AGPL-3.0 自進化上下文資料庫，整合代理記憶、知識 RAG 與技能；透過 MCP 用戶端／Agent Plugins 通用整合接入 DeepSeek Harness，無原生 dsh 模式。
DSH 整合
生態系相關
作者聲明
安全稽核
未稽核
最後核實
2026-08-21
授權條款
AGPL-3.0
01它能幫你完成什麼？
Give an AI agent persistent memory, knowledge and skills in one browsable context database
Memories, resources and skills unified under the `viking://` protocol, browsable with ls/tree/find, L0/L1/L2 tiered on-demand loading, and observable retrieval trajectories
Developers building stateful AI agents who want deterministic, file-like access to agent memory instead of a black-box vector store
Wire long-term memory into a coding agent such as Claude Code, Codex, Cursor, MCP clients or LangChain
OpenViking recall injected into the agent's context and session memory auto-committed after each session
Coding-agent users who want cross-session memory and recall without manual note-taking
Run a ready-made AI agent on top of the context database
VikingBot agent started alongside the server via `openviking-server --with-bot`, chatted with via `ov chat`
Users who want an out-of-the-box agent built on OpenViking instead of integrating their own
02如何將外掛接入 DeepSeek Harness？
先決條件
- Python 3.10 or higher
- A model provider to configure during init — Volcengine, OpenAI, Codex OAuth, Kimi, GLM, or local Ollama
安裝步驟
- 01
`pip install openviking --upgrade`
- 02
`openviking-server init` — interactive wizard: providers, models, writes `~/.openviking/ov.conf`
- 03
`openviking-server doctor` — validate setup
- 04
`openviking-server` — start the server (background: `nohup openviking-server > openviking.log 2>&1 &`)
驗證整合成功
- Run `openviking-server doctor` — checks the config file, Python version, provider connectivity, and disk space without a running server
- With the server running, the bundled `ov` client CLI works, e.g. `ov status`
03DSH 整合程度與能力邊界
No DeepSeek Harness-specific integration is documented in the README; OpenViking runs as a standalone context/memory server that agents reach through its generic integrations (MCP clients, Agent Plugins 1.0)
viking:// virtual filesystem for all context
memories, resources (docs, repos, web pages) and skills→one `viking://` URI tree browsed deterministically via `ov ls` / `tree` / `find` / `grep`
L0/L1/L2 tiered on-demand loading
any entry written into OpenViking→L0 abstract (~100 tokens), L1 overview (~2k tokens), L2 details — loaded only as deep as the task requires, cutting token spend
every entry is processed into three tiers on write (asynchronous semantic processing)Sessions become long-term memory
committed agent sessions→user preferences and agent experience extracted into long-term memory
after a session commits, OpenViking asynchronously extracts memory in the backgroundAgent integrations & ov CLI
Claude Code, Codex, OpenClaw, Hermes, Cursor, TRAE, OpenCode, MCP clients, LangChain/LangGraph and more→OpenViking recall injected into the agent's context with auto-committed session memory
`openviking-server init` writes `~/.openviking/ov.conf`; integrations modify the target agent's configuration
04適合誰？何時不該用？
適合
- Developers building stateful AI agents who want deterministic, file-like access to agent memory instead of a black-box vector store
- Coding-agent users who want cross-session memory and recall without manual note-taking
- Users who want an out-of-the-box agent built on OpenViking instead of integrating their own
不適合
- The server requires Python 3.10 or higher, plus a configured model provider (Volcengine, OpenAI, Codex OAuth, Kimi, GLM, or local Ollama) before it can serve context.
- The README documents no DeepSeek Harness (dsh)-specific integration; DSH users must rely on the generic integration lanes it lists (MCP clients, Agent Plugins 1.0), so a working dsh hookup is not guaranteed by the README alone.
- The project self-describes as early-stage, and the OpenViking Helper desktop console is still in beta for macOS and Windows x64 only.
05相容性、維護與安全提醒
- The server requires Python 3.10 or higher, plus a configured model provider (Volcengine, OpenAI, Codex OAuth, Kimi, GLM, or local Ollama) before it can serve context.
- The README documents no DeepSeek Harness (dsh)-specific integration; DSH users must rely on the generic integration lanes it lists (MCP clients, Agent Plugins 1.0), so a working dsh hookup is not guaranteed by the README alone.
- The project self-describes as early-stage, and the OpenViking Helper desktop console is still in beta for macOS and Windows x64 only.
AGPL-3.0 · actively maintained (latest release v0.4.15, 2026-08-18)
06常見問題
OpenViking 如何接入 DeepSeek Harness？
README 未提及 DeepSeek Harness 專屬整合。OpenViking 提供通用接入通道——MCP 用戶端與 Agent Plugins 1.0，另有 Claude Code、Codex、Cursor、TRAE、OpenCode 等專門指南，dsh 可走 MCP 用戶端這一通用通道連接伺服器。
是原生整合還是 MCP？
README 既未聲明原生 dsh 支援，也未提供 dsh 外掛；在其列出的整合方式中，MCP 用戶端是通用的機器對機器接入通道，具體設定見其文件站，README 本身未展開。
安裝前需要什麼條件？
Python 3.10 以上，並需在 `openviking-server init` 精靈中設定模型供應商——火山引擎、OpenAI、Codex OAuth、Kimi、GLM 或本機 Ollama（精靈可自動偵測並安裝 Ollama 執行環境）。
我的資料會存在哪裡？
自架模式下資料保存在本機伺服器，設定寫入 `~/.openviking/ov.conf`；模型呼叫發往你設定的供應商（選本機 Ollama 則完全離線）。官方也提供託管在火山引擎上的選用 SaaS 版本。
有什麼限制？
專案自述仍處於早期階段；OpenViking Helper 桌面主控台僅以 Beta 形式提供 macOS 與 Windows x64 版本；正式環境使用建議依獨立 HTTP 服務方式部署。
07相關的 DSH 工作流程
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Harness 工程新手教程，從 0 到 1 系統學習智慧體工作流框架。
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收錄最全、更新最快的技能Skills商店：精選原創技能包（涵蓋文件處理、內容創作、程式設計開發、機器學習、自動化工作流），全部打包好可直接安裝使用！同時自動抓取GitHub上萬個Skills專案，按分類、更新時間、Star數量整理。The most comprehensive and frequently updated AI Agent skill library, featuring curated skill packs across document processing, content creation, programming, machine learning, automated workflows, and many more domains.
08資料與來源
Integrations inject OpenViking recall into your agent's context and auto-commit session memory:
- [MCP clients](https://docs.openviking.ai/en/agent-integrations/06-mcp-clients)
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。