Semantic Kernel Integration - Helicone OSS LLM Observability

Introduction#

Semantic Kernel is Microsoft's open-source SDK for building AI agents and orchestrating LLM workflows across multiple languages (.NET, Python, Java). By integrating Helicone AI Gateway with Semantic Kernel, you can:

Route to different models & providers with automatic failover through a single endpoint
Unified billing with pass-through billing or bring your own keys
Monitor all requests with automatic cost tracking in one dashboard

Note

This integration requires only one line change to your existing Semantic Kernel code - adding the AI Gateway endpoint.

Integration Steps#

Create an account + Generate an API Key

Note

You'll also need to configure your provider API keys (OpenAI, Anthropic, etc.) at Helicone Providers for BYOK (Bring Your Own Keys).

Set environment variables

# Your Helicone API keyexport HELICONE_API_KEY=<your-helicone-api-key>

Create a .env file in your project:

HELICONE_API_KEY=sk-helicone-...

Add the AI Gateway endpoint to your Semantic Kernel configuration

using Microsoft.SemanticKernel;using Microsoft.SemanticKernel.ChatCompletion;using DotNetEnv;// Load environment variablesEnv.Load();var heliconeApiKey = Environment.GetEnvironmentVariable("HELICONE_API_KEY");// Create kernel buildervar builder = Kernel.CreateBuilder();// Add OpenAI chat completion with Helicone AI Gateway endpointbuilder.AddOpenAIChatCompletion(    modelId: "gpt-4.1-mini",                                // Any model from Helicone registry    apiKey: heliconeApiKey,                                 // Your Helicone API key    endpoint: new Uri("https://ai-gateway.helicone.ai/v1")  // Helicone AI Gateway);var kernel = builder.Build();

Info

The only change from a standard Semantic Kernel setup is adding the endpoint parameter. Everything else stays the same!

Use the chat service normally

Your existing Semantic Kernel code continues to work without any changes:

using Microsoft.SemanticKernel.ChatCompletion;// Get the chat servicevar chatService = kernel.GetRequiredService<IChatCompletionService>();// Create chat historyvar chatHistory = new ChatHistory();chatHistory.AddUserMessage("What is the capital of France?");// Get responsevar response = await chatService.GetChatMessageContentAsync(chatHistory);Console.WriteLine(response.Content);

View requests in the Helicone dashboard

All your Semantic Kernel requests are now visible in your Helicone dashboard:

Request/response bodies
Latency metrics
Token usage and costs
Model performance analytics
Error tracking

Tip

While you're here, why not give us a star on GitHub? It helps us a lot!

Migration Example#

Here's what migrating an existing Semantic Kernel application looks like:

Before (Direct OpenAI)#

var builder = Kernel.CreateBuilder();builder.AddOpenAIChatCompletion(    modelId: "gpt-4o-mini",    apiKey: openAiApiKey);var kernel = builder.Build();

After (Helicone AI Gateway)#

var builder = Kernel.CreateBuilder();builder.AddOpenAIChatCompletion(    modelId: "gpt-4.1-mini",                                // Use Helicone model names    apiKey: heliconeApiKey,                                 // Your Helicone API key    endpoint: new Uri("https://ai-gateway.helicone.ai/v1")  // Add this line!);var kernel = builder.Build();

That's it! Just one additional parameter and you're routing through Helicone's AI Gateway.

Complete Working Example#

Here's a full example that tests multiple models:

using Microsoft.SemanticKernel;using Microsoft.SemanticKernel.ChatCompletion;using DotNetEnv;// Load environmentEnv.Load();var apiKey = Environment.GetEnvironmentVariable("HELICONE_API_KEY");if (string.IsNullOrEmpty(apiKey)){    Console.WriteLine("❌ HELICONE_API_KEY not found in environment");    return;}Console.WriteLine("🚀 Testing multiple models through Helicone AI Gateway\n");// Test different modelsawait TestModel("gpt-4.1-mini", "OpenAI GPT-4.1 Mini");await TestModel("claude-opus-4-1", "Anthropic Claude Opus 4.1");await TestModel("gemini-2.5-flash-lite", "Google Gemini 2.5 Flash Lite");Console.WriteLine("\n✅ All models tested!");Console.WriteLine("🔍 Check your dashboard: https://us.helicone.ai/dashboard");async Task TestModel(string modelId, string modelName){    try    {        var builder = Kernel.CreateBuilder();        // Configure with Helicone AI Gateway        builder.AddOpenAIChatCompletion(            modelId: modelId,            apiKey: apiKey,            endpoint: new Uri("https://ai-gateway.helicone.ai/v1")        );        var kernel = builder.Build();        var chatService = kernel.GetRequiredService<IChatCompletionService>();        var chatHistory = new ChatHistory();        chatHistory.AddUserMessage("Say hello in one sentence.");        Console.Write($"🤖 Testing {modelName}... ");        var response = await chatService.GetChatMessageContentAsync(chatHistory);        Console.WriteLine("✅");        Console.WriteLine($"   Response: {response.Content}\n");    }    catch (Exception ex)    {        Console.WriteLine("❌");        Console.WriteLine($"   Error: {ex.Message}\n");    }}