What this error means
AzureOpenAI with AAD bearer token passed via api_key works in 2.33.0 but returns 401 Unauthorized in 2.34.0 is a OpenAI API failure pattern reported for developers trying to fix azure openai authentication regression where aad bearer tokens stopped working after sdk upgrade, causing production auth failures. 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#3282 (May 20, 2026). This is a breaking SDK regression affecting Azure OpenAI enterprise users who authenticate via AAD bearer tokens. The 401 error blocks all API calls after upgrading past v2.33.0. Directly maps to 'OpenAI API' category since it impacts paid Azure OpenAI usage and billing.
Common causes
- GitHub issue openai/openai-python#3282 (May 20, 2026). This is a breaking SDK regression affecting Azure OpenAI enterprise users who authenticate via AAD bearer tokens. The 401 error blocks all API calls after upgrading past v2.33.0. Directly maps to 'OpenAI API' category since it impacts paid Azure OpenAI usage and billing.
Quick fixes
- Confirm the exact error signature matches
AzureOpenAI with AAD bearer token passed via api_key works in 2.33.0 but returns 401 Unauthorized in 2.34.0. - 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.