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.

翻译：基于大语言模型的多智能体系统在增强单一LLM应对实际应用中复杂多样任务方面展现出显著潜力。尽管该领域已取得长足进展，但目前缺乏整合现有方法的统一化代码库，导致重复实现工作、不公平比较以及研究者面临高入门门槛。为应对这些挑战，我们提出MASLab——一个面向基于LLM的多智能体系统的统一化、综合性且便于研究的代码库。(1) MASLab集成了跨多个领域的20余种已确立方法，每种方法均通过逐步输出与其官方实现的比对进行严格验证；(2) MASLab提供一个包含多样化基准测试的统一环境，以确保方法间的公平比较，保持输入一致性与标准化评估协议；(3) MASLab采用共享精简结构实现方法部署，降低了理解与扩展的门槛。基于MASLab，我们开展了覆盖10余个基准测试和8种模型的广泛实验，为研究者呈现当前MAS方法的清晰全景视图。MASLab将持续演进，追踪领域前沿发展，并诚邀来自更广泛开源社区的贡献。