Developed a persistent memory feature for chat agents within the MemMachine/MemMachine repository, focusing on enabling agents to recall user interactions and preferences across sessions. This work involved integrating LlamaIndex with MemMachine, establishing a persistent-memory architecture that supports cross-session personalization and lays the groundwork for smarter conversational agents. The implementation leveraged Python for both API integration and full stack development, with an emphasis on chatbot functionality. By allowing chat agents to remember user context, the solution addressed the challenge of continuity in user experience and set the stage for future enhancements in personalized conversation and user engagement without addressing bug fixes during this period.