Daemon Backend Codex

Codex Daemon Backend — Flag Discovery Entrypoint

The installed CLI's own help is the authority for Codex flags; this page is only the entrypoint. Conversion rules, key safety, and persistence live in the parent reference/cli-backends/SKILL.md.

Discover flags from the installed CLI

1. Run, in bash: codex --version, codex --help, and codex exec --help. The daemon backend wraps codex exec, so codex exec --help is the relevant flag surface. These are local read-only commands; no session is started.
2. Translate what you found into backend_options with the parent's generic conversion rules. Nothing Codex-specific is added to that contract here.

Example: reasoning effort via the generic config route

Codex exposes most of its tunables as repeated -c, --config <key=value> overrides (see codex exec --help for the key=value syntax). Through backend_options, a list value repeats the flag once per item:

{
  "backend": "codex",
  "tasks": [{
    "task": "Implement and validate the change.",
    "tools": [],
    "backend_options": {
      "config": ["model_reasoning_effort=\"ultra\""]
    }
  }]
}
// argv: --config model_reasoning_effort="ultra"

The effort vocabulary belongs to the installed CLI and the selected model — LingTai does not validate, enumerate, or simulate effort levels. A value like ultra passes through and the CLI/model decides its semantics (or rejects it).

Installation

Use OpenAI's current official Codex documentation for installation and upgrades; this manual maintains no vendor install/update commands or pinned version. Any actual install or update still needs its owner's authorization. Verify the resulting codex --version and codex exec --help before relying on a flag.

Configuration

Auth & API key

The external CLI uses its existing authorized account and credential context. Consult installed help and OpenAI's documentation for subscription/API-key or other provider configuration; do not infer CLI account selection from a LingTai preset or pool label. This manual grants no login/logout, account/profile switch, credential read/copy or configuration-repair authority. Never print credentials or whole auth/config files while diagnosing; escalate an unavailable approved account rather than silently switching it.

Models

Model names and reasoning levels belong to the installed CLI and selected account. Query its help/vendor documentation instead of copying a "latest" model list or vendor configuration schema here. Per-run model selection passes through backend_options ("model": "<name>" → --model <name>), under the parent's generic conversion rules and the task's actual model/authority contract.

Style axis: Codex vs Claude Code

Codex and Claude Code are both LingTai daemon backends; the choice between them is about style, not just run shape:

• Codex — tightly-scoped diffs, deterministic refactors, mechanical validation sweeps. More conservative; the right choice when the change is well-specified and the scope is clear.
• Claude Code — exploratory code reading, multi-file edits, skill/doc work, PR composition. See reference/backends/claude-p/SKILL.md.

Capability note

Codex CLI is open source and built in Rust. Beyond the headless codex exec surface the daemon wraps, the CLI also ships an interactive TUI (native Vim editing, plugin management, hooks) and a Chrome extension, plus a headless remote-control app-server. Those are interactive features; the daemon backend only exercises codex exec, whose live surface is codex exec --help — not a static catalog here.

Official docs: https://developers.openai.com/codex/

Harness boundary

Codex currently declares no reserved-flag list at the validation layer, so nothing is refused for this backend beyond the generic key/value safety rules. Still, do not re-set harness-owned surfaces (--json, sandbox/approval bypass, or mcp_servers.daemon_common.* config keys): breaking them silently breaks progress/result extraction and completion enforcement.

Troubleshooting

Installation failures, missing/invalid credentials, and plugin-marketplace issues are the installed CLI's own concerns — use installed help and OpenAI's official docs above; do not print or cat a credential file/env var into agent-visible output while diagnosing. The one failure mode that is actually LingTai's contract to diagnose:

1. Agent appears stuck while codex exec runs. You likely used synchronous CLI for work that should have been daemon-backed or supervised in the background. Inspect the child process and worktree; if needed, kill the child so the blocking call returns, then resume with the Codex daemon backend or a supervised background wrapper that records logs, timeout, cancellation path, and recovery notes.