[{"_1":2,"_358":-5,"_359":-5},"loaderData",{"_3":4,"_24":25,"_261":262},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":22,"_23":-5},"attributes",[15],"bot","id",27958635,"membership","referenceId","019abdf8-e51f-71df-bc47-de8c3de3f15e","unreadReleaseNotesCount",2,"userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_26":27,"_28":-5,"_29":30,"_31":43,"_180":181,"_260":162},"availableLocales",[],"githubUser","locale",{"_31":32,"_39":40,"_41":42},"mcpServer",{"_33":34,"_35":36,"_37":38},"description","A Model Context Protocol integration for the MemOS memory system, optimized for personal AI assistant scenarios with intelligent memory management and retrieval capabilities.","displayName","MemOS-MCP","readme","# MemOS - MCP Integration\n\nMemOS记忆系统的MCP（Model Context Protocol）集成版本，专为个人AI助手场景优化。\n\n基于原始[MemOS项目](https://github.com/MemTensor/MemOS)，针对Claude Desktop和MCP协议进行了深度优化。\n\n## 🚀 特性\n\n### 核心功能\n- **智能记忆管理**: 基于向量数据库的语义记忆存储和检索\n- **MCP协议支持**: 完整的Model Context Protocol集成\n- **多模型支持**: 支持Qwen3、SiliconFlow等多种嵌入和重排模型\n- **时间感知检索**: 基于时间衰减的智能记忆排序\n- **缓存优化**: LRU缓存系统，显著提升检索性能\n\n### 技术架构\n- **分层设计**: 表示层、业务逻辑层、数据访问层的清晰分离\n- **MVP管理器**: 统一的记忆管理接口，支持增强版和基础版自动切换\n- **容量管理**: 智能的记忆容量管理和自动压缩\n- **并发控制**: 多进程安全的并发访问控制\n\n- **Website**: https://memos.openmem.net/\n- **Documentation**: https://memos-docs.openmem.net/home/overview/\n- **API Reference**: https://memos-docs.openmem.net/docs/api/info/\n- **Source Code**: https://github.com/MemTensor/MemOS\n\n## 📈 Performance Benchmark\n\nMemOS demonstrates significant improvements over baseline memory solutions in multiple reasoning tasks.\n\n| Model | Avg. Score | Multi-Hop | Open Domain | Single-Hop | Temporal Reasoning |\n|-------------|------------|-----------|-------------|------------|---------------------|\n| **OpenAI** | 0.5275 | 0.6028 | 0.3299 | 0.6183 | 0.2825 |\n| **MemOS** | **0.7331** | **0.6430** | **0.5521** | **0.7844** | **0.7321** |\n| **Improvement** | **+38.98%** | **+6.67%** | **+67.35%** | **+26.86%** | **+159.15%** |\n\n> 💡 **Temporal reasoning accuracy improved by 159% compared to the OpenAI baseline.**\n\n### Details of End-to-End Evaluation on LOCOMO\n\n> [!NOTE]\n> 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.\n\n $\"END2END$ \n\n## ✨ Key Features\n\n- **🧠 Memory-Augmented Generation (MAG)**: Provides a unified API for memory operations, integrating with LLMs to enhance chat and reasoning with contextual memory retrieval.\n- **📦 Modular Memory Architecture (MemCube)**: A flexible and modular architecture that allows for easy integration and management of different memory types.\n- **💾 Multiple Memory Types**:\n - **Textual Memory**: For storing and retrieving unstructured or structured text knowledge.\n - **Activation Memory**: Caches key-value pairs (`KVCacheMemory`) to accelerate LLM inference and context reuse.\n - **Parametric Memory**: Stores model adaptation parameters (e.g., LoRA weights).\n- **🔌 Extensible**: Easily extend and customize memory modules, data sources, and LLM integrations.\n\n## 🚀 Getting Started\n\nHere's a quick example of how to create a **`MemCube`**, load it from a directory, access its memories, and save it.\n\n```python\nfrom memos.mem_cube.general import GeneralMemCube\n\n# Initialize a MemCube from a local directory\nmem_cube = GeneralMemCube.init_from_dir(\"examples/data/mem_cube_2\")\n\n# Access and print all memories\nprint(\"--- Textual Memories ---\")\nfor item in mem_cube.text_mem.get_all():\n print(item)\n\nprint(\"\\n--- Activation Memories ---\")\nfor item in mem_cube.act_mem.get_all():\n print(item)\n\n# Save the MemCube to a new directory\nmem_cube.dump(\"tmp/mem_cube\")\n```\n\nWhat 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:\n\n```python\nfrom memos.configs.mem_os import MOSConfig\nfrom memos.mem_os.main import MOS\n\n\n# init MOS\nmos_config = MOSConfig.from_json_file(\"examples/data/config/simple_memos_config.json\")\nmemory = MOS(mos_config)\n\n# create user\nuser_id = \"b41a34d5-5cae-4b46-8c49-d03794d206f5\"\nmemory.create_user(user_id=user_id)\n\n# register cube for user\nmemory.register_mem_cube(\"examples/data/mem_cube_2\", user_id=user_id)\n\n# add memory for user\nmemory.add(\n messages=[\n {\"role\": \"user\", \"content\": \"I like playing football.\"},\n {\"role\": \"assistant\", \"content\": \"I like playing football too.\"},\n ],\n user_id=user_id,\n)\n\n# Later, when you want to retrieve memory for user\nretrieved_memories = memory.search(query=\"What do you like?\", user_id=user_id)\n# output text_memories: I like playing football, act_memories, para_memories\nprint(f\"text_memories: {retrieved_memories['text_mem']}\")\n```\n\nFor more detailed examples, please check out the [`examples`](./examples) directory.\n\n## 📦 Installation\n\n> [!WARNING]\n> MemOS is compatible with Linux, Windows, and macOS.\n>\n> However, if you're using macOS, please note that there may be dependency issues that are difficult to resolve.\n>\n> For example, compatibility with macOS 13 Ventura is currently challenging.\n\n### Install via pip\n\n```bash\npip install MemoryOS\n```\n\n### Development Install\n\nTo contribute to MemOS, clone the repository and install it in editable mode:\n\n```bash\ngit clone https://github.com/MemTensor/MemOS.git\ncd MemOS\nmake install\n```\n\n### Optional Dependencies\n\n#### Ollama Support\n\nTo use MemOS with [Ollama](https://ollama.com/), first install the Ollama CLI:\n```bash\ncurl -fsSL https://ollama.com/install.sh | sh\n```\n\n#### Transformers Support\n\nTo use functionalities based on the `transformers` library, ensure you have [PyTorch](https://pytorch.org/get-started/locally/) installed (CUDA version recommended for GPU acceleration).\n\n## 💬 Community & Support\n\nJoin our community to ask questions, share your projects, and connect with other developers.\n\n- **GitHub Issues**: Report bugs or request features in our GitHub Issues.\n- **GitHub Pull Requests**: Contribute code improvements via Pull Requests.\n- **GitHub Discussions**: Participate in our GitHub Discussions to ask questions or share ideas.\n- **Discord**: Join our Discord Server.\n- **WeChat**: Scan the QR code to join our WeChat group.\n\n $\"QR$ \n\n## 📜 Citation\n\n> [!NOTE]\n> We publicly released the Short Version on **May 28, 2025**, making it the earliest work to propose the concept of a Memory Operating System for LLMs.\n\nIf you use MemOS in your research, we would appreciate citations to our papers.\n\n```bibtex\n\n@article{li2025memos_long,\n title={MemOS: A Memory OS for AI System},\n author={Li, Zhiyu and Song, Shichao and Xi, Chenyang and Wang, Hanyu and Tang, Chen and Niu, Simin and Chen, Ding and Yang, Jiawei and Li, Chunyu and Yu, Qingchen and Zhao, Jihao and Wang, Yezhaohui and Liu, Peng and Lin, Zehao and Wang, Pengyuan and Huo, Jiahao and Chen, Tianyi and Chen, Kai and Li, Kehang and Tao, Zhen and Ren, Junpeng and Lai, Huayi and Wu, Hao and Tang, Bo and Wang, Zhenren and Fan, Zhaoxin and Zhang, Ningyu and Zhang, Linfeng and Yan, Junchi and Yang, Mingchuan and Xu, Tong and Xu, Wei and Chen, Huajun and Wang, Haofeng and Yang, Hongkang and Zhang, Wentao and Xu, Zhi-Qin John and Chen, Siheng and Xiong, Feiyu},\n journal={arXiv preprint arXiv:2507.03724},\n year={2025},\n url={https://arxiv.org/abs/2507.03724}\n}\n\n@article{li2025memos_short,\n title={MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models},\n 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},\n journal={arXiv preprint arXiv:2505.22101},\n year={2025},\n url={https://arxiv.org/abs/2505.22101}\n}\n\n@article{yang2024memory3,\nauthor = {Yang, Hongkang and Zehao, Lin and Wenjin, Wang and Wu, Hao and Zhiyu, Li and Tang, Bo and Wenqiang, Wei and Wang, Jinbo and Zeyun, Tang and Song, Shichao and Xi, Chenyang and Yu, Yu and Kai, Chen and Xiong, Feiyu and Tang, Linpeng and Weinan, E},\ntitle = {Memory$^3$: Language Modeling with Explicit Memory},\njournal = {Journal of Machine Learning},\nyear = {2024},\nvolume = {3},\nnumber = {3},\npages = {300--346},\nissn = {2790-2048},\ndoi = {https://doi.org/10.4208/jml.240708},\nurl = {https://global-sci.com/article/91443/memory3-language-modeling-with-explicit-memory}\n}\n```\n\n## 🙌 Contributing\n\nWe welcome contributions from the community! Please read our [contribution guidelines](https://memos-docs.openmem.net/contribution/overview) to get started.\n\n## 📄 License\n\nMemOS is licensed under the [Apache 2.0 License](./LICENSE).\n\n## 📰 News\n\nStay up to date with the latest MemOS announcements, releases, and community highlights.\n\n- **2025-07-07** – 🎉 *MemOS 1.0 (Stellar) Preview Release*: A SOTA Memory OS for LLMs is now open-sourced.\n- **2025-07-04** – 🎉 *MemOS Paper Released*: [MemOS: A Memory OS for AI System](https://arxiv.org/abs/2507.03724) was published on arXiv.\n- **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.\n- **2024-07-04** – 🎉 *Memory3 Model Released at WAIC 2024*: The new memory-layered architecture model was unveiled at the 2024 World Artificial Intelligence Conference.\n- **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.\n","name","English","tag","en-US",{"_44":45,"_13":46,"_49":34,"_50":34,"_35":36,"_16":51,"_52":53,"_107":108,"_115":-5,"_116":117,"_118":-5,"_119":120,"_169":170,"_175":-5,"_60":151,"_176":117,"_63":177,"_178":179},"addedAt","2025-07-16T17:00:24.205529Z",[47,48],"language:python","license_grade:a","descriptionMarkdown","descriptionPlainText",9299,"integrations",[54,65,72,79,86,93,100],{"_55":56,"_33":62,"_63":64},"brand",{"_57":58,"_39":59,"_60":61},"logoSvgPath","M3.8423 0a1.0037 1.0037 0 0 0-.922.6078c-.1536.3687-.0438.6275.2938 1.1113l6.9185 8.3597-1.0223 1.1058a1.0393 1.0393 0 0 0 .003 1.4229l1.2292 1.3135-5.4391 6.4444c-.2803.299-.4538.823-.2971 1.1986a1.0253 1.0253 0 0 0 .9585.635.9133.9133 0 0 0 .6891-.3405l5.783-6.126 7.4902 8.0051a.8527.8527 0 0 0 .6835.2597.9575.9575 0 0 0 .8777-.6138c.1577-.377-.017-.7502-.306-1.1407l-7.0518-8.3418 1.0632-1.13a.9626.9626 0 0 0 .0089-1.3165L4.6336.4639s-.3733-.4535-.768-.463zm0 .272h.0166c.2179.0052.4874.2715.5644.3639l.005.006.0052.0055 10.169 10.9905a.6915.6915 0 0 1-.0072.945l-1.0666 1.133-1.4982-1.7724-8.5994-10.39c-.3286-.472-.352-.6183-.2592-.841a.7307.7307 0 0 1 .6704-.4401Zm14.341 1.5701a.877.877 0 0 0-.6554.2418l-5.6962 6.1584 1.6944 1.8319 5.3089-6.5138c.3251-.4335.479-.6603.3247-1.0292a1.1205 1.1205 0 0 0-.9763-.689zm-7.6557 12.2823 1.3186 1.4135-5.7864 6.1295a.6494.6494 0 0 1-.4959.26.7516.7516 0 0 1-.706-.4669c-.1119-.2682.0359-.6864.2442-.9083l.0051-.0055.0047-.0055z","arXiv","slug","arxiv","Supports academic paper citation and publication, with references to arXiv papers related to memory systems for LLMs.","uid","ms3wnrr8sk",{"_55":66,"_33":70,"_63":71},{"_57":67,"_39":68,"_60":69},"M20.317 4.3698a19.7913 19.7913 0 00-4.8851-1.5152.0741.0741 0 00-.0785.0371c-.211.3753-.4447.8648-.6083 1.2495-1.8447-.2762-3.68-.2762-5.4868 0-.1636-.3933-.4058-.8742-.6177-1.2495a.077.077 0 00-.0785-.037 19.7363 19.7363 0 00-4.8852 1.515.0699.0699 0 00-.0321.0277C.5334 9.0458-.319 13.5799.0992 18.0578a.0824.0824 0 00.0312.0561c2.0528 1.5076 4.0413 2.4228 5.9929 3.0294a.0777.0777 0 00.0842-.0276c.4616-.6304.8731-1.2952 1.226-1.9942a.076.076 0 00-.0416-.1057c-.6528-.2476-1.2743-.5495-1.8722-.8923a.077.077 0 01-.0076-.1277c.1258-.0943.2517-.1923.3718-.2914a.0743.0743 0 01.0776-.0105c3.9278 1.7933 8.18 1.7933 12.0614 0a.0739.0739 0 01.0785.0095c.1202.099.246.1981.3728.2924a.077.077 0 01-.0066.1276 12.2986 12.2986 0 01-1.873.8914.0766.0766 0 00-.0407.1067c.3604.698.7719 1.3628 1.225 1.9932a.076.076 0 00.0842.0286c1.961-.6067 3.9495-1.5219 6.0023-3.0294a.077.077 0 00.0313-.0552c.5004-5.177-.8382-9.6739-3.5485-13.6604a.061.061 0 00-.0312-.0286zM8.02 15.3312c-1.1825 0-2.1569-1.0857-2.1569-2.419 0-1.3332.9555-2.4189 2.157-2.4189 1.2108 0 2.1757 1.0952 2.1568 2.419 0 1.3332-.9555 2.4189-2.1569 2.4189zm7.9748 0c-1.1825 0-2.1569-1.0857-2.1569-2.419 0-1.3332.9554-2.4189 2.1569-2.4189 1.2108 0 2.1757 1.0952 2.1568 2.419 0 1.3332-.946 2.4189-2.1568 2.4189Z","Discord","discord","Provides community support through Discord server integration.","14zq6dhdmh",{"_55":73,"_33":77,"_63":78},{"_57":74,"_39":75,"_60":76},"M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12","GitHub","github","Enables communication with GitHub through issues, discussions, and pull requests for bug reporting and community support.","5ol0zxl71w",{"_55":80,"_33":84,"_63":85},{"_57":81,"_39":82,"_60":83},"M16.361 10.26a.894.894 0 0 0-.558.47l-.072.148.001.207c0 .193.004.217.059.353.076.193.152.312.291.448.24.238.51.3.872.205a.86.86 0 0 0 .517-.436.752.752 0 0 0 .08-.498c-.064-.453-.33-.782-.724-.897a1.06 1.06 0 0 0-.466 0zm-9.203.005c-.305.096-.533.32-.65.639a1.187 1.187 0 0 0-.06.52c.057.309.31.59.598.667.362.095.632.033.872-.205.14-.136.215-.255.291-.448.055-.136.059-.16.059-.353l.001-.207-.072-.148a.894.894 0 0 0-.565-.472 1.02 1.02 0 0 0-.474.007Zm4.184 2c-.131.071-.223.25-.195.383.031.143.157.288.353.407.105.063.112.072.117.136.004.038-.01.146-.029.243-.02.094-.036.194-.036.222.002.074.07.195.143.253.064.052.076.054.255.059.164.005.198.001.264-.03.169-.082.212-.234.15-.525-.052-.243-.042-.28.087-.355.137-.08.281-.219.324-.314a.365.365 0 0 0-.175-.48.394.394 0 0 0-.181-.033c-.126 0-.207.03-.355.124l-.085.053-.053-.032c-.219-.13-.259-.145-.391-.143a.396.396 0 0 0-.193.032zm.39-2.195c-.373.036-.475.05-.654.086-.291.06-.68.195-.951.328-.94.46-1.589 1.226-1.787 2.114-.04.176-.045.234-.045.53 0 .294.005.357.043.524.264 1.16 1.332 2.017 2.714 2.173.3.033 1.596.033 1.896 0 1.11-.125 2.064-.727 2.493-1.571.114-.226.169-.372.22-.602.039-.167.044-.23.044-.523 0-.297-.005-.355-.045-.531-.288-1.29-1.539-2.304-3.072-2.497a6.873 6.873 0 0 0-.855-.031zm.645.937a3.283 3.283 0 0 1 1.44.514c.223.148.537.458.671.662.166.251.26.508.303.82.02.143.01.251-.043.482-.08.345-.332.705-.672.957a3.115 3.115 0 0 1-.689.348c-.382.122-.632.144-1.525.138-.582-.006-.686-.01-.853-.042-.57-.107-1.022-.334-1.35-.68-.264-.28-.385-.535-.45-.946-.03-.192.025-.509.137-.776.136-.326.488-.73.836-.963.403-.269.934-.46 1.422-.512.187-.02.586-.02.773-.002zm-5.503-11a1.653 1.653 0 0 0-.683.298C5.617.74 5.173 1.666 4.985 2.819c-.07.436-.119 1.04-.119 1.503 0 .544.064 1.24.155 1.721.02.107.031.202.023.208a8.12 8.12 0 0 1-.187.152 5.324 5.324 0 0 0-.949 1.02 5.49 5.49 0 0 0-.94 2.339 6.625 6.625 0 0 0-.023 1.357c.091.78.325 1.438.727 2.04l.13.195-.037.064c-.269.452-.498 1.105-.605 1.732-.084.496-.095.629-.095 1.294 0 .67.009.803.088 1.266.095.555.288 1.143.503 1.534.071.128.243.393.264.407.007.003-.014.067-.046.141a7.405 7.405 0 0 0-.548 1.873c-.062.417-.071.552-.071.991 0 .56.031.832.148 1.279L3.42 24h1.478l-.05-.091c-.297-.552-.325-1.575-.068-2.597.117-.472.25-.819.498-1.296l.148-.29v-.177c0-.165-.003-.184-.057-.293a.915.915 0 0 0-.194-.25 1.74 1.74 0 0 1-.385-.543c-.424-.92-.506-2.286-.208-3.451.124-.486.329-.918.544-1.154a.787.787 0 0 0 .223-.531c0-.195-.07-.355-.224-.522a3.136 3.136 0 0 1-.817-1.729c-.14-.96.114-2.005.69-2.834.563-.814 1.353-1.336 2.237-1.475.199-.033.57-.028.776.01.226.04.367.028.512-.041.179-.085.268-.19.374-.431.093-.215.165-.333.36-.576.234-.29.46-.489.822-.729.413-.27.884-.467 1.352-.561.17-.035.25-.04.569-.04.319 0 .398.005.569.04a4.07 4.07 0 0 1 1.914.997c.117.109.398.457.488.602.034.057.095.177.132.267.105.241.195.346.374.43.14.068.286.082.503.045.343-.058.607-.053.943.016 1.144.23 2.14 1.173 2.581 2.437.385 1.108.276 2.267-.296 3.153-.097.15-.193.27-.333.419-.301.322-.301.722-.001 1.053.493.539.801 1.866.708 3.036-.062.772-.26 1.463-.533 1.854a2.096 2.096 0 0 1-.224.258.916.916 0 0 0-.194.25c-.054.109-.057.128-.057.293v.178l.148.29c.248.476.38.823.498 1.295.253 1.008.231 2.01-.059 2.581a.845.845 0 0 0-.044.098c0 .006.329.009.732.009h.73l.02-.074.036-.134c.019-.076.057-.3.088-.516.029-.217.029-1.016 0-1.258-.11-.875-.295-1.57-.597-2.226-.032-.074-.053-.138-.046-.141.008-.005.057-.074.108-.152.376-.569.607-1.284.724-2.228.031-.26.031-1.378 0-1.628-.083-.645-.182-1.082-.348-1.525a6.083 6.083 0 0 0-.329-.7l-.038-.064.131-.194c.402-.604.636-1.262.727-2.04a6.625 6.625 0 0 0-.024-1.358 5.512 5.512 0 0 0-.939-2.339 5.325 5.325 0 0 0-.95-1.02 8.097 8.097 0 0 1-.186-.152.692.692 0 0 1 .023-.208c.208-1.087.201-2.443-.017-3.503-.19-.924-.535-1.658-.98-2.082-.354-.338-.716-.482-1.15-.455-.996.059-1.8 1.205-2.116 3.01a6.805 6.805 0 0 0-.097.726c0 .036-.007.066-.015.066a.96.96 0 0 1-.149-.078A4.857 4.857 0 0 0 12 3.03c-.832 0-1.687.243-2.456.698a.958.958 0 0 1-.148.078c-.008 0-.015-.03-.015-.066a6.71 6.71 0 0 0-.097-.725C8.997 1.392 8.337.319 7.46.048a2.096 2.096 0 0 0-.585-.041Zm.293 1.402c.248.197.523.759.682 1.388.03.113.06.244.069.292.007.047.026.152.041.233.067.365.098.76.102 1.24l.002.475-.12.175-.118.178h-.278c-.324 0-.646.041-.954.124l-.238.06c-.033.007-.038-.003-.057-.144a8.438 8.438 0 0 1 .016-2.323c.124-.788.413-1.501.696-1.711.067-.05.079-.049.157.013zm9.825-.012c.17.126.358.46.498.888.28.854.36 2.028.212 3.145-.019.14-.024.151-.057.144l-.238-.06a3.693 3.693 0 0 0-.954-.124h-.278l-.119-.178-.119-.175.002-.474c.004-.669.066-1.19.214-1.772.157-.623.434-1.185.68-1.382.078-.062.09-.063.159-.012z","Ollama","ollama","Supports integration with Ollama for local model deployment and inference.","zbqkihfgx2",{"_55":87,"_33":91,"_63":92},{"_57":88,"_39":89,"_60":90},"M22.2819 9.8211a5.9847 5.9847 0 0 0-.5157-4.9108 6.0462 6.0462 0 0 0-6.5098-2.9A6.0651 6.0651 0 0 0 4.9807 4.1818a5.9847 5.9847 0 0 0-3.9977 2.9 6.0462 6.0462 0 0 0 .7427 7.0966 5.98 5.98 0 0 0 .511 4.9107 6.051 6.051 0 0 0 6.5146 2.9001A5.9847 5.9847 0 0 0 13.2599 24a6.0557 6.0557 0 0 0 5.7718-4.2058 5.9894 5.9894 0 0 0 3.9977-2.9001 6.0557 6.0557 0 0 0-.7475-7.0729zm-9.022 12.6081a4.4755 4.4755 0 0 1-2.8764-1.0408l.1419-.0804 4.7783-2.7582a.7948.7948 0 0 0 .3927-.6813v-6.7369l2.02 1.1686a.071.071 0 0 1 .038.052v5.5826a4.504 4.504 0 0 1-4.4945 4.4944zm-9.6607-4.1254a4.4708 4.4708 0 0 1-.5346-3.0137l.142.0852 4.783 2.7582a.7712.7712 0 0 0 .7806 0l5.8428-3.3685v2.3324a.0804.0804 0 0 1-.0332.0615L9.74 19.9502a4.4992 4.4992 0 0 1-6.1408-1.6464zM2.3408 7.8956a4.485 4.485 0 0 1 2.3655-1.9728V11.6a.7664.7664 0 0 0 .3879.6765l5.8144 3.3543-2.0201 1.1685a.0757.0757 0 0 1-.071 0l-4.8303-2.7865A4.504 4.504 0 0 1 2.3408 7.872zm16.5963 3.8558L13.1038 8.364 15.1192 7.2a.0757.0757 0 0 1 .071 0l4.8303 2.7913a4.4944 4.4944 0 0 1-.6765 8.1042v-5.6772a.79.79 0 0 0-.407-.667zm2.0107-3.0231l-.142-.0852-4.7735-2.7818a.7759.7759 0 0 0-.7854 0L9.409 9.2297V6.8974a.0662.0662 0 0 1 .0284-.0615l4.8303-2.7866a4.4992 4.4992 0 0 1 6.6802 4.66zM8.3065 12.863l-2.02-1.1638a.0804.0804 0 0 1-.038-.0567V6.0742a4.4992 4.4992 0 0 1 7.3757-3.4537l-.142.0805L8.704 5.459a.7948.7948 0 0 0-.3927.6813zm1.0976-2.3654l2.602-1.4998 2.6069 1.4998v2.9994l-2.5974 1.4997-2.6067-1.4997Z","OpenAI","openai","Includes benchmarking against OpenAI models and potential integration capabilities.","305sjdzjfw",{"_55":94,"_33":98,"_63":99},{"_57":95,"_39":96,"_60":97},"M12.005 0L4.952 7.053a9.865 9.865 0 000 14.022 9.866 9.866 0 0014.022 0c3.984-3.9 3.986-10.205.085-14.023l-1.744 1.743c2.904 2.905 2.904 7.634 0 10.538s-7.634 2.904-10.538 0-2.904-7.634 0-10.538l4.647-4.646.582-.665zm3.568 3.899a1.327 1.327 0 00-1.327 1.327 1.327 1.327 0 001.327 1.328A1.327 1.327 0 0016.9 5.226 1.327 1.327 0 0015.573 3.9z","PyTorch","pytorch","Provides integration with PyTorch for model acceleration and GPU support.","ah059vw3g0",{"_55":101,"_33":105,"_63":106},{"_57":102,"_39":103,"_60":104},"M8.691 2.188C3.891 2.188 0 5.476 0 9.53c0 2.212 1.17 4.203 3.002 5.55a.59.59 0 0 1 .213.665l-.39 1.48c-.019.07-.048.141-.048.213 0 .163.13.295.29.295a.326.326 0 0 0 .167-.054l1.903-1.114a.864.864 0 0 1 .717-.098 10.16 10.16 0 0 0 2.837.403c.276 0 .543-.027.811-.05-.857-2.578.157-4.972 1.932-6.446 1.703-1.415 3.882-1.98 5.853-1.838-.576-3.583-4.196-6.348-8.596-6.348zM5.785 5.991c.642 0 1.162.529 1.162 1.18a1.17 1.17 0 0 1-1.162 1.178A1.17 1.17 0 0 1 4.623 7.17c0-.651.52-1.18 1.162-1.18zm5.813 0c.642 0 1.162.529 1.162 1.18a1.17 1.17 0 0 1-1.162 1.178 1.17 1.17 0 0 1-1.162-1.178c0-.651.52-1.18 1.162-1.18zm5.34 2.867c-1.797-.052-3.746.512-5.28 1.786-1.72 1.428-2.687 3.72-1.78 6.22.942 2.453 3.666 4.229 6.884 4.229.826 0 1.622-.12 2.361-.336a.722.722 0 0 1 .598.082l1.584.926a.272.272 0 0 0 .14.047c.134 0 .24-.111.24-.247 0-.06-.023-.12-.038-.177l-.327-1.233a.582.582 0 0 1-.023-.156.49.49 0 0 1 .201-.398C23.024 18.48 24 16.82 24 14.98c0-3.21-2.931-5.837-6.656-6.088V8.89c-.135-.01-.27-.027-.407-.03zm-2.53 3.274c.535 0 .969.44.969.982a.976.976 0 0 1-.969.983.976.976 0 0 1-.969-.983c0-.542.434-.982.97-.982zm4.844 0c.535 0 .969.44.969.982a.976.976 0 0 1-.969.983.976.976 0 0 1-.969-.983c0-.542.434-.982.969-.982z","WeChat","wechat","Offers community support and integration through WeChat groups.","ap5j1qa50f","namespace",{"_109":110,"_60":114},"owner",{"_111":112},"githubAccount",{"_113":114},"login","qinshu1109","readmeMarkdown","recentUsage",0,"redditPostExternalId","repository",{"_121":122,"_136":137,"_140":141,"_160":-5,"_161":162,"_163":-5,"_164":165},"argumentsJsonSchema",{"_123":124,"_133":134,"_128":135},"properties",{"_125":126,"_130":131},"PUBMED_EMAIL",{"_33":127,"_128":129},"Your email address (required by NCBI)","type","string","PUBMED_API_KEY",{"_33":132,"_128":129},"Optional API key for higher rate limits","required",[125],"object","githubProject",{"_37":38,"_138":139},"url","https://github.com/qinshu1109/memos-MCP","githubRepository",{"_142":143,"_144":145,"_146":147,"_16":148,"_149":150,"_39":151,"_109":152,"_154":117,"_155":156,"_158":159},"defaultBranch","main","fullName","qinshu1109/memos-MCP","githubTreeId",339957,20056,"language","Python","memos-MCP",{"_113":114,"_63":153},"a5g3w36ejs","recentStargazers","spdxLicense",{"_39":157},"Apache 2.0","stargazers",3,"glamaJson","inspectable",false,"npmPackage","supportedPlatforms",[166,167,168],"LINUX","WINDOWS","MACOS","scores",{"_171":172,"_173":-5,"_174":-5},"license",100,"quality","security","simpleIcon","toolCount","p7vpttx1wc","updatedAt","2025-11-24T18:31:16.692794Z","meta",[182,185,187,194,197,199,202,205,208,211,214,216,219,222,224,226,228,230,232],{"_183":184},"title","MemOS-MCP | Glama",{"_186":34,"_39":33},"content",{"_188":189,"_190":191,"_192":193},"href","https://glama.ai/mcp/servers/@qinshu1109/memos-MCP","rel","canonical","tagName","link",{"_186":36,"_195":196},"property","og:title",{"_186":34,"_195":198},"og:description",{"_186":200,"_195":201},"https://glama.ai/logo.png","og:logo",{"_186":203,"_195":204},"https://glama.ai/mcp/servers/@qinshu1109/memos-MCP/og-image","og:image",{"_186":206,"_195":207},"630","og:image:height",{"_186":209,"_195":210},"1200","og:image:width",{"_186":212,"_195":213},"website","og:type",{"_186":189,"_195":215},"og:url",{"_186":217,"_195":218},"Glama – MCP Hosting Platform","og:site_name",{"_186":220,"_39":221},"summary_large_image","twitter:card",{"_186":217,"_39":223},"twitter:title",{"_186":34,"_39":225},"twitter:description",{"_186":203,"_39":227},"twitter:image",{"_186":206,"_39":229},"twitter:image:height",{"_186":209,"_39":231},"twitter:image:width",{"_233":234},"script:ld+json",{"_235":236,"_237":238},"@context","http://schema.org","@graph",[239],{"_235":240,"_241":242,"_243":244},"https://schema.org","@type","BreadcrumbList","itemListElement",[245,252,255,258],{"_241":246,"_247":248,"_39":249,"_250":251},"ListItem","item","https://glama.ai/","Glama","position",1,{"_241":246,"_247":253,"_39":254,"_250":22},"https://glama.ai/mcp","MCP",{"_241":246,"_247":256,"_39":257,"_250":159},"https://glama.ai/mcp/servers","Servers",{"_241":246,"_247":189,"_39":36,"_250":259},4,"visitorIsMaintainer","routes/_public/mcp/servers/~namespace/~slug/_pages/_explorer/blob/~commit/~/_route",{"_263":264,"_31":270,"_302":303,"_180":317,"_314":341,"_342":343},"blob",{"_186":265,"_266":267,"_268":269},"# Rerankers module for MemOS\n","fileName","__init__.py","size",29,{"_44":45,"_13":271,"_49":34,"_50":34,"_35":36,"_16":51,"_52":272,"_107":287,"_115":-5,"_116":117,"_118":-5,"_119":290,"_169":301,"_175":-5,"_60":151,"_176":117,"_63":177,"_178":179},[47,48],[273,275,277,279,281,283,285],{"_55":274,"_33":62,"_63":64},{"_57":58,"_39":59,"_60":61},{"_55":276,"_33":70,"_63":71},{"_57":67,"_39":68,"_60":69},{"_55":278,"_33":77,"_63":78},{"_57":74,"_39":75,"_60":76},{"_55":280,"_33":84,"_63":85},{"_57":81,"_39":82,"_60":83},{"_55":282,"_33":91,"_63":92},{"_57":88,"_39":89,"_60":90},{"_55":284,"_33":98,"_63":99},{"_57":95,"_39":96,"_60":97},{"_55":286,"_33":105,"_63":106},{"_57":102,"_39":103,"_60":104},{"_109":288,"_60":114},{"_111":289},{"_113":114},{"_121":291,"_136":296,"_140":297,"_160":-5,"_161":162,"_163":-5,"_164":300},{"_123":292,"_133":295,"_128":135},{"_125":293,"_130":294},{"_33":127,"_128":129},{"_33":132,"_128":129},[125],{"_37":38,"_138":139},{"_142":143,"_144":145,"_146":147,"_16":148,"_149":150,"_39":151,"_109":298,"_154":117,"_155":299,"_158":159},{"_113":114,"_63":153},{"_39":157},[166,167,168],{"_171":172,"_173":-5,"_174":-5},"member",{"_304":305,"_308":309,"_310":311,"_146":312,"_313":162,"_314":267,"_315":316,"_268":269,"_128":263},"commit",{"_306":307},"sha","9a5512ce5eafdcfd9a9a9bc8669d6681b6dd1cb6","contentStoredObjectPointerId",495123,"fullPath","src/memos/rerankers/__init__.py",339932,"indexable","path","safeToDisplayAsText",true,[318,320,322,324,326,327,328,330,331,332,333,334,335,336,337,338,339,340],{"_183":319},"__init__.py | MemOS-MCP | Glama",{"_186":321,"_39":33},"Enterprise-grade security, privacy, and features like agents, MCP, prompt templates, and more.",{"_188":323,"_190":191,"_192":193},"https://glama.ai/mcp/servers/@qinshu1109/memos-MCP/blob/9a5512ce5eafdcfd9a9a9bc8669d6681b6dd1cb6/src/memos/rerankers",{"_186":325,"_195":196},"__init__.py | MemOS-MCP",{"_186":321,"_195":198},{"_186":200,"_195":201},{"_186":329,"_195":204},"https://glama.ai/generated-images/og?title=__init__.py+%7C+MemOS-MCP",{"_186":206,"_195":207},{"_186":209,"_195":210},{"_186":212,"_195":213},{"_186":323,"_195":215},{"_186":217,"_195":218},{"_186":220,"_39":221},{"_186":217,"_39":223},{"_186":321,"_39":225},{"_186":329,"_39":227},{"_186":206,"_39":229},{"_186":209,"_39":231},"src/memos/rerankers","treeMembers",[344,348,352,354],{"_304":345,"_310":346,"_314":347,"_128":263},{"_306":307},"src/memos/rerankers/base.py","base.py",{"_304":349,"_310":350,"_314":351,"_128":263},{"_306":307},"src/memos/rerankers/factory.py","factory.py",{"_304":353,"_310":311,"_314":267,"_128":263},{"_306":307},{"_304":355,"_310":356,"_314":357,"_128":263},{"_306":307},"src/memos/rerankers/siliconflow.py","siliconflow.py","actionData","errors"]

MCP directory API