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Semantic Kernel project preview
Developer ToolsAlternative to AutoGenAlternative to CrewAIAlternative to LangChain

Semantic Kernel

Integrate cutting-edge LLM technology quickly and easily into C#, Python, and Java apps.

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LicenseMIT
GitHub stars21.5k
Last commit1 weeks ago
Tags6 topics
Python AiMicrosoft LlmPlugin ConnectorsArtificial IntelligenceEnterprise Ai SdkCsharp Ai
Overview

Why consider Semantic Kernel?

Semantic Kernel is Microsoft’s enterprise SDK that integrates LLMs (OpenAI, Azure OpenAI, Hugging Face) into conventional programming languages (C#, Python, Java). It combines prompt engineering, semantic functions, vector memory connectors, and plugin architectures.

Guided learning

Learn Semantic Kernel by building

Practical setup notes, real use cases, and copy-ready examples in one focused guide.

1 min read 2 sections
In this guide2 sections

Overview of Semantic Kernel

Semantic Kernel connects AI plugins, planner orchestrators, and enterprise databases into native .NET and Python services.

Installation

bash
1# .NET
2dotnet add package Microsoft.SemanticKernel
3
4# Python
5pip install semantic-kernel

Semantic Kernel is licensed under the permissive MIT License.

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Semantic Kernel FAQs

Semantic Kernel is listed as a Developer Tools tool on TiloBox. Review the overview, features, and official documentation on this page to decide whether it solves your specific workflow.

Start with the project's GitHub repository and official website for supported installation and deployment instructions. Test the setup with representative data or a small project before rolling it out more widely.

Semantic Kernel is listed under the MIT license. Read the complete license text and the project's notices before using, modifying, or distributing the software.

Production readiness depends on your requirements. Review maintenance activity, security practices, documentation, backup and upgrade procedures, and compatibility with your stack; then validate it in a non-production environment.

Semantic Kernel is listed as an alternative to AutoGen. Compare the core workflow, deployment model, integrations, and licensing against your must-have requirements before switching.