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• License: MIT License
• Author: Microsoft
• Requires: Python >=3.10
• Provides-Extra: anthropic , autogen , aws , azure , chroma , copilotstudio , faiss , google , hugging-face , mcp , milvus , mistralai , mongo , notebooks , ollama , onnx , oracledb , pandas , pinecone , postgres , qdrant , realtime , redis , sql , usearch , weaviate

Classifiers

• Development Status
  • 5 - Production/Stable
• Framework
  • Pydantic :: 2
• Intended Audience
  • Developers
• License
  • OSI Approved :: MIT License
• Programming Language
  • Python :: 3
  • Python :: 3.10
  • Python :: 3.11
  • Python :: 3.12
• Typing
  • Typed

Get Started with Semantic Kernel Python

[!IMPORTANT] Semantic Kernel is now Microsoft Agent Framework! Microsoft Agent Framework (MAF) is the enterprise‑ready successor to Semantic Kernel. Microsoft Agent Framework is now available at version 1.0 as a production-ready release: stable APIs, and a commitment to long-term support. Whether you're building a single assistant or orchestrating a fleet of specialized agents, Microsoft Agent Framework 1.0 gives you enterprise-grade multi-agent orchestration, multi-provider model support, and cross-runtime interoperability via A2A and MCP.

Learn more about Semantic Kernel and Agent Framework here: Semantic Kernel and Microsoft Agent Framework on the Agent Framework blog, and try out the Semantic Kernel migration guide.

Highlights

• Flexible Agent Framework: build, orchestrate, and deploy AI agents and multi-agent systems
• Multi-Agent Systems: Model workflows and collaboration between AI specialists
• Plugin Ecosystem: Extend with Python, OpenAPI, Model Context Protocol (MCP), and more
• LLM Support: OpenAI, Azure OpenAI, Hugging Face, Mistral, Google AI, ONNX, Ollama, NVIDIA NIM, and others
• Vector DB Support: Azure AI Search, Elasticsearch, Chroma, and more
• Process Framework: Build structured business processes with workflow modeling
• Multimodal: Text, vision, audio

Quick Install

pipinstall--upgradesemantic-kernel
# Optional: Add integrations
pipinstall--upgradesemantic-kernel[hugging_face]
pipinstall--upgradesemantic-kernel[all]

Supported Platforms:

• Python: 3.10+
• OS: Windows, macOS, Linux

1. Setup API Keys

Set as environment variables, or create a .env file at your project root:

OPENAI_API_KEY=sk-...
OPENAI_CHAT_MODEL_ID=...
...
AZURE_OPENAI_API_KEY=...
AZURE_OPENAI_ENDPOINT=...
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=...
...

You can also override environment variables by explicitly passing configuration parameters to the AI service constructor:

chat_service = AzureChatCompletion(
 api_key=...,
 endpoint=...,
 deployment_name=...,
 api_version=...,
)

See the following setup guide for more information.

2. Use the Kernel for Prompt Engineering

Create prompt functions and invoke them via the Kernel:

importasyncio
fromsemantic_kernelimport Kernel
fromsemantic_kernel.connectors.ai.open_aiimport OpenAIChatCompletion
fromsemantic_kernel.functionsimport KernelArguments

kernel = Kernel()
kernel.add_service(OpenAIChatCompletion())

prompt = """
1) A robot may not injure a human being...
2) A robot must obey orders given it by human beings...
3) A robot must protect its own existence...

Give me the TLDR in exactly {{$num_words}} words."""


async defmain():
 result = await kernel.invoke_prompt(prompt, arguments=KernelArguments(num_words=5))
 print(result)


asyncio.run(main())
# Output: Protect humans, obey, self-preserve, prioritized.

3. Directly Use AI Services (No Kernel Required)

You can use the AI service classes directly for advanced workflows:

importasyncio
importasyncio

fromsemantic_kernel.connectors.ai.open_aiimport OpenAIChatCompletion, OpenAIChatPromptExecutionSettings
fromsemantic_kernel.contentsimport ChatHistory


async defmain():
 service = OpenAIChatCompletion()
 settings = OpenAIChatPromptExecutionSettings()

 chat_history = ChatHistory(system_message="You are a helpful assistant.")
 chat_history.add_user_message("Write a haiku about Semantic Kernel.")
 response = await service.get_chat_message_content(chat_history=chat_history, settings=settings)
 print(response.content)

"""
 Output:

 Thoughts weave through context, 
 Semantic threads interlace— 
 Kernel sparks meaning.
 """


asyncio.run(main())

4. Build an Agent with Plugins and Tools

Add Python functions as plugins or Pydantic models as structured outputs;

Enhance your agent with custom tools (plugins) and structured output:

importasyncio
fromtypingimport Annotated
frompydanticimport BaseModel
fromsemantic_kernel.agentsimport ChatCompletionAgent
fromsemantic_kernel.connectors.ai.open_aiimport AzureChatCompletion, OpenAIChatPromptExecutionSettings
fromsemantic_kernel.functionsimport kernel_function, KernelArguments


classMenuPlugin:
 @kernel_function(description="Provides a list of specials from the menu.")
 defget_specials(self) -> Annotated[str, "Returns the specials from the menu."]:
 return """
 Special Soup: Clam Chowder
 Special Salad: Cobb Salad
 Special Drink: Chai Tea
 """

 @kernel_function(description="Provides the price of the requested menu item.")
 defget_item_price(
 self, menu_item: Annotated[str, "The name of the menu item."]
 ) -> Annotated[str, "Returns the price of the menu item."]:
 return "$9.99"


classMenuItem(BaseModel):
 # Used for structured outputs
 price: float
 name: str


async defmain():
 # Configure structured outputs format
 settings = OpenAIChatPromptExecutionSettings()
 settings.response_format = MenuItem

 # Create agent with plugin and settings
 agent = ChatCompletionAgent(
 service=AzureChatCompletion(),
 name="SK-Assistant",
 instructions="You are a helpful assistant.",
 plugins=[MenuPlugin()],
 arguments=KernelArguments(settings),
 )

 response = await agent.get_response("What is the price of the soup special?")
 print(response.content)

 # Output:
 # The price of the Clam Chowder, which is the soup special, is $9.99.


asyncio.run(main())

You can explore additional getting started agent samples here.

5. Multi-Agent Orchestration

Coordinate a group of agents to iteratively solve a problem or refine content together:

importasyncio
fromsemantic_kernel.agentsimport ChatCompletionAgent, GroupChatOrchestration, RoundRobinGroupChatManager
fromsemantic_kernel.agents.runtimeimport InProcessRuntime
fromsemantic_kernel.connectors.ai.open_aiimport AzureChatCompletion


defget_agents():
 return [
 ChatCompletionAgent(
 name="Writer",
 instructions="You are a creative content writer. Generate and refine slogans based on feedback.",
 service=AzureChatCompletion(),
 ),
 ChatCompletionAgent(
 name="Reviewer",
 instructions="You are a critical reviewer. Provide detailed feedback on proposed slogans.",
 service=AzureChatCompletion(),
 ),
 ]


async defmain():
 agents = get_agents()
 group_chat = GroupChatOrchestration(
 members=agents,
 manager=RoundRobinGroupChatManager(max_rounds=5),
 )
 runtime = InProcessRuntime()
 runtime.start()
 result = await group_chat.invoke(
 task="Create a slogan for a new electric SUV that is affordable and fun to drive.",
 runtime=runtime,
 )
 value = await result.get()
 print(f"Final Slogan: {value}")

 # Example Output:
 # Final Slogan: "Feel the Charge: Adventure Meets Affordability in Your New Electric SUV!"

 await runtime.stop_when_idle()


if __name__ == "__main__":
 asyncio.run(main())

For orchestration-focused examples, see these orchestration samples.

More Examples & Notebooks

• Getting Started with Agents: Practical agent orchestration and tool use
• Getting Started with Processes: Modeling structured workflows with the Process framework
• Concept Samples: Advanced scenarios, integrations, and SK patterns
• Getting Started Notebooks: Interactive Python notebooks for rapid experimentation

Semantic Kernel Documentation

• Getting Started with Semantic Kernel Python
• Agent Framework Guide
• Process Framework Guide

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