What this error means

Assistant message missing reasoning_content — LiteLLM strips it when serializing response from turn 1 is a LiteLLM failure pattern reported for developers trying to fix multi-turn chat failures when using deepseek v4 pro through litellm proxy where assistant messages lose reasoning content on sequential turns. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #26395 in BerriAI/litellm (April 23, 2026). Second conversation turn fails because LiteLLM strips reasoning_content field when serializing assistant messages between turns. Common configuration — many teams use LiteLLM as proxy for DeepSeek models. Specific, actionable error with clear symptom reproduction steps.

Common causes

  • GitHub Issue #26395 in BerriAI/litellm (April 23, 2026). Second conversation turn fails because LiteLLM strips reasoning_content field when serializing assistant messages between turns. Common configuration — many teams use LiteLLM as proxy for DeepSeek models. Specific, actionable error with clear symptom reproduction steps.

Quick fixes

  1. Confirm the exact error signature matches Assistant message missing reasoning_content — LiteLLM strips it when serializing response from turn 1.
  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.