Memory-Os: AI-Powered Personal Knowledge Operating System

Memory-OS is an AI-powered Personal Knowledge Operating System designed to unify personal and development-related information through a conversational command-line interface. The system integrates external knowledge sources such as GitHub, Gmail, and Notion through a connector-based architecture and processes collected information for efficient retrieval. It uses a local sentence-transformer model to generate embeddings, Qdrant for vector storage, Neo4j for knowledge graph relationships, and SQLite for structured data management. A hybrid Retrieval-Augmented Generation (RAG) pipeline combines semantic search, keyword-based retrieval, and graph-based context to generate relevant and context-aware responses using a Groq-powered Large Language Model (LLM). Memory-OS also provides an interactive terminal interface, a full-screen Textual TUI, workspace management, incremental synchronization, Docker-based service provisioning, health diagnostics, backup and restore functionality, and performance monitoring. The primary objective is to reduce information fragmentation by enabling users to search, connect, and understand their accumulated knowledge through natural-language queries instead of manually navigating multiple applications.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-30
DOI
https://doi.org/10.5281/zenodo.23798879
Primary Topic
Information Retrieval and Search Behavior
Type
article
Field-Weighted Citation Impact
0.00
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article

Memory-Os: AI-Powered Personal Knowledge Operating System

Ananthu AS, Anirudh T, Mrs. Baby Kalpana Y, Gokull NT et al.
2 links
Zenodo (CERN European Organization for Nuclear Research)
Information Retrieval and Search Behavior
article

Memory-Os: AI-Powered Personal Knowledge Operating System

Ananthu AS, Anirudh T, Mrs. Baby Kalpana Y, Gokull NT, Arun A
article en

Abstract

Memory-OS is an AI-powered Personal Knowledge Operating System designed to unify personal and development-related information through a conversational command-line interface. The system integrates external knowledge sources such as GitHub, Gmail, and Notion through a connector-based architecture and processes collected information for efficient retrieval. It uses a local sentence-transformer model to generate embeddings, Qdrant for vector storage, Neo4j for knowledge graph relationships, and SQLite for structured data management. A hybrid Retrieval-Augmented Generation (RAG) pipeline combines semantic search, keyword-based retrieval, and graph-based context to generate relevant and context-aware responses using a Groq-powered Large Language Model (LLM). Memory-OS also provides an interactive terminal interface, a full-screen Textual TUI, workspace management, incremental synchronization, Docker-based service provisioning, health diagnostics, backup and restore functionality, and performance monitoring. The primary objective is to reduce information fragmentation by enabling users to search, connect, and understand their accumulated knowledge through natural-language queries instead of manually navigating multiple applications.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 6%
Information Retrieval and Search Behavior
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Memory-Os: AI-Powered Personal Knowledge Operating System — Ananthu AS, Anirudh T, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS