dsh-mneme

dsh-mneme

中文 | English

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🇨🇳 dsh-mneme（中文）

记忆主权，归还于你 —— 记忆不再是黑盒，而是你读得懂、改得动的 Markdown。

dsh-mneme 是一个 DeepSeek Harness (DSH) 插件，为 Agent 提供持久的跨会话记忆能力。Mneme（Μνήμη）——希腊记忆女神 Mnemosyne 之名，掌管记忆与梦境，正如 autoDream 在后台巩固记忆。

不同于把记忆锁进数据库的插件，Mneme 把记忆写成你读得懂的 Markdown——你始终握着记忆的主权：看得见、改得动、删得掉，记忆这回事不该让 Agent 一个人说了算。

✨ 特性一览

• 🧠 记忆主权：SQLite + 可人工编辑的 Markdown 镜像，双向同步——记忆透明、可审查、归你所有
• autoDream 梦境巩固：后台自动去重 / 合并 / 归档 / 冲突裁决（fail-safe 校验），越用越精炼
• 6 个模型工具：memory_save / memory_search / memory_list / memory_update / memory_delete / memory_forget
• 自动注入 + 会话摘要：新会话自动带入相关记忆，会话结束自动提炼偏好 / 决策 / 教训
• Web 记忆面板：官方设置面板内嵌，按类型浏览、全文搜索 + 语义（向量）搜索
• 用户设置 + 自定义指令：用户画像、行为规则每轮注入；注册斜杠命令
• 向量搜索：OpenAI 兼容 embeddings API，语义匹配字面不同但意思相近的记忆

🔮 语义增强（完全离线，v0.2+）

完全离线的语义记忆引擎——embedding、rerank、搜索全在本地，零 API 成本：

• 本地 Embedding：三后端可选——ONNX（Xenova/bge-small-zh-v1.5，离线）/ Ollama / OpenAI 兼容，失败自动逐级降级，最差回退关键词搜索
• Rerank 精排：Xenova/bge-reranker-base 对召回候选交叉编码精排，提升 Top-K 准确率
• autoDream 语义增强：对记忆向量聚类（clusterMemories），自动发现主题相近 / 疑似矛盾的记忆，巩固更精准
• 搜索流水线：混合召回（关键词 + 向量）→ Rerank → Top-K

在 cordis.patch.yml 配置 embedProvider（默认 openai 保持 v0.1 行为，切到 local 即完全离线）。无需数据迁移。

📖 详见 语义架构 · 本地模型部署 · v0.1 迁移

📦 安装（DSH）

# 安装插件（自动注册 bundle 层）
dsh plugin --profile web add @modusensus/dsh-mneme
dsh web

需要 Node 24+（node:sqlite）。安装 / 配置 / 架构详见 插件完整文档。

📁 仓库结构

dsh-mneme/   插件本体（npm 包 @modusensus/dsh-mneme）
docs/        设计文档与实施计划

🧪 本地开发

cd dsh-mneme
npm install
npm test          # 198 个测试
npm run stress    # 三轴线压测（长会话检索 / 冲突仲裁 / 多 Agent 并发）
npm run sync      # src → lib 同步（发布时自动执行）

📄 文档

文档	路径
插件完整文档（功能 / 安装 / 配置 / 架构）	dsh-mneme/README.md
语义架构	dsh-mneme/docs/SEMANTIC.md
本地模型部署指南	dsh-mneme/docs/LOCAL_MODEL.md
v0.1 迁移说明	dsh-mneme/docs/MIGRATION.md
插件设计	docs/superpowers/specs/2026-08-13-dsh-mneme-design.md
autoDream 设计	docs/superpowers/specs/2026-08-13-dsh-mneme-autodream-design.md
实施计划（核心插件）	docs/superpowers/plans/2026-08-13-dsh-memory.md
实施计划（autoDream）	docs/superpowers/plans/2026-08-13-dsh-memory-autodream.md

📜 License

MIT

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🇬🇧 dsh-mneme (English)

Memory sovereignty, returned to you — memory is no longer a black box, but Markdown you can read and edit.

dsh-mneme is a DeepSeek Harness (DSH) plugin that gives agents persistent cross-session memory. Mneme (Μνήμη) — named after Mnemosyne, the Greek goddess of memory and dreams, mirroring how autoDream consolidates memories in the background.

Unlike plugins that lock memory inside a database, Mneme writes memory as human-readable Markdown — memory sovereignty stays with you: see it, edit it, delete it. Memory shouldn't be decided by the agent alone.

✨ Features

• 🧠 Memory sovereignty: SQLite + human-editable Markdown mirror, two-way sync — memory is transparent, auditable, and yours
• autoDream consolidation: background dedup / merge / archive / conflict resolution (fail-safe validation), refined with use
• 6 model tools: memory_save / memory_search / memory_list / memory_update / memory_delete / memory_forget
• Auto-injection + session summary: relevant memories injected at session start, preferences / decisions / lessons distilled at session end
• Web memory panel: embedded in the official settings panel — browse by type, full-text + semantic (vector) search
• User settings + custom commands: user profile and behavior rules injected every turn; register slash commands
• Vector search: OpenAI-compatible embeddings API for semantic matching of differently-worded but related memories

🔮 Semantic Enhancement (fully offline, v0.2+)

A fully-offline semantic memory engine — embedding, rerank and search all run locally, zero API cost:

• Local embedding: three interchangeable backends — ONNX (Xenova/bge-small-zh-v1.5, offline) / Ollama / OpenAI-compatible — degrading automatically, falling back to keyword search at worst
• Rerank: Xenova/bge-reranker-base cross-encoder re-ranking of recall candidates for sharper Top-K
• autoDream semantic boost: vector clustering (clusterMemories) surfaces topically-close or potentially conflicting memories for more precise consolidation
• Search pipeline: hybrid recall (keyword + vector) → rerank → Top-K

Configure embedProvider in cordis.patch.yml (default openai keeps v0.1 behavior; switch to local for fully offline). No data migration needed.

📖 See Semantic architecture · Local model guide · v0.1 migration

📦 Install (DSH)

# Install the plugin (auto-registers the bundle layer)
dsh plugin --profile web add @modusensus/dsh-mneme
dsh web

Requires Node 24+ (node:sqlite). Full install / config / architecture docs in the plugin README.

📁 Repository Structure

dsh-mneme/   plugin package (npm @modusensus/dsh-mneme)
docs/        design docs & implementation plans

🧪 Local Development

cd dsh-mneme
npm install
npm test          # 198 tests
npm run stress    # three-axis stress test (long-session retrieval / conflict arbitration / concurrent agents)
npm run sync      # src → lib sync (runs automatically on publish)

📄 Docs

Doc	Path
Full plugin docs (features / install / config / architecture)	dsh-mneme/README.md
Semantic architecture	dsh-mneme/docs/SEMANTIC.md
Local model guide	dsh-mneme/docs/LOCAL_MODEL.md
v0.1 migration	dsh-mneme/docs/MIGRATION.md
Plugin design	docs/superpowers/specs/2026-08-13-dsh-mneme-design.md
autoDream design	docs/superpowers/specs/2026-08-13-dsh-mneme-autodream-design.md
Implementation plan (core)	docs/superpowers/plans/2026-08-13-dsh-memory.md
Implementation plan (autoDream)	docs/superpowers/plans/2026-08-13-dsh-memory-autodream.md

📜 License

MIT