What this error means

Bedrock and Vertex _make_status_error methods missing 413→RequestTooLargeError and 529→OverloadedError mapping (canonical Anthropic client already maps these) is a Anthropic API failure pattern reported for developers trying to fix anthropic sdk bedrock/vertex clients not raising correct exception types for 413 request too large and 529 overloaded errors. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Reported on anthropics/anthropic-sdk-python#1544 (2026-05-14). The Bedrock and Vertex clients' _make_status_error methods are missing two status codes that the canonical client maps: 413→RequestTooLargeError and 529→OverloadedError. Production impact for users calling Claude via AWS Bedrock or GCP Vertex. Category: Anthropic API (SDK error mapping issue).

Common causes

  • Reported on anthropics/anthropic-sdk-python#1544 (2026-05-14). The Bedrock and Vertex clients' _make_status_error methods are missing two status codes that the canonical client maps: 413→RequestTooLargeError and 529→OverloadedError. Production impact for users calling Claude via AWS Bedrock or GCP Vertex. Category: Anthropic API (SDK error mapping issue).

Quick fixes

  1. Confirm the exact error signature matches Bedrock and Vertex _make_status_error methods missing 413→RequestTooLargeError and 529→OverloadedError mapping (canonical Anthropic client already maps these).
  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.