What this error means

Bifrost v1.5.2: OpenAI→Anthropic tool-payload translation breaks all tool-using requests routed to Anthropic — GatewayClientRequestError: FailoverError is a Bifrost failure pattern reported for developers trying to fix bifrost proxy failing to translate openai tool specs to anthropic format. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

v1.5.2 regression. OpenAI-format tool specs routed to Anthropic produce schema validation error 400. Same payload works for all other providers. Breaks every tool-using client through Bifrost.

Common causes

  • Bifrost v1.5.2 breaks all tool-using clients (OpenClaw, OpenAI SDK) when routing to Anthropic models. The OpenAI→Anthropic tool-payload translation produces invalid schema. Same request works fine to OpenAI, Gemini, xAI, Ollama.
  • v1.5.2 regression. OpenAI-format tool specs routed to Anthropic produce schema validation error 400. Same payload works for all other providers. Breaks every tool-using client through Bifrost.

Quick fixes

  1. Confirm the exact error signature matches Bifrost v1.5.2: OpenAI→Anthropic tool-payload translation breaks all tool-using requests routed to Anthropic — GatewayClientRequestError: FailoverError.
  2. Check the Bifrost 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.