Microevolution and macroevolution describe evolutionary change at different scales: short-term genetic and phenotypic variation within populations, and long-term patterns of diversification and extinction. Despite their interconnected nature, they have often been studied separately, and the reciprocal causal links between them remain poorly understood due to their operation on different timescales and the complexity of the processes involved, making mechanistic approaches particularly challenging. To bridge this operational gap, we introduce a novel bottom-up, process-based computational framework that integrates genotype-to-phenotype mapping, fitness evaluation under environmental constraints, and biotic interactions shaping ecological niches and adaptive pressures, while incorporating lower-level mechanisms such as mutation, gene flow, and gene-pool expansion through stochastic duplication of genes. Its modular design accommodates diverse microevolutionary mechanisms to study the emergence of large-scale eco-evolutionary patterns from explicit individual-level processes. The framework allows addressing research questions ranging from the formation of spatiotemporal biodiversity patterns to the role of eco-evolutionary feedbacks in macroevolution. It provides an open-ended platform that serves both as a theoretical tool for testing evolutionary hypotheses and as a flexible environment for exploratory simulations. To illustrate its heuristic potential, we present proof-of-concept simulations under biologically plausible conditions that reproduce multiple well-documented macroevolutionary patterns-such as biphasic diversification, saturating and exponential-like biodiversity trends, speciation-extinction correlations, species duration distributions, and niche structuring-as emergent phenomena. Beyond reproducing patterns, the simulations reveal underlying mechanisms, including trial-and-error dynamics in long-term adaptation, high species turnover maintaining biodiversity equilibrium, and self-organized niche occupancy. These findings establish the framework as a versatile tool for investigating the complex interplay of ecological and evolutionary forces shaping biodiversity. By capturing emergent dynamics from mechanistic microevolutionary processes without imposing predefined constraints, the model provides a unique perspective on long-term evolutionary change, contributing to a broader theoretical toolkit for studying macroevolutionary patterns under controlled conditions. Future extensions could assess how variations in environmental dynamics, genomic architecture, or species interactions influence evolutionary trajectories, refining our understanding of biodiversity evolution.