CF

Build AI Agents with CrewAI

2h 51m 42s
English
Paid

Build AI Agents with CrewAI is a 26-lesson 2 hours 51 minutes self-paced course by Zero To Mastery. Learn to build intelligent, collaboratively working AI agents with CrewAI.

Course facts

Lessons
26
Duration
2 hours 51 minutes
Level
All levels
Language
English
Updated
Instructor
Zero To Mastery
Price
Premium
Learn to build intelligent, collaboratively working AI agents with CrewAI. Master the organization of multi-agent workflows using LLM, tools, and real task distribution. Gain practical experience to improve your skills in the field of AI!

What you will learn:

  • Create and organize multi-agent AI workflows using CrewAI
  • Design agent roles such as Supervisor, Researcher, and Action Executor
  • Utilize LLM for decision-making and personalized task execution
  • Create tool-based actions for data retrieval and API interaction
  • Connect multiple agents for dynamic collaboration
  • Configure agent prompts to manage their behavior and outputs

If you are interested in AI that actually performs tasks rather than just discussing them, this brief course is for you. You will learn to create AI agents that collaborate, delegate, and act using the intuitive structure of CrewAI.

We will start by defining roles for each agent, then link them with tools and actions that allow data retrieval, decision-making, and effective collaboration. You will progress from setting up simple workflows to organizing full-fledged AI systems.

By the end of the course, you will create a multi-agent AI interview coach powered by CrewAI!

Who teaches Build AI Agents with CrewAI? Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

What lessons are included in Build AI Agents with CrewAI?

This is a demo lesson (10:00 remaining)

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#1: Introduction: Why AI Agents Are The Future
All Course Lessons (26)
#Lesson TitleDurationAccess
1
Introduction: Why AI Agents Are The Future Demo
11:13
2
What We're Building (Panic! I Have a Job Interview Tomorrow!)
02:27
3
Getting an OpenAI API Key
04:31
4
CrewAI Setup
04:50
5
Get the Data
05:54
6
About CrewAI
07:51
7
AI Interviewer Agent
07:32
8
Interview Prep Task
03:46
9
Assemble First Crew
05:05
10
Second AI Agent
07:15
11
Adding Knowledge Sources
08:14
12
Game Plan for Pro Level
04:01
13
CrewAI Setup
06:30
14
AI Research Agent
08:11
15
AI Research Tasks
05:08
16
AI Coach and Interview Tasks
06:24
17
Assembling First Crew and Exporting
09:26
18
Interviewing and Coaching Tasks
06:43
19
Second AI Crew
07:06
20
Hierarchical Process
06:34
21
Andrei Asks For Your Help!
01:37
22
CrewAI Setup
03:51
23
Research AI Agent and 1st Task
08:31
24
Research and Sentiment Tasks
09:46
25
AI Scoring Agent and Task
07:38
26
Final AI Crew
11:38
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Frequently asked questions

What prerequisites are needed before enrolling in this course?
Before enrolling, students should have a basic understanding of artificial intelligence concepts and some familiarity with using APIs, as the course involves obtaining and using an OpenAI API key. Prior experience with AI development platforms will be beneficial but is not mandatory.
What will I build during the course?
Throughout the course, students will build a series of AI agents capable of performing tasks collaboratively. This includes an AI interviewer agent, a research agent, and an AI coach. By the end of the course, students will have assembled an AI crew capable of handling complex workflows and tasks such as interview preparation and research.
Who is the target audience for this course?
This course is designed for individuals interested in advancing their skills in AI, specifically in building intelligent and collaborative AI agents. It is suitable for software developers, AI enthusiasts, and professionals looking to integrate AI agents into their workflows or projects.
How does the depth and scope of this course compare to similar courses?
The course offers a practical approach focused on organizing multi-agent workflows using CrewAI. Unlike some introductory AI courses, this one delves into task distribution among agents and the integration of various tools to enhance functionality, providing a specialized understanding of collaborative AI systems.
What specific tools or platforms will I learn to use?
Students will learn to use CrewAI for organizing AI agents and managing workflows. The course also covers the use of the OpenAI API for developing intelligent agents. Emphasis is placed on practical application and integration of these tools to build a cohesive AI agent system.
What topics are not covered in this course?
The course does not cover the foundational theories of AI in depth, nor does it address machine learning model building from scratch. It focuses specifically on using pre-existing AI tools and APIs to construct and manage collaborative AI agents in practical scenarios.
How much time should I expect to commit to this course?
The course consists of 26 lessons, and while the exact runtime is not specified, students should plan to spend several hours on each lesson to fully grasp the material and complete the practical tasks. The time commitment will vary depending on prior experience with AI technologies.