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.
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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.
Plugins are collections of KernelFunctions — regular methods annotated with descriptions that LLMs can discover, understand, and invoke. Supports both 'semantic functions' (prompt templates) and 'native functions' (compiled code) with identical interfaces.
Use Case:
Exposing a company's existing order management API as a plugin so an AI assistant can check order status, process returns, and update shipping information.
When enabled, the kernel automatically detects when the LLM wants to call a function, executes it, and feeds the result back — handling the complete tool-use loop including parallel function calls and type conversion.
Use Case:
Building a financial assistant that automatically queries portfolio data, calculates metrics, and generates reports by calling multiple functions during a single conversation.
Request and response filters that intercept AI service calls, function invocations, and prompt rendering. Follows the middleware pattern familiar to ASP.NET developers for logging, content moderation, PII redaction, or token tracking.
Use Case:
Implementing enterprise compliance by adding a PII detection filter that scrubs sensitive data before it reaches the LLM and logs all AI interactions for regulatory review.
Unified IMemoryStore interface with implementations for Azure AI Search, Qdrant, Pinecone, ChromaDB, Redis, and PostgreSQL (pgvector). Handles embedding generation, storage, and semantic search through a single consistent API.
Use Case:
Adding semantic search to a knowledge management application where employees ask natural language questions about internal documents.
Template system supporting Handlebars and custom formats with variable injection, conditional blocks, and embedded function calls. Templates are versioned and can be loaded from files for prompt management as code.
Use Case:
Managing a library of prompt templates for different customer service scenarios, with version control and environment-specific variable injection.
Workflow orchestration for building multi-step processes with state management, event-driven transitions, and error handling. Supports human-in-the-loop steps, parallel execution, and conditional branching.
Use Case:
Creating an automated document review pipeline where documents flow through AI analysis, human review, and approval gates with state tracked throughout.
Free
Azure usage costs
Variable by provider
Azure pricing
Ready to get started with Microsoft Semantic Kernel?
View Pricing Options →Enterprise applications requiring AI integration with existing business systems and databases
Multi-step AI workflows that combine reasoning, planning, and tool execution
Conversational AI applications with persistent memory and context across sessions
AI-powered business process automation with human-in-the-loop capabilities
Development of custom AI agents that need to access multiple external APIs and services
Applications requiring provider flexibility to switch between different LLM models
Microsoft Semantic Kernel works with these platforms and services:
We believe in transparent reviews. Here's what Microsoft Semantic Kernel doesn't handle well:
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.
Redesigned function calling with automatic parameter validation and retry logic.
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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.
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