AIOps MCP – Log anomaly detection using Isolation Forest

What it does

In the maker’s words, at launch

I built an open-source AIOps MCP (Monitoring & Control Plane) that detects anomalies in logs using Isolation Forest. It accepts logs from agents, apps, or collectors, parses and extracts features, and identifies unusual patterns in real time. Alerts can be sent to Slack, Webhooks, or PagerDuty. It’s lightweight, easy to deploy with Kubernetes & Helm, and designed to plug into existing observability stacks. I built this to experiment with combining ML-based anomaly detection and flexible alerting for DevOps/SRE teams. Most AIOps platforms are either too heavyweight or closed-source — I wanted something minimal yet effective. You can try it by running the FastAPI app locally or deploying with Helm. Contributions are welcome — I’d love feedback on features, detection accuracy, and real-world use cases! GitHub: https://github.com/kishorealliiita/aioops-mcp-iforest

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