Open-source AI agent for investigating production incidents and finding root causes. Works with any stack — Kubernetes, VMs, cloud providers, databases, and SaaS platforms. We are a Cloud Native Computing Foundation sandbox project. Originally created by Robusta.Dev, with major contributions from Microsoft.
New: Operator Mode — Find Problems 24/7 in the Background
Most AI agents are great at troubleshooting problems, but still need a human to notice something is wrong and trigger an investigation. Operator mode fixes that — HolmesGPT runs in the background 24/7, spots problems before your customers notice, and messages you in Slack with the fix. Connect the GitHub integration and it can even open PRs to fix what it finds.
While the operator itself runs in Kubernetes, health checks can query any data source Holmes is connected to — VMs, cloud services, databases, SaaS platforms, and more.
Features
- Petabyte-scale data: Server-side filtering, JSON tree traversal, and tool output transformers keep large payloads out of context windows
- Memory-safe execution: Per-tool memory limits, streaming large results to disk, and automatic output budgeting prevent OOM kills when querying large observability datasets
- Deep integrations: Prometheus, Grafana, Datadog, Kubernetes, and many more—plus any REST API
- Bidirectional alert integrations: Fetch alerts from AlertManager, PagerDuty, OpsGenie, or Jira—and write findings back
- Any LLM provider: OpenAI, Anthropic, Azure, Bedrock, Gemini, and more
- No Kubernetes required: Works with any infrastructure — VMs, bare metal, cloud services, or containers
How it Works
HolmesGPT uses an agentic loop to query live observability data from multiple sources and identify root causes.
🔗 Data Sources
HolmesGPT integrates with popular observability and cloud platforms. The following data sources ("toolsets") are built-in. Add your own.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
