Introduction
Large Language Models (LLMs) are excellent at generating text, answering questions, and summarizing information. However, on their own, they cannot access databases, call APIs, retrieve business data, send emails, or perform actions in external systems.
To make AI applications truly useful, developers need a way to connect AI models with real-world tools and services. This is where Semantic Kernel Plugins come into play.
Microsoft Semantic Kernel provides a framework for building AI-powered applications in .NET, and plugins are one of its most powerful features. They allow AI models to interact with external systems and perform actions beyond simple text generation.
In this article, you'll learn what Semantic Kernel Plugins are, how they work, and how to use them to build more capable AI applications.
What Is Semantic Kernel?
Semantic Kernel is Microsoft's open-source SDK for integrating AI capabilities into applications.
It helps developers build:
AI assistants
Copilots
Chatbots
AI agents
Enterprise AI solutions
Workflow automation systems
Semantic Kernel acts as a bridge between AI models and application code.
Instead of directly managing prompts and API calls, developers can use Semantic Kernel abstractions to create structured AI workflows.
What Are Semantic Kernel Plugins?
A plugin is a collection of functions that an AI model can invoke to perform specific tasks.
Think of plugins as tools available to the AI.
Examples include:
Querying a database
Sending emails
Calling REST APIs
Retrieving customer information
Checking inventory
Creating support tickets
Accessing internal systems
Without plugins:
User
│
▼
AI Model
│
▼
Text Response
With plugins:
User
│
▼
AI Model
│
▼
Plugin
│
▼
External System
│
▼
AI Response
This allows AI applications to perform meaningful actions rather than simply generating text.
Why Plugins Matter
Modern AI applications need access to real-world information.
Consider a customer asking:
What is the status of my order?
An LLM alone cannot answer this because it does not have access to your company's order database.
With plugins, the AI can:
Identify the user's intent.
Call an order lookup function.
Retrieve order information.
Generate a personalized response.
This transforms the AI from a conversational tool into a business assistant.
Installing Semantic Kernel
Create a new .NET project:
dotnet new console
Install Semantic Kernel:
dotnet add package Microsoft.SemanticKernel
You can then configure the kernel.
using Microsoft.SemanticKernel;
var builder = Kernel.CreateBuilder();
Kernel kernel = builder.Build();
The kernel becomes the central component that manages AI interactions and plugins.
Creating Your First Plugin
A plugin is typically a C# class containing methods decorated with Semantic Kernel attributes.
Example:
using Microsoft.SemanticKernel;
public class WeatherPlugin
{
[KernelFunction]
public string GetWeather(string city)
{
return $"The weather in {city} is sunny.";
}
}
This plugin exposes a weather function that AI models can call when needed.
Registering a Plugin
After creating a plugin, register it with the kernel.
kernel.ImportPluginFromType<WeatherPlugin>();
The plugin is now available to the AI application.
When users ask weather-related questions, the model can invoke the function automatically.
Example: Customer Support Plugin
Let's create a plugin that retrieves customer information.
public class CustomerPlugin
{
[KernelFunction]
public string GetCustomerStatus(int customerId)
{
return "Premium Customer";
}
}
Register it:
kernel.ImportPluginFromType<CustomerPlugin>();
Now the AI can access customer-related information through the plugin.
How Plugin Invocation Works
Suppose a user asks:
What is the status of customer 1001?
Workflow:
User Question
│
▼
Semantic Kernel
│
▼
Customer Plugin
│
▼
Business Data
│
▼
Generated Response
The AI determines that the plugin should be called and incorporates the returned information into its response.
Working with External APIs
Plugins can also connect to REST APIs.
Example:
public class ExchangeRatePlugin
{
[KernelFunction]
public async Task<string> GetRate()
{
using var client =
new HttpClient();
return await client.GetStringAsync(
"https://api.example.com/rates"
);
}
}
This allows AI applications to access real-time information.
Common API integrations include:
Weather services
Payment systems
CRM platforms
ERP systems
Inventory management tools
Creating Database Plugins
Many enterprise applications require database access.
Example:
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