
Configuring OpenTelemetry in .NET
FreeAdd observability to your ASP.NET Core applications.
Free · Opens the source repo
What Configuring OpenTelemetry in .NET does
Configuring OpenTelemetry in .NET is a skill designed for developers looking to implement distributed tracing, metrics, and logging in their ASP.NET Core applications. By leveraging the .NET OpenTelemetry SDK, this skill guides users through the process of adding observability features to their applications, making it easier to monitor performance and troubleshoot issues across microservices. It is particularly useful for those who need to set up OTLP exporters, create custom metrics, or ensure proper trace correlation across distributed systems.
The skill provides a step-by-step workflow that begins with installing the necessary OpenTelemetry NuGet packages. It emphasizes the importance of integrating the correct packages for ASP.NET Core instrumentation and logging, ensuring that developers avoid common pitfalls, such as installing the wrong packages or missing dependencies. With clear examples and code snippets, users can easily configure their applications to collect and export telemetry data effectively.
In addition to basic setup instructions, the skill also covers advanced topics like creating custom spans for business operations. This feature allows developers to gain deeper insights into specific processes within their applications, enabling better performance monitoring and debugging. By automatically correlating logs with traces, users can quickly identify issues and understand the flow of requests through their services.
This skill is ideal for software engineers and architects who are responsible for maintaining observability in modern cloud-native applications. It is especially relevant for teams adopting microservices architectures, where understanding the interactions between services is crucial for maintaining application health and performance.
When to use it
Use this skill when you need to implement distributed tracing, metrics, and logging in an ASP.NET Core application, particularly in microservices environments.
When not to use it
Avoid this skill if you only need application-level logging or if you are using Application Insights SDK directly, as it employs a different API.
What you can build with it
Implementing Distributed Tracing
Use this skill to add distributed tracing capabilities to your ASP.NET Core application, allowing you to monitor requests across services.
Setting Up OTLP Exporters
Utilize the skill to configure OTLP exporters for sending telemetry data to your observability backend.
Creating Custom Metrics
Leverage this skill to define and collect custom metrics that are relevant to your business operations, enhancing your application's observability.
How to install Configuring OpenTelemetry in .NET
View source1. Install with the skills CLI
npx skills add dotnet/skills/configuring-opentelemetry-dotnet --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by dotnetConfiguring OpenTelemetry in .NET
When to Use
- Adding distributed tracing to an ASP.NET Core application
- Setting up OpenTelemetry exporters (OTLP is the primary protocol; Jaeger accepts OTLP natively; Prometheus OTLP ingestion requires explicit opt-in)
- Creating custom metrics or trace spans for business operations
- Troubleshooting distributed trace context propagation across services
When Not to Use
- The user wants application-level logging only (use ILogger, Serilog)
- The user is using Application Insights SDK directly (different API)
- The user needs APM with a commercial vendor's proprietary SDK
Inputs
| Input | Required | Description |
|---|---|---|
| ASP.NET Core project | Yes | The application to instrument |
| Observability backend | No | Where to export: OTLP collector, Aspire dashboard, Jaeger (accepts OTLP natively) |
Workflow
Step 1: Install the correct packages
There are many OpenTelemetry NuGet packages. Install exactly these:
# Core SDK + ASP.NET Core instrumentation + logging integration
dotnet add package OpenTelemetry.Extensions.Hosting
dotnet add package OpenTelemetry.Instrumentation.AspNetCore
dotnet add package OpenTelemetry.Instrumentation.Http
# Exporter
dotnet add package OpenTelemetry.Exporter.OpenTelemetryProtocol # OTLP exporter for traces, metrics, AND logs
# Optional — dev/local debugging only (do NOT include in production deployments)
# dotnet add package OpenTelemetry.Exporter.Console
Do NOT install OpenTelemetry alone — you need OpenTelemetry.Extensions.Hosting for proper DI integration.
Optional: additional auto-instrumentation packages
Install only the packages that match the libraries your application uses:
dotnet add package OpenTelemetry.Instrumentation.SqlClient # SQL Server queries
dotnet add package OpenTelemetry.Instrumentation.EntityFrameworkCore # EF Core
dotnet add package OpenTelemetry.Instrumentation.GrpcNetClient # gRPC calls
dotnet add package OpenTelemetry.Instrumentation.Runtime # GC, thread pool metrics
Step 2: Configure all signals in Program.cs
using OpenTelemetry.Resources;
using OpenTelemetry.Trace;
using OpenTelemetry.Metrics;
using OpenTelemetry.Logs;
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddOpenTelemetry()
.ConfigureResource(resource => resource
.AddService(serviceName: builder.Environment.ApplicationName))
.WithTracing(tracing => tracing
.AddAspNetCoreInstrumentation(options =>
{
// Filter out health check endpoints from traces
options.Filter = httpContext =>
!httpContext.Request.Path.StartsWithSegments("/healthz");
})
.AddHttpClientInstrumentation(options =>
{
options.RecordException = true;
})
// Optional: add SQL instrumentation if using SqlClient directly
// .AddSqlClientInstrumentation(options =>
// {
// options.SetDbStatementForText = true;
// options.RecordException = true;
// })
// Custom activity sources (must match ActivitySource names in your code)
.AddSource("MyApp.Orders")
.AddSource("MyApp.Payments")
.AddSource("MyApp.Messaging"))
.WithMetrics(metrics => metrics
.AddAspNetCoreInstrumentation()
.AddHttpClientInstrumentation()
// Optional: .AddRuntimeInstrumentation() for GC and thread pool metrics
// (requires OpenTelemetry.Instrumentation.Runtime package)
// Custom meters (must match Meter names in your code)
.AddMeter("MyApp.Metrics"))
.WithLogging(logging =>
{
logging.IncludeScopes = true;
// logging.IncludeFormattedMessage = true; // Enable if you need the formatted message string in log exports
})
// Single OTLP exporter for all signals — reads OTEL_EXPORTER_OTLP_ENDPOINT
// env var (defaults to http://localhost:4317). Override via environment variable
// or appsettings.json configuration.
.UseOtlpExporter();
Step 3: Understanding log–trace correlation
The .WithLogging() call in Step 2 integrates ILogger with OpenTelemetry:
- Each log entry automatically includes TraceId and SpanId for correlation with traces
- The service resource from
.ConfigureResource()propagates to logs automatically UseOtlpExporter()applies to logs alongside traces and metrics- No additional packages or separate
SetResourceBuildercall needed
Step 4: Create custom spans (Activities) for business operations
using System.Diagnostics;
using Microsoft.Extensions.Logging;
public class OrderService
{
// Create an ActivitySource matching what you registered in Step 2
private static readonly ActivitySource ActivitySource = new("MyApp.Orders");
private readonly ILogger<OrderService> _logger;
public OrderService(ILogger<OrderService> logger) => _logger = logger;
public async Task<Order> ProcessOrderAsync(CreateOrderRequest request)
{
// Start a new span
using var activity = ActivitySource.StartActivity("ProcessOrder");
// Add attributes (tags) to the span
activity?.SetTag("order.customer_id", request.CustomerId);
activity?.SetTag("order.item_count", request.Items.Count);
try
{
// Child span for validation
using (var validationActivity = ActivitySource.StartActivity("ValidateOrder"))
{
await ValidateOrderAsync(request);
validationActivity?.SetTag("validation.result", "passed");
}
// Child span for payment
using (var paymentActivity = ActivitySource.StartActivity("ProcessPayment",
ActivityKind.Client)) // Client = outgoing call
{
paymentActivity?.SetTag("payment.method", request.PaymentMethod);
await ProcessPaymentAsync(request);
}
var order = new Order { Id = Guid.NewGuid(), CustomerId = request.CustomerId, Status = "Completed" };
activity?.SetTag("order.status", "completed");
activity?.SetStatus(ActivityStatusCode.Ok);
return order;
}
catch (Exception ex)
{
activity?.SetStatus(ActivityStatusCode.Error, ex.Message);
// Log via ILogger — OpenTelemetry captures this with trace correlation.
// Prefer logging over activity.RecordException() as OTel is deprecating
// span events for exception recording in favor of log-based exceptions.
_logger.LogError(ex, "Order processing failed for customer {CustomerId}", request.CustomerId);
throw;
}
}
}
Critical: ActivitySource name must match AddSource("...") in configuration. Unmatched sources are silently ignored — this is the #1 debugging issue.
Step 5: Create custom metrics
Use IMeterFactory (injected via DI) to create meters — this ensures proper lifetime management and testability.
using System.Diagnostics;
using System.Diagnostics.Metrics;
public class OrderMetrics
{
private readonly Counter<long> _ordersProcessed;
private readonly Histogram<double> _orderProcessingDuration;
private readonly UpDownCounter<int> _activeOrders;
public OrderMetrics(IMeterFactory meterFactory)
{
// Meter name must match AddMeter("...") in configuration
var meter = meterFactory.Create("MyApp.Metrics");
// Counter — use for things that only go up
_ordersProcessed = meter.CreateCounter<long>(
"orders.processed", "orders", "Total orders successfully processed");
// Histogram — use for measuring distributions (latency, sizes)
_orderProcessingDuration = meter.CreateHistogram<double>(
"orders.processing_duration", "ms", "Time to process an order");
// UpDownCounter — use for things that go up AND down
_activeOrders = meter.CreateUpDownCounter<int>(
"orders.active", "orders", "Currently processing orders");
}
public void RecordOrderProcessed(string region, double durationMs)
{
// Tags enable dimensional filtering (by region, status, etc.)
var tags = new TagList
{
{ "region", region },
{ "order.type", "standard" }
};
_ordersProcessed.Add(1, tags);
_orderProcessingDuration.Record(durationMs, tags);
}
}
Register OrderMetrics in DI:
builder.Services.AddSingleton<OrderMetrics>();
Step 6: Configure context propagation for distributed scenarios
Trace context propagation is automatic for HTTP calls when using AddHttpClientInstrumentation(). For non-HTTP scenarios:
using System;
using System.Collections.Generic;
using System.Diagnostics;
using OpenTelemetry.Context.Propagation;
// ActivitySource should be static — register via .AddSource("MyApp.Messaging") in Step 2
private static readonly ActivitySource MessageSource = new("MyApp.Messaging");
// Manual context propagation (e.g., across message queues)
// On the SENDING side:
var propagator = Propagators.DefaultTextMapPropagator;
var activityContext = Activity.Current?.Context ?? default;
var context = new PropagationContext(activityContext, Baggage.Current);
var carrier = new Dictionary<string, string>();
propagator.Inject(context, carrier, (dict, key, value) => dict[key] = value);
// Send carrier dictionary as message headers
// On the RECEIVING side:
var parentContext = propagator.Extract(default, carrier,
(dict, key) => dict.TryGetValue(key, out var value) ? new[] { value } : Array.Empty<string>());
Baggage.Current = parentContext.Baggage;
using var activity = MessageSource.StartActivity("ProcessMessage",
ActivityKind.Consumer,
parentContext.ActivityContext); // Links to parent trace!
Validation
- Traces appear in the observability backend (Jaeger, Aspire dashboard, etc.)
- HTTP requests automatically create spans with correct verb, URL, status code
- Custom
ActivitySourcenames matchAddSource()registrations - Custom
Meternames matchAddMeter()registrations - Logs include TraceId and SpanId for correlation
- Health check endpoints are filtered from traces
- Exception details appear on error spans
Common Pitfalls
| Pitfall | Solution |
|---|---|
ActivitySource.StartActivity returns null | Source name doesn't match any AddSource() — names must match exactly |
| Traces not appearing in exporter | Check OTLP endpoint: gRPC uses port 4317, HTTP uses 4318 |
| Missing HTTP client spans | Ensure AddHttpClientInstrumentation() is registered; it works for both IHttpClientFactory/DI and new HttpClient() (use IHttpClientFactory for lifetime management) |
| High cardinality tags | Don't use user IDs, request IDs, or UUIDs as metric tags — explodes storage |
| OTLP gRPC vs HTTP mismatch | Default is gRPC (port 4317); if collector only accepts HTTP, set OtlpExportProtocol.HttpProtobuf |
Meter / ActivitySource lifecycle | ActivitySource should be static; create Meter via IMeterFactory from DI (not new Meter()) for proper lifetime management and testability |
Frequently asked questions about Configuring OpenTelemetry in .NET
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