Most successful networks, including economic, societal, transport and vasculature networks, are dynamic. Continuous adaptation of network links ensures optimal network performance when challenged with dynamic external influences. Yet, how can continuous adaptation in a dynamic environment be aligned with the observation that stimuli trigger permanent changes in adaptive networks? We can show that adaptation dynamics allow a network to memorize information about the position of an applied stimulus within its network morphology. Both our numerical simulations and analytical calculations identify that the irreversible dynamics of vanishing network links encode memory. This not only opens an entirely new perspective on adaptive networks such as our vasculature but provides an analytically tractable theory of memory formation in disordered systems [1]. Currently we are working on understanding memory capacity of adaptive networks. In the video abstract we show how morphological changes of flow networks can be linked with information storage and information flow. We address different phases of network evolutions(with or without stimulus) as memory writing(when network evolves with stimulus) and noise source (when network evolved without stimulus) and we probe the network with stimulus from different direction and address the energy measurement as output.