New: Operator Mode — Find Problems 24/7 in the Background

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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 c

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HolmesGPT — The CNCF SRE Agent

Installation | Docs |

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.

Data Source Notes
AKS Azure Kubernetes Service cluster and node health diagnostics
ArgoCD Get status, history and manifests and more of apps, projects and clusters
AWS RDS events, instances, slow query logs, and more (MCP)
Azure Azure resources and diagnostics (MCP)
Azure SQL Database health, performance, connections, and slow queries
Confluence Private runbooks and documentation
Confluence (MCP) Private runbooks and documentation (MCP)
Coralogix Retrieve logs for any resource
Crossplane Troubleshoot Crossplane providers, compositions, claims, and managed resources
Datadog Query logs, metrics, and traces
Docker Get images, logs, events, history and more
Elasticsearch / OpenSearch Query logs, cluster health, shard and index diagnostics
GCP Google Cloud Platform resources (MCP)
GitHub Repositories, issues, and pull requests (MCP)
GitLab Projects, merge requests, issues, and CI/CD pipelines (MCP)
Jenkins (MCP) Build status, pipeline logs, and job history (MCP)
Grafana Query and analyze dashboard configurations and panels
Helm Release status, chart metadata, and values
Internet Public runbooks, community docs, etc.
Kafka Fetch metadata, list consumers and topics or find lagging consumer groups
Kubernetes Pod logs, K8s events, and resource status (kubectl describe)
Kubernetes Remediation (MCP) Apply fixes like scaling, rollbacks, and resource edits (MCP)
Loki Query logs for Kubernetes resources or any query
MariaDB MariaDB database queries and diagnostics (MCP)
MongoDB Query data, diagnose performance, inspect schemas, find slow operations
MongoDB Atlas Cluster health, slow queries, and performance diagnostics
NewRelic Investigate alerts, query tracing data
OpenShift Projects, routes, builds, security context constraints, and deployment configs
Prefect (MCP) Workflow orchestration monitoring, flow runs, and worker health (MCP)
Prometheus Investigate alerts, query metrics and generate PromQL queries
RabbitMQ Partitions, memory/disk alerts, troubleshoot split-brain scenarios and more
Robusta Multi-cluster monitoring, historical change data, runbooks, PromQL graphs and more
ServiceNow Query tables and incident records
Sentry Error tracking, issues, and performance monitoring (MCP)
Slab Team knowledge base and runbooks on demand
Splunk Log search and analysis (MCP)
SQL Databases PostgreSQL, MySQL, ClickHouse, MariaDB, SQL Server, SQLite
Tempo Fetch trace info, debug issues like high latency in application
VictoriaLogs Query logs from VictoriaLogs using LogsQL
VictoriaMetrics Query metrics from a Prometheus-compatible TSDB (vmsingle / vmcluster)
Zabbix Monitor hosts, problems, events, triggers, and historical metrics

See the full list of built-in toolsets for additional integrations including Cilium, KubeVela, Notion, and more.

🚀 End-to-End Automation

HolmesGPT can fetch alerts/tickets to investigate from external systems, then write the analysis back to the source or Slack.

Integration Status Notes
Slack Demo. Available via Robusta
Microsoft Teams Available via Robusta
Prometheus/AlertManager Robusta or HolmesGPT CLI
PagerDuty HolmesGPT CLI only
OpsGenie HolmesGPT CLI only
Jira HolmesGPT CLI only
GitHub HolmesGPT CLI only

Installation

Read the installation documentation to learn how to install HolmesGPT.

Supported LLM Providers

Read the LLM Providers documentation to learn how to set up your LLM API key.

Using HolmesGPT

See the walkthrough documentation for usage guides, including:

🔐 Data Privacy

By design, HolmesGPT has read-only access and respects RBAC permissions. It is safe to run in production environments.

License

Distributed under the Apache 2.0 License. See LICENSE for more information.

Community

Join our community to discuss the HolmesGPT roadmap and share feedback:

Support

If you have any questions, feel free to message us on HolmesGPT Slack Channel

How to Contribute

Please read our CONTRIBUTING.md for guidelines and instructions.

For help, contact us on Slack or ask DeepWiki AI your questions.

Please make sure to follow the CNCF code of conduct - details here.

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Evidence46/100medium confidence
Why MDRSS assigned this score
  • Production catalog audit 2026-08-04
  • Taxonomy classified from title, annotation, source and Markdown signals
  • Agent usefulness evaluated from structure, procedures, examples, evidence and retrieval value
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