What is Semantic Kernel Semantic Kernel is a model-agnostic SDK open-sourced by Microsoft, with the goal of enabling developers to quickly and conveniently integrate cutting-edge LLM technology into applications. It is used to build, orchestrate, and deploy AI agents and multi-agent systems, and is suitable for both simple chatbots and complex multi-agent workflows, with an emphasis on enterprise-grade reliability and flexibility.
Important note: the project team has clearly stated that Semantic Kernel has now been replaced by Microsoft Agent Framework (MAF). MAF is the enterprise-grade successor and has now released a 1.0 production-ready version, with stable APIs and a long-term support commitment. MAF 1.0 provides enterprise-grade multi-agent orchestration, multi-provider model support, and cross-runtime interoperability through A2A and MCP. The official Agent Framework blog introduced content related to Semantic Kernel and Agent Framework, and provides a Semantic Kernel migration guide.
Main capabilities
- Model flexibility: can connect to any LLM, with built-in support for OpenAI, Azure OpenAI, Hugging Face, NVidia, etc.
- Agent framework: build modular AI agents with access to tools/plugins, memory, and planning capabilities.
- Multi-agent systems: orchestrate complex workflows through collaborating specialized agents.
- Plugin ecosystem: can be extended through native code functions, prompt templates, OpenAPI specifications, or Model Context Protocol (MCP).
- Vector database support: seamless integration with Azure AI Search, Elasticsearch, Chroma, etc.
- Multimodal support: can handle text, vision, and audio input.
- Local deployment: can run using Ollama, LMStudio, or ONNX.
- Process Framework: model complex business processes as structured workflows.
- Enterprise-ready: built for observability, security, and stable APIs.
System requirements
- .NET: .NET 10.0+
- Operating system support: Windows,macOS, Linux
- Model flexibility: can connect to any LLM, with built-in support for OpenAI, Azure OpenAI, Hugging Face, NVidia, etc.
- Agent framework: build modular AI agents with access to tools/plugins, memory, and planning capabilities
- Multi-agent systems: orchestrate complex workflows completed by collaborating specialized agents
- Plugin ecosystem: extensible through native code functions, prompt templates, OpenAPI specifications, or Model Context Protocol (MCP)
- Vector database support: seamless integration with Azure AI Search, Elasticsearch, Chroma, etc.
- Multimodal support: handle text, vision, and audio input
- Local deployment: can run with Ollama, LMStudio, or ONNX
- Process framework: model complex business processes as structured workflows
- Enterprise-ready: built for observability, security, and stable APIs
How to use (types of examples provided in the material) The material shows the topics of several code examples, including: Basic Agent - Python (basic agent, Python version); Basic Agent - .NET (basic agent, .NET version); Agent with Plugins - Python (agent with plugins, Python version); Agent with Plugin - .NET (agent with plugin, .NET version); Multi-Agent System - Python (multi-agent system, Python version). There are also instructions on setting environment variables for AI services before use, as well as tips for using OpenAI directly, and a pointer to the semantic-kernel-java build instructions. Because the code snippets in the original material are omitted or not provided in full, the specific code content and installation commands are not yet provided.
Use cases
- Build simple chatbots or complex multi-agent workflows.
- Applications that need to connect to multiple LLM providers and want to remain model-agnostic.
- Agent systems that need to extend capabilities through plugins, OpenAPI, and MCP.
- Projects that need vector database integration, multimodal input handling, or local deployment (Ollama/LMStudio/ONNX).
- Enterprises that need to model complex business processes as structured workflowsApplications.
Limitations and notes
- It has been officially announced that Semantic Kernel is being replaced by Microsoft Agent Framework (MAF); new projects may be better suited to adopting MAF directly, and a migration guide is provided.
- The .NET version requires .NET 10.0+.
- The material does not provide specific pricing or fee information, so it is not yet provided.
- The material does not provide complete code examples or installation command details, so the related code content is not yet provided.
Community and contributions The project welcomes community contributions and suggestions. You can participate through discussions in the GitHub repository; bug reports and fixes are welcome. For new features, components, or extensions, it is recommended to open an issue for discussion before submitting a PR. You can read the documentation, learn how to contribute, ask questions in GitHub discussions or the Discord community, participate in regular office hours and community events, and follow the team blog. The project adopts the Microsoft Open Source Code of Conduct, uses the MIT License, and the copyright belongs to Microsoft Corporation.
