LiteLLM Drops reasoning_content When Converting Anthropic Thinking Blocks to OpenAI Format

What this error means

LiteLLM Anthropic to OpenAI conversion drops reasoning_content, breaks multi-turn with reasoning models is a LiteLLM / Anthropic API failure pattern reported for developers trying to fix litellm reasoning_content missing after anthropic to openai format conversion. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Thinking blocks stored in custom thinking_blocks field instead of standard reasoning_content. Breaks multi-turn with reasoning models that expect reasoning_content in subsequent turns.

Common causes

• When LiteLLM converts Anthropic responses with thinking blocks to OpenAI format, reasoning_content is not populated. Breaks multi-turn conversations with reasoning models.
• Thinking blocks stored in custom thinking_blocks field instead of standard reasoning_content. Breaks multi-turn with reasoning models that expect reasoning_content in subsequent turns.

Quick fixes

1. Confirm the exact error signature matches LiteLLM Anthropic to OpenAI conversion drops reasoning_content, breaks multi-turn with reasoning models.
2. Check the LiteLLM / Anthropic 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.

Sources checked

• https://github.com/BerriAI/litellm/issues/27946

Evidence note: Thinking blocks stored in custom thinking_blocks field instead of standard reasoning_content. Breaks multi-turn with reasoning models that expect reasoning_content in subsequent turns.

Related errors

• LiteLLM cache_control dropped
• Anthropic API thinking blocks error

FAQ

What should I check first?

Start with the exact LiteLLM Anthropic to OpenAI conversion drops reasoning_content, breaks multi-turn with reasoning models text and the smallest action that reproduces it.

Can I ignore this error?

No. Treat it as a failed LiteLLM / Anthropic API workflow until the root cause is understood.

Is this guaranteed to have one fix?

No. The imported evidence supports the troubleshooting path above, but tool behavior can vary by account, plan, version, provider, and local configuration.

How do I know the fix worked?

Rerun the same command, editor action, or request. The fix is working when that action completes without LiteLLM Anthropic to OpenAI conversion drops reasoning_content, breaks multi-turn with reasoning models.