dsh-mnemon
Composable three-tier memory control plane for DeepSeek Harness: persistent runtime context, searchable project documents, pluggable long-term memory, guarded strategies, WebUI, and headless tools.
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Composable three-tier memory control plane for DeepSeek Harness: persistent runtime context, searchable project documents, pluggable long-term memory, guarded strategies, WebUI, and headless tools.
Repository-root manifest for the DeepSeek Harness plugin that lives in extensions/dsh — the same package published to npm as dsh-deja. It re-exports the subdirectory so the plugin installs from a bare git URL or from github:vshulcz/deja-vu#path:extensions/dsh; the npm package is the shorter path. The repository itself is the Go project behind the deja binary.
Knowledge graph memory for DeepSeek Harness and OpenClaw — cross-session recall, PageRank, communities, and vector search
Install the full dsh-mnemon integration from the Mnemon repository.
Cross-session project memory for DeepSeek Harness: seven-layer SQLite memory, first-turn snapshot injection, per-message keyword hits, memory_remember/search/project tools, automatic reflection with reflection-fold UI, and idle-triggered dream consolidati
Curated file-based long-term memory for DeepSeek Harness — discovery manifest for the installable plugin in adapters/dsh/plugin/
Noema long-term memory plugin for DSH: durable, inspectable agent memory with recall tools and a settings page.
Recent conversations stay vivid. Older ones fade into summaries, not oblivion. StrataGate gives DeepSeek Harness six-layer, time-decaying memory, while lasting events and relationships settle into a knowledge graph. Bring your memories from other AIs with
Bounded, layered, approval-gated, auditable cross-session memory for DeepSeek Harness — a capability seam (ctx.memory service + local SQLite provider + memory tool + frozen snapshot injection), not another memory warehouse
Structured memory engine for DeepSeek Harness. Offline semantic search, entity-attribute-timeline, autoDream self-consolidation, and human-editable Markdown storage.
Personal assistant plugin for deepseek-harness that organizes commitments from conversations, tracks plans across sessions, and delivers low-interruption reminders.
DSH-KRouter — Agent knowledge OS. Self-evolution. Timer on by default; your vault-page API key or logged-in CLI is the key. Cursor, Codex, Claude Code, DeepSeek Harness. No vector store.
DeepSeek Harness (DSH) local-first cross-session project memory: bounded cache-friendly Hot Memory, BM25 recall/cache, provenance, lifecycle and bilingual Web GUI; no embeddings, external service or bundled runtime dependencies.
Claude Code- and Codex-inspired long-term memory for DeepSeek Harness
Long-term memory for DeepSeek Harness: multi-channel retrieval and an evolving memory store.
L0~L3 分层蒸馏记忆插件 for DeepSeek Harness:自动捕获对话(L0)、抽取原子记忆(L1)、整合场景块(L2)、蒸馏核心画像/团队方法论(L3),并在模型步骤前自动召回注入。移植自 MemoryCore (TencentDB Agent Memory) 的管线设计。
Persistent project memory for dsh agents: index docs (PDF/Markdown/text) and code symbols into a searchable per-workspace store, recall them with cited sources, and keep experience entries (problems -> solutions) searchable on demand.
SGME 仓库级 DSH 插件包装(真实插件包在 adapters/dsh/sgme-bridge/,dsh-sgme 已发布 npm)。本文件让 dsh plugin add github:freehul/sgme 直接可装,并让 dshfind 能推导出 npm 安装命令。
Cross-session project memory for DeepSeek Harness: agent-maintained Markdown memory with progressive-disclosure topic files, digest-deduplicated injection, opt-in silent distillation, seven memory tools, and a live settings panel.
DeepSeek Harness 长期记忆与自进化插件:L0 对话捕获 → L1 记忆提取 → L2 场景归纳 → L3 用户画像,自动召回注入 + 技能合成,纯本地。
OpenViking retrieval, resource management, auto-recall and session memory for DeepSeek Harness.
分层 Token 优化管道 v2:输出阶梯 / 结果缓存 / 文件 diff / 工具裁剪 + MCP 懒加载 / 压缩调度 / 缓存命中率,基于 DSH 真实插件 API
TaskPack: an open, offline task-handoff container. Task Passport is the durable state; TaskPack is the box it travels in.
Standalone DeepSeek Harness plugin: continual harness self-evolution. The agent persists and refines reusable prompt notes, memories, skill contracts, and subagent specs through small evidence-backed edits, with automatic refinement gates, rollback, and prompt injection.