📖 MemoryOS-MCP Getting Started

🔧 Core Tools

1. add_memory

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.

2. retrieve_memory

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.

3. get_user_profile

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

1. Install dependencies

cd memoryos-mcp
pip install -r requirements.txt

2. configuration

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"
  "embedding_model_name":"BAAI/bge-m3"
}

3. Start the server

python server_new.py --config config.json

4. Test

python test_comprehensive.py

5. Configure it on Cline and other clients

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

📖MemoryOS_Chromadb Getting Started

1. Install dependencies

cd memoryos-chromadb
pip install -r requirements.txt

2. Test

The edit information is in comprehensive_test.py
    memoryos = Memoryos(
        user_id='travel_user_test',
        openai_api_key='',
        openai_base_url='',
        data_storage_path='./comprehensive_test_data',
        assistant_id='travel_assistant',
        embedding_model_name='BAAI/bge-m3',
        mid_term_capacity=1000,
        mid_term_heat_threshold=13.0,
        mid_term_similarity_threshold=0.7,
        short_term_capacity=2
    )
python3 comprehensive_test.py
# Make sure to use a different data storage path when switching embedding models.

📖Docker Getting Started

You can run MemoryOS using Docker in two ways: by pulling the official image or by building your own image from the Dockerfile. Both methods are suitable for quick setup, testing, and production deployment.

Option 1: Pull the Official Image

# Pull the latest official image
docker pull ghcr.io/bai-lab/memoryos:latest

docker run -it --gpus=all ghcr.io/bai-lab/memoryos /bin/bash

Option 2: Build from Dockerfile

# Clone the repository
git clone https://github.com/BAI-LAB/MemoryOS.git
          
cd MemoryOS

# Build the Docker image (make sure Dockerfile is present)
docker build -t memoryos .

docker run -it --gpus=all memoryos /bin/bash

📖Playground Getting Started

cd MemoryOS/memoryos-playground/memdemo/

python3 app.py

After launching the main interface, fill in the corresponding User ID, OpenAI API Key, Model, and API Base URL.

After entering the system, you can use the Help button to view the functions of each button.

The user's memory is stored under MemoryOS-main/memoryos-playground/memdemo/data

🎯Reproduce

cd eval
Configure API keys and other settings in the code
python3 main_loco_parse.py
python3 evalution_loco.py

☑️ Todo List

MemoryOS is continuously evolving! Here's what's coming:

• Ongoing🚀: Integrated Benchmarks: Standardized benchmark suite with a cross-model comparison for Mem0, Zep, and OpenAI
• 🏗️ Enabling seamless Memory exchange and integration across diverse systems.

Have ideas or suggestions? Contributions are welcome! Please feel free to submit issues or pull requests! 🚀

📖 Documentation

A more detailed documentation is coming soon 🚀, and we will update in the Documentation page.

📣 Citation

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}, 
}

🎯 Contact us

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: baiting@bupt.edu.cn

📣Follow our WeChat official account, join the WeChat group or https://discord.gg/SqVj7QvZ to get the latest updates.

🌟 Star History

Disclaimer

This project, MemoryOS (Memory Operation System), is developed by the BaiJia AI team and has no affiliation with memoryOS (https://memoryos.com). The use of the name "MemoryOS" herein is solely for academic discussion purposes.