huggingface / agents-course
This repository contains the Hugging Face Agents Course.
AI Architecture Analysis
This repository is indexed by RepoMind. By analyzing huggingface/agents-course in our AI interface, you can instantly generate complete architecture diagrams, visualize control flows, and perform automated security audits across the entire codebase.
Our Agentic Context Augmented Generation (Agentic CAG) engine loads full source files into context, avoiding the fragmentation of traditional RAG systems. Ask questions about the architecture, dependencies, or specific features to see it in action.
Embed this Badge
Showcase RepoMind's analysis directly in your repository's README.
Repository Summary (README)
PreviewThe Hugging Face Agents Course
If you like the course, don't hesitate to ⭐ star this repository. This helps us to make the course more visible 🤗.
Content
The course is divided into 4 units. These will take you from the basics of agents to a final assignment with a benchmark.
Sign up here (it's free) 👉 https://bit.ly/hf-learn-agents
You can access the course here 👉 https://hf.co/learn/agents-course
| Unit | Topic | Description |
|---|---|---|
| 0 | Welcome to the Course | Welcome, guidelines, necessary tools, and course overview. |
| 1 | Introduction to Agents | Definition of agents, LLMs, model family tree, and special tokens. |
| 1 Bonus | Fine-tuning an LLM for Function-calling | Learn how to fine-tune an LLM for Function-Calling |
| 2 | Frameworks for AI Agents | Overview of smolagents, LangGraph and LlamaIndex. |
| 2.1 | The Smolagents Framework | Learn how to build effective agents using the smolagents library, a lightweight framework for creating capable AI agents. |
| 2.2 | The LlamaIndex Framework | Learn how to build LLM-powered agents over your data using indexes and workflows using the LlamaIndex toolkit. |
| 2.3 | The LangGraph Framework | Learn how to build production-ready applications using the LangGraph framework giving you control tools over the flow of your agent. |
| 2 Bonus | Observability and Evaluation | Learn how to trace and evaluate your agents. |
| 3 | Use Case for Agentic RAG | Learn how to use Agentic RAG to help agents respond to different use cases using various frameworks. |
| 4 | Final Project - Create, Test and Certify Your Agent | Automated evaluation of agents and leaderboard with student results. |
| 3 Bonus | Agents in Games with Pokemon | Explore the exciting intersection of AI Agents and games. |
Prerequisites
- Basic knowledge of Python
- Basic knowledge of LLMs
Contribution Guidelines
If you want to contribute to this course, you're welcome to do so. Feel free to open an issue or join the discussion in the Discord. For specific contributions, here are some guidelines:
Small typo and grammar fixes
If you find a small typo or grammar mistake, please fix it yourself and submit a pull request. This is very helpful for students.
New unit
If you want to add a new unit, please create an issue in the repository, describe the unit, and why it should be added. We will discuss it and if it's a good addition, we can collaborate on it.
Citing the project
To cite this repository in publications:
@misc{agents-course,
author = {Burtenshaw, Ben and Thomas, Joffrey and Simonini, Thomas and Paniego, Sergio},
title = {The Hugging Face Agents Course},
year = {2025},
howpublished = {\url{https://github.com/huggingface/agents-course}},
note = {GitHub repository},
}