MemOS 1.0: ææēģ (Stellar) 
MemOS 1.0: ææēģ (Stellar)
**MemOS** is an operating system for Large Language Models (LLMs) that enhances them with long-term memory capabilities. It allows LLMs to store, retrieve, and manage information, enabling more context-aware, consistent, and personalized interactions.
- **Website**: https://memos.openmem.net/
- **Documentation**: https://memos.openmem.net/docs/home
- **API Reference**: https://memos.openmem.net/docs/api/info
- **Source Code**: https://github.com/MemTensor/MemOS
## ð Performance Benchmark
MemOS demonstrates significant improvements over baseline memory solutions in multiple reasoning tasks.
| Model | Avg. Score | Multi-Hop | Open Domain | Single-Hop | Temporal Reasoning |
|-------------|------------|-----------|-------------|------------|---------------------|
| **OpenAI** | 0.5275 | 0.6028 | 0.3299 | 0.6183 | 0.2825 |
| **MemOS** | **0.7331** | **0.6430** | **0.5521** | **0.7844** | **0.7321** |
| **Improvement** | **+38.98%** | **+6.67%** | **+67.35%** | **+26.86%** | **+159.15%** |
> ðĄ **Temporal reasoning accuracy improved by 159% compared to the OpenAI baseline.**
### Details of End-to-End Evaluation on LOCOMO
> [!NOTE]
> Comparison of LLM Judge Scores across five major tasks in the LOCOMO benchmark. Each bar shows the mean evaluation score judged by LLMs for a given method-task pair, with standard deviation as error bars. MemOS-0630 consistently outperforms baseline methods (LangMem, Zep, OpenAI, Mem0) across all task types, especially in multi-hop and temporal reasoning scenarios.
## âĻ Key Features
- **ð§ Memory-Augmented Generation (MAG)**: Provides a unified API for memory operations, integrating with LLMs to enhance chat and reasoning with contextual memory retrieval.
- **ðĶ Modular Memory Architecture (MemCube)**: A flexible and modular architecture that allows for easy integration and management of different memory types.
- **ðū Multiple Memory Types**:
- **Textual Memory**: For storing and retrieving unstructured or structured text knowledge.
- **Activation Memory**: Caches key-value pairs (`KVCacheMemory`) to accelerate LLM inference and context reuse.
- **Parametric Memory**: Stores model adaptation parameters (e.g., LoRA weights).
- **ð Extensible**: Easily extend and customize memory modules, data sources, and LLM integrations.
## ð Getting Started
Here's a quick example of how to create a **`MemCube`**, load it from a directory, access its memories, and save it.
```python
from memos.mem_cube.general import GeneralMemCube
# Initialize a MemCube from a local directory
mem_cube = GeneralMemCube.init_from_dir("examples/data/mem_cube_2")
# Access and print all memories
print("--- Textual Memories ---")
for item in mem_cube.text_mem.get_all():
print(item)
print("\n--- Activation Memories ---")
for item in mem_cube.act_mem.get_all():
print(item)
# Save the MemCube to a new directory
mem_cube.dump("tmp/mem_cube")
```
What about **`MOS`** (Memory Operating System)? It's a higher-level orchestration layer that manages multiple MemCubes and provides a unified API for memory operations. Here's a quick example of how to use MOS:
```python
from memos.configs.mem_os import MOSConfig
from memos.mem_os.main import MOS
# init MOS
mos_config = MOSConfig.from_json_file("examples/data/config/simple_memos_config.json")
memory = MOS(mos_config)
# create user
user_id = "b41a34d5-5cae-4b46-8c49-d03794d206f5"
memory.create_user(user_id=user_id)
# register cube for user
memory.register_mem_cube("examples/data/mem_cube_2", user_id=user_id)
# add memory for user
memory.add(
messages=[
{"role": "user", "content": "I like playing football."},
{"role": "assistant", "content": "I like playing football too."},
],
user_id=user_id,
)
# Later, when you want to retrieve memory for user
retrieved_memories = memory.search(query="What do you like?", user_id=user_id)
# output text_memories: I like playing football, act_memories, para_memories
print(f"text_memories: {retrieved_memories['text_mem']}")
```
For more detailed examples, please check out the [`examples`](./examples) directory.
## ðĶ Installation
> [!WARNING]
> Currently, MemOS primarily supports Linux platforms. You may encounter issues on Windows and macOS temporarily.
### Install via pip
```bash
pip install MemoryOS
```
### Development Install
To contribute to MemOS, clone the repository and install it in editable mode:
```bash
git clone https://github.com/MemTensor/MemOS.git
cd MemOS
make install
```
### Optional Dependencies
#### Ollama Support
To use MemOS with [Ollama](https://ollama.com/), first install the Ollama CLI:
```bash
curl -fsSL https://ollama.com/install.sh | sh
```
#### Transformers Support
To use functionalities based on the `transformers` library, ensure you have [PyTorch](https://pytorch.org/get-started/locally/) installed (CUDA version recommended for GPU acceleration).
## ðŽ Community & Support
Join our community to ask questions, share your projects, and connect with other developers.
- **GitHub Issues**: Report bugs or request features in our GitHub Issues.
- **GitHub Pull Requests**: Contribute code improvements via Pull Requests.
- **GitHub Discussions**: Participate in our GitHub Discussions to ask questions or share ideas.
- **Discord**: Join our Discord Server.
- **WeChat**: Scan the QR code to join our WeChat group.
## ð Citation
If you use MemOS in your research, please cite our paper:
```bibtex
@article{li2025memos,
title={MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models},
author={Li, Zhiyu and Song, Shichao and Wang, Hanyu and Niu, Simin and Chen, Ding and Yang, Jiawei and Xi, Chenyang and Lai, Huayi and Zhao, Jihao and Wang, Yezhaohui and others},
journal={arXiv preprint arXiv:2505.22101},
year={2025}
}
```
## ð Contributing
We welcome contributions from the community! Please read our [contribution guidelines](./docs/contribution/overview.md) to get started.
## ð License
MemOS is licensed under the [Apache 2.0 License](./LICENSE).
## ð° News
Stay up to date with the latest MemOS announcements, releases, and community highlights.
- **2025-07-07** â ð *MemOS 1.0 (Stellar) Preview Release*: A SOTA Memory OS for LLMs is now open-sourced.
- **2025-05-28** â ð *Short Paper Uploaded*: [MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models](https://arxiv.org/abs/2505.22101) was published on arXiv.
- **2024-07-04** â ð *Memory3 Model Released at WAIC 2024*: The new memory-layered architecture model was unveiled at the 2024 World Artificial Intelligence Conference.
- **2024-07-01** â ð *Memory3 Paper Released*: [Memory3: Language Modeling with Explicit Memory](https://arxiv.org/abs/2407.01178) introduces the new approach to structured memory in LLMs.