What this error means
SDK parses response text on response.output_text.done event before terminal response.incomplete status — incomplete_details.reason not yet known is a OpenAI API failure pattern reported for developers trying to fix incorrect parsing of structured output json when openai api returns incomplete responses (truncation or max_tokens). Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
OpenAI openai-python #3263 (2026-05-18): Python SDK's streaming structured output helper auto-parses on done event before knowing if response will be marked incomplete. Affects paid API users using Pydantic models with responses API. Category: OpenAI API (direct API library error). Not covered in covered-errors.md (distinct from generic 429/model-not-found).
Common causes
- OpenAI openai-python #3263 (2026-05-18): Python SDK's streaming structured output helper auto-parses on done event before knowing if response will be marked incomplete. Affects paid API users using Pydantic models with responses API. Category: OpenAI API (direct API library error). Not covered in covered-errors.md (distinct from generic 429/model-not-found).
Quick fixes
- Confirm the exact error signature matches
SDK parses response text on response.output_text.done event before terminal response.incomplete status — incomplete_details.reason not yet known. - 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.