State-of-the-art navigated transcranial magnetic stimulation (nTMS) systems can display the TMS coil position relative to the structural magnetic resonance image (MRI) of the subject's brain and calculate the induced electric field. However, the local effect of TMS propagates via the white-matter network to different areas of the brain, and currently there is no commercial or research neuronavigation system that can highlight in real time the brain's structural connections during TMS. This lack of real-time visualization may overlook critical inter-individual differences in brain connectivity and does not provide the opportunity to target brain networks. In contrast, real-time tractography enables on-the-fly parameter tuning and detailed exploration of connections, which is computationally inefficient and limited with offline methods. To target structural brain connections, particularly in network-based treatments like major depressive disorder, a real-time tractography-based neuronavigation solution is needed to account for each individual's unique brain connectivity. The objective of this work is to develop a real-time tractography-assisted TMS neuronavigation system and investigate its feasibility. We propose a modular framework that seamlessly integrates offline (preparatory) analysis of diffusion MRI data with online (real-time) probabilistic tractography using the parallel transport approach. For tractography and neuronavigation, we combine our open source software Trekker and InVesalius, respectively. We evaluate our system using synthetic data and MRI scans of four healthy volunteers obtained using a multi-shell high-angular resolution diffusion imaging protocol. The feasibility of our online approach is assessed by studying four major TMS targets via comparing streamline count and overlap against offline tractography results based on filtering of one hundred million streamlines. Our development of a real-time tractography-assisted TMS neuronavigation system showcases advanced tractography techniques, with interactive parameter tuning and real-time visualization of thousands of streamlines via an innovative uncertainty visualization method. Our analysis reveals considerable variability among subjects and TMS targets in the streamline count, for example, while 15,000 streamlines were observed for the TMS target on the visual cortex (V1) of subject #4, in the case of subject #3's V1, no streamlines were obtained. Overlap analysis against offline tractograms demonstrated that real-time tractography can quickly cover a substantial part of the target areas' connectivity, often surpassing the coverage of offline approaches within seconds. For instance, significant portions of Broca's area and the primary motor cortex were effectively visualized after generating tens of thousands of streamlines, highlighting the system's efficiency and feasibility in capturing brain connectivity in real-time. Overall, our work shows that real-time tractography-assisted TMS neuronavigation is feasible. With our system, it is possible to target specific brain regions based on their structural connectivity, and to aim for the fiber tracts that make up the brain's networks. Real-time tractography provides new opportunities for TMS targeting through novel visualization techniques without compromising structural connectivity estimates when compared to the offline approach.