@zilliz/memsearch-dsh
MemSearch plugin for DeepSeek Harness (DSH).
It gives DSH persistent, cross-agent memory on the same .memsearch/memory/
markdown store used by the Claude Code, Codex, OpenClaw, and OpenCode plugins,
backed by a Milvus hybrid search index.
capture ── session/event turn/end ──> summarize (dsh-headless agent default, or custom-llm) ──> memory/YYYY-MM-DD.md
inject ── agent/pre-step step 1 ──> memsearch search ──> returned chunks injected (zero cost otherwise)
recall ── ctx.skills.register(memory-recall) ──> search → expand → transcript
review ── web UI dock panel ──> GET/POST /memsearch-dsh/* ──> list candidates / queue review / install
Prerequisites
-
Install memsearch:
uv tool install "memsearch[onnx]"
-
A DSH profile (web / headless / tui) you want to attach memory to.
-
Node >= 22.19 (DSH's requirement).
Install
From npm (recommended)
dsh plugin --profile web add @zilliz/memsearch-dsh
From source (development)
dsh plugin --profile web add /path/to/memsearch/plugins/dsh
dsh plugin is a pnpm forwarder: it links the package into the profile,
detects the dsh.bundle declaration in package.json, and appends it to
the profile's bundle layers. cordis.patch.yml inside the package then
inserts the memsearch row into the profile's plugin tree.
Replace web with your profile name (headless, tui, ...), and restart
DSH for the profile (or start a new session) so the plugin mounts.
Manual patch insertion (no dsh plugin)
Append this row to the profile's cordis.patch.yml and make sure
@zilliz/memsearch-dsh is resolvable from the profile's node_modules (for example a
link: dependency):
- insert:
- id: memsearch
name: '@zilliz/memsearch-dsh'
Verify it loaded
Start DSH and check the session log for the plugin mount, or confirm the
memory-recall skill is available through the skill tool. Captured turns
land in <project>/.memsearch/memory/YYYY-MM-DD.md.
Configuration
The plugin is configured through the profile's cordis.patch.yml config
block (patch the memsearch row you inserted). All keys are optional.
| Key | Type | Default | Meaning |
|---|
captureEnabled | bool | true | Capture completed turns into memory. |
injectEnabled | bool | true | Inject returned memory candidates before each turn's first step. |
summarizeEnabled | bool | true | Summarize turns before writing (on failure a short unavailable note is written, never a raw dump). |
summarizeMode | string | auto | Summarizer backend. auto (default) mirrors the other platform plugins: if [plugins.dsh.summarize] provider is set in memsearch config, it uses custom-llm; otherwise dsh-headless (zero-config DSH agent). Explicit dsh-headless / custom-llm pin the backend. |
Everything else — provider/model, Milvus, collection, memory dir — comes from
memsearch config / environment, exactly like the other platform plugins
(no per-plugin config fields):
- Summarize provider/model →
[plugins.dsh.summarize] provider / model
in ~/.memsearch/config.toml (or [llm.providers.*]; see the
custom-llm section below).
- Milvus →
[milvus] uri in memsearch config.
- Collection → derived from the project path (
derive-collection.sh),
or --collection passed to the memsearch CLI.
- Memory dir →
MEMSEARCH_DIR env (explicit → global scope), else
<project>/.memsearch.
Maintenance tasks (PROJECT.md / USER.md / skills)
Optional background upkeep, aligned with the other platform plugins. Each task
is disabled by default; enable the ones you want in ~/.memsearch/config.toml:
[plugins.dsh.project_review]
enabled = true # maintain .memsearch/PROJECT.md
[plugins.dsh.user_profile]
enabled = true # maintain .memsearch/USER.md
[plugins.dsh.memory_to_skill]
enabled = true # distill recurring workflows into skill candidates
min_occurrences = 3 # how often a workflow must recur before distilling
Common settings per task: provider (native = a one-shot DSH headless
agent, default), model, min_interval_hours (default 24), input_dir,
output_file. Candidates land in .memsearch/skill-candidates/ (git-tracked)
and are never installed automatically — installing is a human step (see
the memory-to-skill skill in the other platform plugins).
Example override layer (add this to the profile's own cordis.patch.yml):
- id: memsearch
config:
summarizeMode: dsh-headless # pin the headless backend (default is auto)
Summarization modes
Two backends are available, selected by summarizeMode — the same
"configured choice" the Claude Code / Codex / OpenClaw / OpenCode plugins
offer (each can summarize with their own LLM or a headless agent + small
model). The default (auto) matches theirs: configure a provider and you get
a direct LLM call; configure nothing and you get a headless agent.
auto (default) — mirrors the other platform plugins:
- if
[plugins.dsh.summarize] provider is set in memsearch config
(~/.memsearch/config.toml, same place the other plugins read), use
custom-llm with that provider/model;
- otherwise use
dsh-headless (zero-config DSH agent).
This means the plugin behaves like the other four: configure a provider
→ direct LLM; configure nothing → headless.
dsh-headless — boots a one-shot DSH headless agent
(dsh --profile headless "<summarize task>") to write the notes, mirroring
how the other plugins reuse their own agent's headless mode. Zero-config for
anyone already using DSH: the sub-agent's model is the deployment's
agent-default-model — the user layer of ~/.dsh/settings.yaml (the same
selection the Web UI model settings write) wins over any patch, so the
[plugins.dsh.summarize] provider/model do NOT apply here — change the
model in DSH settings (agent-default-model: in ~/.dsh/settings.yaml, or
the Web UI model picker) instead. The boot is asynchronous and fire-and-forget,
so the few seconds of headless startup never block the conversation. Requires
dsh on PATH or DSH_CLI set to the CLI
entry. The sub-agent is booted with MEMSEARCH_DSH_SUMMARIZE=1; the plugin
checks that flag and stays inert (no capture / inject / skill) inside the
summarizer, so the summarizer's own session is never re-captured in a loop.custom-llm — scripts/summarize.py imports memsearch's
[llm.providers.*] config and calls the LLM directly. Lightweight: one
python process, no DSH boot, no extra CLI dependency. Choose this when you
want a specific small model (e.g. an official deepseek-v4-flash key in
memsearch config) without booting an agent. Provider selection
(most specific first):
[plugins.dsh.summarize] provider (or the summarizeProvider CLI
argument summarize.py receives from it) — looked up in
[llm.providers.<name>]; a missing entry fails loudly (visible error),
never a silent empty write.llm.provider when it names a configured provider or is a raw type.compact.llm_provider (deprecated) or openai as a final default.
There is no automatic fallback between modes: the backend you configure
(or auto resolves) is the backend used. If it fails (missing dsh CLI, bad
provider config), a short unavailable note is written with the reason — the
plugin never silently switches to an LLM you did not configure.
A failed summarization writes a short unavailable note (mirroring Claude
Code's behavior — memory stays clean, the transcript anchor keeps the raw
content reachable for progressive disclosure), and logs a visible warning
through the DSH logger.
How it works
- Capture — listens on
session/event for turn/end, renders the turn
([User] / [Assistant] / [Tool call] lines), then fire-and-forget
summarizes (if enabled) and appends it to the session's own
memory/YYYY-MM-DD.md with the shared anchor format
<!-- session:<id> turn:<N> db:<path> -->. The project directory comes from
session.header.cwd, so a long-lived web surface captures every project it
hosts, not just the process's boot directory. Turns are serialized (LLM
summarize calls never overlap) and captureExists dedup keeps each turn
idempotent if its event replays.
- Inject — on
agent/pre-step at step 1, runs a bounded memsearch search
over the user's question. Only when the search returns chunks does it inject
them plus a [memsearch] Retrieved memory context attached. marker;
otherwise the decision is returned unchanged (zero context cost). The model
still evaluates whether each candidate chunk is relevant.
- Recall — registers a
memory-recall skill (invocable through DSH's
native skill tool) that performs search → expand → transcript drill-down
and returns a curated summary.
- Maintenance — runs the shared maintenance runner (PROJECT.md / USER.md
upkeep and memory-to-skill distillation), triggered on
session/disposed
plus a 6-hourly fallback timer. Each task is a due-state machine: it runs at
most once per min_interval_hours (default 24h) and only when enabled in
memsearch config ([plugins.dsh.project_review], [plugins.dsh.user_profile],
[plugins.dsh.memory_to_skill]), mirroring the other platform plugins. The
maintenance work is executed by a one-shot DSH headless agent (the same
dsh --profile headless mechanism as summarization), booted with
MEMSEARCH_DSH_SUMMARIZE=1 so the plugin stays inert inside it.
- Skill review panel (web only) — a non-blocking dock strip above the
composer (registered into the
conversation.input.dock slot) lists skill
candidates distilled into .memsearch/skill-candidates/. It is served by the
plugin's browser half (client.js, declared through dsh.client in
package.json) and talks to the host through two JSON routes on the DSH web
server:
GET /memsearch-dsh/skill-candidates?sessionId=<id> — lists candidates
(pending first) parsed from each candidate's meta.json.
Uninstall
dsh plugin --profile web remove @zilliz/memsearch-dsh
Removing the dependency drops the profile-layer entry; the memory markdown
files and the Milvus index are left untouched.
Development
- The plugin is plain ESM with no build step —
dsh plugin add links the
checkout directly, so edits are live after a profile reload.
- The browser half (
client.js) is a prebuilt client-module bundle: it
registers its factory with window.__ModuleLoader__.load({ id, factory })
(the lazy CJS table the web shell serves at /plugins/<id>/client.js) and
exports the Cordis client plugin shape (inject + apply). It is checked
in as-is; edit it directly and keep the __ModuleLoader__ registration
format (mirror the shipped @deepseek-ai/dsh-client-ui-* lib/client.js
bundles).
- Python helpers under
scripts/ are linted with the repo's ruff config and
tested under plugins/dsh/tests/.
- Keep the memory-write format byte-compatible with the other platform
plugins; see
plugins/opencode/scripts/capture-daemon.py for the canonical
writer.
