CrewAI - Multi-Agent AI Framework

Table of Contents

Introduction

Prerequisites

Installation and Setup

Core Concepts

Creating Agents

Defining Tasks

Crew Orchestration

Tool Integration

Sequential vs Parallel Execution

Memory and Context Management

Delegation Between Agents

Real-World Use Case: Research Agent Team

Real-World Use Case: Coding Agent Team

Best Practices

Conclusion

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Introduction

CrewAI is a cutting-edge framework for orchestrating autonomous AI agents that work together as a collaborative team. Inspired by real-world crew dynamics, it enables developers to create sophisticated multi-agent systems where each agent has a specific role, expertise, and set of tools.

Key features of CrewAI:

• Role-based agents: Define agents with specific roles, goals, and backstories
• Task management: Create structured tasks with expected outputs and dependencies
• Flexible orchestration: Sequential, parallel, and hierarchical execution modes
• Tool ecosystem: Rich set of built-in tools plus custom tool creation
• Memory systems: Short-term, long-term, and entity memory for context persistence
• Delegation: Agents can delegate tasks to other agents when appropriate

Multi-agent systems outperform single-agent approaches for complex tasks because they allow specialization, parallel processing, and collaborative problem-solving — much like a human team.

In this tutorial, you will learn how to create agents, define tasks, orchestrate crews, integrate tools, and build real-world multi-agent applications.

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Prerequisites

• Python 3.10 or higher
• An OpenAI API key (or other supported LLM provider)
• Basic understanding of Large Language Models (LLMs)
• Familiarity with Python async patterns (for parallel execution)

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Installation and Setup

# Install CrewAI with all tools
pip install crewai[tools]

Or minimal installation
pip install crewai

Set up environment variables
export OPENAIAPIKEY="your-openai-api-key"

Optional: for other LLM providers
export ANTHROPICAPIKEY="your-anthropic-key"

Verify the installation:

import crewai
print(f"CrewAI version: {crewai.version}")

from crewai import Agent, Task, Crew
print("All core components imported successfully")

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Core Concepts

CrewAI is built around three core abstractions:

Agent: An autonomous entity with a role, goal, backstory, and tools

Task: A specific piece of work with a description, expected output, and assigned agent

Crew: An orchestrator that manages agents and coordinates task execution

from crewai import Agent, Task, Crew, Process

The simplest possible crew
agent = Agent(
    role="Greeting Agent",
    goal="Greet the user warmly",
    backstory="You are a friendly agent that welcomes users.",
    verbose=True,
)

task = Task(
    description="Greet the user and wish them a productive day.",
    expectedoutput="A warm, personalized greeting message.",

    agent=agent,
)

crew = Crew(
    agents=[agent],
    tasks=[task],
    process=Process.sequential,
    verbose=True,
)

result = crew.kickoff()
print(result)

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Creating Agents

Agents are the core workers in CrewAI. Each agent should have a clearly defined role and area of expertise.

from crewai import Agent
from crewaitools import SerperDevTool, WebsiteSearchTool

Research analyst agent
researchanalyst = Agent(

    role="Senior Research Analyst",