dsh-memory 🧠

Cross-session memory plugin for DeepSeek Harness (dsh).

YAML-backed remember / recall / view / list / forget / pin tools with embedding search, title-first injection, and two-level (session/auto) automatic injection — the agent "naturally carries" memory without having to remember to call a tool.

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✨ Features

Feature	Description
remember / recall / view / list / forget / pin	Write, semantic search, expand-by-id, list (titles), soft-delete, force-inject pinning
Title-first injection	Memories inject as summary titles (with id), not full text — compact prompts; details expand via the view tool
Two-level injection	injectLevel: session memories are resident (injected once at session start, OpenClaw-style); auto memories arrive via topic retrieval
Once-per-session injection	Fixed memories inject on the session's FIRST step, then ride the history (no per-round re-render — token + prefix-cache friendly)
Embedding search	HybridSearch (keyword 2-gram + local embedding via ollama, RRF fusion) — catches paraphrases keyword search misses
Tuned matching	Generic-word downweighting (文件/测试/文档…), ≥2-token keyword hits, retrieval gate skips test-chatter ("别管/只是测试")
Provenance labeling	Injected memories are prefixed "…from the memory store (not conversation history)" so the model never mistakes them for dialogue
Data safety	Serialized write queue (no lost updates), corrupt-file quarantine + backup, atomic writes, mtime cache invalidation

📦 Install

# From source (inside a dsh checkout)
pnpm install
pnpm run build

# Register in your profile patch (e.g. ~/.dsh/profiles/web/cordis.patch.yml)
- insert:
    - id: dsh-memory
      name: '@towzai/dsh-memory'
      config:
        file: /path/to/memory.yaml
        injectLimit: 8

Requires a local ollama instance with an embedding model (default qwen3-embedding:0.6b; override via DSH_MEMORY_EMBED_MODEL).

🔨 Build (from source)

Requires the dsh checkout's dependencies (the plugin's peer deps live there). On Windows you can junction them instead of reinstalling:

# in the plugin repo
cmd /c mklink /J node_modules <path-to-dsh-checkout>\node_modules
npm run build   # outputs lib/index.js

🛠️ Tools

Tool	Purpose
remember	Save a memory (content / title / category / tags / importance / forceInject / injectLevel / source)
recall	Hybrid search (keyword + embedding) top-N
view	Expand ONE memory's full content by id (title-first injection's detail entry)
list	List memories as titles (category/tag/importance filters, full for content)
forget	Soft-delete (marks retired, keeps history)
pin	Toggle forceInject — pinned memories always appear at session start

🗂️ Storage

Single YAML file (default memory.yaml). Each entry:

- id: MEM-20260814-001
  title: "Summary title (auto-derived if absent)"   # injected as title
  content: "..."
  category: preference | project | lesson | fact
  tags: [tag1, tag2]
  importance: high | normal | low
  created: 2026-08-14
  updated: 2026-08-14
  source: user | agent | conversation
  retired: false
  forceInject: false           # full behavioural rules, always resident
  injectLevel: session | auto  # resident vs on-demand
  vector: [...]   # 1024-dim embedding, auto-computed on write

🔧 Architecture

src/
├── index.ts    # plugin entry: tools + session-first injection + pre-step listener
├── storage.ts  # Storage interface + YamlStorage (queue, mtime cache, quarantine)
├── search.ts   # KeywordSearch / EmbeddingSearch / HybridSearch (RRF) / OllamaEmbedder
├── inject.ts   # selectForInjection / renderSection (title-first) / deriveTitle / entryTitle
├── dynamic.ts  # DynamicInjector (per-session dedup + retrieval gate + fixed-set exclusion)
└── types.ts    # data model (+ reserved fields: vector/scope/weight for future)

Design doc: docs/2026-08-13-dsh-memory-design.md.

⚠️ Notes

• Embedding model change invalidates stored vectors (dimension mismatch is detected and logged)
• The plugin is developed against dsh v0.1.0-rc.5; peer dependency ranges may need bumping

🔗 References

This plugin is part of the DeepSeek Harness (dsh) plugin ecosystem.

• Official repo: deepseek-ai/deepseek-harness
• Plugin docs: docs/user/develop — plugin lifecycle, config, and publish guide
• Ecosystem topic: dsh-plugin

📄 License

MIT