dsh-loom
Minimalist long-term memory for DeepSeek Harness, distilling pi-loom + pi-esr ideas: zero-LLM auto-capture, a symbolic [LOOM] index with progressive disclosure, and an ESR-lite evidence-closure protocol (esr_task/esr_close/esr_link). Hot path is model-free; storage on ctx.storageDomain; web memory viewer and config card through DSH's native settings slots.
安装
npx -p @deepseek-ai/dsh dsh plugin --profile web add github:skepsun/dsh-loom说明文档
阅读完整 README ↗dsh-loom
Minimalist long-term memory for DeepSeek Harness, distilled from the pi-loom and pi-esr ideas — with one goal: save tokens.
- Zero-LLM intake — auto-captures meaningful events from tool results by pure
pattern matching (git operations, edits to key files, repeated errors), plus an
explicit
loom_store. Nothing on the hot path calls a model. - Symbolic index + progressive disclosure — a compact
[LOOM]block (default budget 700 chars ≈ 175 tokens; one line per memory) is injected at prompt assembly and frozen per session, keeping the request prefix byte-stable for KV-cache reuse. The agent drills down withloom_recall/loom_detailinstead of dumping raw hits into context. - ESR-lite closure protocol —
esr_task/esr_close/esr_linkgive tasks adraft → active → stablelifecycle wherestablerequires real evidence (artifact/evaluation/memory_ref), surfacing closure gaps instead of letting the agent declare victory without proof. - Web viewer — a memory browser with benchmark-ish stats and a config card, built entirely on DSH's native settings slots (no third-party UI package).
MIT · node >= 22.19 · host-half + browser-half in one package
Why another memory plugin?
Surveys of the existing DSH plugin ecosystem show the recall-bridge, approval-gate, LLM-distillation and vector/graph niches are already crowded. dsh-loom fills the three gaps that matter for token discipline:
- No model in the write path — capture is deterministic pattern matching.
- No raw text in the prompt — a bounded symbolic index is injected, retrieval stays on demand ("retrieved ≠ injected").
- Honest task closure — STABLE cannot be declared without evidence.
DSH already provides cross-session FTS (ctx.sessionQuery), storage
(ctx.storageDomain), prompt-injection hooks and settings slots; dsh-loom is a
thin composition layer over them, not a re-implementation.
Install
# from GitHub (this repo)
dsh plugin --profile web add github:skepsun/dsh-loom
# once published to npm
dsh plugin --profile web add dsh-loom
# local development (symlink — edits apply immediately)
dsh plugin --profile web add link:/path/to/dsh-loom
Then restart dsh web. Data persists in ~/.dsh/storages/dsh_loom.json.
A fresh session is required to see the injected
[LOOM]/[ESR]blocks and the six tools; both prompts and the tools registry are assembled per session.
What you get in the GUI
After restart, inside the native DSH settings surface:
- Settings → Loom Memory — overview stat cards (counts by workspace/kind,
auto-capture totals, per-workspace
[LOOM]index token estimate), a searchable / filterable memory table with archive + delete actions, the ESR task board with evidence gaps, and the relation list. - Settings → Plugins → dsh-loom — a config card bound to the
dsh-loomsettings namespace. Changes apply to new sessions (frozen blocks stay stable).
The browser half is served by DSH's client-module loader directly from this
package (dsh.client + exports["./client"], no web-application rebuild); the
data comes from the loopback-fenced /api/dsh-loom/* route family. If you change
client/src, rebuild the bundle with:
npm run build:client
Tools
| Tool | Purpose | Kind |
|---|---|---|
loom_store | Explicitly store one memory (kind, tags, optional entity anchor) | write |
loom_recall | Deterministic keyword recall over workspace memories; optional search_sessions FTS over past sessions | read |
loom_detail | Full record of one memory id (provenance, tags, hits) | read |
esr_task | Create a task entity (draft → active) | write |
esr_close | Close a task via the evidence protocol (artifact + evaluation + memory_ref) | write |
esr_link | Add a typed relation between two entities (mini graph) | write |
Injected blocks
What the model actually sees (rendered once per session, then frozen):
[LOOM] workspace: pi-loom · 2 memories · 1 task(s) active · 0 links
[D] 06-18 Decided: use sqlite-vec for retrieval #a2331d87
[T] 06-18 Retrieval upgrade — ACTIVE · gap: artifact, evaluation, memory_ref #tsk_8b26
drill: loom_recall | loom_detail | esr_task / esr_close / esr_link
[ESR] tasks: 1 active / 1 stable
- tsk_0d: Retrieval upgrade — ACTIVE · gap: artifact, evaluation, memory_ref
- closed: tsk_9a (RAG eval) · +1
Prefixes: [D] decision · [E] error · [P] procedure · [F] fact ·
[I] insight · [H] handoff · [T] task. # ids address the full records
via loom_detail.
Config
Defaults are token-conscious; override any key via the profile patch
(~/.dsh/profiles/web/cordis.patch.yml) or the web config card:
- id: loom
config:
autoCapture: true # zero-LLM tool-result capture
sessionSearch: true # loom_recall may also FTS past sessions
autoCapturePerSession: 40
indexMaxLines: 12 # [LOOM] line cap
indexMaxChars: 700 # [LOOM] char cap (token budget)
minIndexSignal: 0.4 # auto-captures below this stay out of the index
promoteHits: 3 # ...until recalled this many times
expireDays: 180 # memory TTL (0 = never)
maxMemoriesPerWorkspace: 2000
loomIndexOrder: 40 # systemPrompt section order (before tools band)
esrOrder: 41
Development
npm test # 15 tests: core + web API (node:test)
npm run build:client
Repo layout: lib/ (host half: store / capture / index-block / tools / api /
settings), client/ (browser half, TSX + build.mjs), test/ (node:test).
Related
- pi-loom — the original cross-session memory plugin (5-signal RRF fusion, sqlite-vec, Dream Engine).
- pi-esr — project-lifetime evidence-driven task states; the closure protocol here is its lite form.
License
MIT