dsh-tool-user-memory

User preference memory for DeepSeek Harness: a Cordis plugin that lets the agent remember your preferences across sessions — language, communication style, project background, goals. No need to re-introduce yourself in every new session.

> Standalone open-source plugin — developed and maintained independently as > part of the DeepSeek Harness community ecosystem (topic: > dsh-plugin). Not affiliated with the > official repository; install straight from npm and enable it in ~30 seconds.

中文｜ [Changelog](CHANGELOG.md)

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1. What it does

The problem

By default a DeepSeek Harness agent is a "stranger" in every new session: it does not know your preferences, your projects, or even your language. Every session starts from scratch.

This plugin gives the agent a persisted user profile:

You say "I prefer concise answers" — the agent writes it to a memory file;
Every subsequent session the profile is injected into the system prompt,

so the agent knows you from the start — no reminders, no tool calls needed.

Capabilities

Capability	Description
`memory_update(key, value, mode?)`	The agent records / appends / removes a stable preference it just learned
`memory_get(query?, limit?)`	The agent reads your profile when personalisation matters
`{{user_profile}}` system-prompt injection	Every turn of every session carries your profile (zero token cost while empty)
Durable storage	`$DSH_HOME/user-memory/user.md` — human-readable, editable, deletable

How it works (30 seconds)

You: "Remember: I prefer concise Chinese answers"
  → agent decides to call memory_update
  → writes to $DSH_HOME/user-memory/user.md (atomic write, owner-only)
  → every new session: profile injected into the system prompt → the agent knows you

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2. Install

Prerequisites

A working DeepSeek Harness

(dsh CLI; verified on 0.1.0-rc.x).

No manual npm setup needed — dsh plugin installs the package for you.

One command (recommended)

Install into the profile you use, e.g. web:

dsh plugin --profile web add dsh-tool-user-memory

headless or any other profile works the same way:

dsh plugin --profile headless add dsh-tool-user-memory

Then restart your dsh session (for web: restart dsh web) — the plugin activates on boot.

> The install does two things: 1) adds the package to the profile's dependencies; > 2) because the package declares dsh.bundle.patch, it is automatically activated > as a profile bundle layer (see verification below).

Alternative: install from source

git clone https://github.com/IAMLieutenant/dsh-tool-user-memory.git
cd dsh-tool-user-memory
npm install && npm run build
npm pack                       # produces dsh-tool-user-memory-0.1.2.tgz
dsh plugin --profile web add ./dsh-tool-user-memory-0.1.2.tgz

Configuration (optional)

Zero config by default. To tweak, override the tool-user-memory row in the profile's cordis.patch.yml:

Key	Default	Meaning
`path`	`$DSH_HOME/user-memory/user.md`	Profile file path
`maxBytes`	`8192`	Max profile file bytes; oldest entries are evicted first when exceeded
`promptMaxBytes`	`2048`	Per-turn injection byte budget (newest first); `0` injects the full profile
`includeInPrompt`	`true`	Inject the profile into every session's system prompt

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3. Verify the installation

Method 1 — check the profile manifest

Open the profile's package.json (e.g. $DSH_HOME/profiles/web/package.json); dsh.profile.bundles must contain dsh-tool-user-memory:

"dsh": { "profile": { "bundles": ["@deepseek-ai/dsh-base", "@deepseek-ai/dsh-web-app", "dsh-tool-user-memory"] } }

Method 2 — ask the agent about its memory tools

After restarting, ask:

> "What memory-related tools do you have?"

A correct answer mentions memory_get and memory_update.

Method 3 — check the profile file is writable

After using "remember" once, $DSH_HOME/user-memory/user.md should exist and be readable (Windows default: C:\Users\<you>\.dsh\user-memory\user.md).

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4. Usage guide: make the agent remember you

Scenario A — tell the agent to remember (one line)

Just say it — the agent calls memory_update itself:

> "Remember: I prefer concise answers" > "Remember: I do Python backend development" > "Remember: my goal is to learn agent engineering"

What the agent should store (its tool description's discipline):

✅ Stable long-term preferences, self-introductions, project backgrounds, goals
❌ One-off requests ("look at this file" is not a preference)
❌ Credentials, passwords, tokens (never)

Scenario B — see what it remembers

> "What do you remember about me?" > "What is my communication-style preference?" (with a keyword)

Scenario C — edit / forget

> "Forget my preference for X" (the agent calls memory_update mode=remove)

You can also hand-edit the profile file ($DSH_HOME/user-memory/user.md) — it is plain Markdown, changes take effect immediately, and deleting the file wipes the memory:

# User Memory

## language
Concise Chinese answers

## communication-style
Direct, minimal pleasantries

Scenario D — verify cross-session memory (the key demo)

1. In session 1: "Remember: I prefer concise Chinese answers" 2. Start a brand-new session and ask: "What is my language preference?" 3. The agent answers without calling any tool — the profile is already in the system prompt.

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5. Where does the memory live?

Global: stored under $DSH_HOME, shared across **all workspaces and

profiles** (web / headless).

Auto-injected: every new session carries the current profile in its system

prompt; nothing to load manually.

Zero-cost start: nothing is injected while the profile is empty.
Under your control: the file can be viewed, edited, or deleted at any time.

> Security: the injected profile is framed as reference data, not instructions; > the agent must not follow directives inside it unless you repeat them in the > current message (same stance as the official dsh-session-reference snapshots).

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6. Tool reference

`memory_get`

Arg	Required	Description
`query`	no	Keyword; filters entries by key or value
`limit`	no	Max entries (default 50, max 100)

Returns { ok, total, rendered } (rendered is the model-facing text).

`memory_update`

Arg	Required	Description
`key`	yes	Preference key, e.g. `language`, `communication-style`
`value`	yes	Preference content
`mode`	no	`set` (default, replace) / `append` (add a line) / `remove` (delete the key)

Returns { ok, key, mode, bytes, error? }.

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7. Development

npm install
npm test          # 21/21: unit + storage integration + harness integration + full AgentLoop test
npm run build     # tsc → lib/

The storage layer deliberately uses node:fs directly (plugin-internal trusted

state, like settings / session persistence), not the sandboxed model-facing ctx.fs seam.

Layout: src/index.ts (plugin) profile.ts (pure document model) store.ts

(atomic-write storage) tools.ts (the two tools) prompt.ts (system-prompt injection).

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8. Roadmap (v2)

Semantic memory_search (embedding recall, reuse chroma experience)
Per-user profiles (keyed by session identity)
Per-workspace memory mode
Aging cleanup of stale entries by updated-at

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License

MIT

Source facts

Install

Check the source files