| name | maf-skill |
| description | Microsoft Agent Framework (MAF) Python reference. Use when creating, configuring, or explaining MAF Python agents, selecting the correct packages, understanding the FoundryChatClient/Agent API, managing credentials with AzureCliCredential, or setting up Azure AI Foundry projects. Covers GA v1+ patterns, package names, imports, env vars, and key rules. Subskills available: maf-update (for migrating outdated MAF samples). |
Microsoft Agent Framework — Python Reference
MAF evolves rapidly. Always consult the Microsoft Learn MCP docs before implementing or reviewing code. Never rely on cached internal knowledge of MAF APIs.
Package
pip install agent-framework
requirements.txt entry:
agent-framework
azure-identity
python-dotenv
Do not use agent-framework-azure-ai — that is the old pre-GA package name.
Canonical Imports
from agent_framework import Agent
from agent_framework.foundry import FoundryChatClient
from azure.identity import AzureCliCredential
from dotenv import load_dotenv
Agent Setup Pattern
import asyncio
import os
from agent_framework import Agent
from agent_framework.foundry import FoundryChatClient
from azure.identity import AzureCliCredential
from dotenv import load_dotenv
async def main() -> None:
load_dotenv()
client = FoundryChatClient(
project_endpoint=os.getenv("AZURE_PROJECT_ENDPOINT"),
model=os.getenv("AZURE_MODEL_DEPLOYMENT"),
credential=AzureCliCredential(),
)
agent = Agent(
client=client,
name="MyAgent",
instructions="You are a helpful assistant.",
)
result = await agent.run("Hello")
print(f"Agent: {result}")
if __name__ == "__main__":
asyncio.run(main())
Key Rules
| Rule | Detail |
|---|
| Package | agent-framework (not agent-framework-azure-ai) |
| Credential | azure.identity.AzureCliCredential (sync) — not the .aio variant |
| Context manager | Do not use async context managers (async with) for client or agent |
load_dotenv() | Must be called explicitly — MAF never auto-loads .env |
agent.run() result | Returns a plain str — no .text attribute |
| Env var prefix | Always use AZURE_ prefix: AZURE_PROJECT_ENDPOINT, AZURE_MODEL_DEPLOYMENT |
| Model default | Use gpt-4o-mini as the default model unless otherwise specified |
| Auth in production | Prefer ManagedIdentityCredential over AzureCliCredential for deployed workloads |
Environment Variables
AZURE_PROJECT_ENDPOINT=https://<project>.services.ai.azure.com/api/projects/<name>
AZURE_MODEL_DEPLOYMENT=gpt-4o-mini
Streaming
print("Agent: ", end="", flush=True)
async for chunk in agent.run("Tell me a fun fact.", stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
print()
References
