What this error means

LiteLLM Responses API silently drops cache_control on input_text content blocks during transformation is a LiteLLM / Anthropic API failure pattern reported for developers trying to fix litellm cache_control not forwarding to anthropic when using responses api. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Bug confirmed in litellm v1.83.14. In _transform_responses_api_content_to_chat_completion_content(), input_text handler builds output dict with only type and text, ignoring cache_control. Tools correctly preserve cache_control at lines ~1400-1401.

Common causes

  • Prompt caching directives sent via Responses API are silently lost, causing higher API costs and slower responses. Tools correctly preserve cache_control but text content does not.
  • Bug confirmed in litellm v1.83.14. In _transform_responses_api_content_to_chat_completion_content(), input_text handler builds output dict with only type and text, ignoring cache_control. Tools correctly preserve cache_control at lines ~1400-1401.

Quick fixes

  1. Confirm the exact error signature matches LiteLLM Responses API silently drops cache_control on input_text content blocks during transformation.
  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.