What this error means

OpenAI's Unknown parameter 400 errors get misclassified as 429 RateLimitError when the rejected field name contains 'rate_limit' substring (e.g., _litellm_rate_limit_descriptors) is a LiteLLM failure pattern reported for developers trying to stop false positive rate_limit classification when openapi rejects unknown parameters containing rate_limit in their names; fix exception mapping in litellm proxy. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

BerriAI/litellm#27915 identifies a regex-based substring match that catches too broadly: any upstream 400 containing 'rate_limit' in the field name gets routed to RateLimitError path. Causes incorrect retry/backoff behavior for genuine parameter errors.

Common causes

  • BerriAI/litellm#27915 identifies a regex-based substring match that catches too broadly: any upstream 400 containing 'rate_limit' in the field name gets routed to RateLimitError path. Causes incorrect retry/backoff behavior for genuine parameter errors.

Quick fixes

  1. Confirm the exact error signature matches OpenAI's Unknown parameter 400 errors get misclassified as 429 RateLimitError when the rejected field name contains 'rate_limit' substring (e.g., _litellm_rate_limit_descriptors).
  2. Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
  3. Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests.

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.