(arXiv paper: https://arxiv.org/abs/2305.14177) The ChemGymRL Open Source Library enables the use of Reinforcement Learning (RL) algorithms to train agents towards the target of operating individual chemistry benches given specific material targets. The environment can be thought of as a virtual chemistry laboratory consisting of different stations (or benches) where a variety of tasks can be completed. The laboratory consists of three basic elements: vessels, shelves, and benches. Vessels contain materials, in pure or mixed form, with each vessel tracking the hidden internal state of their contents. Whether an agent can determine this state, through measurement or reasoning, is up to the design of each bench and the user’s goals. A shelf can hold any vessels not currently in use, as well as the resultants (or output vessels) of previous experiments. Benches are sub-environments which enact various physical or chemical processes on the vessels. Each bench recreates a simplified version of one task in a material design pipeline and has an observation and action space specific to the task at hand. ChemGymRL is designed in a modular fashion so that new benches can be added or modified with minimal difficulty or changes to the source code. A bench must be able to receive a set of initial experimental supplies, possibly including vessels, and return the results of the intended experiment, also including modified vessels. The details and methods of how the benches interact with the vessels between these two points are completely up to the user, including the goal of the bench. In this initial version of ChemGymRL we have implemented some core benches, which we describe in the following sections and which will allow us to demonstrate an example workflow.