What this error means

ERROR :root: LiteLLM call failed: litellm.APIConnectionError: OllamaException - litellm.Timeout: Connection timed out after 600.0 seconds. is a LiteLLM failure pattern reported for developers trying to fix litellm hanging 600 seconds on ollama connection timeout instead of raising proper error quickly; understand why stream_timeout and regular timeout settings don't work for watsonx/ollama backends. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Sources: https://github.com/BerriAI/litellm/issues/7996 (Jan 2025), https://github.com/BerriAI/litellm/issues/7870 (stream_timeout not working for watsonx). Users running LiteLLM proxy routing to Ollama/watsonx get stuck waiting 600s. High commercial value for teams using LiteLLM as paid proxy. Category: LiteLLM per mapping rules.

Common causes

  • Sources: https://github.com/BerriAI/litellm/issues/7996 (Jan 2025), https://github.com/BerriAI/litellm/issues/7870 (stream_timeout not working for watsonx). Users running LiteLLM proxy routing to Ollama/watsonx get stuck waiting 600s. High commercial value for teams using LiteLLM as paid proxy. Category: LiteLLM per mapping rules.

Quick fixes

  1. Confirm the exact error signature matches ERROR :root: LiteLLM call failed: litellm.APIConnectionError: OllamaException - litellm.Timeout: Connection timed out after 600.0 seconds..
  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.