Memori and EverMemOS
A SQL-native memory layer designed for multi-agent systems complements rather than competes with a long-term memory OS, as the former provides structured persistence while the latter handles temporal state management across distributed agents.
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 EverMemOS
EverMind-AI/EverMemOS
Long-term memory for your 24/7 OpenClaw agents across LLMs and platforms.
Provides structured memory extraction from conversations using LLM-based encoding, organizes data into episodes and user profiles stored across MongoDB/Milvus/Elasticsearch, and exposes a REST API for retrieval with BM25, semantic embedding, and agentic search capabilities. Integrates directly with OpenClaw agents and supports TEN Framework for real-time applications, Claude Code plugins, and computer-use scenarios requiring persistent context across sessions.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work