Cooperative perception aims to address the inherent limitations of single autonomous driving systems through information exchange among multiple agents. Previous research has primarily focused on single-frame perception tasks. However, the more challenging cooperative sequential perception tasks, such as cooperative 3D multi-object tracking, have not been thoroughly investigated.Therefore, we propose CoopTrack, a fully instance-level end-to-end framework for cooperative tracking, featuring learnable instance association, which fundamentally differs from existing approaches.CoopTrack transmits sparse instance-level features that significantly enhance perception capabilities while maintaining low transmission costs. Furthermore, the framework comprises three key components: Multi-Dimensional Feature Extraction (MDFE), Cross-Agent Alignment (CAA), and Graph-Based Association (GBA), which collectively enable comprehensive instance representation with semantic and motion features, and adaptive cross-agent association and fusion based on graph learning.Experiments on the V2X-Seq dataset demonstrate that, benefiting from its sophisticated design, CoopTrack achieves state-of-the-art performance, with 39.0\% mAP and 32.8\% AMOTA. Codes and visualization results are provided in the supplementary materials.