LiteLLM stream_timeout not enforced on first chunk causes silent hangs in production APIs

What this error means

stream_timeout not enforced on first chunk; timeout should raise error at N seconds but actual behavior delays error raising significantly beyond configured timeout is a LiteLLM failure pattern reported for developers trying to fix litellm stream_timeout enforcement so timeout errors trigger at correct time rather than being ignored on first chunk, preventing silent hangs in production api calls. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #19909 on BerriAI/litellm (opened Jan 27, 2026). LiteLLM fails to enforce stream_timeout on first chunk — timeout error only raised much later than expected. Affects teams with strict SLA requirements on LLM API responses. Production-grade bug with clear commercial impact.

Common causes

• GitHub Issue #19909 on BerriAI/litellm (opened Jan 27, 2026). LiteLLM fails to enforce stream_timeout on first chunk — timeout error only raised much later than expected. Affects teams with strict SLA requirements on LLM API responses. Production-grade bug with clear commercial impact.

Quick fixes

1. Confirm the exact error signature matches stream_timeout not enforced on first chunk; timeout should raise error at N seconds but actual behavior delays error raising significantly beyond configured timeout.
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/19909

Evidence note: GitHub Issue #19909 on BerriAI/litellm (opened Jan 27, 2026). LiteLLM fails to enforce stream_timeout on first chunk — timeout error only raised much later than expected. Affects teams with strict SLA requirements on LLM API responses. Production-grade bug with clear commercial impact.

Related errors

• LiteLLM

FAQ

What should I check first?

Start with the exact stream_timeout not enforced on first chunk; timeout should raise error at N seconds but actual behavior delays error raising significantly beyond configured timeout 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 stream_timeout not enforced on first chunk; timeout should raise error at N seconds but actual behavior delays error raising significantly beyond configured timeout.