# <a href="https://hf.co/learn/agents-course" target="_blank">The Hugging Face Agents Course</a>

If you like the course, **don't hesitate to ⭐ star this repository**. This helps us to **make the course more visible 🤗**.

<img src="https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/please_star.gif" alt="Star the repo" />

## 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) 👉 <a href="https://bit.ly/hf-learn-agents" target="_blank">https://bit.ly/hf-learn-agents</a>

You can access the course here 👉 <a href="https://hf.co/learn/agents-course" target="_blank">https://hf.co/learn/agents-course</a>

| Unit    | Topic                                                                                                          | Description                                                                                                                            |
|---------|----------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|
| 0       | [Welcome to the Course](https://huggingface.co/learn/agents-course/en/unit0/introduction)                      | Welcome, guidelines, necessary tools, and course overview.                                                                             |
| 1       | [Introduction to Agents](https://huggingface.co/learn/agents-course/en/unit1/introduction)                     | Definition of agents, LLMs, model family tree, and special tokens.                                                                     |
| 1 Bonus | [Fine-tuning an LLM for Function-calling](https://huggingface.co/learn/agents-course/bonus-unit1/introduction) | Learn how to fine-tune an LLM for Function-Calling                                                                                     |
| 2       | [Frameworks for AI Agents](https://huggingface.co/learn/agents-course/unit2/introduction)                      | Overview of `smolagents`, `LangGraph` and `LlamaIndex`.                                                                                |
| 2.1     | [The Smolagents Framework](https://huggingface.co/learn/agents-course/unit2/smolagents/introduction)           | Learn how to build effective agents using the `smolagents` library, a lightweight framework for creating capable AI agents.            |
| 2.2     | [The LlamaIndex Framework](https://huggingface.co/learn/agents-course/unit2/llama-index/introduction)          | Learn how to build LLM-powered agents over your data using indexes and workflows using the `LlamaIndex` toolkit.                       |
| 2.3     | [The LangGraph Framework](https://huggingface.co/learn/agents-course/unit2/langgraph/introduction)             | 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](https://huggingface.co/learn/agents-course/bonus-unit2/introduction)            | Learn how to trace and evaluate your agents.                                                                                           |
| 3       | [Use Case for Agentic RAG](https://huggingface.co/learn/agents-course/unit3/agentic-rag/introduction)          | 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](https://huggingface.co/learn/agents-course/unit4/introduction)          | Automated evaluation of agents and leaderboard with student results.                                                                   |
| 3 Bonus | [Agents in Games with Pokemon](https://huggingface.co/learn/agents-course/bonus-unit3/introduction)            | 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](https://discord.gg/UrrTSsSyjb). 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:

```bibtex
@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},
}
```
