chore: bump version to v1.1.3 (#471)

## Description

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Summary:

• Memory & Retrieval Core: async add (plain & preference), Preference
Memory pipeline, Reranker strategy suite, BM25 for TreeTextMemory,
MemReader structural parsing.
• Scheduler & Observability: API scheduler modularization
(schema/utils/analyzer), Redis ORM for history sync & mixture search,
metrics + request logs, Nacos-based dynamic config.
• Data & Infra: PolarDB graph backend with pool/timeout & fixes, unified
graph factory (Nebula/Neo4j/PolarDB), Milvus interface & item
optimizations, enhanced logging.
• Evaluation: PrefEval standardization; LoCoMo/LongMemEval/PersonaMem
pipeline upgrades; new utilities (e.g., mirix_utils.py).
• Stability & Fixes: query scheduling, message schema, Tree search
inputs, self-input prompts, SQLite list users, PolarDB value mapping;
pool/timeout tuning, usage data removal, graph-call toggle.
• Compatibility: adopt new async path/schemas/metrics; align preference
fields; migrate tests to Redis ORM; configure PolarDB pool/timeout
before rollout.

• 记忆与检索内核：新增 async add（明文/偏好）、偏好记忆全链路、Reranker 策略集、TreeTextMemory 引入
BM25、MemReader 结构化解析。
• 调度与可观测性：API 调度模块化（schema/utils/analyzer）、Redis ORM（历史同步与混合搜索）、metrics
指标与请求日志、Nacos 动态配置。
• 数据与基础设施：PolarDB 图后端（连接池/超时与修复）、图工厂统一（Nebula/Neo4j/PolarDB）、Milvus
接口与数据项优化、日志链路增强。
• 评估体系：PrefEval 字段标准化；LoCoMo/LongMemEval/PersonaMem 评测升级；新增工具（如
mirix_utils.py）。
• 稳定性与修复：修复查询调度、消息 schema、树检索输入、自输入提示、SQLite 用户列表、PolarDB
值映射；连接池/超时优化、移除 usage 数据、图调用开关。
• 兼容性：适配异步路径/新 schema/metrics；偏好字段按新规范；测试迁移至 Redis ORM；启用 PolarDB
前配置连接池与超时并压测。
Fix: #424
#426
#423
#443
#384
#406
#445

• Scheduler/Query: fixed query-schedule edge cases
(#424).
• Schemas: corrected message schema inconsistencies
(#426).
• Search I/O: fixed TreeTextMemory searcher input mismatch
(#423).
• Prompts: fixed self-input prompt error
(#443).
• Storage: fixed SQLite list-users error
(#384).
• Graph/DB: corrected PolarDB value/graph issues
(#406,
#445).

• 调度/查询：修复查询调度边界问题（https://github.com/MemTensor/MemOS/pull/424）。
• Schema：修正消息 schema 不一致（https://github.com/MemTensor/MemOS/pull/426）。
• 检索 I/O：修复 Tree 搜索器输入不匹配（https://github.com/MemTensor/MemOS/pull/423）。
• 提示：修复自输入提示错误（https://github.com/MemTensor/MemOS/pull/443）。
• 存储：修复 SQLite 用户列表错误（https://github.com/MemTensor/MemOS/pull/384）。
• 图/数据库：修正 PolarDB
值与图相关问题（https://github.com/MemTensor/MemOS/pull/406、https://github.com/MemTensor/MemOS/pull/445）。

Docs Issue/PR: (docs-issue-or-pr-link)

Reviewer: @(reviewer)

## Checklist:

- [ ] I have performed a self-review of my own code | 我已自行检查了自己的代码
- [ ] I have commented my code in hard-to-understand areas |
我已在难以理解的地方对代码进行了注释
- [ ] I have added tests that prove my fix is effective or that my
feature works | 我已添加测试以证明我的修复有效或功能正常
- [ ] I have created related documentation issue/PR in
[MemOS-Docs](https://github.com/MemTensor/MemOS-Docs) (if applicable) |
我已在 [MemOS-Docs](https://github.com/MemTensor/MemOS-Docs) 中创建了相关的文档
issue/PR（如果适用）
- [ ] I have linked the issue to this PR (if applicable) | 我已将 issue
链接到此 PR（如果适用）
- [ ] I have mentioned the person who will review this PR | 我已提及将审查此 PR
的人

Author: CaralHsi

README.md

@@ -43,7 +43,7 @@
 
 ---
 
-<img src="https://statics.memtensor.com.cn/memos/sota_score.jpg" alt="SOTA SCORE">
+<img src="https://cdn.memtensor.com.cn/img/1762436050812_3tgird_compressed.png" alt="SOTA SCORE">
 
 **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.
 
@@ -52,6 +52,28 @@
 - **API Reference**: https://memos-docs.openmem.net/docs/api/info/
 - **Source Code**: https://github.com/MemTensor/MemOS
 
+## 📰 News
+
+Stay up to date with the latest MemOS announcements, releases, and community highlights.
+
+
+- **2025-11-06** - 🎉 MemOS v1.1.3 (Async Memory & Preference):
+  Millisecond-level async memory add (support plain-text-memory and
+  preference memory); enhanced BM25, graph recall, and mixture search; full
+  results & code for LoCoMo, LongMemEval, PersonaMem, and PrefEval released.
+- **2025-10-30** - 🎉 MemOS v1.1.2 (API & MCP Update):
+API architecture overhaul and full MCP (Model Context Protocol) support — enabling models, IDEs, and agents to read/write external memory directly.
+- **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/)
+- **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.
+- **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.
+- **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.
+- **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.
+- **2025-07-07** – 🎉 *MemOS 1.0 (Stellar) Preview Release*: A SOTA Memory OS for LLMs is now open-sourced.
+- **2025-07-04** – 🎉 *MemOS Paper Released*: [MemOS: A Memory OS for AI System](https://arxiv.org/abs/2507.03724) was published on arXiv.
+- **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.
+- **2024-07-04** – 🎉 *Memory3 Model Released at WAIC 2024*: The new memory-layered architecture model was unveiled at the 2024 World Artificial Intelligence Conference.
+- **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.
+
 ## 📈 Performance Benchmark
 
 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
 ## 📄 License
 
 MemOS is licensed under the [Apache 2.0 License](./LICENSE).
-
-## 📰 News
-
-Stay up to date with the latest MemOS announcements, releases, and community highlights.
-
-
-- **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/)
-- **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.
-- **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.
-- **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.
-- **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.
-- **2025-07-07** – 🎉 *MemOS 1.0 (Stellar) Preview Release*: A SOTA Memory OS for LLMs is now open-sourced.
-- **2025-07-04** – 🎉 *MemOS Paper Released*: [MemOS: A Memory OS for AI System](https://arxiv.org/abs/2507.03724) was published on arXiv.
-- **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.
-- **2024-07-04** – 🎉 *Memory3 Model Released at WAIC 2024*: The new memory-layered architecture model was unveiled at the 2024 World Artificial Intelligence Conference.
-- **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.

pyproject.toml

@@ -4,7 +4,7 @@
 ##############################################################################
 
 name = "MemoryOS"
-version = "1.1.2"
+version = "1.1.3"
 description = "Intelligence Begins with Memory"
 license = {text = "Apache-2.0"}
 readme = "README.md"

src/memos/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.1.2"
+__version__ = "1.1.3"
 
 from memos.configs.mem_cube import GeneralMemCubeConfig
 from memos.configs.mem_os import MOSConfig