Telemetry data - LogBrew

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

Release-aware

Logs, traces, issues, and actions include release and environment so agents can compare versions safely.

Grouped issues

Errors group by stable fingerprint so teams can track one issue across many occurrences.

Action events

Actions are product events, such as signups, checkouts, imports, or failed jobs.

Agent-readable

CLI and API reads return predictable JSON for coding agents and local tools.

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:

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

​Product model