Semantic Kernel: Definition and Examples

Semantic Kernel is an AI orchestration framework created by Microsoft, designed to enable developers to easily integrate language models like GPT-4, Claude, or Mistral into their existing applications. It acts as an intermediary layer between application code and LLMs, managing model calls, prompt construction, and execution of native functions.

The fundamental principle of Semantic Kernel is based on the notion of 'plugins' that combine semantic functions (parameterized prompts) and native functions (regular code in C#, Python, or Java). The kernel orchestrates these plugins to accomplish complex tasks by automatically determining which functions to call and in what order using a planner system.

One of the major strengths of Semantic Kernel is its management of contextual memory. The framework allows storing and retrieving relevant information via vector embeddings, offering applications a form of long-term memory. This enables the language model to work with enriched context without exceeding its context window limits.

Semantic Kernel is part of the broader ecosystem of agentic frameworks. It natively supports the function-calling protocol of modern LLMs, making it a particularly suitable tool for building autonomous agents capable of reasoning, planning, and acting in interaction with external systems such as databases, APIs, or files.