MemOS

  MemOS 2.0 Stardust（星尘）

Give your Agent persistent memory and the ability to grow.

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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

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())

🧠 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
• 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.

##### 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.

1. Create a MemOS API Key.
2. Install the cloud plugin into the default DSH web profile:

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

1. Add the API Key to ~/.dsh/.credentials.yaml:

MEMOS_API_KEY: mpg-your-key

1. Add the minimal plugin configuration to ~/.dsh/settings.yaml:

memos-cloud:
     apiKeyEnv: MEMOS_API_KEY

1. Restart the DSH Web profile:

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
• 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):

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):

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

Install (Windows 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.