Rootly-AI-Labs

Rootly-AI-Labs / rootly-graphify-importer

Public

Turn Rootly incidents, alerts, and teams into a queryable knowledge graph. Visualize service dependencies, on-call coverage gaps, and cross-incident patterns.

rootly.com graphify in incident-management sre
11
2
69% credibility
Found Apr 12, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Tool that fetches incidents, alerts, and teams from Rootly to build an interactive knowledge graph for pattern discovery.

How It Works

1
📖 Discover the tool

You hear about a simple way to turn your team's incident history into a visual map of problems, teams, and fixes.

2
🛠️ Get it ready

With one easy step, you add this helper to your computer so it's all set up.

3
🔗 Link your incident records

You share a private password from your incident tracker so it can pull your real data safely.

4
⚡ Pull data and create map

It grabs your recent incidents, alerts, and teams, then builds a colorful, clickable map showing what's connected.

5
🔍 Explore patterns

Open the map in your web browser and use simple filters for teams, severity, or time to spot repeating issues and weak spots.

6
Want deeper insights?
👀
Just explore

Use the map's filters and colors to understand your incidents visually.

🧠
Add AI insights

Let an AI scan for patterns, causes, and suggestions to make the map even smarter.

🎉 Unlock team wisdom

Now you see which services fail together, team coverage gaps, and action items that need attention – all in one clear picture.

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Star Growth

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AI-Generated Review

What is rootly-graphify-importer?

This Python tool pulls incidents, alerts, teams, and service data from the Rootly API into a local knowledge graph you can query and visualize. It reveals service dependencies, on-call coverage gaps, and cross-incident patterns via an interactive HTML graph with filters for teams, severity, status, and time ranges. Run `graphify rootly` after setting your API key to get graph.html for exploration and a report on god nodes and suggested questions.

Why is it gaining traction?

Unlike raw Rootly dashboards, it builds a graph-based view spotting hidden correlations—like services failing together or alert noise funnels—without embeddings or vector DBs. Semantic enrichment via Claude Code or Codex adds inferred edges for patterns, with caching for token-efficient re-runs. Filters and Leiden clustering make complex incident histories scannable in seconds.

Who should use this?

SREs and incident responders at Rootly shops analyzing postmortems for recurring issues. On-call managers hunting coverage gaps or single points of failure across teams. DevOps leads visualizing alert-to-incident flows to cut noise.

Verdict

Worth a spin for Rootly users needing graph-powered insights—solid docs and CLI make it dead simple despite 11 stars and 0.7% credibility score signaling early maturity. Pair with semantic mode for depth, but test on recent data first as it's a fresh importer.

(187 words)

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