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agent-framework-workshop

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# Agent Framework Workshop Guide

## Introduction

Welcome to the Microsoft Agent Framework workshop! This project is designed to help you learn and practice implementing multi-agent systems using Agent Framework. The repository contains a series of progressive exercises that will guide you through building increasingly complex agent systems, from simple single-agent interactions to sophisticated multi-agent collaborative scenarios and MCP servers.

## Setup

### Prerequisites

- GitHub Codespaces
- (or run locally with [Python](https://www.python.org/) 3.9+ installed, see instructions below if needed)
- **Azure subscription**

You must have an active Azure subscription and be able to log into the Azure Portal to use Azure services in these labs. Don't have a subscription? Most of the content in these labs provide thorough walkthroughs and explanations of the code and output, so whether or not you are actually able to run the labs, you can still learn a lot by reading through them!
- An Azure OpenAI API key or OpenAI API key (For production ready deployments, you should refrain from using keys, and switch to [managed identities](https://learn.microsoft.com/entra/identity/managed-identities-azure-resources/overview))
- [Visual Studio Code](https://code.visualstudio.com/)
- [UV package manager](https://docs.astral.sh/uv/getting-started/installation/)

When not using Codespaces, follow below instructions:

### Installation

1. **Launch VS Code**
    ```cmd
    git clone <repo-url>
    cd agent-framework-workshop
    ```

2. **Clone the repository**

3. **Local Dependencies Setup**

The project is managed by pyproject.toml and [uv package manager](https://docs.astral.sh/uv/getting-started/installation/).

For local execution init the .venv environment with uv:

```bash
uv sync
```
This creates a .venv folder in your project directory and installs all dependencies defined in pyproject.toml into that environment.

3. **Activate the virtual environment:**

**Windows:**
   ```cmd
   .venv\Scripts\activate
   ```
*Note*: After this step all project-related commands should run inside the created virtual environment

4. **Copy the environment variables template:**

```cmd
    copy .env.example .env
    ```

5. **(Optional) Register Jupyter kernel:**

```cmd
    python -m ipykernel install --user --name=agent-framework-workshop
    ```
This adds your virtual environment as a selectable kernel in Jupyter. You might need to reload the window (Command Palette -> "Developer: Reload Window") for the kernel to appear in VSCode.

### AI Endpoints Setup

1. Navigate to the Azure Portal `https://portal.azure.com/#home`
2. Login using your credentials
3. Open a new tab in your browser and go to `https://ai.azure.com` (you should be logged in already)
4. Create a new project if none are available.
5. Deploy a GPT-4o model. You can follow the numbered steps in the screenshot below:

- Navigate to `models + endpoints`

- Deploy a new model (gpt-4o for this workshop)

6. Get the API endpoint and key from:

7. Copy and paste the deployment name, endpoint and API key into a `.env` file. You can find an example in `.env.example`.

```python
# Azure OpenAI configuration
AZURE_OPENAI_ENDPOINT=https://<resource>.openai.azure.com/
# Note that cognitiveservices endpoints also work: https://<resource>.cognitiveservices.azure.com/
AZURE_OPENAI_API_KEY=<your-api-key>
AZURE_OPENAI_API_VERSION=2025-03-01-preview
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=gpt-4o-2024-11-20
AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=text-embedding-ada-002
```

## Getting Started
Start with the first exercise and progress through them sequentially.

Use documentation on https://learn.microsoft.com/en-us/agent-framework/
to help you with getting up to speed and figuring out what steps to take to complete the exercises.

## Project Structure

```
agent-framework-workshop/
├── 01-single-agents/    
│   ├── 01.1-single-agents.ipynb      # Agent fundamentals
│   ├── 01.2-single-agent-mcp.ipynb   # MAF + MCP example
├── 02-workflows/                     # Agent creation and orchestration
│   ├── 02.1-intro-workflows.ipynb    # Intro to core concepts
│   ├── 02.2-orchestrations.ipynb     # Multi-agent orchestrations
├── 03-rag/                           # Retrieval-Augmented Generation
│   ├── 03.0-setup-guide.md           # Setup instructions for RAG (AI Search only)
│   ├── 03.1-rag-fundamentals.ipynb   # RAG fundamentals with programmatic indexing
│   ├── 03.2-context-providers.ipynb  # Context providers
│   ├── data/                         # Data files for RAG exercises
│   └── images/                       # Images for RAG notebooks
├── 04-human-in-the-loop/             # Human-in-the-loop patterns
│   ├── 04.1-hitl-intro.ipynb         # HITL introduction
│   ├── 04.2-hitl-workflows.ipynb     # HITL workflows
│   └── images/                       # Images for HITL notebooks
├── guides/                           # Additional setup guides
│   ├── blob-storage-ai-search-integration.md  # Production setup with Blob Storage + AI Search
│   └── images/                       # Images for guides
├── mcp/
│   ├── contoso.db                    # Sample database
│   └── mcp_server.py                 # MCP server implementation
├── readme_images/                    # Images for README
├── workshop_utils.py                 # Shared utility functions
└── .env.example                      # Environment variables template
```

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