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Semantic Kernel

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

License: MIT Python package Nuget package Discord

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

Key Features

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()],
    arguments=KernelArguments(settings)
)

response = await agent.get_response(messages="What is the price of the soup special?")
print(response.content)

# Output:
# The price of the Clam Chowder, which is the soup special, is $9.99.

asyncio.run(main())

Agent with Plugin - .NET

using System.ComponentModel; using Microsoft.SemanticKernel; using Microsoft.SemanticKernel.Agents; using Microsoft.SemanticKernel.ChatCompletion;

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();

kernel.Plugins.Add(KernelPluginFactory.CreateFromType());

ChatCompletionAgent agent = new() { Name = "SK-Assistant", Instructions = "You are a helpful assistant.", Kernel = kernel, Arguments = new KernelArguments(new PromptExecutionSettings() { FunctionChoiceBehavior = FunctionChoiceBehavior.Auto() })

};

await foreach (AgentResponseItem response in agent.InvokeAsync("What is the price of the soup special?")) { Console.WriteLine(response.Message); }

sealed class MenuPlugin { [KernelFunction, Description("Provides a list of specials from the menu.")] public string GetSpecials() => """ Special Soup: Clam Chowder Special Salad: Cobb Salad Special Drink: Chai Tea """;

[KernelFunction, Description("Provides the price of the requested menu item.")]
public string GetItemPrice(
    [Description("The name of the menu item.")]
    string menuItem) =>
    "$9.99";

}

Multi-Agent System - Python

Build a system of specialized agents that can collaborate:

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

billing_agent = ChatCompletionAgent( service=AzureChatCompletion(), name="BillingAgent", instructions="You handle billing issues like charges, payment methods, cycles, fees, discrepancies, and payment failures." )

refund_agent = ChatCompletionAgent( service=AzureChatCompletion(), name="RefundAgent", instructions="Assist users with refund inquiries, including eligibility, policies, processing, and status updates.", )

triage_agent = ChatCompletionAgent( service=OpenAIChatCompletion(), name="TriageAgent", instructions="Evaluate user requests and forward them to BillingAgent or RefundAgent for targeted assistance." " Provide the full answer to the user containing any information from the agents", plugins=[billing_agent, refund_agent], )

thread: None

async def main() -> None: print("Welcome to the chat bot!\n Type 'exit' to exit.\n Try to get some billing or refund help.") while True: user_input = input("User:> ")

    if user_input.lower().strip() == "exit":
        print("\n\nExiting chat...")
        return False

    response = await triage_agent.get_response(
        messages=user_input,
        thread=thread,
    )

    if response:
        print(f"Agent :> {response}")

Agent :> I understand that you were charged twice for your subscription last month, and I'm here to assist you with resolving this issue. Here’s what we need to do next:

1. Billing Inquiry:

- Please provide the email address or account number associated with your subscription, the date(s) of the charges, and the amount charged. This will allow the billing team to investigate the discrepancy in the charges.

2. Refund Process:

- For the refund, please confirm your subscription type and the email address associated with your account.

- Provide the dates and transaction IDs for the charges you believe were duplicated.

Once we have these details, we will be able to:

- Check your billing history for any discrepancies.

- Confirm any duplicate charges.

- Initiate a refund for the duplicate payment if it qualifies. The refund process usually takes 5-10 business days after approval.

Please provide the necessary details so we can proceed with resolving this issue for you.

if name == "main": asyncio.run(main())

Where to Go Next

  1. 📖 Try our Getting Started Guide or learn about Building Agents
  2. 🔌 Explore over 100 Detailed Samples
  3. 💡 Learn about core Semantic Kernel Concepts

API References

Troubleshooting

Common Issues

Getting Help

Join the community

We welcome your contributions and suggestions to the SK community! One of the easiest ways to participate is to engage in discussions in the GitHub repository. Bug reports and fixes are welcome!

For new features, components, or extensions, please open an issue and discuss with us before sending a PR. This is to avoid rejection as we might be taking the core in a different direction, but also to consider the impact on the larger ecosystem.

To learn more and get started:

Contributor Wall of Fame

semantic-kernel contributors

Code of Conduct

This project has adopted theMicrosoft Open Source Code of Conduct. For more information, see theCode of Conduct FAQor contact opencode@microsoft.comwith any additional questions or comments.

License

Copyright (c) Microsoft Corporation. All rights reserved.

Licensed under the MIT license.