MIT-licensed Agent OS for replayable AI coding workflows — interview, Seed, execute, evaluate, evolve. Connects to DeepSeek Harness both ways: run `ooo` tools natively inside dsh via the dsh-ouroboros plugin, or drive the pipeline with `--llm-backend dsh`.
DSH integration
Native runtime
Author-claimed
Safety audit
Unaudited
Last verified
2026-08-21
License
MIT
01What can it help you accomplish?
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
02How to install into DeepSeek Harness
Prerequisites
- 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
Installation steps
- 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
Verify the integration
Not specified by the author
Rollback
- Run `ouroboros uninstall` — removes all configuration, MCP registration, and data (details in UNINSTALL.md)
03DSH integration and capability boundaries
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
04Who is it for? When not to use it?
Good for
- 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
Not for
- 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.
05Compatibility, maintenance and safety notes
- 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)
06Frequently asked questions
How do I use Ouroboros inside DeepSeek Harness?
Install the dsh-ouroboros plugin with `dsh plugin --profile <your-profile> add "github:Q00/ouroboros#main&path:integrations/dsh-plugin"`, then type `ooo interview` / `ooo auto` directly in dsh chat — the same `ouroboros_interview` / `ouroboros_auto` tools run natively inside.
Is the DeepSeek Harness integration native or MCP?
Both directions exist. Inside dsh, the plugin's tools run natively — driven turn by turn as `mcp__ouroboros__ouroboros_interview` in dsh chat. The other way, Ouroboros can drive DeepSeek Harness's ACP server by pointing its pipeline at DeepSeek models with `--llm-backend dsh` or `OUROBOROS_LLM_BACKEND=dsh`.
What do I need before installing?
DeepSeek Harness (dsh) for the plugin direction. For pip/uv installs, Python >= 3.12. The one-command installer auto-detects available runtimes; if you install as an MCP server, use version 0.51.1 or later.
What limits should I know about?
Never mix `[mcp]` with `[claude]`/`[all]` extras in one interpreter, and host registration needs `uvx --isolated` or `pipx`. In the workflow itself, an ambiguity score above 0.2 blocks Seed generation unless you explicitly pass `force`.
Where do my data and model calls go?
Ouroboros is a local-first runtime: execution events are persisted locally via event sourcing (SQLAlchemy + aiosqlite). Model calls go to the backend you configure — with `--llm-backend dsh`, that's DeepSeek's own models via DeepSeek Harness.
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08Data and sources
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…
This page is generated from the project’s public documentation, repository metadata and a structured parse of DSH Plugins; last verified on 2026-08-21. Found an error? Submit a correction.
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