What Tools and Libraries Does ai-agents-for-beginners Depend On?

The microsoft/ai-agents-for-beginners repository depends on 20+ Python packages across five categories: Azure AI services, Microsoft's Agent Framework, Model Context Protocol, OpenAI SDK, and utility libraries for data processing and notebook execution.

This open-source learning kit from Microsoft demonstrates how to build production-ready AI agents using Azure AI Foundry and modern agentic protocols. All dependencies are declaratively managed in requirements.txt, making the environment reproducible for beginners and experienced developers alike. This guide breaks down every dependency group with specific file paths, method signatures, and runnable code examples straight from the source.

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Azure AI and Identity Libraries

The Azure AI stack forms the production backbone of the repository. These four packages enable authentication, model inference, project orchestration, and retrieval-augmented generation.

Core Azure Packages

Package	Purpose	Source File Reference
azure-identity	Azure AD authentication via DefaultAzureCredential	Used across all Azure-connected notebooks
azure-ai-inference	Chat completions and model inference endpoints	01-intro-to-ai-agents/code_samples/01-python-agent-framework.ipynb
azure-ai-projects	Project-level orchestration and resource management	Foundation for multi-agent workflows
azure-search-documents	Vector and keyword search for RAG implementations	05-agentic-rag lessons

Authentication Example

The DefaultAzureCredential class from azure-identity provides seamless authentication across local development and Azure deployments:

from azure.identity import DefaultAzureCredential
from azure.ai.inference import ChatCompletionsClient

credential = DefaultAzureCredential()
endpoint = "https://<your-project>.openai.azure.com"
client = ChatCompletionsClient(endpoint=endpoint, credential=credential)

response = client.chat_completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello, AI!"}]
)
print(response.choices[0].message.content)

This pattern appears in 01-intro-to-ai-agents/code_samples/01-python-agent-framework.ipynb as the entry point for Azure-based agent development.

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Microsoft Agent Framework

The agent-framework and a2a-sdk packages provide the core abstraction layer for defining agents, registering tools, and orchestrating multi-step workflows.

Framework Components

Package	Role	Key Classes/Decorators
agent-framework	Core agent definition and execution	Agent, Tool, @Tool
a2a-sdk	Agent-to-Agent protocol implementation	A2A communication primitives

Agent Definition Example

The 14-microsoft-agent-framework/code-samples/14-sequential.ipynb notebook demonstrates the declarative agent pattern:

from agent_framework import Agent, Tool

@Tool
def echo(text: str) -> str:
    """Simply returns the provided text."""
    return text

my_agent = Agent(
    name="EchoAgent",
    description="Echoes back whatever the user says",
    tools=[echo],
    model="gpt-4o"  # works with Azure or OpenAI

)

print(my_agent.run("Say hello!"))

The framework abstracts the underlying LLM provider, enabling identical agent code to run against Azure OpenAI or OpenAI's native API.

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Model Context Protocol (MCP)

The mcp[cli] package implements the Model Context Protocol, a standardized mechanism for serializing, exchanging, and tracing agent context across distributed components.

MCP Usage Pattern

As shown in 11-agentic-protocols/code_samples/11-mcp-agent-framework.ipynb:

from mcp import Context, serialize_context, deserialize_context

ctx = Context(user_id="alice", session_id="1234")
payload = serialize_context(ctx)

# Later, possibly in another agent

restored = deserialize_context(payload)
print(restored.session_id)   # → "1234"

MCP enables traceability and context preservation when agents hand off work to other agents or resume sessions from persisted state.

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OpenAI SDK

The openai package provides a direct alternative to Azure AI for students without Azure subscriptions or those wanting to compare model behavior.

Direct OpenAI Usage

import openai

openai.api_key = "sk-…"   # loaded from .env in real notebooks

response = openai.ChatCompletion.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is the capital of France?"}]
)
print(response.choices[0].message.content)

This pattern appears in introductory notebooks when Azure credentials are unavailable, ensuring the learning materials remain accessible.

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Data and Utility Libraries

The remaining packages support notebook execution, data processing, HTTP communication, and environment management.

Category	Libraries	Purpose
HTTP & Async	httpx, nest-asyncio, uvicorn	Modern HTTP client, Jupyter async loop patching, ASGI server for tool endpoints
Jupyter	ipykernel	Notebook kernel execution
Data Processing	numpy, pandas, pillow	Numerical arrays, tabular data, image manipulation
Environment	python-dotenv	Load .env files for secrets and configuration

FastAPI Tool Server Example

The uvicorn + fastapi combination enables agents to expose custom tools as HTTP endpoints, as demonstrated in multi-agent workflow examples:

from fastapi import FastAPI
import uvicorn

app = FastAPI()

@app.get("/weather")
def get_weather(city: str):
    return {"city": city, "temp_c": 22}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

Agents invoke this endpoint as an external tool, enabling integration with existing APIs and services.

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Summary

• Azure AI stack (azure-identity, azure-ai-inference, azure-ai-projects, azure-search-documents) provides production-grade authentication, inference, and retrieval capabilities.
• Microsoft Agent Framework (agent-framework, a2a-sdk) offers the core abstraction for defining agents and enabling agent-to-agent communication.
• Model Context Protocol (mcp[cli]) standardizes context serialization and tracing across distributed agent components.
• OpenAI SDK (openai) serves as an accessible alternative for learners without Azure subscriptions.
• Utility libraries (httpx, uvicorn, pandas, pillow, python-dotenv, etc.) support notebook execution, data processing, HTTP communication, and environment management.

All dependencies are declared in requirements.txt and loaded via python-dotenv from .env files, ensuring reproducible, secure environments for learning AI agent development.

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Frequently Asked Questions

What is the main dependency file in ai-agents-for-beginners?

The requirements.txt file at the repository root contains all 20+ Python dependencies. It is referenced in setup instructions and used by pip install -r requirements.txt to create a reproducible environment.

Can I run the notebooks without an Azure subscription?

Yes. The openai package allows direct API calls to OpenAI's models, and several introductory notebooks include fallback paths that use python-dotenv to load OpenAI keys instead of Azure credentials.

What is the purpose of the mcp[cli] package?

The mcp[cli] package implements the Model Context Protocol, which standardizes how agents serialize, exchange, and trace context across sessions and between different agent instances. It is demonstrated in the "11-agentic-protocols" lesson.

How does the Microsoft Agent Framework differ from using Azure AI directly?

The agent-framework provides higher-level abstractions like the Agent class and @Tool decorator that handle tool registration, conversation state, and orchestration patterns (sequential, concurrent, conditional). It can target either Azure AI or OpenAI through the same API, whereas direct Azure AI usage requires manual management of endpoints and credentials.

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