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Curated file-based long-term memory for DeepSeek Harness — human-readable markdown notes, one plain-file store shared across hosts (git-ignored when it lives inside a project repo), and a deterministic index cap enforced by ctx.tools.guard()

The Engramory curated-memory discipline as an installable plugin ([npm: dsh-engramory](https://www.npmjs.com/package/dsh-engramory)): a deterministic 200-line / 25 KB cap on the `MEMORY.md` index via `ctx.tools.guard()` — growth denied, a shrinking rewrite always passes — plus the protocol registered as a runtime skill. The store is plain markdown, one file per fact, shared with Claude Code, Codex, Kiro, and OpenClaw.

Noema long-term memory plugin for DSH: durable, inspectable agent memory with recall tools and a settings page.

Mnemon 심층 통합: 로컬 3단계 메모리(Runtime Memory, 검색 가능한 Documents, 감독형 Memory Spaces)를 제공합니다.

세션 간 메모리: 사람이 직접 편집 가능한 Markdown 미러를 갖춘 SQLite, 백그라운드 통합(중복 제거, 병합, 충돌 해결), 6가지 메모리 도구를 제공합니다.

Technical-preview local graph memory for DSH and Codex: recalls owner-global and current-project evidence before each turn, writes USER and ASSISTANT records separately, and preserves project, session, actor, and source provenance for a visual atlas and cited knowledge pages.

Long-term memory & self-evolving plugin for DSH: L0 capture → L1 memory extraction → L2 scene grouping → L3 user persona, auto recall injection, skill synthesis, fully local (SQLite FTS5 + jieba, optional vector recall).

File-based cross-session memory: plain Markdown memory files with automatic retrieval injection, format-compatible with Claude Code's CLAUDE.md and memory files, so migrating is a manual file copy.

CBDC 권한 게이팅이 적용된 제한적 로컬 메모리: SQLite + FTS5 클레임, 설명 가능한 use/verify/ignore 판단이 포함된 범위 기반 회상과 전체 감사 추적, /memory 명령어, 호출당 최대 3개 클레임/1200자 삽입, 추가 모델 호출 없음

Biomimetic memory system for DeepSeek Harness: plain-Markdown data layer, memory tools, frozen-snapshot injection, graded approval gating, structured audits, memory metabolism (dream), memory pins, semantic recall, /memory command, and cross-session retrieval.

Faithful port of the hermes-agent MEMORY.md / USER.md mechanism for DSH: bounded dual-bank memory (2200/1375 chars), a single `memory` tool (add/replace/remove/batch), frozen-snapshot injection per session, nudge reminders, DSH-native JSON storage — zero external dependencies, zero LLM cost.

DeepSeek Harness용 구조화된 장기 메모리 시스템 -- 자동 주입되는 유닛 기반 요약/전문 쌍

Native StrataGate layered long-term memory for DeepSeek Harness

Long-term memory injected at session start: preferences/facts/summaries/knowledge in global and per-project scopes, budgeted recall via `agent/pre-step`, durable JSON store.

Layered memory pipeline for DSH: auto-distills conversations into atomic facts, scene summaries and a persona profile (L0–L3), with hybrid BM25 + vector retrieval, chat/work family separation, and context injection before each model step.

Automatic, local-first cross-session memory for DeepSeek Harness: layered Event/Element cards, evidence-gated recall, and expansion back to original turns and tool results.

DeepSeek Harness용 영구 장기 페르소나 메모리: MEMORY.md/USER.md 저장, memory + memory_search 도구, 요청별 컨텍스트 주입, 비밀 정보 스캔. pi-hermes-memory의 설계 철학을 이식