m365-agents-py
oleh microsoft
Bangun agen enterprise untuk Microsoft 365, Teams, dan Copilot Studio menggunakan Microsoft Agents SDK dengan hosting aiohttp, perutean AgentApplication, respons streaming, dan autentikasi berbasis MSAL.
npx skills add https://github.com/microsoft/agent-skills --skill m365-agents-pyMicrosoft 365 Agents SDK (Python)
Build enterprise agents for Microsoft 365, Teams, and Copilot Studio using the Microsoft Agents SDK with aiohttp hosting, AgentApplication routing, streaming responses, and MSAL-based authentication.
Before implementation
- Use the microsoft-docs MCP to verify the latest API signatures for AgentApplication, start_agent_process, and authentication options.
- Confirm package versions on PyPI for the microsoft-agents-* packages you plan to use.
Important Notice - Import Changes
⚠️ Breaking Change: Recent updates have changed the Python import structure from
microsoft.agentstomicrosoft_agents(using underscores instead of dots).
Installation
pip install microsoft-agents-hosting-core
pip install microsoft-agents-hosting-aiohttp
pip install microsoft-agents-activity
pip install microsoft-agents-authentication-msal
pip install microsoft-agents-copilotstudio-client
pip install python-dotenv aiohttp
Environment Variables (.env)
CONNECTIONS__SERVICE_CONNECTION__SETTINGS__CLIENTID=<client-id>
CONNECTIONS__SERVICE_CONNECTION__SETTINGS__CLIENTSECRET=<client-secret>
CONNECTIONS__SERVICE_CONNECTION__SETTINGS__TENANTID=<tenant-id>
# Optional: OAuth handlers for auto sign-in
AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__GRAPH__SETTINGS__AZUREBOTOAUTHCONNECTIONNAME=<connection-name>
# Optional: Azure OpenAI for streaming (AAD auth via DefaultAzureCredential)
AZURE_OPENAI_ENDPOINT=<endpoint>
AZURE_OPENAI_API_VERSION=<version>
# Optional: Copilot Studio client
COPILOTSTUDIOAGENT__ENVIRONMENTID=<environment-id>
COPILOTSTUDIOAGENT__SCHEMANAME=<schema-name>
COPILOTSTUDIOAGENT__TENANTID=<tenant-id>
COPILOTSTUDIOAGENT__AGENTAPPID=<app-id>
Core Workflow: aiohttp-hosted AgentApplication
import logging
from os import environ
from dotenv import load_dotenv
from aiohttp.web import Request, Response, Application, run_app
from microsoft_agents.activity import load_configuration_from_env
from microsoft_agents.hosting.core import (
Authorization,
AgentApplication,
TurnState,
TurnContext,
MemoryStorage,
)
from microsoft_agents.hosting.aiohttp import (
CloudAdapter,
start_agent_process,
jwt_authorization_middleware,
)
from microsoft_agents.authentication.msal import MsalConnectionManager
# Enable logging
ms_agents_logger = logging.getLogger("microsoft_agents")
ms_agents_logger.addHandler(logging.StreamHandler())
ms_agents_logger.setLevel(logging.INFO)
# Load configuration
load_dotenv()
agents_sdk_config = load_configuration_from_env(environ)
# Create storage and connection manager
STORAGE = MemoryStorage()
CONNECTION_MANAGER = MsalConnectionManager(**agents_sdk_config)
ADAPTER = CloudAdapter(connection_manager=CONNECTION_MANAGER)
AUTHORIZATION = Authorization(STORAGE, CONNECTION_MANAGER, **agents_sdk_config)
# Create AgentApplication
AGENT_APP = AgentApplication[TurnState](
storage=STORAGE, adapter=ADAPTER, authorization=AUTHORIZATION, **agents_sdk_config
)
@AGENT_APP.conversation_update("membersAdded")
async def on_members_added(context: TurnContext, _state: TurnState):
await context.send_activity("Welcome to the agent!")
@AGENT_APP.activity("message")
async def on_message(context: TurnContext, _state: TurnState):
await context.send_activity(f"You said: {context.activity.text}")
@AGENT_APP.error
async def on_error(context: TurnContext, error: Exception):
await context.send_activity("The agent encountered an error.")
# Server setup
async def entry_point(req: Request) -> Response:
agent: AgentApplication = req.app["agent_app"]
adapter: CloudAdapter = req.app["adapter"]
return await start_agent_process(req, agent, adapter)
APP = Application(middlewares=[jwt_authorization_middleware])
APP.router.add_post("/api/messages", entry_point)
APP["agent_configuration"] = CONNECTION_MANAGER.get_default_connection_configuration()
APP["agent_app"] = AGENT_APP
APP["adapter"] = AGENT_APP.adapter
if __name__ == "__main__":
run_app(APP, host="localhost", port=environ.get("PORT", 3978))
AgentApplication Routing
import re
from microsoft_agents.hosting.core import (
AgentApplication, TurnState, TurnContext, MessageFactory
)
from microsoft_agents.activity import ActivityTypes
AGENT_APP = AgentApplication[TurnState](
storage=STORAGE, adapter=ADAPTER, authorization=AUTHORIZATION, **agents_sdk_config
)
# Welcome handler
@AGENT_APP.conversation_update("membersAdded")
async def on_members_added(context: TurnContext, _state: TurnState):
await context.send_activity("Welcome!")
# Regex-based message handler
@AGENT_APP.message(re.compile(r"^hello$", re.IGNORECASE))
async def on_hello(context: TurnContext, _state: TurnState):
await context.send_activity("Hello!")
# Simple string message handler
@AGENT_APP.message("/status")
async def on_status(context: TurnContext, _state: TurnState):
await context.send_activity("Status: OK")
# Auth-protected message handler
@AGENT_APP.message("/me", auth_handlers=["GRAPH"])
async def on_profile(context: TurnContext, state: TurnState):
token_response = await AGENT_APP.auth.get_token(context, "GRAPH")
if token_response and token_response.token:
# Use token to call Graph API
await context.send_activity("Profile retrieved")
# Invoke activity handler
@AGENT_APP.activity(ActivityTypes.invoke)
async def on_invoke(context: TurnContext, _state: TurnState):
invoke_response = Activity(
type=ActivityTypes.invoke_response, value={"status": 200}
)
await context.send_activity(invoke_response)
# Fallback message handler
@AGENT_APP.activity("message")
async def on_message(context: TurnContext, _state: TurnState):
await context.send_activity(f"Echo: {context.activity.text}")
# Error handler
@AGENT_APP.error
async def on_error(context: TurnContext, error: Exception):
await context.send_activity("An error occurred.")
Streaming Responses with Azure OpenAI
from openai import AsyncAzureOpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
from microsoft_agents.activity import SensitivityUsageInfo
# AAD token provider (preferred over AZURE_OPENAI_API_KEY)
token_provider = get_bearer_token_provider(
DefaultAzureCredential(),
"https://cognitiveservices.azure.com/.default",
)
# Module-level singleton: client lives for the agent app lifetime.
CLIENT = AsyncAzureOpenAI(
api_version=environ["AZURE_OPENAI_API_VERSION"],
azure_endpoint=environ["AZURE_OPENAI_ENDPOINT"],
azure_ad_token_provider=token_provider,
)
@AGENT_APP.message("poem")
async def on_poem_message(context: TurnContext, _state: TurnState):
# Configure streaming response
context.streaming_response.set_feedback_loop(True)
context.streaming_response.set_generated_by_ai_label(True)
context.streaming_response.set_sensitivity_label(
SensitivityUsageInfo(
type="https://schema.org/Message",
schema_type="CreativeWork",
name="Internal",
)
)
context.streaming_response.queue_informative_update("Starting a poem...\n")
# Stream from Azure OpenAI
streamed_response = await CLIENT.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are a creative assistant."},
{"role": "user", "content": "Write a poem about Python."}
],
stream=True,
)
try:
async for chunk in streamed_response:
if chunk.choices and chunk.choices[0].delta.content:
context.streaming_response.queue_text_chunk(
chunk.choices[0].delta.content
)
finally:
await context.streaming_response.end_stream()
OAuth / Auto Sign-In
@AGENT_APP.message("/logout")
async def logout(context: TurnContext, state: TurnState):
await AGENT_APP.auth.sign_out(context, "GRAPH")
await context.send_activity(MessageFactory.text("You have been logged out."))
@AGENT_APP.message("/me", auth_handlers=["GRAPH"])
async def profile_request(context: TurnContext, state: TurnState):
user_token_response = await AGENT_APP.auth.get_token(context, "GRAPH")
if user_token_response and user_token_response.token:
# Use token to call Microsoft Graph
async with aiohttp.ClientSession() as session:
headers = {
"Authorization": f"Bearer {user_token_response.token}",
"Content-Type": "application/json",
}
async with session.get(
"https://graph.microsoft.com/v1.0/me", headers=headers
) as response:
if response.status == 200:
user_info = await response.json()
await context.send_activity(f"Hello, {user_info['displayName']}!")
Copilot Studio Client (Direct to Engine)
import asyncio
from msal import PublicClientApplication
from microsoft_agents.activity import ActivityTypes, load_configuration_from_env
from microsoft_agents.copilotstudio.client import (
ConnectionSettings,
CopilotClient,
)
# Token cache (local file for interactive flows)
class LocalTokenCache:
# See samples for full implementation
pass
def acquire_token(settings, app_client_id, tenant_id):
pca = PublicClientApplication(
client_id=app_client_id,
authority=f"https://login.microsoftonline.com/{tenant_id}",
)
token_request = {"scopes": ["https://api.powerplatform.com/.default"]}
accounts = pca.get_accounts()
if accounts:
response = pca.acquire_token_silent(token_request["scopes"], account=accounts[0])
return response.get("access_token")
else:
response = pca.acquire_token_interactive(**token_request)
return response.get("access_token")
async def main():
settings = ConnectionSettings(
environment_id=environ.get("COPILOTSTUDIOAGENT__ENVIRONMENTID"),
agent_identifier=environ.get("COPILOTSTUDIOAGENT__SCHEMANAME"),
)
token = acquire_token(
settings,
app_client_id=environ.get("COPILOTSTUDIOAGENT__AGENTAPPID"),
tenant_id=environ.get("COPILOTSTUDIOAGENT__TENANTID"),
)
# CopilotClient does not implement the context manager protocol.
copilot_client = CopilotClient(settings, token)
# Start conversation
act = copilot_client.start_conversation(True)
async for action in act:
if action.text:
print(action.text)
# Ask question
replies = copilot_client.ask_question("Hello!", action.conversation.id)
async for reply in replies:
if reply.type == ActivityTypes.message:
print(reply.text)
asyncio.run(main())
Best Practices
- This skill is async-first (aiohttp-based). Use async handlers and
async withfor aiohttp sessions. - Always use context managers for clients and async credentials. Wrap every client in
with Client(...) as client:(sync) orasync with Client(...) as client:(async). For asyncDefaultAzureCredentialfromazure.identity.aio, also useasync with credential:so tokens and transports are cleaned up. - Use
microsoft_agentsimport prefix (underscores, not dots). - Use
MemoryStorageonly for development; use BlobStorage or CosmosDB in production. - Always use
load_configuration_from_env(environ)to load SDK configuration. - Include
jwt_authorization_middlewarein aiohttp Application middlewares. - Use
MsalConnectionManagerfor MSAL-based authentication. - Call
end_stream()in finally blocks when using streaming responses. - Use
auth_handlersparameter on message decorators for OAuth-protected routes. - Keep secrets in environment variables, not in source code.
Reference Links
| Resource | URL |
|---|---|
| Microsoft 365 Agents SDK | https://learn.microsoft.com/en-us/microsoft-365/agents-sdk/ |
| GitHub samples (Python) | https://github.com/microsoft/Agents-for-python |
| PyPI packages | https://pypi.org/search/?q=microsoft-agents |
| Integrate with Copilot Studio | https://learn.microsoft.com/en-us/microsoft-365/agents-sdk/integrate-with-mcs |
Lebih banyak skill dari microsoft
oss-growth
microsoft
Persona peretas pertumbuhan OSS
official
microsoft-foundry
microsoft
Menyebarkan, mengevaluasi, dan mengelola agen Foundry secara menyeluruh: pembuatan Docker, push ACR, pembuatan agen yang dihosting/dengan prompt, memulai kontainer, evaluasi batch, evaluasi berkelanjutan, alur kerja pengoptimal prompt, agent.yaml, kurasi kumpulan data dari jejak. GUNAKAN UNTUK: menyebarkan agen ke Foundry, agen yang dihosting, membuat agen, memanggil agen, mengevaluasi agen, menjalankan evaluasi batch, evaluasi berkelanjutan, pemantauan berkelanjutan, status evaluasi berkelanjutan, mengoptimalkan prompt, meningkatkan prompt, pengoptimal prompt, mengoptimalkan instruksi agen, meningkatkan agen...
officialdevelopmentdevops
azure-ai
microsoft
Gunakan untuk Azure AI: Search, Speech, OpenAI, Document Intelligence. Membantu pencarian, pencarian vektor/hibrida, ucapan-ke-teks, teks-ke-ucapan, transkripsi, OCR. KAPAN: AI Search, pencarian kueri, pencarian vektor, pencarian hibrida, pencarian semantik, ucapan-ke-teks, teks-ke-ucapan, transkripsi, OCR, konversi teks ke ucapan.
officialdevelopmentapi
azure-deploy
microsoft
Jalankan deployment Azure untuk aplikasi yang SUDAH DISIAPKAN dan memiliki file .azure/deployment-plan.md serta infrastruktur yang sudah ada. JANGAN gunakan skill ini saat pengguna meminta untuk MEMBUAT aplikasi baru — gunakan azure-prepare sebagai gantinya. Skill ini menjalankan perintah azd up, azd deploy, terraform apply, dan az deployment dengan pemulihan kesalahan bawaan. Membutuhkan .azure/deployment-plan.md dari azure-prepare dan status tervalidasi dari azure-validate. KAPAN: "jalankan azd up", "jalankan azd deploy", "jalankan deployment",...
officialdevopsaws
azure-storage
microsoft
Layanan Azure Storage termasuk Blob Storage, File Shares, Queue Storage, Table Storage, dan Data Lake. Menjawab pertanyaan tentang tingkat akses penyimpanan (hot, cool, cold, archive), kapan menggunakan setiap tingkat, dan perbandingan tingkat. Menyediakan penyimpanan objek, berbagi file SMB, pengiriman pesan asinkron, NoSQL key-value, dan analitik big data. Termasuk manajemen siklus hidup. GUNAKAN UNTUK: blob storage, file shares, queue storage, table storage, data lake, unggah file, unduh blob, akun penyimpanan, tingkat akses,...
officialdevelopmentdatabase
azure-diagnostics
microsoft
Debug masalah produksi Azure menggunakan AppLens, Azure Monitor, resource health, dan triase aman. KAPAN: debug masalah produksi, troubleshoot app service, CPU tinggi app service, kegagalan deployment app service, troubleshoot container apps, troubleshoot functions, troubleshoot AKS, kubectl tidak bisa terhubung, kegagalan kube-system/CoreDNS, pod pending, crashloop, node tidak siap, kegagalan upgrade, analisis log, KQL, insights, kegagalan image pull, masalah cold start, kegagalan health probe,...
officialdevopsdevelopment
azure-prepare
microsoft
Siapkan aplikasi Azure untuk deployment (infra Bicep/Terraform, azure.yaml, Dockerfiles). Gunakan untuk membuat/memodernisasi atau membuat+men-deploy; bukan untuk migrasi lintas-cloud (gunakan azure-cloud-migrate). JANGAN GUNAKAN UNTUK: aplikasi copilot-sdk (gunakan azure-hosted-copilot-sdk). KAPAN: "membuat aplikasi", "membangun aplikasi web", "membuat API", "membuat API HTTP serverless", "membuat frontend", "membuat backend", "membangun layanan", "memodernisasi aplikasi", "memperbarui aplikasi", "menambahkan autentikasi", "menambahkan caching", "hosting di Azure", "membuat dan...
officialdevelopmentdevops
azure-validate
microsoft
Validasi pra-penyebaran untuk kesiapan Azure. Lakukan pemeriksaan mendalam pada konfigurasi, infrastruktur (Bicep atau Terraform), penetapan peran RBAC, izin identitas terkelola, dan prasyarat sebelum menyebarkan. KAPAN: validasi aplikasi saya, periksa kesiapan penyebaran, jalankan pemeriksaan awal, verifikasi konfigurasi, periksa apakah siap untuk menyebarkan, validasi azure.yaml, validasi Bicep, uji sebelum menyebarkan, pecahkan kesalahan penyebaran, validasi Azure Functions, validasi function app, validasi serverless...
officialdevopstesting