dsh-memory

Cross-session semantic memory for DeepSeek Harness — installed into any profile, so your agent actually remembers you across sessions.

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dsh-memory registers two tools for the web / headless profiles, so your agent can remember you across sessions:

• memory_add — persist a fact worth keeping long-term, auto-vectorized on the way in.
• memory_search — recall relevant facts by semantics (cosine similarity), with a keyword fallback.
• /mem <question> — type this in the composer and the agent will call memory_search first (results render as a collapsible tool card), then answer from memory.

Under the hood: Zhipu embedding-3 (2048-dim) + SQLite (node:sqlite) + cosine similarity. Zero new infrastructure — no vector database, no sidecar containers. Hundreds to thousands of memories recall in milliseconds.

Why

Your agent forgets everything between sessions. dsh-memory gives it a durable, semantic recall layer without adding a new service to run. It solves the "who am I / what did we agree on" problem with a few hundred bytes of SQLite and one HTTP call per write.

Features

• Zero-config, never crashes — a missing Zhipu key, a missing database, a missing tools service, or a failing embedding call all degrade gracefully. This plugin will never take the DSH tree down (see Design guarantee).
• Semantic recall with keyword fallback — memory_search scores by embedding cosine similarity first, then weights keyword hits; when embeddings are unavailable it falls back to pure keyword (bigram) matching.
• 8s embedding timeout — on a bad or missing network it fails fast and falls back to keyword search instead of hanging the session.

Installation

This plugin follows the official DSH bundle convention (dsh.bundle in package.json), so dsh plugin recognizes it and activates it as a configuration layer — not a plain dependency.

Install into the target profile (e.g. web):

dsh plugin --profile web add dsh-memory

After installing, restart DSH web for it to take effect (the bundle layer is only composed at startup):

systemctl restart dsh    # or restart however you run DSH

Local / pre-release install:

# Put this directory into the profile's node_modules and append "dsh-memory"
# to dsh.profile.bundles in package.json, then restart.

Configuration

Everything is optional — skip it all and the plugin still works (falling back to keyword search):

Config	Description	Default
enabled	false disables tool registration	true
semantic	false skips embeddings, keyword-only	true
memoryDbPath	SQLite database path	~/.dsh-memory/memories.db
zhipuEnvPath	Path to the Zhipu credentials .env file	~/.dsh-memory/.memenv
forceMemoryWords	Force-memory signal words (array). When any word appears in a user message, the agent must call memory_add first, then answer	[] (disabled by default)

forceMemoryWords example (set in your profile's cordis.patch.yml, not in the public repo):

- id: dsh-memory
  config:
    forceMemoryWords:
      - 记住
      - 重要
      - 珍贵
      - 特别
      - 务必
      - 一定
      - 必须

Zhipu credentials are read from environment variables first, then from the file at zhipuEnvPath:

ZHIPU_API_KEY=your-zhipu-key
ZHIPU_BASE_URL=https://open.bigmodel.cn/api/paas/v4/

Get a Zhipu key for free at open.bigmodel.cn (embedding-3 is billed per use).

Design guarantee

This plugin was born from a real incident: a misconfigured plugin sent a DSH web profile into a crash-loop. So dsh-memory is deliberately defensive:

1. apply() is wrapped in an overall try/catch — no error is ever thrown up into the DSH tree.
2. inject only declares tools (a service dsh-base always provides). It does not depend on dsh-llm, agents, or http.
3. A missing key / database / service, or a failed embedding call → it logs and degrades, never interrupting startup.
4. Errors raised inside a tool's execute() are caught and returned as ordinary results, never bubbling up into a session exception.

Tools

• memory_add — write a fact worth remembering; vectorized automatically.
• memory_search — semantically recall the most relevant facts for a question.
• /mem — a manual override: "search memory first, then answer." If you're not sure the agent will recall on its own, type /mem <question> and it will run a semantic search, inject the results, and answer from them.

Storage schema

memories(id, text, category, source, created_at, updated_at)
memory_embeddings(memory_id, dim, vector)   -- 2048-dim JSON array

License

MIT