Observability

This document describes how we configure OpenTelemetry (OTel) instrumentation, what we export, and how to investigate system behavior using metrics, traces, and logs.

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OTel Setup

The system uses OpenTelemetry SDKs in Go (API, bridge, workers) and Python (adapters). Configuration is driven by environment variables:

• OTEL_EXPORTER_OTLP_ENDPOINT – OTLP collector endpoint.
• OTEL_EXPORTER_OTLP_HEADERS – additional headers for exports.
• OTEL_SERVICE_NAME – service identifier ("duragraph-api", "duragraph-bridge", "duragraph-pyworker", etc.).
• OTEL_LOG_LEVEL – log verbosity for OTel instrumentation.
• OTEL_RESOURCE_ATTRIBUTES – additional dimensions (deployment, project, region).

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Telemetry Data

Traces

• Run lifecycle (/runs endpoints → Translator → Bridge → Temporal client).
• Activities (e.g. llm_call, tool).
• SSE streaming events correlation.
• Search attributes (e.g. run_id, thread_id, assistant_id) are added as trace attributes.

Metrics

• Run start latency histogram.
• Workflow activity duration.
• Active runs count.
• SSE stream lag and dropped events.
• Error rates.

Logs

• Structured logs with run_id / thread_id context.
• Logs emitted from API requests, workflow execution, and activity handlers.

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Grafana Dashboards & Queries

Typical Grafana panels:

• Run Start Latency (p95):

  histogram_quantile(0.95, sum(rate(run_start_seconds_bucket[5m])) by (le))

• Run Success Rate:

  sum(rate(run_completed_total{status="success"}[5m]))
  /
  sum(rate(run_completed_total[5m]))

• SSE Stream Gaps:

  sum(rate(sse_gap_total[5m])) by (endpoint)

• Worker Activity Duration (avg):

  rate(activity_duration_seconds_sum[5m])
  /
  rate(activity_duration_seconds_count[5m])

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Correlating Run IDs to Traces

Every run_id is attached as:

• A trace attribute (run_id).
• A log field.
• A metric label.

This allows navigation between:

• API request logs filtered by run_id.
• Grafana panels filtering by run_id.
• Trace explorer views (Jaeger/Tempo) that show span trees for individual runs.

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