Large language models (LLMs) are increasingly used as web-facing agents for information-seeking tasks over live websites. Many such tasks require multi-step in-site navigation and cross-page evidence aggregation, a setting known as web traversal. Existing LLM-based web agents, including recent explore–critic architectures, typically store interaction history as an ever-growing unstructured text trace and mix high-level planning with low-level page actions. This makes long-horizon behavior brittle, obscures the source of evidence, and often leads to redundant browsing or premature stopping. To mitigate these issues, we propose WebTraveler, a hierarchical multi-agent framework that separates high-level information-seeking planning from local web exploration and maintains a structured trajectory memory of decision-relevant signals. This design provides compact information interfaces for evidence sufficiency checking and webpage navigation, improving long-horizon stability and cross-page evidence aggregation. To obtain scalable supervision, we further introduce path-guided data synthesis, which uses executable navigation paths to collect trajectories, removes explicit path hints, and filters invalid samples through answer-consistency checks. Experiments show that WebTraveler consistently outperforms previous methods across LLM scales, especially on tasks requiring deeper navigation, with further gains from path-guided supervision. Moreover, WebTraveler also shows generalization to broader web navigation tasks. Code is available at https://github.com/RongchangLi/WebTraveler.