Superlog

Description

Superlog is AI-native observability that fixes bugs on autopilot. You connect your app and Superlog groups incidents, investigates production telemetry, and prepares fixes, delivering pull requests with production fixes directly in Slack. It ingests OTLP data, groups errors into incidents, and dispatches AI agents to investigate root causes and open fix PRs automatically.

Superlog's incident control plane fingerprints and groups similar errors into clear-cut incidents, then provides a summary, a severity score (SEV1-3), and an impact assessment instead of repeated error logs, with a custom evaluation suite keeping assessments terse and relevant. It can also mark benign patterns, such as browser-extension errors, as noise so they do not alert the team. For fixes, agents can use custom context including custom MCPs, a Notion knowledge base, and Linear tickets, a memory system improves accuracy from every comment and review, and PR autopilot follows up on PRs, passes them through review, and addresses your comments.

A related Responder mode acts as an AI SRE agent that watches Sentry, Datadog, and Slack, traces each alert through your codebase, filters noise, and opens pull requests from Slack. Superlog installs in one prompt with an OTel-first setup, offers connectors for Vercel, Railway, Render, AWS, and Cloudflare, and includes a Slack bot you can @-mention. Pricing starts with a free tier (1M spans, 5M logs, 10M metric points, 30-day retention, 50 investigations/month), a pay-as-you-go plan, and paid plans from $150/month, billing telemetry per signal and investigations as credits.

Superlog's Core Features

• OpenTelemetry (OTLP) ingestion with one-prompt install

• Error fingerprinting and grouping into clear incidents

• Severity scoring (SEV1-3) and impact assessment

• AI agents that investigate root causes automatically

• Fix PRs delivered in Slack with PR autopilot follow-up

• Custom context via MCPs, Notion, and Linear plus a memory system

• Noise filtering for benign error patterns

• Connectors for Vercel, Railway, Render, AWS, and Cloudflare

How to use Superlog?

1. 1

   Connect your app: Install in one prompt with an OTel-first setup so telemetry flows in.

2. 2

   Group incidents: Let Superlog fingerprint and group similar errors into clear incidents.

3. 3

   Review severity: See a summary, severity score, and impact assessment for each incident.

4. 4

   Investigate automatically: Have AI agents root-cause the issue against your telemetry and code.

5. 5

   Merge the fix: Receive a fix PR in Slack, review it, and let PR autopilot address comments.

Superlog's Use Cases

• Automated bug fixing
• Incident triage
• Root cause analysis
• Alert response
• Noise reduction

FAQ from Superlog

• What does Superlog do?

  It provides AI-native observability that groups errors into incidents, investigates production telemetry with AI agents, and delivers pull requests with fixes in Slack.

• How does it reduce alert noise?

  Superlog fingerprints and groups similar errors into single incidents and can mark benign patterns, such as browser-extension errors, as noise so future occurrences do not alert the team.

• What is Responder?

  Responder is an AI SRE agent that watches Sentry, Datadog, and Slack, traces each alert through your codebase, filters noise, and opens pull requests from Slack with the root cause and evidence.

• How does Superlog produce good fixes?

  Agents investigate with access to your codebase, logs, and telemetry, and can use custom context such as custom MCPs, a Notion knowledge base, and Linear tickets, while a memory system learns from every comment and review.

• How does pricing work?

  There is a free tier with generous telemetry and 50 investigations per month, a pay-as-you-go plan, and paid plans from $150/month. Telemetry is billed per signal and each completed investigation counts as one credit.