Course focus: The Hugging Face Agents Course is organized into four main units, taking learners from agent fundamentals through frameworks and agentic RAG to a final evaluated project.
What you will learn: The course introduces agent concepts and then compares smolagents, LlamaIndex, and LangGraph. Bonus material covers function-calling fine-tuning, observability, and evaluation.
Recommended order: Complete unit 0 and unit 1 before choosing a framework. Build the smallest tool-using agent in unit 2, write failure cases, and only then continue to agentic RAG and the final benchmark.
Before you begin: Basic Python knowledge is the stated prerequisite. Practical exercises may also require a Hugging Face account, model access, and compute depending on the chosen unit.
Who it suits: Useful for Python learners who want a project-based agent course and exposure to several widely used frameworks.
Important limits: Trying every framework at once creates unnecessary complexity. Framework APIs change, so use the course's current environment files and verify package versions before debugging code. Use restricted tokens, review every tool an agent can call, cap iterations and cost, and avoid granting file-system or browser access during early exercises.
Project and license: Repository content is provided under Apache-2.0; check the current LICENSE before reuse. Repository: https://github.com/huggingface/agents-course
Break the goal into input, action, and output before adding review checkpoints.
Keep examples, failure conditions, and review criteria so the tutorial stays reusable.