CrewAI
Framework for orchestrating role-based AI agents that collaborate to solve complex tasks. Each agent has specialized roles, goals, and tools, working together like a software development team.
Why CrewAI?
You need multiple AI agents working together on complex tasks
You want role-based agent collaboration
You're building autonomous AI workflows
Signal Breakdown
What drives the Trust Score
Download Trend
Last 12 months
Tradeoffs & Caveats
Know before you commitYou need simple single-agent interactions
You're working in JavaScript/TypeScript primarily
You want a more lightweight agent framework
Pricing
Free tier & paid plans
Open-source free · CrewAI+: free beta
Enterprise pricing TBD
Platform still maturing
Alternative Tools
Other options worth considering
Often Used Together
Complementary tools that pair well with CrewAI
Learning Resources
Docs, videos, tutorials, and courses
Get Started
Repository and installation options
View on GitHub
github.com/crewAIInc/crewAI
pip install crewaiQuick Start
Copy and adapt to get going fast
from crewai import Agent, Task, Crew, Process
researcher = Agent(
role='Research Analyst',
goal='Find the latest trends in AI tools',
backstory='Expert researcher with deep knowledge of developer ecosystems',
verbose=True,
)
writer = Agent(
role='Content Writer',
goal='Write engaging articles about developer tools',
backstory='Skilled technical writer who makes complex topics accessible',
)
research_task = Task(description='Research the top 5 AI tools of 2025', agent=researcher)
write_task = Task(description='Write a blog post based on the research', agent=writer)
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_task],
process=Process.sequential,
)
result = crew.kickoff()
print(result)Code Examples
Common usage patterns
Agents with tools
Give agents access to web search and code execution
from crewai import Agent, Task, Crew
from crewai_tools import SerperDevTool, CodeInterpreterTool
search_tool = SerperDevTool()
code_tool = CodeInterpreterTool()
analyst = Agent(
role='Data Analyst',
goal='Analyze data and produce insights',
backstory='Expert data analyst with Python skills',
tools=[search_tool, code_tool],
verbose=True,
)
task = Task(
description='Find the npm download trends for React vs Vue in 2025 and plot a chart',
agent=analyst,
expected_output='A Python script that generates a chart',
)
crew = Crew(agents=[analyst], tasks=[task])
result = crew.kickoff()Hierarchical process
Use a manager agent to delegate to specialist agents
from crewai import Agent, Task, Crew, Process
manager = Agent(role='Project Manager', goal='Coordinate the team', backstory='...')
dev = Agent(role='Developer', goal='Write code', backstory='...')
qa = Agent(role='QA Engineer', goal='Test the code', backstory='...')
tasks = [
Task(description='Build a REST API for user authentication', agent=dev),
Task(description='Write integration tests for the API', agent=qa),
]
crew = Crew(
agents=[manager, dev, qa],
tasks=tasks,
process=Process.hierarchical,
manager_agent=manager,
)
crew.kickoff()Memory and context sharing
Enable long-term memory so agents learn across runs
from crewai import Crew, Process
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_task],
memory=True, # Enable long-term memory
embedder={
"provider": "openai",
"config": {"model": "text-embedding-3-small"},
},
verbose=True,
)
result = crew.kickoff(inputs={"topic": "AI orchestration frameworks"})Community Notes
Real experiences from developers who've used this tool