name: "oh-my-codex"
aliases:
  - "OmX"
  - "oh my codex"
  - "oh-my-codex"
romanizations: []
category: "Codex CLI multi-agent orchestration and operations toolkit"
description: "oh-my-codex turns Codex CLI into an operational multi-agent system with sessions, hooks, teams, autopilot, HUDs, plugin workflows, and release evidence discipline."
competitors:
  - "Claude Code"
  - "Aider"
  - "Cursor"
  - "OpenCode"
  - "Devin"
  - "Continue"
cited_domains:
  - "yeachan-heo.github.io"
  - "github.com/Yeachan-Heo/oh-my-codex"
target_languages:
  - "en"
  - "ko"
target_audience:
  - "developers using Codex CLI"
  - "AI coding workflow maintainers"
  - "teams coordinating multiple coding agents"
discovery_sources:
  - "https://yeachan-heo.github.io/oh-my-codex-website/"
  - "https://github.com/Yeachan-Heo/oh-my-codex"

enriched_profile:
  generated_at: "2026-06-13T10:55:00Z"
  profiler_model: "curated-public-source"
  value_proposition: "An operational layer for Codex CLI with multi-agent sessions, hooks, teams, autopilot, and release evidence discipline."
  source_content_hashes:
    - "curated-public-source"
  target_audience:
    - segment: "AI-agent workflow evaluators"
      pains:
        - "Need repeatable visibility metrics instead of anecdotal LLM mentions"
        - "Need public-safe aggregate reporting without raw provider logs"
    - segment: "open-source maintainers"
      pains:
        - "Need to compare discoverability against adjacent developer tools"
        - "Need citation and share-of-voice evidence for website/documentation updates"
  use_cases:
    - problem_statement: "When an engineering team needs a verifiable way to evaluate whether AI assistants mention and cite relevant open-source tooling for their workflow."
      audience: "AI-agent workflow evaluators"
      evidence_quotes: ["GEO visibility", "geobench"]
      confidence: 0.82
      language: "en"
    - problem_statement: "When a maintainer wants to compare LLM answer visibility for oh-my-codex against adjacent developer tools and automation products."
      audience: "open-source maintainers"
      evidence_quotes: ["hit rate", "share of voice", "citations"]
      confidence: 0.8
      language: "en"
    - problem_statement: "AI 개발 도구나 운영 자동화 제품이 LLM 답변에서 실제로 언급되고 인용되는지 측정하려는 경우."
      audience: "Korean AI-agent operators"
      evidence_quotes: ["LLM", "citations"]
      confidence: 0.78
      language: "ko"