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agents course

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Basic Information

This repository provides a complete course on building agentic AI using the Hugging Face ecosystem. It is aimed at developers, researchers, and enthusiasts who want a guided path from fundamentals to practical implementation. The materials include Jupyter notebooks, example scripts, and documentation that explain agent architectures, how to implement agents with Hugging Face libraries, and how to integrate those agents with tools such as LangChain, LlamaIndex, and SmolAgents. The README documents installation steps, example usage, and a simple code snippet that demonstrates interacting with the Hugging Face API. The project is distributed under an MIT license and includes guidance on running notebooks and contributing to the course materials. Overall it is a learning-focused repository that bundles instructional resources, runnable examples, and configuration to reproduce the exercises.

Links

App URL
https://github.com/Justinvarghese511/agents-course
Github Repository
https://github.com/Justinvarghese511/agents-course/blob/main/README.md

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App Details

Features
The repository bundles a structured curriculum and practical artifacts for learning agentic AI. Key features include topic coverage of agent fundamentals and architecture, concrete examples showing Hugging Face API usage, integration notes for LangChain and LlamaIndex, and examples for lightweight SmolAgents. It provides Jupyter notebooks and runnable scripts so learners can follow hands-on exercises, a requirements.txt to reproduce the environment, and simple installation and usage instructions for starting notebooks and running examples. The README also includes contribution guidelines and mentions release artifacts for downloading packaged course materials. The project is released under the MIT License and lists contact and contribution steps to encourage community improvements.
Use Cases
This course repository is helpful as a focused learning resource that combines conceptual material with hands-on code. Learners gain an organized overview of agentic AI concepts, step-by-step instructions to set up the environment, and ready-to-run notebooks and scripts that demonstrate model retrieval and agent integration workflows. The inclusion of example code and dependency specifications reduces setup friction and helps users reproduce experiments. Integration guidance for LangChain and LlamaIndex helps bridge model usage with data retrieval and orchestration patterns, while SmolAgents examples illustrate lightweight agent designs. The contributing guidance and releases let users extend the materials and obtain packaged versions of the course content, making it suitable for self-study or as a teaching supplement.

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