Langchain4j Testing Strategies Agent Skill

Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java applications.

13k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

316

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies

The instruction itself

19 sections, as written by the author

LangChain4J Testing Strategies

Overview

Patterns for unit testing with mocks, integration testing with Testcontainers, and end-to-end validation of RAG systems, AI Services, and tool execution.

When to Use

Unit testing AI services: When you need fast, isolated tests for services using LangChain4j AiServices
Integration testing LangChain4j components: When testing real ChatModel, EmbeddingModel, or RAG pipelines with Testcontainers
Mocking AI models: When you need deterministic responses without calling external APIs
Testing LLM-based Java applications: When validating RAG workflows, tool execution, or retrieval chains

Instructions

1. Unit Testing with Mocks

Use mock models for fast, isolated testing. See references/unit-testing.md.

ChatModel mockModel = mock(ChatModel.class);
when(mockModel.generate(any(String.class)))
    .thenReturn(Response.from(AiMessage.from("Mocked response")));

var service = AiServices.builder(AiService.class)
        .chatModel(mockModel)
        .build();

2. Configure Testing Dependencies

Setup Maven/Gradle dependencies. See references/testing-dependencies.md.

langchain4j-test - Guardrail assertions
testcontainers - Containerized testing
mockito - Mock external dependencies
assertj - Fluent assertions

3. Integration Testing with Testcontainers

Test with real services. See references/integration-testing.md.

@Testcontainers
class OllamaIntegrationTest {
    @Container
    static GenericContainer<?> ollama = new GenericContainer<>(
        DockerImageName.parse("ollama/ollama:0.5.4")
    ).withExposedPorts(11434);

    @Test
    void shouldGenerateResponse() {
        // Verify container is healthy
        assertTrue(ollama.isRunning());
        await().atMost(30, TimeUnit.SECONDS)
            .until(() -> ollama.getLogs().contains("API server listening"));

        ChatModel model = OllamaChatModel.builder()
                .baseUrl(ollama.getEndpoint())
                .build();

        // Verify model responds before running tests
        assertDoesNotThrow(() -> model.generate("ping"));

        String response = model.generate("Test query");
        assertNotNull(response);
    }
}

4. Advanced Features

Streaming, memory, error handling patterns in references/advanced-testing.md.

5. Testing Workflow

Follow the testing pyramid from references/workflow-patterns.md:

70% Unit Tests: Fast, isolated with mocks
20% Integration Tests: Real services with health checks
10% End-to-End Tests: Complete workflows

70% Unit Tests ─ Mock ChatModel, guardrails, edge cases
20% Integration Tests ─ Testcontainers, vector stores, RAG
10% End-to-End Tests ─ Complete user journeys

Troubleshooting

Container fails to start: Check Docker daemon is running, verify image exists, increase timeout
Model not responding: Verify baseUrl is correct, check container logs, ensure model is loaded
Test timeout: Increase @Timeout duration for slow models, check container resource limits
Flaky tests: Add retry logic or health checks before assertions

Examples

Unit Test

@Test
void shouldProcessQueryWithMock() {
    ChatModel mockModel = mock(ChatModel.class);
    when(mockModel.generate(any(String.class)))
        .thenReturn(Response.from(AiMessage.from("Test response")));

    var service = AiServices.builder(AiService.class)
            .chatModel(mockModel)
            .build();

    String result = service.chat("What is Java?");
    assertEquals("Test response", result);
}

Integration Test with Testcontainers

@Testcontainers
class RAGIntegrationTest {
    @Container
    static GenericContainer<?> ollama = new GenericContainer<>(
        DockerImageName.parse("ollama/ollama:0.5.4")
    );

    @BeforeAll
    static void waitForContainerReady() {
        await().atMost(60, TimeUnit.SECONDS)
            .until(() -> ollama.getLogs().contains("API server listening"));
    }

    @Test
    void shouldCompleteRAGWorkflow() {
        assertTrue(ollama.isRunning());

        var chatModel = OllamaChatModel.builder()
                .baseUrl(ollama.getEndpoint())
                .build();

        var embeddingModel = OllamaEmbeddingModel.builder()
                .baseUrl(ollama.getEndpoint())
                .build();

        var store = new InMemoryEmbeddingStore<>();
        var retriever = EmbeddingStoreContentRetriever.builder()
                .chatModel(chatModel)
                .embeddingStore(store)
                .embeddingModel(embeddingModel)
                .build();

        var assistant = AiServices.builder(RagAssistant.class)
                .chatLanguageModel(chatModel)
                .contentRetriever(retriever)
                .build();

        String response = assistant.chat("What is Spring Boot?");
        assertNotNull(response);
        assertTrue(response.contains("Spring"));
    }
}

Best Practices

Use @BeforeEach/@AfterEach for test isolation
Never call real APIs in unit tests; use mocks
Include @Timeout for external service calls
Test both success and error handling scenarios
Validate response coherence and edge cases

Common Patterns

Mock Strategy

ChatModel mockModel = mock(ChatModel.class);
when(mockModel.generate(anyString())).thenReturn(Response.from(AiMessage.from("Mocked")));
when(mockModel.generate(eq("Hello"))).thenReturn(Response.from(AiMessage.from("Hi")));
when(mockModel.generate(contains("Java"))).thenReturn(Response.from(AiMessage.from("Java")));

Assertion Helpers

assertThat(response).isNotNull().isNotEmpty();
assertThat(response).containsAll(expectedKeywords);
assertThat(response).doesNotContain("error");

Reference Documentation

Testing Dependencies - Maven/Gradle configuration
Unit Testing - Mock models, guardrails
Integration Testing - Testcontainers, real services
Advanced Testing - Streaming, memory, error handling
Workflow Patterns - Test pyramid, best practices

Constraints and Warnings

AI responses are non-deterministic; use mocks for reliable unit tests
Avoid real API calls in tests to prevent costs and rate limiting
Integration tests require Docker; use container health checks
RAG tests need properly seeded embedding stores
Mock-based tests cannot guarantee actual LLM behavior; supplement with integration tests
Use test-specific configuration profiles; never affect production data

Other skills for the same job

different authors, same section of the catalogue

Webapp Testing

by anthropics

vendor ×12

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

6k tokens scripts

Finishing A Development Branch

by ZhanlinCui

×7

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

1k tokens

Test Driven Development

by w95

×7

Use when implementing any feature or bugfix, before writing implementation code

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

10k tokens scripts

Verification Before Completion

by ZhanlinCui

×6

Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.

Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

19k tokens

How to use it

Copy the folder

Take giuseppe-trisciuoglio/langchain4j-testing-strategies from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.