superloglabs/superlog
Open-source observability tool that uses AI agents to self-heal your software observed · 2026-08-28
Health v2 · maintenance only
58/100
- Activity 99
- Release rhythm 35
- Longevity 6
Flags: no_releases young
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 92
- days_rel: n/a
- days_push: 7
- n_releases_24m: 0
Adoption not part of the score
1402 stars · 105 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Superlog is an open-source agentic observability platform that ingests OpenTelemetry traces, logs, and metrics, groups noisy error signals into incidents, and dispatches AI agents to investigate root causes and open fix PRs automatically. It ships as a self-hostable community edition (web app, OTLP ingest proxy, workers, Postgres/ClickHouse backend) plus a hosted cloud offering with Datadog/Sentry imports and Slack integration.
Use cases
- self-host an OpenTelemetry observability stack for traces logs and metrics
- group noisy errors into incidents automatically
- have an AI agent investigate production errors and open fix PRs
- import errors from Datadog or Sentry into an open-source workspace
- query high-cardinality telemetry locally with ClickHouse
- get incident context in my coding agent via MCP
- monitor Vercel Railway Render AWS or Cloudflare logs without manual wiring
- ask questions about my telemetry in Slack
When to choose
- you want an open-source, self-hosted alternative to Datadog or Sentry with OpenTelemetry-first ingestion
- your team wants automated root-cause investigation and AI-generated fix PRs from production errors
- you need a local-first telemetry workspace with incident grouping and ClickHouse-backed queries
- you want incident context available to coding agents through an MCP server
When to avoid
- you need a battle-tested, long-proven APM for large enterprise fleets with strict compliance requirements
- you only need simple log aggregation without AI investigation features
- you cannot run Docker/Postgres/ClickHouse infrastructure or prefer a fully managed vendor
- your telemetry stack is not OpenTelemetry-based and you lack Datadog/Sentry to import from
Facets
service · maturity active
monitoring tracing logging alerting agent-framework mcp llm-inference monitoring artificial-intelligence developer-tools self-hosted self-hosted cloud observability opentelemetry agentic-observability incident-management self-healing otlp clickhouse fix-prs datadog sentry devops ai-agents docker nodejs web-server
5 sources
- readme: https://github.com/superloglabs/superlog · fetched 2026-08-28 · 15d4301ed633
- homepage: https://superlog.sh · fetched 2026-08-29 · ceb17a9c830a
- site_page: https://docs.superlog.sh · fetched 2026-08-29 · 5a91b0d9c34c
- site_page: https://superlog.sh/changelog · fetched 2026-08-29 · 8b6256ff23b3
- site_page: https://superlog.sh/pricing · fetched 2026-08-29 · 9bde9405aaca
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| superloglabs/superlog | main | 58 |
For agents
markdown · JSON · MCP: product_card(name="superloglabs/superlog")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem