CrewAI - Multi-Agent Orchestration Framework
Build teams of autonomous AI agents that collaborate to solve complex tasks.
When to use CrewAI
Use CrewAI when:
- Building multi-agent systems with specialized roles
- Need autonomous collaboration between agents
- Want role-based task delegation (researcher, writer, analyst)
- Require sequential or hierarchical process execution
- Building production workflows with memory and observability
- Need simpler setup than LangChain/LangGraph
Key features:
- Standalone: No LangChain dependencies, lean footprint
- Role-based: Agents have roles, goals, and backstories
- Dual paradigm: Crews (autonomous) + Flows (event-driven)
- 50+ tools: Web scraping, search, databases, AI services
- Memory: Short-term, long-term, and entity memory
- Production-ready: Tracing, enterprise features
Use alternatives instead:
- LangChain: General-purpose LLM apps, RAG pipelines
- LangGraph: Complex stateful workflows with cycles
- AutoGen: Microsoft ecosystem, multi-agent conversations
- LlamaIndex: Document Q&A, knowledge retrieval
Quick start
Installation
bash
# Core framework
pip install crewai
# With 50+ built-in tools
pip install 'crewai[tools]'
Create project with CLI
bash
# 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)
python
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
python
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
python
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
python
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:
python
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_task],
process=Process.sequential # Task 1 → Task 2 → Task 3
)
Hierarchical
Auto-creates a manager agent that delegates and coordinates:
python
crew = Crew(
agents=[researcher, writer, analyst],
tasks=[research_task, write_task, analyze_task],
process=Process.hierarchical, # Manager delegates tasks
manager_llm="gpt-4o" # LLM for manager
)
Using tools
Built-in tools (50+)
bash
pip install 'crewai[tools]'
python
from crewai_tools import (
SerperDevTool, # Web search
ScrapeWebsiteTool, # Web scraping
FileReadTool, # Read files
PDFSearchTool, # Search PDFs
WebsiteSearchTool, # Search websites
CodeDocsSearchTool, # Search code docs
YoutubeVideoSearchTool, # Search YouTube
)
# Assign tools to agent
researcher = Agent(
role="Researcher",
goal="Find accurate information",
backstory="Expert at finding data online.",
tools=[SerperDevTool(), ScrapeWebsiteTool()]
)
Custom tools
python
from crewai.tools import BaseTool
from pydantic import Field
class CalculatorTool(BaseTool):
name: str = "Calculator"
description: str = "Performs mathematical calculations. Input: expression"
def _run(self, expression: str) -> str:
try:
result = eval(expression)
return f"Result: {result}"
except Exception as e:
return f"Error: {str(e)}"
# Use custom tool
agent = Agent(
role="Analyst",
goal="Perform calculations",
tools=[CalculatorTool()]
)
YAML configuration (recommended)
Project structure
my_project/
├── src/my_project/
│ ├── config/
│ │ ├── agents.yaml # Agent definitions
│ │ └── tasks.yaml # Task definitions
│ ├── crew.py # Crew assembly
│ └── main.py # Entry point
└── pyproject.toml
agents.yaml
yaml
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
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
crew.py
python
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool
@CrewBase
class MyProjectCrew:
"""My Project crew"""
@agent
def researcher(self) -> Agent:
return Agent(
config=self.agents_config['researcher'],
tools=[SerperDevTool()],
verbose=True
)
@agent
def reporting_analyst(self) -> Agent:
return Agent(
config=self.agents_config['reporting_analyst'],
verbose=True
)
@task
def research_task(self) -> Task:
return Task(config=self.tasks_config['research_task'])
@task
def reporting_task(self) -> Task:
return Task(
config=self.tasks_config['reporting_task'],
output_file='report.md'
)
@crew
def crew(self) -> Crew:
return Crew(
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
verbose=True
)
main.py
python
from my_project.crew import MyProjectCrew
def run():
inputs = {
'topic': 'AI Agents',
'year': 2025
}
MyProjectCrew().crew().kickoff(inputs=inputs)
if __name__ == "__main__":
run()
Flows - Event-driven orchestration
For complex workflows with conditional logic, use Flows:
python
from crewai.flow.flow import Flow, listen, start, router
from pydantic import BaseModel
class MyState(BaseModel):
confidence: float = 0.0
class MyFlow(Flow[MyState]):
@start()
def gather_data(self):
return {"data": "collected"}
@listen(gather_data)
def analyze(self, data):
self.state.confidence = 0.85
return analysis_crew.kickoff(inputs=data)
@router(analyze)
def decide(self):
return "high" if self.state.confidence > 0.8 else "low"
@listen("high")
def generate_report(self):
return report_crew.kickoff()
# Run flow
flow = MyFlow()
result = flow.kickoff()
See Flows Guide for complete documentation.
Memory system
python
# Enable all memory types
crew = Crew(
agents=[researcher],
tasks=[research_task],
memory=True, # Enable memory
embedder={ # Custom embeddings
"provider": "openai",
"config": {"model": "text-embedding-3-small"}
}
)
Memory types: Short-term (ChromaDB), Long-term (SQLite), Entity (ChromaDB)
LLM providers
python
from crewai import LLM
llm = LLM(model="gpt-4o") # OpenAI (default)
llm = LLM(model="claude-sonnet-4-5-20250929") # Anthropic
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434") # Local
llm = LLM(model="azure/gpt-4o", base_url="https://...") # Azure
agent = Agent(role="Analyst", goal="Analyze data", llm=llm)
CrewAI vs alternatives
| Feature | CrewAI | LangChain | LangGraph | |
|---|
| Best for | Multi-agent teams | General LLM apps | Stateful workflows | |
| Learning curve | Low | Medium | Higher | |
| Agent paradigm | Role-based | Tool-based | Graph-based | |
| Memory | Built-in | Plugin-based | Custom | |
Best practices
- Clear roles - Each agent should have a distinct specialty
- YAML config - Better organization for larger projects
- Enable memory - Improves context across tasks
- Set max_iter - Prevent infinite loops (default 15)
- Limit tools - 3-5 tools per agent max
- Rate limiting - Set max_rpm to avoid API limits
Common issues
Agent stuck in loop:
python
agent = Agent(
role="...",
max_iter=10, # Limit iterations
max_rpm=5 # Rate limit
)