Title: MARLlib: Extending RLlib for Multi-agent Reinforcement Learning. Abstract: Despite the fast development of multi-agent reinforcement learning (MARL) methods, there is a lack of commonly-acknowledged baseline implementation and evaluation platforms. As a result, an urgent need for MARL researchers is to develop an integrated library suite, similar to the role of RLlib in single-agent RL, that delivers reliable MARL implementation and replicable evaluation in various bechmarks. To fill such a research gap, in this paper, we propose Multi-Agent RLlib (MARLlib), a comprehensive MARL algorithm library that facilitates RLlib for solving multi-agent problems. With a novel design of agent-level distributed dataflow, MARLlib manages to unify tens of algorithms, including different types of independent learning, centralized critic, and value decomposition methods; this leads to a highly composable integration of MARL algorithms that are not possible to unify before. Furthermore, MARLlib goes beyond current work by integrating diverse environment interfaces and providing flexible parameter sharing strategies; this allows to create versatile solutions to  cooperative, competitive, and mixed tasks with minimal code modifications for end users. A plethora of experiments  are conducted to substantiate the correctness of our implementation, based on which we further derive new insights on the relationship between the  performance and the design of algorithmic components. With MARLlib, we expect researchers to be able to tackle broader real-world multi-agent problems with trustworthy solutions. Our code\footnote{\url{https://github.com/ICLR2023Paper4242/MARLlib}} and documentation\footnote{\url{https://iclr2023marllib.readthedocs.io/}} are released for reference.