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

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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