What this error means

InvalidRequestError: 400 InvalidRequestError {'message': "Invalid type: 'None'. Expected one of ('string', 'list[string]', 'list[{\"type\": \"text\", \"text\": ...}\]') - 'content' from {'role': 'user'}.", 'type': 'invalid_request_error', 'param': 'messages', 'code': None} is a OpenAI API failure pattern reported for developers trying to fix openai chat completions api rejecting requests where user message content is null or empty — common in streaming/conversational uis after user cancels input. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Very common developer mistake: sending messages with null/empty content field after refactor or streaming cancellation. Not in covered list (which covers 401/429/model-not-found but NOT invalid request payload 400s). Strong commercial value as it breaks production chat apps.

Common causes

  • Very common developer mistake: sending messages with null/empty content field after refactor or streaming cancellation. Not in covered list (which covers 401/429/model-not-found but NOT invalid request payload 400s). Strong commercial value as it breaks production chat apps.

Quick fixes

  1. Confirm the exact error signature matches InvalidRequestError: 400 InvalidRequestError {'message': "Invalid type: 'None'. Expected one of ('string', 'list[string]', 'list[{\"type\": \"text\", \"text\": ...}\]') - 'content' from {'role': 'user'}.", 'type': 'invalid_request_error', 'param': 'messages', 'code': None}.
  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.