We develop an agent based model of strategic link formation in Research and Development (R&D) networks. In our model, firms are represented as nodes, and their R&D alliances as links. Empirical evidence has shown that the growth and evolution of these networks are driven by two types of mechanisms. Endogenous mechanisms depend on the previous history of the network, i.e. they take into account existing alliance patterns. Exogenous mechanisms do not depend on the properties of the network but on those of newcomers, i.e. they include the exploratory search for firms that are not part of the network yet. Despite the observation of these two mechanisms, research has focused only on either of the two. To overcome this limitation, we develop a general modeling framework that includes both mechanisms and allows to tune their relative importance in the formation of links. The model contains additional ingredients derived from empirical observations, such as the heterogeneous propensity to form alliances and the presence of circles of influence, i.e. clusters of firms in the network. We test our model against the Thomson Reuters SDC Platinum dataset, one of the most complete datasets available nowadays, listing cross-country R&D alliances from 1984 to 2009. Interestingly, by fitting only three macroscopic properties of the network, this framework is able to reproduce a number of microscopic measures characterizing the network topology, including the distributions of degree, local clustering, path length and component size, and the emergence of network clusters. Furthermore, by estimating the link probabilities towards newcomers and established firms from the available data, we find that endogenous mechanisms are predominant over the exogenous ones in the network formation. This quantifies the importance of existing network structures in selecting partners for R&D alliances.