CrewAI Tutorial · Course Guide

This course is based on the CrewAI 1.x official documentation, spanning 26 chapters from zero to enterprise-grade multi-agent applications.

Model Integration Conventions

CrewAI supports hundreds of models through LiteLLM. Per course requirements, all examples use OpenAI-compatible third-party models:

from crewai import LLM

llm = LLM(
    model="openai/deepseek-chat",          # provider/model format
    base_url="https://api.deepseek.com/v1",
    api_key="sk-xxx",
    temperature=0.7,
)

What You Will Learn

Stage	Chapters	Content
Getting Started	01–05	Core concepts, first Crew, deep dive into Agent/Task/Crew
Intermediate	06–12	LLM configuration, tooling system, hierarchical process, memory, knowledge bases, structured output
Advanced	13–19	Planning & fault tolerance, callback events, Flow workflows, engineering project structure, multi-Crew orchestration
Production Practice	20–26	MCP, evaluation & training, observability, deployment & service, three capstone projects, best practices checklist

Official Reference Sources

This course is strictly aligned with these authoritative sources:

Source	Link
docs.crewai.com	https://docs.crewai.com/
github.com	https://github.com/joaomdmoura/crewAI

Content version baseline: CrewAI 1.15+

Prerequisites

• Python basics; recommended to complete chapters 1–6 of this site's Agno tutorial first for a solid Agent concept foundation;
• Python 3.10–3.13 environment (CrewAI official requirement).

👉 Start with Chapter 1.