Microsoft Semantic Kernel

Overview

Microsoft Semantic Kernel is an open-source SDK that lets you easily combine cutting-edge AI with traditional programming languages. It provides a unified programming model for integrating LLMs with your existing codebase, with first-class support for .NET, Python, and Java.

Key Features

🏗️ Enterprise-Grade Architecture

Semantic Kernel is designed for enterprise use:

• Multi-Language Support: First-class support for C#, Python, and Java
• Dependency Injection: Built-in DI container for clean architecture
• Async/Await Native: Full async support for all operations
• Testable Design: Easy to mock and test all components

🔌 Plugin Architecture

Create reusable plugins that can be composed:

// C# example
public class WeatherPlugin
{
    [KernelFunction("get_weather")]
    [Description("Gets the current weather for a location")]
    public async Task<string> GetWeather(
        [Description("The location to get weather for")] string location)
    {
        return await weatherService.GetWeatherAsync(location);
    }
}

// Register plugin
kernel.Plugins.AddFromObject(new WeatherPlugin());

# Python example
from semantic_kernel.functions import kernel_function

class WeatherPlugin:
    @kernel_function(
        description="Gets the current weather for a location",
        name="get_weather"
    )
    async def get_weather(self, location: str) -> str:
        return await self.weather_service.get_weather(location)

🧠 Memory Connectors

Built-in memory with multiple storage backends:

// C# - Vector memory
var memory = new MemoryBuilder()
    .WithOpenAITextEmbeddingGeneration("text-embedding-3-small", "api-key")
    .WithMemoryStore(new VolatileMemoryStore())
    .Build();

// Python - Multiple connectors
from semantic_kernel.connectors.memory.chroma import ChromaMemoryStore
from semantic_kernel.connectors.memory.pinecone import PineconeMemoryStore
from semantic_kernel.connectors.memory.qdrant import QdrantMemoryStore

🤖 Agent Orchestration

Built-in agent framework:

var agent = new ChatCompletionAgent
{
    Name = "Assistant",
    Instructions = "You are a helpful assistant.",
    Kernel = kernel,
    ChatCompletionService = openAIService
};

var result = await agent.InvokeAsync("What's the weather in Seattle?");

🔗 Multiple Model Providers

Support for numerous AI providers:

Provider	Status	Models
OpenAI	✅ Full	GPT-4o, GPT-4 Turbo, o1
Azure OpenAI	✅ Full	All Azure models
Anthropic	✅ Full	Claude 3.5 Sonnet, Opus
Google	✅ Full	Gemini 1.5 Pro, 2.0
Hugging Face	✅ Full	Open models
Ollama	✅ Full	Local models
Mistral	✅ Full	Mistral models

Installation

# .NET
dotnet add package Microsoft.SemanticKernel

# Python
pip install semantic-kernel

# Java (Maven)
<dependency>
    <groupId>com.microsoft.semantic-kernel</groupId>
    <artifactId>semantic-kernel-api</artifactId>
    <version>1.15.0</version>
</dependency>

Quick Start

// C# - Complete example
using Microsoft.SemanticKernel;

var kernel = Kernel.CreateBuilder()
    .AddOpenAIChatCompletion("gpt-4o", "api-key")
    .Build();

var result = await kernel.InvokePromptAsync("What is AI?");
Console.WriteLine(result);

# Python - Complete example
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

kernel = Kernel()
kernel.add_service(OpenAIChatCompletion(service_id="chat", ai_model_id="gpt-4o"))

result = await kernel.invoke_prompt("What is AI?")
print(result)

Advanced Patterns

Semantic Functions

Define AI-powered functions with prompts:

// C# - Semantic function from string
var summarize = kernel.CreateFunctionFromPrompt(
    "Summarize the following text: {{$input}}",
    new PromptExecutionSettings
    {
        Temperature = 0.3,
        MaxTokens = 500
    }
);

var summary = await kernel.InvokeAsync(summarize, new { input = longText });

Planning and Orchestration

var planner = new SequentialPlanner(kernel);
var plan = await planner.CreatePlanAsync("Book a flight to Paris and find a hotel");
var result = await plan.InvokeAsync();

Memory with RAG

// Add memory
var memory = new MemoryBuilder()
    .WithOpenAITextEmbeddingGeneration("text-embedding-3-small", "api-key")
    .WithMemoryStore(new VolatileMemoryStore())
    .Build();

await memory.SaveInformationAsync("collection", "AI is transformative", "id1");

// Query memory
var result = await memory.SearchAsync("collection", "What is AI?");

Function Calling

kernel.Plugins.AddFromType<CalendarPlugin>();
kernel.Plugins.AddFromType<EmailPlugin>();

var result = await kernel.InvokePromptAsync(
    "Schedule a meeting with the team tomorrow at 2pm"
);

Pros

• ✅ Enterprise-Ready: Designed by Microsoft for enterprise scenarios
• ✅ Multi-Language: Excellent support for .NET, Python, and Java
• ✅ Azure Integration: First-class integration with Azure AI services
• ✅ Clean Architecture: Well-organized, testable code structure
• ✅ Active Development: Backed by Microsoft with regular updates
• ✅ Documentation: Comprehensive docs with enterprise focus
• ✅ No Vendor Lock-in: Works with any OpenAI-compatible endpoint

Cons

• ❌ Smaller Community: Less community activity than LangChain
• ❌ Fewer Integrations: Smaller ecosystem of pre-built connectors
• ❌ Learning Curve: Enterprise patterns can be complex for simple use cases
• ❌ JavaScript Gap: No official JavaScript/TypeScript support

Use Cases

Use Case	Why Semantic Kernel
Enterprise .NET Apps	Native integration with C# and .NET ecosystem
Azure AI Services	First-class support for Azure OpenAI and Cognitive Services
Multi-language Projects	Support for C#, Python, and Java in one codebase
Plugin Architecture	Compose reusable AI-powered plugins
Enterprise RAG	Memory connectors with enterprise storage backends

Comparison with Alternatives

Feature	Semantic Kernel	LangChain	Microsoft AutoGen	Vercel AI SDK
Paradigm	Plugin-based	Chain-based	Multi-agent conversation	TypeScript SDK
.NET Support	✅ Excellent	❌ No	✅ Yes	❌ No
Java Support	✅ Yes	❌ No	❌ No	❌ No
Enterprise Ready	✅ Yes	⚠️ Moderate	✅ Yes	⚠️ Moderate
Azure Integration	✅ Native	⚠️ Via plugins	✅ Native	❌ No
Learning Curve	Medium	High	Medium	Low
Best for	.NET/Enterprise	Complex apps	Multi-agent teams	TS/Next.js

Best Practices

1. Use dependency injection — Leverage built-in DI for clean architecture
2. Create reusable plugins — Encapsulate AI functionality in plugins
3. Use semantic functions — Define AI-powered functions with prompts
4. Leverage memory connectors — Use appropriate storage backend for your needs
5. Plan before executing — Use SequentialPlanner for complex tasks
6. Test with mocks — All components are designed for testability

Troubleshooting

Issue	Solution
Plugin not loading	Verify plugin class has [KernelFunction] attribute
Memory not persisting	Check memory store configuration and connection
Function calling fails	Ensure function signatures match expected types
Azure auth errors	Verify Azure OpenAI endpoint and key configuration
Planning fails	Provide clearer task description to planner

Resources

• Official Docs: https://learn.microsoft.com/en-us/semantic-kernel/
• GitHub: https://github.com/microsoft/semantic-kernel
• Samples: https://github.com/microsoft/semantic-kernel/tree/main/samples
• NuGet: https://www.nuget.org/packages/Microsoft.SemanticKernel/

Comparison

Feature	Semantic Kernel	LangChain	CrewAI
.NET Support	✅ Excellent	❌ No	❌ No
Java Support	✅ Good	❌ No	❌ No
Enterprise Ready	✅ Yes	⚠️ Moderate	⚠️ Moderate
Azure Integration	✅ Native	⚠️ Via plugins	❌ No
Community Size	Medium	Large	Medium
JavaScript	❌ No	✅ Yes	❌ No

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Last updated: May 2026