Autonomous GUI and web agents frequently generate long, multi-step execution traces with rich environmental states, making manual inspection and fault localization challenging. Existing visualization tools typically render isolated single-run trajectories, obscuring fine-grained behavioral discrepancies across parallel runs on the same task. We introduce TRACEFORK, an interactive system designed to align, compare, and inspect paired same-task trajectories produced by AI agents or human demonstrators. TRACEFORK ingests execution traces from standardized benchmarks (e.g., WebArena, VisualWebArena) and custom user logs via an extensible normalization schema. Using a multi-modal dynamic programming alignment engine, the system collapses shared prefixes onto a single trunk, isolates behavioral divergence points into distinct branches, and identifies downstream reconvergence via conservative rejoin cues. Across a library of 1,722 tasks and 1,387 aligned trajectory pairs (33,337 steps), TRACEFORK achieves a median alignment latency of 45.5 ms per pair (p95 320.4 ms). On a manually annotated pilot subset, it achieves an F1 score of 0.910 for divergence localization, offering an open-source, deterministic diagnostic workbench for the agent research community.