Semantic Kernel — AI Orchestration SDK by Microsoft

Introduction

Semantic Kernel is an open-source SDK from Microsoft that lets developers integrate large language models into applications written in C#, Python, or Java. It provides a lightweight orchestration layer that connects prompts, native code functions, and external services into composable AI pipelines.

What Semantic Kernel Does

• Orchestrates calls to OpenAI, Azure OpenAI, Hugging Face, and other LLM providers through a unified connector interface
• Defines reusable prompt templates with variable substitution and prompt engineering helpers
• Lets you register native functions alongside AI functions so the model can call your own code
• Supports automatic function calling (tool use) where the model decides which plugin to invoke
• Provides planner components that decompose complex goals into multi-step execution plans

Architecture Overview

Semantic Kernel is built around a lightweight Kernel object that acts as a dependency-injection container for AI services, plugins, and memory connectors. Plugins bundle prompt functions and native functions under a single namespace. When a user request arrives, the kernel resolves the appropriate AI service, renders the prompt template, sends it to the model, and optionally routes tool-call responses back through registered functions. Memory connectors integrate vector stores for RAG scenarios.

Self-Hosting & Configuration

• Install via NuGet (Microsoft.SemanticKernel), pip (semantic-kernel), or Maven for Java
• Configure AI service credentials through environment variables or builder methods
• Add plugins by decorating methods with @kernel_function (Python) or [KernelFunction] (C#)
• Connect vector databases like Azure AI Search, Qdrant, or Chroma for retrieval-augmented generation
• Deploy as part of any .NET, Python, or Java application; no separate server process required

Key Features

• Multi-language support across C#, Python, and Java with consistent API design
• Built-in automatic function calling that maps model tool-use requests to your code
• Prompt template engine with Handlebars and Jinja2 syntax support
• Telemetry integration via OpenTelemetry for tracing AI calls in production
• Process framework for building long-running, stateful AI workflows with step-based orchestration

Comparison with Similar Tools

• LangChain — broader ecosystem with more integrations but heavier; Semantic Kernel is more tightly integrated with .NET and Azure
• LlamaIndex — focused on retrieval and indexing; Semantic Kernel emphasizes orchestration and function calling
• Haystack — pipeline-centric approach; Semantic Kernel uses a kernel-plugin model
• AutoGen — optimized for multi-agent conversations; Semantic Kernel targets single-agent tool orchestration
• Spring AI — Java-native alternative; Semantic Kernel covers C#, Python, and Java

FAQ

Q: Does Semantic Kernel require Azure? A: No. It works with OpenAI, Hugging Face, Ollama, and any OpenAI-compatible endpoint. Azure is optional.

Q: Can I use it in production? A: Yes. Microsoft uses Semantic Kernel internally in Copilot products. It follows semver and has stable releases.

Q: How does function calling work? A: You register native functions as plugins. When the model returns a tool-call response, the kernel automatically invokes the matching function and feeds the result back.

Q: Is there a visual planner or UI? A: No built-in UI. The planner is a code-level component. Community tools like Kernel Memory add higher-level abstractions.

Sources

• https://github.com/microsoft/semantic-kernel
• https://learn.microsoft.com/en-us/semantic-kernel/overview/