agents-course README

The project's README from github.com/huggingface/agents-course. As one markdown file: README.md.

The 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 🤗.

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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

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},
}

agents-course (huggingface/agents-course)

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这是什么?这是 Hugging Face 官方的 Agents 课程:一门免费的实践型课程,教你如何构建 AI 智能体,从基础知识逐步讲到智能体框架,最后是一个可获得证书的结业项目。
这是一门可以自己阅读学习的课程,还是需要去某个地方注册?这是一门需要在线注册的免费课程;本仓库存放课程内容,内容按编号的单元和加餐单元组织,你可以按自己的节奏学习。
我应该从哪里开始?先从第 0 单元(欢迎来到课程)开始,了解环境配置和学习指南,然后依次学习第 1 至 4 单元,内容涵盖智能体基础、框架以及可获得证书的结业项目。
我需要先懂 Python 或机器学习吗?只需要基础的 Python 知识和基本的大语言模型(LLM)概念,不需要深厚的机器学习背景。
学完之后真的能拿到证书吗?能。第 4 单元是结业项目,你的智能体会被自动评测,并在排行榜上排名,由此即可获得证书。
这门课教授哪些智能体框架?课程涵盖 smolagents(一个轻量级的智能体库)、LlamaIndex(用于基于你的数据通过工作流构建智能体)以及 LangGraph(用于构建可用于生产环境、可控的智能体流程)。
宝可梦加餐单元是讲什么的?加餐单元(3 Bonus)探讨如何构建可以玩宝可梦游戏的 AI 智能体,将智能体设计与游戏环境结合起来。
这门课会讲检索增强型智能体吗?会。第 3 单元专门讲解 Agentic RAG,介绍如何在不同框架下,把检索增强生成应用到智能体的使用场景中。
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