Built a couple of things with Semantic Kernel. Both some private test projects, but also two customer facing applications and one internal.
It's heavily tilted towards OpenAI and it's offerings (either through OpenAI API or through Azure). However, it works decent enough for other alternatives as well, like: huggingface or ollama. Compared to the others (CrewAI etc). I kind of feel like Semantic Kernel hasn't really solved observe ability yet. Sure you can connect what ever logging/metric solution .Net supports, but it's not as seamless like the others. Semantic Kernel is available in .Net, Java and Python. But it's quite obvious .Net is a lot more polished then the others. Python usually gets new features faster, or at least pocs or previews.
Some learnings from it all:
- It's quite easy to get started with
- I like the distinction between native plugins and textbased ones (if a plugin should run code or not)
- There is a feeling of black magic in the background, in the sense of observe ability
- A bit more manual work to get things in order, compared to the alternatives
- Rapid development, it's quite clear the development team from Microsoft is doing a lot of work with this library
All and all, if you feel comfortable with writing C#, then Semantic Kernel is totally a viable option. If you prefer python over anything else, then I would say llamaindex or langchain is probably a better option (for now).
Thanks. I would have preferred to use Go instead of Python, but somehow the language is not picking up a lot in terms of new LLM frameworks.
As of now, I am using very light weight abstractions over prompts in python and that gets the job done. But, it is way too early and I can see how pipelining multiple LLM calls would need a good library that is not too complex and involved. In the end it is just a API call and you hope for the best result :)
Since you prefer go, you might be interested in one of my pet projects. Where I've glued together some libraries, which lets you basically code all interactions with LLM's through lua. The project is written in go.
Currently it only supports ollama, but I've been thinking about adding support for more providers
As you can see, it's in a very early stage. I'm not a go developer, and I use this repository as a way to explore things both within Ollama and with go.
I'll probably add more things as the time goes by, but it isn't something I hack on every day or for that matter week. Just something to poke around and explore things with.
It's heavily tilted towards OpenAI and it's offerings (either through OpenAI API or through Azure). However, it works decent enough for other alternatives as well, like: huggingface or ollama. Compared to the others (CrewAI etc). I kind of feel like Semantic Kernel hasn't really solved observe ability yet. Sure you can connect what ever logging/metric solution .Net supports, but it's not as seamless like the others. Semantic Kernel is available in .Net, Java and Python. But it's quite obvious .Net is a lot more polished then the others. Python usually gets new features faster, or at least pocs or previews.
Some learnings from it all:
- It's quite easy to get started with
- I like the distinction between native plugins and textbased ones (if a plugin should run code or not)
- There is a feeling of black magic in the background, in the sense of observe ability
- A bit more manual work to get things in order, compared to the alternatives
- Rapid development, it's quite clear the development team from Microsoft is doing a lot of work with this library
All and all, if you feel comfortable with writing C#, then Semantic Kernel is totally a viable option. If you prefer python over anything else, then I would say llamaindex or langchain is probably a better option (for now).
edit: updated some formatting