssistant.class)
    .chatLanguageModel(mockModel)
    .build();

String answer = assistant.ask("What is the capital of France?");
assertThat(answer).isEqualTo("The capital of France is Paris.");

For more complex scenarios, Mockito works naturally since ChatLanguageModel is an interface:

@ExtendWith(MockitoExtension.class)
class TranslatorTest {

    @Mock
    ChatLanguageModel mockModel;

    @Test
    void translatesText() {
        AiMessage fakeResponse = new AiMessage("Bonjour le monde");
        when(mockModel.generate(anyList())).thenReturn(new Response<>(fakeResponse));

        Translator translator = AiServices.builder(Translator.class)
            .chatLanguageModel(mockModel).build();

        assertThat(translator.translate("Hello world", "French"))
            .isEqualTo("Bonjour le monde");
    }
}

For integration tests that require a real LLM but want cost control, use Ollama with a small local model (e.g., tinyllama) via Testcontainers. This gives you real model behavior without OpenAI billing and can run in CI pipelines. The langchain4j-ollama module combined with the Testcontainers Ollama image enables fully automated integration test suites with no API keys required.

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