Ouroboros

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O U R O B O R O S

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It gets smarter on its own. We just hold the line.
_{Skip the prompt engineering. The agent runs, fails, and gets smarter every generation. The grading command and expected result never make it into the success contract we hand it.
The Agent OS for replayable AI coding workflows}

Quick Start · Why · Results · How It Works · Commands · Philosophy · Guide

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

_{One command installs it. Then run ooo setup once inside your coding agent — details in Quick Start.}

_{Separate runs, separate hosts. Different tasks on purpose — the engine is what is shared, not the prompt}

Terminal CLI — a task-management CLI: ouroboros init start asking about ordering and scope, then reporting an ambiguity score	ChatGPT (Codex) — called as an integration, on a video-publishing harness: the interview, its advisory lanes, and the ambiguity ledger	Claude Code — a YouTube automation task, with the six advisory lanes running in parallel before the interview submits
Hermes (Discord) — a kart-racing game, run as a chat bot, ending at Final ambiguity: 0.15	DeepSeek Harness — an OSS-trend outreach script, driven from a dsh chat: mcp__ouroboros__ouroboros_interview turn by turn, fan-out results submitted between rounds	Kiro — recording on the way

Turn a vague idea into a verified, working codebase -- across Claude Code, Codex CLI, OpenCode, Hermes, Gemini, Kiro, Copilot, Pi, Zcode, Goose, GJC, Antigravity, and Grok.

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.

Naming note. A separate, unaffiliated open-source project also uses the
name "Ouroboros" — Anton Razzhigaev's self-modifying, autonomous-memory agent
at github.com/razzant/ouroboros. No shared code, no relationship. This
project locks a specification before executing rather than rewriting its own
architecture; if you're looking for the latter, that's the other one.

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 | OUROBOROS_INSTALL_REF=readme bash

First command — open your AI coding agent and run these in order:

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

ooo setup is a one-time configuration step. ooo interview is the first
workflow command and starts the Socratic interview. After setup, Codex follows
its currently selected model and Claude Code starts with its recommended model
settings. Choose Directly configure models only when you want to pin a
stage to a specific model; it opens the local settings screen in your browser.
You can return to those settings any time with ooo config.

Or from a plain terminal, without an agent host:

$ ouroboros init start --orchestrator "I want to build a task management CLI tool"

_{That recording is this exact command. It is at the top of this page so you can see the tool before installing it.}

_{ouroboros setup refresh on one machine. It installs into the hosts that machine actually has, each in the shape that host expects: rules and skills for Codex, skills for Hermes, a plugin and an AGENTS.md for OpenCode, bridges for Pi and GJC. Your machine will show whichever of the thirteen you have installed.}

Works with Claude Code, Codex CLI, GitHub Copilot CLI, OpenCode, Hermes, Gemini, Kiro CLI, Pi CLI, Zcode, Goose, GJC, Antigravity CLI, and Grok Build CLI. The installer detects available runtimes and registers the MCP server where the host supports it. For explicit selection, run ouroboros setup --runtime <opencode|kiro|copilot|gemini|pi|zcode|goose|gjc|antigravity|grok> 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.

DeepSeek support. Ouroboros speaks DeepSeek two ways. Point the interview/Seed/QA pipeline at DeepSeek's own models with --llm-backend dsh (ouroboros mcp serve --runtime claude-cli --llm-backend dsh, or OUROBOROS_LLM_BACKEND=dsh) — this drives DeepSeek Harness's ACP server under the hood. Or go the other way: install the dsh-ouroboros plugin (dsh plugin --profile <your-profile> add "github:Q00/ouroboros#main&path:integrations/dsh-plugin") and type ooo interview / ooo auto directly in the DeepSeek Harness chat — the same ouroboros_interview / ouroboros_auto tools run natively inside it, Socratic questions and all. Both directions, including what the dsh backend needs beyond the one variable, are in the DeepSeek Harness guide.

codex plugin marketplace add Q00/ouroboros
codex plugin add ouroboros@ouroboros

ooo setup
ooo interview "Build a task management CLI"

pipx install 'ouroboros-ai[mcp]'       # or: uv tool install 'ouroboros-ai[mcp]'
ouroboros setup --runtime kiro         # detects Kiro CLI, registers MCP server, and
                                        # writes OUROBOROS_RUNTIME=kiro into
                                        # ~/.kiro/settings/mcp.json (the trusted,
                                        # setup-managed location -- a project .env
                                        # is untrusted input and this key is ignored there)

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

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

pip install ouroboros-ai                # base
pip install 'ouroboros-ai[claude]'        # + default Claude Agent SDK profile (MCP 1.x)
pip install 'ouroboros-ai[claude-cli]'    # + dependency-free Claude CLI worker profile
pip install 'ouroboros-ai[claude-sdk]'    # + explicit alias for the Claude SDK profile
pip install 'ouroboros-ai[litellm]'       # + LiteLLM multi-provider; Python 3.12-3.13
pip install 'ouroboros-ai[mcp]'           # + MCP server/client support
pip install 'ouroboros-ai[tui]'           # + Textual terminal UI
pip install 'ouroboros-ai[all]'           # MCP 1.x app bundle; excludes the MCP 2 server
ouroboros setup                         # configure runtime

brew tap q00/tap
brew install ouroboros-ai
ouroboros setup                         # configure runtime

ouroboros uninstall

Python >= 3.12 required. LiteLLM-bearing profiles support Python 3.12-3.13. See Platform Support and pyproject.toml.

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 when an existing environment shadows the [mcp] profile (#2012). This matters if you install through a downstream package rather than PyPI, since those can lag.

_{Most people find out they were unclear about three files into the review.
If that feels familiar, star Q00/ouroboros on GitHub so the next person it could save can find it.}

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

How It Compares

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

The Loop

The ouroboros -- a serpent devouring its own tail -- is not decoration. It IS the architecture:

    Interview -> Seed -> Execute -> Evaluate
        ^                           |
        +---- Evolutionary Loop ----+

Each cycle does not repeat -- it evolves. The output of evaluation feeds back as input for the next generation, until the system truly knows what it is building.

"This is where the Ouroboros eats its tail: the output of evaluation
becomes the input for the next generation's seed specification."
-- reflect.py

Convergence is reached when ontology similarity >= 0.95 -- when the system has questioned itself into clarity.

Ralph: The Loop That Never Stops

ooo ralph runs the evolutionary loop persistently -- across session boundaries -- until convergence is reached. Each step is stateless: the EventStore reconstructs the full lineage, so even if your machine restarts, the serpent picks up where it left off.

Ralph Cycle 1: evolve_step(lineage, seed) -> Gen 1 -> action=CONTINUE
Ralph Cycle 2: evolve_step(lineage)       -> Gen 2 -> action=CONTINUE
Ralph Cycle 3: evolve_step(lineage)       -> Gen 3 -> action=CONVERGED
                                                +-- Ralph stops.
                                                    The ontology has stabilized.

Commands

Inside AI coding agent sessions, use ooo <cmd> skills. From the terminal, use the ouroboros CLI.