ai agents masterclass
Basic Information
This repository contains the code and materials for the AI Agents Masterclass series by coleam00. It is organized into numbered folders where each folder corresponds to a weekly video demonstrating how to build an AI agent. The code in each folder is the exact code used in the accompanying video and is intended to be run locally by learners. The repo is aimed at developers who want a hands-on way to learn how to give large language models the ability to interact with external systems and perform tasks. It includes examples, a basic architecture diagram, and instructions for setting up a Python environment, configuring API keys through .env files, and executing the example scripts shown in the lessons.
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App Details
Features
Folder-per-video structure so each lesson has its own runnable example and resources. Each lesson folder includes a .env.example file showing required environment variables and a requirements.txt for Python dependencies. The README provides step-by-step setup commands for creating a virtual environment and installing packages on Windows, macOS, and Linux. Code in the repo matches the instructor"s live demonstrations and is intended to be used for follow-along learning. The repository also contains supplemental non-sequential content from the author’s channel and a simple diagram illustrating agent architecture. Guidance emphasizes minimal setup and replicable examples rather than a packaged framework.
Use Cases
This repo provides practical, executable examples so developers can follow video lessons and quickly reproduce agent behaviors on their machine. Learners get concrete starter code, environment examples for storing API keys, and cross-platform setup instructions that reduce friction when running sample scripts. The materials are organized to let users progress week-by-week, reuse code from each lesson, and extend examples into more complex multi-agent integrations. By matching the instructor"s code exactly, the repo helps bridge conceptual learning with practical implementation and gives a reproducible foundation for building agents that interact with external tools and services.
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