Tommy-yw

Tommy-yw / RunbookHermes

Public

Hermes-native AIOps agent for evidence-driven incident response, approval-gated remediation, and runbook learning.

61
6
100% credibility
Found May 02, 2026 at 61 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

RunbookHermes extends the Hermes AI agent into a production incident-response console for payment systems, integrating observability tools for evidence collection, safe remediation with approvals, and runbook generation.

How It Works

1
🔍 Discover RunbookHermes

While dealing with a payment system outage, you find this tool that helps AI handle incidents automatically.

2
🚀 Start the demo setup

Run the simple demo to create a test payment environment with monitoring, so you can see it in action.

3
📊 Open the web console

See your services' health, logs, and traces in a live dashboard that feels like your own control room.

4
🚨 Report an incident

Tell it about a problem like rising errors, and it gathers evidence from metrics and logs on its own.

5
🛡️ Review and approve fixes

Check the smart analysis, approve safe actions, and watch it rollback or restart services carefully.

6
📚 Learn from the fix

After resolving, it creates a reusable guide from what worked, so next time is even faster.

✅ Incidents handled smarter

Your payment system stays reliable, with AI turning every fix into shared knowledge for your team.

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Star Growth

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AI-Generated Review

What is RunbookHermes?

RunbookHermes is a Python-based, hermes-native AIOps agent on GitHub that automates evidence-driven incident response, approval-gated remediation, and runbook learning for production systems like payment services. It ingests alerts from webhooks, Alertmanager, or messaging apps, pulls real observability data from Prometheus, Loki, and Jaeger, then generates root-cause summaries, action plans, and reusable skills via a web console. Developers get a full incident command center with monitoring dashboards, approval workflows, and CLI access through Hermes profiles.

Why is it gaining traction?

It stands out by layering AIOps workflows on Hermes' agent runtime—routing models, tools, memory, and safety—without rebuilding from scratch, delivering controlled remediation that verifies recovery post-fix. The local Docker Compose demo spins up a faulting payment stack for instant testing, while production hooks for Feishu/WeCom and executors like Kubernetes make it deployable fast. Early adopters notice the shift from guesswork to evidence-backed runbooks that accumulate operational knowledge.

Who should use this?

SREs and oncall engineers triaging microservices incidents in payment, e-commerce, or high-availability setups with Prometheus/Loki stacks. DevOps teams wanting human-in-loop gates before rollbacks or restarts, especially those already using Hermes for agentic tools. Ops leads evaluating AIOps for reducing MTTR without blind automation.

Verdict

Promising for Hermes users dipping into incident automation—solid docs, demo env, and safety rails make it playable today despite 61 stars and 1.0% credibility score signaling early maturity. Try the local payment sim before prod; pair with a real model provider for full value.

(198 words)

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