插件目录 / Knowledge / dsh-vector-memory
dsh-vector-memory
已验证 · 实测可装 Bryan-cmf
功能简介
DSH-Plugin: durable agent memory core - mem_save/mem_search/mem_health backed by storageDomain (survives restarts), 記憶 view tab.
可用 — 实测通过,早期项目
DSH-Plugin: durable agent memory core - mem_save/mem_search/mem_health backed by storageDomain (survives restarts), 記憶 view tab. 实测能干净安装、正常启动。早期项目,但功能可用。
「已验证」表示我们的自动化 CI 在干净 profile 里实际执行了 dsh plugin add 并启动成功——仅此而已。功能描述与版本兼容性均为作者声明。这不是安全审计,也不代表对第三方代码的背书。
README
⚠️ 此套件已併入
@bryan-cmf/dsh-insights(2026-08-17)。此 repo 僅保留作合併前歷史,後續迭代請到 dsh-insights。
🧠 Vector Memory — DSH-Plugin
可持久化的 Agent 記憶核心——
mem_save/mem_search/mem_health,跨 session、跨重啟,零外部依賴。
A DeepSeek Harness plugin
that gives your agents durable, cross-session memory — the samemem_save / mem_search contract as the originalBryan-cmf/vector-memory MCP
server, but natively on DSH.
Why a native rewrite (and what changed)
The 2026 memory landscape made self-hosting a Qdrant + BGE-m3 stack a
commodity (Zep / Mem0 / Letta / LangMem all offer managed embeddings + graph
memory). This package keeps the agent-facing contract and drops the
infrastructure:
| Aspect | Old (MCP server) | This plugin |
|---|---|---|
| Storage | Qdrant (Docker) + BGE-m3 (~2GB) | DSH storageDomain — durable JSON backend, zero setup |
| Retrieval | Vector similarity | v1: deterministic keyword scoring + recency; embeddings pluggable in v2 |
| Interface | MCP stdio (Claude/Cursor/…) | Native tools (mem_save / mem_search / mem_health) + vectorMemory service |
| Persistence | Manual install script | Bundle row — it just works |
Tools
| Tool | Effect |
|---|---|
mem_save |
{ content, tags?, ttlDays? } → durable memory, returns id |
mem_search |
{ query, limit? } → ranked hits (score, content, tags, created) |
mem_health |
record count, expired count, domain status, TTL policy |
Other plugins can inject: ['vectorMemory'] and call save / search /health directly.
Dashboard
The client half registers a 「記憶」view tab (order 30, right of
chat / 軌跡 / 觀測) listing this session's saved/searched items live.
Install
"dsh": { "profile": { "bundles": ["@bryan-cmf/dsh-vector-memory"] } }
⚠️ This row publishes the
vectorMemoryservice — it must live in the
host composition (process-global). If you mount it from an agent preset
instead, wrap it in a group with anisolaterealm (see the DSH
composition docs).
Requirements
- DSH
>= 0.1.0-rc.6— services:tools,storageDomain(domain layer +
a storage backend, e.g.dsh-storage-json, are part of the shippeddsh-basebundle)
Configuration
| Key | Default | Meaning |
|---|---|---|
ttlDays |
90 |
Default memory TTL; 0 = forever |
maxResults |
10 |
Default mem_search result count |
Roadmap
- v2: pluggable embedding backends (hosted embedders or Qdrant REST)
- v2: MCP stdio dual-exit so the same store serves Claude Desktop / Cursor
- v2: memory curation (merge/dedupe/forget jobs)
Development
pnpm install --store-dir .pnpm-store
pnpm build
License
MIT
安装
装一次目录插件,之后本站所有插件都能让 DeepSeek Harness 自动找、自动装:
dsh plugin add dshbase-catalog 然后对 agent 说「帮我装 dsh-vector-memory」,它会在目录里找到并自动安装。文档:dshbase-catalog · 已验证场景包。
该插件是 GitHub 源码(未发 npm)——直接从仓库装:
Web profile:
dsh plugin --profile web add github:Bryan-cmf/dsh-vector-memory Headless(CLI)profile:
dsh plugin --profile headless add github:Bryan-cmf/dsh-vector-memory 实测报告
验证通过:从 GitHub 源码完成 L1 安装 + L2 加载 + L3 运行(dsh 0.1.0-rc.6)。