專有 EULA（私有 Beta）。為 AI 編碼 Agent 打造結構化 Agent Memory：雙時態知識圖譜，以 DeepSeek Harness 外掛形式經 dsh CLI 安裝，本機索引、25+ MCP 工具、17 個技能、零 LLM 呼叫。
DSH 整合
相容
作者聲明
安全稽核
未稽核
最後核實
2026-08-21
授權條款
NOASSERTION
01它能幫你完成什麼？
Give DeepSeek Harness agents persistent structural agent memory of the codebase across sessions
A live bi-temporal knowledge graph of every function, class, call edge and version, queryable in milliseconds via 25+ MCP tools and 17 agent skills
Developers running coding agents on DeepSeek Harness who want shared, replay-aware code context without agents re-reading files every session
Assess blast radius and replay refactors with full causal awareness before changing code
Impact analysis with risk rating (`get_impact`), diff-to-symbol scope mapping (`detect_changes`), and six-mode temporal evolution queries
Engineers doing refactors, incident investigation or code review on large repos who need to know what breaks before they change it
Index a large codebase locally with zero LLM calls and zero API cost
A 50k-file repo indexed in under 90 seconds by Rust + Tree-sitter parsers — 20+ languages plus framework-aware scanners, fully local
Teams with large monorepos or strict privacy requirements who can't send source code through LLM APIs
02如何將外掛接入 DeepSeek Harness？
先決條件
- Node.js ≥ 18 (README requirements table)
- DeepSeek Harness CLI — `@deepseek-ai/dsh`, which provides the `dsh` command (installed globally or run via npx)
- Git repository history available — required for temporal analysis
- Private beta access — Memtrace rolls out access in batches via the waitlist at memtrace.io
安裝步驟
- 01
Install DeepSeek Harness: `npm install -g @deepseek-ai/dsh`
$ npm install -g @deepseek-ai/dsh
- 02
Add the Memtrace plugin: `dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace` (or the npx variant without a global CLI)
$ dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace
- 03
Optional: pin a local binary with `npm install -g memtrace` and `MEMTRACE_BIN=memtrace` so the first launch doesn't fetch it via npx
$ npm install -g memtrace
- 04
Ask the agent to index the workspace, then pull blast radius, evolution, or an architecture briefing
驗證整合成功
作者未說明
復原
- `memtrace uninstall` — removes skills, MCP server, plugin, settings
- `npm uninstall -g memtrace`; if npm uninstall already ran, the cleanup script is at `~/.memtrace/uninstall.js`
03DSH 整合程度與能力邊界
Installed as a DeepSeek Harness plugin via the dsh CLI (`dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace`); the bundle registers Memtrace's skills and starts `memtrace mcp` inside the Harness profile.
Structural local indexing
any codebase — 20+ programming languages plus YAML / HCL / JSON / TOML / SQL and framework-aware scanners (Express, NestJS, FastAPI, Django, GitHub Actions, Terraform, …)→a live knowledge graph with symbols as nodes (functions, classes, interfaces, types, endpoints) and CALLS / IMPLEMENTS / IMPORTS / EXPORTS / CONTAINS edges, built deterministically with Rust + Tree-sitter — zero LLM calls
writes a local graph index / MemDB on disk; the first index is CPU/RAM intensiveBi-temporal engine
the indexed repo plus its Git history→time-travel queries via six scoring algorithms (impact, novelty, recency, directional, compound, overview) — every symbol carries its full version history
25+ MCP tools + 17 agent skills
natural-language requests from the agent (find / who-calls / what-changed / blast-radius / architecture)→hybrid BM25 + semantic search, relationship analysis, graph algorithms (PageRank, Louvain communities), Cypher queries — skills fire automatically based on what you ask
the DSH plugin bundle starts a local `memtrace mcp` server and registers skill files inside the Harness profileImpact analysis & cross-repo API topology
a symbol, diff, or service boundary→blast radius with risk rating, dead-code detection, complexity hotspots, and the HTTP call graph between repositories
network traffic limited to license validation, aggregate node/edge counts and opt-out crash telemetry — no source code, file paths or symbol names
04適合誰？何時不該用？
適合
- Developers running coding agents on DeepSeek Harness who want shared, replay-aware code context without agents re-reading files every session
- Engineers doing refactors, incident investigation or code review on large repos who need to know what breaks before they change it
- Teams with large monorepos or strict privacy requirements who can't send source code through LLM APIs
不適合
- Memtrace is in private beta — access is rolled out in batches via the waitlist at memtrace.io, so new users may need to wait for a cohort before they can use it.
- Proprietary EULA (GitHub reports NOASSERTION): the indexer and MemDB database are closed-source; free for individual developers during beta and after GA.
- The first index is CPU/RAM intensive — minimum 4 cores, 8 GB RAM, 5 GB disk and Node.js ≥ 18; 8+ cores and 16–32 GB RAM recommended for large monorepos.
05相容性、維護與安全提醒
- Memtrace is in private beta — access is rolled out in batches via the waitlist at memtrace.io, so new users may need to wait for a cohort before they can use it.
- Proprietary EULA (GitHub reports NOASSERTION): the indexer and MemDB database are closed-source; free for individual developers during beta and after GA.
- The first index is CPU/RAM intensive — minimum 4 cores, 8 GB RAM, 5 GB disk and Node.js ≥ 18; 8+ cores and 16–32 GB RAM recommended for large monorepos.
Proprietary EULA per README (GitHub reports NOASSERTION) · actively maintained (latest release v1.1.5, 2026-08-20)
06常見問題
Memtrace 如何整合 DeepSeek Harness？
先安裝 Harness CLI（npm install -g @deepseek-ai/dsh），再執行 dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace。此外掛會註冊 Memtrace 的技能，並在 Harness profile 內啟動 memtrace mcp。
是原生執行還是 MCP？
以 DSH 外掛形式安裝：外掛套件會在 Harness profile 中啟動本機 memtrace mcp 服務並註冊 17 個 Agent 技能，技能依你的提問自動觸發，不需手動撰寫提示詞。
安裝需要哪些前置條件？
Node.js ≥ 18 與 DeepSeek Harness CLI；時序分析還需要 Git 儲存庫歷史。另外 Memtrace 目前為私有 Beta，可能需要先在 memtrace.io 排隊取得存取權。
程式碼會上傳到雲端嗎？
不會。索引完全在本機完成，唯一的網路流量是授權驗證、節點／邊數量的彙總統計與可關閉的崩潰遙測，不含原始碼、檔案路徑或符號名稱；用 MEMTRACE_TELEMETRY=off 可關閉遙測。
對機器資源有什麼要求？
首次索引較耗 CPU/記憶體：最低 4 核、8 GB 記憶體、5 GB 磁碟；大型 monorepo 建議 8 核以上、16–32 GB 記憶體。後續查詢與增量重建索引會輕量許多。
07相關的 DSH 工作流程
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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
Memtrace runs as a [DeepSeek Harness](https://github.com/deepseek-ai/deepseek-harness) plugin.
npx -y @deepseek-ai/dsh plugin --profile web add github:syncable-dev/dsh-plugin-memtrace
That bundle registers Memtrace's skills and starts `memtrace mcp` inside the Harness profile. First launch may fetch the…
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
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