Features
- Role-based agent design with personas
- Sequential and hierarchical task workflows
- Built-in tool integration and delegation
- Memory and context sharing between agents
Pros
- Intuitive role-based agent modeling
- Easy to set up multi-agent collaborations
- Good abstractions for common agent patterns
Cons
- Python-only framework
- Less flexible than LangGraph for custom workflows
- Agent interactions can be unpredictable
Overview
CrewAI is a framework for orchestrating autonomous AI agents that work together to complete tasks. Its key concept is the “crew”: a team of AI agents, each with a defined role, goal, and backstory, collaborating on a set of tasks with specified workflows.
The framework uses a role-playing approach where each agent is given a persona (e.g., “Senior Research Analyst” or “Technical Writer”) that guides its behavior. Tasks are assigned to agents with clear descriptions and expected outputs, and agents can delegate subtasks to each other when needed.
CrewAI supports sequential workflows (tasks executed in order), hierarchical workflows (a manager agent coordinates worker agents), and allows agents to share context and memory for coherent multi-step outputs.
When to Use
Choose CrewAI when building multi-agent systems with clearly defined roles and task flows. It is ideal for content generation pipelines, research workflows, and other scenarios where breaking work into specialized agent roles is natural.
Getting Started
pip install crewaifrom crewai import Agent, Task, Crew
researcher = Agent(role="Researcher", goal="Find accurate information")
writer = Agent(role="Writer", goal="Create clear content")
task = Task(description="Write a summary of AI trends", agent=writer)
crew = Crew(agents=[researcher, writer], tasks=[task])
result = crew.kickoff()