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43 | 43 |
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44 | 44 | --- |
45 | 45 |
|
46 | | -<img src="https://statics.memtensor.com.cn/memos/sota_score.jpg" alt="SOTA SCORE"> |
| 46 | +<img src="https://cdn.memtensor.com.cn/img/1762436050812_3tgird_compressed.png" alt="SOTA SCORE"> |
47 | 47 |
|
48 | 48 | **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. |
49 | 49 |
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52 | 52 | - **API Reference**: https://memos-docs.openmem.net/docs/api/info/ |
53 | 53 | - **Source Code**: https://github.com/MemTensor/MemOS |
54 | 54 |
|
| 55 | +## 📰 News |
| 56 | + |
| 57 | +Stay up to date with the latest MemOS announcements, releases, and community highlights. |
| 58 | + |
| 59 | + |
| 60 | +- **2025-11-06** - 🎉 MemOS v1.1.3 (Async Memory & Preference): |
| 61 | + Millisecond-level async memory add (support plain-text-memory and |
| 62 | + preference memory); enhanced BM25, graph recall, and mixture search; full |
| 63 | + results & code for LoCoMo, LongMemEval, PersonaMem, and PrefEval released. |
| 64 | +- **2025-10-30** - 🎉 MemOS v1.1.2 (API & MCP Update): |
| 65 | +API architecture overhaul and full MCP (Model Context Protocol) support — enabling models, IDEs, and agents to read/write external memory directly. |
| 66 | +- **2025-09-10** - 🎉 *MemOS v1.0.1 (Group Q&A Bot)*: Group Q&A bot based on MemOS Cube, updated KV-Cache performance comparison data across different GPU deployment schemes, optimized test benchmarks and statistics, added plaintext memory Reranker sorting, optimized plaintext memory hallucination issues, and Playground version updates. [Try PlayGround](https://memos-playground.openmem.net/login/) |
| 67 | +- **2025-08-07** - 🎉 *MemOS v1.0.0 (MemCube Release)*: First MemCube with word game demo, LongMemEval evaluation, BochaAISearchRetriever integration, NebulaGraph support, enhanced search capabilities, and official Playground launch. |
| 68 | +- **2025-07-29** – 🎉 *MemOS v0.2.2 (Nebula Update)*: Internet search+Nebula DB integration, refactored memory scheduler, KV Cache stress tests, MemCube Cookbook release (CN/EN), and 4b/1.7b/0.6b memory ops models. |
| 69 | +- **2025-07-21** – 🎉 *MemOS v0.2.1 (Neo Release)*: Lightweight Neo version with plaintext+KV Cache functionality, Docker/multi-tenant support, MCP expansion, and new Cookbook/Mud game examples. |
| 70 | +- **2025-07-11** – 🎉 *MemOS v0.2.0 (Cross-Platform)*: Added doc search/bilingual UI, MemReader-4B (local deploy), full Win/Mac/Linux support, and playground end-to-end connection. |
| 71 | +- **2025-07-07** – 🎉 *MemOS 1.0 (Stellar) Preview Release*: A SOTA Memory OS for LLMs is now open-sourced. |
| 72 | +- **2025-07-04** – 🎉 *MemOS Paper Released*: [MemOS: A Memory OS for AI System](https://arxiv.org/abs/2507.03724) was published on arXiv. |
| 73 | +- **2025-05-28** – 🎉 *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. |
| 74 | +- **2024-07-04** – 🎉 *Memory3 Model Released at WAIC 2024*: The new memory-layered architecture model was unveiled at the 2024 World Artificial Intelligence Conference. |
| 75 | +- **2024-07-01** – 🎉 *Memory3 Paper Released*: [Memory3: Language Modeling with Explicit Memory](https://arxiv.org/abs/2407.01178) introduces the new approach to structured memory in LLMs. |
| 76 | + |
55 | 77 | ## 📈 Performance Benchmark |
56 | 78 |
|
57 | 79 | MemOS demonstrates significant improvements over baseline memory solutions in multiple memory tasks, |
@@ -265,19 +287,3 @@ We welcome contributions from the community! Please read our [contribution guide |
265 | 287 | ## 📄 License |
266 | 288 |
|
267 | 289 | MemOS is licensed under the [Apache 2.0 License](./LICENSE). |
268 | | - |
269 | | -## 📰 News |
270 | | - |
271 | | -Stay up to date with the latest MemOS announcements, releases, and community highlights. |
272 | | - |
273 | | - |
274 | | -- **2025-09-10** - 🎉 *MemOS v1.0.1 (Group Q&A Bot)*: Group Q&A bot based on MemOS Cube, updated KV-Cache performance comparison data across different GPU deployment schemes, optimized test benchmarks and statistics, added plaintext memory Reranker sorting, optimized plaintext memory hallucination issues, and Playground version updates. [Try PlayGround](https://memos-playground.openmem.net/login/) |
275 | | -- **2025-08-07** - 🎉 *MemOS v1.0.0 (MemCube Release)*: First MemCube with word game demo, LongMemEval evaluation, BochaAISearchRetriever integration, NebulaGraph support, enhanced search capabilities, and official Playground launch. |
276 | | -- **2025-07-29** – 🎉 *MemOS v0.2.2 (Nebula Update)*: Internet search+Nebula DB integration, refactored memory scheduler, KV Cache stress tests, MemCube Cookbook release (CN/EN), and 4b/1.7b/0.6b memory ops models. |
277 | | -- **2025-07-21** – 🎉 *MemOS v0.2.1 (Neo Release)*: Lightweight Neo version with plaintext+KV Cache functionality, Docker/multi-tenant support, MCP expansion, and new Cookbook/Mud game examples. |
278 | | -- **2025-07-11** – 🎉 *MemOS v0.2.0 (Cross-Platform)*: Added doc search/bilingual UI, MemReader-4B (local deploy), full Win/Mac/Linux support, and playground end-to-end connection. |
279 | | -- **2025-07-07** – 🎉 *MemOS 1.0 (Stellar) Preview Release*: A SOTA Memory OS for LLMs is now open-sourced. |
280 | | -- **2025-07-04** – 🎉 *MemOS Paper Released*: [MemOS: A Memory OS for AI System](https://arxiv.org/abs/2507.03724) was published on arXiv. |
281 | | -- **2025-05-28** – 🎉 *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. |
282 | | -- **2024-07-04** – 🎉 *Memory3 Model Released at WAIC 2024*: The new memory-layered architecture model was unveiled at the 2024 World Artificial Intelligence Conference. |
283 | | -- **2024-07-01** – 🎉 *Memory3 Paper Released*: [Memory3: Language Modeling with Explicit Memory](https://arxiv.org/abs/2407.01178) introduces the new approach to structured memory in LLMs. |
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