Semantic Kernel

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

Cre

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