What this error means

RouterRateLimitError: No deployments available for selected model, Try again in X seconds (no Retry-After header) is a LiteLLM failure pattern reported for developers trying to fix missing retry-after header in litellm routerratelimiterror so downstream clients can properly handle rate limit cooldown. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Open issue on BerriAI/litellm with associated PR. RouterRateLimitError raised without Retry-After header when all model deployments are in cooldown after upstream 429s. Affects automated retry logic in production systems.

Common causes

  • LiteLLM is widely used as an AI model gateway. When all deployments are in cooldown, the lack of Retry-After header means downstream clients (OpenAI SDK, custom agents) can't programmatically retry, causing cascading failures.
  • Open issue on BerriAI/litellm with associated PR. RouterRateLimitError raised without Retry-After header when all model deployments are in cooldown after upstream 429s. Affects automated retry logic in production systems.

Quick fixes

  1. Confirm the exact error signature matches RouterRateLimitError: No deployments available for selected model, Try again in X seconds (no Retry-After header).
  2. Check the LiteLLM 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.