<!-- mobian-agent-page publisher="dailydev" canonical="https://daily.dev/posts/crewai-in-python-coordinating-teams-of-ai-agents-real-python-vsoav1ste" -->

---
title: CrewAI in Python: Coordinating Teams of AI Agents – Real...
description: CrewAI is a Python framework for building teams of specialized AI agents that coordinate on complex tasks. Rather than asking a single model to do everything,...
canonical: https://daily.dev/posts/crewai-in-python-coordinating-teams-of-ai-agents-real-python-vsoav1ste
twitter:card: summary_large_image
twitter:site: @dailydotdev
og:type: website
og:site_name: daily.dev
og:title: CrewAI in Python: Coordinating Teams of AI Agents – Real Python | daily.dev
og:description: CrewAI is a Python framework for building teams of specialized AI agents that coordinate on complex tasks. Rather than asking a single model to do everything,...
og:url: https://daily.dev/posts/crewai-in-python-coordinating-teams-of-ai-agents-real-python-vsoav1ste
og:image: https://api.daily.dev/og/posts/VsOav1STe.png
og:image:alt: CrewAI in Python: Coordinating Teams of AI Agents – Real Python
og:image:width: 1200
og:image:height: 630
og:locale: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# CrewAI in Python: Coordinating Teams of AI Agents – Real Python

**[Real Python](https://daily.dev/sources/rpython)** · 17 min read · 1 upvotes · 0 comments

## Summary

CrewAI is a Python framework for building teams of specialized AI agents that coordinate on complex tasks. Rather than asking a single model to do everything, you define agents with distinct roles, goals, and backstories, then chain them into sequential workflows where outputs flow automatically between tasks. The tutorial covers installation with Gemini, creating single and multi-agent crews, using the context parameter for explicit task dependencies, equipping agents with tools like web scraping, and understanding production trade-offs including API cost multiplication, verbose debugging, and the difference between CrewAI's task-based communication vs. conversational frameworks like AG2.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://realpython.com/crewai-python>

## Questions this post answers

### How does CrewAI differ from AG2 (the AutoGen fork) for multi-agent coordination?

CrewAI coordinates agents through task-based structured outputs, automatically passing results from one task to the next, with no built-in agent negotiation or debate. AG2, the actively maintained community fork of Microsoft AutoGen, instead supports conversational, back-and-forth coordination between agents. Choose CrewAI for predictable role-based pipelines and AG2 for chat-driven agent discussion.

_daily.dev helps developers weighing CrewAI against AG2 compare coordination styles before committing to an architecture._

### How do I control which task outputs feed into a specific task in CrewAI?

Use the context parameter on a Task, passing a list of the tasks whose outputs should be provided as input, for example context=[research_task]. This works even with parallel tasks run via async_execution=True, since a task with multiple context dependencies waits until all of them finish before starting, giving explicit control beyond the automatic sequential default.

_developers wiring multi-agent pipelines can track patterns like this on daily.dev while designing task dependencies._

### Why do CrewAI multi-agent crews cost more to run than a single LLM call?

Each agent in a CrewAI crew makes its own separate LLM API call, so a three-agent crew running sequential tasks costs roughly three times a single-agent call, and costs rise further if agents iterate or invoke tools. Token usage can be checked via crew.usage_metrics after calling kickoff(), and a cheaper model can be assigned to tool-calling operations through the function_calling_llm parameter.

_teams budgeting for agentic workloads use daily.dev to stay ahead of cost trade-offs before scaling a crew to production._

## Similar posts on daily.dev

- [CrewAI: A Practical Guide to Role-Based Agent Orchestration](https://daily.dev/posts/crewai-a-practical-guide-to-role-based-agent-orchestration-5ei4ywdxf) · DigitalOcean Community · 1 upvotes · 0 comments
- [Building Multiagent Systems for Workflow Automation With CrewAI](https://daily.dev/posts/building-multiagent-systems-for-workflow-automation-with-crewai-uc8gsfe1m) · The New Stack · 0 upvotes · 0 comments
- [From Chaos to Coordination: The Power of Multi AI Agent Systems with CrewAI](https://daily.dev/posts/from-chaos-to-coordination-the-power-of-multi-ai-agent-systems-with-crewai-x5fnonqnm) · Faun · 2 upvotes · 0 comments
- [How to Build Your Own Agentic AI System Using CrewAI](https://daily.dev/posts/how-to-build-your-own-agentic-ai-system-using-crewai-lroizkkxm) · Towards Data Science · 0 upvotes · 0 comments
- [Build a multi-agent AI system using CrewAI, Gemini, and CircleCI](https://daily.dev/posts/build-a-multi-agent-ai-system-using-crewai-gemini-and-circleci-ye03vxnbq) · CircleCI · 0 upvotes · 0 comments

---

Tags: [#python](https://daily.dev/tags/python), [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents)

[View this post on daily.dev](https://daily.dev/posts/crewai-in-python-coordinating-teams-of-ai-agents-real-python-vsoav1ste)

```json
{"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]}
{"@context":"https://schema.org","@type":"TechArticle","headline":"CrewAI in Python: Coordinating Teams of AI Agents – Real Python","url":"https://daily.dev/posts/crewai-in-python-coordinating-teams-of-ai-agents-real-python-vsoav1ste","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/crewai-in-python-coordinating-teams-of-ai-agents-real-python-vsoav1ste"},"datePublished":"2026-07-29T14:13:37.601Z","dateModified":"2026-09-14T06:16:05.780Z","description":"CrewAI is a Python framework for building teams of specialized AI agents that coordinate on complex tasks. Rather than asking a single model to do everything,...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/0bda2fc238153d593b06606657921d67?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/0bda2fc238153d593b06606657921d67?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Real Python","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Real Python","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/rpython","url":"https://daily.dev/sources/rpython"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/crewai-in-python-coordinating-teams-of-ai-agents-real-python-vsoav1ste","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"python,llm,ai-agents","timeRequired":"PT17M"}
{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Real Python","item":"https://daily.dev/sources/rpython"},{"@type":"ListItem","position":3,"name":"CrewAI in Python: Coordinating Teams of AI Agents – Real Python"}]}
{"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/crewai-in-python-coordinating-teams-of-ai-agents-real-python-vsoav1ste#faq","mainEntity":[{"@type":"Question","name":"How does CrewAI differ from AG2 (the AutoGen fork) for multi-agent coordination?","acceptedAnswer":{"@type":"Answer","text":"CrewAI coordinates agents through task-based structured outputs, automatically passing results from one task to the next, with no built-in agent negotiation or debate. AG2, the actively maintained community fork of Microsoft AutoGen, instead supports conversational, back-and-forth coordination between agents. Choose CrewAI for predictable role-based pipelines and AG2 for chat-driven agent discussion. daily.dev helps developers weighing CrewAI against AG2 compare coordination styles before committing to an architecture."}},{"@type":"Question","name":"How do I control which task outputs feed into a specific task in CrewAI?","acceptedAnswer":{"@type":"Answer","text":"Use the context parameter on a Task, passing a list of the tasks whose outputs should be provided as input, for example context=[research_task]. This works even with parallel tasks run via async_execution=True, since a task with multiple context dependencies waits until all of them finish before starting, giving explicit control beyond the automatic sequential default. developers wiring multi-agent pipelines can track patterns like this on daily.dev while designing task dependencies."}},{"@type":"Question","name":"Why do CrewAI multi-agent crews cost more to run than a single LLM call?","acceptedAnswer":{"@type":"Answer","text":"Each agent in a CrewAI crew makes its own separate LLM API call, so a three-agent crew running sequential tasks costs roughly three times a single-agent call, and costs rise further if agents iterate or invoke tools. Token usage can be checked via crew.usage_metrics after calling kickoff(), and a cheaper model can be assigned to tool-calling operations through the function_calling_llm parameter. teams budgeting for agentic workloads use daily.dev to stay ahead of cost trade-offs before scaling a crew to production."}}]}
```

