Documentation
All docs are available as raw Markdown for LLM consumption. See llms.txt for the machine-readable index.
Getting Started
- Getting Started 5-minute quickstart — add NarrativeTrace to a Java project, capture your first trace
Core Concepts
- Annotations @Narrated, @OnError, @NotTraced, @NarrativeSummary reference
- Capture Levels Five levels from OFF to DETAIL — two-gate architecture
- Output Formats Markdown, JSON, and prose rendering from a single trace
- Clarity Diagnostics Code quality scoring from runtime narrative
- Diagrams Mermaid and PlantUML sequence diagram generation
Integrations
- Spring Boot @EnableNarrativeTrace, async support, Spring Boot starter
- JUnit 5 Per-test trace files, scenario framing, clarity reporting
- JUnit 4 @Rule-based integration for legacy test suites
- SLF4J Bridge MDC fields, coexistence with existing logging
- Java Agent Zero-code ASM bytecode transformation
- Micrometer Cross-thread context propagation for @Async and CompletableFuture
- Gradle Plugin clarityCheck and clarityScan tasks for CI integration
Pro Tier
- Pro Overview AI integration, advanced analytics, and compliance features — early access
- Aggregation Flow summaries, migration diffs, path frequency analysis
- Dependency Graphs Runtime dependency visualization as Mermaid graphs
- MCP Server Model Context Protocol server for AI coding agents — coming soon
- Audit & SecOps @AuditEvent, @SecurityEvent, compliance policies
- Audit Specification Deterministic inference, field masking, data classification
Architecture
- Narrative/Value Separation Core design principle — separating structure from runtime values
- Output Format Specification Canonical JSON, Markdown, and prose format specs
- Module Structure Free and Pro module organization and dependencies