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Memory Fortress for DeepSeek Harness: 5-track layered memory, self-evolution, knowledge graph (SQLite+FTS5), status pill. 本地记忆堡垒：五轨记忆·自我进化·知识图谱·状态灯。

Persistent memory for DeepSeek Harness with curated facts, automatic turn retention, and graph- and vector-ranked recall.

DeepSeek Harness를 위한 로컬, 감사 가능한 개인 메모리: 선호 사항, 프로젝트 규칙, 워크플로 및 오류 교훈을 체인지로그가 기록된 JSON 파일로 워크스페이스에 저장하며, 온보딩, 자동 프로젝트 규칙 추출 및 증거 기반 교훈을 제공합니다.

DeepSeek Harness에 타임라인 기억을 보강합니다: 고정 폭의 추가 전용 사실 로그 + agent가 스스로 유지하는 요약 피라미드로, 고정된 읽기 예산 아래에서 recall 비용이 기억량에 따라 증가하지 않습니다.

세션 시작 시 주입되는 장기 기억: 전역 및 프로젝트별 범위의 선호·사실·요약·지식, `agent/pre-step`을 통한 예산 기반 회상, 영속 JSON 저장소.

기존 Claude / ChatGPT 기록을 DSH로 가져오기: Claude 내보내기의 memories.json을 제로 토큰으로 즉시 가져오고, 로컬 Claude Code 트랜스크립트는 내보내기 없이 사용하며, 대화는 DSH에 이미 설정된 모델로 증류합니다. 모든 메모리는 코드로 소스와 대조된 축자적 증거를 동반하며, 의역은 폐기 후 집계되고 assistant 턴에서만 발견된 증거는 항상 저장이 아닌 검토로 보냅니다.

DSH의 무손실 세션 로그를 기반으로 한 출처 표기 메모리: 정제된 사실마다 `(sessionId, eventRange)` 인용이 붙어 원본 로그 발췌로 바로 펼쳐볼 수 있음

Cross-session long-term memory: captures durable facts from each turn, auto-associates them by shared entity and tag, and recalls with BM25 plus spreading activation over the link graph.

记忆核心（工作区版 memory-eternal-ws）：按 DSH 工作区自动切库的本地 Markdown 记忆库插件。每个项目用自己的 .dsh/memory-vault，跨项目知识走全局库。fork 自 dsh-memory-eternal v0.7.0，保留自动沉淀 / 自动召回 / 知识卡 / 知识图谱 / 审核中心 / 回收站 / 配置页全部功能。

Claude-compatible memory for DeepSeek Harness: a user-chosen memory directory with MEMORY.md index and topic files, the memory tool, and a settings page

Progressive long-term memory for DSH: sharded markdown bodies under ~/.dsh/memory/topics/, with only a live-generated index (shard · count · path · per-entry line numbers) injected into the system prompt — so the prompt cost stays flat however large the m

Read-only enhancer for a file-based long-term memory library (markdown cards in git): a memory_find search tool, a small budgeted memory digest injected into the system prompt, and a settings page with library overview, health-check status and toggles.

Memory archive and prompt viewer for DeepSeek Harness: read back what compaction folded out of context - browse past sessions or Tavern playthrough archives (summary, original text, state) with full-text search - and inspect the prompt each turn actually sent, listing every assembled system section with its order, character count, offset, hash and mutability, plus an assembly map, registry export, and honest diagnostics when a section is empty or its log cannot be read. Optionally provisions a roleplay agent preset.

On-demand memory for DeepSeek Harness: session logs are distilled into layered markdown (a summary, an index and per-topic files), and a mem_query tool returns only the matched entries rather than the whole index. Nothing is injected per turn, there are no dependencies, and every entry keeps a source pointer back to the conversation it came from.

Layered cross-session memory for DSH that pushes only what changed. The first round injects every active entry, later rounds push only added / updated / removed ones, and nothing at all is injected when nothing changed. Facts and decisions carry semantic keys (one active truth per key), the model may only write to an inbox that a human promotes, an entry can carry a review date that raises a one-time reminder when it comes due, search is relevance-ranked with Chinese matched by bigram, and a sidebar tab tells the lifecycle story instead of just listing files — entries are grouped into awaiting confirmation / in use / archived, viewable by type, tag or date, and the tab can promote a candidate, withdraw or archive a standing entry, restore an archived one and delete a candidate, alongside the global and workspace instruction files and the current injection size. A zero-dependency CLI ships with it, so the store is usable without DSH.

Persistent, searchable, per-project memory for the DeepSeek Harness: decisions, rules, and sprint context in a queryable DuckDB file, rules enforced on every model request, with a full management UI in the Web Client

Polaris Memory: local-first project memory plugin for DeepSeek Harness. Uses per-project .dsh-memory.json; provides memory_read/recall/write/update/delete tools, /memory command and settings UI, with tiered injection (resident user profile + scene-triggered memories with P1/P2/P3 importance), semantic recall (IDF + synonym layer), and a hard-guard mechanism for bottom-line memories (detect deny-semantics -> authorize -> block AI deletion; user can confirm-override).

Integrated memory and context management plugin: four-layer memory system (L2-L4), context compression/trimming/freezing, prompt orchestration, input box UI buttons, and enterprise security with role-based access control.

Bridges the agent to a persistent OKF memory vault over MCP stdio: a Python server (SQLite FTS5 + Markdown) with 10 tools for searching and storing decisions, facts, learnings, conventions, profiles and sources.

Session-memory autopilot: watches DSH sessions and extracts OKF entries in-process through the harness LLM (ctx.llm, deepseek-official by default) at idle, commit, compaction and end checkpoints, storing them via the vault MCP server.

Persistent conversation memory for DeepSeek Harness: memory tab, automatic AI naming, per-AI privacy isolation, sidebar AI names, and automatic experience archiving. A static profile plugin available in every preset without disappearing on restart.

A /remember command that writes facts into the AGENTS.md instruction hierarchy dsh already loads, plus a Memory tab showing which instructions the loader kept within its byte budget.

Agent-curated long-horizon memory (ctx.memory) for DeepSeek Harness: durable project-scoped memory banks with a provider registry and a shipped local backend — a standalone plugin with no upstream harness changes required.

Memory triage for DeepSeek Harness: at the end of each turn it decides which entries to keep and which to archive, routes project-specific ones into a project bucket while global ones stay in the main store, merges near-duplicate entries, and falls back to an LLM when the rules are unsure — automatic actions are recoverable archives only, never deletions.