RunbookAI – Hypothesis-driven incident investigation agent(open source)

Hacker News

RunbookAI is an open-source CLI agent that investigates production incidents using hypothesis-driven reasoning. When an alert fires, it forms ranked hypotheses, runs targeted queries against your infrastructure (AWS, Kubernetes, CloudWatch), and systematically narrows to root cause. Key design decisions: - Hypothesis branching/pruning instead of broad exploration. Forms 3-5 hypotheses, tests each with causal queries, prunes dead ends, branches deeper on strong evidence (max depth: 4). - Every mutation requires human approval. Full audit trail of what the agent thought, what it queried, and why. - Pulls context from your runbooks, postmortems, and architecture docs (Confluence, Notion, Google Drive, or local markdown). The agent doesn't investigate in a vacuum. - Deep Claude Code integration — auto-injects relevant operational context into coding sessions via MCP. Try the demo without any API keys: npx @runbook-agent/runbook demo GitHub: https://github.com/Runbook-Agent/RunbookAI Would love to hear any questions/feedback!

Visit product View on Hacker NewsFeb 28, 2026

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

98%98% 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.

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

34%34% 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: google · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

24%24% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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