PlanVer is a natural language task planning verification agent. Task planning is the field of based on a model of the world deciding how to reach a goal, an incredibly relevant task as agents move into the real world. In contrast to vague formulations, such as creative writing or travel planning, task planning requires attention to detail, long-term decision making and frequently back tracking. Scientific opinion on the planning capabilities of LLMs, LRMs and agents has long been mixed and in flux, so PlanVer hopes to benchmark and further this research through AgentBeat. Powered by the field of classical planning PlanVer turns PDDL tasks into natural language tasks that can be formally verified, similarly to the AutoPlanBench paper. These natural language tasks are then provided to the purple agent which replies with a plan. The plans are then validated by the green PlanVer agent, guaranteeing that only plans which correctly and formally solve the underlying task are approved. PlanVer currently supports a diverse set of 30 domains, different forms of problems, each with 30 concrete instances. These are taken from the AutoScale benchmark set designed to challenge even custom built solvers and includes tasks from the very simple to the incredibly difficult. Adding new domains is also simple and readily supported.