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

  1. 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.
  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.