ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems
<|reference_start|>ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems: Recently, there has been increasing interest in using Large Language Models (LLMs) to construct complex multi-agent systems to perform tasks such as compiling literature reviews, drafting consumer reports, and planning vacations. Many tools and libraries exist for helping create such systems, however none support recursive multi-agent systems -- where the models themselves flexibly decide when to delegate tasks and how to organize their delegation structure. In this work, we introduce ReDel: a toolkit for recursive multi-agent systems that supports custom tool-use, delegation schemes, event-based logging, and interactive replay in an easy-to-use web interface. We show that, using ReDel, we are able to achieve significant performance gains on agentic benchmarks and easily identify potential areas of improvements through the visualization and debugging tools. Our code, documentation, and PyPI package are open-source and free to use under the MIT license.<|reference_end|>
@article{zhu2024redel:,
    title={ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems},
    author={Andrew Zhu, Liam Dugan, Chris Callison-Burch},
    journal={arXiv preprint arXiv:2408.02248},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02248},
    primaryClass={cs.CL cs.MA cs.SE}
}