FeaturedUdemy
AI Agents with Hugging Face smolagents
4.8(256)2k studentsADVANCEDEnglish
Korshub's take
The Korshub Take
This advanced course explores the rapidly evolving field of autonomous AI agents using smolagents, Hugging Face's lightweight Python framework. It provides a comprehensive progression from building a single agent to coordinating complex multi-agent architectures that reason and act.
Who It's For
Advanced developers and AI practitioners who want to build sophisticated, memory-enabled AI agents capable of writing code and orchestrating multi-step tasks.
Key Takeaways
- Framework Basics: Learn to use Hugging Face's smolagents to build code agents from scratch that can generate and execute Python.
- Tooling & Memory: Extend agent capabilities by integrating built-in and custom tools, and add memory to handle natural follow-up interactions.
- Advanced RAG: Implement retrieval-augmented generation (RAG) and build agentic RAG systems that reason over multiple steps across large document collections.
- Multi-Agent Coordination: Design multi-agent systems managed by a central coordinator, utilizing planning intervals, callbacks, and output validation for reliable results.
About this course
AI agents are changing how we work with data and software. From automating workflows to helping users navigate complex tasks, agents can search, reason, and act on your behalf. In this course, you’ll learn how to build agents using smolagents, a lightweight Python framework developed by Hugging Face.
Get Hands-On With Code Agents and Tools
You’ll start by understanding what makes code agents different and why they're so powerful. Then, you’ll build your first agent from scratch, using smolagents to generate and execute Python code. You’ll also learn how to plug in built-in tools and create custom tools to extend what your agents can do.
Make Agents Smarter With RAG and Memory
Next, you’ll use retrieval-augmented generation (RAG) to help agents pull info from large document collections. You’ll take things further by building agentic RAG systems—agents that reason over multiple steps to get better answers. You’ll also learn how to add memory so agents can handle follow-up questions naturally and keep track of what’s already been done.
Coordinate Multi-Agent Systems and Validate Outputs
In the final chapter, you’ll build multi-agent systems that coordinate specialist agents through a manager. You’ll add planning intervals, use callbacks for insight into agent behavior, and validate final answers, so your agents stay reliable and user-friendly.
By the end of the course, you’ll know how to build agents that think ahead, work together, and get things done.
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