Skip to main content
HomePython

Course

Building Agentic Workflows with LlamaIndex

AdvancedSkill Level
4.7+
54 reviews
Updated 03/2026
Build AI agentic workflows that can plan, search, remember, and collaborate, using LlamaIndex.
Start Course for Free
PythonArtificial Intelligence2 hr5 videos15 Exercises1,250 XPStatement of Accomplishment

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies

Group

Training 2 or more people?

Try DataCamp for Business

Course Description

Build Smart, Capable AI Agents from Scratch

Learn how to build intelligent agentic workflows from the ground up using LlamaIndex, a powerful framework for orchestrating AI behavior. Starting with a single-agent setup and moving toward multi-agent collaboration, you'll create a system that can perform research, generate reports, and reflect on its own outputs.

Create and Orchestrate Agentic Workflows

You’ll begin by setting up a simple AI agent using the AgentWorkflow class and connecting it to functionalities such as searching the web. You’ll learn how to give your agents “memory” using context and make their responses feel more dynamic with streaming outputs. As the course progresses, you’ll explore advanced capabilities like event handling, concurrent execution, and creating custom workflows using step-based logic. You'll also implement self-reflection loops to allow agents to improve their own outputs.

Design Multi-Agent Systems with LlamaIndex

In the final step, you’ll move beyond single-agent systems by designing teams of AI agents that work together. You’ll build workflows where each agent takes on a specialized role—like researching, writing, and reviewing—and shares information through a common context. You’ll experiment with sequential, looping, and branching workflows, gaining insights into how to manage complex tasks with multiple agents.

Prerequisites

Intermediate Python
1

Building an Agent

This chapter introduces the core concepts and setup needed to build agentic workflows using LlamaIndex. Get hands-on practice with tools like Tavily and OpenAI models, define your first tool function, and create an intelligent agent capable of interacting with the web. By the end, you'll have a fully functional AI agent built with LlamaIndex, ready to tackle real-world tasks.
Start Chapter
2

Creating a Deep Research Workflow

Why stop at one agent when you can build a team? In this chapter, you'll unlock the power of multi-agent systems, where specialized agents work together like a research squad, each with a unique role. You'll build your own collaborative agent workflow that searches, writes, and reviews, showing how teamwork makes your AI smarter and more scalable.
Start Chapter
Building Agentic Workflows with LlamaIndex
Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance reviewEnroll Now

Don’t just take our word for it

*4.7
from 54 reviews
80%
17%
2%
2%
0%
  • Matt
    4 days ago

  • Chandan
    5 days ago

  • Edgar
    2 weeks ago

  • Kim Chuan
    2 weeks ago

  • Michael
    5 weeks ago

  • Kelvin
    6 weeks ago

Edgar

Kim Chuan

Michael

FAQs

What will I be able to build after completing this course?

You will build a single AI agent that interacts with the web, then create a multi-agent research workflow where specialized agents collaborate on searching, writing, and reviewing tasks.

What tools and APIs does this course use alongside LlamaIndex?

You use Tavily for web search and OpenAI models for language generation. These are integrated as tools within the LlamaIndex framework to build functional agents.

Do I need prior experience with LLMs or AI agents?

No specialized AI experience is required. The course is beginner level, though you should have a solid grasp of Intermediate Python and Introduction to Python.

How is the course organized?

It has two chapters and 17 exercises. Chapter 1 builds a single agent with tool functions, and Chapter 2 creates a collaborative multi-agent deep research workflow.

What makes multi-agent workflows useful compared to a single agent?

Multi-agent systems divide complex tasks among specialized agents, each with a unique role. This makes your AI smarter, more scalable, and better at handling research and reporting tasks.

    Join over 19 million learners and start Building Agentic Workflows with LlamaIndex today!

    Create Your Free Account

    or

    By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

    Grow your data skills with DataCamp for Mobile

    Make progress on the go with our mobile courses and daily 5-minute coding challenges.