LLM-based multi-agent systems (MAS) have demonstrated significant potential in enhancing single LLMs to address complex and diverse tasks in practical applications. Despite considerable advancements, the field lacks a unified codebase that consolidates existing methods, resulting in redundant re-implementation efforts, unfair comparisons, and high entry barriers for researchers. To address these challenges, we introduce MASLab, a unified, comprehensive, and research-friendly codebase for LLM-based MAS. (1) MASLab integrates over 20 established methods across multiple domains, each rigorously validated by comparing step-by-step outputs with its official implementation. (2) MASLab provides a unified environment with various benchmarks for fair comparisons among methods, ensuring consistent inputs and standardized evaluation protocols. (3) MASLab implements methods within a shared streamlined structure, lowering the barriers for understanding and extension. Building on MASLab, we conduct extensive experiments covering 10+ benchmarks and 8 models, offering researchers a clear and comprehensive view of the current landscape of MAS methods. MASLab will continue to evolve, tracking the latest developments in the field, and invite contributions from the broader open-source community.
View on arXiv@article{ye2025_2505.16988,
title={ MASLab: A Unified and Comprehensive Codebase for LLM-based Multi-Agent Systems },
author={ Rui Ye and Keduan Huang and Qimin Wu and Yuzhu Cai and Tian Jin and Xianghe Pang and Xiangrui Liu and Jiaqi Su and Chen Qian and Bohan Tang and Kaiqu Liang and Jiaao Chen and Yue Hu and Zhenfei Yin and Rongye Shi and Bo An and Yang Gao and Wenjun Wu and Lei Bai and Siheng Chen },
journal={arXiv preprint arXiv:2505.16988},
year={ 2025 }
}