Rules have a number of desirable properties, such as easy to understand, can infer new knowledge, and communicate with other inference systems. One weakness of the previous rule induction systems is that they only represent rules within a knowledge base (KB) and therefore cannot generalize to more open and complex real-world rules. Recently, the language model (LM)-based rule generation are proposed to enhance expressive power of the rules.We revisit the differences between KB-based rule induction and LM-based rule generation. We argue that current LM-based methods are "learning rules from rules". This limits these methods to producing "canned" rles whose patterns are from the annotated rules, while discarding the rich expressive power of LMs for free text.In this paper, we propose the open rule induction problem, which aims to induce open rules of the knowledge in LMs. We propose Orion (open rule induction) system, which automatically mines open rules from LMs without supervision of annotated rules. We conducted extensive experiments to verify the quality and quantity of the inducted open rules. Surprisingly, when applying the open rules in the downstream task (i.e. relation extraction), these automatically inducted rules even outperformed the manually annotated rules.