Semantic Kernel — Microsoft AI Agent Framework

What it is

Semantic Kernel is Microsoft's open-source AI agent framework that provides a lightweight SDK for integrating LLMs into Python, .NET, and Java applications. It uses a plugin architecture where AI capabilities (called 'skills') are composed with traditional code functions. Semantic Kernel supports multiple AI models, multi-agent orchestration, and vector database integration.

Semantic Kernel is for enterprise developers building AI-powered applications on the Microsoft stack, as well as teams that need a framework supporting multiple programming languages and model providers.

The project is actively maintained with regular releases and a growing user community. Documentation covers common use cases, and the open-source nature means you can inspect the source code, contribute fixes, and adapt the tool to your specific requirements.

How it saves time or tokens

Semantic Kernel abstracts the differences between OpenAI, Azure OpenAI, Hugging Face, and other model providers behind a unified interface. You write your agent logic once and swap models without code changes. The planner component automatically breaks complex tasks into steps, reducing the prompt engineering needed to orchestrate multi-step workflows.

How to use

1. Install the Semantic Kernel SDK for your language (pip, NuGet, or Maven).
2. Configure a kernel instance with your AI model provider and API keys.
3. Register plugins (functions) and invoke them through the kernel's planner or direct function calling.

Example

import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

kernel = sk.Kernel()
kernel.add_service(OpenAIChatCompletion(
    service_id='chat',
    ai_model_id='gpt-4',
    api_key='your-key'
))

# Define a semantic function
summarize = kernel.add_function(
    plugin_name='text',
    function_name='summarize',
    prompt='Summarize this text in 2 sentences: {{$input}}'
)

result = await kernel.invoke(summarize, input='Long document text here...')
print(result)

Related on TokRepo

• AI Tools for Agents -- Agent frameworks and orchestration tools
• Multi-Agent Frameworks -- Compare multi-agent approaches

Common pitfalls

• The Python and .NET SDKs have different API surfaces. Code examples from one language do not translate directly to another.
• The automatic planner can produce unexpected step sequences. For production use, define explicit function chains instead of relying on the planner for critical workflows.
• Vector store connectors require separate packages. The base SDK does not include database drivers for Pinecone, Weaviate, or Qdrant.

Before adopting this tool, evaluate whether it fits your team's existing workflow. Read the official documentation thoroughly, and start with a small proof-of-concept rather than a full migration. Community forums, GitHub issues, and Stack Overflow are valuable resources when you encounter edge cases not covered in the documentation.