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Semantic Kernel–Agent Framework

In this post I show you the recently introduced Semantic Kernel agents feature and how it simplifies building your own AI agents. But maybe I should start with a short recap about Semantic Kernel.

On the documentation pages, Semantic Kernel is described like this:

Semantic Kernel is a lightweight, open-source development kit that lets you easily build AI agents and integrate the latest AI models into your C#, Python, or Java codebase. It serves as an efficient middleware that enables rapid delivery of enterprise-grade solutions.

It gives you all the building blocks required to build your own agent; a chat completion model, a plugin system, a planner and more. However until recently you had to bring all this building blocks together yourself.

Here is a small code snippet I copied from an existing project:

There are a lot of things going on in the code above and if you have hard time to understand all of this I have some good news for you. Starting with the Python (1.6.0) and .NET releases (1.18.0 RC1), Semantic Kernel now provides a first-class abstraction for agents.

To use it, we first need to add the following NuGet package:

dotnet add package Microsoft.SemanticKernel.Agents.Core

Let’s rewrite the code above to use the new agent abstraction:

This is already an improvement but you still have to manage the chat history yourself.

If you are using an OpenAI based model, you can go one step further and use the OpenAI assistant abstraction so that the state is managed for you:

Nice!

Remark: Everything I showing here is still in preview and will probably change in the future.

More information

semantic-kernel/dotnet/samples/GettingStartedWithAgents at main · microsoft/semantic-kernel (github.com)

Introducing enterprise multi-agent support in Semantic Kernel | Semantic Kernel (microsoft.com)

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