.NET Claude SDK: Practical Setup and First Use

Why this matters

Most teams are not trying to build an AI platform on day one. They just want one thing to work well: summarize text, draft a response, extract action items, or answer a question from internal knowledge. The Claude SDK gives you a direct .NET path into that.

What the official SDK gives you

Official C# package: Anthropic
.NET Standard 2.0+ support
Messages API access for Claude models
Simple client creation and async request flow

Official repo and docs used for this article:

anthropics/anthropic-sdk-csharp
Claude SDK for C# documentation on Anthropic platform docs

Install and first request

Start with a clean package install, then create one client and one message call. That is enough to prove the SDK works in your solution.

dotnet add package Anthropic

using Anthropic;
using Anthropic.Models.Messages;

var client = new AnthropicClient();

var request = new MessageCreateParams
{
    Model = "claude-opus-4-6",
    MaxTokens = 1024,
    Messages =
    [
        new()
        {
            Role = Role.User,
            Content = "Summarize this incident report in 5 bullets."
        }
    ]
};

var message = await client.Messages.Create(request);
Console.WriteLine(message);

How I structured it in a real .NET app

Keep the SDK behind one service class.
Read the API key from environment or secure config.
Return structured results to the UI or API layer.
Log request metadata, not sensitive prompt content.

public sealed class ClaudeService
{
    private readonly AnthropicClient _client = new();

    public async Task<string> SummarizeAsync(string input)
    {
        var request = new MessageCreateParams
        {
            Model = "claude-opus-4-6",
            MaxTokens = 512,
            Messages =
            [
                new() { Role = Role.User, Content = $"Summarize this text:\n{input}" }
            ]
        };

        var response = await _client.Messages.Create(request);
        return response.ToString();
    }
}

Simple request flow

Practical examples to start with

Summarize incident notes into action items.
Rewrite support replies into clearer language.
Draft release notes from a change list.
Summarize long emails or architecture notes for stakeholders.

Delivery notes

Keep the API key outside source control.
Wrap the SDK in a service so the app can change providers later.
Start with one bounded use case instead of trying to AI-enable everything.
Log prompt length and request IDs, not raw sensitive data.

What I built first in a real team

I started with a small internal helper that summarized release notes, support logs, or meeting text. It was small enough to ship, useful enough for the team to notice, and simple enough to prove whether AI belonged in the workflow.

Practical endpoint wrapper

A good production shape is a single API endpoint that validates input, calls one service, and returns structured output the UI can use directly.

app.MapPost("/api/ai/summarize", async (
    SummaryRequest request,
    ClaudeService service) =>
{
    if (string.IsNullOrWhiteSpace(request.Text))
        return Results.BadRequest("Text is required.");

    var summary = await service.SummarizeAsync(request.Text);
    return Results.Ok(new { summary });
});

public sealed record SummaryRequest(string Text);

public sealed class ClaudeService
{
    private readonly AnthropicClient _client = new();

    public async Task<string> SummarizeAsync(string input)
    {
        var request = new MessageCreateParams
        {
            Model = "claude-opus-4-6",
            MaxTokens = 256,
            Messages =
            [
                new() { Role = Role.User, Content = $"Summarize this text:\n{input}" }
            ]
        };

        var response = await _client.Messages.Create(request);
        return response.ToString();
    }
}