agent-framework-core 1.0.0b251211

Microsoft Agent Framework for building AI Agents with Python. This is the core package that has all the core abstractions and implementations.

Get Started with Microsoft Agent Framework

Highlights

• Flexible Agent Framework: build, orchestrate, and deploy AI agents and multi-agent systems
• Multi-Agent Orchestration: Group chat, sequential, concurrent, and handoff patterns
• Plugin Ecosystem: Extend with native functions, OpenAPI, Model Context Protocol (MCP), and more
• LLM Support: OpenAI, Azure OpenAI, Azure AI, and more
• Runtime Support: In-process and distributed agent execution
• Multimodal: Text, vision, and function calling
• Cross-Platform: .NET and Python implementations

Quick Install

pip install agent-framework-core --pre
# Optional: Add Azure AI integration
pip install agent-framework-azure-ai --pre

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=...
OPENAI_RESPONSES_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.

2. Create a Simple Agent

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.

3. Directly Use Chat Clients (No Agent Required)

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

import asyncio
from agent_framework.openai import OpenAIChatClient
from agent_framework import ChatMessage, Role

async def main():
    client = OpenAIChatClient()

    messages = [
        ChatMessage(role=Role.SYSTEM, text="You are a helpful assistant."),
        ChatMessage(role=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())

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 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.

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 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())

Note: GroupChat, Sequential, and Concurrent orchestrations are available today. See examples in:

• python/samples/getting_started/workflows/orchestration/
• group_chat_simple_selector.py
• group_chat_prompt_based_manager.py

More Examples & 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
• .NET Workflows Samples: Advanced multi-agent patterns (.NET)

Agent Framework Documentation

• Agent Framework Repository
• Python Package Documentation
• .NET Package Documentation
• Design Documents
• Learn Documentation