semantic-kernel README

The project's README from github.com/microsoft/semantic-kernel. As one markdown file: README.md.

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

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<ChatMessageContent> 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<MenuPlugin>());

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<ChatMessageContent> 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, ChatHistoryAgentThread
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: ChatHistoryAgentThread = 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 the Microsoft Open Source Code of Conduct. For more information, see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

License

Copyright (c) Microsoft Corporation. All rights reserved.

Licensed under the MIT license.

semantic-kernel (microsoft/semantic-kernel)

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28.7K ·GitHub
Semantic-kernel
这是什么？Semantic Kernel 是微软推出的一款不依赖特定模型的 SDK，用于构建、编排和部署 AI 智能体及多智能体系统，小到简单的聊天机器人，大到复杂的多智能体工作流都可以胜任。
它是能加到我自己的应用里的库吗？支持哪些语言？它是一个可以集成到你应用中的 SDK，提供 Python 3.10+、.NET 10.0+ 和 Java（JDK 17+）版本，可在 Windows、macOS 和 Linux 上运行。
我怎么才能把第一个智能体跑起来？先为你的 AI 服务（Azure OpenAI 或 OpenAI）设置一个环境变量，然后按照对应语言的安装步骤和快速入门操作，就能用 Python 或 .NET 搭建一个基础的助手。
需要 API 密钥吗？它支持哪些模型服务商？需要，你得能访问一个 LLM 服务——SDK 内置了对 OpenAI、Azure OpenAI、Hugging Face、NVIDIA 等多家服务商的支持。
README 里说 Semantic Kernel 现在变成 Microsoft Agent Framework 了，我还应该用它来开发吗？该项目现在指向 Microsoft Agent Framework（MAF）——它的企业级后续版本，已达到 1.0 版本，API 稳定并支持长期维护；同时官方还提供了 Semantic Kernel 的迁移指南。
我可以不用云端 API，改用本地模型来运行吗？可以——README 中列出了通过 Ollama、LMStudio 或 ONNX 进行本地部署的方式，智能体可以运行在你自己电脑上的模型。
建好一个智能体之后，它能做什么？智能体是模块化的：它们可以使用工具/插件、记忆和规划能力，你还可以通过原生代码函数、提示词模板、OpenAPI 规范或 MCP 来扩展它们。
多个智能体可以一起协作完成任务吗？可以——Semantic Kernel 能编排多智能体系统，让各司其职的智能体协作处理复杂的工作流，它的 Process Framework 还能把业务流程建模为结构化的工作流。
智能体的记忆或搜索数据存在哪里？它开箱即用地集成了多种向量数据库，包括 Azure AI Search、Elasticsearch 和 Chroma。
它能处理图片和音频吗，还是只能处理文字？它内置了多模态支持：智能体可以处理文字、图像和音频输入。
What are similar projects?hive, ruflo, agent-framework, owl, turnstone, harness-sdk, autogen, agentscope
Which topics is it in?multi-agent-systems, microsoft, vector-database, agent-framework, dotnet, c#, multimodal, orchestration, ollama, artificial-intelligence, sdk, multi-agent
Creator
microsoft
Size
105 MB
Stars
28.7K
Updated
CommitOct 16Releasedotnet-1.81.0Oct 06
Age
3y
Commits · 90 Days