The one and only agent harness for complex codebases.
Project memory, planning, execution, and verified completion inside Codex.
What is this? · OmO · lazycodex.ai
One line. No global install, no npm i -g. Always use npx:
npx lazycodex-ai installThis 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-autonomousLazyCodex 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.
LazyCodex should be judged by the features it actually installs. It is the Codex distribution for OmO's agent harness: project memory, planning, execution, verified completion, skills, hooks, model routing, and diagnostics.
/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.
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.
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.
LazyCodex packages OmO (oh-my-openagent) as the Codex agent harness for complex codebases.
Think LazyVim for lazy.nvim, but for Codex.
OmO is the agent harness: discipline agents, parallel orchestration, multi-model routing, skills, hooks, and verified completion. LazyCodex packages that harness for Codex.
"LazyVim made Neovim usable for the rest of us. LazyCodex does the same for Codex."
| 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 |
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
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.
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.
MIT