Calmo

Automatically captures stack traces, request context, and system state from production errors through instrumentation hooks or log aggregation integrations. Enriches raw error data with source maps, variable snapshots, and execution timeline context to reconstruct the exact state when failures occurred, enabling developers to understand root causes without reproduction.

Uses LLM-based analysis to examine error traces, logs, and system state to generate hypotheses about failure root causes. The system patterns-matches against known failure modes, analyzes code paths, and correlates timing with system events to produce ranked explanations with confidence scores and suggested fixes, reducing manual investigation time.

Generates code patches or configuration changes directly from error analysis results. The system understands the error context, examines the relevant source code, and produces targeted fixes (bug patches, configuration corrections, dependency updates) with explanations of why the fix resolves the issue. Fixes are presented as diffs or pull request suggestions.

Traces errors across microservices and distributed systems by correlating request IDs, timing, and service dependencies. Automatically maps which upstream service failures caused downstream errors, reconstructs the full request path through the system, and identifies the true origin of failures that manifest in multiple services. Uses distributed tracing standards (OpenTelemetry, Jaeger) for integration.

Uses semantic analysis and pattern matching to group similar errors across different manifestations. Errors with identical root causes but different stack traces, error messages, or triggering conditions are automatically clustered together. Deduplication reduces alert fatigue by surfacing unique issues rather than variants of the same problem, and enables trend analysis across error families.

Ranks errors by business impact using context about user count affected, service criticality, error frequency trends, and business metrics. Combines error severity with impact analysis to surface the most urgent issues first. Learns from past incident severity to improve prioritization over time, and suppresses low-impact errors to reduce noise.

Automatically generates incident response runbooks from error analysis, historical incident data, and known remediation patterns. Produces step-by-step guides for on-call engineers including diagnostic commands, rollback procedures, and escalation paths. Runbooks are customized to the specific error and organization's infrastructure, and improve over time as incidents are resolved.

Reconstructs the execution context of production errors by replaying the request through the system with captured state. Captures variable values, function arguments, and execution flow at error time, then allows engineers to step through the execution path interactively. Integrates with IDE debuggers for familiar debugging experience without requiring local reproduction.

Monitors application performance metrics (latency, throughput, resource usage) and automatically detects regressions by comparing against historical baselines. Uses statistical analysis to identify significant performance changes, correlates regressions with code deployments and infrastructure changes, and generates hypotheses about performance causes. Integrates with APM systems for metric collection.