MemoryOS is designed to provide a memory operating system for personalized AI agents, enabling more coherent, personalized, and context-aware interactions. Drawing inspiration from memory management principles in operating systems, it adopts a hierarchical storage architecture with four core modules: Storage, Updating, Retrieval, and Generation, to achieve comprehensive and efficient memory management. On the LoCoMo benchmark, the model achieved average improvements of 49.11% and 46.18% in F1 and BLEU-1 scores.

• [new] 🔥 [2025-06-15]:🛠️ Open-sourced MemoryOS-MCP released! Now configurable on agent clients for seamless integration and customization. 👉 MemoryOS-MCP
• [2025-05-30]: 📄 Paper-Memory OS of AI Agent is available on arXiv: https://arxiv.org/abs/2506.06326.
• [2025-05-30]: Initial version of MemoryOS launched! Featuring short-term, mid-term, and long-term persona Memory with automated user profile and knowledge updating.

All model calls use the OpenAI API interface; you need to supply the API key and base URL.

memoryos/
├── __init__.py            # Initializes the MemoryOS package
├── __pycache__/           # Python cache directory (auto-generated)
├── long_term.py           # Manages long-term persona memory (user profile, knowledge)
├── memoryos.py            # Main class for MemoryOS, orchestrating all components
├── mid_term.py            # Manages mid-term memory, consolidating short-term interactions
├── prompts.py             # Contains prompts used for LLM interactions (e.g., summarization, analysis)
├── retriever.py           # Retrieves relevant information from all memory layers
├── short_term.py          # Manages short-term memory for recent interactions
├── updater.py             # Processes memory updates, including promoting information between layers
└── utils.py               # Utility functions used across the library

1. Initialization: Memoryos is initialized with user and assistant IDs, API keys, data storage paths, and various capacity/threshold settings. It sets up dedicated storage for each user and assistant.
2. Adding Memories: User inputs and agent responses are added as QA pairs. These are initially stored in short-term memory.
3. Short-Term to Mid-Term Processing: When short-term memory is full, the Updater module processes these interactions, consolidating them into meaningful segments and storing them in mid-term memory.
4. Mid-Term Analysis & LPM Updates: Mid-term memory segments accumulate "heat" based on factors like visit frequency and interaction length. When a segment's heat exceeds a threshold, its content is analyzed:
   • User profile insights are extracted and used to update the long-term user profile.
   • Specific user facts are added to the user's long-term knowledge.
   • Relevant information for the assistant is added to the assistant's long-term knowledge base.
5. Response Generation: When a user query is received:
   • The Retriever module fetches relevant context from short-term history, mid-term memory segments, the user's profile & knowledge, and the assistant's knowledge base.
   • This comprehensive context is then used, along with the user's query, to generate a coherent and informed response via an LLM.

• Python >= 3.10
• pip install -i https://pypi.org/simple/ MemoryOS-BaiJia

conda create -n MemoryOS python=3.10
conda activate MemoryOS
pip install -i https://pypi.org/simple/ MemoryOS-BaiJia

import os
from memoryos import Memoryos

# --- Basic Configuration ---
USER_ID = "demo_user"
ASSISTANT_ID = "demo_assistant"
API_KEY = "YOUR_OPENAI_API_KEY"  # Replace with your key
BASE_URL = ""  # Optional: if using a custom OpenAI endpoint
DATA_STORAGE_PATH = "./simple_demo_data"
LLM_MODEL = "gpt-4o-mini"

def simple_demo():
    print("MemoryOS Simple Demo")
    
    # 1. Initialize MemoryOS
    print("Initializing MemoryOS...")
    try:
        memo = Memoryos(
            user_id=USER_ID,
            openai_api_key=API_KEY,
            openai_base_url=BASE_URL,
            data_storage_path=DATA_STORAGE_PATH,
            llm_model=LLM_MODEL,
            assistant_id=ASSISTANT_ID,
            short_term_capacity=7,  
            mid_term_heat_threshold=5,  
            retrieval_queue_capacity=7,
            long_term_knowledge_capacity=100
        )
        print("MemoryOS initialized successfully!\n")
    except Exception as e:
        print(f"Error: {e}")
        return

    # 2. Add some basic memories
    print("Adding some memories...")
    
    memo.add_memory(
        user_input="Hi! I'm Tom, I work as a data scientist in San Francisco.",
        agent_response="Hello Tom! Nice to meet you. Data science is such an exciting field. What kind of data do you work with?"
    )
     
    test_query = "What do you remember about my job?"
    print(f"User: {test_query}")
    
    response = memo.get_response(
        query=test_query,
    )
    
    print(f"Assistant: {response}")

if __name__ == "__main__":
    simple_demo()

Saves the content of the conversation between the user and the AI assistant into the memory system, for the purpose of building a persistent dialogue history and contextual record.

Retrieves related historical dialogues, user preferences, and knowledge information from the memory system based on a query, helping the AI assistant understand the user’s needs and background.

Obtains a user profile generated from the analysis of historical dialogues, including the user’s personality traits, interest preferences, and relevant knowledge background.

cd memoryos-mcp
pip install -r requirements.txt

Edit config.json：

{
  "user_id": "user ID",
  "openai_api_key": "OpenAI API key",
  "openai_base_url": "https://api.openai.com/v1",
  "data_storage_path": "./memoryos_data",
  "assistant_id": "assistant_id",
  "llm_model": "gpt-4o-mini"
}

python server_new.py --config config.json

python test_comprehensive.py

Copy the mcp.json file over, and make sure the file path is correct.

command": "/root/miniconda3/envs/memos/bin/python"
#This should be changed to the Python interpreter of your virtual environment

Contributions are welcome! Please feel free to submit issues or pull requests.

📣 If you find this project useful, please consider citing our paper:

@misc{kang2025memoryosaiagent,
      title={Memory OS of AI Agent}, 
      author={Jiazheng Kang and Mingming Ji and Zhe Zhao and Ting Bai},
      year={2025},
      eprint={2506.06326},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2506.06326}, 
}

BaiJia AI is a research team guided by Associate Professor Bai Ting from Beijing University of Posts and Telecommunications, dedicated to creating emotionally rich and super-memory brains for AI agents. Cooperation and Suggestions: [email protected]

百家AI是北京邮电大学白婷副教授指导的研究小组, 致力于为硅基人类打造情感饱满、记忆超凡的大脑。
合作与建议：[email protected]
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