TraceMind is an early-stage platform being built to help engineers investigate failures across distributed microservices by correlating logs, traces, service context and AI-assisted analysis.
TraceMind is currently under active development. We are building and validating the core observability pipeline, distributed tracing, centralized logging and AI-assisted incident investigation capabilities.
Connecting distributed services into one investigation workflow.
The initial focus is making distributed failures easier to understand.
Correlate events across multiple microservices using a common trace ID.
Collect structured application logs from distributed services.
Use AI to analyze incident context and suggest probable causes and debugging steps.
Group related failures into incidents instead of investigating isolated errors.
Analyze service relationships, logs and traces to identify probable root causes.
Future work will explore AI agents for continuous incident investigation.
TraceMind is being developed incrementally.
Structured logging, trace correlation, Kafka and centralized storage.
Correlate failures across distributed services.
AI-powered incident explanation and debugging recommendations.
Anomaly detection, error clustering and incident similarity.
Explore autonomous investigation and incident-response assistance.