1. Project Overview & Quickstart (MemTensor/MemOS)

[!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.

Get started →

👾 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
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.
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.
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	MemOS Cloud Plugin	Local Plugin
Best for	Your app, fully managed	Teams on own infra	OpenClaw users, zero ops	DeepSeek Harness, Hermes, or OpenClaw; on-device
Setup	Get an API key	docker compose up	openclaw plugins install	npm install + agent-specific setup
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:

python

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):

bash

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):

bash

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:

python

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 ✨

MemOS gives OpenClaw, Hermes, and DeepSeek Harness a shared local memory core; the managed MemOS Cloud Plugin is available for OpenClaw and DeepSeek Harness 🏃🏻

🔌 Plugin	💡 Core Features	🧩 Resources
🧠 memos-local-plugin 2.0		🌐 Website · 📖 Docs · 🐙 GitHub · 📦 NPM
☁️ MemOS Cloud Plugin		🖥️ MemOS Dashboard · 📖 Full Tutorial

1. MemOS Cloud Plugin

Use MemOS Cloud for persistent memory in OpenClaw or DeepSeek Harness — 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:

bash

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.

DeepSeek Harness

Connect DeepSeek Harness to MemOS Cloud through its native plugin mechanism. Before the first model step of each user request, the plugin recalls relevant cloud memories; after a successful turn, it saves the new user and assistant messages back to MemOS Cloud.

Create a MemOS API Key.

Install the cloud plugin into the default DSH web profile:

bash

npx @deepseek-ai/dsh plugin --profile web add @memtensor/memos-cloud-dsh-plugin@latest

Add the API Key to ~/.dsh/.credentials.yaml:
yaml
```
MEMOS_API_KEY: mpg-your-key
```
Add the minimal plugin configuration to ~/.dsh/settings.yaml:
yaml
```
memos-cloud:
  apiKeyEnv: MEMOS_API_KEY
```
Restart the DSH Web profile:
bash
```
npx @deepseek-ai/dsh web
```

The cloud plugin is fail-open: a temporary MemOS Cloud outage does not interrupt the current DSH task.

2. Local Plugin (OpenClaw, Hermes, and DeepSeek Harness)

You use DeepSeek Harness, Hermes Agent, or OpenClaw and want 100% on-device memory — nothing leaves your machine.

Repo: MemTensor/MemOS · apps/memos-local-plugin
NPM: [@memtensor/memos-local-plugin](https://www.npmjs.com/package/@memtensor/memos-local-plugin)
Docs: https://memos-docs.openmem.net/cn/openclaw/local_plugin
DeepSeek Harness: integration details
Viewer dashboard: see apps/memos-local-plugin/viewer/

Install for DeepSeek Harness (macOS / Linux):

bash

curl -fsSL https://raw.githubusercontent.com/MemTensor/MemOS/main/apps/memos-local-plugin/install.sh | bash -s -- --agent dsh --profile web

Install for OpenClaw or Hermes (macOS / Linux):

bash

curl -fsSL https://raw.githubusercontent.com/MemTensor/MemOS/main/apps/memos-local-plugin/install.sh | bash

Install (Windows PowerShell):

powershell

irm https://raw.githubusercontent.com/MemTensor/MemOS/main/apps/memos-local-plugin/install.ps1 -OutFile "$env:TEMP\memos-install.ps1"; powershell -ExecutionPolicy Bypass -File "$env:TEMP\memos-install.ps1"

Requires Node.js and an already-installed DeepSeek Harness, OpenClaw, or Hermes. The installer deploys MemOS to the selected agent runtime; the DeepSeek Harness target installs it as an out-of-tree DSH bundle, while the OpenClaw and Hermes targets write the initial config.yaml in their respective agent homes.

Features: hybrid retrieval (FTS5 + vector), smart dedup, tiered skill evolution (L1 traces / L2 policies / L3 world model), multi-agent collaboration, local-first SQLite storage.

🤝 Community

GitHub Issues: https://github.com/MemTensor/MemOS/issues
GitHub Discussions: https://github.com/MemTensor/MemOS/discussions
Discord: https://discord.gg/Txbx3gebZR
WeChat: scan the QR code to join the group.

📚 Citation

If you use MemOS in your research, please cite:

bibtex

@article{li2025memos_long,
  title={MemOS: A Memory OS for AI System},
  author={Li, Zhiyu and Song, Shichao and Xi, Chenyang and Wang, Hanyu and Tang, Chen and Niu, Simin and Chen, Ding and Yang, Jiawei and Li, Chunyu and Yu, Qingchen and Zhao, Jihao and Wang, Yezhaohui and Liu, Peng and Lin, Zehao and Wang, Pengyuan and Huo, Jiahao and Chen, Tianyi and Chen, Kai and Li, Kehang and Tao, Zhen and Ren, Junpeng and Lai, Huayi and Wu, Hao and Tang, Bo and Wang, Zhenren and Fan, Zhaoxin and Zhang, Ningyu and Zhang, Linfeng and Yan, Junchi and Yang, Mingchuan and Xu, Tong and Xu, Wei and Chen, Huajun and Wang, Haofeng and Yang, Hongkang and Zhang, Wentao and Xu, Zhi-Qin John and Chen, Siheng and Xiong, Feiyu},
  journal={arXiv preprint arXiv:2507.03724},
  year={2025},
  url={https://arxiv.org/abs/2507.03724}
}

@article{li2025memos_short,
  title={MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models},
  author={Li, Zhiyu and Song, Shichao and Wang, Hanyu and Niu, Simin and Chen, Ding and Yang, Jiawei and Xi, Chenyang and Lai, Huayi and Zhao, Jihao and Wang, Yezhaohui and others},
  journal={arXiv preprint arXiv:2505.22101},
  year={2025},
  url={https://arxiv.org/abs/2505.22101}
}

⚖️ License

MemOS is licensed under the Apache 2.0 License.

2. Official Technical Reference & Guides (MemTensor/MemOS-docs)

MemOS Docs

This repository contains the documentation website for MemOS project.

Project Structure

text

MemOS-Docs/
├── app/                    # Main application code
│   ├── components/        # Vue components
│   ├── composables/       # Vue composables
│   ├── pages/            # Vue pages and routing
│   └── assets/           # Static assets like CSS
├── content/              # Documentation content
│   ├── en/              # English documentation
│   ├── cn/              # Chinese documentation
│   └── api.json         # OpenAPI Documentation
├── public/              # Public static assets
│   └── assets/         # Images and other media
├── envConfig/          # Environment configurations
│   ├── config.dev.ts   # Development config
│   ├── config.pre.ts   # Pre-production config
│   └── config.prod.ts  # Production config
├── i18n/               # Internationalization
│   └── locales/       # Translation files
└── nuxt.config.ts     # Nuxt configuration

Features

📝 Markdown-based documentation
🌐 Multi-language support (English & Chinese)
🔍 Full-text search
📱 Mobile-friendly responsive design
⚡️ Fast static site generation

Setup

Make sure to install the dependencies:

bash

pnpm install

Development

Start the development server on http://localhost:3000:

bash

pnpm dev

Build

Build the documentation site:

bash

# Build with default settings (en locale, dev environment)
pnpm run publish

# Build for specific environment
pnpm run publish --env=prod

Environment Configuration

The project supports different environments:

dev: Development environment
pre: Pre-production environment
prod: Production environment

Configure environment-specific settings in envConfig/config.[env].ts.

Writing Documentation

Documentation is written in Markdown format and organized by language:

English docs: content/en/
Chinese docs: content/cn/

Navigation structure is defined in content/[lang]/settings.yml.

Contributing

Fork the repository
Create your feature branch
Commit your changes
Push to the branch
Create a Pull Request

MemOS Documentation

Docker & Self-Hosted Deployment Verified OCI

1. Project Overview & Quickstart (MemTensor/MemOS)

👾 MemOS: Memory Operating System for LLM & AI Agents

Key Features

News

📊 Performance

🎯 What MemOS Is For

🚀 Quick Start

☁️ Use the Cloud API (Hosted)

🖥️ Self-Host the MemOS Service

🧠 MemOS Plugin: Persistent Memory for Your AI Agents ✨

1. MemOS Cloud Plugin

DeepSeek Harness

2. Local Plugin (OpenClaw, Hermes, and DeepSeek Harness)

🤝 Community

📚 Citation

⚖️ License

2. Official Technical Reference & Guides (MemTensor/MemOS-docs)

MemOS Docs

Project Structure

Features

Setup

Development

Build

Environment Configuration

Writing Documentation

Contributing

Table of Contents

Docker & Self-Hosted Deployment Verified OCI

1. Project Overview & Quickstart (MemTensor/MemOS) Copy Chapter

👾 MemOS: Memory Operating System for LLM & AI Agents Copy Chapter

Key Features Copy Section

News Copy Section

📊 Performance Copy Chapter

🎯 What MemOS Is For Copy Chapter

🚀 Quick Start Copy Chapter

☁️ Use the Cloud API (Hosted) Copy Section

🖥️ Self-Host the MemOS Service Copy Section

🧠 MemOS Plugin: Persistent Memory for Your AI Agents ✨ Copy Section

1. MemOS Cloud Plugin

DeepSeek Harness

2. Local Plugin (OpenClaw, Hermes, and DeepSeek Harness)

🤝 Community Copy Chapter

📚 Citation Copy Chapter

⚖️ License Copy Chapter

2. Official Technical Reference & Guides (MemTensor/MemOS-docs) Copy Chapter

MemOS Docs

Project Structure Copy Chapter

Features Copy Chapter

Setup Copy Chapter

Development Copy Chapter

Build Copy Chapter

Environment Configuration Copy Chapter

Writing Documentation Copy Chapter

Contributing Copy Chapter

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1. Project Overview & Quickstart (MemTensor/MemOS)

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📊 Performance

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🖥️ Self-Host the MemOS Service

🧠 MemOS Plugin: Persistent Memory for Your AI Agents ✨

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⚖️ License

2. Official Technical Reference & Guides (MemTensor/MemOS-docs)

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