Tolga Kayhan

Betalgo.Ranul.Anthropic

Hey there, fellow developers! 👋 I'm excited to share a new tool I've been working on that I think many of you, especially those working with AI in the .NET ecosystem, will find useful.

The Birth of a New Library

As some of you might know, I previously created the Betalgo.OpenAI library for working with OpenAI's API. That experience got me thinking about other AI services, particularly Anthropic's Claude. After mulling over the idea for a while (and getting sidetracked by other projects), I finally decided to dive in and create a .NET library for Anthropic's Claude AI.

What's in a Name?

You might notice that unlike my previous OpenAI library, this one uses a different namespace: Betalgo.Ranul.Anthropic. This is part of a larger project called Ranul-Tinga (more on that in a future post!), which aims to create a universal API for accessing various AI providers.

The Challenges of Building an AI Library

Creating this library came with its share of interesting challenges. Here's a peek into some of the hurdles I encountered and how I approached them:

1. Documentation Navigation

   Anthropic's documentation is generally good, but like most APIs, it had its nuances. Some information was distributed across different sections, requiring a bit of detective work to piece everything together. It's a common challenge in the fast-moving world of AI development, where keeping documentation perfectly up-to-date is a Herculean task. Who knows, maybe AI will solve this problem for us in the future!

2. API Differences

   While Anthropic's API shares similarities with OpenAI's, there are notable differences. One major issue I've encountered with some AI APIs is their lack of consideration for strongly typed languages. Anthropic's API fares better in this regard, but there were still some areas that required careful thought to integrate smoothly with .NET's type system.

3. Future-Proofing

   A significant challenge was figuring out the best way to introduce API fields to developers while keeping the library flexible for potential future changes. It's a delicate balance between providing a robust structure and maintaining adaptability.

4. Minimalism, Readability, and Runtime Independence

   I set myself the challenge of allowing developers to use the library with minimal lines of code. The goal was to make it easy to read and less daunting to start using. In theory, you can now create a constructor, call the messaging method with a new message request, and print it to the screen in just three lines of code.

   var messageRequest = new MessageRequest {
       Messages = [Message.FromUser("What is the capital of France?")],
       Model = "claude-3-opus-20240229",
       MaxTokens = 100 
   };
   var messageResponse = await anthropicService.Messages.Create(messageRequest);
   if (messageResponse.Successful) Console.WriteLine(messageResponse.ToString());
   

   Moreover, I put a lot of effort into minimizing runtime dependencies. This approach, which will be a key differentiator of the Ranul-Tinga project from other AI frameworks, reduces the reliance on runtime code. It's one of my pet peeves when a project heavily depends on runtime evaluation, so I tried to avoid this as much as possible. The result is a more predictable and easier-to-debug library.

These challenges shaped the development process and ultimately led to some of the unique features of this library. It's been an exciting journey of problem-solving and optimization, always with the end-user - you, the developers - in mind.

The Power of AI in Development

Interestingly, I used my previous project, Mergy, to help create the README file for this library. I'm also planning to use it to generate wiki pages. It's gratifying to see a tool I created becoming useful in my own development process.

What's Next?

Now that the library is ready, the next step is to let developers know it exists. I'm planning to reach out to the Anthropic team – perhaps they'd be interested in including this SDK in their documentation.

Wrapping Up

Creating this library has been an interesting journey, full of challenges but also rewarding insights. I hope it will make working with Anthropic's Claude AI easier for .NET developers.

I'm excited to see what you'll build with this library. Whether you're an AI enthusiast, a seasoned developer, or just getting started with integrating AI into your applications, I hope this tool makes your journey a little smoother.

Give it a try and let me know what you think. Your feedback and real-world use cases will be invaluable as we continue to improve and expand this library.

Happy coding! 🚀
Tolga.

GitHub Repository: Link to the .NET Library for Anthropic Claude repository