HolmesGPT – OSS AI Agent for On-Call and Observability

Hacker News

Hi HN, I'm Natan from Robusta.dev. We open-sourced HolmesGPT, an AI agent to help on-call engineers triage and respond to alerts. It pulls observability data from your existing tools and use an LLM to correlate data, highlight the most important logs to look at, and (experimentally) open pull requests to fix the problem. We started with integrations for: * Observability tools to fetch metrics, logs, and traces * Incident response (bi-directional OpsGenie/PagerDuty) * ITSM (ServiceNow coming soon) * Slack (start investigating by tagging @holmes on an any alert) We're soon adding MCP support to bring more data into investigations. One unique feature is multimodal output - the ability to render graphs or diagrams related to the incident. That's only in our commercial platform right now, but we're thinking how to bring it to the OSS in a way that makes sense. We built this because we were tired digging through dashboards while half-asleep at 3AM. We're releasing the core as open source because we think for this to succeed it has to be able to access data from all different sources - and OSS is the best way to encourage that. Too many solutions are locked into a specific vendor that limit usefulness. (E.g. DataDog AI can't tap data in Splunk when investigating. But in the real world everyone has multiple observability tools.) We’d love feedback from the HN community - thoughts, ideas, and of course PRs.

Visit product View on Hacker NewsMar 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, mcp, slack · Missing: mac, agents, macos

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, existing · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, soon · Missing: plus, intuitive, reviews

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real world · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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