Get Started with Microsoft Agent Framework for Python Developers

Quick Install

We recommend two common installation paths depending on your use case.

1. Development mode

If you are exploring or developing locally, install the standard framework package set:

pip install agent-framework

This installs the core and the standard integration packages. Experimental Lab modules are installed separately with pip install agent-framework-lab.

2. Selective install

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

# Core + Microsoft Foundry integration
pip install agent-framework-foundry

# Core + Microsoft Copilot Studio integration (preview package)
pip install agent-framework-copilotstudio --pre

# Core + both Microsoft Copilot Studio and Microsoft Foundry integration
pip install --pre agent-framework-copilotstudio agent-framework-foundry

This selective approach is useful when you know which integrations you need, and it is the recommended way to set up lightweight environments. Released packages such as agent-framework, agent-framework-core, and agent-framework-foundry no longer require --pre, while preview connectors such as agent-framework-copilotstudio still do.

Supported Platforms:

• Python: 3.11–3.15
• OS: Windows, macOS, Linux

Python 3.10 is no longer supported because it has reached upstream end of life. Package metadata still permits Python 3.10 installation, but CI no longer runs unit or integration tests on it and compatibility is not guaranteed. Use Python 3.11–3.15 for supported environments, subject to integration-specific limits below. Raising the installation minimum will be handled separately.

Python 3.15 limitations

pip install agent-framework resolves and imports on Python 3.15, but it does not provide exactly the same feature set as Python 3.14:

1. Setup API Keys

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

OPENAI_API_KEY=sk-...
OPENAI_MODEL=...
...
AZURE_OPENAI_API_KEY=...
AZURE_OPENAI_ENDPOINT=...
AZURE_OPENAI_MODEL=...
...
FOUNDRY_PROJECT_ENDPOINT=...
FOUNDRY_MODEL=...

For the generic OpenAI clients (OpenAIChatClient and OpenAIChatCompletionClient), configuration resolves in this order:

1. Explicit Azure inputs such as credential or azure_endpoint
2. OPENAI_API_KEY / explicit OpenAI API-key parameters
3. Azure environment fallback such as AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY

This means mixed shells default to OpenAI when OPENAI_API_KEY is present. To force Azure routing, pass an explicit Azure input such as credential=AzureCliCredential().

You can also override environment variables by explicitly passing configuration parameters to the chat client constructor:

from agent_framework.openai import OpenAIChatClient

client = OpenAIChatClient(
    api_key='',
    azure_endpoint='',
    model='',
    api_version='',
)

See the following setup guide for more information.

2. Create a Simple Agent

Create agents and invoke them directly:

import asyncio
from agent_framework import Agent
from agent_framework.openai import OpenAIChatClient

async def main():
    agent = Agent(
        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.

3. Directly Use Chat Clients (No Agent Required)

You can use the chat client classes directly for advanced workflows:

import asyncio
from agent_framework import Message
from agent_framework.openai import OpenAIChatClient

async def main():
    client = OpenAIChatClient()

    messages = [
        Message("system", ["You are a helpful assistant."]),
        Message("user", ["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())

Connection and credential lifetimes

Chat clients support async context managers for deterministic cleanup:

from agent_framework import Agent, Message
from agent_framework.openai import OpenAIChatClient

async with OpenAIChatClient() as client:
    response = await client.get_response([Message("user", ["Hello!"])])

# Alternatively, let the agent enter and exit its chat client's context.
async with Agent(client=OpenAIChatClient(), instructions="Be helpful.") as agent:
    response = await agent.run("Hello!")

Calling clients without a context manager remains supported; use await client.close() when finished. Context exit releases connections created by the connector, including on exceptions. Caller-supplied SDK clients, HTTP transports, and credentials remain caller-owned. In particular, closing an Azure SDK connection does not close the credential supplied to it. Scope credentials outside the agent or client that uses them:

from azure.identity.aio import AzureCliCredential

async with AzureCliCredential() as credential:
    async with OpenAIChatClient(
        azure_endpoint="https://YOUR-RESOURCE.openai.azure.com",
        model="YOUR-DEPLOYMENT",
        credential=credential,
    ) as client:
        response = await client.get_response([Message("user", ["Hello!"])])

Treat closing a resource-owning client as the end of its lifetime; reopening is SDK-dependent. Avoid overlapping agent contexts sharing a connector-owned connection, since exiting either agent closes that connection. To share an SDK client across agents, inject it into their chat clients and manage its lifetime outside those agent contexts. BaseChatClient provides default context methods and a no-op close(), so custom subclasses do not need to implement lifecycle methods unless they own resources. Protocol-only chat clients do not need context methods. Inherited context exit also supports existing subclasses whose close() is synchronous.

4. Build an Agent with Tools and Functions

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 Agent
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 = Agent(
        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.

5. Multi-Agent Orchestration

Coordinate multiple agents to collaborate on complex tasks using orchestration patterns:

import asyncio
from agent_framework import Agent
from agent_framework.openai import OpenAIChatClient


async def main():
    # Create specialized agents
    writer = Agent(
        client=OpenAIChatClient(),
        name="Writer",
        instructions="You are a creative content writer. Generate and refine slogans based on feedback."
    )

    reviewer = Agent(
        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, Concurrent, Group Chat, Handoff, and Magentic orchestrations, see the orchestration samples.

More Examples & Samples

• Getting Started with Agents: Basic agent creation and tool usage
• Chat Client Examples: Direct chat client usage patterns
• Foundry Integration: Microsoft Foundry integration
• Workflow Samples: Advanced multi-agent patterns

Agent Framework Documentation

• Agent Framework Repository
• Python Package Documentation
• .NET Package Documentation
• Design Documents
• Learn: Agent Framework Overview
• Learn: Quick Start
• Learn: Tutorials