# Semantic Kernel — Integrate LLMs into Enterprise Applications

> An open-source SDK from Microsoft that lets developers quickly integrate large language models into C#, Python, and Java applications with planners, plugins, and memory connectors.

## Install

Save as a script file and run:

# Semantic Kernel — Integrate LLMs into Enterprise Applications

## Quick Use
```bash
dotnet add package Microsoft.SemanticKernel
```
```csharp
using Microsoft.SemanticKernel;

var kernel = Kernel.CreateBuilder()
    .AddOpenAIChatCompletion("gpt-4o", apiKey)
    .Build();

var result = await kernel.InvokePromptAsync("Summarize: {{$input}}", new() { ["input"] = text });
Console.WriteLine(result);
```

## Introduction
Semantic Kernel is Microsoft's open-source SDK for building AI agents and integrating LLMs into applications. It provides abstractions for prompt orchestration, plugin registration, memory management, and multi-step planning so that developers can compose AI capabilities using familiar programming patterns.

## What Semantic Kernel Does
- Provides a unified interface to call OpenAI, Azure OpenAI, Hugging Face, and other LLM providers
- Enables function calling and tool use through a typed plugin system
- Supports automatic planning where the AI decomposes goals into executable steps
- Includes memory connectors for vector stores like Qdrant, Pinecone, and Azure AI Search
- Offers process and agent frameworks for multi-turn and multi-agent orchestration

## Architecture Overview
The kernel acts as a central orchestrator that connects AI services, plugins (native functions and prompt functions), and memory. Planners take a user goal and produce a sequence of plugin calls. Filters intercept requests for logging, retry, and content safety. The architecture uses dependency injection patterns familiar to .NET and enterprise Java developers.

## Self-Hosting & Configuration
- Install via NuGet (C#), pip (Python), or Maven (Java) depending on your stack
- Configure AI service endpoints through builder patterns or dependency injection
- Register plugins as classes with decorated methods or as prompt template folders
- Connect vector memory via the provided connectors or implement the memory interface
- Use the provided middleware filters for telemetry, caching, and error handling

## Key Features
- First-class support for C#, Python, and Java with idiomatic APIs in each language
- Auto function calling that lets the LLM decide which plugins to invoke
- Handlebars and Liquid prompt template engines for dynamic prompt construction
- Built-in agents framework supporting multi-agent collaboration patterns
- Enterprise-ready with OpenTelemetry tracing and responsible AI filters

## Comparison with Similar Tools
- **LangChain** — Python/JS-focused with a wider ecosystem; Semantic Kernel targets enterprise .NET and Java shops
- **Haystack** — pipeline-based approach for RAG; Semantic Kernel is broader with planning and agents
- **AutoGen** — focuses on multi-agent conversation; Semantic Kernel provides a full SDK with plugins and memory
- **Spring AI** — Java-only; Semantic Kernel covers C#, Python, and Java in one project
- **LlamaIndex** — specializes in data indexing and retrieval; Semantic Kernel is a general orchestration SDK

## FAQ
**Q: Which LLM providers does Semantic Kernel support?**
A: OpenAI, Azure OpenAI, Hugging Face, Google Gemini, Mistral, Ollama, and any OpenAI-compatible API endpoint.

**Q: Can I use Semantic Kernel without Azure?**
A: Yes. It works with any supported LLM provider and has no Azure dependency beyond the optional Azure OpenAI connector.

**Q: How does the planner work?**
A: The planner sends available plugin descriptions to the LLM, which generates a step-by-step plan that the kernel then executes by calling the appropriate functions.

**Q: Is Semantic Kernel production-ready?**
A: Yes. It is used internally at Microsoft and follows semantic versioning with stable 1.x releases for C# and Python.

## Sources
- https://github.com/microsoft/semantic-kernel
- https://learn.microsoft.com/en-us/semantic-kernel/

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Author: Script Depot