Documentation Index
Fetch the complete documentation index at: https://docs.stellartrail.io/llms.txt
Use this file to discover all available pages before exploring further.
Monitoring & Health
StellarTrail provides a suite of health endpoints and integrates with several observability tools to ensure reliable operation across all services.Health Endpoints
All health endpoints are served under/api/health. Public endpoints require no authentication. Admin endpoints require the x-admin-secret header.
| Endpoint | Auth | Purpose |
|---|---|---|
/api/health | No | Basic status check with overall degradation signal |
/api/health/live | No | Kubernetes-style liveness probe |
/api/health/ready | No | Readiness probe with dependency checks |
/api/health/detailed | Admin | Full dependency details including memory, uptime, and provider status |
/api/health/queues | No | Queue summary with aggregate job counts |
/api/health/queues/details | Admin | Per-queue job statistics |
/api/health/coach | No | AI Coach service availability |
/api/health/charting | No | Market data provider availability |
status field (typically ok or degraded). The /ready endpoint returns 503 when the server cannot accept traffic; all other public endpoints return 200 unless a service is fully unavailable.
Background Queues
StellarTrail uses BullMQ backed by Redis for asynchronous job processing. Nine queues handle distinct workloads:| Queue | Purpose | Concurrency |
|---|---|---|
usage | Persist usage records | 5 |
trade-index | Vector indexing of trades | 3 |
email | Transactional email delivery | 5 |
cleanup | Maintenance and data cleanup | 1 |
brokerage-sync | Plaid brokerage synchronization | 2 |
reports | Performance report generation | 3 |
notifications | User notification dispatch | 5 |
market-snapshot | Daily market summary snapshots | 1 |
ai-signal-generation | AI-powered trading signal generation | 1 |
Monitoring Tools
StellarTrail integrates the following tools for production observability:- Sentry — Error tracking and performance monitoring. Captures unhandled exceptions across both the server and client with full stack traces and request context.
- PostHog — Product analytics and feature flag management. Tracks user engagement, feature adoption, and conversion funnels.
- Pino — Structured JSON logging on the server. Provides request-level tracing with correlation IDs for debugging production issues.
- LangSmith — LLM observability and tracing. Records AI Coach prompt chains, token usage, and latency for each conversation turn.
- BullBoard — Admin dashboard for inspecting queue state. Provides a visual interface for monitoring job progress, retrying failed jobs, and draining queues.