What this error means

@openai/codex codex-cli v0.133.0 fails during startup when using an OpenRouter model provider profile; codex_models_manager refreshes the available model catalog is a OpenAI API failure pattern reported for developers trying to fix codex cli crashing on startup when configured with openrouter model provider, preventing all agent operations. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue #24286 on openai/codex (2026-05-24). The failure happens before prompt handling — codex models manager crashes while refreshing the model catalog from OpenRouter. Affects paid Codex users who rely on OpenRouter as a model provider. Tier 0 GitHub REST API result.

Common causes

  • GitHub issue #24286 on openai/codex (2026-05-24). The failure happens before prompt handling — codex models manager crashes while refreshing the model catalog from OpenRouter. Affects paid Codex users who rely on OpenRouter as a model provider. Tier 0 GitHub REST API result.

Quick fixes

  1. Confirm the exact error signature matches @openai/codex codex-cli v0.133.0 fails during startup when using an OpenRouter model provider profile; codex_models_manager refreshes the available model catalog.
  2. Check the OpenAI API account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

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.