Master Microsoft Semantic Kernel with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Define your first Semantic Kernel use case and success metric. Connect a foundation model and configure credentials. Attach retrieval/tools and set guardrails for execution. Run evaluation datasets to benchmark quality and latency. Deploy with monitoring, alerts, and iterative improvement loops.
💡 Quick Start: Follow these 1 steps in order to get up and running with Microsoft Semantic Kernel quickly.
No. While Azure OpenAI has the deepest integration, there are official connectors for OpenAI, Google Gemini, Hugging Face, Mistral, and Ollama. The IChatCompletionService interface lets you write custom connectors for any provider. The framework is provider-agnostic by design despite Microsoft's Azure emphasis.
If you're in a .NET-first organization or need tight Azure integration, Semantic Kernel is the clear choice. For pure Python projects, LangChain has a larger ecosystem, more integrations, and a bigger community. Semantic Kernel's Python SDK is capable but typically 2-3 months behind the C# SDK in features.
Semantic Kernel supports loading prompt templates from YAML files with metadata. Store these in version control alongside your code. Each template can specify model-specific settings for different LLM providers. The framework supports runtime template compilation with Handlebars syntax.
Yes, though it's not its primary strength. The Agent Framework (experimental) supports creating multiple agents with different personalities that can participate in group chats. For complex multi-agent orchestration, consider pairing Semantic Kernel's plugin system with a dedicated agent framework or using the Process Framework.
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Tutorial updated March 2026