We recommend two common installation paths depending on your use case.
If you are exploring or developing locally, install the entire framework with all sub-packages:
pip install agent-framework --preThis installs the core and every integration package, making sure that all features are available without additional steps. The --pre flag is required while Agent Framework is in preview. This is the simplest way to get started.
If you only need specific integrations, you can install at a more granular level. This keeps dependencies lighter and focuses on what you actually plan to use. Some examples:
# Core only
# includes Azure OpenAI and OpenAI support by default
# also includes workflows and orchestrations
pip install agent-framework-core --pre
# Core + Azure AI integration
pip install agent-framework-azure-ai --pre
# Core + Microsoft Copilot Studio integration
pip install agent-framework-copilotstudio --pre
# Core + both Microsoft Copilot Studio and Azure AI integration
pip install agent-framework-microsoft agent-framework-azure-ai --preThis selective approach is useful when you know which integrations you need, and it is the recommended way to set up lightweight environments.
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=...
...
AZURE_AI_PROJECT_ENDPOINT=...
AZURE_AI_MODEL_DEPLOYMENT_NAME=...You can also override environment variables by explicitly passing configuration parameters to the chat client constructor:
from agent_framework.azure import AzureOpenAIChatClient
chat_client = AzureOpenAIChatClient(
api_key='',
endpoint='',
deployment_name='',
api_version='',
)See the following setup guide for more information.
Create agents and invoke them directly:
import asyncio
from agent_framework import ChatAgent
from agent_framework.openai import OpenAIChatClient
async def main():
agent = ChatAgent(
chat_client=OpenAIChatClient(),
instructions="""
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 5 words.
"""
)
result = await agent.run("Summarize the Three Laws of Robotics")
print(result)
asyncio.run(main())
# Output: Protect humans, obey, self-preserve, prioritized.You can use the chat client classes directly for advanced workflows:
import asyncio
from agent_framework import ChatMessage
from agent_framework.openai import OpenAIChatClient
async def main():
client = OpenAIChatClient()
messages = [
ChatMessage(role="system", text="You are a helpful assistant."),
ChatMessage(role="user", text="Write a haiku about Agent Framework.")
]
response = await client.get_response(messages)
print(response.messages[0].text)
"""
Output:
Agents work in sync,
Framework threads through each task—
Code sparks collaboration.
"""
asyncio.run(main())Enhance your agent with custom tools and function calling:
import asyncio
from typing import Annotated
from random import randint
from pydantic import Field
from agent_framework import ChatAgent
from agent_framework.openai import OpenAIChatClient
def get_weather(
location: Annotated[str, Field(description="The location to get the weather for.")],
) -> str:
"""Get the weather for a given location."""
conditions = ["sunny", "cloudy", "rainy", "stormy"]
return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
def get_menu_specials() -> str:
"""Get today's menu specials."""
return """
Special Soup: Clam Chowder
Special Salad: Cobb Salad
Special Drink: Chai Tea
"""
async def main():
agent = ChatAgent(
chat_client=OpenAIChatClient(),
instructions="You are a helpful assistant that can provide weather and restaurant information.",
tools=[get_weather, get_menu_specials]
)
response = await agent.run("What's the weather in Amsterdam and what are today's specials?")
print(response)
"""
Output:
The weather in Amsterdam is sunny with a high of 22°C. Today's specials include
Clam Chowder soup, Cobb Salad, and Chai Tea as the special drink.
"""
if __name__ == "__main__":
asyncio.run(main())You can explore additional agent samples here.
Coordinate multiple agents to collaborate on complex tasks using orchestration patterns:
import asyncio
from agent_framework import ChatAgent
from agent_framework.openai import OpenAIChatClient
async def main():
# Create specialized agents
writer = ChatAgent(
chat_client=OpenAIChatClient(),
name="Writer",
instructions="You are a creative content writer. Generate and refine slogans based on feedback."
)
reviewer = ChatAgent(
chat_client=OpenAIChatClient(),
name="Reviewer",
instructions="You are a critical reviewer. Provide detailed feedback on proposed slogans."
)
# Sequential workflow: Writer creates, Reviewer provides feedback
task = "Create a slogan for a new electric SUV that is affordable and fun to drive."
# Step 1: Writer creates initial slogan
initial_result = await writer.run(task)
print(f"Writer: {initial_result}")
# Step 2: Reviewer provides feedback
feedback_request = f"Please review this slogan: {initial_result}"
feedback = await reviewer.run(feedback_request)
print(f"Reviewer: {feedback}")
# Step 3: Writer refines based on feedback
refinement_request = f"Please refine this slogan based on the feedback: {initial_result}\nFeedback: {feedback}"
final_result = await writer.run(refinement_request)
print(f"Final Slogan: {final_result}")
# Example Output:
# Writer: "Charge Forward: Affordable Adventure Awaits!"
# Reviewer: "Good energy, but 'Charge Forward' is overused in EV marketing..."
# Final Slogan: "Power Up Your Adventure: Premium Feel, Smart Price!"
if __name__ == "__main__":
asyncio.run(main())For more advanced orchestration patterns including Sequential, GroupChat, Concurrent, Magentic, and Handoff orchestrations, see the orchestration samples.
- Getting Started with Agents: Basic agent creation and tool usage
- Chat Client Examples: Direct chat client usage patterns
- Azure AI Integration: Azure AI integration
- Workflow Samples: Advanced multi-agent patterns
- Agent Framework Repository
- Python Package Documentation
- .NET Package Documentation
- Design Documents
- Learn docs are coming soon.