HolmesGPT
Open SourceAbout HolmesGPT
HolmesGPT is an open-source SRE agent for investigating production incidents across any infrastructure. It includes an Operator Mode that runs in the background to find problems continuously. It can connect to a GitHub integration to open pull requests addressing issues it finds. The operator itself runs in Kubernetes, while health checks can query other connected data sources. Users can connect additional data sources for deeper investigations. The project is part of the Cloud Native Computing Foundation sandbox.
Products
- Operator Mode: Runs in the background 24/7 to find problems and message fixes via Slack.
- Deployment Verification: Deploys a health check alongside an app to verify the new version is healthy.
- Scheduled Health Checks: Continuously monitors services and catches regressions automatically.
- CLI: Runs HolmesGPT from the terminal.
- UI/TUI: Provides a web interface or K9s plugin for using HolmesGPT.
Used for
- Investigating production incidents across infrastructure.
- Finding problems in the background around the clock.
- Verifying that a newly deployed version is healthy.
- Monitoring services continuously to catch regressions.
- Opening PRs to fix issues found during investigation.
From HolmesGPT's own website, read 2026-10-23. How we build listings.
How to evaluate AIOps & Incident Management
This is how the CIOPages Research Team evaluates this category. It is not an assessment of HolmesGPT. The category covers two kinds of product, so both frameworks are here. Most buyers need one of them.
Incident Management & On-Call
25%
Alerting & On-Call Reliability
Does the page actually arrive? Multi-channel delivery (push, SMS, phone, email) with carrier redundancy, escalation if unacknowledged, on-call schedule and rotation math, override and coverage-gap handling, and the resilience of the notification pipeline itself — this is the one job that cannot fail
20%
Alert Noise Reduction & Correlation
Event grouping and de-duplication, intelligent correlation across signals, suppression and maintenance windows, dynamic routing by service ownership, and whether the platform measurably lowers pages-per-incident rather than just forwarding every alert your monitoring emits
20%
Incident Response & Comms Orchestration
Chat-native (Slack/Teams) war-room automation, roles and severity workflows, automatic timeline capture, stakeholder and status-page updates, runbook/automation actions, and how little manual coordination an incident commander has to do under pressure
15%
Post-Incident Learning & Analytics
Blameless retro/postmortem templates, auto-drafted timelines and summaries, action-item tracking to closure, MTTA/MTTR and on-call-load reporting, recurring-incident and follow-up trend analysis — the loop that actually reduces recurrence
10%
Integration & Ecosystem Fit
Depth of connectors to your monitoring/observability, ITSM, CI/CD, and chat; bidirectional ticket sync; webhook and API coverage for automation; and how naturally it slots into the tools your responders already live in rather than demanding a new home base
10%
AI / AI-SRE Capability
Autonomous triage and investigation agents, AI root-cause hypotheses and likely-culprit-commit detection, auto-generated summaries and retros, and scheduling/override agents — weighing genuine, evaluable assistance against demo-ware, and checking what data the AI is trained on and where it runs
IT Operations Management (ITOM)
25%
Event Correlation & Noise Reduction
Deduplication and flap suppression, cross-source correlation (topology-based, time-based, text/ML-based), how an alert storm collapses into a single situation, transparency of the correlation logic (open-box vs. black-box), and the realistic false-positive and missed-incident rate on YOUR event volume
20%
Discovery, Topology & CMDB
Agent and agentless discovery breadth (cloud, on-prem, network, containers, mainframe), automated service mapping and dependency graphs, how the topology is kept current as infrastructure churns, and whether correlation depends on a CMDB you must maintain or a graph the platform builds itself
20%
AIOps Depth: Root Cause & Remediation
Probable-root-cause identification and how it is explained, causal vs. purely statistical inference, anomaly and early-warning detection, closed-loop and agentic remediation, and how models behave on noisy, incomplete production data rather than curated demo streams
15%
Integration & Tool Coverage
Breadth of inbound monitoring/observability connectors, bidirectional ITSM and incident-tool integration (ServiceNow, Jira, PagerDuty), ChatOps/collaboration hooks, open APIs and webhooks, and whether the platform plays as an overlay or expects to own the stack
10%
Scale & Performance on Real Telemetry
Sustained events-per-second ingest, query and correlation latency at peak, multi-tenant and multi-region operation, data retention and cost at your volume, and graceful behavior during the event surge of a major incident — exactly when the platform matters most
10%
Operations, Workflow & Automation
Operator and NOC ergonomics, runbook and workflow automation, on-call and escalation routing, service-health and SLA reporting for business owners, role-based access, and audit logging of automated actions
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Quick Facts
holmesgpt.devCategoryObservability & Monitoring
SubcategoryAIOps & Incident Management
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