crewai-agents

name	crewai-agents
description	Use when "CrewAI", "multi-agent systems", "agent orchestration", "AI crews", or asking about "autonomous agents", "agent collaboration", "role-based agents", "agent workflows", "AI team coordination"
version	1.0.0

CrewAI Multi-Agent Orchestration

Build teams of autonomous AI agents that collaborate on complex tasks.

When to Use

• 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

Quick Start

pip install crewai
pip install 'crewai[tools]'  # With 50+ built-in tools

Simple Crew

from crewai import Agent, Task, Crew, Process

# 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 trends.",
    verbose=True
)

writer = Agent(
    role="Technical Writer",
    goal="Create clear, engaging content about technical topics",
    backstory="You excel at explaining complex concepts.",
    verbose=True
)

# 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.",
    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
)

# Create and run crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    verbose=True
)

result = crew.kickoff(inputs={"topic": "AI Agents"})
print(result.raw)

Process Types

Sequential

Tasks execute in order:

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:

crew = Crew(
    agents=[researcher, writer, analyst],
    tasks=[research_task, write_task, analyze_task],
    process=Process.hierarchical,
    manager_llm="gpt-4o"
)

Using Tools

from crewai_tools import (
    SerperDevTool,       # Web search
    ScrapeWebsiteTool,   # Web scraping
    FileReadTool,        # Read files
    PDFSearchTool,       # Search PDFs
)

researcher = Agent(
    role="Researcher",
    goal="Find accurate information",
    backstory="Expert at finding data online.",
    tools=[SerperDevTool(), ScrapeWebsiteTool()]
)

Custom Tools

from crewai.tools import BaseTool

class CalculatorTool(BaseTool):
    name: str = "Calculator"
    description: str = "Performs mathematical calculations."

    def _run(self, expression: str) -> str:
        try:
            return f"Result: {eval(expression)}"
        except Exception as e:
            return f"Error: {str(e)}"

YAML Configuration

agents.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}.

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.

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.
  expected_output: >
    A detailed report in markdown format.
  agent: reporting_analyst
  output_file: report.md

Memory System

crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    memory=True,  # Enable memory
    embedder={
        "provider": "openai",
        "config": {"model": "text-embedding-3-small"}
    }
)

LLM Providers

from crewai import LLM

llm = LLM(model="gpt-4o")  # OpenAI
llm = LLM(model="claude-sonnet-4-5-20250929")  # Anthropic
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")  # Local

agent = Agent(role="Analyst", goal="Analyze data", llm=llm)

Best Practices

1. Clear roles - Each agent should have a distinct specialty
2. YAML config - Better organization for larger projects
3. Enable memory - Improves context across tasks
4. Set max_iter - Prevent infinite loops (default 15)
5. Limit tools - 3-5 tools per agent max
6. Rate limiting - Set max_rpm to avoid API limits

Common Issues

Agent stuck in loop:

agent = Agent(
    role="...",
    max_iter=10,
    max_rpm=5
)

Task not using context:

task2 = Task(
    description="...",
    context=[task1],  # Explicitly pass context
    agent=writer
)

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

Resources

• GitHub: https://github.com/crewAIInc/crewAI
• Docs: https://docs.crewai.com
• Tools: https://github.com/crewAIInc/crewAI-tools