A community in a network is a set of nodes that are densely and closely connected within the set, yet sparsely connected to nodes outside of it. Detecting communities in large networks helps solve many real-world problems. However, detecting such communities in a complex network by focusing on the whole network is costly. Instead, one can focus on finding overlapping communities starting from one or more seed nodes of interest. Moreover, on the online setting the network is given as a stream of higher order structures, i.e., triangles of nodes to be clustered into communities.In this paper, we propose an on online local graph community detection algorithm that uses motifs, such as triangles of nodes. We provide experimental results and compare it to another algorithm named COEUS. We use two public datasets, one of Amazon data and the other of DBLP data. Furthermore, we create and experiment on a new dataset that consists of web pages and their links by using the Internet Archive. This latter dataset provides insights to better understand how working with motifs is different than working with edges.