HolmesGPT SRE Agent
An SRE agent for investigating production incidents and finding root causes.
Per-dimension scores and reasoning
Evidence shows: README claims read-only access by default and respects RBAC, but no implementation details; Kubernetes Remediation toolset can apply fixes, but user confirmation mechanism not explicit; data flow transparency limited, no details on data handling/storage; sensitive data handling not specified; dependency security: pyproject.toml pins multiple CVE-fixed versions, showing attention; external effects: writable operations exist (e.g., Kubernetes Remediation), but default behavior not clear; rollback mechanism not explicit; source attribution: README and LICENSE clearly identify Robusta Dev LTD and major contributors. Deductions: user confirmation, data flow transparency, sensitive data handling, external effects, and rollback lack concrete implementation evidence.
Evidence shows: extensive test suite (pytest config, CI workflow), but static review cannot verify test pass; dependency availability: many dependencies listed, but no lock file or mirror; failure messages: error handling shown in tests and docs, but not fully assessed. Deductions: test execution unverified, dependency locking unclear.
Evidence shows: README lists many data sources and scenarios (Kubernetes, cloud, databases); capability boundaries via toolsets, but not explicit limits; trigger precision: CLI and API interfaces, but trigger conditions not detailed; environment fit: supports multiple LLM providers and deployment methods. Deductions: capability boundaries and trigger precision lack detailed documentation.
Evidence shows: README well-structured with installation, usage, data sources sections; install notes point to external docs; naming stable, project name and CLI command consistent; examples and FAQ links present; known limitations not explicitly listed; license Apache-2.0 with full LICENSE file; version in pyproject.toml is 0.0.0, but no CHANGELOG; maintenance responsibility via CONTRIBUTING and community channels. Deductions: known limitations and versioning/changelog missing.
Evidence shows: output usability: CLI and API return structured results (e.g., JSON); marginal value: automated investigation and remediation features provide clear value; cost-benefit: open-source free, but requires LLM API key, cost depends on usage. Deductions: no actual performance or cost data.
Evidence shows: README claims (e.g., read-only) not verified in code; cross-source corroboration limited, only README and pyproject.toml; fact-inference separation unclear, README feature descriptions may include inferences. Deductions: lack of code-level verification and independent sources.
- Static review cannot verify test pass or actual behavior; dynamic testing recommended.
- User confirmation and rollback mechanisms not explicit; caution when using writable operations.
- Dependency locking unclear; use lock file for reproducibility.
What does this agent do, and when should you use it?
HolmesGPT is an open-source SRE agent for production-incident investigation and root-cause analysis, and it is a CNCF Sandbox project. It uses an agentic loop to query live observability data from multiple sources and assemble an investigation. The repository describes a CLI workflow for interactive questions, Prometheus-alert investigations, and CI/CD troubleshooting. Built-in toolsets span Kubernetes, Prometheus, Grafana, Datadog, cloud platforms, databases, logging systems, and ticketing systems, with several integrations delivered through MCP. Its Kubernetes-based Operator mode can run deployment verification and scheduled health checks, then send findings to Slack.
HolmesGPT reads alerts, logs, metrics, traces, resource status, database diagnostics, and runbook content from connected toolsets. Its agentic loop makes follow-on queries across data sources to investigate an incident and produce root-cause findings; the README describes server-side filtering, JSON tree traversal, and tool output transformers for controlling context input. It can fetch items to investigate from AlertManager, PagerDuty, OpsGenie, or Jira, then write findings back to the originating system or Slack. In Kubernetes, Operator mode can run deployment verification and scheduled health checks; with the GitHub MCP integration connected, the README says it can open PRs to fix identified problems.
- An SRE responding to a Prometheus or AlertManager alert who needs to combine metrics, logs, and Kubernetes resource status.
- A platform team deploying Kubernetes workloads that wants deployment verification after a new application version is released.
- An on-call engineer who needs to pull incidents from PagerDuty, OpsGenie, or Jira and return the investigation outcome to that system.
- A team using Datadog, Grafana, Loki, Tempo, or Elasticsearch/OpenSearch that needs to correlate multiple observability sources during an incident.
- An operations team coordinating incidents in Slack or Microsoft Teams that wants automated findings delivered to its chat workflow.
- An infrastructure team operating VMs, bare metal, cloud services, or containers without making Kubernetes a prerequisite for incident investigation.
What are this agent's strengths and limitations?
- Its agentic loop is designed to combine logs, metrics, traces, resource status, and runbook material in one incident investigation.
- The README specifically describes server-side filtering, output transformation, disk streaming, and per-tool memory limits for large observability datasets.
- It can begin from alerting or ticketing systems and write findings back to the source or to Slack.
- It offers both CLI use and Kubernetes-based continuous Operator mode, while the core product does not require Kubernetes.
- It names multiple LLM providers, including OpenAI, Anthropic, Azure, Bedrock, and Gemini, rather than documenting a single-provider dependency.
- The supplied material lacks install commands, CLI examples, configuration schemas, and version requirements, adding uncertainty to first deployment.
- Investigation coverage depends on connected data sources, their RBAC permissions, and an LLM API key; no offline mode is documented.
- Operator mode explicitly runs in Kubernetes, so continuous background checks require a Kubernetes environment.
- Many important integrations depend on external services or MCP, including GitHub, AWS, Azure, GCP, Jenkins, GitLab, and Sentry.
- The README describes the default design as read-only, but Kubernetes Remediation (MCP) and GitHub PR creation introduce potentially mutating capabilities that require careful permission and configuration boundaries.
How do you install or deploy this agent?
The supplied material says installation is covered by HolmesGPT CLI installation documentation and that a supported LLM provider API key must be configured. It does not provide a copyable install command, version requirement, configuration schema, or runtime prerequisites, so no verified command can be given from this material alone. For Operator mode, the material explicitly states that the operator runs in Kubernetes.
How do you use this agent?
The supplied material confirms CLI workflows for interactive mode, Prometheus-alert investigation, and CI/CD troubleshooting, and it confirms the need for an LLM API key and connected data sources. It does not include CLI subcommands, flags, configuration keys, or a first runnable invocation, so accurate copyable usage steps cannot be derived from it. The documented flow is to connect toolsets, let HolmesGPT query live data and generate investigation findings, or use Operator mode for deployment verification and scheduled health checks.