OWL, short for Optimized Workforce Learning, is an open-source multi-agent framework from the CAMEL-AI community for automating real-world tasks. Multiple specialized agents collaborate like a small workforce, browsing the web, operating tools, and completing end-to-end jobs that a single chatbot would fumble. The project has over 20,000 stars on GitHub and was actively developed in October 2026.
OWL builds on the CAMEL-AI framework and adds training and coordination techniques that teach agent teams to work together effectively. Instead of one generalist agent doing everything, OWL assigns roles across a workforce of agents that plan, search, write, and execute, mirroring how human teams divide labor. It has posted leading results on agent benchmarks such as GAIA, and it supports web interaction and a wide range of tools out of the box. Everything is open source, so researchers and builders can reproduce results, swap models, and extend the workforce with their own agents.
OWL is free and open source. You can clone the repository and run it yourself; the only costs are the model API calls or local compute your agents consume.
OWL is aimed at AI researchers and engineers exploring multi-agent systems, and at builders who need agents that finish multi-step, real-world tasks rather than single-turn chats. It is a solid foundation if you want a workforce of agents instead of one assistant.