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AI Agents with Hugging Face smolagents

AdvancedSkill Level
4.8+
206 reviews
Updated 09/2025
Learn how to build intelligent agents that reason, act, and solve real-world tasks using Python.
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PythonArtificial Intelligence3 hr10 videos30 Exercises2,300 XP2,063Statement of Accomplishment

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Course Description

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 ToolsYou’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 MemoryNext, 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 OutputsIn 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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What you'll learn

  • Understand how smolagents' code agents work and why they’re powerful
  • Build agents that solve real-world tasks using Python
  • Create custom tools to extend what agents can do
  • Design multi-agent workflows to solve more complex problems

Prerequisites

Working with Hugging FaceRetrieval Augmented Generation (RAG) with LangChain
1

Introduction to Hugging Face smolagents

Discover what makes code agents special and how they use Python to reason and act. Build your first agent with smolagents, add built-in and community tools for web access, and create custom tools to connect agents with data.
Start Chapter
2

Agentic RAG and Multi-Step Agents

Transform your traditional RAG pipeline into an agentic system that retrieves information iteratively and reasons across multiple steps. Build stateful tools to support advanced retrieval, guide agents with planning intervals to improve outcomes, and use callbacks to track and customize agent behavior at runtime.
Start Chapter
3

Multi-Agent Systems, Memory and Validation

Tackle complex workflows by orchestrating teams of specialized agents under a coordinating manager. Add memory to retain context across interactions, debug agent behavior using execution traces and reasoning steps, and implement robust validation strategies to ensure high-quality, reliable responses.
Start Chapter
AI Agents with Hugging Face smolagents
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*4.8
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FAQs

What is smolagents and why should I learn it?

smolagents is a lightweight Python framework by Hugging Face for building AI agents that generate and execute code. It is well suited for creating agents that search, reason, and act autonomously.

Does the course cover retrieval-augmented generation for agents?

Yes. Chapter 2 teaches you to build agentic RAG systems where agents iteratively retrieve information from document collections and reason across multiple steps for better answers.

What is a multi-agent system and will I build one?

A multi-agent system coordinates specialist agents through a manager agent. In Chapter 3, you build such systems, add memory for context retention, and implement output validation.

How many prerequisites does this advanced course have?

Ten prerequisites are listed, covering Python fundamentals, LangChain, OpenAI API, Hugging Face, prompt engineering, and RAG. Strong Python and LLM experience is expected.

Will I learn to create custom tools for my agents?

Yes. Chapter 1 teaches you to build custom tools that connect agents with external data and functionality, extending what your agents can do beyond built-in capabilities.

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