Anthropic API Overloaded Error — Client-side Retry Logic Fails on Rate Limit

What this error means

overloaded_error: We're experiencing high demand — please retry after X seconds is a Anthropic API failure pattern reported for developers trying to fix handling of anthropic api overloaded_error (429) responses when making multiple concurrent requests through sdk or proxy. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #672 in anthropics/anthropic-sdk-python shows client-side retry logic fails to properly handle Anthropic’s overloaded_error. Affects production workflows using Claude API with automatic retries. High commercial value as API usage is pay-per-token and failures block paid operations. Category mapped to Anthropic API per approved mapping.

Common causes

• GitHub Issue #672 in anthropics/anthropic-sdk-python shows client-side retry logic fails to properly handle Anthropic’s overloaded_error. Affects production workflows using Claude API with automatic retries. High commercial value as API usage is pay-per-token and failures block paid operations. Category mapped to Anthropic API per approved mapping.

Quick fixes

1. Confirm the exact error signature matches overloaded_error: We're experiencing high demand — please retry after X seconds.
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.

Sources checked

• https://github.com/anthropics/anthropic-sdk-python/issues/672

Evidence note: GitHub Issue #672 in anthropics/anthropic-sdk-python shows client-side retry logic fails to properly handle Anthropic’s overloaded_error. Affects production workflows using Claude API with automatic retries. High commercial value as API usage is pay-per-token and failures block paid operations. Category mapped to Anthropic API per approved mapping.

Related errors

• Anthropic API

FAQ

What should I check first?

Start with the exact overloaded_error: We're experiencing high demand — please retry after X seconds text and the smallest action that reproduces it.

Can I ignore this error?

No. Treat it as a failed Anthropic API workflow until the root cause is understood.

Is this guaranteed to have one fix?

No. The imported evidence supports the troubleshooting path above, but tool behavior can vary by account, plan, version, provider, and local configuration.

How do I know the fix worked?

Rerun the same command, editor action, or request. The fix is working when that action completes without overloaded_error: We're experiencing high demand — please retry after X seconds.