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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesTo send .NET application traces to Langfuse, register OpenTelemetry tracing, export over OTLP to the Langfuse endpoint for your region, and configure the project’s Basic-auth credentials and ingestion-version header. In ASP.NET Core, the OpenTelemetry instrumentation package can capture inbound HTTP requests; add application spans for work that matters, then use Langfuse experiment metadata or an evaluation workflow to assess traces.
What you need before configuring the exporter
This walkthrough uses ASP.NET Core because its OpenTelemetry instrumentation has a documented integration path. A worker service or console application can also export OTLP, but needs instrumentation appropriate to its workload; ASP.NET Core request instrumentation will not apply. The .NET target, model provider, hosting region, and provider SDK are not specified here, so select package versions compatible with your runtime and verify provider-specific instrumentation separately.
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- A Langfuse project’s public and secret keys. Treat the secret key as a credential and provide it through a deployment environment or secret manager, not committed source.
- The OTLP traces endpoint for the Langfuse region where your project resides.
- The OpenTelemetry .NET hosting integration, ASP.NET Core instrumentation, and OTLP exporter packages. The official OpenTelemetry ASP.NET Core guide demonstrates these packages; choose versions that fit your application’s supported runtime and dependency policy.
Register OpenTelemetry tracing in ASP.NET Core
Register tracing in the application’s service collection, give the service a useful resource name, add ASP.NET Core instrumentation, and configure the OTLP exporter. The following is the registration shape; endpoint, protocol, and headers are deployment settings rather than values to hard-code into a public example.
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builder.Services.AddOpenTelemetry()
.ConfigureResource(resource => resource
.AddService(serviceName: builder.Environment.ApplicationName))
.WithTracing(tracing => tracing
.AddAspNetCoreInstrumentation()
.AddOtlpExporter(options =>
{
// Set the Langfuse OTLP endpoint, protocol, and headers here.
}));The OpenTelemetry .NET exporter documentation describes OTLP over HTTP/protobuf and gRPC. Use the protocol and endpoint combination supported by the destination. For Langfuse, configure the region-specific traces endpoint under the /api/public/otel base path, Basic authentication using the project’s public and secret keys, and x-langfuse-ingestion-version=4 for current v4 ingestion. See Langfuse’s OpenTelemetry integration documentation for the endpoint and header pattern. Keep credentials outside source control.
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Add spans for application and model work
ASP.NET Core instrumentation can create spans for inbound HTTP requests, but it does not establish that an unspecified model-provider SDK records model names, prompts, completions, token usage, or cost. Check the chosen provider’s integration documentation for those fields; where coverage is absent, add instrumentation around meaningful operations.
.NET’s tracing API uses ActivitySource and Activity. OpenTelemetry’s official .NET documentation explains: “The Tracing API is implemented by the System.Diagnostics API, repurposing existing constructs like ActivitySource and Activity to be OpenTelemetry-compliant under the covers.” See the .NET instrumentation documentation.
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Use a clear span hierarchy: an inbound request can contain application-operation spans, which in turn cover meaningful model or tool calls. Attach useful attributes supported by your instrumentation and data-handling policies. Avoid assuming that traces automatically contain model inputs or outputs: capture only what you intend to observe and are permitted to store.
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- Run the application and make a representative request that exercises the operation you instrumented.
- In Langfuse, inspect the resulting trace. Confirm that it arrived in the intended project and that the service name, span hierarchy, and relevant attributes are useful.
- If the trace is absent or incomplete, check the region-specific endpoint, OTLP protocol, Basic-auth header construction, ingestion-version header, and whether the relevant code path actually creates spans.
- For a short-lived console or evaluation process, flush or shut down the tracer provider after work completes so queued telemetry has an opportunity to export.
This is a configuration pattern, not a claim that a particular application or provider integration has been tested. The exporter guide covers exporter configuration; Langfuse’s OTel guide covers its ingestion settings.
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Choose how Langfuse should evaluate traces
There are two distinct goals: compare application behavior against a fixed set of cases, or assess traces from live traffic. Choose the workflow based on which question you need to answer.
Run a repeatable dataset experiment
For offline regression evaluation, organize representative inputs as a dataset, run the application task for each item, and record outputs and evaluator scores. Langfuse’s experiment documentation describes dataset items, task functions, and optional evaluators through supported SDK workflows. Its Python or TypeScript examples should not be mistaken for a .NET runner package.
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For direct OpenTelemetry experiment ingestion, Langfuse groups traces into experiment runs when the required experiment and item metadata are attached to spans. Follow the attributes and structure in the OpenTelemetry experiment integration documentation. For a .NET application, this direct OTEL route is the documented option unless you introduce a separately supported client.
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Evaluate production traces
If the goal is to score live traffic rather than rerun a fixed dataset, use Langfuse’s online evaluation setup and scoring mechanisms. The scoring documentation describes scoring, while online evaluation documentation covers evaluation of production traces. Keep online monitoring distinct from dataset regression runs: they answer different questions and use different inputs.
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Decide what a useful comparison measures
When comparing prompts, models, or application variants, consider task correctness against expected output, policy or safety compliance where relevant, latency, token usage or cost when the model integration records it, and robustness across representative cases. These are evaluation dimensions to choose, not guaranteed trace fields or measured outcomes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check for legacy Langfuse ingestion
Langfuse’s Public API documentation identifies OTLP trace ingestion as the supported trace-ingestion route and lists November 16, 2026 as the Langfuse Cloud sunset date for the legacy Ingestion API. That date is upcoming as of October 4, 2026. New integrations should use the OTLP route; if an existing exporter targets the legacy API, check its configuration and plan migration.
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