Memori and MemoryOS
A SQL-native vector storage layer complements a memory operating system by providing the persistent, queryable backend infrastructure that a personalized agent OS would build upon for structured memory operations.
About Memori
MemoriLabs/Memori
SQL Native Memory Layer for LLMs, AI Agents & Multi-Agent Systems
This tool helps developers give their AI agents and large language models (LLMs) the ability to remember past interactions and learn from what they do, not just what they say. It takes conversations and actions from your agents and uses them to provide relevant context for future interactions. This is for developers building AI agents, multi-agent systems, or applications that use LLMs, who want their AI to have persistent, long-term memory.
About MemoryOS
BAI-LAB/MemoryOS
[EMNLP 2025 Oral] MemoryOS is designed to provide a memory operating system for personalized AI agents.
This project helps create AI agents that remember and personalize interactions more effectively. By managing an agent's 'memories' (like conversation history, preferences, and knowledge), it enables more consistent and context-aware responses. It takes in various types of information an AI agent encounters and processes it to output a more personalized and coherent agent interaction, making it ideal for anyone building or deploying personalized AI assistants or conversational AI.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work