Semantic Kernel 101

Part 1: Understanding the framework and building your first Agent

Semantic kernel is a lightweight framework which make it easier to develop AI-powered applications. It falls into the category of AI orchestrators like Llama-Index, LangChain, TaskWeaver and so on. So you migth ask yourself: how is Semantic Kernel different and what are its differentiators?

This is the type of question that we will try to cover in this series of articles, exploring the main features and capabilities of Semantic Kernel. However, as a disclaimer, my opinion is that there is no “better” or “worse” orchestrator, but rather they might serve different purposes depending on your unique use cases or, more practically, on the existing skills within your organization.

Having said that, let’s start exploring Semantic Kernel and use it to build a first AI Agent.

What is Semantic Kernel?

As an AI orchestrator, Semantic Kernel comes with a set of pre-built components that are the typical assets needed when building AI apps, including Metaprompts, Memory, VectorDBs and so on. Additionally, it has some differentiating features that needs to be introduced.

Note: to get started with SK, you can refer to the official documentation here and the main GitHub…