HAhamzmu·Mar 16, 2026Submission
Open Source tool to detect On-Call Burnout from incident response patterns
5 points · Open discussionRootly-AI-Labs
on-call-health
Python2026–2026
Rootly-AI-Labs / on-call-health
on-call-health
On-call Health: identify signs that incident responders are overworked.
3Distinct people
6Mentions
0Explicit recommenders
46GitHub stars
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What it is
On-call Health: identify signs that incident responders are overworked.
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by week6 dated mentions
Observed mentions: 2026-02-11 14:26:11 UTC to 2026-03-16 03:42:01 UTC. Weeks start Monday, UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Feb 9 | 2026-02-09 00:00:00 UTC | 2026-02-16 00:00:00 UTC | 1 | 1 | 0 |
| Feb 16 | 2026-02-16 00:00:00 UTC | 2026-02-23 00:00:00 UTC | 1 | 1 | 0 |
| Feb 23 | 2026-02-23 00:00:00 UTC | 2026-03-02 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2 | 2026-03-02 00:00:00 UTC | 2026-03-09 00:00:00 UTC | 1 | 1 | 0 |
| Mar 9 | 2026-03-09 00:00:00 UTC | 2026-03-16 00:00:00 UTC | 1 | 1 | 0 |
| Mar 16 | 2026-03-16 00:00:00 UTC | 2026-03-23 00:00:00 UTC | 2 | 1 | 1 |
What Hacker News says
6 sourcesHi all! I'm one of the developers at Rootly, an incident management platform.
We built On-Call Health, an open source tool from Rootly AI Labs that helps teams detect early burnout from incident response patterns. It connects to tools engineers already use (Rootly, PagerDuty, GitHub, Slack, Jira, Linear) to surface workload trends that traditional incident dashboards miss.
What we noticed:
-Incident volume alone doesn’t show the full picture. When alerts happen matter as much as how many occur
-After-hours interruptions add up. Late-night pages, weekend work, and repeated disruptions often drive burnout more than raw incident volume.
-Workload snowballs. Incident response happens alongside development work, reviews, tickets, and meetings.
-Look for trends, not only metrics. Changes in workload trends reveal signs of stress before dashboar
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ds
What On-Call Health does:
-Detects workload patterns (after-hours, consecutive on call days, uneven incidents, etc.)
-Compute a risk level from incident response data, engineering workloads, and work-pattern signals
-Tracks trends relative to each engineer’s baseline instead of static thresholds
-Slack check ins to combine operational data with how responders feel
The goal is to help teams catch burnout signals early. Try it with mock data here and checkout the blog to learn more:
Website: oncallhealth.ai
GitHub: github.com/Rootly-AI-Labs/On-Call-Health
Blog: dev.to/hamza_2315/on-call-burnout-what-incident-data-doesnt-show-2kap
Would love to hear feedback from the community. Feel free to comment below or open an issue on github!
Show HN: On-Call Health – spot burnout before it hits your engineers
We built On-Call Health because our industry has gotten very good at keeping systems healthy, but does little for the engineers on call managing them.
On-call can be rough: 97% of engineers experience stress during incidents, 87% after, and 56% experience sleep disruption even without getting paged.
We integrate with devtools like Rootly, PagerDuty, Linear, Jira, and Slack (for self-reported data). The tool doesn't provide a diagnosis, but it surfaces signals that a team or individual might be struggling.
It's free to use and open-source (we also have a hosted version with mock data if you want to try it out quickly). Happy to answer questions about the data or how we built it.
2 points · Open discussionCatch exhaustion before it burns out your engineers
1 points · Open discussionIdentify signs that incident responders are overworked
1 points · Open discussionShow HN: On-Call Health – Spot signs of overload in incident responders
Hi HN,
We built On-Call Health to help teams detect signs of overload in on-call incident responders. Burnout is too common for SREs and other on-call engineers and that’s who we serve at Rootly. We hope to put a dent in this problem with this tool.
Here is the project GitHub repo https://github.com/Rootly-AI-Labs/On-Call-Health, and here is the hosted version https://oncallhealth.ai. The easiest way to try the tool is to log into the hosted version, which has mock data.
The tool uses two types of inputs:
- Observed signals from tools like Rootly, PagerDuty, GitHub, Linear, and Jira (incident volume and severity, after-hours activity, task load…)
- Self-reported check-ins, where responders periodically share how they're feeling
We provide a "risk level", a compound score derived from objective data. The self-reported check-in feature is t
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aking inspiration from the Ecological Momentary Assessment (EMA), a research methodology also used by Apple Health's State of Mind feature.
We provide trend data for all metrics, for both teams and individuals, to help managers spot anomalies that may require investigation. Our tool doesn't provide a diagnosis, nor is it a medical tool; it simply highlights signals.
It can help spot two types of potential issues:
1. Existing high load: when setting up the tool, teams and individuals with a high risk level should be looked at. A high score doesn't always mean there's a problem – for example, some people thrive on high-severity incidents – but it can be a sign that something is already wrong.
2. Growing risk: over time, if risk levels are steeply climbing above a team or individual baseline.
Users can consume the findings via our dashboard, AI-generated summaries, our API, or our MCP server.
The project is fully open source and self-hostable, and the hosted version is available at no cost. We have a ton of ideas to improve the tool to make on-call suck les,s and we are happily accepting PR and welcome feedback on our GitHub repo. You can reach out directly to me at jj@rootly.com