ouroboros

# 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

Demo videos for each runtime.

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.

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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:

Layer	Repo	Role	What it gives you
Shell (terminal client)	Ouro-labs/ourocode	Native terminal UI for running ooo workflows across Claude / Codex / Gemini CLIs in one session	TUI, wonderTool decision pickers, MCP pane state, command discovery
Apps (domain workflows)	Ouro-labs/ouroboros-plugins	UserLevel plugin contract — composes core primitives into installable domain programs (PR ops, Jira sync, incidents, releases)	Plugin manifest, scoped permissions, audit/provenance, reference plugins
OS (this repo)	Q00/ouroboros	Agent OS core — Seed, Ledger, Runtime, MCP, safety boundaries	ooo commands, spec-first workflow engine, multi-runtime adapter

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.

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Why Ouroboros?

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

Problem	What Happens	Ouroboros Fix
Vague prompts	AI guesses, you rework	Socratic interview exposes hidden assumptions
No spec	Architecture drifts mid-build	Immutable seed spec locks intent before code
Manual QA	"Looks good" is not verification	3-stage automated evaluation gate

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Quick Start

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"

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.

Needs codex on your PATH and uvx on the host (the plugin's MCP descriptor launches the server with it). Install uv with pipx install uv, pip install --user uv, or brew install uv.

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

Start a new Codex session, then run these commands in order:

ooo setup
ooo interview "Build a task management CLI"

ooo setup is the one-time runtime preparation. Once ready, Ouroboros follows Codex's current default model; choose Directly configure models only when you want to pin a specific model for a pipeline stage.

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)

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. Model-ID mapping is catalog-gated: the prior direct and OpenRouter Opus 5 pins resolve to claude-opus-5 when that ID is present; current Anthropic API pins convert only their trailing numeric separator and only when the discovered catalog contains the exact candidate. Unknown IDs remain unchanged so Copilot reports an explicit unavailable-model error instead of silently selecting a different model. Leave role models unset so setup writes a discovered ID, or set a Copilot-valid ID explicitly. See the Copilot runtime guide.

See the GitHub Copilot CLI runtime guide for full details.

For experimental live task activity, keep runtime_backend: copilot and opt into the ACP transport:

ouroboros config set orchestrator.copilot_transport acp

The default remains cli (copilot -p). ACP streams assistant text and tool progress through the existing event store; authentication is still owned by Copilot, and no TCP listener or display is required.

Claude Code plugin only (no Python package or global Python to install; the host needs uv, which provides both uvx for the MCP server and the skills' Python >= 3.12 fallback):

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[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

Core and non-LiteLLM installs support Python 3.12-3.14. LiteLLM-bearing installs ([litellm], [all], and source --extra all) support Python 3.12-3.13; use Python 3.13 for current examples. See Platform Support.

The recommended standalone installation is ouroboros-ai[mcp,tui] followed by an explicit MCP v2-compatible runtime selection. The example uses --runtime claude-cli; substitute another compatible runtime such as codex, opencode, hermes, gemini, goose, kiro, copilot, pi, or gjc. Use [claude] and [claude-sdk] only in isolated MCP 1.x environments.

pip install 'ouroboros-ai[mcp]' is valid for embedding the MCP client/server library in an already isolated Python environment, but host registration requires uvx --isolated --python '>=3.12' or pipx. Use pipx install 'ouroboros-ai[mcp]' or uv tool install 'ouroboros-ai[mcp]' before ouroboros setup --runtime <claude-cli|codex|opencode|hermes|gemini|goose|kiro|copilot|pi|gjc>; setup exits without changing runtime configuration when neither isolated launcher is available.

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.

Homebrew (macOS/Linux):

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

Self-hosted tap, not yet in homebrew-core. Installs the same package published to PyPI.

See runtime guides: Claude Code · Codex CLI · Hermes · OpenCode · Kiro CLI · Gemini CLI · GitHub Copilot CLI · Zcode · Pi JSON mode · OMP (Oh My Pi) · Goose · GJC · Antigravity CLI · Grok Build CLI · Megacode harness

ouroboros uninstall

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

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.

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What You Get

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

Step	Before	After
Interview	"Build me a task CLI"	12 hidden assumptions exposed, ambiguity scored to 0.19
Seed	No spec	Immutable specification with acceptance criteria, ontology, constraints
Evaluate	Manual review	3-stage gate: Mechanical (free) -> Semantic -> Multi-Model Consensus

interview  ->  Socratic questioning exposed 12 hidden assumptions
seed       ->  Crystallized answers into an immutable spec (Ambiguity: 0.15)
run        ->  Fanned acceptance criteria out to agents in dependency order
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.

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How It Compares

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

	Vanilla AI Coding	Ouroboros
Vague prompt	AI guesses intent, builds on assumptions	Socratic interview forces clarity before code
Spec validation	No spec -- architecture drifts mid-build	Immutable seed spec locks intent; ambiguity gate (<= 0.2) blocks premature code without explicit force
Evaluation	"Looks good" / manual QA	3-stage automated gate: Mechanical -> Semantic -> Multi-Model Consensus
Rework rate	High -- wrong assumptions surface late	Low -- assumptions surface in the interview, not in the PR review

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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.

Phase	What Happens
Interview	Socratic questioning exposes hidden assumptions
Seed	Answers crystallize into an immutable specification
Execute	Acceptance criteria fan out to agents in dependency order
Evaluate	3-stage gate: Mechanical ($0) -> Semantic -> Multi-Model Consensus
Evolve	Wonder ("What do we still not know?") -> Reflect -> next generation

"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.

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Commands

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

Skill (ooo)	CLI equivalent	What It Does
ooo setup	ouroboros setup	Register runtime and configure project (one-time)
ooo interview	ouroboros init start	Socratic questioning -- expose hidden assumptions
ooo idk	(via skill)	Calibrate interview wording to your topic knowledge
ooo auto	ouroboros auto	Goal → A-grade Seed → execution handoff with bounded loops
ooo seed	(generated by interview)	Crystallize into immutable spec
ooo run	ouroboros run seed.yaml	Execute the seed's acceptance criteria
ooo evaluate	(via MCP)	3-stage verification gate
ooo evolve	(via MCP)	Evolutionary loop until ontology converges
ooo unstuck	(via MCP)	5 lateral thinking personas when you are stuck
ooo status	ouroboros status executions / ouroboros status execution <id>	Session tracking + (MCP-only) drift detection
ooo resume-session	ouroboros resume	List in-flight sessions and re-attach commands
ooo cancel	ouroboros cancel execution [<id>|--all]	Cancel stuck or orphaned executions
ooo ralph	(via MCP)	Persistent loop until verified
ooo tutorial	(interactive)	Interactive hands-on learning
ooo help	ouroboros --help	Full reference
ooo pm	(via MCP)	PM-focused interview + PRD generation
ooo qa	(via skill)	General-purpose QA verdict for any artifact
ooo update	ouroboros update	Check for updates + upgrade to latest
ooo brownfield	(via skill)	Scan and manage brownfield repo/worktree defaults
ooo publish	(skill/runtime surface; uses gh CLI)	Publish a Seed as GitHub Epic/Task issues for team workflows

ooo idk adjusts interview wording in the active conversation without answering the pending question or lowering requirements rigor. It works with the Claude skill relay and intercepted interviews on Codex, Copilot, Gemini, OpenCode, Hermes, Pi, OMP, GJC, and Kiro. Calibration is not saved: run ooo idk again after reopening a saved session. See the calibration contract.

Not all skills have direct CLI equivalents. Some (evaluate, evolve, unstuck, ralph, publish) 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.

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The Nine Minds

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

Agent	Role	Core Question
Socratic Interviewer	Questions-only. Never builds.	"What are you assuming?"
Ontologist	Finds essence, not symptoms	"What IS this, really?"
Seed Architect	Crystallizes specs from dialogue	"Is this complete and unambiguous?"
Evaluator	3-stage verification	"Did we build the right thing?"
Contrarian	Challenges every assumption	"What if the opposite were true?"
Hacker	Finds unconventional paths	"What constraints are actually real?"
Simplifier	Removes complexity	"What's the simplest thing that could work?"
Researcher	Stops coding, starts investigating	"What evidence do we actually have?"
Architect	Identifies structural causes	"If we started over, would we build it this way?"

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Under the Hood

src/ouroboros/
+-- bigbang/        Interview, ambiguity scoring, brownfield explorer
+-- router/         Skill dispatch (`ooo <skill>` -> MCP tool); PAL tier routing is in orchestrator/
+-- 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

Key internals:

• PAL Router -- Frugal (1x) -> Standard (10x) -> Frontier (30x) with escalation on retry (no automatic downgrade)
• Drift -- Goal (50%) + Constraint (30%) + Ontology (20%) weighted measurement, threshold <= 0.3
• Brownfield -- Auto-detects config files across multiple language ecosystems
• Evolution -- Up to 30 generations, convergence at ontology similarity >= 0.95
• Stagnation -- Detects spinning, oscillation, no-drift, and diminishing returns patterns
• Agent OS runtime -- Replayable execution contract across capability discovery, policy, directives, event journal, and agent processes
• Runtime backends -- Pluggable abstraction layer (orchestrator.runtime_backend config) with first-class support for every canonical backend returned by runtime_backend_choices(), including GJC, Antigravity, Grok, and Zcode; same workflow spec, different execution engines

See Architecture for the full design document.

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From Wonder to Ontology

Wonder -> "How should I live?" -> "What IS 'live'?" -> Ontology -- Socrates

Every great question leads to a deeper question -- and that deeper question is always ontological: not "how do I do this?" but "what IS this, really?"

   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?"

This is not abstraction for its own sake. When you answer "What IS a task?" -- deletable or archivable? solo or team? -- you eliminate an entire class of rework. The ontological question is the most practical question.

Ouroboros embeds this into its architecture through the Double Diamond:

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

The first diamond is Socratic: diverge into questions, converge into ontological clarity. The second diamond is pragmatic: diverge into design options, converge into verified delivery. Each diamond requires the one before it -- you cannot design what you have not understood.

The Interview does not end when you feel ready -- it ends when the math says you are ready. Ouroboros quantifies ambiguity as the inverse of weighted clarity:

Ambiguity = 1 - Sum(clarity_i * weight_i)

Each dimension is scored 0.0-1.0 by the LLM (temperature 0.1 for reproducibility), then weighted:

Dimension	Greenfield	Brownfield
Goal Clarity -- Is the goal specific?	40%	35%
Constraint Clarity -- Are limitations defined?	30%	25%
Success Criteria -- Are outcomes measurable?	30%	25%
Context Clarity -- Is the existing codebase understood?	--	15%

Threshold: Ambiguity <= 0.2. A score above that blocks Seed generation. Passing force explicitly is what gets past it, and the CLI puts that choice on screen next to continue and cancel. The gate is a default worth arguing with, not a lock.

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

Why 0.2? Because at 80% weighted clarity, the remaining unknowns are small enough that code-level decisions can resolve them. Above that threshold, you are still guessing at architecture.

The evolutionary loop does not run forever. It stops when consecutive generations produce ontologically identical schemas. Similarity is measured as a weighted comparison of schema fields:

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

Component	Weight	What It Measures
Name overlap	50%	Do the same field names exist in both generations?
Type match	30%	Do shared fields have the same types?
Exact match	20%	Are name, type, AND description all identical?

Threshold: Similarity >= 0.95 -- the loop converges and stops evolving.

But raw similarity is not the only signal. The system also detects pathological patterns:

Signal	Condition	What It Means
Stagnation	Similarity >= 0.95 for 3 consecutive generations	Ontology has stabilized
Oscillation	Gen N ~ Gen N-2 (period-2 cycle)	Stuck bouncing between two designs
Repetitive feedback	>= 70% question overlap across 3 generations	Wonder is asking the same things
Hard cap	30 generations reached	Safety valve

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

Two mathematical gates, one philosophy: do not build until you are clear (Ambiguity <= 0.2), do not stop evolving until you are stable (Similarity >= 0.95).

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Contributing

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

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Sponsors

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.

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