CrewAI

Information

CrewAI is a framework for orchestrating collaborative, role-based AI agents. It allows you to create teams of agents that work together to complete complex tasks, mimicking a human organization.

Main Functionalities and Features

• Role-Based Agents: Define agents with specific roles, goals, and backstories.
• Autonomous Delegation: Agents can decide to delegate tasks to other agents.
• Process-Driven: Supports sequential, hierarchical, and consensual processes.
• Tool Integration: Easily add custom tools or use existing ones from LangChain.
• Output Validation: Ensure the output of one agent meets the requirements for the next.
• Memory: Short-term, long-term, and entity memory for agents.

Installation

Python (Universal)

CrewAI requires Python 3.10 to 3.13. It is recommended to use a virtual environment.

pip install crewai crewai-tools

macOS / Linux

Ensure you have Python installed. You may need to use pip3.

python3 -m venv .venv
source .venv/bin/activate
pip install crewai crewai-tools

Windows

Using PowerShell:

python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install crewai crewai-tools

Setup for Developer

The fastest way to start a new CrewAI project is using the CLI.

1. Create a new project:

   crewai create crew my_cool_crew
   

2. Install dependencies:

   cd my_cool_crew
   crewai install
   

3. Configure environment: Update the .env file with your LLM API keys:

   OPENAI_API_KEY=sk-...
   

4. Run the crew:

   crewai run
   

Setup with Docker for Developer

Using Docker ensures a consistent environment for all team members.

docker-compose.yaml:

version: '3.8'
services:
  crew:
    image: python:3.11-slim
    volumes:
      - .:/app
    working_dir: /app
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
    command: sh -c "pip install crewai crewai-tools && python main.py"

Usage, tips and tricks

Basic Code Example

from crewai import Agent, Task, Crew, Process

# Define Agents
researcher = Agent(
  role='Senior Research Analyst',
  goal='Uncover cutting-edge developments in AI',
  backstory='You are an expert at identifying emerging trends.'
)

writer = Agent(
  role='Content Strategist',
  goal='Write engaging technical articles',
  backstory='You specialize in simplifying complex AI topics.'
)

# Define Tasks
research_task = Task(description='Analyze 2024 AI trends', agent=researcher)
write_task = Task(description='Write a blog post based on research', agent=writer)

# Form the Crew
my_crew = Crew(
  agents=[researcher, writer],
  tasks=[research_task, write_task],
  process=Process.sequential
)

result = my_crew.kickoff()
print(result)

Tips

• Backstories: The more detailed the backstory, the better the agent performs.
• Hierarchical Process: Use a Manager agent for complex projects with many agents.
• Local Models: Use Ollama with CrewAI by setting the base_url or using the LLM class.

Similar Software

• AgentKit: Blockchain interaction for AI agents.
• LangGraph: Graph-based orchestration for stateful agent workflows.
• AutoGen: Microsoft framework for multi-agent conversations.
• PydanticAI: Python framework for building LLM applications with strong typed inputs and outputs.

See also

• CrewAI Official Website
• CrewAI Documentation
• AI Agent
• AI
• LLM