ouroboros

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

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:

Layer	Repo	Role	What it gives you
Shell (terminal client)	Q00/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)	Q00/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.

---

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

---

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, Kiro CLI, and Pi CLI. The installer detects Claude Code, Codex CLI, and Hermes CLI automatically and registers the MCP server where the host supports it. For OpenCode, Kiro, GitHub Copilot CLI, Gemini CLI, or Pi CLI, run ouroboros setup --runtime <opencode|kiro|copilot|gemini|pi> 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.

<strong>Kiro CLI quick start</strong>

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.

<strong>GitHub Copilot CLI quick start</strong>

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.

<strong>Other install methods</strong>

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 · Pi JSON mode

<strong>Uninstall</strong>

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:

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

<strong>What just happened?</strong>

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 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
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

---

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	Double Diamond: Discover -> Define -> Design -> Deliver
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.

---

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 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 via Double Diamond decomposition
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

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.

See the CLI reference for full details.

---

The Nine Minds

Nine agents, each a different mode of thinking. 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?"

---

Under the Hood

<strong>Architecture overview -- Python >= 3.12</strong>

src/ouroboros/
+-- bigbang/        Interview, ambiguity scoring, brownfield explorer
+-- routing/        PAL Router -- 3-tier cost optimization (1x / 10x / 30x)
+-- execution/      Double Diamond, hierarchical AC decomposition
+-- 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)
+-- 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 auto-escalation on failure, auto-downgrade on success
• 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 Claude Code, Codex CLI, OpenCode, Hermes, Gemini, Goose, Kiro, Copilot, and Pi; same workflow spec, different execution engines

See Architecture for the full design document.

---

From Wonder to Ontology

<strong>The philosophical engine behind Ouroboros</strong>

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.

<strong>Ambiguity Score: The Gate Between Wonder and Code</strong>

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 -- only then can a Seed be generated.

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.

<strong>Ontology Convergence: When the Serpent Stops</strong>

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).

---

Contributing

git clone https://github.com/Q00/ouroboros
cd ouroboros
uv sync --all-groups && uv run pytest

Issues · Discussions · Contributing Guide

---

Star History

---

<em>"The beginning is the end, and the end is the beginning."</em>

<strong>The serpent does not repeat -- it evolves.</strong>

<code>MIT License</code>