Getting Started with Semantic Kernel on .NET 8 (2025)

Are you wondering how to talk to an LLM from your .NET app without wrestling with brittle HTTP calls or rolling your own prompt‑orchestration layer? You’re not alone – I fought that dragon too until I discovered Semantic Kernel (SK) and life suddenly felt like debugging with IntelliSense again instead of Notepad.

In the next ten minutes you’ll install SK on .NET 8, run your first “Hello AI” prompt, and learn the common pitfalls that bite new adopters – so you can skip the pain and go straight to building cool Gen AI features.

Why Semantic Kernel Matters in the Gen AI Stack

When ChatGPT exploded onto the scene, many teams (mine included) duct‑taped OpenAI calls into their apps. It worked, but the code was fragile, state management was messy, and iterating on prompts felt like copy‑pasting SQL in 2005. SK solves that by giving you:

1. A programmable kernel that abstracts prompt, memory, and planning concerns.
2. Plugin model to mix and match skills (both native C# methods and prompt templates).
3. Pipeline flexibility – swap OpenAI with Azure OpenAI, OSS models, or your own fine‑tunes by flipping configuration, not code.

Think of SK as the Entity Framework for LLMs: you focus on logic, it handles the plumbing.

NuGet vs Source‑Build: Which One & When?

• NuGet – ideal for 99 % of projects. You pull the latest Microsoft.SemanticKernel package and stay productive.
• Source‑build – handy when you need to debug SK internals, submit a PR, or ride the bleeding edge before preview packages hit NuGet. Clone the repo, run dotnet build -p:Configuration=Release, and reference the local output.

If you’re evaluating SK, start with NuGet; you can switch later in under five minutes.

Prerequisites

Heads‑up: If you’re behind a proxy, set HTTP_PROXY and HTTPS_PROXY env vars before running the sample or you’ll meet a friendly SocketException.

Installing the NuGet Package

Choose the Right Package

• Microsoft.SemanticKernel – meta‑package that lights up all core features. Perfect for quick starts.
• Microsoft.SemanticKernel.Connectors.OpenAI – pulls in only the OpenAI connector. Use when you need a slimmer dependency tree.
• Microsoft.SemanticKernel.Connectors.Qdrant / Memory.AzureCosmosDB / Skills.Excel – pick ’n’ mix à la carte.

For this tutorial we’ll grab the meta‑package:

# From an empty folder or existing solution
dotnet new console -n HelloSK
cd HelloSK

dotnet add package Microsoft.SemanticKernel --version 1.2.0

Preview train arriving! SK ships weekly preview builds tagged -preview.*. If you need a fix that merged yesterday, add --prerelease to the command above or pin an explicit suffix, e.g. 1.3.0-preview.24308.2.

Restore & Verify

dotnet restore

You should see Microsoft.SemanticKernel and its friends in obj/project.assets.json. If nuget.org is blocked, configure an internal mirror via NuGet.Config.

Your First Kernel in 30 Lines

Let’s write the tiniest usable kernel that says hi.

// Program.cs
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Connectors.OpenAI;

var openAiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY")
    ?? throw new InvalidOperationException("Set OPENAI_API_KEY env var");

IKernel kernel = new KernelBuilder()
    .AddOpenAIChatCompletion(
        modelId: "gpt-3.5-turbo",
        apiKey: openAiKey)
    .Build();

var result = await kernel.InvokePromptAsync("Write a cheerful greeting from an AI assistant.");

Console.WriteLine(result.GetValue<string>());

Run it:

dotnet run

And voilà – GPT replies:

🌞 Hello, human! I’m thrilled to help you build amazing things today. Let’s get started!

What just happened?

• KernelBuilder wires dependencies (logging, memory, skills) behind the scenes.
• AddOpenAIChatCompletion registers the GPT 3.5 chat endpoint.
• InvokePromptAsync sends the string, SK wraps it in a system template, handles retries, and yields a typed result.

Under the Hood: Prompt Flow

1. System prompt → 2. User prompt → 3. Kernel pipeline → 4. LLM → 5. Post‑processing / memories / skills.

This abstraction lets you bolt on vector storage, function calling, or agentic planning without rewriting your call sites.

Troubleshooting Cheat Sheet

Tip: Run dotnet --list-sdks – if you see preview builds older than May 2025, they sometimes hijack the tooling path. Uninstall or reorder the PATH entries.

FAQ: Semantic Kernel First‑Steps

Conclusion: You’re Talking to an LLM from .NET in Under 10 Minutes

Today you:

• Installed Semantic Kernel via NuGet on .NET 8.
• Built a minimal KernelBuilder and sent a prompt.
• Learned the landmines (SDK mismatches, proxies, preview builds) that derail first‑timers.

Go ahead – fork this sample, add a memory store, maybe wire up function calling, and you’ll be three commits away from a conversational copilots feature in your product.

What’s next? In Part 2 we’ll dive into SK’s core abstractions – Functions, Memories, and Planners – and see how they compose into full‑blown AI agents.

Have you tried SK already? Which connector or skill are you most excited about? Let me know in the comments!