SK does a lot of the same things that Langhain does at a high level. The most useful bits for us are prompt templating[0], "inlining" some functions like `reca

SK does a lot of the same things that Langhain does at a high level. The most useful bits for us are prompt templating[0], "inlining" some functions like `recall` into the text of the prompt [1], and service container [2] (useful if you are using multiple LLM services and models for different types of prompts/flows). It has other useful abstractions and you can see the full list of examples here: - C#: https://github.com/microsoft/semantic-kernel/tree/main/dotnet/samples/Concepts https://github.com/microsoft/semantic-kernel/tree/main/dotne... - python: https://github.com/microsoft/semantic-kernel/tree/main/python https://github.com/microsoft/semantic-kernel/tree/main/pytho... --- [0] https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/PromptTemplates/PromptyFunction.cs https://github.com/microsoft/semantic-kernel/blob/main/dotne... [1] https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/Memory/TextMemoryPlugin_MultipleMemoryStore.cs#L283 https://github.com/microsoft/semantic-kernel/blob/main/dotne... [2] https://github.com/microsoft/semantic-kernel/blob/main/dotnet/samples/Concepts/Kernel/CustomAIServiceSelector.cs https://github.com/microsoft/semantic-kernel/blob/main/dotne...