MIT 授權的本地優先 agent memory 中樞 + 本地 Agent 執行環境：透過 `memmy-memory init` 安裝的 memory Skills 與本地記憶 API（127.0.0.1:18960），讓 DeepSeek Harness 類編碼 Agent 共享同一套長期記憶，支援桌面應用程式、CLI/TUI 與 BYOK。
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
MIT
01它能幫你完成什麼？
Give every coding agent one shared, persistent agent memory so project context carries over between agents and sessions
A local-first memory layer that keeps project goals, decisions, constraints, and failed attempts, then brings the relevant context to the next Agent — no need to re-introduce anything again
Developers running DeepSeek Harness-style coding agents (Codex, Claude Code, Cursor, OpenClaw) whose agents keep losing context between sessions
Convert months of existing agent history into searchable long-term memory within minutes
Automatic scan of Cursor, Claude Code, Codex, OpenCode, OpenClaw, Hermes, WorkBuddy, Pi and qwenwork histories, distilled into personal long-term memory plus a personalized "First Meeting Report"
Teams and individuals with months of conversations in other agents who want that context to become a reusable knowledge asset instead of starting from scratch
Wire memory search and writes into external agents and scripts
`memmy-memory` CLI (init / health / search / add / get) against the local memory service at http://127.0.0.1:18960, with memory Skills installed into each supported agent as needed
Developers who want agents or shell scripts to query and append shared memory programmatically, including coding agents like DeepSeek Harness via installed Skills
02如何將外掛接入 DeepSeek Harness？
先決條件
- Memmy desktop app or CLIs, downloaded from the official website (https://memmy.bot/) or GitHub Releases
- An account (sign-up grants Agent task trial tokens) or BYOK: your own model API key configured in ~/.memmy/config.yaml
- Only for building from source: Node.js >= 22 and npm
安裝步驟
- 01
Get Memmy from the official website (https://memmy.bot/) or GitHub Releases, launch the desktop app and choose Account mode or API Key mode
- 02
In API Key mode, configure the primary model and pass a connection test; optionally configure Embedding, ASR, image generation, memory summary, and skill evolution models
- 03
Enter the main workbench and send your first task; open "Tools" to connect messaging channels or third-party tools, open "Memory" to scan Agent history sources
- 04
To give external agents memory access, run `memmy-memory init` — it writes the Memory config and installs Skills for each Agent as needed
驗證整合成功
- Run `memmy status` to check config, workspace, model, and provider status
- Run `memmy-memory health` to check the memory service
03DSH 整合程度與能力邊界
Local agent memory hub whose `memmy-memory init` CLI writes the Memory config and installs memory Skills into external agents, letting DeepSeek Harness-style coding agents read/write one shared long-term memory via the local memory service; the repo is tagged `dsh-plugin`, though the README documents no dsh-specific flow
Cross-Agent shared long-term memory
project goals, decisions, constraints, failed attempts and agent conversations→one shared memory layer reused across Codex, Claude Code, Cursor and OpenClaw without re-introducing context
memory, configuration and app state are written to your machine by default (~/.memmy)history onboarding reads the local history files of your existing agentsMemOS-powered memory engine with history onboarding
scattered conversations and behavior from supported agents (Cursor, Claude Code, Codex, OpenCode, OpenClaw, Hermes, WorkBuddy, Pi, qwenwork)→structured, searchable, reusable long-term memory plus a personalized "First Meeting Report"
scanning imports and stores other agents' history into Memmy's local memoryLocal Agent runtime with multiple entry points
tasks issued from the desktop app, `memmy` CLI/TUI, or any OpenAI-compatible client→the same Agents, memory, and configuration served from every entry point; `memmy serve` exposes an OpenAI-compatible API on :18990
`memmy serve` opens a local API on port 18990; the memory service listens on 127.0.0.1:18960Extensible tools via Skills and MCP
Skills, MCP servers, messaging channels (Telegram, Discord, WeChat, Feishu, DingTalk) and productivity tools (GitHub, Gmail, Notion, Slack, Jira)→agents that go from conversation to real task execution, including managed Chromium browser tools for local page inspection
the desktop app and scripts/dev-start.sh prepare a managed Chromium build before the Agent Gateway startsconnected third-party tools and channels are invoked over the network under your credentials
04適合誰？何時不該用？
適合
- Developers running DeepSeek Harness-style coding agents (Codex, Claude Code, Cursor, OpenClaw) whose agents keep losing context between sessions
- Teams and individuals with months of conversations in other agents who want that context to become a reusable knowledge asset instead of starting from scratch
- Developers who want agents or shell scripts to query and append shared memory programmatically, including coding agents like DeepSeek Harness via installed Skills
05相容性、維護與安全提醒
- Account mode grants Agent task trial tokens whose amount and usage are shown in the app; once the trial credits run out or expire, continued agent tasks require switching to BYOK with your own model API key.
- Building from source requires Node.js >= 22 and npm, and on Windows scripts/dev-start.sh must be run in Git Bash.
- The README documents memory-Skill installation for external agents generically and the repo is tagged `dsh-plugin`, but no DeepSeek Harness-specific setup is documented — usage inside dsh is inferred via the installed memory Skills / MCP ecosystem.
MIT · actively maintained (latest release v1.0.9, 2026-08-20)
06常見問題
Memmy 如何接入 DeepSeek Harness 這類編碼 Agent？
執行 `memmy-memory init`，它會寫入 Memory 設定並視需要為各個 Agent 安裝 memory Skills，之後這些 Agent 透過本地記憶服務（預設 http://127.0.0.1:18960）讀寫同一套記憶。README 未提供 dsh 專屬步驟——儲存庫標示了 `dsh-plugin` 主題，接入走的是通用的 Skills / MCP 生態。
記憶資料存在哪裡？會上傳雲端嗎？
Memmy 採本地優先架構：記憶、設定與應用程式狀態預設保存在你的機器上，無需上傳雲端。記憶服務監聽 127.0.0.1:18960，並提供受控存取機制，只有授權來源才能呼叫記憶能力。
需要自己準備 API Key 嗎？
帳號模式註冊即送 Agent 任務體驗額度（目前額度與用量以應用程式內顯示為準）。額度用完或過期後，切換到 API Key（BYOK）模式，在 ~/.memmy/config.yaml 中設定自己的模型服務商即可。
支援匯入哪些 Agent 的歷史紀錄？
目前支援 Cursor、Claude Code、Codex、OpenCode、OpenClaw、Hermes、WorkBuddy、Pi 和 qwenwork。幾分鐘內即可把歷史對話轉換為長期記憶，並產生個人化的「初次見面報告」。
Memmy 有哪些使用入口？
桌面應用程式、`memmy` CLI/TUI，以及透過 `memmy serve` 啟動的 OpenAI 相容 API（連接埠 :18990）——三個入口共享同一套 Agent、記憶與設定。
07相關的 DSH 工作流程
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yao
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在桌面、移動端、瀏覽器或 API 上統一管理你的智慧體與工作區，看板式任務追蹤，支援自託管。
08資料與來源
memmy-memory init # Write the Memory config and install Skills for each Agent as needed
Connect more tools through Skills and MCP, taking the Agent from conversation to real task execution.
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。