What this error means

Background responses failures lack a stable code/name that maps to an exception class — HTTP poll returns 200 OK with status=failed but no typed exception is raised is a OpenAI API failure pattern reported for developers trying to developer using client.responses.create(background=true) cannot handle failure cases programmatically because the sdk returns 200 with failed payload instead of raising a typed exception. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Found on openai/openai-python#3212 (open, 2026-05-08, 3 comments). SDK-level bug in OpenAI Responses API — background polling always returns 200 even on failure, so no exception type maps to it. Blocks production error handling on paid API. Maps to approved category OpenAI API.

Common causes

  • Found on openai/openai-python#3212 (open, 2026-05-08, 3 comments). SDK-level bug in OpenAI Responses API — background polling always returns 200 even on failure, so no exception type maps to it. Blocks production error handling on paid API. Maps to approved category OpenAI API.

Quick fixes

  1. Confirm the exact error signature matches Background responses failures lack a stable code/name that maps to an exception class — HTTP poll returns 200 OK with status=failed but no typed exception is raised.
  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.