What this error means
exceeded retry limit, last status: 429 Too Many Requests, request id: 9f9fe320aa92b10b-FRA is a OpenAI API failure pattern reported for developers trying to fix 429 rate limit error handling in openai codex app when hitting response endpoint ddos-rate limits, distinguishing between usage quotas and request frequency caps. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
GitHub issue #22122 on openai/codex, opened ~6 days ago. Clear 429 error with request ID. Users confused between rate limit vs usage limit errors. Source: https://github.com/openai/codex/issues/22122.
Common causes
- GitHub issue #22122 on openai/codex, opened ~6 days ago. Clear 429 error with request ID. Users confused between rate limit vs usage limit errors. Source: https://github.com/openai/codex/issues/22122.
Quick fixes
- Confirm the exact error signature matches
exceeded retry limit, last status: 429 Too Many Requests, request id: 9f9fe320aa92b10b-FRA. - Check the OpenAI API account, local tool state, and provider configuration involved in the failing workflow.
- Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests.
Platform/tool-specific checks
- Verify the command, editor, extension, or API client that produced the error.
- Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
- Avoid deleting credentials, local model data, or project settings until the failing scope is clear.
Step-by-step troubleshooting
- Capture the exact error message and the command, editor action, or request that triggered it.
- Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
- Review the source evidence below and compare it with your environment.
- Apply one change at a time and rerun the smallest failing action.
- Keep the working fix documented for the team or deployment environment.
How to prevent it
- Keep provider/tool configuration documented.
- Record non-secret diagnostics such as tool version, provider name, model name, and command path.
- Add a lightweight check before CI or production workflows depend on the tool.