Name: ouroboros
Author: Q00

Turn a vague idea into a verified, working codebase -- across Claude Code, Codex CLI, OpenCode, and Hermes.

Ouroboros is an Agent OS for AI coding: a local-first runtime layer that turns non-deterministic agent work into a replayable, observable, policy-bound execution contract. It replaces ad-hoc prompting with a structured specification-first workflow: interview, crystallize, execute, evaluate, evolve.

The Ouroboros Agent OS Stack

Like any OS, Ouroboros is split into a stable OS layer of primitives, an application layer of domain workflows, and a shell that humans actually sit in front of. Three repos, one stack:

How they connect:

  ourocode  ──►  ooo / ouroboros-plugins  ──►  ouroboros core (Seed · Ledger · MCP · Runtime)
   shell             user-level apps                        kernel

The kernel (ouroboros) owns the contract: every action becomes a Seed-bound, ledger-recorded, replayable event — regardless of which LLM executes it.
Plugins (ouroboros-plugins) declare scoped capabilities against that contract, so domain workflows (review a PR, triage a Linear ticket, run a release) stay auditable and policy-bound instead of being one-off prompts.
Ourocode is the terminal shell: it surfaces MCP state, interview questions, and wonderTool decisions as first-class TUI elements, so you can drive the OS without leaving the keyboard or switching between CLIs.

Use ouroboros alone with any supported CLI, layer plugins on for domain workflows, or install ourocode when you want a unified terminal cockpit.

Disclaimer. The Ouroboros project and community are not affiliated with any cryptocurrency, token, memecoin, or trading community — including, but not limited to, any "ouroboros" tickers on pump.fun or other launchpads. This is an open-source developer tool. We do not issue, endorse, or hold any coins. Any token claiming association with this project is unauthorized.

Why Ouroboros?

Most AI coding fails at the input, not the output. The bottleneck is not AI capability -- it is human clarity.

Quick Start

Install — one command, everything auto-detected:

curl -fsSL https://raw.githubusercontent.com/Q00/ouroboros/main/scripts/install.sh | bash

Build — open your AI coding agent and go:

> ooo interview "I want to build a task management CLI"

Works with Claude Code, Codex CLI, GitHub Copilot CLI, OpenCode, Hermes, Gemini, and Kiro CLI. The installer detects Claude Code, Codex CLI, and Hermes CLI automatically and registers the MCP server. For OpenCode, Kiro, or GitHub Copilot CLI, run ouroboros setup --runtime <opencode|kiro|copilot> after installation. The Copilot CLI runtime live-discovers its model catalog via the GitHub Copilot models API and lets you pick a default during setup.

pip install 'ouroboros-ai[claude]'
ouroboros setup            # detects Kiro CLI and registers MCP server

Set runtime in .env:

OUROBOROS_RUNTIME=kiro

Then use ooo commands inside a Kiro CLI session.

gh auth login                                # one-time GitHub auth (used for live model discovery)
pipx install 'ouroboros-ai[mcp]'             # or: uv tool install 'ouroboros-ai[mcp]'
ouroboros setup --runtime copilot            # discovers models live, picks a default,
                                             # registers MCP server in ~/.copilot/mcp-config.json

Restart your Copilot CLI session, then use ooo commands inside it. Hyphenated Anthropic model IDs (claude-opus-4-6) used elsewhere in your config are auto-mapped to the dotted Copilot form (claude-opus-4.6) at runtime, so existing configs keep working when you switch backends.

See the GitHub Copilot CLI runtime guide for full details.

Claude Code plugin only (no system package):

claude plugin marketplace add Q00/ouroboros && claude plugin install ouroboros@ouroboros

Then run ooo setup inside a Claude Code session.

pip / uv / pipx:

pip install ouroboros-ai                # base
pip install ouroboros-ai[claude]        # + Claude Code deps
pip install ouroboros-ai[litellm]       # + LiteLLM multi-provider
pip install ouroboros-ai[mcp]           # + MCP server/client support
pip install ouroboros-ai[tui]           # + Textual terminal UI
pip install ouroboros-ai[all]           # everything (claude + litellm + mcp + tui)
ouroboros setup                         # configure runtime

Legacy compatibility: ouroboros-ai[dashboard] is still accepted as a compatibility alias/no-op; it does not install dashboard runtime payload. ouroboros-ai[all] includes that no-op alias only for compatibility.

See runtime guides: Claude Code · Codex CLI · Hermes · OpenCode · Kiro CLI · Gemini CLI · GitHub Copilot CLI

ouroboros uninstall

Removes all configuration, MCP registration, and data. See UNINSTALL.md for details.

Python >= 3.12 required. See pyproject.toml for the full dependency list.

What You Get

After one loop of the Ouroboros cycle, a vague idea becomes a verified codebase:

interview  ->  Socratic questioning exposed 12 hidden assumptions
seed       ->  Crystallized answers into an immutable spec (Ambiguity: 0.15)
run        ->  Executed via Double Diamond decomposition
evaluate   ->  3-stage verification: Mechanical -> Semantic -> Consensus

Use ooo <cmd> inside your AI coding agent session, or ouroboros init start, ouroboros run seed.yaml, etc. from the terminal.

The serpent completed one loop. Each loop, it knows more than the last.

How It Compares

AI coding tools are powerful -- but they solve the wrong problem when the input is unclear.

| | Vanilla AI Coding | Ouroboros | | :------------------ | :--------------------------------------- | :------------------------------------------------------------------------------ | | **Vague promp

ouroboros

Related Skills

The Ouroboros Agent OS Stack

Why Ouroboros?

Quick Start

What You Get

How It Compares