A Practitioner’s Guide to DeepSeek Harness Memory Plugins
A practitioner’s guide compares DeepSeek Harness memory options across capture, retrieval, storage, deployment, and governance. It reviews MemOS Local, MemOS Cloud, OpenViking, Hindsight, memsearch, ReMe, dsh-auto-memory, and Mnemosyne as options for personal or project-level memory. The guide says Mnemosyne lacks a documented DSH-specific adapter and installation guidance, while most reviewed plugins focus on personal or project memory rather than automatically promoting individual memories into trusted team rules. It advises users to pin compatible versions and run a minimal validation before installation because community plugins change rapidly and the guide provides no independent performance benchmarks.
The guide gives DSH users a practical framework for evaluating memory integrations, including when information is written and recalled, where it is stored, how it is deployed, and how personal memory differs from governed team knowledge.
DeepSeek Harness Memory 插件总结：
一篇面向实践者的 DSH Memory 插件选型与落地指南 本文基于 DSH 官方文档、插件目录、插件维护者公开 README 和已核验的官方接入说明整理。社区插件迭代很快，文中能力描述以公开资料为准；正式安装前仍应锁定 DSH、Node/Python、插件包和后端服务版本，完成最小可运行验证。
引言：Memory 的问题，最终会走向两层
Coding Agent 每次开启新会话都从零开始，就很难成为稳定的工作搭档：它不知道项目约定、上次已经验证过的排查路径，也不知道用户反复纠正过哪些偏好。
Memory 插件解决的是一条完整链路：从对话和任务结果中提取值得保留的信息，按当前任务召回相关内容，把内容注入本轮上下文，并在记忆错误时允许纠正、降权、过期和回滚。
但“能记住”只是第一步。一个人的编码偏好可以直接服务本人；一条团队规则却会影响所有成员、项目和 Agent。因此，Memory 的落地天然分成两个问题：
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