What this error means
Unknown beta requested: 'realtime' — invalid_beta error on OpenAI Realtime API is a OpenAI API failure pattern reported for developers trying to fix openai realtime api invalid_beta error after openai-beta realtime header deprecation. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
LiteLLM PR #27690 (May 2026) documents the OpenAI API deprecation. realtime_translation_testing CircleCI job fails on every PR with: {"error": {"code": "invalid_beta", "message": "Unknown beta requested: 'realtime'."}} followed by TimeoutError: Timed out waiting for 'session.created'. OpenAI deprecated the OpenAI-Beta: realtime=v1 header.
Common causes
- OpenAI deprecated the
OpenAI-Beta: realtime=v1header on the live Realtime API. Applications still sending this header receive{'error': {'code': 'invalid_beta', 'message': "Unknown beta requested: 'realtime'."}}followed by a TimeoutError waiting for 'session.created'. This breaks CI/CD pipelines and production integrations that haven't been updated. - LiteLLM PR #27690 (May 2026) documents the OpenAI API deprecation. realtime_translation_testing CircleCI job fails on every PR with: {"error": {"code": "invalid_beta", "message": "Unknown beta requested: 'realtime'."}} followed by TimeoutError: Timed out waiting for 'session.created'. OpenAI deprecated the OpenAI-Beta: realtime=v1 header.
Quick fixes
- Confirm the exact error signature matches
Unknown beta requested: 'realtime' — invalid_beta error on OpenAI Realtime API. - 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.