Compositional visual reasoning methods, which translate a complex query into a structured composition of feasible visual tasks, have exhibited a strong potential in complicated multimodal tasks like visual question answering, language-guided image editing, etc. Empowered by recent advances in large language models (LLMs), this multimodal challenge has been brought to a new stage by treating LLMs as few-shot/zero-shot planners, i.e., visual-language programming. Such methods, despite their numerous merits, suffer from challenges due to LLM planning mistakes or inaccuracy of visual execution modules, lagging behind the non-compositional models. In this work, we devise a “plug-and-play” method, ExoViP, to correct the errors at both the planning and execution stages through introspective verification. We employ verification modules as “exoskeletons” to enhance current vision-language programming schemes. Specifically, our proposed verification module utilizes a mixture of three sub-verifiers to validate predictions after each reasoning step, subsequently calibrating the visual module predictions and refining the reasoning trace planned by LLMs. Experimental results on two representative vision-language programming methods showcase consistent improvements on five compositional reasoning tasks on standard benchmarks. In light of this, we believe ExoViP can foster better performance and generalization on open-domain multimodal challenges.