> ## Documentation Index
> Fetch the complete documentation index at: https://docs.logbrew.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Telemetry data

> How LogBrew organizes logs, traces, issues, actions, and releases for humans and AI agents.

LogBrew keeps observability data organized around projects, services, releases,
environments, and signal types. Public docs focus on the product contract: what
to send, how signals relate, and how humans or AI agents read the data back.

## Product model

<CardGroup cols={2}>
  <Card title="Release-aware" icon="tag">
    Logs, traces, issues, and actions include release and environment so agents
    can compare versions safely.
  </Card>

  <Card title="Grouped issues" icon="bug">
    Errors group by stable fingerprint so teams can track one issue across many
    occurrences.
  </Card>

  <Card title="Action events" icon="mouse-pointer-click">
    Actions are product events, such as signups, checkouts, imports, or failed
    jobs.
  </Card>

  <Card title="Agent-readable" icon="bot">
    CLI and API reads return predictable JSON for coding agents and local tools.
  </Card>
</CardGroup>

## What to send

* `release` and `environment` on every telemetry envelope.
* Stable names for services, actions, and issue fingerprints.
* Trace context when logs, spans, actions, and issues belong to the same user
  journey.
* Canonical severity values: `info`, `warning`, `error`, and `critical`.
* Useful attributes for debugging, without secrets or unnecessary personal data.

## What agents can rely on

Agents should start broad, then narrow with filters:

```bash theme={null}
logbrew read releases --environment production --json
logbrew read issues --release api@1.2.3 --status unresolved --json
logbrew read logs --release api@1.2.3 --since 24h --json
logbrew read actions --name checkout_failed --since 24h --json
logbrew read trace <trace_id> --json
```