Q00/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

# macOS / Linux / WSL 2
curl -fsSL https://raw.githubusercontent.com/Q00/ouroboros/main/scripts/install.sh | OUROBOROS_INSTALL_REF=readme-hero bash

# Windows (PowerShell) — no Python needed; installs Git and uv for you
irm https://raw.githubusercontent.com/Q00/ouroboros/main/scripts/install.ps1 | iex

_{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 — the Kiro CLI running the Ouroboros interview flow, turning a vague request into a structured, testable Seed

Turn a vague idea into a verified, working codebase -- across Claude Code, Codex CLI, OpenCode, Hermes, Gemini, Kiro, Copilot, Pi, OMP, 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.

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.

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

Install — one command, everything auto-detected:

# macOS / Linux / WSL 2
curl -fsSL https://raw.githubusercontent.com/Q00/ouroboros/main/scripts/install.sh | OUROBOROS_INSTALL_REF=readme bash

# Windows (PowerShell 5.1+ or pwsh 7+) — nothing to install first
irm https://raw.githubusercontent.com/Q00/ouroboros/main/scripts/install.ps1 | iex

The Windows installer installs Git and uv through winget when they are missing, lets uv download its own Python, then installs ouroboros-ai and wires the host it finds. Native Windows is experimental and Codex CLI needs WSL 2; see platform support.

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, OMP 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|omp|zcode|goose|gjc|antigravity|grok> after installation. Copilot live-discovers its subscription catalog via the GitHub Copilot models API; Kiro's settings picker queries the authenticated CLI with kiro-cli chat --listmodels -f json, so account and enterprise allow-list changes appear without a hardcoded model table.

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[mcp,tui]' && ouroboros setup --runtime claude-cli  # recommended MCP v2 default
pip install 'ouroboros-ai[claude]'      # Claude Agent SDK profile (MCP 1.x, isolated)
pip install 'ouroboros-ai[claude-cli]'  # dependency-free Claude CLI worker
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 v2 server/client without the GUI
pip install 'ouroboros-ai[tui]'         # settings GUI only
pip install 'ouroboros-ai[all]'         # MCP 1.x app bundle; excludes MCP 2 by design
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.}

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

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

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.

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.

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

Not all skills have direct CLI equivalents. Some (evaluate, evolve, unstuck, ralph, publish, maintain) are available through agent skills, runtime rules, or MCP tools rather than a direct ouroboros <subcommand> shell command. /resume is reserved for Claude Code's built-in session picker; use ooo resume-session for Ouroboros in-flight sessions. Claude Code also reserves /run, /status, /help, and /config. The safe direct skill forms are /ouroboros:ouroboros-run, /ouroboros:ouroboros-status, /ouroboros:ouroboros-help, and /ouroboros:ouroboros-config; the familiar ooo run, ooo status, ooo help, and ooo config phrases remain supported.

See the CLI reference for full details.

Nine highlighted agents below, with 12 more specialized agents available (21 total). All loaded on-demand, never preloaded:

src/ouroboros/
+-- bigbang/        Interview, ambiguity scoring, brownfield explorer
+-- routing/        PAL Router -- 3-tier cost optimization (1x / 10x / 30x)
+-- execution/      (deprecated — logic moved to orchestrator/ and mcp/tools/)
+-- evaluation/     Mechanical -> Semantic -> Multi-Model Consensus
+-- evolution/      Wonder / Reflect cycle, convergence detection
+-- resilience/     4-pattern stagnation detection, 5 lateral personas
+-- observability/  3-component drift measurement, auto-retrospective
+-- persistence/    Event sourcing (SQLAlchemy + aiosqlite), checkpoints
+-- orchestrator/   Runtime abstraction layer (Claude Code, Codex CLI, OpenCode, Hermes, Gemini, Kiro, Copilot, Pi, OMP, GJC, Goose, Antigravity, Grok, Zcode)
+-- core/           Types, errors, seed, ontology, security
+-- providers/      LiteLLM adapter (100+ models)
+-- mcp/            MCP client/server integration
+-- plugin/         Plugin system (skill/agent auto-discovery)
+-- tui/            Terminal UI dashboard
+-- cli/            Typer-based CLI

   Wonder                          Ontology
"What do I want?"    ->    "What IS the thing I want?"
"Build a task CLI"   ->    "What IS a task? What IS priority?"
"Fix the auth bug"   ->    "Is this the root cause, or a symptom?"

    * Wonder          * Design
   /  (diverge)      /  (diverge)
  /    explore      /    create
 /                 /
* ------------ * ------------ *
 \                 \
  \    define       \    deliver
   \  (converge)     \  (converge)
    * Ontology        * Evaluation

Ambiguity = 1 - Sum(clarity_i * weight_i)

Example (Greenfield):

  Goal: 0.9 * 0.4  = 0.36
  Constraint: 0.8 * 0.3  = 0.24
  Success: 0.7 * 0.3  = 0.21
                        ------
  Clarity             = 0.81
  Ambiguity = 1 - 0.81 = 0.19  <= 0.2 -> Ready for Seed

Similarity = 0.5 * name_overlap + 0.3 * type_match + 0.2 * exact_match

Gen 1: {Task, Priority, Status}
Gen 2: {Task, Priority, Status, DueDate}     -> similarity 0.78 -> CONTINUE
Gen 3: {Task, Priority, Status, DueDate}     -> similarity 1.00 -> CONVERGED

git clone https://github.com/Q00/ouroboros
cd ouroboros
uv sync --python 3.13 --all-groups
uv run --python 3.13 --no-sync pytest

Issues · Discussions · Contributing Guide

Ouroboros is MIT-licensed and built in the open. If it saves you rework — or you want the loop to keep evolving — consider sponsoring. Sponsorship directly funds maintenance, new runtime integrations, and sponsor-only deep-dive content.

Every sponsor keeps the serpent evolving. Thank you.

These numbers are generated from GitHub data and refreshed automatically; caching may delay updates.

"The beginning is the end, and the end is the beginning."

The serpent does not repeat -- it evolves.

MIT License