Semantic Kernel

Microsoft SDK for adding models, plugins and agents to .NET, Python and Java apps

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What is Semantic Kernel?

Semantic Kernel is an open-source Microsoft SDK that lets existing applications call language models through ordinary code. It provides abstractions for prompts, plugins, planners and agents across C#, Python and Java, so a .NET or Java service can add AI features without adopting a new runtime or rewriting its architecture. Plugins wrap existing functions and APIs so models can call them, memory and vector store connectors handle retrieval, and agent support builds on the same primitives. It is designed for teams that already have an application and want to add intelligence to it.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

  • One consistent SDK across C#, Python and Java
  • Plugin abstractions that expose existing code to models
  • Planner and agent patterns for multi-step tasks
  • Vector store and memory connectors for retrieval
  • Prompt templating with function and variable support
  • Open source with Microsoft backing and regular releases

Pros & cons

Strengths

  • Fits existing .NET and Java codebases naturally
  • Plugin model reuses functions you already have
  • Truly cross-language, unusual in this category

Watch out for

  • API surface has changed across major versions
  • Code-first, with no visual builder for non-developers
  • You still own hosting, observability and cost control

Best for & use cases

enterprise developers, .net and java applications, plugin architectures and ai features

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FAQ

What is a plugin here?

A wrapper around a function or API that a model can call. It is how your existing business logic becomes something the assistant can use, without duplicating that logic inside prompts.

Which languages are supported?

C#, Python and Java share the same concepts, which is useful for organisations running services in more than one stack. Feature parity between them is close but not always identical.

Is it suitable for small projects?

It can be, but its value shows most when you already have an application with functions worth exposing. For a quick prototype, a lighter library is often faster to adopt.