# 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.

Repository: https://github.com/Q00/ouroboros
Canonical: https://ross.abutalabs.com/products/ouroboros
Homepage: https://ouroboros.page/
Language: Python
License: MIT
License Family: permissive
Topics: ai-agent, agent-os, llm-orchestration, agentic-ai, claude-code, cli, developer-tools, mcp, ai-coding-agent, codex, opencode, github-copilot, llm-evaluation, coding-agent, loop-engineering, deepseek, deepseek-harness, dsh, dsh-plugin
Last push: 2026-08-25T17:46:01+00:00

## Health v2 (maintenance only)
Score: 78/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 16
- inputs: {"age_days": 232, "days_push": 8, "days_rel": 10, "gap_med": 1, "n_releases_24m": 118}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5687, forks 569 (observed 2026-08-28T04:09:27.742820+00:00)

## What it is
Ouroboros is an 'Agent OS' that manages AI coding agent workflows through interview-gated requirement capture, staged evaluation, and a budgeted evolution loop, keeping grading criteria hidden from the agent's success contract. It ships as a CLI (pip install ouroboros-ai) and MCP server/plugin supporting 13 coding agent runtimes including Claude Code, Codex CLI, Gemini CLI, OpenCode, and GitHub Copilot.

## Use cases
- run AI coding agents in a verifiable spec-then-verify loop
- hide grading assertions from the agent so it cannot game evaluation
- turn a vague idea into a reviewed spec via an ambiguity-scoring interview
- orchestrate the same workflow across Claude Code, Codex, Gemini CLI and other runtimes
- evaluate and iterate on agent-generated code with staged verification
- record replayable evidence of AI coding work

## When to choose
- you use coding agents like Claude Code or Codex and want trustworthy, spec-pinned verification of their output
- you want an agent loop that improves across generations without leaking the success criteria
- you need one orchestration engine across multiple agent runtimes

## When to avoid
- you need a simple one-shot code generator without evaluation overhead
- you don't use any supported coding agent runtime
- you want a lightweight prompt library rather than a full workflow engine

## Facets
- artifact type: cli-tool
- maturity: active
- function: agent-framework, llm-inference, mcp, cli, developer-tools, workflow-automation, testing
- domain: large-language-models, developer-tools
- platform: cli, python, cross-platform, windows
- tags: agent-os, llm-orchestration, agentic-ai, claude-code, codex-cli, gemini-cli, opencode, github-copilot, llm-evaluation, coding-agent, loop-engineering, spec-driven-development, interview-gated, mcp-server, self-improving-agent, ai-agents, automation, command-line, linux, macos

## Member repositories
- Q00/ouroboros (main) score 78

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:27.742820+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:54:13.528469+00:00, confidence not recorded.
  - readme: https://github.com/Q00/ouroboros (fetched 2026-08-28T04:09:27.742820+00:00, sha d1fa7d3b99ca)
  - homepage: https://ouroboros.page/ (fetched 2026-08-29T08:49:22.798025+00:00, sha 9a2867a80874)
  - site_page: https://ouroboros.page/learn/en/install (fetched 2026-08-29T08:49:22.808036+00:00, sha 529e3f343e94)
  - site_page: https://ouroboros.page/learn/en (fetched 2026-08-29T08:49:22.810211+00:00, sha 4589e7ac6e55)
- Data as of 2026-08-30T08:39:29.467469+00:00.