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MemTensor

MemOS

MemTensor's memory OS for LLM agents: one API over graph-structured, multi-modal memory with hybrid retrieval and skill evolution — hosted, self-hosted (Neo4j + Qdrant) or local plugins.

11,610 1,060 TypeScript Apache-2.0updated 5 days ago
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Curator's take

Pick MemOS when you want a full memory stack rather than a vector wrapper: graph-structured memories you can inspect and correct in natural language, isolated or shared 'memory cubes' across users and agents, async ingestion, and traces that crystallize into reusable skills. The fastest path is a plugin — local SQLite for Hermes, OpenClaw or DeepSeek Harness, zero infra. Self-hosting the service means running Neo4j and Qdrant plus LLM and embedder keys, which is heavy for a single-user bot. Read the headline LoCoMo/LongMemEval numbers with care: the comparison runs on OmniMemEval, MemTensor's own harness. Skip it if you only need session recall for one coding agent — claude-mem or engrim is far less machinery.

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Give your Agent persistent memory and the ability to grow.

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MemOS agent ecosystem: OpenClaw, Hermes, and DeepSeek Harness

[!TIP] New: Connect MemOS to DeepSeek Harness (dsh)

Add automatic recall, background capture, hybrid retrieval, and a local Memory Viewer to DeepSeek Harness—powered by the same MemOS core used across agent ecosystems.

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👾 MemOS: Memory Operating System for LLM & AI Agents

MemOS is a Memory Operating System for LLMs and AI agents that unifies store / retrieve / manage for long-term memory, enabling context-aware and personalized interactions with KB, multi-modal, tool memory, and enterprise-grade optimizations built in.

Key Features

  • Unified Memory API: A single API to add, retrieve, edit, and delete memory—structured as a graph, inspectable and editable by design, not a black-box embedding store.
  • Multi-Modal Memory: Natively supports text, images, tool traces, and personas, retrieved and reasoned together in one memory system.
  • Multi-Cube Knowledge Base Management: Manage multiple knowledge bases as composable memory cubes, enabling isolation, controlled sharing, and dynamic composition across users, projects, and agents.
  • Asynchronous Ingestion via MemScheduler: Run memory operations asynchronously with millisecond-level latency for production stability under high concurrency.
  • Memory Feedback & Correction: Refine memory with natural-language feedback—correcting, supplementing, or replacing existing memories over time.

News

  • 2026-08-17 · 🐋 MemOS Connects with DeepSeek Harness MemOS now brings persistent memory to DeepSeek Harness through both local and cloud plugins. DSH can automatically recall relevant context before a task and retain new experience after a successful turn, without modifying its core.

  • 2026-07-02 · 🏆 MemOS Advances Agent and User Memory Benchmarks With MemOS, OpenClaw improves average task completion from 36.63% to 50.87% across five agent tasks. MemOS also achieves 88.83 on LoCoMo and 89.20 on LongMemEval, and leads in OmniMemEval, a unified evaluation of 14 commercial memory products across ten datasets.

  • 2026-05-09 · 🧠 memos-local-plugin 2.0