What is Semantic Kernel
Semantic Kernel is an open-source AI SDK that enables developers to build AI applications by integrating large language models with external knowledge sources and tools. It provides a flexible framework to orchestrate AI prompts, memory, and plugins for complex AI workflows.Semantic Kernel is an AI software development kit (SDK) that enables developers to build AI applications by connecting large language models with external data and tools.How it works
Semantic Kernel acts as a middleware framework that connects large language models (LLMs) with external resources like databases, APIs, and user memory. It orchestrates prompt templates, memory management, and plugin calls to create complex AI workflows. Think of it as a conductor that coordinates AI model calls and external tools to produce intelligent, context-aware responses.
It uses a modular design where developers define skills (reusable prompt templates or functions) and memory (context storage) that the kernel manages. When an AI request is made, the kernel dynamically composes prompts, retrieves relevant context, and invokes external plugins if needed, then returns the AI-generated output.
Concrete example
This Python example shows how to initialize Semantic Kernel with an OpenAI-compatible LLM, add a simple prompt skill, and invoke it to generate a response.
import os
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
# Initialize kernel
kernel = sk.Kernel()
# Add OpenAI chat completion service
kernel.add_service(OpenAIChatCompletion(
service_id="chat",
api_key=os.environ["OPENAI_API_KEY"],
ai_model_id="gpt-4o-mini"
))
# Define a simple prompt skill
prompt = "Write a short poem about spring."
# Invoke the skill
response = kernel.run("chat", prompt)
print(response)A gentle breeze whispers through the trees, Spring awakens with vibrant ease. Flowers bloom, colors bright, Nature dances in warm sunlight.
When to use it
Use Semantic Kernel when you need to build AI applications that require:
- Integration of
large language modelswith external data sources or APIs. - Context management and memory to maintain stateful conversations or workflows.
- Composable AI skills and plugins for modular, reusable AI components.
It is not ideal if you only need simple one-off LLM calls without external integration or memory management.
Key terms
| Term | Definition |
|---|---|
| Semantic Kernel | An AI SDK to build AI apps by connecting LLMs with external tools and memory. |
| Kernel | The core orchestrator managing AI calls, memory, and plugins. |
| Skill | Reusable prompt templates or functions that define AI behaviors. |
| Memory | Context storage to maintain state across AI interactions. |
| Plugin | External service or API integrated into the AI workflow. |
Key Takeaways
-
Semantic Kernelenables modular AI app development by connecting LLMs with external data and tools. - It manages context and memory to support stateful, complex AI workflows.
- Use it when your AI app requires integration beyond simple prompt calls.
- The SDK supports multiple LLM providers via pluggable services.
- Skills and plugins make AI behaviors reusable and composable.