Point your logs at HeliosLogs and search, chart, monitor, and investigate them. A pipelined query language, dashboards, alerting, and an AI agent that does the digging.
Ingest with a single request, query with a language built for logs, and let the agent do the digging.
Push NDJSON over HTTP — or point an existing shipper at the drop-in endpoints.
curl 'https://logs.example.com/api/ingest?index=app' \
-H 'content-type: application/x-ndjson' \
--data-binary @app.ndjsonFull-text and fields up front, pipeline operators for the analytics.
service:checkout level:error
| timechart span=5m count by statusSkip the query. Describe the problem and read the answer.
Why did checkout 5xx spike at 14:00, and which dependency is to blame?
Dashboards, fast search, and an AI agent that charts its own findings — all in the same UI.
Search with a histogram, and live tail, and AI assisted investigations.
A production service-health dashboard — totals, error tiers, and live charts.
Setup AI monitors in plain English; the agent monitors the system and alerts
Keep your shippers. HeliosLogs speaks the protocols your tools already emit, so you can migrate without re-instrumenting a thing.
Most log stacks are a project to operate. HeliosLogs is one process.
# laptop or production — the same binary
helios serve --port 7300 --data-dir ./data
# scale out: point several nodes at one store
helios serve --shared-store s3://logs-bucket/heliosSREs, platform teams, and security engineers who want answers from their logs — not a second job operating the log system.
Full-text, fields, and pipeline analytics over high-cardinality logs — with field discovery and live tail.
Threshold and AI monitors watch on a schedule and alert you — with the agent's reasoning attached to the alert.
RBAC, SAML SSO, scoped API keys, an encrypted control plane, and a FIPS 140-3 build for regulated environments.
Grab the binary, send some logs, and run your first query — the quickstart walks you through it end to end.