This sample demonstrates how to build an agent using Agent Framework in Python with the Microsoft Agent 365 SDK. It covers:

• Observability: End-to-end tracing, caching, and monitoring for agent applications
• Notifications: Services and models for managing user notifications
• Tools: Model Context Protocol tools for building advanced agent solutions
• Hosting Patterns: Hosting with Microsoft 365 Agents SDK

This sample uses the Microsoft Agent 365 SDK for Python.

For comprehensive documentation and guidance on building agents with the Microsoft Agent 365 SDK, including how to add tooling, observability, and notifications, visit the Microsoft Agent 365 Developer Documentation.

• Python 3.x
• Microsoft Agent 365 SDK
• Agent Framework (agent-framework-azure-ai)
• Azure/OpenAI API credentials

On every incoming message, the A365 platform populates activity.from_property with basic user information — always available with no API calls or token acquisition:

The sample logs these fields at the start of every message turn and injects the display name into the LLM system instructions for personalized responses.

When a user installs (hires) or uninstalls (removes) the agent, the A365 platform sends an InstallationUpdate activity — also referred to as the agentInstanceCreated event. The sample handles this in on_installation_update in host_agent_server.py:

if action == "add":
    await context.send_activity("Thank you for hiring me! Looking forward to assisting you in your professional journey!")
elif action == "remove":
    await context.send_activity("Thank you for your time, I enjoyed working with you.")

To test with Agents Playground, use Mock an Activity → Install application to send a simulated installationUpdate activity.

Agent365 agents can send multiple discrete messages in response to a single user prompt in Teams. This is achieved by calling send_activity multiple times within a single turn.

Important: Streaming responses are not supported for agentic identities in Teams. The SDK detects agentic identity and buffers the stream into a single message. Use send_activity directly to send immediate, discrete messages to the user.

The sample demonstrates this in on_message (host_agent_server.py):

# Message 1: immediate ack — reaches the user right away
await context.send_activity("Got it — working on it…")

# Send typing indicator immediately (awaited so it arrives before the LLM call starts).
await context.send_activity(Activity(type="typing"))

# Background loop refreshes the "..." animation every ~4s (it times out after ~5s).
async def _typing_loop():
    try:
        while True:
            await asyncio.sleep(4)
            await context.send_activity(Activity(type="typing"))
    except asyncio.CancelledError:
        pass  # Expected on cancel.

typing_task = asyncio.create_task(_typing_loop())
try:
    response = await agent.process_user_message(...)
    # Message 2: the LLM response
    await context.send_activity(response)
finally:
    typing_task.cancel()
    try:
        await typing_task
    except asyncio.CancelledError:
        pass

Each send_activity call produces a separate Teams message. You can call it as many times as needed to send progress updates, partial results, or a final answer.

• Typing indicators show a "..." progress animation in Teams
• They have a built-in ~5-second visual timeout and must be refreshed in a loop every ~4 seconds
• Only visible in 1:1 chats and small group chats — not in channels

To set up and test this agent, refer to the Configure Agent Testing guide for complete instructions.

For a detailed explanation of the agent code and implementation, see the Agent Code Walkthrough.

For issues, questions, or feedback:

• Issues: Please file issues in the GitHub Issues section
• Documentation: See the Microsoft Agents 365 Developer documentation
• Security: For security issues, please see SECURITY.md

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

• Microsoft Agent 365 SDK - Python repository
• Microsoft 365 Agents SDK - Python repository
• Agent Framework documentation
• Python API documentation

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Copyright (c) Microsoft Corporation. All rights reserved.

Licensed under the MIT License - see the LICENSE file for details.