MemOS

<div align="center"> <h1 align="center"> <a href="https://memos.openmem.net/"> <img src="https://statics.memtensor.com.cn/logo/memos_color_m.png" alt="MemOS Logo" width="48"/> </a>&nbsp; MemOS 2.0&ensp;Stardust（星尘） </h1> <p align="center"> <a href="https://memos-docs.openmem.net/home/overview/"><img src="https://img.shields.io/badge/Docs-Get--Start-002FA7?labelColor=gray&style=for-the-badge&logo=googledocs&logoColor=white" alt="Docs"></a> <a href="https://arxiv.org/abs/2507.03724"><img src="https://img.shields.io/badge/ArXiv-2507.03724-B31B1B?labelColor=gray&style=for-the-badge&logo=arxiv&logoColor=white" alt="ArXiv"></a> <a href="https://x.com/MemOS_dev"><img src="https://img.shields.io/badge/Follow-MemOS-000000?labelColor=gray&style=for-the-badge&logo=x&logoColor=white" alt="X"></a> <a href="https://discord.gg/Txbx3gebZR"><img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Fv10%2Finvites%2FTxbx3gebZR%3Fwith_counts%3Dtrue&query=%24.approximate_presence_count&suffix=%20online&label=Discord&color=404EED&labelColor=gray&style=for-the-badge&logo=discord&logoColor=white" alt="Discord"></a> <br> <a href="https://github.com/IAAR-Shanghai/Awesome-AI-Memory"><img src="https://img.shields.io/badge/Resources-Awesome--AI--Memory-8A2BE2?labelColor=gray&style=for-the-badge&logo=awesomelists&logoColor=white" alt="Resources"></a> </p> <p align="center"> <strong>Give your Agent persistent memory and the ability to grow.</strong><br/> </p> <p align="center"> <strong>English</strong> | <a href="README_ZH.md">中文</a> </p> </div> <div align="center"> <img width="1660" alt="MemOS agent ecosystem: OpenClaw, Hermes, and DeepSeek Harness" src="./assets/readme/memos-agent-ecosystem.png" /> </div>

[!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 →

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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 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:

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-full 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())

<a id="memos-plugin"></a>

🧠 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 i