LangChain vs Semantic Kernel: Which Agent Framework to Use?

LangChain and Semantic Kernel both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.

LangChain is the most popular agent framework.

Semantic Kernel is Microsoft's enterprise SDK for building AI agents.

Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.

Pick LangChain if langChain adds value when you need production integrations (vector stores, specific LLM providers, deployment tooling). But if you want to understand what's happening — or your use case is straightforward — the plain Python version is easier to debug, modify, and reason about. The tradeoffs in its intro should match how your team already thinks about agents; Semantic Kernel will feel like translation if they don't.

Pick Semantic Kernel if semantic Kernel earns its complexity in enterprise environments with Azure OpenAI, .NET backends, and existing Microsoft infrastructure. But the core agent pattern — LLM call, tool dispatch, loop — is identical to what you can build in 60 lines of Python. The tradeoffs in its intro should match how your team already thinks about agents; LangChain will feel like translation if they don't.

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. LangChain has its own class hierarchy and tool registration conventions; Semantic Kernel has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.