What is CrewAI? (original) (raw)

Last Updated : 4 Apr, 2026

CrewAI is an open source framework that enables multiple AI agents to collaborate on complex tasks, where each agent has a specific role and works together efficiently with memory and context awareness.

• Uses multiple agents with defined roles to handle different parts of a task.
• Supports collaboration similar to a team working together on a shared goal.
• Maintains context and memory for smoother execution and better decisions.
• Applicable in areas like content creation, research, software development and customer support.

CrewAI

Setting Up Environment

To start using CrewAI, set up the environment by installing required packages and configuring API access.

Step 1: Install CrewAI

We will install the crewai package using pip to make all the necessary functionality available

!pip install crewai

Step 2: Set API Key

Configure your API key (e.g., Gemini API key) to enable model access and functionality.

Python `

import os os.environ["GOOGLE_API_KEY"] = "your api key"

`

Implementation

In this section, we will see how to use CrewAI by setting up a team of agents to work together on a party planning task.

1. Import Required Libraries

Before we start working with CrewAI, we need to import the required libraries. These libraries provide the essential functions to define agents, tasks and crews.

• Agent defines the individual agents that perform tasks.
• Task describes the tasks assigned to agents.
• Crew groups agents and tasks together for execution. Python `

from crewai import Agent, Task, Crew

`

2. Define Agents

Agents in CrewAI are the entities that perform specific tasks.

• Agents are defined with key attributes including role (what the agent does), goal (the outcome it aims to achieve) and backstory (context describing its skills and expertise).
• Use allow_delegation to control whether the agent can assign tasks to other agents.
• Set verbose=True to enable detailed explanations of the agent’s actions and reasoning.
• Create agents for roles such as party planner, food coordinator, decorator and entertainment manager. Python `

party_planner = Agent( role="Party Planner", goal="Create the party plan, including the theme, timeline, and guest list.", backstory=( "You organize the vision for the party, create a timeline, and ensure all aspects are planned. " "You send out invitations and coordinate with the other agents." ), allow_delegation=False, verbose=True, llm="gemini/gemini-2.5-flash" )

food_beverage_coordinator = Agent( role="Food & Beverage Coordinator", goal="Organize the food and drinks for the party, ensuring there’s enough variety for all guests.", backstory=( "You handle the food and drink preparations, whether it’s cooking, ordering, or working with caterers. " "You make sure guests have plenty to eat and drink throughout the event." ), allow_delegation=False, verbose=True, llm="gemini/gemini-2.5-flash" )

decorator = Agent( role="Decorator", goal="Make the party venue look great, fitting the theme and making it fun for guests.", backstory=( "You decorate the venue to match the theme and create a welcoming and festive environment. " "You ensure the venue is ready when the guests arrive." ), allow_delegation=False, verbose=True, llm="gemini/gemini-2.5-flash" )

entertainment_guest_relations = Agent( role="Entertainment & Guest Relations Coordinator", goal="Organize entertainment, games, and manage guest interactions to ensure a fun party.", backstory=( "You make sure the guests have fun, whether it’s through music, games, or other activities. " "You also help guests with seating and ensure the event flows smoothly." ), allow_delegation=False, verbose=True, llm="gemini/gemini-2.5-flash" )

`

3. Assigning Tasks

• Define tasks for each agent based on their role, goal and responsibilities.
• Link each task to a specific agent so it is executed accordingly.
• Ensure tasks cover different aspects like planning, food, decoration and entertainment.
• Each agent performs its assigned task using the configured LLM (e.g., Gemini) for execution. Python `

party_plan_task = Task( description="Create a party plan including theme, timeline, and guest list.", expected_output="Complete party plan with theme, timeline, and invitations.", agent=party_planner )

food_task = Task( description="Organize food and drinks menu and set up food stations.", expected_output="Food and drinks ready for the party.", agent=food_beverage_coordinator )

decor_task = Task( description="Decorate the venue according to the theme.", expected_output="Venue decorated and ready for guests.", agent=decorator )

entertainment_task = Task( description="Organize music, games, and manage guest interactions.", expected_output="A fun and engaging atmosphere with happy guests.", agent=entertainment_guest_relations )

`

4. Creating and Managing a Crew

• Create a Crew by combining all defined agents and their assigned tasks into a single workflow.
• This enables agents to collaborate and coordinate on the shared goal (e.g., party planning).
• The Crew manages execution flow, ensuring each agent performs its task using the configured LLM. Python `

party_crew = Crew(agents=[party_planner, food_beverage_coordinator, decorator, entertainment_guest_relations], tasks=[party_plan_task, food_task, decor_task, entertainment_task], verbose=True)

`

5. Executing the Workflow

• Start the Crew execution to begin the workflow.
• Agents perform their assigned tasks based on their roles and goals.
• The Crew coordinates the process and ensures tasks are completed using the configured LLM. Python `

party_result = party_crew.kickoff(inputs={})

`

**Output:

Download full code from here

Applications

• Supports event planning by coordinating tasks like planning, food, decoration and entertainment.
• Enhances content creation by enabling agents to research, write and review content.
• Assists in software development through code generation, review and validation.
• Enables market research by collecting data, analyzing trends and generating insights.