Langmuir turbulence, prevalent in the ocean surface boundary layer, induces significant vertical mixing, influencing temperature, salinity, mixed layer depth, and the distribution of biogeochemical tracers. However, resolving Langmuir turbulence in ocean and climate models is not feasible; thus, its mixing effects are typically parameterized, often within an existing turbulent mixing framework such as the K-profile parameterization (KPP). Here, we propose a novel scheme for Langmuir mixing effects within the KPP framework. Unlike the original KPP, which determines boundary layer depth using the bulk Richardson number, the KPP adopted here employs an alternative integral criterion to define boundary layer depth. This integral-criterion KPP exhibits markedly reduced sensitivity to model vertical resolution compared to the original KPP. We compare the performance of this new Langmuir mixing scheme with previous schemes using a 1D model across five distinct scenarios, including large eddy simulation (LES) results and field observations. The comparisons demonstrate that the new Langmuir mixing scheme, tailored for the integral-criterion KPP framework, overall outperforms previous schemes while retaining reduced sensitivity to vertical resolution.