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 + dashboard)
ouroboros setup                         # configure runtime

Legacy compatibility: ouroboros-ai[dashboard] is still accepted as a compatibility alias while extras migrate.

See runtime guides: Claude Code · Codex CLI · Hermes · OpenCode

ouroboros uninstall

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

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.

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)
+-- 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
• Runtime backends -- Pluggable abstraction layer (orchestrator.runtime_backend config) with first-class support for Claude Code, Codex CLI, and Hermes; same workflow spec, different execution engines

See Architecture for the full design document.

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:

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.

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

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

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

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).