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
- 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. - Check the Anthropic API account, local tool state, and provider configuration involved in the failing workflow.
- 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
- Capture the exact error message and the command, editor action, or request that triggered it.
- Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
- Review the source evidence below and compare it with your environment.
- Apply one change at a time and rerun the smallest failing action.
- 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.