LiteLLM Proxy Failover Fails Due to APIConnectionError in cooldown_handlers

What this error means

APIConnectionError is a LiteLLM failure pattern reported for developers trying to fix litellm proxy failover not working when a deployment host is unreachable. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Bug report (2026-05-07) identifies root cause: cooldown_handlers.py line 57 has hardcoded ignored_strings=[‘APIConnectionError’], causing _is_cooldown_required() to return False, preventing failover to healthy deployments in the same model group.

Common causes

• LiteLLM is a paid proxy/router for multi-model LLM deployments. When failover doesn’t work due to hardcoded APIConnectionError exclusion, all requests to dead hosts fail without routing to healthy deployments — causing production outages.
• Bug report (2026-05-07) identifies root cause: cooldown_handlers.py line 57 has hardcoded ignored_strings=[‘APIConnectionError’], causing _is_cooldown_required() to return False, preventing failover to healthy deployments in the same model group.

Quick fixes

1. Confirm the exact error signature matches APIConnectionError.
2. Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
3. Check the build output, project root, and deployment platform configuration before redeploying.

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/BerriAI/litellm/issues/27362

Evidence note: Bug report (2026-05-07) identifies root cause: cooldown_handlers.py line 57 has hardcoded ignored_strings=[‘APIConnectionError’], causing _is_cooldown_required() to return False, preventing failover to healthy deployments in the same model group.

Related errors

• LiteLLM router cooldown not triggered for connection errors
• LiteLLM deployment failover not working

FAQ

What should I check first?

Start with the exact APIConnectionError text and the smallest action that reproduces it.

Can I ignore this error?

No. Treat it as a failed LiteLLM 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 APIConnectionError.