MIT 授權、本機優先的 agent memory 核心，透過 Cordis 外掛生命週期原生載入 DeepSeek Harness：類型化知識圖譜、跨工作階段自動召回與 gm_* 工具，SQLite 儲存並可選向量檢索。
DSH 整合
原生執行環境
作者聲明
安全稽核
未稽核
最後核實
2026-08-21
授權條款
MIT
01它能幫你完成什麼？
Give DeepSeek Harness agents durable, traceable cross-session memory
A typed knowledge graph (TASK / SKILL / EVENT nodes with typed edges) in local SQLite, with relevant memory automatically recalled in new sessions — even after DSH restarts
DeepSeek Harness users who want their agent to remember past tasks, fixes and reusable skills across sessions
Shrink context by recalling only relevant memory instead of replaying full history
A relevant local subgraph injected into the prompt during Prompt Assembly; the author's seven-turn benchmark measured an ~75% token reduction at turn R7
DSH users hitting context limits or paying for redundant history replay in long workflows
Explicitly record and search long-term knowledge inside DSH
gm_record persists TASK / SKILL / EVENT nodes deterministically; gm_search runs long-term graph search; gm_status and gm_stats expose store and graph state
DSH users who need deterministic control over what critical knowledge is remembered and why a memory was recalled
02如何將外掛接入 DeepSeek Harness？
先決條件
- Node.js `22.19+` or `24+`
- DeepSeek Harness (dsh) with the web profile — the README uses `npx @deepseek-ai/dsh`; local acceptance was tested on DSH `0.1.0-rc.5`
安裝步驟
- 01
Clone and build the tarball from source (the beta is not on npm yet): `git clone https://github.com/adoresever/graph-memory.git && cd graph-memory && npm install && npm test && npm run build && npm pack`
$ git clone https://github.com/adoresever/graph-memory.git && cd graph-memory && npm install && npm test && npm run build && npm pack
- 02
Install the generated tarball into the DSH web profile: `npx @deepseek-ai/dsh plugin --profile web add /absolute/path/to/graph-memory-1.6.0-beta.1.tgz`
$ npx @deepseek-ai/dsh plugin --profile web add /absolute/path/to/graph-memory-1.6.0-beta.1.tgz
- 03
Optionally dump the config to confirm registration: `npx @deepseek-ai/dsh --profile web --dump-config`
$ npx @deepseek-ai/dsh --profile web --dump-config
- 04
Start DSH web: `npx @deepseek-ai/dsh web`
$ npx @deepseek-ai/dsh web
- 05
Optional vector retrieval: export `GRAPH_MEMORY_EMBEDDING_API_KEY`, `GRAPH_MEMORY_EMBEDDING_BASE_URL`, `GRAPH_MEMORY_EMBEDDING_MODEL`, `GRAPH_MEMORY_EMBEDDING_DIMENSIONS` before `dsh web`
$ dsh web
驗證整合成功
- Confirm that `graph-memory/dsh` is enabled under Settings → Plugins → Plugin list
- Use `gm_status` to check store path, graph counts, vector coverage, mode, and dimensions
03DSH 整合程度與能力邊界
Native DSH plugin loaded by the Cordis plugin lifecycle via the cordis.patch.yml bundle entry — registers gm_* tools, auto-recall during Prompt Assembly, and DSH Credentials access
Typed knowledge-graph memory core
DSH session conversation events→TASK / SKILL / EVENT nodes with typed edges (USED_SKILL, SOLVED_BY, REQUIRES, PATCHES, CONFLICTS_WITH) plus episodic provenance, stored in local SQLite
Writes a local SQLite database at $DSH_HOME/graph-memory/graph-memory.db (normally ~/.dsh/graph-memory/graph-memory.db)Dual-path recall with graph ranking
the current user query during Prompt Assembly→a deduplicated relevant local subgraph (vector or FTS5 search + community expansion + Personalized PageRank) injected into the prompt
Optional network calls to OpenAI-compatible embedding providers (DashScope, OpenAI, local) when vector retrieval is configuredgm_* tools for record, search and observability
explicit agent or user tool calls inside DSH→gm_record persists knowledge deterministically; gm_search runs long-term graph search; gm_status and gm_stats report store, extraction, recall, vector and community state
gm_record writes new nodes into the local graph storeSafe, local-first context assembly
recalled memory nodes→recalled history injected as untrusted reference material that cannot override current user instructions
Memory data stays in the user's local profile by default; API keys come from host credentials or environment variables, never the database
04適合誰？何時不該用？
適合
- DeepSeek Harness users who want their agent to remember past tasks, fixes and reusable skills across sessions
- DSH users hitting context limits or paying for redundant history replay in long workflows
- DSH users who need deterministic control over what critical knowledge is remembered and why a memory was recalled
不適合
- The current build is 1.6.0-beta.1 and DeepSeek Harness is still in Developer Preview, which may introduce compatibility-breaking changes; local acceptance was tested on DSH 0.1.0-rc.5.
- npm registry publication is pending, so installation requires cloning the repository and building the tarball from source instead of a one-command npm install.
- Automatic extraction depends on auxiliary-model output stability, so critical knowledge should be persisted explicitly with gm_record; DSH does not yet expose gm_update and gm_maintain.
05相容性、維護與安全提醒
- The current build is 1.6.0-beta.1 and DeepSeek Harness is still in Developer Preview, which may introduce compatibility-breaking changes; local acceptance was tested on DSH 0.1.0-rc.5.
- npm registry publication is pending, so installation requires cloning the repository and building the tarball from source instead of a one-command npm install.
- Automatic extraction depends on auxiliary-model output stability, so critical knowledge should be persisted explicitly with gm_record; DSH does not yet expose gm_update and gm_maintain.
MIT · actively maintained (last push 2026-08-14; current beta 1.6.0-beta.1)
06常見問題
Graph Memory 如何接入 DeepSeek Harness？原生還是 MCP？
原生接入。它透過 cordis.patch.yml 由 DSH/Cordis 外掛生命週期載入，整合 Session、Tool、Agent Loop、Prompt Assembly、LLM 與 Credentials 接縫，不是透過 MCP 旁路模擬，也不需要 fork DSH。
安裝前需要什麼？
Node.js 22.19+ 或 24+，以及 DeepSeek Harness。目前 beta 尚未發布到 npm，需要複製儲存庫執行 `npm install`、`npm test`、`npm run build`、`npm pack`，再用 `npx @deepseek-ai/dsh plugin --profile web add` 安裝產生的 tarball。
一定要設定 embedding API key 嗎？
不需要。Embedding 為選配，未設定時召回會自動退回 FTS5 詞彙檢索。若需要向量檢索，設定 GRAPH_MEMORY_EMBEDDING_* 環境變數即可接入任何 OpenAI 相容服務（DashScope、OpenAI 或本機 provider）。
記憶資料存在哪裡？
本機 SQLite 資料庫：$DSH_HOME/graph-memory/graph-memory.db（通常為 ~/.dsh/graph-memory/graph-memory.db）。資料預設保留在使用者本機 profile，API key 來自東道主憑證或環境變數，不會寫入資料庫。
目前有哪些限制？
目前版本為 1.6.0-beta.1，且 DeepSeek Harness 仍處於 Developer Preview，可能出現破壞相容性的變更；DSH 端暫未提供 gm_update 與 gm_maintain，Pro 視覺化圖譜工作台也尚未發布。
07相關的 DSH 工作流程
weknora
作者 tencent
開源的 LLM 知識庫平臺：把原始文件轉化為可檢索的 RAG 知識庫、自主推理智慧體和自動維護的 Wiki 系統，支援多租戶部署。
honcho
作者 plastic-labs
用於構建有狀態 AI 智慧體的記憶庫，支援跨會話連續學習與上下文工程。
mirage
作者 strukto-ai
全球首個面向 AI 智慧體的統一虛擬檔案系統。
reme
作者 agentscope-ai
ReMe：面向 AI Agent 的記憶管理套件——幫 Agent「記住我、最佳化我」，支援 RAG 與長期記憶。
08資料與來源
Loaded by the DSH/Cordis plugin lifecycle, not simulated through an MCP side channel.
Integrates Session, Tool, Agent Loop, Prompt Assembly, LLM, and Credentials seams.
此頁面根據專案公開文件、儲存庫中繼資料與 DSH Plugins 的結構化解析所產生；最後核實於 2026-08-21。發現錯誤？提交更正。
最佳 DeepSeek Harness 外掛
從全目錄挑出的 12 個值得優先安裝的外掛，涵蓋各個分類。