The paper introduces AGENTSCoDRIVER, a novel framework that leverages large language models (LLMs) to enable multi-vehicle collaborative driving. The framework consists of five modules: observation, reasoning engine, cognitive memory, reinforcement reflection, and communication. These modules enable the system to accumulate knowledge, make decisions, and communicate with other vehicles for collaborative driving. The paper highlights the limitations of current autonomous driving systems, such as lack of interpretability, generalization, and continuous learning capabilities, and addresses these issues by integrating LLMs. Extensive experiments demonstrate the effectiveness of AGENTSCoDRIVER in various scenarios, showing superior performance in lifelong learning and collaboration compared to existing approaches. The framework is the first to use LLMs for multi-vehicle collaborative driving, making significant contributions to the field of connected and autonomous driving.The paper introduces AGENTSCoDRIVER, a novel framework that leverages large language models (LLMs) to enable multi-vehicle collaborative driving. The framework consists of five modules: observation, reasoning engine, cognitive memory, reinforcement reflection, and communication. These modules enable the system to accumulate knowledge, make decisions, and communicate with other vehicles for collaborative driving. The paper highlights the limitations of current autonomous driving systems, such as lack of interpretability, generalization, and continuous learning capabilities, and addresses these issues by integrating LLMs. Extensive experiments demonstrate the effectiveness of AGENTSCoDRIVER in various scenarios, showing superior performance in lifelong learning and collaboration compared to existing approaches. The framework is the first to use LLMs for multi-vehicle collaborative driving, making significant contributions to the field of connected and autonomous driving.