What Is the Microsoft AI Agents for Beginners Repository? A Complete Guide

The Microsoft AI Agents for Beginners repository is a comprehensive, self-contained educational course that teaches developers how to build, design, and deploy AI agents using Microsoft's Agent Framework (MAF) and Azure AI Foundry.

This open-source project from Microsoft provides 15+ structured lessons covering everything from agent fundamentals to advanced patterns like multi-agent collaboration and metacognition. Whether you're new to AI agents or looking to adopt Microsoft's tooling, this repository serves as both curriculum and codebase.

What You'll Learn From This Course

The Microsoft AI Agents for Beginners repository follows a progressive learning path. Each lesson combines conceptual explanations with hands-on code samples.

Core Curriculum Structure

The course spans 00-course-setup through 15-browser-use, with each folder containing:

• A README explaining the lesson's concepts and learning objectives
• A Jupyter notebook or Markdown file with step-by-step walkthroughs
• A code_samples/ subfolder with runnable Python and .NET implementations

According to [AGENTS.md](https://github.com/microsoft/ai-agents-for-beginners/blob/main/AGENTS.md), lessons cover design patterns including tool use, retrieval-augmented generation (RAG), planning, multi-agent workflows, and agentic protocols.

Multi-Language Accessibility

A standout feature of the Microsoft AI Agents for Beginners repository is its automatic translation pipeline. The course is written once in English and translated into 50+ languages via the Co-op Translator GitHub Action.

Translation files live in translations/ subfolders (e.g., translations/zh-TW/README.md), making the curriculum accessible to global developers without manual localization efforts.

Key Technologies: MAF and Azure AI Foundry

The Microsoft AI Agents for Beginners repository centers on two core technologies:

Microsoft Agent Framework (MAF)

MAF provides the structural foundation for building agents. It handles prompt engineering, state management, and workflow orchestration under the hood, letting developers focus on business logic rather than boilerplate.

Azure AI Foundry Agent Service V2

The course integrates with Azure AI Foundry Agent Service V2 for model hosting and deployment. All samples use Azure OpenAI endpoints, with optional compatibility for OpenAI-compatible providers like MiniMax.

Hands-On Code Example: Hotel Booking Agent

To understand how the Microsoft AI Agents for Beginners repository teaches practical implementation, examine this minimal agent from 14-microsoft-agent-framework/code-samples/hotel_booking_workflow_sample.py:

from agent_framework import Agent, AzureAIProjectAgentProvider

# Load credentials from .env (see .env.example)

provider = AzureAIProjectAgentProvider(
    endpoint="https://<your-project>.openai.azure.com/",
    deployment_name="gpt-4o",
    api_key="<YOUR_API_KEY>"
)

# Define a simple agent that can ask for a check-in date

class HotelBookingAgent(Agent):
    async def run(self, state):
        # MAF handles prompt engineering under the hood

        response = await self.llm.complete(
            "User wants to book a hotel. Ask for the check-in date."
        )
        return {"message": response.text}

# Instantiate and execute

agent = HotelBookingAgent(provider=provider)
result = await agent.run({})
print(result["message"])

This sample demonstrates MAF's abstraction layer: developers subclass Agent, implement run(), and let the framework handle LLM interaction.

Running the Sample

1. Install dependencies from [requirements.txt](https://github.com/microsoft/ai-agents-for-beginners/blob/main/requirements.txt):

pip install -r requirements.txt

2. Copy environment template and configure credentials:

cp .env.example .env

# Edit .env with your Azure AI Foundry values

3. Execute the script:

python hotel_booking_workflow_sample.py

Cross-language parity is demonstrated in .NET samples under 08-multi-agent/code_samples/workflows-agent-framework/dotNET/.

Repository Structure and Key Files

Path	Purpose
[README.md](https://github.com/microsoft/ai-agents-for-beginners/blob/main/README.md)	Course overview, badges, and quickstart
[AGENTS.md](https://github.com/microsoft/ai-agents-for-beginners/blob/main/AGENTS.md)	Detailed lesson breakdown with design patterns
[requirements.txt](https://github.com/microsoft/ai-agents-for-beginners/blob/main/requirements.txt)	Python dependencies (MAF, Azure SDKs)
.env.example	Template for Azure credentials
00-course-setup/README.md	Environment setup instructions
14-microsoft-agent-framework/code-samples/hotel_booking_workflow_sample.py	Complete MAF workflow example
11-agentic-protocols/code_samples/mcp-agents/client/client.py	MCP protocol client implementation

Summary

The Microsoft AI Agents for Beginners repository delivers:

• 15+ progressive lessons covering agent fundamentals through advanced patterns
• Microsoft Agent Framework (MAF) integration with Azure AI Foundry
• Runnable code samples in Python and .NET with clear setup instructions
• Automatic translation to 50+ languages via Co-op Translator
• Production-ready patterns including tool use, RAG, planning, and multi-agent workflows

Whether you're building your first agent or evaluating Microsoft's agent stack, this repository provides both the conceptual foundation and practical implementation guidance.

Frequently Asked Questions

What prerequisites do I need for the Microsoft AI Agents for Beginners course?

You need basic Python or .NET knowledge, an Azure subscription for AI Foundry access, and familiarity with API concepts. The 00-course-setup lesson walks through installing dependencies, configuring virtual environments, and obtaining Azure credentials.

Can I use the Microsoft AI Agents for Beginners repository without Azure?

The code samples are designed for Azure AI Foundry, but MAF includes optional support for OpenAI-compatible providers like MiniMax. You would need to modify the AzureAIProjectAgentProvider configuration to point to alternative endpoints.

How does the Microsoft Agent Framework differ from LangChain or AutoGen?

MAF is Microsoft's opinionated framework that tightly integrates with Azure AI Foundry and handles prompt engineering, state management, and workflow orchestration internally. Unlike LangChain's modular chain composition or AutoGen's conversation-centric multi-agent design, MAF emphasizes structured workflows with built-in Azure enterprise features.

Is the Microsoft AI Agents for Beginners repository suitable for production deployments?

The repository is explicitly educational, but it demonstrates production patterns using the same MAF and Azure AI Foundry components that Microsoft uses in enterprise scenarios. For production, you would add error handling, observability, security hardening, and scalability considerations beyond the course scope.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}