# Semantic Kernel — AI Orchestration SDK by Microsoft

> Build AI agents and integrate LLMs into .NET, Python, and Java apps with Microsoft's open-source AI orchestration framework.

## Install

Save as a script file and run:

# Semantic Kernel — AI Orchestration SDK by Microsoft

## Quick Use
```bash
# Python
pip install semantic-kernel
```
```python
import semantic_kernel as sk
kernel = sk.Kernel()
kernel.add_service(sk.connectors.AzureOpenAI(deployment_name="gpt-4o", endpoint="..."))
result = await kernel.invoke_prompt("Summarize: {{$input}}", input_variables={"input": text})
```

## 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/

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Author: Script Depot