Codex's native answer to scheduled agent loops: define a prompt plus a schedule in the Codex app and it runs in the cloud on cadence — nightly dependency audits, morning issue triage — with no terminal open. The Codex-side equivalent of Claude Code Routines.
Define an Automation in the Codex app: a prompt + schedule that runs in the cloud on cadence (e.g., nightly dependency audit, morning triage of new issues), no terminal open.
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Verify each pass by running the relevant tests or checks — self-reported success does not count. Keep changes minimal and never touch files outside the task’s scope.
An outer-planner/inner-coder loop from an official OpenAI recipe: an Agents SDK orchestrator plans and verifies while Codex CLI, wrapped as an MCP server, performs one bounded code change per turn.
Wrap Codex CLI as an MCP server and drive it from an OpenAI Agents SDK orchestrator loop — the outer agent plans/verifies, the inner Codex call does one bounded code change per turn.
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Verify each pass by running the relevant tests or checks — self-reported success does not count. Keep changes minimal and never touch files outside the task’s scope.
An event-triggered loop where a red GitHub Actions build invokes `codex exec` non-interactively with the failing logs and repo, and Codex proposes a fix as a PR or patch artifact — one bounded iteration per CI failure.
CI job: on workflow failure, run `codex exec` non-interactively with the failing logs + repo, have it generate and propose a fix (PR or patch artifact) automatically. `codex exec` runs one task and exits, so each CI failure is one bounded iteration.
OpenAI's first-party loop recipe: a script alternates a Codex review pass that emits machine-readable findings with a repair pass fed those findings verbatim, looping until validation passes, attempts run out, progress stalls, or a decision needs human review.
Script alternates two `codex exec` calls: (a) review pass with a JSON schema output ("list remaining issues as machine-readable findings"), (b) repair pass fed those findings verbatim. Loop while findings remain, capped by max attempts. Stops for one of four reasons: validation passes, max attempts reached, remaining delta stops changing, or next decision needs human review.
Anthropic's first-party file-as-memory harness for long-running agents: every fresh-context session recovers state from a progress file and the git log, does one unit of work, updates the file, commits, and exits.
Long-running agent harness: each fresh-context session starts by reading `claude-progress.txt` + git log to recover state, does one unit of work, updates the progress file, commits, exits. Initializer session sets up the file; coder sessions loop.
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Verify each pass by running the relevant tests or checks — self-reported success does not count. Keep changes minimal and never touch files outside the task’s scope.
Anthropic's first-party take on the Ralph loop: a Claude Code plugin that runs the iterate-fresh-context pattern with a managed stop and iteration mechanism built in.
Install the `ralph-wiggum` plugin from the anthropics/claude-code repo; it wraps the Ralph loop with a managed stop/iteration mechanism inside Claude Code.
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Verify each pass by running the relevant tests or checks — self-reported success does not count. Keep changes minimal and never touch files outside the task’s scope.