What this error means

Bedrock and Vertex client _make_status_error missing 413/529 status codes — returns generic APIStatusError instead of RequestTooLargeError / OverloadedError is a Anthropic API failure pattern reported for developers trying to fix missing error type mapping so bedrock/vertex users get specific typed exceptions (413→requesttoolargeerror, 529→overloadederror) matching canonical anthropic client behavior. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Pull request anthropics/anthropic-sdk-python#1544 (May 14, 2026, still open). Canonical Anthropic SDK already handles 529 overloaded and 413 request-too-large errors, but Bedrock/Vertex path bypasses this. Enterprise users on AWS Bedrock and GCP Vertex AI receive generic errors without actionable retry logic. Maps to 'Anthropic API' category — affects paid Bedrock/Vertex usage pricing and requires proper error handling.

Common causes

  • Pull request anthropics/anthropic-sdk-python#1544 (May 14, 2026, still open). Canonical Anthropic SDK already handles 529 overloaded and 413 request-too-large errors, but Bedrock/Vertex path bypasses this. Enterprise users on AWS Bedrock and GCP Vertex AI receive generic errors without actionable retry logic. Maps to 'Anthropic API' category — affects paid Bedrock/Vertex usage pricing and requires proper error handling.

Quick fixes

  1. Confirm the exact error signature matches Bedrock and Vertex client _make_status_error missing 413/529 status codes — returns generic APIStatusError instead of RequestTooLargeError / OverloadedError.
  2. Check the Anthropic API account, local tool state, and provider configuration involved in the failing workflow.
  3. Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests.

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.