<p>Developing large-scale collective adaptive systems for safety-critical applications requires an extensive effort, involving the interplay of distributed programming techniques and mathematical proofs of real-time guarantees. This effort could be significantly reduced by allowing the system developer to rely on libraries of predefined algorithms. By exploiting such algorithms, distributed behaviour and (hard) real-time guarantees for the final application could be automatically inferred, effectively shifting the verification burden from the system designer to the algorithm developer. Following earlier work on real-time guarantees for aggregate computing algorithms, we argue that aggregate computing could provide a convenient framework towards this aim. As a first step, we give a detailed description of different kinds of models that can interpret corresponding classes of aggregate programs as mathematical functions. Then, building on such models, we investigate the problem of how real-time behaviour constraints can be specified in a compositional way, proposing a few composable specification patterns, and singling out a number of potential building block library algorithms that could constitute such a real-time aggregate computing library. We evaluate our proposal by means of examples, describing a series of example algorithms for each proposed model, and by investigating two possible compositions of some of them in an archetypal scenario of distributed estimation of the network diameter. In these two examples, we experimentally prove the effectiveness of the models by comparing the results of the interpretation with the simulations results, achieving a close match. Overall, the proposed framework provides a roadmap towards a real-time aggregate computing library&#xa0;with the potential of providing a valuable asset for supporting the rigorous engineering of safety-critical large-scale collective adaptive systems.</p>