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korrel8r

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Correlation of observability signals.

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# Korrel8r

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- **[User Guide](https://korrel8r.github.io/korrel8r)** - Complete installation, usage, and reference guide
- **[GitHub Issues](https://github.com/korrel8r/korrel8r/issues)** - Report bugs or request features
- **[Contributing](CONTRIBUTING.md)** - Contributing and development

## What is Korrel8r?

Korrel8r is a rule-based correlation engine for Kubernetes clusters.

It connects cluster resources and observability signals — logs, metrics, alerts, traces, network flows — into a graph of related data.
It searches multiple back-ends with different APIs and query languages automatically.
Given any starting point — an alert, a pod, a deployment — it finds related data across multiple signal stores,
such as Prometheus, Loki, Tempo, and the Kubernetes API.

Neighborhood Search
: Find everything reachable within N steps.
  _"Show me everything related to this pod"_ — returns owning deployments, logs, metrics, network flows, and more.

Goal Search
: Find paths to a specific type of data.
  _"Find all logs related to this alert"_ — korrel8r connects the alert to deployments, deployments to pods, pods to logs.

### Use cases

Interactive troubleshooting
: The [OpenShift troubleshooting panel](https://docs.redhat.com/en/documentation/red_hat_openshift_cluster_observability_operator/1-latest/html/ui_plugins_for_red_hat_openshift_cluster_observability_operator/troubleshooting-ui-plugin) displays interactive correlation graphs using Korrel8r as a back-end. Click to jump to relevant resources directly, without manually discovering the intermediate relationships.

AI-agent troubleshooting
: Korrel8r provides an [MCP](https://korrel8r.github.io/korrel8r/docs/reference/mcp/) interface for AI agents. Its rule-based engine searches and correlates large data sets faster than an AI agent can. It builds a small, focused graph of relevant signals, so the AI can concentrate on understanding the problem rather than spending tokens discovering connections.

Custom automation
: Korrel8r provides REST and MCP interfaces, can run as a cluster service or a command, and can be used as a Go library.
Embed it in custom automation solutions that need correlation data.

### Extending

Rules
: Take data from a "start" class to generate a "goal" query for related data.
Rules are written as YAML files using Go template syntax.

Domains
: A "domain" defines a signal type, query language, and data store.
New domains can be added to the engine. Coding is required.

## Installing

``` bash
# Latest release
go install github.com/korrel8r/korrel8r/cmd/korrel8r@latest # Executable
docker pull quay.io/korrel8r/korrel8r:latest                # Image

# Daily images from main for development, not tested for release.
docker pull quay.io/korrel8r/korrel8r:dev-latest
```

See all available images at: https://quay.io/repository/korrel8r/korrel8r?tab=tags

## Contributing

We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for how to get started.

- **[Project Board](https://github.com/orgs/korrel8r/projects/3)** - Current work and project status
- **[Pull Requests](https://github.com/korrel8r/korrel8r/pulls)** - Submit contributions

### AI-Assisted Development

Korrel8r includes custom commands for Claude Code to streamline development:
- `/generate-rule` - Interactive assistant for creating correlation rules.
- See [.claude/commands/](.claude/commands/) for available commands.

## License

This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.

korrel8r

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