Anthropic Agent SDK vs Semantic Kernel: Which Agent Framework to Use?

Anthropic Agent SDK 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.

The Anthropic Agent SDK packages Claude Code's agent loop as a library.

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 Anthropic Agent SDK if the Anthropic Agent SDK's real value is packaging Claude Code's battle-tested agent loop with built-in tools and MCP integration. If you want a production agent that reads files, runs commands, and connects to services, it saves significant plumbing. For understanding how agents work, the plain version is more instructive. 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; Anthropic Agent SDK 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. Anthropic Agent SDK 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.