The AutoAgents framework can be extended to handle tasks that require long-term planning and reasoning by incorporating a memory mechanism that allows agents to retain and utilize past experiences and knowledge. This memory mechanism can be divided into long-term memory, short-term memory, and dynamic memory, as mentioned in the context. By enabling agents to access and leverage historical information, they can make more informed decisions and plans for future tasks. Additionally, introducing reinforcement learning techniques can help agents learn from their actions and outcomes over time, improving their planning and reasoning abilities for long-term tasks.