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It supports auto-instrumentation across Node.js, Python, Java, and Go, and exports telemetry to backends like Jaeger, Grafana, Datadog, and Honeycomb through a flexible Collector pipeline.\n\n## Instructions\n\n- When adding tracing, create spans with meaningful names, set span kinds (`CLIENT`, `SERVER`, `PRODUCER`, `CONSUMER`), add business-relevant attributes, and use W3C Trace Context for propagation.\n- When adding metrics, choose the right instrument type: Counter for monotonic values, Histogram for distributions like latency, UpDownCounter for fluctuating values, and Gauge for point-in-time readings.\n- When setting up auto-instrumentation, use the language-specific packages (`@opentelemetry\u002Fauto-instrumentations-node`, `opentelemetry-instrumentation` for Python, etc.) to capture HTTP, database, and messaging spans without code changes.\n- When configuring the OTel Collector, define pipelines with receivers (OTLP, Prometheus), processors (batch, memory_limiter, tail_sampling), and exporters (OTLP, Jaeger, Datadog) in the collector config.\n- When deploying Collectors, choose sidecar mode for per-pod collection, agent mode for per-node, or gateway mode for centralized processing.\n- When setting resource attributes, always include `service.name`, `service.version`, and `deployment.environment`, and use cloud\u002Fcontainer resource detectors for infrastructure metadata.\n- When naming attributes, follow OTel semantic conventions (`http.request.method`, `db.system`, `messaging.system`) instead of inventing custom names.\n\n## Examples\n\n### Example 1: Add distributed tracing to a Node.js microservice\n\n**User request:** \"Instrument my Express API with OpenTelemetry tracing\"\n\n**Actions:**\n1. Install `@opentelemetry\u002Fauto-instrumentations-node` and OTLP exporter\n2. Configure SDK with service name, version, and `BatchSpanProcessor`\n3. Set up OTLP exporter pointing to the Collector endpoint\n4. Add custom spans with business attributes for key operations\n\n**Output:** An auto-instrumented Express API sending traces to the OTel Collector with correlated spans across services.\n\n### Example 2: Set up an OTel Collector pipeline\n\n**User request:** \"Configure an OTel Collector to receive traces and export to Grafana Tempo\"\n\n**Actions:**\n1. Define OTLP gRPC receiver in the Collector config\n2. Add batch processor and memory_limiter for production safety\n3. Configure Tempo exporter with endpoint and authentication\n4. Wire the traces pipeline: receiver -> processor -> exporter\n\n**Output:** A Collector config file routing traces from applications to Grafana Tempo with batching and memory protection.\n\n## Guidelines\n\n- Always set `service.name` and `service.version` as resource attributes.\n- Use semantic conventions for attribute names; never invent custom names when a standard exists.\n- Configure `BatchSpanProcessor` in production, not `SimpleSpanProcessor`, to avoid blocking the application.\n- Set `memory_limiter` processor on the Collector to prevent OOM crashes.\n- Sample in production: `TraceIdRatioBased(0.1)` captures 10% of traces, sufficient for most services.\n- Add custom attributes to spans for business context (`user.tier`, `feature.flag`, `order.total`).\n- Never log sensitive data in span attributes (PII, secrets, 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