LangChain vs Semantic Kernel: Which Agent Framework to Use?

LangChain LangChain is the most popular agent framework.

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

Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.

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. LangChain is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Semantic Kernel would force you to translate.

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. Semantic Kernel is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; LangChain would force you to translate.

Both LangChain and Semantic Kernel pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.