What this error means
Responses API background=True returns status='failed' with HTTP 200 — SDK raises no typed exception, no machine-readable failure identifier is a OpenAI API failure pattern reported for developers trying to get stable exception class for failed openai responses api background runs so production code can catch and handle failures reliably.. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
GitHub issue openai/openai-python#3212: When Responses API runs in background mode and terminates with status='failed' or 'incomplete', the SDK returns HTTP 200 and never invokes the status-code dispatcher, so no typed exception is raised. Callers have no way to distinguish failure types programmatically. Created 2026-05-08, 3 comments.
Common causes
- GitHub issue openai/openai-python#3212: When Responses API runs in background mode and terminates with status='failed' or 'incomplete', the SDK returns HTTP 200 and never invokes the status-code dispatcher, so no typed exception is raised. Callers have no way to distinguish failure types programmatically. Created 2026-05-08, 3 comments.
Quick fixes
- Confirm the exact error signature matches
Responses API background=True returns status='failed' with HTTP 200 — SDK raises no typed exception, no machine-readable failure identifier. - Check the OpenAI API account, local tool state, and provider configuration involved in the failing workflow.
- 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
- 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.