What this error means

ReadTimeout: ollama-cloud stream drop after ~120-145s on long responses is a Ollama failure pattern reported for developers trying to fix ollama cloud streaming read timeout after ~2 minutes on long generation tasks with large context. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Ollama Cloud proxy drops streaming connections after 120-145s during long generation with large context. No ping/keepalive during tool composition. Log shows: 'ollama-cloud stream drop (ReadTimeout) after 144.6s — reconnecting, retry 2/3'. Reported 2026-05-12.

Common causes

  • Developers using Ollama Cloud for long-form code generation hit streaming timeouts mid-task. Without ping/keepalive support, connections drop during tool composition or extended generation, causing lost work and failed retries.
  • Ollama Cloud proxy drops streaming connections after 120-145s during long generation with large context. No ping/keepalive during tool composition. Log shows: 'ollama-cloud stream drop (ReadTimeout) after 144.6s — reconnecting, retry 2/3'. Reported 2026-05-12.

Quick fixes

  1. Confirm the exact error signature matches ReadTimeout: ollama-cloud stream drop after ~120-145s on long responses.
  2. Check the Ollama 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.