mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
95fd5ec658
* renamed AzureAIINferenceEmbeddings and lazy load azure-cosmos and env var rename * updated coverage * fix readme
101 lines
3.5 KiB
Markdown
101 lines
3.5 KiB
Markdown
# Microsoft Agent Framework Python Weather Agent sample (M365 Agents SDK)
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This sample demonstrates a simple Weather Forecast Agent built with the Python Microsoft Agent Framework, exposed through the Microsoft 365 Agents SDK compatible endpoints. The agent accepts natural language requests for a weather forecast and responds with a textual answer. It supports multi-turn conversations to gather required information.
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## Prerequisites
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- Python 3.11+
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- [uv](https://github.com/astral-sh/uv) for fast dependency management
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- [devtunnel](https://learn.microsoft.com/azure/developer/dev-tunnels/get-started?tabs=windows)
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- `agentsplayground` for playground/testing
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- Access to OpenAI or Azure OpenAI with a model like `gpt-4o-mini`
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## Configuration
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Set the following environment variables:
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```bash
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# Common
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export PORT=3978
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export USE_ANONYMOUS_MODE=True # set to false if using auth
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# OpenAI
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export OPENAI_API_KEY="..."
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export OPENAI_CHAT_COMPLETION_MODEL="..."
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```
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## Installing Dependencies
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From the repository root or the sample folder:
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```bash
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uv sync
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```
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## Running the Agent Locally
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```bash
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# Activate environment first if not already
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source .venv/bin/activate # (Windows PowerShell: .venv\Scripts\Activate.ps1)
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# Run the weather agent demo
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python m365_agent_demo/app.py
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```
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The agent starts on `http://localhost:3978`. Health check: `GET /api/health`.
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## QuickStart using Agents Playground
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1. Install (if not already):
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```bash
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winget install agentsplayground
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```
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2. Start the Python agent locally: `python m365_agent_demo/app.py`
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3. Start the playground: `agentsplayground`
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4. Chat with the Weather Agent.
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## QuickStart using WebChat (Azure Bot)
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To test via WebChat you can provision an Azure Bot and point its messaging endpoint to your agent.
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1. Create an Azure Bot (choose Client Secret auth for local tunneling).
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2. Create a `.env` file in this sample folder with the following (replace placeholders):
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```bash
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# Authentication / Agentic configuration
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USE_ANONYMOUS_MODE=False
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CONNECTIONS__SERVICE_CONNECTION__SETTINGS__CLIENTID="<client-id>"
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CONNECTIONS__SERVICE_CONNECTION__SETTINGS__CLIENTSECRET="<client-secret>"
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CONNECTIONS__SERVICE_CONNECTION__SETTINGS__TENANTID="<tenant-id>"
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CONNECTIONS__SERVICE_CONNECTION__SETTINGS__SCOPES=https://graph.microsoft.com/.default
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AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__TYPE=AgenticUserAuthorization
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AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__SCOPES=https://graph.microsoft.com/.default
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AGENTAPPLICATION__USERAUTHORIZATION__HANDLERS__AGENTIC__SETTINGS__ALTERNATEBLUEPRINTCONNECTIONNAME=https://graph.microsoft.com/.default
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```
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3. Host dev tunnel:
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```bash
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devtunnel host -p 3978 --allow-anonymous
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```
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4. Set the bot Messaging endpoint to: `https://<tunnel-host>/api/messages`
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5. Run your local agent: `python m365_agent_demo/app.py`
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6. Use "Test in WebChat" in Azure Portal.
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> Federated Credentials or Managed Identity auth types typically require deployment to Azure App Service instead of tunneling.
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## Troubleshooting
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- 404 on `/api/messages`: Ensure you are POSTing and using the correct tunnel URL.
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- Empty responses: Check model key / quota and ensure environment variables are set.
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- Auth errors when anonymous disabled: Validate MSAL config matches your Azure Bot registration.
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## Further Reading
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- [Microsoft 365 Agents SDK](https://learn.microsoft.com/microsoft-365/agents-sdk/)
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- [Devtunnel docs](https://learn.microsoft.com/azure/developer/dev-tunnels/)
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