Features
- Multi-language support (C#, Python, Java)
- Plugin architecture for extending AI capabilities
- Planner for automatic task decomposition
- Memory and embedding management
Pros
- Enterprise-grade with Microsoft backing
- Strong C#/.NET integration for enterprise teams
- Clean plugin-based architecture
Cons
- More verbose than lightweight frameworks
- C# is the primary focus, other languages lag behind
- Enterprise orientation may feel heavy for startups
Overview
Semantic Kernel is Microsoft’s open-source SDK for integrating AI models into enterprise applications. It provides a lightweight, extensible programming model that treats AI capabilities as plugins, making it easy to combine LLM calls with existing code and services.
The SDK uses a plugin architecture where AI capabilities (semantic functions) and traditional code (native functions) are unified under the same abstraction. This allows AI-powered features to be seamlessly integrated into existing application architectures without requiring a complete rewrite.
Semantic Kernel’s planner can automatically decompose complex user requests into sequences of plugin calls, enabling AI-driven orchestration of business processes. It supports multiple LLM providers including OpenAI, Azure OpenAI, and Anthropic.
When to Use
Choose Semantic Kernel for enterprise .NET/C# applications that need to integrate AI capabilities. It is ideal for teams already in the Microsoft ecosystem who want a structured, plugin-based approach to AI integration.
Getting Started
dotnet add package Microsoft.SemanticKernelusing Microsoft.SemanticKernel;
var kernel = Kernel.CreateBuilder()
.AddOpenAIChatCompletion("gpt-4o", apiKey)
.Build();
var result = await kernel.InvokePromptAsync("Summarize: {{$input}}");