The Hugging Face Agents Course

Content

The course is divided into 4 units. These will take you from the basics of agents to a final assignment with a benchmark.

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

Small typo and grammar fixes

New unit

Citing the project

@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},
}