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Use Codex as a supervised coding workflow: give it a bounded task and the right repository context, ask for a plan before a substantial change, run the work in a suitable project environment, then inspect and test the resulting diff before carrying it forward. Threads, worktrees, repository instructions, and scheduled Automations can help organize that loop, but none replaces your review.
How do I get started with the Codex app?
Sign in with a ChatGPT account in the Codex client you intend to use, then select the repository or project for the task. OpenAI lists the ChatGPT desktop app, CLI, IDE extension, and web as Codex clients; availability and limits can differ by plan and workspace. See the Codex plan and access guidance for current details.
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- Choose the client and sign in. Use the client that fits where you work: desktop, command line, IDE, or web.
- Select the project or repository. Keep the task scoped to the codebase and files it actually concerns.
- Provide relevant context only. Point to the files, components, examples, and requirements that matter; avoid dumping unrelated material into the request.
- Check the environment before running work. Confirm that the project setup, credentials, network permissions, and workspace policies are appropriate for the task.
Cloud tasks have distinct eligibility and workspace settings. OpenAI says cloud environments are created and published on desktop or web, and cloud work can continue while your computer sleeps. Whether that option is available to you depends on your account and workspace configuration; consult the current Help Center guidance rather than relying on a fixed plan allowance.
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How should I ask Codex to make a code change?
For a substantial change, start by asking Codex for an implementation plan in Ask mode. Review the plan, refine it if necessary, then use it in a follow-up request in Code Mode. OpenAI’s Codex app announcement links to practices for structuring work; the practices put it plainly: “Codex works best when it’s given structure, context, and room to iterate.”
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What to include in a useful request
- Outcome: State what should work or change when the task is complete.
- Scope: Name relevant paths, components, or boundaries, and say what should remain untouched.
- Constraints: Include compatibility, style, performance, security, or other project requirements that matter.
- Examples: Provide an input/output example, reproduction steps, or a reference implementation when useful.
- Verification: Identify the tests or project checks that should be run, if known.
Think of the request as a focused issue or pull-request description rather than a broad wish list. Break unrelated changes into separate tasks. If a smaller follow-up comes to mind while Codex is working, capture it in the task queue instead of expanding the original request into an unwieldy job.
How can I give Codex repository-specific context?
For guidance that should apply across tasks in a repository, use an AGENTS.md file. OpenAI’s practices recommend recording project conventions, business logic, known quirks, and dependencies there. Keep it specific and maintained: durable repository guidance belongs in the file, while one-off requirements belong in the individual task request.
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Also make the environment usable. Appropriate startup scripts and environment variables can reduce setup friction. Network access should be configured deliberately for the task rather than granted without limits; enterprise policies may restrict it. The available permissions and safeguards depend on the Codex client settings and workspace management.
Can Codex work on more than one task at once?
Codex organizes agent work in project threads and includes Git worktree support. A worktree is an isolated copy of a repository, which can let parallel tasks proceed without editing the same local working state. This is most useful when tasks are meaningfully independent and each result can be reviewed on its own.
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| Workflow | When it fits | What to watch |
|---|---|---|
| One task in the current working copy | The change is tightly coupled to current work or needs shared local files and state. | Concurrent edits can interfere; finish or coordinate overlapping work before proceeding. |
| Parallel tasks in separate worktrees | Tasks can be isolated and you want work on the same repository to proceed in parallel. | Review and integrate each result separately. Isolation does not resolve design conflicts or guarantee compatible changes. |
Choose based on how much the tasks share files or Git state, whether you need a result locally while another task continues, and how much review and integration effort you can take on. Threads help organize work; worktrees separate working copies, but you remain responsible for deciding whether the changes fit together.
How do I review Codex’s changes?
Review the changes in the task thread before treating them as ready. The app supports inspecting and commenting on a diff, and opening the changes in an editor for manual review or edits.
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- Compare the diff with the request. Check that the intended outcome is present and that unrelated files or behavior have not changed.
- Check repository requirements. Examine correctness, project conventions, edge cases, and any security or compatibility requirements relevant to the task.
- Run appropriate project checks. Use the repository’s relevant tests, linters, builds, or other verification steps; review affordances are not a guarantee that generated changes are correct.
- Give focused follow-up direction. Comment on a specific part of the diff or make manual edits in the editor, then review the revised result.
OpenAI’s app announcement describes agents as limited by default to editing in the folder or branch where they work, with permission requested for commands requiring elevated permissions such as network access. That announcement is not a guarantee of identical controls in every setup: client settings and workspace policies determine the boundaries you actually use. OpenAI’s security and operational controls overview describes managed sandbox, approval, and network policies in OpenAI’s own deployment context.
Should I run Codex locally or in the cloud?
The choice depends on the task’s environment and whether work must continue independently of your computer. The documented capabilities support these practical decision points; they are not a performance comparison.
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| Consideration | Local work | Cloud work |
|---|---|---|
| Repository and environment | Useful when the required checkout, tools, and setup are already available in your local workflow. | Requires a configured cloud environment for the repository and task. |
| Computer availability | Work depends on the local setup being available. | Cloud tasks can continue while your computer sleeps, according to OpenAI. |
| Credentials and network | Use the permissions and credentials available in the local environment, subject to its controls. | Check cloud credentials, network configuration, and workspace policy before assigning work. |
| Access | Client availability and usage are subject to plan and workspace limits. | Cloud access has separate eligibility and workspace settings; check current account configuration. |
Prefer the environment that has the necessary project context and appropriately scoped permissions. Do not assume that cloud access, network access, or a particular usage allowance is universal.
Can Codex run recurring tasks?
Yes. Codex app Automations combine instructions with optional skills, run on a schedule, and send their results to a review queue. OpenAI cites issue triage, CI failure summaries, release briefs, and bug checks as examples. The announcement says: “When an Automation finishes, the results land in a review queue so you can jump back in and continue working if needed.”
Use recurring runs for bounded tasks whose output a person can inspect. Treat the review queue as part of the automation, not an optional extra: examine the result, correct or refine it when needed, and only then decide whether to continue with the suggested work.
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OpenAI’s February 2, 2026 app announcement describes an internal racing-game example built by Codex using more than 7 million tokens from one initial user prompt. That is a vendor illustration, not an independent benchmark, a typical usage estimate, or a promise that a similarly broad request will produce a reliable result. The official material cited here does not establish an independent comparative benchmark for the app.
For dependable day-to-day work, prioritize a clear scope, useful repository context, an appropriate environment, and a deliberate review-and-test loop over the apparent size of any demonstration.
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