OPENTAB is an open-domain table reasoning framework that leverages Large Language Models (LLMs) to handle tasks requiring knowledge not explicitly trained on. The framework addresses the challenges of structured table data by using a retriever to fetch relevant tables and a reasoner to generate SQL programs for efficient parsing. The final response is formulated using an LLM-based reader. OPENTAB significantly outperforms baselines in both open- and closed-domain settings, achieving up to 21.5% higher accuracy. The framework includes a Generative Reranking & Sequential Reasoning (GRSR) strategy to prioritize tables based on query similarity, enhancing prediction accuracy. Ablation studies validate the effectiveness of the proposed components, demonstrating the system's robustness and adaptability. OPENTAB is open-sourced and designed to handle large-scale tabular data efficiently, making it suitable for various applications.OPENTAB is an open-domain table reasoning framework that leverages Large Language Models (LLMs) to handle tasks requiring knowledge not explicitly trained on. The framework addresses the challenges of structured table data by using a retriever to fetch relevant tables and a reasoner to generate SQL programs for efficient parsing. The final response is formulated using an LLM-based reader. OPENTAB significantly outperforms baselines in both open- and closed-domain settings, achieving up to 21.5% higher accuracy. The framework includes a Generative Reranking & Sequential Reasoning (GRSR) strategy to prioritize tables based on query similarity, enhancing prediction accuracy. Ablation studies validate the effectiveness of the proposed components, demonstrating the system's robustness and adaptability. OPENTAB is open-sourced and designed to handle large-scale tabular data efficiently, making it suitable for various applications.