Getting started

This page sets up everything the Build multi-agent solutions with the Agent Framework lab needs. Every task begins here — complete this page first. Each task is written so you can then do it on its own; if you’re working through the whole lab in one sitting, you only need to do this setup once.

Your scenario: you work at Tailwind Traders, an outdoor-gear retailer that also runs guided trips. Across the lab you’ll build the automation behind Tailwind Traders operations, starting with one agent and growing to a coordinated team of them.

Note: Some of the technologies used in this lab are in preview or in active development. You may experience some unexpected behavior, warnings, or errors.

Prerequisites

Before starting, ensure you have:

• An Azure subscription with sufficient permissions and quota to provision Azure AI resources
• Visual Studio Code installed on your local machine
• Python 3.13 or later installed
• Git installed on your local machine
• Basic familiarity with Python

* Python 3.14 is available, but some dependencies are not yet compiled for that release. The lab has been successfully tested with Python 3.13.12.

Create a Microsoft Foundry project

You need a Foundry project and a deployed model for every task. You can create these in the portal (the default), or provision them with one command using the Azure Developer CLI (azd).

Option A — Create the project in the portal (default)

Microsoft Foundry uses projects to organize models, resources, data, and other assets.

1. In a web browser, open the Foundry portal at https://ai.azure.com and sign in using your Azure credentials. Close any tips or quick start panes, and if necessary use the Foundry logo at the top left to navigate to the home page.

   Important: For this lab, you’re using the New Foundry experience.

2. In the top banner, select Start building.

3. When prompted, create a new project and enter a valid name (for example, agents-lab-project).

4. Expand Advanced options and specify:
   • Microsoft Foundry resource: A valid name for your Foundry resource
   • Region: Select one available near you*
   • Subscription: Your Azure subscription
   • Resource group: Select or create a resource group

   * Some Azure AI resources are constrained by regional model quotas. If you hit a quota limit later, you may need to create another resource in a different region.

5. Select Create and wait for your project to be created. When prompted, continue through the welcome dialog.

6. When prompted to deploy a model, deploy a gpt-4o model (or another available chat model). Note the deployment name — you’ll set it as MODEL_DEPLOYMENT_NAME in your .env.

7. From the project overview, copy the Project endpoint — you’ll set it as PROJECT_ENDPOINT.

Option B — Provision with azd (optional, one command)

If you’d rather not click through the portal, the lab ships an optional azd template that creates the Foundry resource, a project, and a model deployment for you.

1. Install the Azure Developer CLI.

2. From the Labfiles/C-build-multi-agent-solutions-with-agent-framework folder, run:

    azd auth login
    azd up
   

3. Answer the prompts (environment name, region). When it finishes, azd writes PROJECT_ENDPOINT and MODEL_DEPLOYMENT_NAME into Python/.env for you.

   Note: When you’re done with the lab, run azd down to delete everything it created.

Get the starter code

1. In VS Code, open the Command Palette (Ctrl+Shift+P), run Git: Clone, and enter:

    https://github.com/MicrosoftLearning/mslearn-ai-agents.git
   

2. Open the cloned repo, then File > Open Folder and select mslearn-ai-agents/Labfiles/C-build-multi-agent-solutions-with-agent-framework/Python. This single folder holds the starter code for every task in this lab — you use one virtual environment and one .env throughout.

3. Right-click requirements.txt and choose Open in Integrated Terminal. Then create a virtual environment and install packages:

    python -m venv labenv
    .\labenv\Scripts\Activate.ps1
    pip install -r requirements.txt
   

4. Open the .env file and set PROJECT_ENDPOINT to your project endpoint and MODEL_DEPLOYMENT_NAME to your model deployment name. Save the file. (If you used azd up, these are already filled in.)

   Tip: In the Foundry Toolkit VS Code extension, right-click your project deployment and select Copy Project Endpoint to get the endpoint URL.

   The .env also ships with SERVER_URL and three *_PORT values pre-filled — those are only used by Task 3 (remote agents), and you normally don’t need to change them.

Check you’re ready for a task

Each task needs specific values in your .env. Before starting a task, run the preflight check from the Labfiles/C-build-multi-agent-solutions-with-agent-framework folder — it reads your .env and tells you what (if anything) is missing:

python setup/check_env.py --task 1

Swap 1 for the task number you’re about to start. That’s it — head to any task: