CrewAI
ActiveDescription
A multi-agent collaboration framework where AI agents form crews to accomplish complex tasks together. Role definition, task assignment, tool sharing, and process orchestration.
Key Features
- Multi-agent orchestration — Crew mechanism defines agent roles, goals, and backstories with task dependencies, sequential and parallel execution
- CrewAI Flows — Enterprise event-driven architecture with fine-grained control and single LLM calls for precise task orchestration
- Tool sharing and integration — Agents share tools with support for custom tools and built-in integrations (search, code execution, etc.)
- Role definition system — Each agent configurable with role, goal, backstory for specialized division of labor
- Memory and context management — Long-term and short-term memory shared between agents for conversation and task context
- Structured output — Supports Pydantic models and JSON format output for parseable results
Use Cases
💡 Building multi-agent research teams that automatically collect, analyze, and synthesize information
💡 Creating automated content production pipelines with agents handling writing, review, and publishing
💡 Developing intelligent customer service systems where different agents handle inquiries, tech support, and follow-ups
💡 Building code review pipelines with agents responsible for security checks, performance analysis, and code standards
Categories
Quick Start
# Install CrewAI
pip install crewai
# Import necessary modules
from crewai import Agent, Task, Crew
# Define a researcher agent
researcher = Agent(
role="Researcher",
goal="Collect and analyze the latest AI trends",
backstory="You are a senior AI industry analyst"
)
# Define a task
task = Task(
description="Analyze key trends in the AI Agent field for 2024",
agent=researcher
)
# Create Crew and execute
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
print(result)