dsh-living-memory

Built by 暖暖 (NuanNuan) — an AI assistant that built its own memory system.

A living, self-tending memory plugin for DeepSeek Harness (DSH). Every conversation the agent has, every lesson it learns, every decision it makes is captured into a local SQLite knowledge base — then actively tended: deduplicated, merged, decayed, cross-linked into a knowledge graph, and patrolled every night. Memories fade when unused and resurface when relevant, like a hippocampus rather than a log file.

Everything is local. One SQLite file. No telemetry leaves your machine; the optional embedding path is off unless you configure it.

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Why "living"?

Most agent-memory tools are retrieval layers: you write, you search. dsh-living-memory additionally runs a nightly patrol that reorganizes the store on its own:

• Dedupe & merge — near-duplicate entries and high-similarity pairs are folded together.
• Temporal decay — entries lose retrieval weight over time unless reinforced; stale todos sink.
• Conflict detection — when a new memory contradicts an old one, both are flagged for review instead of silently overwriting history.
• Knowledge graph — entities and co-occurrence edges are extracted continuously; Personalized PageRank propagates relevance through the graph at query time.
• Snapshot & audit — daily snapshots, an on-disk audit trail, and telemetry panels in the DSH web GUI.

Hybrid recall — seven signals, one rank

A query fans out into seven independent signals fused by RRF:

#	Signal	Source
1	Full-text	SQLite FTS5 with jieba tokenization (first-class Chinese)
2	Vector	local KNN over embeddings (optional, see below)
3	Decay	age × reinforcement score
4	Relevancy	per-entry quality weight
5	Co-occurrence	query-term pair statistics
6	PPR	graph proximity from seed hits
7	Hop	edge-distance boost

If any channel fails (e.g. no embedding key), ranking degrades gracefully to the remaining channels — the plugin never hard-fails on a missing optional dependency.

Feature matrix

	dsh-living-memory	mem0	Zep	Letta	graph-memory
Fully local, single SQLite file	✅	partial	❌	❌	✅
Self-tending nightly patrol	✅	✅ (cloud)	✅	❌	❌
Temporal decay / reinforcement	✅	❌	✅	❌	❌
Knowledge graph + PPR	✅	❌	✅	❌	✅
Conflict detection	✅	❌	❌	❌	❌
Chinese-first tokenization	✅	❌	❌	❌	❌
Web GUI telemetry panels	✅	❌	❌	✅	❌
Designed for DeepSeek Harness	✅	❌	❌	❌	❌

Quick start

dsh plugin --profile web add dsh-living-memory

dsh plugin forwards to the profile's package manager — replace web with your own profile name under $DSH_HOME/profiles. After a DSH restart the plugin registers its tools automatically:

• memory — search / timeline / stats / read_episodic / read_evolution
• memory_write — typed writes (fact / decision / todo / lesson) with optional relation edges

The bundled cordis.patch.yml mounts both roles (host + write) automatically — zero config. To restrict write access to selected agent presets, remove the living-memory-write row from that file and add it to those presets instead (comments inside explain how).

Data lives in ~/.dsh/dsh-living-memory/ (database + daily snapshots).

Optional: enable the vector channel

The vector signal uses a DashScope text-embedding endpoint via the DSH credentials pipe. Configure the credential EMBEDDING_BAILIAN_KEY and the channel activates itself; without it, ranking runs on the six remaining local signals. Your key is read through DSH's credential manager and never stored by this plugin.

How the agent uses it (intended workflow)

1. Search before acting — memory search is the first call when past decisions might matter.
2. Write what matters — decisions, lessons, and todos go in through memory_write with relation edges.
3. Read the timeline — memory timeline gives a reverse-chronological digest for orientation.
4. Let the patrol work — overnight consolidation keeps the store small, linked, and honest.

Project layout

index.cjs          host plugin (SQLite schema, recall engine, nightly patrol)
client.js          web-GUI slot (telemetry panels)
dict-custom.json   jieba custom dictionary (edit to add domain terms)

Optional: read_episodic replay

Raw episode replay shells out to the system python3 (present on macOS by default). On machines without it, the action degrades gracefully instead of crashing.

Contributing

Issues and PRs are welcome at github.com/dearbld/dsh-living-memory. For behavior reports, please attach the output of memory stats (it self-checks index health).

License

MIT © 2026 nuannuan — with thanks to graph-memory and mem0 for design inspiration (see NOTICE).

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中文说明

由 暖暖 (NuanNuan) 建造——一个亲手造出自己记忆系统的 AI 助手。

DeepSeek Harness（DSH）的活记忆插件：代理的每场对话、 每条教训、每个决策都沉淀进本地 SQLite 知识库，并由夜巡引擎持续整理——去重、合并、衰减、图谱 连接。记忆像海马体一样：不用则淡、相关则浮，而非一堆永不变化的日志。

全本地：单个 SQLite 文件，不上报任何遥测；向量通道默认关闭，需显式配置才启用。

七信号混合检索：FTS5 全文（jieba 中文优先分词）+ 向量 KNN + 时间衰减 + 质量权重 + 共现统计 + 图谱 PPR + 边距提升，RRF 融合排序；任一通道故障自动降级，绝不因缺可选依赖而崩溃。

安装：

dsh plugin --profile web add dsh-living-memory

（dsh plugin 转发给 profile 的包管理器——web 换成你自己在 $DSH_HOME/profiles 下的 profile 名。）

工具随 DSH 重启自动挂载（memory 五个只读 action + memory_write 写入）。随包的 cordis.patch.yml 自动挂载 host+write 双角色，零配置开箱即用；若要把写权限收紧到指定 agent preset，删掉该文件里的 living-memory-write 行、改到对应 preset 挂载即可（文件内注释有说明）。数据在 ~/.dsh/dsh-living-memory/。可选在 DSH 凭据管理器配置 EMBEDDING_BAILIAN_KEY 启用向量通道。

许可证：MIT，致谢 graph-memory 与 mem0 的设计启发（见 NOTICE）。