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

# Core framework
pip install crewai

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

Create project with CLI

# 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
)