semantic-kernel
microsoft/semantic-kernel
Integrate cutting-edge LLM technology quickly and easily into your apps
Integrate cutting-edge LLM technology quickly and easily into your apps
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Install
git clone https://github.com/microsoft/semantic-kernelREADME
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 Se