What this error means

OPENAI_API_KEY exists but is not valid for the official OpenAI Responses endpoint is a OpenAI API failure pattern reported for developers trying to fix invalid openai api key for official responses endpoint in github actions ci pipeline despite key being set in repository secrets. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Found at MiaoDX/roboclaws#111 (opened May 19, 2026, ~6 hours ago, labeled blocked/ready-for-human). The OPENAI_API_KEY GitHub secret is confirmed present (timestamp verified), but requests to the official OpenAI Responses endpoint fail with invalid key error. Indicates a mismatch between how the key was generated/scoped vs what the Responses endpoint requires. Blocks CI/CD pipelines for teams paying for OpenAI API usage.

Common causes

  • Found at MiaoDX/roboclaws#111 (opened May 19, 2026, ~6 hours ago, labeled blocked/ready-for-human). The OPENAI_API_KEY GitHub secret is confirmed present (timestamp verified), but requests to the official OpenAI Responses endpoint fail with invalid key error. Indicates a mismatch between how the key was generated/scoped vs what the Responses endpoint requires. Blocks CI/CD pipelines for teams paying for OpenAI API usage.

Quick fixes

  1. Confirm the exact error signature matches OPENAI_API_KEY exists but is not valid for the official OpenAI Responses endpoint.
  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.