Semantic Kernel and LangChain are both geared towards NLP but they have different takes on it. While LangChain revolves around creating sequences of calls known as "Chains", SK employs a "Kernel" to manage these sequences and has a "Planner" to auto-create chains for new user needs.
SK steps up the game with plugins supporting both semantic and native functions, which isn't a feature in LangChain. Also, SK has a memory feature to store context and embeddings, broadening its use case.
Moreover, SK is more welcoming to C# integration alongside Python, and has a knack for blending AI services like OpenAI with conventional coding, which LangChain doesn't offer.
So, in a nutshell, while there are similarities, SK packs more features and a bit of a different approach compared to LangChain.
Hope this clears things up!