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=v1 header 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

  1. Confirm the exact error signature matches Unknown beta requested: 'realtime' — invalid_beta error on OpenAI Realtime API.
  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.