lazycodex

🚀 Install

One line. No global install, no npm i -g. Always use npx:

npx lazycodex-ai install

This is shorthand for npx --yes --package oh-my-openagent omo install --platform=codex. For a fully autonomous, no-TUI setup:

npx lazycodex-ai install --no-tui --codex-autonomous

⚡ Commands

LazyCodex installs these as OmO commands for Codex. Invoke them with the $command syntax shown by the installer.

| Command | Type this | What it does | | --- | --- | --- | | $ulw-loop | $ulw-loop "task" [--completion-promise=TEXT] [--strategy=reset\|continue] | Self-referential loop that runs until Oracle-verified completion. Caps at 500 iterations in ultrawork mode, 100 in normal mode. | | $ulw-plan | $ulw-plan "what to build" | Prometheus strategic planner. Writes a plan to plans/<slug>.md. Never writes product code. | | $start-work | $start-work [plan-name] [--worktree <path>] | Executes a plan until every checkbox is done. Prints ORCHESTRATION COMPLETE. |

Full documentation lives at lazycodex.ai/docs.

Use the built-in workflows

LazyCodex should be judged by the features it actually installs. It is a thin Codex distribution for OmO: project memory, planning, execution, verified completion, skills, hooks, model routing, and diagnostics.

1. /init-deep creates project memory

/init-deep generates hierarchical AGENTS.md context. It scores complex directories, writes local guidance near the code that needs it, and gives future agents landmarks before they edit.

Use it when the repository is too large to explain from memory. Run it again when the shape of the codebase changes.

2. The three command pillars stay up front

Use $ulw-plan when the work needs decisions before implementation. It writes a plan to plans/<slug>.md and does not touch product code.

Use $start-work when a plan is ready. It executes the checklist with durable Boulder progress and stops only when the plan is complete.

Use $ulw-loop when the task should keep moving until the result is verified by evidence instead of a hopeful status update.

3. Skills cover specialized work

The command layer stays simple. The skill layer adds specialist judgment for the actual work:

| Feature | Use it for | | --- | --- | | /init-deep | Hierarchical project memory through AGENTS.md | | $ulw-plan | Decision-complete planning before code changes | | $start-work | Durable plan execution with Boulder progress | | $ulw-loop | Verified completion for open-ended tasks | | review-work | Multi-angle post-implementation review | | remove-ai-slops | Behavior-preserving cleanup of AI-looking code | | frontend-ui-ux | Polished UI surfaces | | programming | Strict TypeScript, Rust, Python, or Go discipline | | LSP | Diagnostics, definitions, references, symbols, and renames | | AST-grep | Structural search and rewrite across code | | rules | Project instructions from AGENTS, rules, and instruction files | | comment-checker | Feedback after edit-like operations |

Start at https://lazycodex.ai.

💤 What is this?

LazyCodex is the lazy way to get OmO (oh-my-openagent) up and running.

Think LazyVim for lazy.nvim, but for Codex.

OmO is the best agent harness: discipline agents, parallel orchestration, multi-model routing, skills, hooks, and more. LazyCodex wraps it so you don't have to think about setup.

"LazyVim made Neovim usable for the rest of us. LazyCodex does the same for Codex."

🧩 What you get

| Feature | Description | | --- | --- | | 🤖 Discipline Agents | Sisyphus orchestrates Hephaestus, Oracle, Librarian. A full AI dev team | | 🔀 Parallel Execution | Multiple agents working simultaneously on subtasks | | 🎯 Multi-Model Routing | Automatic model selection per task category | | 🛠️ Skills System | Extensible skill library for specialized tasks | | 📋 Hooks & Lifecycle | Pre/post hooks for every agent action | | 🔧 Zero Config | Sensible defaults, override when you want |

🧠 Why different GPT models appear

Do not be surprised if an OmO/LazyCodex run shows models like gpt-5.2 with xhigh, gpt-5.4-mini, gpt-5.3-codex, or newer equivalents like gpt-5.5 with xhigh. That is intentional.

OmO does not blindly spend your best model on every subtask. Its source defines task categories and fallback chains so the agent can pick the most appropriate model for the job: quick routes to gpt-5.4-mini for small edits, ultrabrain uses a high-reasoning GPT model for hard logic, and agentic coding paths can use Codex-tuned GPT models when available. See openai-categories.ts and model-requirements.ts.

The point is quota discipline: use the strongest model when the task needs deep reasoning, use a cheaper/faster model when that is enough, and keep parallel agent work efficient instead of burning premium quota on routine steps. This is benchmark-driven routing, not random model churn:

• GPT-5.2 is documented by OpenAI as stronger at code review, bug finding, and complex tool use; the announcement notes that its maximum API reasoning effort uses xhigh.
• GPT-5.3-Codex is OpenAI's Codex-tuned model for agentic software engineering, with public coding-agent benchmarks such as SWE-Bench Pro, Terminal-Bench 2.0, and OSWorld Verified reported in the GPT-5.3-Codex announcement.
• GPT-5.4 mini is positioned for efficient everyday coding, computer use, and subagents; that is why lightweight OmO tasks can land there instead of spending a frontier reasoning model.

Reference links:

• OpenAI GPT-5.2 announcement
• OpenAI GPT-5.2 model docs
• OpenAI GPT-5.3-Codex model docs
• OpenAI GPT-5.4 mini and nano announcement
• OpenAI latest model guide

🏗️ Architecture

LazyCodex is a thin distribution layer. The core engine is oh-my-openagent (OmO), included as a submodule under src/.

lazycodex/
├── src/                     → oh-my-openagent (submodule)
├── packages/
│   └── web/                 → Next.js 15 + Tailwind v4 + opennextjs-cloudflare
│                              (deployed to lazycodex.ai via Cloudflare Workers)
├── .github/workflows/       → web-ci.yml + web-deploy.yml
├── README.md
└── ...

LazyCodex is part of the omo.dev project. omo in Codex, packaged for the lazy.

👷 Maintainer

LazyCodex is maintained by Jobdori, the AI assistant that builds and ships OmO in real-time.

Meet your own Jobdori, Dori. Learn more at sisyphuslabs.ai.

📄 License

MIT