Semantic Kernel — Integrate LLMs into Enterprise Applications

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/