Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
# Create new crew project
crewai create crew my_project
cd my_project
# Install dependencies
crewai install
# Run the crew
crewai run
Simple crew (code-only)
from crewai import Agent, Task, Crew, Process
# 1. Define agents
researcher = Agent(
role="Senior Research Analyst",
goal="Discover cutting-edge developments in AI",
backstory="You are an expert analyst with a keen eye for emerging trends.",
verbose=True
)
writer = Agent(
role="Technical Writer",
goal="Create clear, engaging content about technical topics",
backstory="You excel at explaining complex concepts to general audiences.",
verbose=True
)
# 2. Define tasks
research_task = Task(
description="Research the latest developments in {topic}. Find 5 key trends.",
expected_output="A detailed report with 5 bullet points on key trends.",
agent=researcher
)
write_task = Task(
description="Write a blog post based on the research findings.",
expected_output="A 500-word blog post in markdown format.",
agent=writer,
context=[research_task] # Uses research output
)
# 3. Create and run crew
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_task],
process=Process.sequential, # Tasks run in order
verbose=True
)
# 4. Execute
result = crew.kickoff(inputs={"topic": "AI Agents"})
print(result.raw)
Core concepts
Agents - Autonomous workers
from crewai import Agent
agent = Agent(
role="Data Scientist", # Job title/role
goal="Analyze data to find insights", # What they aim to achieve
backstory="PhD in statistics...", # Background context
llm="gpt-4o", # LLM to use
tools=[], # Tools available
memory=True, # Enable memory
verbose=True, # Show reasoning
allow_delegation=True, # Can delegate to others
max_iter=15, # Max reasoning iterations
max_rpm=10 # Rate limit
)
Tasks - Units of work
from crewai import Task
task = Task(
description="Analyze the sales data for Q4 2024. {context}",
expected_output="A summary report with key metrics and trends.",
agent=analyst, # Assigned agent
context=[previous_task], # Input from other tasks
output_file="report.md", # Save to file
async_execution=False, # Run synchronously
human_input=False # No human approval needed
)
Crews - Teams of agents
from crewai import Crew, Process
crew = Crew(
agents=[researcher, writer, editor], # Team members
tasks=[research, write, edit], # Tasks to complete
process=Process.sequential, # Or Process.hierarchical
verbose=True,
memory=True, # Enable crew memory
cache=True, # Cache tool results
max_rpm=10, # Rate limit
share_crew=False # Opt-in telemetry
)
# Execute with inputs
result = crew.kickoff(inputs={"topic": "AI trends"})
# Access results
print(result.raw) # Final output
print(result.tasks_output) # All task outputs
print(result.token_usage) # Token consumption
Process types
Sequential (default)
Tasks execute in order, each agent completing their task before the next:
researcher:
role: "{topic} Senior Data Researcher"
goal: "Uncover cutting-edge developments in {topic}"
backstory: >
You're a seasoned researcher with a knack for uncovering
the latest developments in {topic}. Known for your ability
to find relevant information and present it clearly.
reporting_analyst:
role: "Reporting Analyst"
goal: "Create detailed reports based on research data"
backstory: >
You're a meticulous analyst who transforms raw data into
actionable insights through well-structured reports.
tasks.yaml
research_task:
description: >
Conduct thorough research about {topic}.
Find the most relevant information for {year}.
expected_output: >
A list with 10 bullet points of the most relevant
information about {topic}.
agent: researcher
reporting_task:
description: >
Review the research and create a comprehensive report.
Focus on key findings and recommendations.
expected_output: >
A detailed report in markdown format with executive
summary, findings, and recommendations.
agent: reporting_analyst
output_file: report.md
