This demonstration illustrates the newly developed Python-based framework, pyDCOP, which implements several state-of-the-art distributed constraint reasoning solution methods, provides utilities to deploy them over distributed infrastructures and also equip the system with resilience capabilities. The idea behind pyDCOP is to distribute agents over an Internet-of-Things infrastructure (e.g. Rapsberry Pis) to install collective decisions, as to implement Ambient Intelligence or Smart Home scenarios. Scenarios are modeled in a dedicated format, translated in a distributed constraint optimization or satisfaction problem, then pushed to the devices which coordinate using chosen protocols as to self-configure in a decentralized manner. Besides configuring the system in an optimal manner, it also provides a resilience framework, which equips the system with adaptation capabilities against unpredictable device removals. This mechanism is based on decision replication and a lightweight DCOP-based reparation mechanism.