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
Automatically intercepts and persists LLM conversations to SQL, then intelligently retrieves relevant context on subsequent queries—achieving 81.95% accuracy on long-context tasks while reducing token usage to ~5% of full-context approaches. Integrates directly with OpenAI, Anthropic, and other LLM providers via SDK wrappers, plus hooks into OpenClaw agents and MCP-compatible tools (Claude Code, Cursor) without requiring code changes. Supports bring-your-own-database deployments for self-hosted setups alongside cloud-hosted options.
About MemoryOS
BAI-LAB/MemoryOS
[EMNLP 2025 Oral] MemoryOS is designed to provide a memory operating system for personalized AI agents.
Implements a hierarchical memory architecture with four core modules (Storage, Updating, Retrieval, Generation) that manages short-term, mid-term, and long-term persona memory through dynamic updates and context-aware retrieval. Exposes memory capabilities via MCP Server with pluggable storage engines (including Chromadb vector database), multiple embedding models (BGE-M3, Qwen), and universal LLM support across OpenAI, Anthropic, Deepseek, and other providers for seamless agent integration.
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