Despite the significant advances in subsymbolic artificial intelligence over the last decade, including novel Large Language Model (LLM) based methods, this technology comes with high, even prohibitive, development and application costs. Recent research suggests that instead of blindly increasing the model sizes and deploying larger numbers of better accelerators, there are benefits from a focus on the model structures and methods employed instead. An approach that has gained in popularity lately that addresses this is neurosymbolic reasoning. By focusing on various ways of combining subsymbolic, mostly neural network-based, computations with classical symbolic reasoning, it promises to alleviate the computation demands of pure deep learning approaches by guiding the learning process with symbolic knowledge. In this paper, we introduce the Modular Hybrid Agent Architecture (MHAgentA), a cognitive agent architecture, along with a Python framework that implements it. This architecture is designed to facilitate the prototyping and deployment of neurosymbolic agents that follow Kahneman’s System 1, System 2 model. We provide a breakdown of a high-level view of the architecture and outline the technical details of its implementation.