The mu opioid receptor (μOR) is a key target for therapeutic and abused drugs. This study uses a proteomics and computational approach to map the proximal proteome of the activated μOR and identify subcellular location, trafficking, and functional partners of GPCR activity. The research reveals that distinct opioid agonists induce differences in the μOR proximal proteome through endocytosis and endosomal sorting. Two novel μOR network components, EYA4 and KCTD12, are identified, which are recruited based on receptor-triggered G protein activation and may form a previously unrecognized buffering system for G protein activity, broadly modulating cellular GPCR signaling. The study demonstrates that the μOR's subcellular location significantly influences its proximal proteome. Three agonists—DAMGO, morphine, and PZM21—induce distinct changes in the μOR's proximal protein environment. DAMGO causes strong internalization and trafficking, while morphine results in less endocytosis, and PZM21 leads to no detectable changes in μOR localization. These findings highlight the need for a systematic approach to identify location-specific proximal proteomes. A computational framework is developed to model receptor trafficking by utilizing spatially specific APEX references. This framework quantitatively deconvolutes the effect of receptor location and proximal interactors in proximity labeling data. The framework enables modeling of receptor trafficking directly from proximity labeling data, minimizing the need for independent measurements with complementary methods. The study identifies novel proteins in the μOR proximal interaction network, including COMMD3 and VPS35, which influence receptor distribution between cell surface and intracellular compartments. EYA4 and KCTD12 are found to be G protein-dependent proteins near the μOR, interacting with G protein subunits rather than the receptor itself. These proteins regulate μOR signaling through G proteins. The study also shows that EYA4 enhances Gs-mediated stimulation of cytoplasmic cAMP and limits the ability of Gi to suppress this response. KCTD12 promotes signal desensitization after the peak and impacts both Gs and Gi-mediated signaling. These findings suggest that EYA4 and KCTD12 have distinct functional effects on GPCR signaling. The study highlights the importance of understanding the proximal interaction networks of GPCRs, as they play a crucial role in cellular responses to ligands. The computational framework developed in this study provides a comprehensive dataset of the proximal protein environment of the μOR, enabling the identification of novel functional interactors and the characterization of ligand-specific effects on GPCR activation. This approach is broadly applicable for assessing and comparing ligand-specific effects on GPCR activation, offering insights into the molecular mechanisms of how chemically distinct ligands can evoke different cellular responses.The mu opioid receptor (μOR) is a key target for therapeutic and abused drugs. This study uses a proteomics and computational approach to map the proximal proteome of the activated μOR and identify subcellular location, trafficking, and functional partners of GPCR activity. The research reveals that distinct opioid agonists induce differences in the μOR proximal proteome through endocytosis and endosomal sorting. Two novel μOR network components, EYA4 and KCTD12, are identified, which are recruited based on receptor-triggered G protein activation and may form a previously unrecognized buffering system for G protein activity, broadly modulating cellular GPCR signaling. The study demonstrates that the μOR's subcellular location significantly influences its proximal proteome. Three agonists—DAMGO, morphine, and PZM21—induce distinct changes in the μOR's proximal protein environment. DAMGO causes strong internalization and trafficking, while morphine results in less endocytosis, and PZM21 leads to no detectable changes in μOR localization. These findings highlight the need for a systematic approach to identify location-specific proximal proteomes. A computational framework is developed to model receptor trafficking by utilizing spatially specific APEX references. This framework quantitatively deconvolutes the effect of receptor location and proximal interactors in proximity labeling data. The framework enables modeling of receptor trafficking directly from proximity labeling data, minimizing the need for independent measurements with complementary methods. The study identifies novel proteins in the μOR proximal interaction network, including COMMD3 and VPS35, which influence receptor distribution between cell surface and intracellular compartments. EYA4 and KCTD12 are found to be G protein-dependent proteins near the μOR, interacting with G protein subunits rather than the receptor itself. These proteins regulate μOR signaling through G proteins. The study also shows that EYA4 enhances Gs-mediated stimulation of cytoplasmic cAMP and limits the ability of Gi to suppress this response. KCTD12 promotes signal desensitization after the peak and impacts both Gs and Gi-mediated signaling. These findings suggest that EYA4 and KCTD12 have distinct functional effects on GPCR signaling. The study highlights the importance of understanding the proximal interaction networks of GPCRs, as they play a crucial role in cellular responses to ligands. The computational framework developed in this study provides a comprehensive dataset of the proximal protein environment of the μOR, enabling the identification of novel functional interactors and the characterization of ligand-specific effects on GPCR activation. This approach is broadly applicable for assessing and comparing ligand-specific effects on GPCR activation, offering insights into the molecular mechanisms of how chemically distinct ligands can evoke different cellular responses.