Problem Statement
Modern microservice architectures generate high volumes of noisy alerts, creating alert fatigue and prolonging Mean Time to Resolution (MTTR). During critical production outages, the primary bottleneck is rarely diagnosing an entirely novel problem—it is finding the engineer or historical post-mortem runbook that solved the exact same failure weeks or months earlier.
To tackle this, I built OpsMind: an autonomous Site Reliability Engineering (SRE) incident responder that cross-references real-time error traces against historical failure memories using Vectorize Hindsight and runs sub-second root-cause diagnosis via Groq.
Architecture & Workflow
OpsMind operates on a continuous feedback loop:
- Alert Ingestion: Ingests service metadata, error signatures, and raw stack traces.
- Persistent Memory Recall (Vectorize Hindsight): Queries an incident memory bank to retrieve verified historical post-mortems and past mitigations matching the error signature.
- Sub-Second LLM Diagnosis (Groq): Combines the recalled runbooks with real-time logs to pinpoint root causes and formulate direct shell/kubectl remediation commands.
- Knowledge Retention (Teach Agent): When an on-call engineer mitigates a novel issue, they commit the resolution, root cause, and runbook command back to OpsMind, which retains it permanently in Hindsight for future alerts.
Key Features
- Semantic Memory Bank: Retains unstructured incident post-mortems without manual vector chunking or embedding pipeline overhead.
- Deterministic Fallback Engine: Features an in-memory runbook cache to maintain uptime during upstream network interruptions.
- SRE Control Plane: A responsive Streamlit dashboard allowing teams to simulate outages, review recalled memories, and submit verified resolutions.
Links & Demos
- Live Application: https://opsmind-incident-agent.streamlit.app
- GitHub Repository: https://github.com/MDZubair01/opsmind-incident-agent
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