What this error means

LiteLLM BadRequestError: reasoning_content must be passed back to the API (DeepSeek V4 Pro multi-turn) is a LiteLLM failure pattern reported for developers trying to fix litellm breaking deepseek v4 pro multi-turn conversations by stripping reasoning_content. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

17 reactions. First turn succeeds, all subsequent turns fail with 400. Root cause: Message.__init__ in litellm/types/utils.py deletes reasoning_content when None. Old R1 behavior (strip) conflicts with V4 Pro requirement (preserve).

Common causes

  • DeepSeek V4 Pro requires reasoning_content to be passed back in multi-turn conversations, but LiteLLM strips it during serialization (carried over from R1 behavior)
  • 17 reactions. First turn succeeds, all subsequent turns fail with 400. Root cause: Message.__init__ in litellm/types/utils.py deletes reasoning_content when None. Old R1 behavior (strip) conflicts with V4 Pro requirement (preserve).

Quick fixes

  1. Confirm the exact error signature matches LiteLLM BadRequestError: reasoning_content must be passed back to the API (DeepSeek V4 Pro multi-turn).
  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.