AI: Semantic Kernel vs LangChain

Semantic Kernel 101

Currently, the Copilot approach rules the artificial intelligence (AI) world, and in that world, Microsoft created Semantic Kernel as a framework for building its own Copilots. Now, you can use Semantic Kernel too. Semantic Kernel (SK) is an open-source AI framework, created by Microsoft for .NET, Python, and Java developers working with Large Language Models (LLMs). Its purpose is threefold: To abstract away the underlying LLMs, APIs and tooling; to handle more complex implementations in a generic way; and to make it easy to integrate your own content. Semantic Kernel is very similar to LangChain, a popular open source LLM framework for Python and JavaScript

Introduction | 🦜️🔗 Langchain

CODE Consulting - Services - Artificial Intelligence (AI)

microsoft/semantic-kernel: Integrate cutting-edge LLM technology quickly and easily into your apps @GitHub (MIT)

C#, Python, Jupyter

LangChain.com

LangChain - Wikipedia

LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.

langchain-ai/langchain: 🦜🔗 Build context-aware reasoning applications  @GitHub (MIT)

Python, Jupyter

Project Jupyter - Wikipedia