--- title: "semantic-kernel" type: "tool" slug: "microsoft-semantic-kernel" canonical_url: "https://www.graphcanon.com/tools/microsoft-semantic-kernel" github_url: "https://github.com/microsoft/semantic-kernel" homepage_url: "https://aka.ms/semantic-kernel" stars: 28280 forks: 4669 primary_language: "C#" license: "MIT" categories: ["ai-agents", "llm-frameworks"] tags: ["llm", "ai", "artificial-intelligence", "openai", "sdk"] updated_at: "2026-07-07T18:22:11.364188+00:00" --- # semantic-kernel > Integrate cutting-edge LLM technology quickly and easily into your apps Semantic Kernel is a model-agnostic SDK for building, orchestrating, and deploying AI agents and multi-agent systems with enterprise-grade reliability. It includes features like model flexibility, agent framework, multi-agent system orchestration, plugin ecosystem, and vector database support. ## Facts - Repository: https://github.com/microsoft/semantic-kernel - Homepage: https://aka.ms/semantic-kernel - Stars: 28,280 · Forks: 4,669 · Open issues: 261 · Watchers: 294 - Primary language: C# - License: MIT - Last pushed: 2026-07-07T15:25:17+00:00 ## Categories - [AI Agents](/categories/ai-agents.md) - [LLM Frameworks](/categories/llm-frameworks.md) ## Tags llm, ai, artificial-intelligence, openai, sdk ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [hermes-agent](/tools/nousresearch-hermes-agent.md) - The self-improving AI agent built by Nous Research (★ 210,880) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,659) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,019) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,298) - [dify](/tools/langgenius-dify.md) - Production-ready platform for agentic workflow development (★ 148,070) ## README (excerpt) ```text # Semantic Kernel > [!IMPORTANT] > Semantic Kernel is now [Microsoft Agent Framework](https://github.com/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](https://devblogs.microsoft.com/agent-framework/semantic-kernel-and-microsoft-agent-framework/), and try out the [Semantic Kernel migration guide](https://learn.microsoft.com/en-us/agent-framework/migration-guide/from-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](https://platform.openai.com/docs/introduction), [Azure OpenAI](https://azure.microsoft.com/en-us/products/ai-services/openai-service), [Hugging Face](https://huggingface.co/), [NVidia](https://www.nvidia.com/en-us/ai-data-science/products/nim-microservices/) 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](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search), [Elasticsearch](https://www.elastic.co/), [Chroma](https://docs.trychroma.com/docs/overview/getting-started), and more - **Multimodal Support**: Process text, vision, and audio inputs - **Local Deployment**: Run with [Ollama](https://ollama.com/), [LMStudio](https://lmstudio.ai/), or [ONNX](https://onnx.ai/) - **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:** ```bash export AZURE_OPENAI_API_KEY=AAA.... ``` **or OpenAI directly:** ```bash export OPENAI_API_KEY=sk-... ``` ### Python ```bash pip install semantic-kernel ``` ### .NET ```bash dotnet add package Microsoft.SemanticKernel dotnet add package Microsoft.SemanticKernel.Agents.Core ``` ### Java See [semantic-kernel-java build](https://github.com/microsoft/semantic-kernel-java/blob/main/BUILD.md) for instructions. ## Quickstart ### Basic Agent - Python Create a simple assistant that responds to user prompts: ```python 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 ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/microsoft-semantic-kernel`](/api/graphcanon/tools/microsoft-semantic-kernel) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_