LiteLLM Continuous False-Positive Slack Alerts for LLM Hanging Requests

What this error means

llm_requests_hanging alert fires continuously for requests below 600s threshold is a LiteLLM failure pattern reported for developers trying to fix litellm llm_requests_hanging alert spam when requests are not actually hanging. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

LiteLLM issue #27855 reports continuous false-positive Slack alerts for llm_requests_hanging. Alert fires for multiple models even though request times are below the 600s threshold. Affects production monitoring reliability.

Common causes

• The llm_requests_hanging Slack alert fires continuously reporting 600s+ request time even when actual request times are well below the threshold. This causes alert fatigue in production monitoring and wastes engineering time investigating false positives.
• LiteLLM issue #27855 reports continuous false-positive Slack alerts for llm_requests_hanging. Alert fires for multiple models even though request times are below the 600s threshold. Affects production monitoring reliability.

Quick fixes

1. Confirm the exact error signature matches llm_requests_hanging alert fires continuously for requests below 600s threshold.
2. Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
3. Compare the failing environment with a known working setup, then change one configuration value at a time.

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/27855

Evidence note: LiteLLM issue #27855 reports continuous false-positive Slack alerts for llm_requests_hanging. Alert fires for multiple models even though request times are below the 600s threshold. Affects production monitoring reliability.

Related errors

• LiteLLM monitoring alert threshold misconfiguration
• LiteLLM request timeout handling bug

FAQ

What should I check first?

Start with the exact llm_requests_hanging alert fires continuously for requests below 600s threshold 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 llm_requests_hanging alert fires continuously for requests below 600s threshold.