microsoft/semantic-kernel

microsoft/semantic-kernel

Description: Integrate cutting-edge LLM technology quickly and easily into your apps

Language: C#

License: MIT

Stars: 28097

Forks: 4639

Open issues: 277

Created: 2023-02-27T17:39:42Z

Pushed: 2026-06-11T00:38:07Z

Default branch: main

Fork: no

Archived: no

README:

Semantic Kernel

> [!IMPORTANT] > Semantic Kernel is now Microsoft Agent Framework! Microsoft Agent Framework (MAF) is the enterprise‑ready successor to Semantic Kernel. Microsoft Agent Framework is now available at version 1.0 as a production-ready release: stable APIs, and a commitment to long-term support. Whether you're building a single assistant or orchestrating a fleet of specialized agents, Microsoft Agent Framework 1.0 gives you enterprise-grade multi-agent orchestration, multi-provider model support, and cross-runtime interoperability via A2A and MCP. > > Learn more about Semantic Kernel and Agent Framework here: Semantic Kernel and Microsoft Agent Framework on the Agent Framework blog, and try out the Semantic Kernel migration guide.

Build intelligent AI agents and multi-agent systems with this enterprise-ready orchestration framework

What is Semantic Kernel?

Semantic Kernel is a model-agnostic SDK that empowers developers to build, orchestrate, and deploy AI agents and multi-agent systems. Whether you're building a simple chatbot or a complex multi-agent workflow, Semantic Kernel provides the tools you need with enterprise-grade reliability and flexibility.

System Requirements

• Python: 3.10+
• .NET: .NET 10.0+
• Java: JDK 17+
• OS Support: Windows, macOS, Linux

Key Features

• Model Flexibility: Connect to any LLM with built-in support for OpenAI, Azure OpenAI, Hugging Face, NVidia and more
• Agent Framework: Build modular AI agents with access to tools/plugins, memory, and planning capabilities
• Multi-Agent Systems: Orchestrate complex workflows with collaborating specialist agents
• Plugin Ecosystem: Extend with native code functions, prompt templates, OpenAPI specs, or Model Context Protocol (MCP)
• Vector DB Support: Seamless integration with Azure AI Search, Elasticsearch, Chroma, and more
• Multimodal Support: Process text, vision, and audio inputs
• Local Deployment: Run with Ollama, LMStudio, or ONNX
• Process Framework: Model complex business processes with a structured workflow approach
• Enterprise Ready: Built for observability, security, and stable APIs

Installation

First, set the environment variable for your AI Services:

Azure OpenAI:

export AZURE_OPENAI_API_KEY=AAA....

or OpenAI directly:

export OPENAI_API_KEY=sk-...

Python

pip install semantic-kernel

.NET

dotnet add package Microsoft.SemanticKernel
dotnet add package Microsoft.SemanticKernel.Agents.Core

Java

See semantic-kernel-java build for instructions.

Quickstart

Basic Agent - Python

Create a simple assistant that responds to user prompts:

import asyncio
from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

async def main():
# Initialize a chat agent with basic instructions
agent = ChatCompletionAgent(
service=AzureChatCompletion(),
name="SK-Assistant",
instructions="You are a helpful assistant.",
)

# Get a response to a user message
response = await agent.get_response(messages="Write a haiku about Semantic Kernel.")
print(response.content)

asyncio.run(main())

# Output:
# Language's essence,
# Semantic threads intertwine,
# Meaning's core revealed.

Basic Agent - .NET

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents;

var builder = Kernel.CreateBuilder();
builder.AddAzureOpenAIChatCompletion(
Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT"),
Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT"),
Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY")
);
var kernel = builder.Build();

ChatCompletionAgent agent =
new()
{
Name = "SK-Agent",
Instructions = "You are a helpful assistant.",
Kernel = kernel,
};

await foreach (AgentResponseItem response
in agent.InvokeAsync("Write a haiku about Semantic Kernel."))
{
Console.WriteLine(response.Message);
}

// Output:
// Language's essence,
// Semantic threads intertwine,
// Meaning's core revealed.

Agent with Plugins - Python

Enhance your agent with custom tools (plugins) and structured output:

import asyncio
from typing import Annotated
from pydantic import BaseModel
from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, OpenAIChatPromptExecutionSettings
from semantic_kernel.functions import kernel_function, KernelArguments

class MenuPlugin:
@kernel_function(description="Provides a list of specials from the menu.")
def get_specials(self) -> Annotated[str, "Returns the specials from the menu."]:
return """
Special Soup: Clam Chowder
Special Salad: Cobb Salad
Special Drink: Chai Tea
"""

@kernel_function(description="Provides the price of the requested menu item.")
def get_item_price(
self, menu_item: Annotated[str, "The name of the menu item."]
) -> Annotated[str, "Returns the price of the menu item."]:
return "$9.99"

class MenuItem(BaseModel):
price: float
name: str

async def main():
# Configure structured output format
settings = OpenAIChatPromptExecutionSettings()
settings.response_format = MenuItem

# Create agent with plugin and settings
agent = ChatCompletionAgent(
service=AzureChatCompletion(),
name="SK-Assistant",
instructions="You are a helpful assistant.",
plugins=[MenuPlugin()],…