What this error means

CrashLoopBackOff with Reason: OOMKilled, Exit Code 137 is a Kubernetes failure pattern reported for developers trying to diagnose and fix pods stuck in crashloopbackoff specifically caused by memory limits being exceeded (oomkilled); determine whether to increase resources or fix memory leaks. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Comprehensive troubleshooting guides available on Dash0, Netdata, Komodor, Sysdig, CNCF blog (updated Mar 2026). CrashLoopBackOff triggered by OOMKilled is the #1 cause of pod restart loops. kubectl logs --previous shows last crash context. Root causes include: application memory leak, insufficient resources.limits.memory, missing init container setup, configmap/secret mount failures. Commercial impact: production cluster instability for teams running microservices on EKS/GKE/AKS. Category mapping: Kubernetes → Cloud Platforms (per category rules). This covers the specific OOMKilled sub-case which is the most impactful trigger.

Common causes

  • Comprehensive troubleshooting guides available on Dash0, Netdata, Komodor, Sysdig, CNCF blog (updated Mar 2026). CrashLoopBackOff triggered by OOMKilled is the #1 cause of pod restart loops. kubectl logs --previous shows last crash context. Root causes include: application memory leak, insufficient resources.limits.memory, missing init container setup, configmap/secret mount failures. Commercial impact: production cluster instability for teams running microservices on EKS/GKE/AKS. Category mapping: Kubernetes → Cloud Platforms (per category rules). This covers the specific OOMKilled sub-case which is the most impactful trigger.

Quick fixes

  1. Confirm the exact error signature matches CrashLoopBackOff with Reason: OOMKilled, Exit Code 137.
  2. Check the Kubernetes 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.