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@@ -0,0 +1,261 @@
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# Get Started with Microsoft Agent Framework for Python Developers
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## Quick Install
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We recommend two common installation paths depending on your use case.
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### 1. Development mode
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If you are exploring or developing locally, install the entire framework with all sub-packages:
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```bash
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pip install agent-framework
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```
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This installs the core and every integration package, making sure that all features are available without additional steps. This is the simplest way to get started.
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### 2. Selective install
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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:
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```bash
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# Core only
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# includes Azure OpenAI and OpenAI support by default
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# also includes workflows and orchestrations
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pip install agent-framework-core
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# Core + Azure AI Foundry integration
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pip install agent-framework-foundry
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# Core + Microsoft Copilot Studio integration (preview package)
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pip install agent-framework-copilotstudio --pre
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# Core + both Microsoft Copilot Studio and Azure AI Foundry integration
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pip install --pre agent-framework-copilotstudio agent-framework-foundry
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```
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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.
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Supported Platforms:
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- Python: 3.10+
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- OS: Windows, macOS, Linux
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## 1. Setup API Keys
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Set as environment variables, or create a .env file at your project root:
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```bash
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OPENAI_API_KEY=sk-...
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OPENAI_MODEL=...
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...
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AZURE_OPENAI_API_KEY=...
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AZURE_OPENAI_ENDPOINT=...
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AZURE_OPENAI_MODEL=...
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...
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FOUNDRY_PROJECT_ENDPOINT=...
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FOUNDRY_MODEL=...
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```
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For the generic OpenAI clients (`OpenAIChatClient` and `OpenAIChatCompletionClient`), configuration
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resolves in this order:
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1. Explicit Azure inputs such as `credential` or `azure_endpoint`
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2. `OPENAI_API_KEY` / explicit OpenAI API-key parameters
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3. Azure environment fallback such as `AZURE_OPENAI_ENDPOINT` and `AZURE_OPENAI_API_KEY`
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This means mixed shells default to OpenAI when `OPENAI_API_KEY` is present. To force Azure routing,
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pass an explicit Azure input such as `credential=AzureCliCredential()`.
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You can also override environment variables by explicitly passing configuration parameters to the chat client constructor:
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```python
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from agent_framework.openai import OpenAIChatClient
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client = OpenAIChatClient(
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api_key='',
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azure_endpoint='',
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model='',
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api_version='',
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)
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```
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See the following [setup guide](samples/01-get-started) for more information.
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## 2. Create a Simple Agent
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Create agents and invoke them directly:
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```python
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import asyncio
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from agent_framework import Agent
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from agent_framework.openai import OpenAIChatClient
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async def main():
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agent = Agent(
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client=OpenAIChatClient(),
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instructions="""
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1) A robot may not injure a human being...
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2) A robot must obey orders given it by human beings...
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3) A robot must protect its own existence...
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Give me the TLDR in exactly 5 words.
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"""
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)
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result = await agent.run("Summarize the Three Laws of Robotics")
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print(result)
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asyncio.run(main())
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# Output: Protect humans, obey, self-preserve, prioritized.
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```
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## 3. Directly Use Chat Clients (No Agent Required)
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You can use the chat client classes directly for advanced workflows:
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```python
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import asyncio
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from agent_framework import Message
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from agent_framework.openai import OpenAIChatClient
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async def main():
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client = OpenAIChatClient()
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messages = [
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Message("system", ["You are a helpful assistant."]),
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Message("user", ["Write a haiku about Agent Framework."])
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]
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response = await client.get_response(messages)
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print(response.messages[0].text)
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"""
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Output:
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Agents work in sync,
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Framework threads through each task—
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Code sparks collaboration.
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"""
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asyncio.run(main())
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```
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## 4. Build an Agent with Tools and Functions
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Enhance your agent with custom tools and function calling:
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```python
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import asyncio
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from typing import Annotated
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from random import randint
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from pydantic import Field
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from agent_framework import Agent
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from agent_framework.openai import OpenAIChatClient
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def get_weather(
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location: Annotated[str, Field(description="The location to get the weather for.")],
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) -> str:
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"""Get the weather for a given location."""
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conditions = ["sunny", "cloudy", "rainy", "stormy"]
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return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
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def get_menu_specials() -> str:
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"""Get today's menu specials."""
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return """
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Special Soup: Clam Chowder
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Special Salad: Cobb Salad
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Special Drink: Chai Tea
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"""
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async def main():
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agent = Agent(
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client=OpenAIChatClient(),
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instructions="You are a helpful assistant that can provide weather and restaurant information.",
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tools=[get_weather, get_menu_specials]
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)
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response = await agent.run("What's the weather in Amsterdam and what are today's specials?")
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print(response)
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"""
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Output:
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The weather in Amsterdam is sunny with a high of 22°C. Today's specials include
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Clam Chowder soup, Cobb Salad, and Chai Tea as the special drink.
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"""
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if __name__ == "__main__":
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asyncio.run(main())
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```
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You can explore additional agent samples [here](samples/02-agents).
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## 5. Multi-Agent Orchestration
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Coordinate multiple agents to collaborate on complex tasks using orchestration patterns:
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```python
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import asyncio
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from agent_framework import Agent
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from agent_framework.openai import OpenAIChatClient
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async def main():
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# Create specialized agents
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writer = Agent(
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client=OpenAIChatClient(),
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name="Writer",
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instructions="You are a creative content writer. Generate and refine slogans based on feedback."
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)
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reviewer = Agent(
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client=OpenAIChatClient(),
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name="Reviewer",
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instructions="You are a critical reviewer. Provide detailed feedback on proposed slogans."
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)
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# Sequential workflow: Writer creates, Reviewer provides feedback
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task = "Create a slogan for a new electric SUV that is affordable and fun to drive."
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# Step 1: Writer creates initial slogan
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initial_result = await writer.run(task)
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print(f"Writer: {initial_result}")
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# Step 2: Reviewer provides feedback
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feedback_request = f"Please review this slogan: {initial_result}"
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feedback = await reviewer.run(feedback_request)
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print(f"Reviewer: {feedback}")
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# Step 3: Writer refines based on feedback
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refinement_request = f"Please refine this slogan based on the feedback: {initial_result}\nFeedback: {feedback}"
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final_result = await writer.run(refinement_request)
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print(f"Final Slogan: {final_result}")
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# Example Output:
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# Writer: "Charge Forward: Affordable Adventure Awaits!"
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# Reviewer: "Good energy, but 'Charge Forward' is overused in EV marketing..."
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# Final Slogan: "Power Up Your Adventure: Premium Feel, Smart Price!"
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if __name__ == "__main__":
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asyncio.run(main())
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```
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For more advanced orchestration patterns including Sequential, Concurrent, Group Chat, Handoff, and Magentic orchestrations, see the [orchestration samples](samples/03-workflows/orchestrations).
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## More Examples & Samples
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- [Getting Started with Agents](samples/02-agents): Basic agent creation and tool usage
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- [Chat Client Examples](samples/02-agents/chat_client): Direct chat client usage patterns
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- [Foundry Integration](https://github.com/microsoft/agent-framework/tree/main/python/packages/foundry): Microsoft Foundry integration
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- [Workflow Samples](samples/03-workflows): Advanced multi-agent patterns
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## Agent Framework Documentation
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- [Agent Framework Repository](https://github.com/microsoft/agent-framework)
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- [Python Package Documentation](https://github.com/microsoft/agent-framework/tree/main/python)
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- [.NET Package Documentation](https://github.com/microsoft/agent-framework/tree/main/dotnet)
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- [Design Documents](https://github.com/microsoft/agent-framework/tree/main/docs/design)
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- Learn docs are coming soon.
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