Overview

Ouroboros is a revolutionary AI coding framework that transforms the AI development lifecycle. It shifts the paradigm from imprecise "prompting" to precise "specifying," creating a repeatable, observable, and policy-bound execution contract for AI-generated code. Designed as a local-first Agent OS, Ouroboros bridges the gap between a vague idea and a fully verified, functional codebase, supporting a range of powerful AI coding tools.

Key Features

• Specification-First Workflow: Replaces ad-hoc prompting with a structured, human-in-the-loop process: interview, crystallize, execute, evaluate, and evolve.
• Socratic Interview: Uncovers hidden assumptions and clarifies intent through deep, iterative questioning, reducing AI guesswork and rework.
• Immutable Seed Specification: Locks in your project's intent before code generation begins, ensuring architecture stability and clear acceptance criteria.
• Automated 3-Stage Evaluation: Guarantees code quality through Mechanical, Semantic, and Multi-Model Consensus verification gates.
• Evolutionary Loop: The core "Ouroboros" architecture enables continuous improvement, where the output of one cycle feeds into the next, driving towards convergence and ontological clarity.
• Broad AI Runtime Support: Seamlessly integrates with Claude Code, Codex CLI, GitHub Copilot CLI, OpenCode, Hermes, Gemini, and Kiro CLI.
• Ambiguity Scoring: Quantifies clarity with a defined threshold (<= 0.2) before code generation can proceed, ensuring a solid foundation.
• Nine Thinking Minds: Leverages specialized AI agents (Socratic Interviewer, Ontologist, Evaluator, etc.) on demand for diverse problem-solving approaches.
• Persistent Execution: The ooo ralph command runs the evolutionary loop continuously across sessions, reconstructing lineage from an EventStore, ensuring no work is lost.
• Drift Detection: Monitors deviations from the intended specification across multiple dimensions, ensuring the AI stays on track.

Typical Use Cases

• Rapid Prototyping: Quickly turn initial concepts into verifiable codebases, iterating with clarity and confidence.
• Complex Software Development: Manage large projects by ensuring a robust, well-defined specification from the outset, minimizing architectural drift.
• Brownfield Development: Integrate AI coding into existing codebases by automatically detecting configurations and managing project evolution.
• AI-Powered QA & Verification: Leverage advanced multi-model consensus for rigorous code evaluation and validation.
• Building Reusable AI Workflows: Create deterministic and replayable AI coding processes that can be shared and executed consistently.
• Enhancing Team Collaboration: Publish specifications and code as GitHub issues (Epics/Tasks) for transparent team workflows.
• From Vague Ideas to Verified Code: The ideal solution for developers who want to move beyond the frustration of ambiguous AI outputs and achieve tangible, reliable results.