Apache-2.0 本地優先、Markdown 原生的代理記憶執行環境；DeepSeek Harness 透過本地 HTTP API（/api/v2）或 MCP 橋接接入，取得持久長期記憶。
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
Apache-2.0
01它能幫你完成什麼？
Give DeepSeek Harness sessions persistent long-term memory
Durable memories stored as readable Markdown under ~/.everos, with local SQLite and LanceDB indexes and keyword search recall via the local HTTP API
Developers running DeepSeek Harness (dsh) or other coding agents who want cross-session context that survives restarts
Try the memory ingest -> extract -> index -> recall loop in minutes
A running local server with one-key setup: memory add, flush, Markdown persistence, cascade indexing, and keyword search
Builders evaluating agent memory options who want a no-infrastructure trial before committing
Ingest multimodal content into agent memory
Image, PDF, audio, and office-file ingestion through /api/v2/memory/add via the optional multimodal extra
Teams whose agent context includes documents, screenshots, and audio rather than plain text
02如何將外掛接入 DeepSeek Harness？
先決條件
- Python 3.12+
- One OpenRouter API key (https://openrouter.ai/keys)
安裝步驟
- 01
Install the package: `uv pip install everos` (or `pip install everos`)
- 02
Run `everos init` — this creates `~/.everos/everos.toml` and `~/.everos/ome.toml`; replace the empty `api_key` in `~/.everos/everos.toml` with your OpenRouter key (use `everos init --root <path>` for a different memory root)
- 03
Start the server: `everos server start`
- 04
Add memories and search them via the HTTP API, e.g. POST /api/v2/memory/add, /api/v2/memory/flush, /api/v2/memory/search (use `"method": "keyword"` in the one-key setup)
- 05
Optional: `uv pip install 'everos[multimodal]'` for image / PDF / audio / office-file ingestion
驗證整合成功
- Open a second terminal and run `curl http://127.0.0.1:8000/health` — look for `"status":"ok"`; with the one-key setup `capabilities.llm` is `true` while embedding and rerank remain `false`
- Add a tiny conversation via /api/v2/memory/add, flush it, then search it back via /api/v2/memory/search — you should see the stored memory in the response
03DSH 整合程度與能力邊界
Local-first memory server that DeepSeek Harness can call through the local HTTP API (/api/v2/memory/add, /flush, /search) or community MCP bridges; the README documents EverOS as a universal long-term memory layer for AI coding assistants
Markdown-native memory persistence
conversations, files, and agent trajectories→canonical readable/editable/diffable .md files under the memory root (~/.everos), Git-versionable
writes Markdown files and syncs local SQLite and LanceDB indexes on diskcascade watcher re-syncs indexes when you edit .md files directlyLocal HTTP memory API with orthogonal retrieval
memory add / flush / search requests with user_id, agent_id, app_id, project_id, session_id→keyword (Tier 1) or hybrid search results from the local index; business endpoints live under /api/v2
starts a local server on 127.0.0.1:8000sends prompts to the configured OpenRouter endpoint when the llm capability is enabledTiered capability upgrades (embedding / rerank / multimodal)
optional [embedding], [rerank], [multimodal] sections in everos.toml→hybrid search, reflection and skill extraction; agentic search and Knowledge Wiki; image / PDF / audio / office ingestion
extra providers mean additional API calls and costsmultimodal extra pulls in everalgo-parser; office documents additionally require LibreOffice as a system dependencyOffline reflection (self-evolving memory)
accumulated episode clusters between sessions→merged episodes and refined user profiles and agent skills without online traffic
requires an embedding provider to be configured; mutates stored memory state during consolidation
04適合誰？何時不該用？
適合
- Developers running DeepSeek Harness (dsh) or other coding agents who want cross-session context that survives restarts
- Builders evaluating agent memory options who want a no-infrastructure trial before committing
- Teams whose agent context includes documents, screenshots, and audio rather than plain text
不適合
- The one-key OpenRouter setup is Tier 1 only (memory add/flush, Markdown persistence, cascade indexing, keyword search). Hybrid search defaults require an embedding provider, and unconfigured features return HTTP 422.
05相容性、維護與安全提醒
- The one-key OpenRouter setup is Tier 1 only (memory add/flush, Markdown persistence, cascade indexing, keyword search). Hybrid search defaults require an embedding provider, and unconfigured features return HTTP 422.
- Office document ingestion (.doc/.docx/.ppt/.pptx/.xls/.xlsx) requires LibreOffice installed on the host; without it, office uploads return HTTP 415 (PDF/image/audio/HTML/email parsing is unaffected).
- Core flows require Python 3.12+ and a paid OpenRouter API key stored in ~/.everos/everos.toml; the README does not document a fully offline (key-less) production mode beyond the educational demo.
Apache-2.0 · actively maintained (latest release v1.2.3, 2026-08-07)
06常見問題
DeepSeek Harness 如何接入 EverOS？
將 EverOS 作為本機記憶伺服器執行（everos server start），在 dsh 工作流程中呼叫其 HTTP API：/api/v2/memory/add、/api/v2/memory/flush、/api/v2/memory/search。README 將 EverOS 定位為 AI 編碼助手的通用長期記憶層，用例區也提供多個編碼 CLI 整合範例可參考。
安裝前需要準備什麼？
Python 3.12+ 和一個 OpenRouter API 金鑰。如果只想先體驗擷取→抽取→索引→召回的完整流程，可直接執行 everos demo，不需要任何金鑰。
記憶資料存在哪裡？
全部存在本機 ~/.everos 目錄（也可用 everos init --root <path> 指定其他路徑），以可讀、可編輯的 Markdown 檔案持久化，並用本地 SQLite 與 LanceDB 建立索引，不需要 MongoDB、Elasticsearch 或 Redis。
單金鑰（Tier 1）模式有什麼限制？
僅涵蓋伺服器啟動、記憶寫入與 flush、Markdown 持久化、級聯索引與關鍵字檢索；混合檢索、反思與知識 Wiki 需另外設定 embedding/rerank 供應商。缺少的能力會在 /health 回報，未設定的功能呼叫會回傳 HTTP 422。
可以擷取文件與圖片嗎？
可以。安裝選配的 everos[multimodal] 擴充後支援圖片、PDF、音訊、HTML、郵件與 Office 文件；其中 Office 格式還需在主機安裝 LibreOffice，否則會回傳 HTTP 415。
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08資料與來源
Universal long-term memory layer for AI coding assistants, powered by EverOS.
EverOS is a Python library and local-first memory runtime for agents and makers. It gives one portable memory layer acro…
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。