How to Set Up CrewAI Multi-Agent Workflows
Build and deploy multi-agent AI systems using CrewAI's role-based framework for complex business tasks.
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What You'll Learn
This intermediate-level guide walks you through how to set up crewai multi-agent workflows step by step. Estimated time: 14 min.
Step 1: Install CrewAI and define agents
Set up CrewAI in your Python project and define specialized agents with roles, goals, and backstories for your use case.
Step 2: Create tasks and workflows
Define tasks that agents will complete, specify dependencies between tasks, and configure the process flow.
Step 3: Add tool integrations
Give agents access to external tools — web search, file operations, APIs, and custom functions they need to complete their tasks.
Step 4: Configure memory and context sharing
Set up agent memory so agents learn from interactions and share context for better collaboration.
Step 5: Deploy and monitor
Deploy your crew to production with proper error handling, logging, and cost monitoring for LLM API usage.
Frequently Asked Questions
When should I use CrewAI vs LangGraph?▾
Use CrewAI for role-based agent teams with clear task delegation. Use LangGraph for complex stateful workflows that need fine-grained control over execution flow.
How many agents should a crew have?▾
Start with 2-3 agents with distinct roles. More agents add coordination overhead. A researcher, writer, and reviewer crew covers most content workflows.
What does a CrewAI workflow cost to run?▾
Cost depends on the LLM used. A 3-agent crew using GPT-4o typically costs $0.50-2.00 per task. Using Claude Haiku can reduce costs to $0.05-0.20 per task.