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Llmwiki — DeepSeek Harness 插件(DSH Plugin)
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dsh-llmwiki

Llmwiki

作为 DeepSeek Harness 长期记忆的本地 Markdown wiki——从 llmwiki 移植而来

插件会安装到这里;不确定时保持 web。

npx -y @deepseek-ai/dsh plugin --profile web add dsh-llmwiki@0.1.1
README兼容性版本

兼容性与来源证明

Llmwiki 以 dsh-llmwiki 发布,当前版本为 0.1.1。Plugin Hub 会校验它的 manifest,并保存精确安装来源,便于复现安装结果。

DSH 兼容范围
*
运行环境
any
发布来源
npm
Registry 更新时间
2026/9/20

版本

0.1.1stable
2026/8/14
0.1.0stable
2026/8/14

相关插件

正在加载相关插件…

最新版
0.1.1
DSH
*
HMR
重启进程
Tree shaking
未声明可安全裁剪
解包体积
39.8 kB
文件数
14
Surface
any
许可证
MIT
发布源
npm
GitHub
★ 2
周下载
130
安全扫描
✓ v0.1.1 扫描通过
最近提交
2026/8/15
查看源码 ↗项目主页 ↗
README Badge

点击下方 Badge 复制 Markdown,粘贴到 README 即可。

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相关插件

继续浏览 memory-context 分类下经过校验的插件。

Contextdsh-context用于上下文洞察和管理的 DeepSeek Harness 插件,提供上下文仪表板和上下文命令,帮助了解上下文的构成及其演变过程。Mnemondsh-mnemon面向 DeepSeek Harness 的可组合三层记忆控制平面:持久化运行时上下文、可搜索的项目文档、可插拔的长期记忆、受保护的策略、WebUI 和无头工具。Memsearch Dsh@zilliz/memsearch-dsh适用于 DeepSeek Harness 的 MemSearch 插件:在多个代理之间共享 Markdown 记忆,支持捕获、步骤前上下文注入、记忆召回技能和技能候选审核面板。Memory@furongjun1999/dsh-memory灵枢(Lingshu·líng shū)DeepSeek Harness 插件:完整大脑——长期记忆/知识飞轮/自我认知/递归反思接入 DSH,对话自动沉淀进 md_cg 认知图(md 文档)

README

dsh-llmwiki

Context Window = RAM, Local Wiki = Disk — long-term memory for DeepSeek Harness, powered by your local Markdown vault.

TypeScript port of llmwiki, packaged as a native dsh plugin.

What it does

Mechanismdsh extension point
Inject relevant wiki knowledge into the same turn's model requestsession/event (agent/inbox/spliced, pre-assembly live event) → ctx.systemPrompt.context()
Teach the model about memoryctx.systemPrompt.section()
memory_search — model recalls prior sessions / curated notesctx.tools.register()
memory_save — model persists durable insightsctx.tools.register()
Auto-capture every turn to chronicle/daily/YYYY-MM-DD.mdsession/event (turn/end)

Retrieval: keyword + wikilink graph + temporal strategies fused with RRF (Reciprocal Rank Fusion), assembled under a token budget, with an LRU + TTL cache. Zero runtime dependencies beyond Node.js.

Vault layout (created automatically)

my-vault/
├── raw/               # Layer 1: session dumps
├── chronicle/daily/   # Layer 2: auto-captured daily logs
├── entities/          # Layer 3: compiled knowledge
├── concepts/
├── comparisons/
├── projects/
└── queries/

Open it with Obsidian, curate Layer-3 notes with [[wikilinks]] — the graph strategy follows them.

Install

Requires Node.js ≥ 22 (same as dsh itself) and a working dsh CLI (npm install -g @deepseek-ai/dsh) with pnpm on PATH.

# from npm
dsh plugin --profile web add dsh-llmwiki

# or from a tarball
dsh plugin --profile web add ./dsh-llmwiki-0.1.1.tgz

# verify the layer, then boot
dsh --profile web --dump-config   # shows a "# == dsh-llmwiki" layer
dsh web                           # logs: [dsh-llmwiki] memory plugin loaded, vault: ...

The package declares dsh.bundle, so dsh plugin add activates it automatically — no manual patching needed.

Configure

The plugin works zero-config (vault defaults to ~/llmwiki-vault). To override, add a row to your profile's cordis.patch.yml (or a --patch overlay) — note the override restates the row by id without insert:

- id: llmwiki
  config:
    vaultPath: /path/to/your/vault   # Obsidian vault welcome
    tokenBudget: 2000
    strategies: [keyword, graph, temporal]
    daysBack: 7
    topK: 5
    autoInject: true
    autoCapture: true

A patch replaces the row's entire config, so restate every key you want to keep.

Config

KeyDefaultMeaning
vaultPath~/llmwiki-vaultMarkdown vault path; structure created if missing
tokenBudget2000Max tokens of injected wiki context
strategies[keyword, graph, temporal]Enabled recall strategies
daysBack7Temporal look-back window
topK5Results per retrieval
priorityrelevanceAssembly priority: relevance / recency / diversity / structured
cacheTtl300Cache TTL seconds
autoInjecttrueInject wiki context on each user message
autoCapturetrueAppend each turn to the daily chronicle

How the pieces map from the Python original

Python (llmwiki)TypeScript (dsh-llmwiki)
core/retriever.pysrc/retriever.ts
core/assembler.pysrc/assembler.ts
core/cache.pysrc/cache.ts
vault/capture.pysrc/capture.ts
search/python_engine.pymerged into retriever.ts (keeps the package zero-dep)
OpenClawMemoryHook adapterthe dsh plugin itself (src/index.ts)

Not yet ported: ripgrep / SQLite FTS engines (the pure-JS engine keeps installs dependency-free — contributions welcome), the LLM-driven curate pipeline (run the Python CLI alongside for now).

License

MIT