Semantic Kernel

Semantic Kernel for .NET

Trigger On

• adding AI-driven prompts, plugins, or orchestration to a .NET app
• reviewing kernel construction, service registration, or plugin usage
• building function-calling patterns with LLMs
• migrating older Semantic Kernel code to current APIs

Documentation

• Semantic Kernel Overview
• Plugins and Functions
• Agent Functions
• GitHub Repository
• Semantic Kernel .NET 1.80.1 release
• Microsoft Agent Framework

References

• patterns.md - Plugin patterns, function calling patterns, multi-agent patterns, prompt templates, and RAG patterns
• anti-patterns.md - Common Semantic Kernel mistakes and how to avoid them

Core Concepts

Workflow

1. Build the Kernel with required services
2. Create Plugins with well-described functions
3. Configure Function Calling for automatic tool use
4. Handle Responses and manage conversation state
5. Test and Observe AI behavior with logging
6. For Semantic Kernel dotnet-1.79.0 and later, keep OpenAPI plugin server URL validation enabled, do not re-enable automatic redirects on the default HttpPlugin or WebFileDownloadPlugin clients without an explicit trusted-host policy, and use the current Microsoft Agent Framework-compatible migration samples when moving SK agent code to Agent Framework.
7. Re-test Cosmos DB vector-store queries, file and document plugins, OpenAPI server-variable URLs, and Ollama reasoning settings after upgrading to 1.79.0. The release fixes the Cosmos vector-store path, rejects mixed-separator UNC paths, URL-encodes OpenAPI server variables, adds Ollama Think, and allows deterministic TimePlugin tests through TimeProvider injection.
8. Treat the Prompty.Core 2.0.0-beta.3 update in 1.79.0 as a breaking dependency change. Re-run prompt-template tests and remove security workarounds that are no longer needed after the vulnerable transitive version is gone.
9. In 1.80.0, re-test OpenAPI plugin HTTP-client defaults and Gemini calls that restrict FunctionChoiceBehavior to a supplied function list. The migrated .NET MEVD providers are no longer owned by Semantic Kernel; follow their redirect guidance and keep vector-provider package references explicit during upgrades.

For 1.80.1, update migrated vector-provider references to the current CommunityToolkit.VectorData package names and re-run connector and OpenAPI plugin tests after the dependency refresh. The release removes retired OpenAI Assistants integration tests; do not interpret that removal as a working Assistants migration path. Use Responses or the current Agent Framework migration guidance for affected integrations.

Kernel Setup

Basic Configuration

var builder = Kernel.CreateBuilder();

builder.AddAzureOpenAIChatCompletion(
    deploymentName: "gpt-4",
    endpoint: config["AzureOpenAI:Endpoint"]!,
    apiKey: config["AzureOpenAI:ApiKey"]!);

// Or OpenAI
builder.AddOpenAIChatCompletion(
    modelId: "gpt-4",
    apiKey: config["OpenAI:ApiKey"]!);

var kernel = builder.Build();

With Dependency Injection

builder.Services.AddKernel()
    .AddAzureOpenAIChatCompletion(
        deploymentName: "gpt-4",
        endpoint: config["AzureOpenAI:Endpoint"]!,
        apiKey: config["AzureOpenAI:ApiKey"]!);

// Register plugins
builder.Services.AddSingleton<WeatherPlugin>();
builder.Services.AddSingleton<OrderPlugin>();

// In your service
public class AiService(Kernel kernel)
{
    public async Task<string> ChatAsync(string message)
    {
        var response = await kernel.InvokePromptAsync(message);
        return response.ToString();
    }
}

Plugin Patterns

Creating a Plugin

public class WeatherPlugin
{
    [KernelFunction]
    [Description("Gets the current weather for a specified city")]
    public async Task<string> GetWeather(
        [Description("The city name, e.g., 'Seattle'")] string city,
        [Description("Temperature unit: 'celsius' or 'fahrenheit'")] string unit = "celsius")
    {
        // Call actual weather API
        var weather = await _weatherService.GetCurrentAsync(city);
        return $"Weather in {city}: {weather.Temperature}° {unit}, {weather.Condition}";
    }

    [KernelFunction]
    [Description("Gets the weather forecast for the next N days")]
    public async Task<string> GetForecast(
        [Description("The city name")] string city,
        [Description("Number of days (1-7)")] int days = 3)
    {
        var forecast = await _weatherService.GetForecastAsync(city, days);
        return FormatForecast(forecast);
    }
}

Plugin Best Practices

Function Calling

Automatic Function Calling

var settings = new OpenAIPromptExecutionSettings
{
    FunctionChoiceBehavior = FunctionChoiceBehavior.Auto()
};

kernel.Plugins.AddFromObject(new WeatherPlugin(), "Weather");
kernel.Plugins.AddFromObject(new OrderPlugin(), "Orders");

var result = await kernel.InvokePromptAsync(
    "What's the weather in Seattle and do I have any pending orders?",
    new KernelArguments(settings));

Manual Function Selection

var settings = new OpenAIPromptExecutionSettings
{
    FunctionChoiceBehavior = FunctionChoiceBehavior.Required(
        [kernel.Plugins["Weather"]["GetWeather"]])
};

Chat Completion Patterns

Multi-Turn Conversation

var chatService = kernel.GetRequiredService<IChatCompletionService>();
var history = new ChatHistory();

history.AddSystemMessage("You are a helpful assistant.");
history.AddUserMessage(userMessage);

var response = await chatService.GetChatMessageContentAsync(
    history,
    executionSettings: new OpenAIPromptExecutionSettings
    {
        FunctionChoiceBehavior = FunctionChoiceBehavior.Auto()
    },
    kernel: kernel);

history.AddAssistantMessage(response.Content!);

Streaming Response

await foreach (var chunk in chatService.GetStreamingChatMessageContentsAsync(
    history, executionSettings, kernel))
{
    Console.Write(chunk.Content);
}

Multi-Agent Plugin Isolation

// WRONG - agents share plugins
var sharedKernel = Kernel.CreateBuilder().Build();
sharedKernel.Plugins.AddFromObject(new AllPlugins());

var agent1 = new ChatCompletionAgent { Kernel = sharedKernel };
var agent2 = new ChatCompletionAgent { Kernel = sharedKernel };
// Both agents have same plugins!

// CORRECT - isolated kernels
var kernel1 = CreateKernelForAgent1();
kernel1.Plugins.AddFromObject(new WeatherPlugin());

var kernel2 = CreateKernelForAgent2();
kernel2.Plugins.AddFromObject(new OrderPlugin());

var agent1 = new ChatCompletionAgent { Kernel = kernel1 };
var agent2 = new ChatCompletionAgent { Kernel = kernel2 };

Anti-Patterns to Avoid

Error Handling

[KernelFunction]
[Description("Places an order for a product")]
public async Task<string> PlaceOrder(
    [Description("Product ID")] string productId,
    [Description("Quantity (1-100)")] int quantity)
{
    // Validate inputs
    if (string.IsNullOrEmpty(productId))
        return "Error: Product ID is required";

    if (quantity < 1 || quantity > 100)
        return "Error: Quantity must be between 1 and 100";

    try
    {
        var order = await _orderService.CreateAsync(productId, quantity);
        return $"Order {order.Id} placed successfully for {quantity} units";
    }
    catch (ProductNotFoundException)
    {
        return $"Error: Product '{productId}' not found";
    }
}

Testing Plugins

[Fact]
public async Task GetWeather_ReturnsFormattedWeather()
{
    var mockWeatherService = new Mock<IWeatherService>();
    mockWeatherService.Setup(w => w.GetCurrentAsync("Seattle"))
        .ReturnsAsync(new Weather { Temperature = 20, Condition = "Sunny" });

    var plugin = new WeatherPlugin(mockWeatherService.Object);

    var result = await plugin.GetWeather("Seattle", "celsius");

    Assert.Contains("20°", result);
    Assert.Contains("Sunny", result);
}

Microsoft Agent Framework

For complex multi-agent scenarios, consider microsoft-agent-framework:

• Multi-agent orchestration
• Agent-to-agent communication
• Enterprise patterns

Deliver

• kernel setup with clear service and plugin composition
• AI features that fit naturally into the existing .NET app
• observable and testable function-calling behavior
• proper plugin isolation for multi-agent scenarios

Validate

• plugins have clear, specific descriptions
• function calling works as expected
• AI flows are logged and debuggable
• input validation prevents hallucination issues
• kernel instances are properly scoped
• deprecated APIs are not used