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This work is licensed under a Creative Commons Attribution 4.0 International License.
MnemoX: An AI-Powered Personalized Memory Assistant with Mood Tracking, Goal Monitoring, and Multi-Modal Interaction
T. Amalraj Victoire, A. Gopinath
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Abstract: This paper presents MnemoX, a web-based AI-powered personal memory assistant designed to help users store, recall, and analyze their daily activities, goals, and cognitive patterns. Built on the Django framework and integrated with the Google Gemini 2.0 API, MnemoX provides a conversational interface that supports natural language memory storage, mood detection, goal tracking, image and PDF analysis, multi-language translation, Wikipedia-backed knowledge retrieval, real-time news aggregation, and voice interaction. The system employs a structured relational database via Django ORM to persist user memories, daily logs, reminders, and goals, while leveraging large language model (LLM) capabilities for intelligent summarization and contextual responses. Experimental observations demonstrate that MnemoX effectively bridges personal productivity tools and conversational AI, offering a unified, user-friendly platform for cognitive augmentation. The system addresses limitations of existing memory tools by combining multi-modal input (text, voice, image, PDF), automatic mood detection, and AI-generated reminders within a single coherent framework.
Keywords: AI memory assistant, natural language processing, mood detection, goal tracking, Django, Google Gemini API, multi-modal interaction, cognitive augmentation, conversational AI
How to Cite:
[1] T. Amalraj Victoire, A. Gopinath, “MnemoX: An AI-Powered Personalized Memory Assistant with Mood Tracking, Goal Monitoring, and Multi-Modal Interaction,” International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2026.13616
This work is licensed under a Creative Commons Attribution 4.0 International License.