[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-73961":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":33,"readmeContent":34,"aiSummary":35,"trendingCount":16,"starSnapshotCount":16,"syncStatus":36,"lastSyncTime":37,"discoverSource":38},73961,"agents-course","huggingface\u002Fagents-course","huggingface","This repository contains the Hugging Face Agents Course. ","",null,"MDX",29269,2104,213,88,0,110,251,709,330,44.97,"Apache License 2.0",false,"main",true,[27,28,29,7,30,31,32],"agentic-ai","agents","course","langchain","llamaindex","smolagents","2026-06-12 02:03:20","# \u003Ca href=\"https:\u002F\u002Fhf.co\u002Flearn\u002Fagents-course\" target=\"_blank\">The Hugging Face Agents Course\u003C\u002Fa>\n\nIf you like the course, **don't hesitate to ⭐ star this repository**. This helps us to **make the course more visible 🤗**.\n\n\u003Cimg src=\"https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fagents-course\u002Fcourse-images\u002Fresolve\u002Fmain\u002Fen\u002Fcommunication\u002Fplease_star.gif\" alt=\"Star the repo\" \u002F>\n\n## Content\n\nThe course is divided into 4 units. These will take you from **the basics of agents to a final assignment with a benchmark**.\n\nSign up here (it's free) 👉 \u003Ca href=\"https:\u002F\u002Fbit.ly\u002Fhf-learn-agents\" target=\"_blank\">https:\u002F\u002Fbit.ly\u002Fhf-learn-agents\u003C\u002Fa>\n\nYou can access the course here 👉 \u003Ca href=\"https:\u002F\u002Fhf.co\u002Flearn\u002Fagents-course\" target=\"_blank\">https:\u002F\u002Fhf.co\u002Flearn\u002Fagents-course\u003C\u002Fa>\n\n| Unit    | Topic                                                                                                          | Description                                                                                                                            |\n|---------|----------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|\n| 0       | [Welcome to the Course](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fagents-course\u002Fen\u002Funit0\u002Fintroduction)                      | Welcome, guidelines, necessary tools, and course overview.                                                                             |\n| 1       | [Introduction to Agents](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fagents-course\u002Fen\u002Funit1\u002Fintroduction)                     | Definition of agents, LLMs, model family tree, and special tokens.                                                                     |\n| 1 Bonus | [Fine-tuning an LLM for Function-calling](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fagents-course\u002Fbonus-unit1\u002Fintroduction) | Learn how to fine-tune an LLM for Function-Calling                                                                                     |\n| 2       | [Frameworks for AI Agents](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fagents-course\u002Funit2\u002Fintroduction)                      | Overview of `smolagents`, `LangGraph` and `LlamaIndex`.                                                                                |\n| 2.1     | [The Smolagents Framework](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fagents-course\u002Funit2\u002Fsmolagents\u002Fintroduction)           | Learn how to build effective agents using the `smolagents` library, a lightweight framework for creating capable AI agents.            |\n| 2.2     | [The LlamaIndex Framework](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fagents-course\u002Funit2\u002Fllama-index\u002Fintroduction)          | Learn how to build LLM-powered agents over your data using indexes and workflows using the `LlamaIndex` toolkit.                       |\n| 2.3     | [The LangGraph Framework](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fagents-course\u002Funit2\u002Flanggraph\u002Fintroduction)             | Learn how to build production-ready applications using the `LangGraph` framework giving you control tools over the flow of your agent. |\n| 2 Bonus | [Observability and Evaluation](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fagents-course\u002Fbonus-unit2\u002Fintroduction)            | Learn how to trace and evaluate your agents.                                                                                           |\n| 3       | [Use Case for Agentic RAG](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fagents-course\u002Funit3\u002Fagentic-rag\u002Fintroduction)          | Learn how to use Agentic RAG to help agents respond to different use cases using various frameworks.                                                                   |\n| 4       | [Final Project - Create, Test and Certify Your Agent](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fagents-course\u002Funit4\u002Fintroduction)          | Automated evaluation of agents and leaderboard with student results.                                                                   |\n| 3 Bonus | [Agents in Games with Pokemon](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fagents-course\u002Fbonus-unit3\u002Fintroduction)            | Explore the exciting intersection of AI Agents and games.        |\n                                                                                   \n## Prerequisites\n\n- Basic knowledge of Python\n- Basic knowledge of LLMs\n\n## Contribution Guidelines\n\nIf 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:\u002F\u002Fdiscord.gg\u002FUrrTSsSyjb). For specific contributions, here are some guidelines:\n\n### Small typo and grammar fixes\n\nIf you find a small typo or grammar mistake, please fix it yourself and submit a pull request. This is very helpful for students.\n\n### New unit\n\nIf 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.\n\n## Citing the project\n\nTo cite this repository in publications:\n\n```bibtex\n@misc{agents-course,\n  author = {Burtenshaw, Ben and Thomas, Joffrey and Simonini, Thomas and Paniego, Sergio},\n  title = {The Hugging Face Agents Course},\n  year = {2025},\n  howpublished = {\\url{https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fagents-course}},\n  note = {GitHub repository},\n}\n```\n","该项目是Hugging Face提供的一个关于构建AI代理的在线课程。核心功能包括从基础概念到使用`smolagents`、`LlamaIndex`和`LangGraph`等框架创建高效AI代理的全过程教学，特别强调了如何微调语言模型以支持函数调用。该课程适合对人工智能特别是代理技术感兴趣的开发者学习，无论是初学者还是有一定经验的人都能从中受益。通过系统化的单元学习，参与者能够掌握构建智能代理所需的关键技能，并在最终项目中应用所学知识进行实践。",2,"2026-06-11 03:48:08","high_star"]