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MemoryBear v0.5.2 Community Release Notes — Where Memory Finds Its Course
Release Date: October 21, 2026 | Codename: ZhenChuan (枕川 · Resting by the River)
MemoryBear v0.5.2 broadens emotion and profile intelligence, improves the accuracy and compatibility of memory data, and brings task-scoped Coding Agent preference retrieval to more Agent workflows. The release also expands Agent cluster conversations, workflow integration, interaction feedback, and configuration portability, creating a more connected foundation for memory-aware collaboration.
🧠 I. Memory Intelligence & Data Foundation
Emotion & Predictive Intelligence
- Expanded Emotion Memory Categories: Emotion classification has expanded from six dimensions to ten categories—neutral, happy, relieved, anxious, sad, angry, frustrated, lonely, confused, and expectant. The emotion radar chart and category list have been updated accordingly, providing a more complete view of emotional memory.
- Richer Profile Data for Predictive Reasoning: Predictive reasoning now reads from the upgraded user profile structure while remaining compatible with existing profile data. This ensures that reasoning workflows continue to receive complete, consistent user context as profile capabilities evolve.
Memory Data Accuracy & Compatibility
- More Accurate “Updated Today” Statistics: The “Updated Today” metric now counts all newly written memories rather than only active memories, providing a more accurate view of daily memory growth within a workspace.
- Broader Memory Data Compatibility: Permanent memories, interest distributions, tags, and user profiles now work more consistently with the upgraded data foundation. Existing capabilities continue to operate reliably after data upgrades, with stable behavior across display, statistics, and duplicate detection.
🔌 II. Preference Engine Carries Coding Habits Across Tasks
- Coding Preferences Across Tasks: Coding Agent can now recall your saved coding preferences in new tasks, eliminating the need to explain details such as code style, naming conventions, and project conventions from scratch each time. Relevant preferences are carried into task execution so subsequent implementations align more closely with your established way of working. Access to this memory capability is authenticated and supports both recalling preferences relevant to the current task and retrieving all preferences currently in effect at once.
🤖 III. Agent Cluster Collaboration
- Expanded Agent Cluster Capabilities: Agent clusters now support direct cluster conversations and can be referenced from Agent nodes in workflows. Conversation interactions have been expanded with regeneration, favorites, negative feedback, and annotation, while cluster LLM configuration now supports JSON-formatted output and deep-thinking options.
- Cluster Configuration Portability: Agent clusters can now be imported and exported, making configurations easier to migrate, reuse, share, and manage across environments.
🧭 IV. Looking Ahead
With ZhenChuan, v0.5.2 turns individual memory signals into clearer, connected context. Richer engine activity, broader emotion coverage, profile compatibility, and task-scoped preference retrieval make memory behavior more visible and dependable across Agents and applications.
The release also extends collaborative intelligence through Agent clusters and a more consistent memory data foundation. As these capabilities mature, MemoryBear is moving toward a platform where long-term preferences, episodic context, knowledge, and Agent collaboration can reinforce one another without adding operational complexity.
Upcoming releases will deepen knowledge base intelligence through FAQ generation, reflection, recall controls, and richer source tracing; advance RAG, predictive reasoning, and multimodal episodic-memory retrieval; and continue refining memory views, application-level retrieval, and ontology-driven responses. We will also strengthen service stability, scalability, and isolation, with specific capabilities and delivery timelines communicated in future official release announcements.
MemoryBear v0.5.2 社区版 发布说明 —— 枕川听忆，万象成章
发布日期： 2026年10月21日 ｜ 版本代号： 枕川（ZhenChuan · Resting by the River）
MemoryBear v0.5.2 扩展情绪与画像智能，提升记忆数据统计的准确性与兼容性，并将按任务检索 Coding Agent 偏好的能力带入更多 Agent 工作流。本版本同时完善 Agent 集群对话、工作流集成、交互反馈与配置迁移能力，为具备长期记忆的智能协作构建更连贯的基础。
🧠 一、记忆智能与数据基础
情绪记忆与预测推演
- 情绪记忆分类扩展：情绪分类由六维扩展为中性、开心、缓解、焦虑、难过、生气、挫败、孤独、困惑和期待十类，并同步更新情绪雷达图及分类列表，使情绪记忆的呈现更加完整细致。
- 预测推演画像数据升级：预测推演现可读取升级后的用户画像结构，并保持对既有画像数据的兼容，确保画像能力演进过程中，推演流程仍能持续获得完整、一致的用户上下文。
记忆数据准确性与兼容性
- “今日更新”统计口径优化：“今日更新”指标由仅统计活跃记忆调整为统计全部新增记忆，更准确地反映工作空间当日的记忆写入与增长情况。
- 记忆数据兼容能力增强：永久记忆、兴趣分布、标签与用户画像等模块进一步适配升级后的数据基础。数据升级后，既有能力仍可稳定运行，并在展示、统计和重复识别等场景中保持一致表现。
🔌 二、偏好引擎延续编码习惯
- 跨任务沿用编码偏好：Coding Agent 现在能够在新任务中找回你记忆中的编码偏好，无需每次从头说明，例如代码风格、命名习惯和项目约定，并将相关内容带入任务执行过程中，使后续实现更贴近你一贯的工作方式。该记忆能力通过身份验证机制提供，既可回忆当前任务相关的偏好，也可一次获取所有生效的偏好。
🤖 三、Agent 集群协作
- Agent 集群能力扩展：Agent 集群新增集群对话能力，并支持在工作流的 Agent 节点中引用集群 Agent。对话过程进一步支持重新生成、收藏、点踩与标注等操作，集群 LLM 配置同时新增 JSON 格式输出与深度思考选项。
- 集群配置迁移与复用：新增 Agent 集群导入和导出能力，便于在不同环境间迁移、复用、共享与管理集群配置。
🧭 四、未来展望
“枕川”让 v0.5.2 中分散的记忆信号沿着更清晰的脉络汇聚。更丰富的引擎动态、更完整的情绪覆盖、持续兼容的画像能力与按任务检索的偏好信息，让记忆在 Agent 和应用中的变化过程更加可见、可靠。
本版本也通过 Agent 集群与更一致的记忆数据基础，进一步拓展协作智能的边界。随着这些能力持续成熟，MemoryBear 正迈向一个让长期偏好、情景上下文、知识与 Agent 协作彼此增强，同时不增加使用复杂度的记忆平台。
后续版本将深化知识库 FAQ 生成、反思、召回控制与来源追溯能力，推进 RAG、预测推演及多模态情景记忆检索，并持续完善记忆视图、应用级检索与本体驱动的响应能力。平台层面也将进一步增强服务的稳定性、扩展性与隔离能力，具体能力及交付时间以后续正式发布信息为准。