Ouroboros
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O U R O B O R O S
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Stop prompting. Start specifying.
Agent OS for replayable, specification-first AI coding workflows
Quick Start · Why · Results · How It Works · Commands · Philosophy
Turn a vague idea into a verified, working codebase -- across Claude Code, Codex CLI, OpenCode, and Hermes.
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.
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, and Kiro CLI. The installer detects Claude Code, Codex CLI, and Hermes CLI automatically and registers the MCP server. For OpenCode, Kiro, or GitHub Copilot CLI, run
ouroboros setup --runtime <opencode|kiro|copilot>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.
Kiro CLI quick start
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.
GitHub Copilot CLI quick start
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.
Other install methods
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 · Kiro CLI · Gemini CLI · GitHub Copilot CLI
Uninstall
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 |
What just happened?
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, orouroboros 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 directouroboros <subcommand>shell command./resumeis reserved for Claude Code's built-in session picker; useooo resume-sessionfor 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
Architecture overview -- Python >= 3.12
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)
+-- 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_backendconfig) with first-class support for Claude Code, Codex CLI, OpenCode, and Hermes; same workflow spec, different execution engines
See Architecture for the full design document.
From Wonder to Ontology
The philosophical engine behind Ouroboros
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.
Ambiguity Score: The Gate Between Wonder and Code
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.
Ontology Convergence: When the Serpent Stops
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
"The beginning is the end, and the end is the beginning."
The serpent does not repeat -- it evolves.
MIT License