The study of how children develop their conversational skills is an important scientific frontier at the crossroad of social, cognitive, and linguistic development with important applications in health, education, and child-oriented AI. While recent advances in machine learning techniques allow us to develop formal theories of conversational development in real-life contexts, progress has been slowed down by the lack of corpora that both approximate naturalistic interaction and provide clear access to children’s non-verbal behavior in face-to-face conversations. This work is an effort to fill this gap. We introduce ChiCo (for Child Conversation), a corpus we built using an online video chat system. Using a weakly structured task (a word-guessing game), we recorded 20 conversations involving either children in middle childhood (i.e., 6 to 12 years old) interacting with their caregivers (condition of interest) or the same caregivers interacting with other adults (a control condition), resulting in 40 individual recordings. Our annotation of these videos has shown that the frequency of children’s use of gaze, gesture and facial expressions mirrors that of adults. Future modeling research can capitalize on this rich behavioral data to study how both verbal and non-verbal cues contribute to the development of conversational coordination.