by Niklas Moser, Dmitri Finkelshtein, Georgy Chargaziya, Stephen J. Cornell, Sara Hamis, Jacob G. Scott, Dagim Shiferaw Tadele, Otso OvaskainenStatistically analysing interacting particle systems remains challenging because the governing equations are analytically intractable. Existing solutions include moment closure methods with pseudolikelihood-based frameworks, and likelihood-free frameworks based on extensive simulations, both relying on heuristic choices whose validity is difficult to predict. As a resolution, we rigorously derive an asymptotically exact expression for the likelihood of agent-based models (ABMs) operating in continuous space and time that can be formulated as reactant–catalyst–product (RCP) models. We derive an expression for the conditional density of agents given information about the current and earlier distributions of neighbouring agents. We utilize this expression to construct an asymptotically exact likelihood that applies to both spatial snapshot and time-series data. We implement the likelihood expression and a Bayesian parameter estimation framework in the R-package agentBayes and demonstrate its utility in biological research and beyond with simulated case studies and empirical data on the evolution of cancer cell populations.