HolmesGPT Not Finding Any Issues? Here's Why.

1. Truncation: Too Much Data

Data overflow causes important information to be truncated. See #437 for summarization improvements.

Solution:

• Use specific namespaces and time ranges
• Target individual components instead of cluster-wide queries

2. Missing Data Access

HolmesGPT can't access logs, metrics, or traces from your observability stack.

Solution:

• Verify toolset configuration connects to Prometheus/Grafana/logs
• Test connectivity: kubectl exec -it <holmes-pod> -- curl http://prometheus:9090/api/v1/query?query=up

3. RBAC Permissions

Service account lacks Kubernetes API permissions.

Error Example:

pods is forbidden: User "system:serviceaccount:default:holmesgpt" cannot get resource "pods"

Solution:

rbac:
  create: true
rbacRules:
  - apiGroups: [""]
    resources: ["pods", "services", "events", "nodes"]
    verbs: ["get", "list", "watch"]

4. Unclear Prompts

Vague questions produce poor results.

Bad:

• "Why is my pod not working?"
• "Check if anything is wrong with my cluster"
• "Something is broken in production and users are complaining"
• "My deployment keeps failing but I don't know why"
• "Can you debug this issue I'm having with my application?"

Good:

• "Why is payment-service pod restarting in production namespace?"
• "What caused memory spike in web-frontend deployment last hour?"

5. Model Issues

Older LLM models lack reasoning capability for complex problems.

Solution:

config:
  model: "gpt-4.1"  # or anthropic/claude-sonnet-4-20250514
  temperature: 0.1
  maxTokens: 2000

Recommended Models:

• anthropic/claude-opus-4-1-20250805 - Most powerful for complex investigations (recommended)
• anthropic/claude-sonnet-4-20250514 - Superior reasoning with faster performance
• gpt-4.1 - Good balance of speed/capability

See benchmark results for detailed model performance comparisons.

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Still stuck?

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