What this error means

litellm.BadRequestError when using llm_as_a_judge guardrail with self-hosted vllm model is a LiteLLM failure pattern reported for developers trying to fix litellm guardrail evaluation errors when using self-hosted vllm models like gemma. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

LiteLLM issue #27767 reports BadRequestError when using llm_as_a_judge guardrail with self-hosted vLLM model (google/gemma-4-31B-it). Both vllm provider and OpenAI compatible endpoint provider fail. Affects AI safety evaluation workflows.

Common causes

  • Developers using LiteLLM's llm_as_a_judge guardrail with self-hosted vLLM models (e.g., google/gemma-4-31B-it) get BadRequestError. Both vllm and openai-compatible provider configurations fail. This blocks AI safety/evaluation pipelines that rely on guardrails.
  • LiteLLM issue #27767 reports BadRequestError when using llm_as_a_judge guardrail with self-hosted vLLM model (google/gemma-4-31B-it). Both vllm provider and OpenAI compatible endpoint provider fail. Affects AI safety evaluation workflows.

Quick fixes

  1. Confirm the exact error signature matches litellm.BadRequestError when using llm_as_a_judge guardrail with self-hosted vllm model.
  2. Check the LiteLLM 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.