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
pip install --upgrade semantic-kernel
# Optional: Add integrations
pip install --upgrade semantic-kernel[hugging_face]
pip install --upgrade semantic-kernel[all]Supported Platforms:
- Python: 3.10+
- OS: Windows, macOS, Linux
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.
Create prompt functions and invoke them via the Kernel:
import asyncio
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
from semantic_kernel.functions import 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 def main():
result = await kernel.invoke_prompt(prompt, arguments=KernelArguments(num_words=5))
print(result)
asyncio.run(main())
# Output: Protect humans, obey, self-preserve, prioritized.You can use the AI service classes directly for advanced workflows:
import asyncio
import asyncio
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAIChatPromptExecutionSettings
from semantic_kernel.contents import ChatHistory
async def main():
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())Add Python functions as plugins or Pydantic models as structured outputs;
Enhance your agent with custom tools (plugins) and structured output:
import asyncio
from typing import Annotated
from pydantic import BaseModel
from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, OpenAIChatPromptExecutionSettings
from semantic_kernel.functions import kernel_function, KernelArguments
class MenuPlugin:
@kernel_function(description="Provides a list of specials from the menu.")
def get_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.")
def get_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"
class MenuItem(BaseModel):
# Used for structured outputs
price: float
name: str
async def main():
# 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.
Coordinate a group of agents to iteratively solve a problem or refine content together:
import asyncio
from semantic_kernel.agents import ChatCompletionAgent, GroupChatOrchestration, RoundRobinGroupChatManager
from semantic_kernel.agents.runtime import InProcessRuntime
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
def get_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 def main():
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.
- 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