skunit

skUnit

skUnit is a semantic testing framework for .NET that makes it easy to test AI-powered applications using simple, readable Markdown scenarios.

Test anything that talks to AI:

• 🤖 IChatClient implementations (Azure OpenAI, OpenAI, Anthropic, etc.)
• 🧠 SemanticKernel applications and plugins
• 🔧 MCP (Model Context Protocol) servers
• 🛠️ Custom AI integrations

Write your tests in Markdown, run them with any test framework (xUnit, NUnit, MSTest), and get live, readable results.

⚡ Quick Start

Here's a simple test scenario in Markdown:

# SCENARIO Mountain Chat

## [USER]
What is the tallest mountain?

## [ASSISTANT]
The tallest mountain is Everest! (OPTIONAL)

### ASSERT SemanticCondition
It mentions Mount Everest.

And here's how to test it with just a few lines of C#:

[Fact]
public async Task SimpleTest()
{
    var markdown = File.ReadAllText("mountain-chat.md");
    var scenarios = ChatScenario.LoadFromText(markdown);

    await ScenarioRunner.RunAsync(scenarios, systemUnderTestClient);
}

Note that in this example, the agent message is just for clarity and is not being used and is optional. So the following test scenario is equivalent:

## [USER]
What is the tallest mountain?

## [ASSISTANT]

### ASSERT SemanticCondition
It mentions Mount Everest.

That's it! ✨ skUnit handles the conversation, calls your AI, and verifies the response makes sense.

🎯 Key Features

1. Start Simple: Basic Chat Scenarios

Test single interactions with basic checks:

## [USER]
Is Everest a mountain or a Tree?

## [ASSISTANT]

### ASSERT ContainsAny
mountain

### ASSERT SemanticCondition
It mentions the mountain

2. Level Up: JSON Validation

Test structured responses with powerful JSON assertions:

# SCENARIO User Info

## [USER]
Give me the most expensive product info as a JSON like this:
{"id": 12, "title": "The product", "price": 0, "description": "the description of the product"}

## [ASSISTANT]
{"id": 12, "title": "Surface Studio 2", "price": 3000, "description: "It is a very high-quality laptop"}

### ASSERT JsonCheck
{
  "id": ["NotEmpty"],
  "title": ["Contains", "Surface"],
  "price": ["Equal", 3000],
  "description": ["SemanticCondition", "It mentions the quality of the laptop."]
}

3. Advanced: Function Call Testing

Verify your AI calls the right functions (MCP maybe) with the right parameters:

# SCENARIO Time Query

## [USER]
What time is it?

## [ASSISTANT]
It's currently 2:30 PM

### ASSERT FunctionCall
{
  "function_name": "get_current_time"
}

Even you can assert the called parameters:

ASSERT FunctionCall

{
"function_name": "GetFoodMenu",
"arguments": {
"mood": ["Equals", "Happy"]
}
}

4. Multi-Turn Conversations

Test complex conversations with multiple exchanges:

# SCENARIO Height Discussion

## [USER]
Is Eiffel tall?

## [ASSISTANT]
Yes it is

### ASSERT SemanticCondition
It agrees that the Eiffel Tower is tall or expresses a positive sentiment.

## [USER]
What about Everest?

## [ASSISTANT]
Yes it is tall too

### ASSERT SemanticCondition
It agrees that Everest is tall or expresses a positive sentiment.

Each scenario can contain multiple sub-scenarios (conversation turns), and each response can have multiple ASSERT statements to verify different aspects of the AI's behavior.

5. Readable Markdown Scenarios

Your test scenarios are just valid Markdown files - easy to read, write, and review:

6. Live Test Results

Watch your tests run in real-time with beautiful, readable output:

7. MCP Server Testing

Test Model Context Protocol servers to ensure your tools work correctly:

# SCENARIO MCP Time Server

## [USER]
What time is it?

## [ASSISTANT]
It's currently 2:30 PM PST

### ASSERT FunctionCall
{
  "function_name": "current_time"
}

### ASSERT SemanticCondition
It mentions a specific time

// Setup MCP server testing
var mcp = await McpClientFactory.CreateAsync(clientTransport);
var tools = await mcp.ListToolsAsync();

var chatClient = new ChatClientBuilder(baseChatClient)
    .ConfigureOptions(options => options.Tools = tools.ToArray())
    .UseFunctionInvocation()
    .Build();

// In your test class constructor:
var assertionClient = /* assertion/evaluation model */;
ScenarioRunner = new ChatScenarioRunner(assertionClient);

// In your test:
await ScenarioRunner.RunAsync(scenarios, chatClient);

🚀 Installation & Setup

1. Install the Package

dotnet add package skUnit

2. Basic Setup

public class MyChatTests
{
    private readonly ChatScenarioRunner _scenarioRunner;
    private readonly IChatClient _chatClient;

    public MyChatTests(ITestOutputHelper output)
    {
        // Configure your AI client (Azure OpenAI, OpenAI, etc.)
        _chatClient = new AzureOpenAIClient(endpoint, credential)
            .GetChatClient(deploymentName)
            .AsIChatClient();
            
        _scenarioRunner = new ChatScenarioRunner(_chatClient, output.WriteLine);
    }

    [Fact]
    public async Task TestChat()
    {
        var markdown = File.ReadAllText("scenario.md");
        var scenarios = ChatScenario.LoadFromText(markdown);
        
        await _scenarioRunner.RunAsync(scenarios, _chatClient);
    }
}

3. Configuration

Set up your AI provider credentials:

{
  "AzureOpenAI_ApiKey": "your-api-key",
  "AzureOpenAI_Endpoint": "https://your-endpoint.openai.azure.com/",
  "AzureOpenAI_Deployment": "your-deployment-name"
}

🧪 Testing Multiple MCP Servers

Test complex scenarios involving multiple MCP servers working together:

// Combine multiple MCP servers
var timeServer = await McpClientFactory.CreateAsync(timeTransport);
var weatherServer = await McpClientFactory.CreateAsync(weatherTransport);

var allTools = new List<AITool>();
allTools.AddRange(await timeServer.ListToolsAsync());
allTools.AddRange(await weatherServer.ListToolsAsync());

var chatClient = new ChatClientBuilder(baseChatClient)
    .ConfigureOptions(options => options.Tools = allTools.ToArray())
    .UseFunctionInvocation()
    .Build();

🧪 Works with Any Test Framework

skUnit is completely test-framework agnostic! Here's the same test with different frameworks:

xUnit

public class GreetingTests
{
    private readonly ChatScenarioRunner ScenarioRunner;
    private readonly IChatClient systemUnderTestClient;

    public GreetingTests()
    {
        var assertionClient = /* assertion/evaluation model */;
        systemUnderTestClient = /* system under test model */;
        ScenarioRunner = new ChatScenarioRunner(assertionClient);
    }

    [Fact]
    public async Task TestGreeting()
    {
        var markdown = File.ReadAllText("greeting.md");
        var scenarios = ChatScenario.LoadFromText(markdown);

        await ScenarioRunner.RunAsync(scenarios, systemUnderTestClient);
    }
}

MSTest

public class GreetingTests : TestClass
{
    private readonly ChatScenarioRunner ScenarioRunner;
    private readonly IChatClient systemUnderTestClient;

    public GreetingTests()
    {
        var assertionClient = /* assertion/evaluation model */;
        systemUnderTestClient = /* system under test model */;
        ScenarioRunner = new ChatScenarioRunner(assertionClient, TestContext.WriteLine);
    }

    [TestMethod]
    public async Task TestGreeting()
    {
        var scenarios = await ChatScenario.LoadFromResourceAsync(
            "MyProject.Scenarios.greeting.md", 
            typeof(GreetingTests).Assembly);
            
        await ScenarioRunner.RunAsync(scenarios, systemUnderTestClient);
    }
}

NUnit

public class GreetingTests
{
    private readonly ChatScenarioRunner ScenarioRunner;
    private readonly IChatClient systemUnderTestClient;

    public GreetingTests()
    {
        var assertionClient = /* assertion/evaluation model */;
        systemUnderTestClient = /* system under test model */;
        ScenarioRunner = new ChatScenarioRunner(assertionClient, TestContext.WriteLine);
    }

    [Test]
    public async Task TestGreeting()
    {
        var markdown = File.ReadAllText("greeting.md");
        var scenarios = ChatScenario.LoadFromText(markdown);

        await ScenarioRunner.RunAsync(scenarios, systemUnderTestClient);
    }
}

The core difference is just the logging integration - use TestContext.WriteLine for MSTest, ITestOutputHelper.WriteLine for xUnit, or TestContext.WriteLine for NUnit. Both patterns show:

• Assertion Client: Created once in the constructor for semantic evaluations
• System Under Test Client: The client whose behavior you're testing, passed to RunAsync

📚 Documentation

• Chat Scenario Spec - Complete guide to writing chat scenarios
• ASSERT Statement Spec - All available assertion types
• Test Framework Integration - How to use skUnit with xUnit, MSTest, NUnit, and more
• MCP Testing Guide - How to test Model Context Protocol servers
• Multi-Modal Support - Working with images and other media
• Scenario Run Options - Mitigate hallucinations with multi-run success thresholds

📋 Requirements

• .NET 8.0 or higher
• AI Provider (Azure OpenAI, OpenAI, Anthropic, etc.) for semantic assertions
• Test Framework (xUnit, NUnit, MSTest - your choice!)

🔒 Advanced: Mitigating Hallucinations with ScenarioRunOptions

LLM outputs can vary between runs. A single spurious response shouldn't fail your build if the model normally behaves correctly.

Use ScenarioRunOptions to execute each scenario multiple times and require only a percentage to pass. This adds statistical robustness without eliminating genuine regressions.

var options = new ScenarioRunOptions
{
    TotalRuns = 3,        // Run the whole scenario three times
    MinSuccessRate = 0.67 // At least 2 of 3 runs must pass
};

// In your test class constructor:
var assertionClient = /* assertion/evaluation model */;
ScenarioRunner = new ChatScenarioRunner(assertionClient);

// In your test:
await ScenarioRunner.RunAsync(scenarios, systemUnderTestClient, options: options);

Recommended starting points:

• Deterministic / low-temp prompts: TotalRuns = 1, MinSuccessRate = 1.0
• Function / tool invocation: TotalRuns = 3, MinSuccessRate = 0.67
• Creative generation: TotalRuns = 5, MinSuccessRate = 0.6
• Critical CI gating: TotalRuns = 5, MinSuccessRate = 0.8

Failure message example:

Only 40% of rounds passed, which is below the required success rate of 80%

Indicates a systematic issue (not just randomness) – investigate prompt, model settings, or assertions.

See full guide: Scenario Run Options

🤝 Contributing

We welcome contributions! Check out our issues or submit a PR.

⭐ Examples

Check out the /demos folder for complete examples:

• Demo.TddRepl - Interactive chat application testing
• Demo.TddMcp - MCP server integration testing
• Demo.TddShop - Complex e-commerce chat scenarios

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Start testing your AI applications with confidence! 🎯