What this error means
AAD bearer token passed via api_key works in 2.33.0 but returns 401 in 2.34.0 and after is a OpenAI API failure pattern reported for developers trying to fix sudden 401 authentication failure when upgrading azure-openai sdk that previously worked on older versions. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
GitHub issue #3282 in openai/openai-python by pfijen, opened May 20 2026. Regression in SDK v2.34.0 breaking Azure AD OAuth auth flow. Affects enterprise users who rely on AAD token-based auth — high commercial value due to billing/production impact.
Common causes
- GitHub issue #3282 in openai/openai-python by pfijen, opened May 20 2026. Regression in SDK v2.34.0 breaking Azure AD OAuth auth flow. Affects enterprise users who rely on AAD token-based auth — high commercial value due to billing/production impact.
Quick fixes
- Confirm the exact error signature matches
AAD bearer token passed via api_key works in 2.33.0 but returns 401 in 2.34.0 and after. - Check the OpenAI API account, local tool state, and provider configuration involved in the failing workflow.
- Verify the account session, API key, provider settings, and environment where the failing tool is running.
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.