Semantic Kernel is Microsoft's open-source SDK for embedding large language models into conventional applications, with first-class support for C#/.NET…
See Semantic Kernel as an enterprise-focused AI integration SDK.
Semantic Kernel is Microsoft's open-source SDK for adding large language model capabilities to ordinary applications. Its distinguishing trait is strong first-class support for C# and .NET — alongside Python and Java — which makes it a natural fit for the enterprise software world where much production code lives.
Rather than being agent-first, it's integration-first: it helps you weave AI functions into existing business applications with the language support, security, and stability enterprises expect. Microsoft has been converging Semantic Kernel and AutoGen into a unified agent offering, but the SDK's core role is embedding AI into apps.
Learn the central abstractions you compose.
The kernel is the heart of the SDK: a container that holds your plugins and connectors and runs AI functions. You register what the model can use with the kernel, then invoke functions through it.
Plugins are collections of functions the model can call, and they come in two flavors. Native functions are ordinary code (a method that queries a database or calls an API). Prompt functions are templated prompts treated as callable units. The model can call either, so you mix real code and LLM steps behind one interface.
See how the kernel reaches models and coordinates work.
Connectors adapt the kernel to specific services: different model providers, embedding models, and vector stores for memory. Because the kernel talks to models through connectors, you can switch providers without rewriting your plugins — useful when an enterprise must stay flexible about vendors.
On top, Semantic Kernel supports function calling so the model automatically invokes the right plugin functions, memory via embeddings for recalling relevant context, and agent orchestration for coordinating multi-step or multi-agent work. These let you go from a single AI function to a full agentic flow within one framework.
Decide when Semantic Kernel fits, and avoid the errors.
Choose Semantic Kernel when you're integrating AI into existing enterprise applications, especially in the .NET/C# ecosystem where the Python-centric frameworks are weaker, or when you value provider flexibility and Microsoft-aligned tooling. It shines at embedding AI functions into real business software rather than building a standalone experimental agent.
If you're in Python building a research-style or heavily multi-agent system, other frameworks may be a more direct fit — match the tool to your stack and goal.
Watch for: vague descriptions on plugin functions, so the model can't choose them well; over-engineering a simple integration into elaborate multi-agent orchestration; ignoring connector-based provider flexibility and hard-coupling to one model; and skipping validation of what native functions do when the model calls them. Keep plugins well-described and the design proportional to the task.
Semantic Kernel is Microsoft's open-source SDK for embedding LLMs into applications, with first-class C#/.NET, Python, and Java support. The kernel is a container holding plugins — native code functions and prompt functions the model can call — and connectors that adapt it to model providers and vector stores, so you can switch providers without rewriting plugins. It adds function calling, memory, and orchestration. Choose it for enterprise/.NET integration and provider flexibility; keep plugins well-described and the design proportional to the task.
You must add an AI assistant to an existing .NET line-of-business app that queries a database. Explain why Semantic Kernel fits, which parts would be native functions versus prompt functions, and how connectors would let you switch model providers later.
What is Semantic Kernel?
Semantic Kernel is an integration-first SDK, notable for enterprise and .NET support, for embedding AI into existing apps.
What is the kernel in Semantic Kernel?
You register plugins and connectors with the kernel and invoke functions through it; it's the heart of the SDK.
What are the two kinds of plugin functions?
Plugins mix real code and LLM steps: native functions run code, prompt functions are reusable templated prompts, behind one interface.
When is Semantic Kernel a strong choice?
SK fits enterprise integration and .NET well; Python-first research or heavily multi-agent systems may suit other frameworks better.