Microsoft's Semantic Kernel provides enterprise-grade AI orchestration with native .NET/Java/Python support, Azure integration, and the security and compliance features large organizations require.
SDK for building AI agents with planners, memory, and connectors. - Enhanced AI-powered platform providing advanced capabilities for modern development and business workflows. Features comprehensive tooling, integrations, and scalable architecture designed for professional teams and enterprise environments.
Microsoft's toolkit that lets your apps use AI to plan, remember, and take action — like giving your software a brain.
Semantic Kernel is Microsoft's SDK for integrating LLMs into conventional enterprise applications, designed to bridge the gap between traditional software engineering and AI capabilities. Available in C#/.NET (primary), Python, and Java, it's built for developers who want to add AI features to existing applications rather than build AI-native systems from scratch.
The core abstraction is the Kernel — a lightweight container that manages AI services (chat completion, embeddings, text-to-image), plugins (collections of functions), and filters (middleware for intercepting AI calls). Plugins are the key differentiator: they're simply classes with annotated methods that the AI can discover and invoke. This means your existing business logic can be exposed to an LLM with minimal refactoring — add a KernelFunction decorator and the AI can call your C# methods.
Semantic Kernel supports both 'semantic functions' (prompt templates) and 'native functions' (regular code), treating them identically within the plugin system. The Planner component can automatically chain multiple functions to accomplish complex goals, though manual orchestration using the newer Handlebars and Stepwise planners offers more predictable results.
The framework integrates deeply with Azure OpenAI Service but supports OpenAI, Hugging Face, and other providers through connectors. The memory system includes vector store abstractions with implementations for Azure AI Search, Qdrant, ChromaDB, and others. Process Framework (experimental) adds workflow orchestration capabilities.
Semantic Kernel's enterprise focus shows in its design: dependency injection support, middleware pipeline for request/response filtering, telemetry integration with OpenTelemetry, and structured logging. It follows .NET conventions that enterprise developers recognize.
The honest take: Semantic Kernel is the right choice for .NET shops adding AI to existing applications. It's not trying to be a multi-agent framework or a research tool — it's a practical SDK for making LLMs accessible within enterprise software architecture. Python developers with no .NET requirements will find LangChain or LlamaIndex more natural, but for C# teams, Semantic Kernel is unmatched in ecosystem fit.
Was this helpful?
Semantic Kernel is Microsoft's enterprise-grade AI orchestration SDK with excellent .NET and Java support. It's the natural choice for Microsoft-stack teams but has less community momentum than Python-first alternatives.
Contact for pricing
Contact for pricing
Contact for pricing
Contact for pricing
Ready to get started with Microsoft Semantic Kernel?
View Pricing Options →Microsoft Semantic Kernel works with these platforms and services:
We believe in transparent reviews. Here's what Microsoft Semantic Kernel doesn't handle well:
Redesigned function calling with automatic parameter validation and retry logic.
Weekly insights on the latest AI tools, features, and trends delivered to your inbox.
In 2026, Semantic Kernel expanded its agent framework with multi-agent orchestration patterns, added native support for Azure AI Agent Service, and introduced process framework for long-running business workflows with step-based execution and event-driven coordination.
AI Agent Builders
Open-source Python framework that orchestrates autonomous AI agents collaborating as teams to accomplish complex workflows. Define agents with specific roles and goals, then organize them into crews that execute sequential or parallel tasks. Agents delegate work, share context, and complete multi-step processes like market research, content creation, and data analysis. Supports 100+ LLM providers through LiteLLM integration and includes memory systems for agent learning. Features 48K+ GitHub stars with active community.
Multi-Agent Builders
Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.
AI Agent Builders
Graph-based workflow orchestration framework for building reliable, production-ready AI agents with deterministic state machines, human-in-the-loop capabilities, and comprehensive observability through LangSmith integration.
AI Agent Builders
Production-ready Python framework for building RAG pipelines, document search systems, and AI agent applications. Build composable, type-safe NLP solutions with enterprise-grade retrieval and generation capabilities.
No reviews yet. Be the first to share your experience!
Get started with Microsoft Semantic Kernel and see if it's the right fit for your needs.
Get Started →Take our 60-second quiz to get personalized tool recommendations
Find Your Perfect AI Stack →Explore 20 ready-to-deploy AI agent templates for sales, support, dev, research, and operations.
Browse Agent Templates →