What this error means

Hooks stop firing after rate-limit stops or live hooks.json edits is a OpenAI API failure pattern reported for developers trying to fix claude code hooks system breaking when rate limits are hit mid-session or when hooks.json is edited dynamically, stopping all further hook processing until restart. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue #21160 on openai/codex, opened ~12 days ago. 1 comment, clearly marked bug. Affects developers using custom hook scripts with rate-limited API usage. Source: https://github.com/openai/codex/issues/21160.

Common causes

  • GitHub issue #21160 on openai/codex, opened ~12 days ago. 1 comment, clearly marked bug. Affects developers using custom hook scripts with rate-limited API usage. Source: https://github.com/openai/codex/issues/21160.

Quick fixes

  1. Confirm the exact error signature matches Hooks stop firing after rate-limit stops or live hooks.json edits.
  2. Check the OpenAI API account, local tool state, and provider configuration involved in the failing workflow.
  3. 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

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. 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.