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You can inspect an application’s OpenTelemetry data inside VS Code without starting a Jaeger container by using the OpenTelemetry for VS Code extension. It runs a local OTLP receiver and displays logs, traces, metrics, and service relationships. Your application still needs an OTLP exporter configured and running; the extension receives and presents telemetry, but does not automatically instrument arbitrary code.
What this VS Code workflow does—and what it replaces
The extension’s Marketplace listing describes a receiver that runs inside VS Code, so for local development you can send telemetry directly from your application to the editor instead of running a separate Jaeger viewer. The listing also says live telemetry is held in memory and cleared when the receiver restarts or you clear the collected data. This makes it useful for an interactive debugging session, not a durable observability service.
OpenTelemetry itself is a vendor-neutral framework for instrumenting, generating, collecting, and exporting telemetry such as traces, metrics, and logs, according to the OpenTelemetry project. The VS Code extension supplies the local receiving and inspection part of that workflow; your app’s instrumentation and exporter remain necessary.
Set up the local OTLP receiver
- Install the extension. In VS Code, open the Extensions view and search for “OpenTelemetry,” or install the Marketplace extension identified as
SukantaSaha.opentelemetry. See the extension listing for its documented features and settings. - Open its view and start the receiver. The listing documents OTLP/gRPC on port
4317and OTLP/HTTP on port4318by default. Confirm the endpoint and protocol your app will use. - Point the application’s exporter at the receiver. The listing says the extension can inject
OTEL_EXPORTER_OTLP_ENDPOINTfor applications launched or debugged from VS Code. It documentsotel.overwriteEnvVarsas the setting to disable that behavior. If your app runs outside VS Code, use the extension’s copy-endpoint commands or configure the exporter yourself. Its example gRPC endpoint ishttp://127.0.0.1:4317. - Generate telemetry and check that it arrives. Confirm that the application has an OTLP-compatible SDK or instrumentation and is actually exporting. Starting the receiver alone does not create logs, spans, or metrics.
For a quick first check, make one request through the app after the receiver is running, then look for the corresponding service and trace in the extension. If nothing appears, check that the exporter’s protocol matches the receiver endpoint, the app is exporting, and the app can reach the configured host and port.
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Use traces and logs to find the failing operation
Filter traces before opening a waterfall
Begin with the request you just reproduced. The listing describes trace filters for status and query terms, including service or name matching, plus duration comparisons. For example, a query such as status=error can narrow the list to failed traces; service/name and duration filters can help isolate a slow path. Open a relevant trace’s waterfall to see its spans and timing, then inspect span attributes for the operation and dependency involved.
Follow the trace into correlated logs
Where logs carry trace or span IDs, the extension can correlate them with trace data. Search and filter the logs, choose which columns to show, and use trace context to move between the request and its related log entries. The listing also documents exporting selected or filtered log rows and importing supported JSON or JSON Lines logs as a read-only instance.
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Navigate to source when location data exists
The extension listing describes source navigation when telemetry includes code-location attributes. That is conditional: a span or log without those attributes may not provide a jump to a source line. Treat source navigation as a convenience when instrumentation supplies the necessary location data, not as a property of every event.
Inspect metrics and service relationships
The extension presents per-service-instance gauges, counters, and histograms as graphs, and describes a service map inferred from spans. That map can show relationships involving services, databases, queues, and external dependencies. The listing marks some graph controls—such as aggregation, time range, and step—as experimental, so their availability or behavior may be less settled than the basic receiver workflow.
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A practical investigation is to start with a failed or slow trace, inspect the span waterfall and related logs, then use the service map or metrics to check whether the issue aligns with a dependency or a broader service pattern. Metrics help add context, but they do not replace examining the specific trace and its attributes.
Know the retention and capacity limits
The Marketplace listing describes bounded, in-memory live data and documents these default per-instance limits:
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| Signal | Documented default limit |
|---|---|
| Logs | 5,000 |
| Traces | 2,000 |
| Metric points per series | 500 |
These are documented settings, not benchmark results or guarantees for every configuration. Restarting the receiver or clearing collected data removes live data. If you need persistence, team sharing, alerting, or production operations, route telemetry to an appropriate backend instead; the extension listing allows routing through an OpenTelemetry Collector.
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Jaeger is optional for the in-editor application-debugging workflow above. It remains one possible backend when you want telemetry stored or shared beyond the local VS Code session. Do not confuse that use with VS Code’s separate Copilot Chat telemetry exporter: Microsoft’s agent monitoring guide documents exporting Copilot Chat telemetry to an OTLP-compatible destination, with Jaeger among its examples alongside Grafana Tempo, Honeycomb, and Datadog.
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| Workflow | What sends telemetry | Where you inspect it | Best fit |
|---|---|---|---|
| Local application debugging | Your instrumented application’s OTLP exporter | OpenTelemetry for VS Code receiver and views | Inspecting current development telemetry in the editor |
| Copilot Chat monitoring | VS Code’s separate Copilot Chat OTel exporter | An OTLP-compatible backend, such as Jaeger | Monitoring Copilot Chat telemetry in a backend workflow |
These workflows are not mutually exclusive: you can use the in-editor receiver for local debugging and a backend for a separate persistence or sharing need. The destination must support the OTLP protocol you configure.
Optional AI-assisted investigation
The extension listing says AI access is disabled by default and can be enabled through a user setting. It describes confirmation for each tool call, secret redaction, result limits, and excluding prompt and completion text from AI-agent spans in data sent to a model. These are statements in the extension listing, not an independent security audit. Review the listing and your organization’s data-handling rules before enabling AI access, particularly if telemetry may contain sensitive attributes.
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