What this error means

KeyError: 'response' — Cloudflare Workers AI example code fails when using LiteLLM provider is a LiteLLM failure pattern reported for developers trying to developer follows litellm docs for cloudflare workers ai setup but encounters keyerror because response structure changed; needs fix or updated example. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue #25999 on BerriAI/litellm — sample usage from LiteLLM docs for Cloudflare Workers AI throws KeyError: 'response'. Breaking change in Cloudflare's API response format. Affects paid LiteLLM proxy users relying on Workers AI models. Distinct from general Cloudflare error coverage (7003, 5xx) as this targets LiteLLM integration-specific parsing bug. Category: LiteLLM.

Common causes

  • GitHub issue #25999 on BerriAI/litellm — sample usage from LiteLLM docs for Cloudflare Workers AI throws KeyError: 'response'. Breaking change in Cloudflare's API response format. Affects paid LiteLLM proxy users relying on Workers AI models. Distinct from general Cloudflare error coverage (7003, 5xx) as this targets LiteLLM integration-specific parsing bug. Category: LiteLLM.

Quick fixes

  1. Confirm the exact error signature matches KeyError: 'response' — Cloudflare Workers AI example code fails when using LiteLLM provider.
  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.