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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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Introduction to Hugging Face smolagents
FreeDiscover 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.
Introduction to Hugging Face smolagents50 xpLet the Agent Do the Math: Expense Insights100 xpWait, You Didn't Write Any Functions?50 xpCreating an Agent With Tools50 xpWriting Code Through Prompts: Compound Interest100 xpLet Agents Search the Web100 xpCreating an Agent With Custom Tools50 xpJust Add @tool: Writing a Custom Tool100 xpCheck, Please! Giving Agents Access to Data100 xpOne Prompt, Many Tools100 xp - 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.
RAG Refresher50 xpRAG in Order: From Manuals to Answers100 xpSo, What’s Actually Happening in RAG?50 xpThe Step That Adds Meaning50 xpIntroduction to Agentic RAG50 xpStateful by Design: Build a RAG Search Tool100 xpConnect the Tool, Unlock Agentic RAG100 xpWorking With Multi-Step Agents50 xpTrack Agent Steps, Monitor the Action100 xpGame Time: Run the Full Agent100 xp - 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.
Introduction to Multi-Agent Systems50 xpDivide and Conquer: Creating Specialist Agents100 xpMeet the Team: Agent Coordination50 xpManaging Agent Memory50 xpDon't Forget: Keeping Memory Between Calls100 xpTracing the Agent’s Code Execution100 xpAgent Final Answer Validation50 xpSay More, Agent: Enforcing Answers Length100 xpSmarter Agents: Validate and Retry100 xpCongratulations!50 xp
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VP of Content, DataCamp
Adel is a Data Science educator, speaker, and Evangelist at DataCamp, where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.
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