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.
- Paper: https://arxiv.org/abs/2506.06326
- Website: https://baijia.online/memoryos/
- Documentation: https://bai-lab.github.io/MemoryOS/docs
- YouTube Video: MemoryOS MCP + RAG Agent That Can Remember Anything
- https://www.youtube.com/watch?v=WHQu8fpEOaU
🏆 TOP Performance in Memory Management
The SOTA results in long-term memory benchmarks, boosting F1 scores by 49.11% and BLEU-1 by 46.18% on the LoCoMo benchmark.🧠 Plug-and-Play Memory Management Architecture
Enables seamless integration of pluggable memory modules—including storage engines, update strategies, and retrieval algorithms.✨ Agent Workflow Create with Ease (MemoryOS-MCP)
Inject long-term memory capabilities into various AI applications by calling modular tools provided by the MCP Server.🌐 Universal LLM Support
MemoryOS seamlessly integrates with a wide range of LLMs (e.g., OpenAI, Deepseek, Qwen ...)
Welcome to our Memory Family, a research line dedicated to exploring AI Memory.
Survey on AI Memory: Theories, Taxonomies, Evaluations, and Emerging Trends
TL;DR: Provides a unified theoretical framework for AI Memory, introducing a comprehensive taxonomy and systematically analyzing memory mechanisms, applications, and evaluation methods.
📄 Paper: http://github.com/BAI-LAB/Survey-on-AI-Memory/blob/main/Survey%20on%20AI%20Memory.pdf
LightSearcher: Efficient DeepSearch via Experiential Memory
TL;DR: Introduces experiential memory into deep search systems, enabling models to learn from successful reasoning trajectories and improve search efficiency.
📄 Paper: https://arxiv.org/abs/2512.06653
Memory OS of AI Agent
TL;DR: Proposes a memory operating system for AI agents that manages short-term, mid-term, and long-term personal memory through hierarchical storage, dynamic updating, retrieval, and generation, improving coherence and personalization in long conversations.
📄 Paper: https://arxiv.org/abs/2506.06326
- [new] 🔥🔥🔥 [2026-01-15]: ✨ReleasedSurvey on AI Memory: Theories, Taxonomies, Evaluations, and Emerging Trends!
- [new] 🔥🔥 [2025-09-11]: 🚀Open-sourced the Playground platform!
- [new] 🔥🔥 [2025-08-21]: 🎉Accepted by EMNLP 2025 main conference!
- [new] 🔥 [2025-07-15]: 🔌 Support for Vector Database Chromadb
- [new] 🔥 [2025-07-15]: 🔌 IntegrateDocker into deployment
- [new][2025-07-14]: ⚡ Acceleration of MCP parallelization
- [new][2025-07-14]: 🔌 Support for BGE-M3 & Qwen3 embeddings on PyPI and MCP.
- [new][2025-07-09]: 📊 Evaluation of the MemoryOS on LoCoMo Dataset: Publicly Available 👉Reproduce.
- [new][2025-07-08]: 🏆 New Config Parameter
- New parameter configuration: similarity_threshold. For configuration file, see 📖 Documentation page.
- [new][2025-07-07]: 🚀5 Times Faster
- The MemoryOS (PYPI) implementation has been upgraded: 5 times faster (reduction in latency) through parallelization optimizations.
- [new][2025-07-07]: ✨R1 models Support Now
- MemoryOS supports configuring and using inference models such as Deepseek-r1 and Qwen3..
- [new][2025-07-07]: ✨MemoryOS Playground Launched
- The Playground of MemoryOS Platform has been launched! 👉MemoryOS Platform. If you need an Invitation Code, please feel free to reach Contact US.
- [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.
| Type | Name | Open Source | Support | Configuration | Description |
|---|---|---|---|---|---|
| Agent Client | Claude Desktop | ❌ | ✅ | claude_desktop_config.json | Anthropic official client |
| Cline | ✅ | ✅ | VS Code settings | VS Code extension | |
| Cursor | ❌ | ✅ | Settings panel | AI code editor | |
| Model Provider | OpenAI | ❌ | ✅ | OPENAI_API_KEY | GPT-4, GPT-3.5, etc. |
| Anthropic | ❌ | ✅ | ANTHROPIC_API_KEY | Claude series | |
| Deepseek-R1 | ✅ | ✅ | DEEPSEEK_API_KEY | Chinese large model | |
| Qwen/Qwen3 | ✅ | ✅ | QWEN_API_KEY | Alibaba Qwen | |
| vLLM | ✅ | ✅ | Local deployment | Local model inference | |
| Llama_factory | ✅ | ✅ | Local deployment | Local fine-tuning deployment |
- ✨ Features
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- 🎯 Quick Start
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- 🔬 How to Reproduce the Results in the Paper
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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
- Python >= 3.10
- conda create -n MemoryOS python=3.10
- conda activate MemoryOS
pip install memoryos-pro -i https://pypi.org/simplegit clone https://github.com/BAI-LAB/MemoryOS.git
cd MemoryOS/memoryos-pypi
pip install -r requirements.txtimportosfrommemoryosimportMemoryos# --- Basic Configuration ---USER_ID="demo_user"ASSISTANT_ID="demo_assistant"API_KEY="YOUR_OPENAI_API_KEY"# Replace with your keyBASE_URL=""# Optional: if using a custom OpenAI endpointDATA_STORAGE_PATH="./simple_demo_data"LLM_MODEL="gpt-4o-mini"defsimple_demo():
print("MemoryOS Simple Demo")
# 1. Initialize MemoryOSprint("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,
#Support Qwen/Qwen3-Embedding-0.6B, BAAI/bge-m3, all-MiniLM-L6-v2embedding_model_name="BAAI/bge-m3"
)
print("MemoryOS initialized successfully!\n")
exceptExceptionase:
print(f"Error: {e}")
return# 2. Add some basic memoriesprint("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.txtEdit 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"
}python server_new.py --config config.jsonpython test_comprehensive.pyCopy 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 environmentcd memoryos-chromadb
pip install -r requirements.txtThe 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.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.
# 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# 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/bashcd MemoryOS/memoryos-playground/memdemo/
python3 app.pyAfter 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
cdeval
Configure API keys and other settings in the code
python3 main_loco_parse.py
python3 evalution_loco.pyMemoryOS 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! 🚀
A more detailed documentation is coming soon 🚀, and we will update in the Documentation page.
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: baiting@bupt.edu.cn
📣Follow our WeChat official account, join the WeChat group or 
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.