CrewAI in Python: Coordinating Teams of AI Agents – Real Python
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Table of Contents
Get Started With CrewAI in Python
Build Your First Multi-Agent Team<br>Define Specialized Agents
Create Tasks and Coordinate Your Team
Control Task Dependencies Explicitly
Expand Agent Capabilities With Tools
Beware of Limitations and Gotchas<br>Multi-Agent Crews Are Expensive to Run
API Keys Are Required for Everything
Communication Is Task-Based, Not Conversational
Sequential Execution Is the Default and Safest Pattern
Verbose Logging Is Essential During Development
Conclusion
Next Steps
Frequently Asked Questions
Mark as Completed
Share
CrewAI in Python: Coordinating Teams of AI Agents
by Farah Abdou
Updated Jul 29, 2026
Reading time estimate 17m
intermediate
ai
Mark as Completed
Share
Table of Contents
Get Started With CrewAI in Python
Build Your First Multi-Agent Team<br>Define Specialized Agents
Create Tasks and Coordinate Your Team
Control Task Dependencies Explicitly
Expand Agent Capabilities With Tools
Beware of Limitations and Gotchas<br>Multi-Agent Crews Are Expensive to Run
API Keys Are Required for Everything
Communication Is Task-Based, Not Conversational
Sequential Execution Is the Default and Safest Pattern
Verbose Logging Is Essential During Development
Conclusion
Next Steps
Frequently Asked Questions
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Have you ever asked ChatGPT to research something, analyze the findings, and then write a polished report, all in one prompt? You probably got something back, but it wasn’t great. That’s because you’re asking a single model to wear way too many hats at once. CrewAI is a Python framework that solves this by letting you build a crew of specialized AI agents, each focusing on one part of the job.
For example, instead of cramming research, validation, and writing into a single prompt, you create a team. One agent researches, another validates, and a third writes. Each focuses on what it does best, working together to complete the task.
By the end of this tutorial, you’ll understand that:
CrewAI coordinates teams where each agent has a specific role , goal , and backstory that influence its behavior.
Sequential workflows automatically pass outputs from one agent to the next, making coordinated pipelines straightforward.
The context parameter gives you fine-grained control over which task outputs feed into other tasks.
You can equip agents with tools like web scraping to expand what they can do beyond text generation.
API costs multiply quickly since each agent makes separate LLM calls, and verbose logging helps you debug agent behavior.
Before you invest time learning CrewAI, you should understand when it’s the right tool for your project. This comparison highlights the key trade-offs:
Use Case<br>Pick CrewAI<br>Pick LangGraph<br>Pick AG2 / AutoGen
You need structured, role-based workflows
You want minimal boilerplate to go from prototype to production
You need complex state machines with conditional branching
Your agents need conversational, chat-driven coordination
CrewAI’s sweet spot is workflows where agents have clear responsibilities and work in a predictable sequence. If you need fine-grained state management with branching logic, LangGraph gives you more control. If your agents need conversational back-and-forth, AG2—the community fork of AutoGen—supports chat-driven coordination patterns and may be a better fit.
Microsoft AutoGen is now in maintenance mode, so new projects typically choose AG2 or Microsoft Agent Framework.
Get Your Code: Click here to download the free sample code you’ll use to build and coordinate teams of AI agents with CrewAI in Python.
Take the Quiz: Test your knowledge with our interactive “CrewAI in Python: Coordinating Teams of AI Agents” quiz. You’ll receive a score upon completion to help you track your learning progress:
Interactive Quiz
CrewAI in Python: Coordinating Teams of AI Agents<br>Check your understanding of CrewAI in Python. Review how to define agent roles, assign tasks, add tools, and coordinate multi-agent workflows.
Get Started With CrewAI in Python
Before you dive into building multi-agent systems with CrewAI, you’ll need to install it and set up an API key for your chosen language model provider. This tutorial uses Google’s Gemini model, which you can access for free through Google AI Studio.
Note: CrewAI is model-agnostic, so you’re not locked into Gemini. It also works with OpenAI, Anthropic’s Claude, Mistral, Groq, and...