autogen VS semantic-kernel

AutoGen is an AI framework by Microsoft designed to streamline multi-agent conversations. AutoGen allows agents to communicate, share information, and make collective decisions. This setup enhances the responsiveness and dynamism of conversations. Developers use AutoGen to tailor agents to specific roles, such as programmer, content writer, CEO, etc. This enhances their ability to handle tasks from simple queries to intricate problem-solving.

I am still playing around with the project but FYI, the parsing for the github repo URL at https://glide.agenticlabs.com/ will fail if there's a trailing slash in the repo link i.e. https://github.com/microsoft/autogen/ won't work but https://github.com/microsoft/autogen will.

New example notebook demoing video transcript translate with whisper.

New release: v0.2.1

I disagree with the conclusion. In software, I've seen 10x engineers in person and I don't think they're replaceable. Whereas, the new college grad or that entry level dev who doesn't design anything and just writes small amounts of code, doing exactly as told is replaceable by an AI. Frameworks similar to Microsoft Autogen(https://github.com/microsoft/autogen) can in theory build agents who can do these tasks with ease whereas a 10x engineer can focus on directing the agents and designing systems.

Microsoft autogen

CompressibleAgent (experimental) can be used to handle long conversations. Notebook: https://github.com/microsoft/autogen/blob/main/notebook/agentchat_compression.ipynb

There is an amazing sample on how to create your own LLM Service class to be used in Semantic Kernel. You can view the Sample here: https://github.com/microsoft/semantic-kernel/blob/3451a4ebbc9db0d049f48804c12791c681a326cb/dotnet/samples/KernelSyntaxExamples/Example16_CustomLLM.cs

This week, I had the chance to explore the SemanticKernel code base, particularly the core plugins. SemanticKernel comes equipped with these built-in plugins:

In this article we'll look at the high-level capabilities building AI orchestration systems in C# with Semantic Kernel, a rapidly maturing open-source AI orchestration framework.

I'm using Semantic Kernel (https://github.com/microsoft/semantic-kernel) and it's really nice. Makes building more complex workflows really simple without sacrificing control.

A bunch of examples (https://github.com/microsoft/semantic-kernel/blob/main/dotne...) for how to handle just about anything you need to do with OAI with a lot less boilerplate.

I haven't tried it but there's Microsoft semantic-kernel.

https://github.com/microsoft/semantic-kernel

Semantic Kernel github.com/microsoft/semantic-kernel

The need for efficient model routing has been a point of discussion in the community. Addressing this, I've submitted a pull request to Semantic-Kernel that introduces an automated multi-model connector.