This article presents a general multilevel structural equation modeling (MSEM) framework for assessing multilevel mediation. The authors argue that traditional multilevel modeling (MLM) approaches have limitations in accommodating mediation pathways with Level-2 outcomes and may conflate between- and within-level components of indirect effects. They propose that MSEM can overcome these limitations by allowing for the separate estimation of within- and between-group components of indirect effects, providing a more accurate understanding of mediation in hierarchical data. The MSEM framework also accommodates mediation models where mediators and outcome variables are assessed at Level 2, such as bottom-up effects. The authors demonstrate that MSEM can subsume both existing and new multilevel mediation models as special cases and provide empirical examples to illustrate the framework's flexibility. They also compare MSEM to MLM, highlighting that MSEM allows for more accurate estimation of indirect effects in designs where the effect of a Level-2 variable on a Level-1 outcome is mediated. The authors advocate for the use of MSEM as a comprehensive system for examining mediation effects in multilevel data.This article presents a general multilevel structural equation modeling (MSEM) framework for assessing multilevel mediation. The authors argue that traditional multilevel modeling (MLM) approaches have limitations in accommodating mediation pathways with Level-2 outcomes and may conflate between- and within-level components of indirect effects. They propose that MSEM can overcome these limitations by allowing for the separate estimation of within- and between-group components of indirect effects, providing a more accurate understanding of mediation in hierarchical data. The MSEM framework also accommodates mediation models where mediators and outcome variables are assessed at Level 2, such as bottom-up effects. The authors demonstrate that MSEM can subsume both existing and new multilevel mediation models as special cases and provide empirical examples to illustrate the framework's flexibility. They also compare MSEM to MLM, highlighting that MSEM allows for more accurate estimation of indirect effects in designs where the effect of a Level-2 variable on a Level-1 outcome is mediated. The authors advocate for the use of MSEM as a comprehensive system for examining mediation effects in multilevel data.