You can inspect application OpenTelemetry (OTel) logs, traces, and metrics inside VS Code without starting a Jaeger container by using the OpenTelemetry for VS Code extension. It runs an OTLP receiver in the editor, then displays incoming telemetry and service relationships. Your application still needs OpenTelemetry instrumentation and an OTLP exporter; the extension receives and displays data rather than instrumenting arbitrary code.
What the VS Code extension does—and what it replaces
The extension’s Marketplace listing describes a local receiver for OTLP data and views for logs, traces, metrics, and service relationships. For local development and debugging, that can replace the separate Jaeger container commonly used just to inspect a trace. The listing says live telemetry is held in memory and cleared when the receiver restarts or collected data is cleared.
OpenTelemetry itself is a vendor-neutral framework for instrumenting, generating, collecting, and exporting telemetry such as traces, metrics, and logs, as the OpenTelemetry project documentation explains. The VS Code extension is one possible destination for data exported by an instrumented application; it is not a substitute for adding instrumentation or configuring an exporter.
Set up the receiver and send your application telemetry
- Install the extension. In VS Code, open the Extensions view, search for “OpenTelemetry,” and install OpenTelemetry for VS Code by Sukanta Saha. Its extension identifier is
SukantaSaha.opentelemetry. The Marketplace listing is the reference for its documented setup and settings. - Start the receiver. Open the OpenTelemetry view and start its receiver. The listing documents OTLP/gRPC on port
4317and OTLP/HTTP on port4318by default. - Configure the app to export OTLP. For an application launched or debugged from VS Code, the extension says it can inject
OTEL_EXPORTER_OTLP_ENDPOINT. You can disable that behavior withotel.overwriteEnvVars. For an app running elsewhere, use the extension’s copy-endpoint commands or configure its SDK with the receiver endpoint. The listing’s gRPC example ishttp://127.0.0.1:4317; match the endpoint and protocol to the exporter you configured. - Generate telemetry. Confirm that the application has an OTLP-compatible SDK and instrumentation configured, then reproduce a request or operation that emits data. If nothing appears, check that the receiver is running, that the application is exporting to the right host, port, and protocol, and that the code path is actually generating telemetry.
Because the receiver is local to VS Code, an application running in a container, VM, or remote host may not be able to reach 127.0.0.1 as the editor’s receiver. In that case, use a reachable endpoint appropriate to your environment or route telemetry through an OpenTelemetry Collector. The extension listing describes Collector routing as an option.
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Use traces and correlated logs to find the failure
- Reproduce one slow or failing operation after the receiver is running, so the resulting telemetry is easy to identify.
- Filter the trace list. The extension documents query filters including
status=error, service or name matching, and duration comparisons. Use an error filter for failed requests or a duration condition for slow ones. - Open the trace waterfall. Inspect the spans to see where time was spent, which operation failed, and which services or dependencies appear in the request path. Check span attributes for additional context.
- Follow the log markers and IDs. Open associated logs and use trace or span IDs to correlate log records with the relevant span. The extension also supports log searching and filtering, column selection, and export of selected or filtered rows.
- Navigate to code when the data supports it. The listing documents source-line navigation when telemetry includes code-location attributes. A trace or log without those attributes will not necessarily link to a source file.
These views help narrow down where an operation failed or slowed down, but they only show information your instrumentation emits. Missing attributes, uninstrumented dependencies, or absent logs limit what you can infer from the display.
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 include services, databases, queues, and external dependencies represented in the telemetry. Some graph aggregation, time-range, and step controls are marked experimental in the listing, so their behavior may change.
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Know the limits of local in-editor inspection
- Data is temporary. Live telemetry is held in memory and is cleared when the receiver restarts or you clear collected data.
- Buffers are bounded. The listing documents default per-instance limits of 5,000 logs, 2,000 traces, and 500 metric points per series. These are documented settings, not a promise of durable retention or a benchmark.
- It is not a shared production backend. If you need long-term retention, team access, alerting, or production operations, export to an appropriate observability backend instead of relying on an editor’s in-memory buffer. A Collector can provide a routing path; choose a backend that supports your requirements and the OTLP protocol in use.
- Jaeger remains an option for a different need. The extension makes a Jaeger container unnecessary for the described local inspection workflow, but Jaeger can still serve as an OTLP backend in arrangements that call for one.
Do not confuse application telemetry with Copilot Chat telemetry
Application telemetry sent to the OpenTelemetry for VS Code receiver is a separate workflow from exporting VS Code Copilot Chat telemetry to a backend. Microsoft’s Copilot Chat OpenTelemetry monitoring guide documents the latter and names Jaeger, Grafana Tempo, Honeycomb, and Datadog as examples of OTLP-compatible destinations. That setup is not a prerequisite for receiving an application’s telemetry in the editor. These workflows can coexist: local in-editor inspection for development and a backend for persistent or shared telemetry serve different purposes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Optional AI-assisted investigation
The extension listing says its 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 exclusion of prompt and completion text from AI-agent spans in data sent to a model. These are the listing’s stated controls, not an independent security audit. The listing also says VS Code 1.95 or later is required for its AI tools; that requirement concerns the optional AI features, not the basic receiver workflow.
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