打造统一代码库，让大模型多智能体研究更高效、可比、易上手。
MASLab: A Unified and Comprehensive Codebase for LLM-based Multi-Agent Systems
- 整合20+主流方法，每项都与官方实现逐步比对验证
- 提供统一环境与10+基准测试，确保评估公平一致
- 结构化设计降低理解门槛，适合新手快速参与
基于大语言模型的多智能体系统在提升单个LLM处理复杂多样任务方面展现出巨大潜力。然而，该领域缺乏统一的代码库，导致重复实现、比较不公平且研究门槛高。为此，我们提出MASLab，一个统一、全面且面向研究的LLM多智能体代码库。 (1) MASLab集成超过20种跨领域的成熟方法，并通过与官方实现逐步骤输出对比，严格验证其正确性。(2) 提供统一环境与多种基准，支持方法间的公平比较，确保输入一致性和标准化评估流程。(3) 所有方法均在共享的简洁结构中实现，显著降低理解与扩展难度。基于MASLab，我们开展了覆盖10多个基准和8种模型的广泛实验，为研究人员呈现当前多智能体方法的全景图。MASLab将持续演进，追踪最新进展，并欢迎开源社区贡献。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。