MemOS
Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings and DeepSeek Harness support.
Install / Use
claude mcp add MemTensor -- npx -y github:MemTensor/MemOSIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AI & Machine LearningSupported Platforms
Tags
Skill content
View source on GitHub👾 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
-
2026-07-02 · 🏆 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.
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2026-05-09 · 🧠 memos-local-plugin 2.0 Official local memory plugin for Hermes Agent and OpenClaw. One core powers self-evolving memory across L1 traces, L2 policies, L3 world models, and crystallized Skills, with local-first storage and feedback-driven retrieval.
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2026-04-10 · 👧🏻 MemOS Hermes Agent Local Plugin Official Hermes Agent memory plugins launched: Hybrid retrieval (FTS5 + vector), smart dedup, tiered skill evolution, multi-agent collaboration. 100% local, zero cloud dependency.
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2026-03-08 · 🦞 MemOS OpenClaw Plugin — Cloud & Local Official OpenClaw memory plugins launched. Cloud Plugin: hosted memory service with 72% lower token usage and multi-agent memory sharing (MemOS-Cloud-OpenClaw-Plugin). Local Plugin (
v1.0.0): 100% on-device memory with persistent SQLite, hybrid search (FTS5 + vector), task summarization & skill evolution, multi-agent collaboration, and a full Memory Viewer dashboard.
📊 Performance
MemOS leads across multiple benchmarks — evaluated against mainstream commercial memory products across 5 user memory and 5 agent memory tasks.
| Benchmark | Score | | --------------- | ----- | | LoCoMo | 88.83 | | LongMemEval | 89.20 | | PersonaMem v2 | 40.58 | | HaluMem | 80.91 | | BEAM-10M | 56.75 | | GDPVal | 62.07 | | LiveCodeBench | 64.96 | | OmniMath | 61.00 | | SWE-Bench | 38.46 | | BrowseComp-Plus | 23.85 |
Evaluated via OmniMemEval — https://github.com/MemTensor/OmniMemEval.
🎯 What MemOS Is For
MemOS gives AI agents long-term memory. Common uses:
- AI assistants with consistent, context-rich conversations
- Customer support that recalls past tickets and user history
- Personalized agents that adapt to individual preferences
- Multi-agent collaboration with shared or isolated memory
🚀 Quick Start
MemOS is built around four entry points. Pick the one that matches your scenario.
| | Cloud API | Self-Host | OpenClaw Cloud Plugin | Local Plugin | | ------------ | ----------------------- | ------------------ | ------------------------ | ------------------------------- | | Best for | Your app, fully managed | Teams on own infra | OpenClaw users, zero ops | Hermes/OpenClaw, 100% on-device | | Setup | Get an API key | docker compose up | openclaw plugins install | npm install + config | | Infra needed | None (hosted) | Neo4j + Qdrant | None (uses MemOS Cloud) | None (local SQLite) | | Data lives | MemOS Cloud | Your servers | MemOS Cloud | Your machine |
☁️ Use the Cloud API (Hosted)
You want to add memory to your app through a fully managed service — no infrastructure to run.
1. Get an API key:
- Sign up on the MemOS dashboard.
- Go to API Keys and copy your key (starts with
mpg-). Keep it server-side.
2. Add and search memories:
import requests
API_KEY = "mpg-..." # keep this server-side
base = "https://memos.memtensor.cn/api/openmem/v1"
headers = {"Authorization": f"Token {API_KEY}", "Content-Type": "application/json"}
# 1. Add a memory
requests.post(f"{base}/add/message", headers=headers, json={
"user_id": "alice",
"conversation_id": "conv_001",
"messages": [{"role": "user", "content": "I like strawberry"}],
})
# 2. Search memories
res = requests.post(f"{base}/search/memory", headers=headers, json={
"query": "What do I like?",
"user_id": "alice",
})
print(res.json())
Next steps:
- MemOS Cloud Getting Started — connect to MemOS Cloud and enable memory in minutes.
- MemOS Cloud Platform — explore the Cloud dashboard, features, and workflows.
🖥️ Self-Host the MemOS Service
You want to run MemOS as a REST service on your own machine or cluster.
Option A — Docker (recommended):
git clone https://github.com/MemTensor/MemOS.git
cd MemOS
cp docker/.env.example .env # fill in your API keys in .env
cd docker
docker compose up # starts MemOS API + Neo4j + Qdrant
The API is served at http://localhost:8000.
Option B — Run with uvicorn (without Docker):
git clone https://github.com/MemTensor/MemOS.git
cd MemOS
cp docker/.env.example .env # fill in your API keys in .env
# Ensure Neo4j and Qdrant are running, then:
cd src
uvicorn memos.api.server_api:app --host 0.0.0.0 --port 8000 --workers 1
See [docker/.env.example](./docker/.env.example) for all configuration options (LLM provider, embedder, vector DB, graph DB, scheduler). The full deployment guide is at https://memos-docs.openmem.net/open_source/getting_started/rest_api_server/.
Try the API:
import requests, json
headers = {"Content-Type": "application/json"}
base = "http://localhost:8000/product"
# 1. Create a memory cube
requests.post(f"{base}/create_cube", headers=headers, data=json.dumps({
"cube_name": "Alice's memory",
"owner_id": "alice",
"cube_id": "alice_cube",
}))
# 2. Add a memory
requests.post(f"{base}/add", headers=headers, data=json.dumps({
"user_id": "alice",
"writable_cube_ids": ["alice_cube"],
"messages": [{"role": "user", "content": "I like strawberry"}],
"async_mode": "sync",
}))
# 3. Search memories
res = requests.post(f"{base}/search", headers=headers, data=json.dumps({
"query": "What do I like?",
"user_id": "alice",
"readable_cube_ids": ["alice_cube"],
}))
print(res.json())
🧠 MemOS Plugin: Persistent Memory for Your AI Agents ✨
Your OpenClaw and Hermes Agents now have the best memory system — choose Cloud Service or Self-hosted to get started 🏃🏻
| 🔌 Plugin | 💡 Core Features | 🧩 Resources | | ------------------------------------------------------------------------------------------------------------- | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | 🧠 memos-local-plugin 2.0 | | 🌐 Website · 📖 Docs · 🐙 GitHub · 📦 NPM | | ☁️ OpenClaw Cloud Plugin | | 🖥️ MemOS Dashboard · 📖 Full Tutorial |
1. OpenClaw Cloud Plugin
You use OpenClaw and want persistent memory via MemOS Cloud — no infrastructure to run.
- Repo: MemTensor/MemOS ·
apps/MemOS-Cloud-OpenClaw-Plugin - NPM:
[@memtensor/memos-cloud-openclaw-plugin](https://www.npmjs.com/package/@memtensor/memos-cloud-openclaw-plugin) - Dashboard: https://memos-dashboard.openmem.net/
- Tutorial: https://memos-docs.openmem.net/openclaw/guide
Install:
openclaw plugins install @memtensor/memos-cloud-openclaw-plugin@latest
openclaw gateway restart
The plugin recalls memories from MemOS Cloud before each agent run and saves new messages back after the run ends.
2
Truncated for display — read the full file on GitHub.
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