Semantic Kernel Guide 2025: Microsoft's AI Orchestration SDK

Install (choose your language)

# Python
pip install semantic-kernel

# .NET
dotnet add package Microsoft.SemanticKernel

# Java (Maven)
# Add to pom.xml: com.microsoft.semantic-kernel:semantickernel-api

quickstart.py

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

async def main():
    kernel = Kernel()
    kernel.add_service(OpenAIChatCompletion(
        ai_model_id="gpt-4o",
        api_key="sk-...",
    ))

    result = await kernel.invoke_prompt(
        "Summarize the key benefits of {{$topic}} in 3 bullet points.",
        topic="vector databases"
    )
    print(result)

asyncio.run(main())

plugins/weather_plugin.py

from semantic_kernel.functions import kernel_function

class WeatherPlugin:
    @kernel_function(
        name="get_current_weather",
        description="Returns current weather for a city",
    )
    def get_current_weather(self, city: str) -> str:
        # Replace with real weather API call
        return f"{city}: 22°C, partly cloudy"

    @kernel_function(
        name="get_forecast",
        description="Returns 5-day weather forecast for a city",
    )
    def get_forecast(self, city: str, days: int = 5) -> str:
        return f"{city} forecast for {days} days: mostly sunny"

Register plugin with kernel

kernel.add_plugin(WeatherPlugin(), plugin_name="Weather")

# The AI can now call Weather-get_current_weather and Weather-get_forecast
result = await kernel.invoke_prompt(
    "What's the weather like in Kyiv and should I bring an umbrella tomorrow?"
)

agent.py

from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.function_choice_behavior import (
    FunctionChoiceBehavior,
)

agent = ChatCompletionAgent(
    service_id="openai",
    kernel=kernel,
    name="WeatherAssistant",
    instructions="You are a helpful weather assistant. Use plugins to answer questions.",
    execution_settings=kernel.get_prompt_execution_settings_from_service_id(
        "openai"
    ),
)

# Agent automatically invokes Weather plugin as needed
async for content in agent.invoke(
    messages=[{"role": "user", "content": "Compare weather in London and Tokyo tomorrow"}]
):
    print(content.content, end="")

from semantic_kernel.connectors.memory.chroma import ChromaMemoryStore
from semantic_kernel.memory import SemanticTextMemory
from semantic_kernel.core_plugins import TextMemoryPlugin

# Connect to Chroma (also works with Qdrant, Pinecone, Azure AI Search, etc.)
memory_store = ChromaMemoryStore(persist_directory="./chroma_db")
memory = SemanticTextMemory(
    storage=memory_store,
    embeddings_generator=kernel.get_service("openai"),
)
kernel.add_plugin(TextMemoryPlugin(memory), "memory")

# Save facts
await memory.save_information("docs", id="faq1",
    text="Prismix tracks 75+ AI service statuses in real time.")

# Retrieve relevant context (AI calls this automatically via TextMemoryPlugin)
results = await memory.search("docs", "how many services does Prismix monitor?")

Program.cs

using Microsoft.SemanticKernel;

var builder = Kernel.CreateBuilder();
builder.AddOpenAIChatCompletion("gpt-4o", Environment.GetEnvironmentVariable("OPENAI_API_KEY")!);
var kernel = builder.Build();

// Add a native plugin
kernel.ImportPluginFromType<MathPlugin>();

// Invoke with auto function calling
var result = await kernel.InvokePromptAsync(
    "What is 1234 times 5678?",
    new KernelArguments(new OpenAIPromptExecutionSettings {
        ToolCallBehavior = ToolCallBehavior.AutoInvokeKernelFunctions
    })
);
Console.WriteLine(result);