Abstract Bayesian inference of phylogenies with MCMC is without a doubt a staple in the study of evolution. Yet, this method still suffers from a practical challenge identified more than two decades ago: designing tree topology proposals that efficiently sample the tree space. In this article, I introduce the concept of tree topology proposals that adapt to the posterior distribution as it is estimated. I use this concept to elaborate two adaptive variants of existing proposals and an adaptive proposal based on a novel design philosophy in which the structure of the proposal is informed by the posterior distribution of trees. I investigate the performance of these proposals by first presenting a metric that captures the performance of each proposals within a mixture. Using this metric, I then compare the adaptive proposals performance to the performance of standard and parsimony-guided proposals on 11 empirical datasets. Using adaptive proposals led to consistent performance gains and resulted in up to 18-fold increases in mixing efficiency and 6-fold increases in converge rate without increasing the computational cost of these analyses. [Bayesian inference; Adaptive tree proposals; Markov chain Monte Carlo; phylogenetics; posterior probability distribution.]