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Welcome to the 🤗 AI Agents Course

The background of the image was generated using Scenario.com

Welcome to the most exciting topic in AI today: Agents!

This free course will take you on a journey, from beginner to expert, in understanding, using and building AI agents.

This first unit will help you onboard:

- Discover the course’s syllabus.
- Choose the path you’re going to take (either self-audit or certification process).
- Get more information about the certification process and the deadlines.
- Get to know the team behind the course.
- Create your Hugging Face account.
- Sign-up to our Discord server, and meet your classmates and us.

Let’s get started!

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What to expect from this course?

In this course, you will:

- 📖 Study AI Agents in theory, design, and practice.
- 🧑‍💻 Learn to use established AI Agent libraries such as smolagents, LlamaIndex, and LangGraph.
- 💾 Share your agents on the Hugging Face Hub and explore agents created by the community.
- 🏆 Participate in challenges where you will evaluate your agents against other students’.
- 🎓 Earn a certificate of completion by completing assignments.

And more!

At the end of this course, you’ll understand how Agents work and how to build your own Agents using the latest libraries and tools.

Don’t forget to sign up to the course!

(We are respectful of your privacy. We collect your email address to be able to send you the links when each Unit is published and give you information about the challenges and updates).

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What does the course look like?

The course is composed of:

- Foundational Units: where you learn Agents concepts in theory.
- Hands-on: where you’ll learn to use established AI Agent libraries to train your agents in unique environments. These hands-on sections will be Hugging Face Spaces with a pre-configured environment.
- Use case assignments: where you’ll apply the concepts you’ve learned to solve a real-world problem that you’ll choose.
- The Challenge: you’ll get to put your agent to compete against other agents in a challenge. There will also be a leaderboard for you to compare the agents’ performance.

This course is a living project, evolving with your feedback and contributions! Feel free to open issues and PRs in GitHub, and engage in discussions in our Discord server.

After you have gone through the course, you can also send your feedback 👉 using this form

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What’s the syllabus?

Here is the general syllabus for the course. A more detailed list of topics will be released with each unit.

Chapter | Topic | Description
--- | --- | ---
0 | Onboarding | Set you up with the tools and platforms that you will use.
1 | Agent Fundamentals | Explain Tools, Thoughts, Actions, Observations, and their formats. Explain LLMs, messages, special tokens and chat templates. Show a simple use case using python functions as tools.
1.5 | Bonus : Fine-tuning an LLM for function calling | Let’s use LoRa and fine-tune a model to perform function calling inside a notebook.
2 | Frameworks | Understand how the fundamentals are implemented in popular libraries : smolagents, LangGraph, LLamaIndex
2.5 | Bonus : Agent Observability and Evaluation | Learn how to trace and evaluate your AI agents to make them ready for production.
3 | Use Cases | Let’s build some real life use cases (open to PRs 🤗 from experienced Agent builders)
4 | Final Assignment | Build an agent for a selected benchmark and prove your understanding of Agents on the student leaderboard 🚀

In addition to the main syllabus, you have 3 bonus units:

- Bonus Unit 1 : Fine-tuning an LLM for Function-calling
- Bonus Unit 2 : Agent Observability and Evaluation
- Bonus Unit 3 : Agents in Games with Pokemon

For instance, in the Bonus Unit 3, you learn to build your Agent to play Pokemon battles 🥊.

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What are the prerequisites?

To be able to follow this course, you should have a:

- Basic knowledge of Python
- Basic knowledge of LLMs (we have a section in Unit 1 to recap what they are)

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What tools do I need?

You only need 2 things:

- A computer with an internet connection.
- A Hugging Face Account: to push and load models, agents, and create Spaces. If you don’t have an account yet, you can create one here (it’s free).

##
The Certification Process

You can choose to follow this course in audit mode, or do the activities and get one of the two certificates we’ll issue.

If you audit the course, you can participate in all the challenges and do assignments if you want, and you don’t need to notify us.

The certification process is completely free:

- To get a certification for fundamentals: you need to complete Unit 1 of the course. This is intended for students that want to get up to date with the latest trends in Agents.
- To get a certificate of completion: you need to complete Unit 1, one of the use case assignments we’ll propose during the course, and the final challenge.

There’s a deadline for the certification process: all the assignments must be finished before July 1st 2025.

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What is the recommended pace?

Each chapter in this course is designed to be completed in 1 week, with approximately 3-4 hours of work per week.

Since there’s a deadline, we provide you a recommended pace:

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How to get the most out of the course?

To get the most out of the course, we have some advice:

1. Join study groups in Discord: studying in groups is always easier. To do that, you need to join our discord server and verify your Hugging Face account.
2. Do the quizzes and assignments: the best way to learn is through hands-on practice and self-assessment.
3. Define a schedule to stay in sync: you can use our recommended pace schedule below or create yours.

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Who are we

About the authors:

###
Joffrey Thomas

Joffrey is a machine learning engineer at Hugging Face and has built and deployed AI Agents in production. Joffrey will be your main instructor for this course.

- Follow Joffrey on Hugging Face
- Follow Joffrey on X
- Follow Joffrey on Linkedin

###
Ben Burtenshaw

Ben is a machine learning engineer at Hugging Face and has delivered multiple courses across various platforms. Ben’s goal is to make the course accessible to everyone.

- Follow Ben on Hugging Face
- Follow Ben on X
- Follow Ben on Linkedin

###
Thomas Simonini

Thomas is a machine learning engineer at Hugging Face and delivered the successful Deep RL and ML for games courses. Thomas is a big fan of Agents and is excited to see what the community will build.

- Follow Thomas on Hugging Face
- Follow Thomas on X
- Follow Thomas on Linkedin

###
Sergio Paniego

Sergio is a machine learning engineer at Hugging Face. He contributed to several sections of Units 2, 3, 4, and the bonus units.

- Follow Sergio on Hugging Face
- Follow Sergio on X
- Follow Sergio on Linkedin

##
Acknowledgments

We would like to extend our gratitude to the following individuals for their invaluable contributions to this course:

- Pedro Cuenca – For his guidance and expertise in reviewing the materials.
- Aymeric Roucher – For his amazing demo spaces ( decoding and final agent ) as well as his help on the smolagents parts.
- Joshua Lochner – For his amazing demo space on tokenization.
- Quentin Gallouédec – For his help on the course content.
- David Berenstein – For his help on the course content and moderation.
- XiaXiao (ShawnSiao) – Chinese translator for the course.
- Jiaming Huang – Chinese translator for the course.

##
I found a bug, or I want to improve the course

Contributions are welcome 🤗

- If you found a bug 🐛 in a notebook, please open an issue and describe the problem.
- If you want to improve the course, you can open a Pull Request.
- If you want to add a full section or a new unit, the best is to open an issue and describe what content you want to add before starting to write it so that we can guide you.

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I still have questions

Please ask your question in our discord server #agents-course-questions.

Now that you have all the information, let’s get on board ⛵

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