This paper introduces SymbCoT, a novel Symbolic Chain-of-Thought framework that enhances the logical reasoning capabilities of large language models (LLMs). Unlike existing methods that rely on external reasoners, SymbCoT is fully LLM-based, integrating symbolic expressions and logical rules into the Chain-of-Thought (CoT) prompting process. The framework consists of four modules: Translator, Planner, Solver, and Verifier. The Translator converts natural language into symbolic format, the Planner breaks down the problem into smaller steps, the Solver applies logical rules to derive conclusions, and the Verifier ensures the correctness of the reasoning process. SymbCoT demonstrates significant improvements over traditional CoT methods on five standard datasets using both First-Order Logic (FOL) and Constraint Optimization (CO) symbolic expressions. It outperforms existing state-of-the-art solutions, particularly in complex reasoning tasks, and provides more faithful, flexible, and explainable reasoning. The system's ability to handle symbolic expressions and logical rules enables it to perform rigorous logical deductions, which are essential for tasks requiring precise reasoning. The framework's key contributions include: (1) a fully LLM-based logical reasoning framework that enhances reasoning capabilities without external tools; (2) the integration of symbolic and natural language expressions to leverage their strengths; and (3) a plan-then-solve architecture with a retrospective verification mechanism to ensure the faithfulness of the reasoning process. Experiments show that SymbCoT significantly improves reasoning performance on logical reasoning tasks, especially when combined with a verification mechanism. It achieves higher accuracy and robustness against translation errors compared to existing methods. The system's ability to handle complex logical reasoning tasks and provide human-readable explanations makes it a promising approach for enhancing LLMs' logical reasoning capabilities. The paper also discusses the limitations of current methods, such as reliance on external solvers and the challenges of handling complex reasoning tasks. It highlights the importance of symbolic reasoning in logical tasks and the potential of combining symbolic expressions with CoT prompting to improve reasoning accuracy and explainability. The study concludes that SymbCoT represents a significant advancement in logical reasoning for LLMs, offering a more robust and reliable approach to symbolic reasoning.This paper introduces SymbCoT, a novel Symbolic Chain-of-Thought framework that enhances the logical reasoning capabilities of large language models (LLMs). Unlike existing methods that rely on external reasoners, SymbCoT is fully LLM-based, integrating symbolic expressions and logical rules into the Chain-of-Thought (CoT) prompting process. The framework consists of four modules: Translator, Planner, Solver, and Verifier. The Translator converts natural language into symbolic format, the Planner breaks down the problem into smaller steps, the Solver applies logical rules to derive conclusions, and the Verifier ensures the correctness of the reasoning process. SymbCoT demonstrates significant improvements over traditional CoT methods on five standard datasets using both First-Order Logic (FOL) and Constraint Optimization (CO) symbolic expressions. It outperforms existing state-of-the-art solutions, particularly in complex reasoning tasks, and provides more faithful, flexible, and explainable reasoning. The system's ability to handle symbolic expressions and logical rules enables it to perform rigorous logical deductions, which are essential for tasks requiring precise reasoning. The framework's key contributions include: (1) a fully LLM-based logical reasoning framework that enhances reasoning capabilities without external tools; (2) the integration of symbolic and natural language expressions to leverage their strengths; and (3) a plan-then-solve architecture with a retrospective verification mechanism to ensure the faithfulness of the reasoning process. Experiments show that SymbCoT significantly improves reasoning performance on logical reasoning tasks, especially when combined with a verification mechanism. It achieves higher accuracy and robustness against translation errors compared to existing methods. The system's ability to handle complex logical reasoning tasks and provide human-readable explanations makes it a promising approach for enhancing LLMs' logical reasoning capabilities. The paper also discusses the limitations of current methods, such as reliance on external solvers and the challenges of handling complex reasoning tasks. It highlights the importance of symbolic reasoning in logical tasks and the potential of combining symbolic expressions with CoT prompting to improve reasoning accuracy and explainability. The study concludes that SymbCoT represents a significant advancement in logical reasoning for LLMs, offering a more robust and reliable approach to symbolic reasoning.