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crewai callback

In this sixth installment of our CrewAI series, we dive deep into the advanced mechanics of task management, specifically focusing on how to master callbacks and retrieve specific task outputs. If you’ve ever wondered how to programmatically react when a task finishes or how to pass a very specific piece of data from one agent’s output to another’s input, this tutorial is designed for you. We move beyond the basics to show you how to maintain granular control over your multi-agent workflows. The core of this video is a hands-on practical implementation. I’ll walk you through the Python code required to set up task_callbacks, explaining how they serve as the perfect "hooks" for logging, notifications, or triggering external API calls. You will also learn the syntax for accessing the output attribute of specific tasks within a crew, ensuring your agents aren't just passing broad context, but are acting on precise, structured information. By the end of this guide, you will be able to build more responsive and intelligent AI crews that behave exactly how you want them to. Whether you are building a data processing pipeline or a complex content generation engine, mastering these two features is a game-changer for any CrewAI developer. Don’t forget to check the resources in the pinned comment for the source code used in this session! Github: [https://github.com/nithishkumar86/CrewAI_C...]