AI-Powered Observability & Log Analysis
OpenCrawling leverages native Spring Boot 4, OpenTelemetry (OTel) correlated spans, and Spring AI Model Context Protocol (MCP) tools to transform complex multi-threaded trace telemetry into automated Root Cause Analysis (RCA) reports.
🔍 Correlated OTel Traces
Every ingestion pipeline stage—Scanning, Extracting, Chunking, Embedding, and Indexing—generates distributed OpenTelemetry spans correlated by unique jobId.
Tracing: Scanning (450ms) → Extracting (1200ms) → Chunking (300ms) → Embedding (1800ms) → Indexing (650ms)
🛠️ System MCP Tools
Exposes system-level Model Context Protocol tools exclusively to the Admin Copilot, enabling LLM agents to query live traces, exception stack traces, and Micrometer performance metrics.
fetch_job_traces(jobId)
get_error_logs(jobId)
query_throughput_metrics(connectorId)
⚡ "Diagnose with AI" Action
System administrators click "Diagnose with AI" in oc-admin-ui to trigger live Root Cause Analysis reports, bottleneck insights, and automated resolution recommendations.
Status: FAILED → RCA: Downstream Vector DB insertion timed out after 30s during pgvector batch flush.