memos

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📌 MemOS: Memory Operating System for 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

• 2025-12-24 · 🎉 MemOS v2.0: Stardust (星尘) Release
  Comprehensive KB (doc/URL parsing + cross-project sharing), memory feedback & precise deletion, multi-modal memory (images/charts), tool memory for agent planning, Redis Streams scheduling + DB optimizations, streaming/non-streaming chat, MCP upgrade, and lightweight quick/full deployment.

  Knowledge Base & Memory

  • Added knowledge base support for long-term memory from documents and URLs

  Feedback & Memory Management

  • Added natural language feedback and correction for memories
  • Added memory deletion API by memory ID
  • Added MCP support for memory deletion and feedback

  Conversation & Retrieval

  • Added chat API with memory-aware retrieval
  • Added memory filtering with custom tags (Cloud & Open Source)

  Multimodal & Tool Memory

  • Added tool memory for tool usage history
  • Added image memory support for conversations and documents

  Data & Infrastructure

  • Upgraded database for better stability and performance

  Scheduler

  • Rebuilt task scheduler with Redis Streams and queue isolation
  • Added task priority, auto-recovery, and quota-based scheduling

  Deployment & Engineering

  • Added lightweight deployment with quick and full modes

  Memory Scheduling & Updates

  • Fixed legacy scheduling API to ensure correct memory isolation
  • Fixed memory update logging to show new memories correctly

• 2025-08-07 · 🎉 MemOS v1.0.0 (MemCube) Release
  First MemCube release with a word-game demo, LongMemEval evaluation, BochaAISearchRetriever integration, NebulaGraph support, improved search capabilities, and the official Playground launch.

  Playground

  • Expanded Playground features and algorithm performance.

  MemCube Construction

  • Added a text game demo based on the MemCube novel.

  Extended Evaluation Set

  • Added LongMemEval evaluation results and scripts.

  Plaintext Memory

  • Integrated internet search with Bocha.
  • Added support for Nebula database.
  • Added contextual understanding for the tree-structured plaintext memory search interface.

  KV Cache Concatenation

  • Fixed the concat_cache method.

  Plaintext Memory

  • Fixed Nebula search-related issues.

• 2025-07-07 · 🎉 MemOS v1.0: Stellar (星河) Preview Release
  A SOTA Memory OS for LLMs is now open-sourced.

• 2025-07-04 · 🎉 MemOS Paper Release
  MemOS: A Memory OS for AI System is available on arXiv.

• 2024-07-04 · 🎉 Memory3 Model Release at WAIC 2024
  The Memory3 model, featuring a memory-layered architecture, was unveiled at the 2024 World Artificial Intelligence Conference.

🚀 Quickstart Guide

☁️ 1、Cloud API (Hosted)

Get API Key

• Sign up on the MemOS dashboard
• Go to API Keys and copy your key

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.

🖥️ 2、Self-Hosted (Local/Private)

1. Get the repository.

   git clone https://github.com/MemTensor/MemOS.git
   cd MemOS
   pip install -r ./docker/requirements.txt

2. Configure docker/.env.example and copy to MemOS/.env

• The OPENAI_API_KEY,MOS_EMBEDDER_API_KEY,MEMRADER_API_KEY and others can be applied for through BaiLian.
• Fill in the corresponding configuration in the MemOS/.env file.

3. Start the service.

• Launch via Docker

  Tips: Please ensure that Docker Compose is installed successfully and that you have navigated to the docker directory (via cd docker) before executing the following command.

  # Enter docker directory
  docker compose up

  For detailed steps, see theDocker Reference.

• Launch via the uvicorn command line interface (CLI)

  Tips: Please ensure that Neo4j and Qdrant are running before executing the following command.

  cd src
  uvicorn memos.api.server_api:app --host 0.0.0.0 --port 8001 --workers 1

  For detailed integration steps, see the CLI Reference.

Basic Usage (Self-Hosted)

• Add User Message

  import requests
  import json
  
  data = {
      "user_id": "8736b16e-1d20-4163-980b-a5063c3facdc",
      "mem_cube_id": "b32d0977-435d-4828-a86f-4f47f8b55bca",
      "messages": [
          {
              "role": "user",
              "content": "I like strawberry"
          }
      ],
      "async_mode": "sync"
  }
  headers = {
      "Content-Type": "application/json"
  }
  url = "http://localhost:8000/product/add"
  
  res = requests.post(url=url, headers=headers, data=json.dumps(data))
  print(f"result: {res.json()}")

• Search User Memory

  import requests
  import json
  
  data = {
      "query": "What do I like",
      "user_id": "8736b16e-1d20-4163-980b-a5063c3facdc",
      "mem_cube_id": "b32d0