Open-Source DevOps · HolmesGPT

holmesgpt

HolmesGPT is an open-source AI agent for investigating production incidents and finding root causes across any infrastructure. It integrates with observability tools like Prometheus, Datadog, and Kubernetes, and can run autonomously in operator mode to detect problems 24/7 and alert teams via Slack.

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Source: GitHub — github.com/HolmesGPT/holmesgpt

2.8k

GitHub stars

403

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	HolmesGPT/holmesgpt
Owner	HolmesGPT
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	2.8k
Forks	403
Open issues	329
Latest release	0.35.0 (2026-07-01)
Last updated	2026-07-08
Source	https://github.com/HolmesGPT/holmesgpt

What holmesgpt is

A Python-based LLM agent framework that executes agentic loops against live observability data sources, with per-tool memory limits, streaming output, and support for multiple LLM providers (OpenAI, Anthropic, Bedrock, etc.). Operator mode runs in Kubernetes for continuous health checks and proactive incident detection.

Quickstart

Get the holmesgpt source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/HolmesGPT/holmesgpt.git2cd holmesgpt3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Autonomous incident investigation and root-cause analysis

Query live observability data from Prometheus, Datadog, Kubernetes logs, and databases to automatically identify root causes and present findings without human-in-the-loop triggering.

24/7 operator mode for continuous health monitoring

Deploy scheduled health checks and deployment verification tests that run continuously in the background, catch regressions early, and open remediation PRs or send Slack alerts.

Multi-tool incident response workflows

Orchestrate investigations across heterogeneous stacks (Kubernetes, VMs, cloud providers, SaaS) with bidirectional alert integrations to fetch alerts from PagerDuty, OpsGenie, or Jira and write findings back.

Implementation considerations

Credential and secret management: Multiple data source integrations (Prometheus, Datadog, Kubernetes, cloud providers) require secure storage of API keys, service account tokens, and connection strings.
LLM provider cost: Operator mode running 24/7 will accumulate usage costs; evaluate quota limits, streaming behavior, and output budgeting to avoid runaway bills.
Context window and memory tuning: Per-tool memory limits and output transformers require testing against your data volumes to avoid OOM or context exhaustion on large queries.
Custom toolset development: Out-of-box integrations may not cover all internal APIs or proprietary systems; plan for custom REST API toolsets or MCP extensions.
Alert feedback loops: Bidirectional integrations (write findings back to Jira, open PRs) require careful RBAC and review processes to prevent unintended automated changes.

When to avoid it — and what to weigh

Real-time sub-second SLA requirements — While designed for performance, HolmesGPT's agentic loop introduces latency dependent on LLM provider response time and data source query speeds. Not suitable for SLA-critical, near-instantaneous decisions.
Closed-source, audited, or air-gapped compliance environments — HolmesGPT sends data to external LLM providers (OpenAI, Anthropic, etc.) by default. Organizations requiring no cloud data egress or proprietary model control should evaluate on-prem alternatives or self-hosted LLM options first.
Minimal operational overhead or zero-touch deployment — Operator mode requires Kubernetes; integration setup demands configuration of multiple data source credentials and toolset definitions. Not a drop-in solution for teams with limited DevOps resources.
Environments with no observability infrastructure — HolmesGPT depends on existing observability platforms (Prometheus, Datadog, Elasticsearch, etc.). Greenfield deployments without monitoring in place will require building that stack first.

License & commercial use

Apache License 2.0 (Apache-2.0): A permissive OSI-approved license allowing commercial use, modification, and distribution with liability and trademark disclaimers. Source code must be disclosed; patent indemnification included.

Commercial use is permitted under Apache-2.0. However, verify that your use case complies with the license (attribution, source availability, no trademark use) and review any SaaS implications if you plan to embed HolmesGPT in a product. Third-party LLM providers (OpenAI, Anthropic) have separate commercial terms; ensure those also permit your use case.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Data egress to LLM providers: Observability data (logs, metrics, traces) is sent to external LLM services; evaluate data classification and compliance. Credential storage: Multiple integrations require API keys and tokens; use external secret management (Kubernetes Secrets, HashiCorp Vault). RBAC: Operator mode can execute remediation (scale pods, open PRs, rollback deployments); enforce least-privilege access. No security audit information provided in data; requires independent review before handling sensitive workloads.

Alternatives to consider

PagerDuty Event Intelligence / Opsgenie Automation

Managed SaaS platforms with built-in incident correlation and automation. Lower operational overhead but less customization and tighter vendor coupling than HolmesGPT.

Moogsoft or Humio (now CrowdStrike) AIOps

Enterprise AIOps platforms with ML-driven root-cause analysis. Proprietary, higher cost, but include compliance features and dedicated support HolmesGPT does not offer.

Custom LLM agent frameworks (LangChain, AutoGen, OpenAI Assistants API)

Build your own incident investigation agent with more control and lower dependencies. Requires significant engineering effort but avoids project risk if HolmesGPT development slows.

Software development agency

Build on holmesgpt with DEV.co software developers

HolmesGPT is a production-ready CNCF sandbox project with active development and strong integrations. Start with a Kubernetes operator deployment or custom toolset for your observability stack. Review data egress and LLM provider costs before full rollout.

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holmesgpt FAQ

Can HolmesGPT work without Kubernetes?

Yes, operator mode is Kubernetes-based, but HolmesGPT core can run on VMs, bare metal, or containers. Operator mode health checks can query any data source (non-K8s infrastructure supported); the agent itself just runs in K8s.

What LLM providers does HolmesGPT support?

OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Gemini, and others. You choose the provider and manage costs; no single default.

How much does it cost to run?

HolmesGPT itself is free (Apache-2.0), but operator mode running 24/7 incurs LLM API costs and integrations fees. Operator mode cost depends on query frequency, data size, and LLM pricing; requires cost modeling per environment.

Can HolmesGPT automatically remediate incidents?

Yes, via Kubernetes remediation MCP (scale pods, rollbacks, edits) or custom toolsets. It can also open PRs on GitHub/GitLab. Remediation is gated by RBAC and tool definitions; not unrestricted.

Software developers & web developers for hire

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