  We introduce a new software toolbox for agent-based simulation. Facilitating
rapid prototyping by offering a user-friendly Python API, its core rests on an
efficient C++ implementation to support simulation of large-scale multi-agent
systems. Our software environment benefits from a versatile message-driven
architecture. Originally developed to support research on financial markets, it
offers the flexibility to simulate a wide-range of different (easily
customisable) market rules and to study the effect of auxiliary factors, such
as delays, on the market dynamics. As a simple illustration, we employ our
toolbox to investigate the role of the order processing delay in normal trading
and for the scenario of a significant price change. Owing to its general
architecture, our toolbox can also be employed as a generic multi-agent system
simulator. We provide an example of such a non-financial application by
simulating a mechanism for the coordination of no-regret learning agents in a
multi-agent network routing scenario previously proposed in the literature.
