@xiaoke8698/dsh-memory-forget
面向 AI 代理的遗忘引擎——记忆 TTL、衰减、驱逐和审计。与记忆编程相反。
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:XIAOke8698/dsh-memory-forget#938db97ebaa9e7bb138a31e5b54bf85e0de99842Memory Forget 以 @xiaoke8698/dsh-memory-forget 发布，当前版本为 0.2.1。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
Remember less. Think clearer.
Forgetting engine for AI agents: memory TTL, decay, eviction, audit. The opposite of memory programming. Zero-dependency core, MIT.
Memory programming is everywhere: Mem0, Letta, Engram all teach agents to remember everything. dsh-memory-forget does the opposite — it turns forgetting from an accident into a design: every memory has a shelf life, dies on schedule, is fully audited, and is injected under a hard token budget.
One-line difference: memory engines answer "how to store and retrieve well"; dsh-memory-forget answers "when to forget, which memory is still trustworthy, how much goes into context, and what is actually in there".
This is not contrarianism — there is evidence: agents get dumber with use, memory pollution is a main cause (CUHK & ZJU); memory poisoning is a real attack class (single-email persistent poisoning, stealthy memory injection). Forgetting is not memory's failure mode — it is memory's governance.
dsh plugin --profile <name> add @xiaoke8698/dsh-memory-forget
Uninstall: dsh plugin --profile <name> remove @xiaoke8698/dsh-memory-forget (memories stay in the workspace file .dsh-memory-forget/store.json; delete that directory to wipe them).
For developers using the engine as a library: npm install @xiaoke8698/dsh-memory-forget (Node >= 20, zero dependencies, ESM).
import { AmnesiaEngine } from '@xiaoke8698/dsh-memory-forget'
const memory = new AmnesiaEngine({ restorable: true })
// remember with a shelf life (plug into the memory bus)
const v = memory.plug({
content: 'validation drink is lapsang',
ttlMs: 60_000,
kind: 'preference',
tags: ['validation'],
})
console.log(v.id, v.expiresAt) // m-xxx 1787469261044
// health readout: active/stale/forgotten counts + token footprint
console.log(memory.status())
// recall renews the decay clock — use it or lose it
memory.recall('lapsang') // matches + touches (sliding TTL)
// forget (unplug): physical delete; audit keeps hash only (content if restorable)
memory.unplug({ id: v.id })
// restore a forgotten memory (restorable mode): new id, new TTL, audit reason 'restored'
const back = memory.restore(v.id)
// budgeted injection selection: dead (stale/forgotten) memories are NEVER selected
const sel = memory.selectForInjection(2000)
console.log(sel.tokens, sel.skippedDead)
// dry-run preview for token cost accounting (does not touch)
console.log(memory.preview())
The engine is in-memory; provide a StoreAdapter for durability:
import { readFile, writeFile } from 'node:fs/promises'
import { AmnesiaEngine } from '@xiaoke8698/dsh-memory-forget'
const memory = new AmnesiaEngine({ restorable: false }, {
async load() {
try { return JSON.parse(await readFile('memories.json', 'utf8')) }
catch { return undefined }
},
async persist(items, audit) {
await writeFile('memories.json', JSON.stringify({ items, audit }))
},
})
await memory.ready
| Method | Meaning |
|---|---|
plug(input) | remember with TTL / pin / scope / tags |
unplug(filter) | forget (by id / query / tags) — physical delete + audit |
touch(id) | access: renew the decay clock (sliding TTL) |
recall(query?) | recall matching active memories and renew each |
restore(id) | plug a forgotten memory back in (restorable mode) |
selectForInjection(budget) | budgeted selection; dead memories excluded |
preview() | dry-run injection preview (token ledger, no touch) |
status() | counts + token footprint + next expiry + recent audit |
auditView(limit) | forget/restore trail (hash only, never content body) |
sweep() | settle all items; expire dead ones (call on turn end) |
| Feature | Description |
|---|---|
| Pluggable memory bus | remember = plug in; forget = unplug — physical delete, no ghost references. Forgetting is a protocol operation, not a failure |
| Shelf life | every memory has TTL + decay curve (Ebbinghaus): active → stale → forgotten |
| Importance: declared or earned | pin: true = explicit forever; or earn it by usage — sliding renewal (use it or lose it): used memories never expire, idle-for-a-full-TTL memories die |
| Recoverable | restore(id) (restorable mode) plugs a forgotten memory back in (new id, new TTL, audited restored); in privacy mode re-remember instead (audit hash lets you verify it is the same content) |
| Audit, hash only | who/what/when/why forgotten or restored; content physically deleted by default, audit keeps SHA-256 |
| Hard injection budget | per-step ≤ maxInjectedTokens (default 2000); dead memories never injected; over budget = select less, never truncate |
| Token ledger | status() reports last-injection accounting; preview() is a non-touching dry run |
| Lightweight | zero embedding, zero LLM extraction/rewriting (zero generation cost on write), zero server process |
| Privacy first | physical deletion by default; recoverability vs. clean deletion is an explicit config switch |
| Multi-agent scoping | scope: workspace / session / team; a sub-agent's temp memories are auto-unplugged when it dies, never inherited by default |
Benchmark design (three-arm, same model, same task set — only the forgetting policy changes):
| Arm | Condition |
|---|---|
| A | no memory (cold start every task) |
| B | full memory (write forever, never forget — typical memory-plugin behavior) |
| C | Amnesia (write with TTL + decay) |
Fairness key: B and C share the same store and retrieval (deterministic strength-ranked selection) — only the forgetting policy is toggled. Memory vendors cannot run this A/B because they cannot turn off their own memory.
Metrics: cumulative context tokens, task success rate, stale-error rate (a written fact changes mid-task; how often does each arm use the outdated fact), steps/time, recall benefit on memory-helps tasks. Task sets: memory-helps (multi-turn preferences, repeated context), memory-hurts (changed facts, expired constraints, poisoned memories), mixed. Full design: docs/design.md §11.
Status: NO comparative results exist yet (M3 not run). What is verified (M1 dynamic-plugin validation) is that the mechanisms work — expiry, restore, audit, budgeted injection, sliding renewal — not that forgetting improves agent outcomes. Until M3 produces data, effectiveness claims rest on the cited research, not on measurements of this package. We will publish the benchmark numbers the moment they exist, with methodology and task sets attached.
Current release v0.2.1 ships the core engine + the DSH official bundle (AmnesiaEngine + Cordis plugin adapter). Not included yet: CLI, skill, MCP server, Client UI, benchmark results.
memf-1)
dsh plugin add (official bundle mechanism)/amnesia command, auto-suggestion (opt-in), deeper visualizationDSH users — installed via the official bundle mechanism (see Install above; dsh.bundle + cordis.patch.yml, DSH docs docs/user/develop/basic/publish.md).
Planned: skill + local CLI (M4b) — one SKILL.md (Anthropic Agent Skills format, shared by Claude Code / Codex / DSH) + a local CLI: copy-and-use, no network, no background process. Claude Code: ~/.claude/skills/dsh-memory-forget/; DSH: skills dir; Codex: skills / AGENTS.md.
Far future: MCP server (M6) — needs server hosting; not committed.
Status: skill / CLI are not shipped yet. Today the npm package is the core engine + DSH official bundle; the DSH dynamic plugin was the session-scoped validation form.
compaction (compression) but no delete/expiry/eviction semantics; memory systems are third-party MCP examples only (off by default)