What this error means

Invalid 'input[1].id': 'chatcmpl-dfa2da3a-1586-4ff7-b64e-f59c692a5d11'. Expected an ID that begins with 'msg' is a LiteLLM failure pattern reported for developers trying to fix litellm response api bridge passing chat completions-style chatcmpl-* ids as message item ids into native openai responses endpoint, causing openai to reject the request. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #27333 in BerriAI/litellm, addressed by PR #28692 (May 23, 2026). Cross-provider handoff (Claude/LiteLLM → OpenAI Responses) fails because bridged output retains chatcmpl-* IDs. OpenAI Responses spec requires msg_* prefixed IDs. High production value — breaks agent multi-step tool call workflows.

Common causes

  • GitHub Issue #27333 in BerriAI/litellm, addressed by PR #28692 (May 23, 2026). Cross-provider handoff (Claude/LiteLLM → OpenAI Responses) fails because bridged output retains chatcmpl-* IDs. OpenAI Responses spec requires msg_* prefixed IDs. High production value — breaks agent multi-step tool call workflows.

Quick fixes

  1. Confirm the exact error signature matches Invalid 'input[1].id': 'chatcmpl-dfa2da3a-1586-4ff7-b64e-f59c692a5d11'. Expected an ID that begins with 'msg'.
  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.