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Basic Information

This repository is a curated collection of open-source projects and resources built around the CrewAI framework. It collects example integrations, tutorials, apps and community contributions that demonstrate how to build and deploy AI agents using CrewAI. The README explains what CrewAI is, sets submission and eligibility guidelines for inclusion, and describes how contributors can add projects. The list emphasizes open-source, developer-oriented projects maintained by individuals or small teams and excludes commercial or company-led proprietary offerings. The repository is intended as a discovery hub for developers, educators and practitioners looking for practical examples, patterns and community-built tools that extend or showcase CrewAI capabilities.

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Features
Organized sections for Integrations, Tutorials and Apps/UI that list projects with title, short description and author. Example integrations include an email agent connecting Gmail with Coinbase and Stripe and a payments integration for automated commerce. Tutorials cover knowledge graph search, multi-agent developer teams, flight and trip planning, Instacart automation, legal assistant and blood report analysis. Additional projects include a CrewAI crews generator and a blog post editor demo. The README includes clear submission guidelines, eligibility criteria, contribution steps and an acknowledgments section. The file tracks recent commits and contributors to show active maintenance and community engagement.
Use Cases
The repository helps developers and community members discover concrete CrewAI implementations and learn by example. It surfaces integrations with real APIs and payments, step‚Äëby‚Äëstep tutorials on multi-agent architectures, domain examples such as legal, healthcare and travel agents, and UI demos to accelerate prototyping. By aggregating community projects it reduces duplication of effort, shows patterns for orchestrating agents, and provides an entry path for contributors through clear submission rules and contribution steps. The collection also highlights authors and active work so users can follow projects, reproduce demos, adapt existing crews for their own use cases, and contribute improvements back to the ecosystem.

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