MemOS and EverMemOS
These appear to be competitors offering similar persistent memory architectures for agent systems, both targeting OpenClaw-based agents with skill reuse capabilities, though MemTensor has broader adoption and MemOS focuses specifically on 24/7 agent continuity.
About MemOS
MemTensor/MemOS
AI memory OS for LLM and Agent systems(moltbot,clawdbot,openclaw), enabling persistent Skill memory for cross-task skill reuse and evolution.
Implements a unified graph-based memory architecture with multi-modal support (text, images, tool traces, personas) and asynchronous ingestion via Redis Streams scheduling, achieving 43.70% accuracy gains over OpenAI Memory while reducing token usage by 35.24%. Integrates natively with OpenClaw agents through both cloud-hosted and local SQLite plugins, featuring hybrid search (FTS5 + vector), automatic task summarization, skill evolution, and natural-language feedback mechanisms for persistent memory refinement.
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.
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