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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.
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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.
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Knowledge graph memory for DeepSeek Harness and OpenClaw — cross-session recall, PageRank, communities, and vector search
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Install the full dsh-mnemon integration from the Mnemon repository.
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Curated file-based long-term memory for DeepSeek Harness — discovery manifest for the installable plugin in adapters/dsh/plugin/
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Noema long-term memory plugin for DSH: durable, inspectable agent memory with recall tools and a settings page.
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Structured memory engine for DeepSeek Harness. Offline semantic search, entity-attribute-timeline, autoDream self-consolidation, and human-editable Markdown storage.
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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.
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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。
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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。
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TaskPack: an open, offline task-handoff container. Task Passport is the durable state; TaskPack is the box it travels in.
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Local-first persistent memory infrastructure for DeepSeek Harness: bounded hot-memory bootstrap, explainable cold recall (exact + Chinese BM25), lease-lock transactional writes, read-only governance, and trajectory review. DSH plugin host; Python stdlib + Markdown core. Zero database / vector service / external service dependencies.
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OpenViking retrieval, resource management, auto-recall and session memory for DeepSeek Harness.
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DeepSeek Harness 的 TencentDB Agent Memory 移植:L0 对话捕获 → L1 结构化记忆提取 → L2 场景/L3 画像,自动召回注入 + 记忆/对话搜索工具;复用现有 ~/.memory-tencentdb/memory-tdai 数据;附 Web UI 设置栏。
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Unified agent memory for DeepSeek Harness — one shared Obsidian vault for every agent (dsh, Codex, Claude, Hermes). Ships the dependency-free Python core (search/promote/adjudicate/forget), a vault template, and a dsh plugin with memory_search / memory_show / memory_submit / memory_status tools. · 统一 Agent 记忆系统:多 Agent 共享一个 Obsidian vault,附零依赖 Python core 与 dsh 插件。
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File-based memory plugin for DeepSeek Harness (DSH) — cross-session memory in transparent markdown files
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agentmemory for DeepSeek Harness (dsh): full memory_* tools, capture hooks, and context injection over the local REST server
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Task-conditioned Chat and Work session memory for DeepSeek Harness
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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.
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Cross-session persistent memory for the DeepSeek Harness: a model-facing memory tool, an always-on index section, and a bundled maintenance skill.
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Native layered memory for DeepSeek Harness with persona files, workspace scope, semantic recall, and background consolidation
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Agent-driven long-term memory for DeepSeek Harness: scoped memory (global + per-workspace), layered entries (fact/knowledge/episodic), time-bucket compaction (day→week→month→year), associative recall (related chains) + memory_relate navigation (multi-hop BFS closure), auto recall injection on user messages (CJK bigram search, tail append), agent-decided content.
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Persistent 5-layer memory system for DeepSeek Harness (entry injection, relevance retrieval, agent tools, and LLM-backed auto-extraction).
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AgentSoul for DeepSeek Harness — a local personality, identity, state and long-term memory layer. Auto-loads as a bundle plugin on every dsh startup.