{"id":25476229,"url":"https://github.com/huggingface/agents-course","last_synced_at":"2025-05-16T12:00:21.542Z","repository":{"id":276810832,"uuid":"917868934","full_name":"huggingface/agents-course","owner":"huggingface","description":"This repository contains the Hugging Face Agents Course. 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This helps us to **make the course more visible 🤗**.\n\n\u003cimg src=\"https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/please_star.gif\" alt=\"Star the repo\" /\u003e\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://bit.ly/hf-learn-agents\" target=\"_blank\"\u003ehttps://bit.ly/hf-learn-agents\u003c/a\u003e\n\nYou can access the course here 👉 \u003ca href=\"https://hf.co/learn/agents-course\" target=\"_blank\"\u003ehttps://hf.co/learn/agents-course\u003c/a\u003e\n\n| Unit    | Topic                                                                                                          | Description                                                                                                                            |\n|---------|----------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|\n| 0       | [Welcome to the Course](https://huggingface.co/learn/agents-course/en/unit0/introduction)                      | Welcome, guidelines, necessary tools, and course overview.                                                                             |\n| 1       | [Introduction to Agents](https://huggingface.co/learn/agents-course/en/unit1/introduction)                     | Definition of agents, LLMs, model family tree, and special tokens.                                                                     |\n| 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                                                                                     |\n| 2       | [Frameworks for AI Agents](https://huggingface.co/learn/agents-course/unit2/introduction)                      | Overview of `smolagents`, `LangGraph` and `LlamaIndex`.                                                                                |\n| 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.            |\n| 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.                       |\n| 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. |\n| 2 Bonus | [Observability and Evaluation](https://huggingface.co/learn/agents-course/bonus-unit2/introduction)            | Learn how to trace and evaluate your agents.                                                                                           |\n| 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.                                                                   |\n| 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.                                                                   |\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://discord.gg/UrrTSsSyjb). 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://github.com/huggingface/agents-course}},\n  note = {GitHub repository},\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhuggingface%2Fagents-course","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhuggingface%2Fagents-course","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhuggingface%2Fagents-course/lists"}