All the sample code (and a bit more) is here: reka-ai/api-examples-dotnet · csharp10-script. The scripts run a small “top 3 restaurants” prompt so you can validate everything quickly.
We’ll make the same request in three ways:
- OpenAI SDK
- Microsoft.Extensions.AI for OpenAI
- Raw HttpClient
What you need
The C# "script" feature used below ships with the upcoming .NET 10 and is currently available in preview. If you prefer not to install a preview SDK, you can run everything inside the provided Dev Container or on GitHub Codespaces. I include a .devcontainer folder with everything set up in the repo.
Set up your API key
We are talking about APIs here so of course you need an API key. Good news is that it's free to sign up with Reka and get one! It's a 2 click process, more details in the repo. The API key is then stored in a .env file and each script loads environment variables using DotNetEnv.Env.Load(), so your key is picked up automatically. I went this way instead of using dotnet user-secrets because I thought it would be the way it would be done in a CI/CD pipeline or a quick script.
Run the demos
From the csharp10-script folder, run any of these scripts. Each line is an alternative
dotnet run 1-try-reka-openai.cs
dotnet run 2-try-reka-ms-ext.cs
dotnet run 3-try-reka-http.cs
You should see a short list of restaurant suggestions.
OpenAI SDK with a custom endpoint
Reka's API are using the OpenAI format, therefore I thought of using the Nuget package OpenAI. To referance a package in a script you use the #:package [package name]@[package version] directive at the top of the file, here an example:
#:package OpenAI@2.3.0
// ...
var baseUrl = "http://api.reka.ai/v1";
var openAiClient = new OpenAIClient(new ApiKeyCredential(REKA_API_KEY), new OpenAIClientOptions
{
Endpoint = new Uri(baseUrl)
});
var client = openAiClient.GetChatClient("reka-flash-research");
string prompt = "Give me 3 nice, not crazy expensive, restaurants for a romantic dinner in Montreal";
var completion = await client.CompleteChatAsync(
new List<ChatMessage>
{
new UserChatMessage(prompt)
}
);
var generatedText = completion.Value.Content[0].Text;
Console.WriteLine($" Result: \n{generatedText}");
The rest of the code is more straightforward, you create a chat client, specify the Reka API URL, select the model and then you send a prompt. And it works just as expected. However not everything was perfect, but before I share more about that part, let talk about Microsoft.Extensions.AI.
Microsoft Extensions AI for OpenAI
Another commun way to use LLM in .NET is to use one ot the Microsoft.Extensions.AI Nuget package. In our case Microsoft.Extensions.AI.OpenAI was used.
#:package Microsoft.Extensions.AI.OpenAI@9.8.0-preview.1.25412.6
// ....
var baseUrl = "http://api.reka.ai/v1";
IChatClient client = new ChatClient("reka-flash-research", new ApiKeyCredential(REKA_API_KEY), new OpenAIClientOptions
{
Endpoint = new Uri(baseUrl)
}).AsIChatClient();
string prompt = "Give me 3 nice, not crazy expensive, restaurants for a romantic dinner in Montreal";
Console.WriteLine(await client.GetResponseAsync(prompt));
As you can see the code is very similar. Create a chat client set the URL, the model, add your propmt, and it works just as well.
That's two ways to use Reka API with differnt SDKs, but maybe you would prefer to go "SDKless", let's see how to do that.
Raw HttpClient calling the REST API
Without any SDK to help, there is a bit more line of codeto write, but it's still pretty straightforward. Let's see the code:
using var httpClient = new HttpClient();
var baseUrl = "http://api.reka.ai/v1/chat/completions";
var requestPayload = new
{
model = "reka-flash-research",
messages = new[]
{
new
{
role = "user",
content = "Give me 3 nice, not crazy expensive, restaurants for a romantic dinner in New York city"
}
}
};
using var request = new HttpRequestMessage(HttpMethod.Post, baseUrl);
request.Headers.Add("Authorization", $"Bearer {REKA_API_KEY}");
request.Content = new StringContent(jsonPayload, Encoding.UTF8, "application/json");
var response = await httpClient.SendAsync(request);
var responseContent = await response.Content.ReadAsStringAsync();
var jsonDocument = JsonDocument.Parse(responseContent);
var contentString = jsonDocument.RootElement
.GetProperty("choices")[0]
.GetProperty("message")
.GetProperty("content")
.GetString();
Console.WriteLine(contentString);
So you create an HttpClient, prepare a request with the right headers and payload, send it, get the response and parse the JSON to extract the text. In this case you have to know the JSON structure of the response, but it follows the OpenAI format.
What did I learn from this experiment?
I used VS Code as IDE while trying the script functionality. One thing that surprised me was that I didn't get any intellisense or autocompletion. I try to disable the DevKit entension and change the setting for OmniSharp but no luck. My guess is that because it's in preview, and it will works just fine in November 2025 when .NET 10 will be released.
In this light ennvironment, I incountered some issues where for some reason I couldn't use a https endpoint, I had to use http. In the raw httpClient script, I had some error with the Reflection that wasn't available. It could be related to the preview or something else, I didn't investigate further.
For the more part, everything worked as expected. You can use C# code to quickly execute some tasks without any project scaffolding. It's a great way to try out the Reka API and see how it works.
What's Next?
While writing those scripts, I encountered multiple issues that aren't related to .NET but more about the SDKs when trying to do more advance functionalities like optimization of the query, and formatting the response output. Since it goes beyond the scope of this post, I will share my findings in a follow up post. Stay tuned!
Video version
Learn more
- Reka docs: docs.reka.ai
- Join the community on Discord: discord invite
- Github: API Example for .NET