Automated planning is an important area of Artificial Intelligence, which has been thoroughly developed in the last decades. In recent years, a significant amount of research has focused on planning languages and systems supporting temporal reasoning, recognizing its importance in modeling and solving real-world complex tasks. Many such languages are action-based, i.e. they model planning problems by specifying which actions can be executed at any given time to affect the environment. Timeline-based planning, a different paradigm originally introduced to support planning and scheduling of space operations, models planning domains as systems composed of a set of independent, but interacting, components, whose behavior over time, the timelines, is governed by a set of temporal constraints. A thorough theoretical study of timeline-based planning languages, and a rigorous comparison with action-based languages, are still missing. We outline recent results and future directions on this front.