Semantic Kernel¶

What it is¶

Semantic Kernel is an open-source SDK from Microsoft that allows developers to integrate LLMs into conventional programming languages like C#, Python, and Java. It uses "plugins" to combine AI capabilities with existing code.

What problem it solves¶

It bridges the gap between AI models and traditional software engineering. It provides a structured way to manage prompts, state, and tool-calling (native functions) while maintaining type safety, standard development practices, and enterprise-grade scalability.

Where it fits in the stack¶

Framework / Enterprise SDK. It acts as the orchestration layer for integrating frontier models like Claude 4.8 and GPT-5.5 into established software ecosystems, particularly the Microsoft stack.

Typical use cases¶

Enterprise App Integration: Adding AI features to existing .NET or Python applications.
Task Automation: Using LLMs to orchestrate a series of native code functions.
Custom Copilots: Building specialized assistants that interact with internal APIs and databases.
Cross-Language AI Strategy: Implementing a unified AI orchestration layer across a polyglot engineering organization.

Strengths¶

Multi-language Support: First-class support for C# / .NET, alongside Python and Java.
Extensible Plugins: Powerful system for wrapping existing business logic as "tools" (plugins).
Microsoft Ecosystem: Native integration with Azure OpenAI, Microsoft Graph, and Azure AI Search.
Planner Evolution: Support for advanced planning mechanisms like FunctionCallingStepwisePlanner to solve complex requests.
Type Safety: Strong typing in C# and structured schemas in Python/Java ensure reliable tool interactions.
MCP 3.0 Compliance: Seamless integration with the Model Context Protocol for cross-platform tool use.

Limitations¶

Complexity: The "Kernel" and "Plugin" abstractions can feel heavy for small projects or simple scripts.
Python Parity: While significantly improved in 2026, some experimental features still land in the .NET version first.
Learning Curve: Requires understanding the "Semantic" vs "Native" function paradigm.

When to use it¶

When building enterprise-grade applications, especially in a .NET environment.
When you need to strictly control how AI interacts with your existing codebase via a formal plugin system.
For building robust, maintainable "Copilot" experiences within corporate software.

When not to use it¶

For quick prototyping or research-focused LLM scripts.
If you don't need the "kernel" abstraction and prefer a more lightweight approach like smolagents.
For simple chatbot applications that don't require integration with native code.

Getting started¶

Installation¶

pip install semantic-kernel

Minimal Python Example¶

import asyncio
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

async def main():
    kernel = Kernel()
    # Configure with GPT-4o or Claude via connectors
    kernel.add_service(OpenAIChatCompletion(ai_model_id="gpt-4o"))

    # Define a simple prompt-based function
    func = kernel.add_function(prompt="What is the capital of {{$input}}?", plugin_name="Geo", function_name="Capital")

    result = await kernel.invoke(func, input="France")
    print(result)

if __name__ == "__main__":
    asyncio.run(main())

CLI examples¶

# Installing the Semantic Kernel CLI tools (if available via dotnet)
dotnet tool install --global Microsoft.SemanticKernel.CLI

# Using the CLI to scaffold a new plugin
sk-cli create plugin --name MyBusinessPlugin --language python

# Testing a plugin via the CLI
sk-cli invoke --plugin MyBusinessPlugin --function MyFunction --input "test data"

API examples¶

Wrapping Native Functions as Plugins¶

from semantic_kernel.functions import kernel_function

class MathPlugin:
    @kernel_function(
        description="Adds two numbers together",
        name="Add"
    )
    def add(self, number1: float, number2: float) -> float:
        return number1 + number2

# Add the plugin to the kernel
kernel.add_plugin(MathPlugin(), plugin_name="Math")

Using Planners for Complex Tasks¶

from semantic_kernel.planners import FunctionCallingStepwisePlanner

# Initialize a planner that can use all registered plugins
planner = FunctionCallingStepwisePlanner()

# Execute a complex task using frontier models
result = await planner.execute(kernel, "Analyze the sales data and provide a summary report.")

Sources / references¶

Contribution Metadata¶

Last reviewed: 2026-07-21
Confidence: high