Meta-learning aims to improve efficiency of learning new tasks by exploiting the inductive biases obtained from related tasks. Previous works consider centralized or federated architectures that rely on central processors, whereas, in this paper, we propose a decentralized meta-learning scheme where the data and the computations are distributed across a network of agents. We provide convergence results for non-convex environments and illustrate the theoretical findings with experiments.