OWL, or Optimized Workforce Learning, is an open-source multi-agent framework built on CAMEL-AI for automating complex real-world tasks. It coordinates specialized agents and toolkits so a group of models can research, browse, execute tools, and collaborate on tasks that would be difficult for a single prompt-response assistant.

The project includes browser and web capabilities, document and data tools, code execution, Model Context Protocol integration, and support for multiple model providers. Its examples and benchmark work emphasize general task-solving, with the repository reporting strong performance on the GAIA benchmark for open-source agent systems.

OWL is intended for researchers and developers exploring general-purpose multi-agent assistants and tool-rich automation. It provides a substantial reference implementation for experimenting with agent roles, collaboration strategies, model choices, and external tool integration in one framework.