MIT 许可的 Agent OS，把访谈 → Seed → 执行 → 评估 → 进化的可回放 AI 编码工作流接入 DeepSeek Harness：通过 dsh-ouroboros 插件在 dsh 内原生运行 `ooo` 工具，或用 `--llm-backend dsh` 调用 DeepSeek 模型。
DSH 适配
原生运行时
作者声明
安全审计
未审计
最后核验
2026-08-21
许可证
MIT
01它能帮你完成什么？
Turn a vague idea into a verified, working codebase with a spec-first workflow
Immutable Seed specification (ambiguity scored, ≤ 0.2 gate), Double Diamond execution, and code verified by a 3-stage evaluation gate: Mechanical → Semantic → Multi-Model Consensus
Developers and teams using AI coding agents — including DeepSeek Harness — who want verified results instead of rework from vague prompts
Run interview / auto workflows natively inside DeepSeek Harness chat
The same `ouroboros_interview` / `ouroboros_auto` tools driven turn by turn in dsh, with advisory fan-out results submitted between rounds
DeepSeek Harness (dsh) users who want structured, policy-bound AI agent automation without leaving dsh chat
Automate multi-stage verification instead of manual QA
3-stage automated evaluation gate — Mechanical (free) → Semantic → Multi-Model Consensus — whose output feeds an evolutionary loop until ontology convergence
Engineering teams that need replayable, observable verification of agent-generated code rather than "looks good" reviews
02如何接入 DeepSeek Harness？
前置条件
- DeepSeek Harness (dsh) with a profile, for the dsh-ouroboros plugin direction
- Python >= 3.12 for pip/uv-based installs (LiteLLM-bearing profiles support 3.12–3.13); the one-command installer auto-detects available runtimes
安装步骤
- 01
Install Ouroboros with one command: `curl -fsSL https://raw.githubusercontent.com/Q00/ouroboros/main/scripts/install.sh | OUROBOROS_INSTALL_REF=readme bash` (alternatives: `pipx install 'ouroboros-ai[mcp]'` or Homebrew `brew tap q00/tap && brew install ouroboros-ai`)
$ curl -fsSL https://raw.githubusercontent.com/Q00/ouroboros/main/scripts/install.sh | OUROBOROS_INSTALL_REF=readme bash
- 02
Run `ooo setup` once inside your coding agent — a one-time configuration step (`ouroboros setup` from a plain terminal)
- 03
Add the plugin to DeepSeek Harness: `dsh plugin --profile <your-profile> add "github:Q00/ouroboros#main&path:integrations/dsh-plugin"`
$ dsh plugin --profile <your-profile> add "github:Q00/ouroboros#main&path:integrations/dsh-plugin"
- 04
Type `ooo interview` / `ooo auto` directly in the DeepSeek Harness chat
验证接入成功
作者未说明
回滚
- Run `ouroboros uninstall` — removes all configuration, MCP registration, and data (details in UNINSTALL.md)
03DSH 适配与能力边界
Official dsh-ouroboros plugin installed via `dsh plugin add` — `ooo interview` / `ooo auto` tools run natively inside DeepSeek Harness chat; also works in reverse via `--llm-backend dsh`, driving Harness's ACP server
Socratic interview + ambiguity-gated Seed
a vague idea or task description (`ooo interview` in an agent session, or `ouroboros init start` from the terminal)→hidden assumptions exposed and an immutable Seed specification; ambiguity must score ≤ 0.2 before Seed generation (or an explicit `force`)
3-stage evaluation gate & evolutionary loop
executed codebase produced from a Seed→Mechanical (free) → Semantic → Multi-Model Consensus verdicts; evaluation output feeds the next generation until ontology similarity >= 0.95
LLM calls to the configured model backend for the semantic and consensus stagesPersistent loop with replayable event sourcing (`ooo ralph`)
a Seed lineage to evolve→persistent, stateless evolution across session boundaries until convergence; the EventStore reconstructs the full lineage after restarts
persists execution events locally via event sourcing (SQLAlchemy + aiosqlite)Multi-runtime MCP integration (13 hosts)
any supported coding-agent runtime: Claude Code, Codex CLI, GitHub Copilot CLI, OpenCode, Hermes, Gemini, Kiro, Pi, Zcode, Goose, GJC, Antigravity, Grok→MCP server registered where the host supports it; the same workflow spec runs across execution engines
setup writes host configuration files (e.g. ~/.kiro/settings/mcp.json, ~/.copilot/mcp-config.json) and registers the MCP server
04适合谁？何时不该用？
适合
- Developers and teams using AI coding agents — including DeepSeek Harness — who want verified results instead of rework from vague prompts
- DeepSeek Harness (dsh) users who want structured, policy-bound AI agent automation without leaving dsh chat
- Engineering teams that need replayable, observable verification of agent-generated code rather than "looks good" reviews
不适合
- Python >= 3.12 is required; LiteLLM-bearing profiles (`[litellm]`, `[all]`) only support Python 3.12–3.13.
- When installing as an MCP server, use 0.51.1 or later — earlier versions can fail at startup with `Failed to reconnect to plugin:ouroboros:ouroboros: -32000`, and downstream packages can lag PyPI.
- Never install `[mcp,claude]`, `[mcp,claude-sdk]`, or `[all,mcp]` in one interpreter; host registration requires `uvx --isolated --python '>=3.12'` or `pipx`, otherwise setup exits without changing runtime configuration.
05兼容性、维护与安全提示
- Python >= 3.12 is required; LiteLLM-bearing profiles (`[litellm]`, `[all]`) only support Python 3.12–3.13.
- When installing as an MCP server, use 0.51.1 or later — earlier versions can fail at startup with `Failed to reconnect to plugin:ouroboros:ouroboros: -32000`, and downstream packages can lag PyPI.
- Never install `[mcp,claude]`, `[mcp,claude-sdk]`, or `[all,mcp]` in one interpreter; host registration requires `uvx --isolated --python '>=3.12'` or `pipx`, otherwise setup exits without changing runtime configuration.
MIT · actively maintained (latest release v0.51.13, 2026-08-19)
06常见问题
如何在 DeepSeek Harness 里使用 Ouroboros？
运行 `dsh plugin --profile <your-profile> add "github:Q00/ouroboros#main&path:integrations/dsh-plugin"` 安装 dsh-ouroboros 插件，然后在 dsh 聊天中直接输入 `ooo interview` / `ooo auto` —— 同一套 `ouroboros_interview` / `ouroboros_auto` 工具会在 dsh 内原生运行。
DeepSeek Harness 集成是原生还是 MCP？
两个方向都支持。在 dsh 内，插件工具原生运行，以 `mcp__ouroboros__ouroboros_interview` 的形式逐轮驱动；反过来，Ouroboros 也可以用 `--llm-backend dsh` 或 `OUROBOROS_LLM_BACKEND=dsh` 把流水线指向 DeepSeek 模型，底层驱动 DeepSeek Harness 的 ACP server。
安装前需要准备什么？
走插件方向需要 DeepSeek Harness（dsh）；走 pip/uv 安装需要 Python >= 3.12。一条命令的安装器会自动检测本机已有的运行时；若作为 MCP server 安装，请使用 0.51.1 及以上版本。
有哪些需要注意的限制？
不要在同一个解释器里混装 `[mcp]` 与 `[claude]`/`[all]` extras；宿主注册需要 `uvx --isolated` 或 `pipx`。工作流层面，模糊度评分高于 0.2 会阻止 Seed 生成，除非显式传入 `force`。
我的数据和模型调用去了哪里？
Ouroboros 是 local-first 运行时：执行事件通过 event sourcing（SQLAlchemy + aiosqlite）保存在本地。模型调用发往你配置的后端——使用 `--llm-backend dsh` 时，就是经由 DeepSeek Harness 调用 DeepSeek 自家模型。
07相关的 DSH 工作流
archify
作者 tt-a1i
为编码智能体生成美观可验证的架构图、时序图与数据流图，输出自包含 HTML，支持动效与清晰导出。
openviking
作者 volcengine
为 AI 智能体打造的自进化上下文数据库，统一智能体记忆、知识 RAG 与技能。
nocobase
作者 nocobase
开源的 AI + 无代码应用搭建平台：AI 在久经生产验证的底层基础设施与所见即所得的无代码界面之上工作，帮你快速构建 CRM、ERP 等业务系统，兼顾速度与可靠性。
learn-harness-engineering
作者 walkinglabs
Harness 工程新手教程，从 0 到 1 系统学习智能体工作流框架。
08数据与来源
and type `ooo interview` / `ooo auto` directly in the DeepSeek Harness chat — the same `ouroboros_interview` / `ouroboro…
Ouroboros speaks DeepSeek two ways. Point the interview/Seed/QA pipeline at DeepSeek's own models with `--llm-backend ds…
driven from a dsh chat: <code>mcp__ouroboros__ouroboros_interview</code> turn by turn, fan-out results submitted between…
页面基于项目公开文档、仓库元数据和 DSH Plugins 的结构化解析生成；最后核验于 2026-08-21。发现错误？提交更正。
最佳 DeepSeek Harness 插件
从全目录挑出的 12 个值得优先安装的插件，覆盖各个分类。