What this error means

API Error: Server is temporarily limiting requests (not your usage limit) · Rate limited is a Claude Code / Anthropic API failure pattern reported for developers trying to developer starts claude code after idle period and immediately gets rate-limited despite not hitting usage limits; wants to understand why and how to disable or work around the spurious throttle. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Source: https://github.com/anthropics/claude-code/issues/60710 (opened 2026-05-19). Reproducible: stop using Claude Code for ~1 hour, resume, immediately hit throttling despite no quota exceeded. This is NOT a usage-quota error but an Anthropic backend artificial rate limit — affects paid users who expect full API access under their plan. Distinct from existing covered errors (which cover standard 429/quota exceeded).

Common causes

  • Source: https://github.com/anthropics/claude-code/issues/60710 (opened 2026-05-19). Reproducible: stop using Claude Code for ~1 hour, resume, immediately hit throttling despite no quota exceeded. This is NOT a usage-quota error but an Anthropic backend artificial rate limit — affects paid users who expect full API access under their plan. Distinct from existing covered errors (which cover standard 429/quota exceeded).

Quick fixes

  1. Confirm the exact error signature matches API Error: Server is temporarily limiting requests (not your usage limit) · Rate limited.
  2. Check the Claude Code / 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.