Q00/ouroboros

Agent OS: the agent gets smarter on its own. We just hold the line: the grading command and expected result never make it into the success contract we hand it. Interview-gated, staged evaluation, budgeted evolution loop. MCP server, 13 runtimes: Claude Code, Codex CLI, Gemini CLI, OpenCode, Copilot, Kiro and more.

What it solves

Ouroboros addresses the problem of vague prompts and architectural drift in AI coding. It prevents AI agents from guessing and requiring multiple rework cycles by replacing ad-hoc prompting with a structured, specification-first workflow that ensures human clarity before code is written.

How it works

Ouroboros acts as an "Agent OS" or runtime layer that sits between the user and various AI coding agents (such as Claude Code, Codex, or GitHub Copilot). It implements a cycle of:

  1. Interview: A Socratic questioning process that exposes hidden assumptions and calculates an ambiguity score.
  2. Seed: The results of the interview are crystallized into an immutable specification containing acceptance criteria, ontology, and constraints.
  3. Execute: The agent runs the task based on the Seed.
  4. Evaluate: A three-stage automated evaluation gate (Mechanical, Semantic, and Multi-Model Consensus) verifies the output.
  5. Evolve: The process repeats and improves based on failures.

Who it’s for

Developers using AI coding agents who want to turn vague ideas into verified, working codebases without constant manual prompt engineering or manual QA.

Highlights

  • Multi-Runtime Support: Works across a wide array of agents including Claude Code, Codex CLI, GitHub Copilot CLI, OpenCode, Gemini, and others.
  • Agent OS Architecture: Split into a Shell (TUI), Apps (domain plugins), and an OS core (the kernel) that manages the Seed and Ledger.
  • Specification-First: Locks intent via an immutable seed spec before execution begins.
  • Automated Evaluation: Replaces "looks good" manual reviews with a structured 3-stage verification gate.

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