Monitor agent usage with OpenTelemetry

This article describes how to enable and configure OpenTelemetry monitoring for Copilot Chat agent interactions in VS Code.

Copilot Chat can export traces, metrics, and events via OpenTelemetry (OTel), giving you visibility into agent interactions, LLM calls, tool executions, and token usage. All signal names and attributes follow the OTel GenAI Semantic Conventions, so the data works with any OTel-compatible backend.

What gets collected

Copilot Chat emits three types of OTel signals: traces, metrics, and events.

Traces

Each agent interaction produces a hierarchical span tree that captures the full execution flow:

invoke_agent copilot                           [~15s]
  ├── chat gpt-4o                              [~3s]  (LLM requests tool calls)
  ├── execute_tool readFile                    [~50ms]
  ├── execute_tool runCommand                  [~2s]
  ├── chat gpt-4o                              [~4s]  (LLM generates final response)
  └── (span ends)

Three span types make up the trace:

When an agent invokes a subagent (for example, through the runSubagent tool), the trace context is automatically propagated. The subagent's invoke_agent span appears as a child of the parent agent's execute_tool span, producing a connected trace tree across async boundaries.

Metrics

Metrics include attributes for filtering, such as gen_ai.request.model, gen_ai.provider.name, gen_ai.tool.name, and error.type.

Events

Resource attributes

All signals carry these resource attributes:

Add custom resource attributes with OTEL_RESOURCE_ATTRIBUTES to filter by team, department, or other organizational boundaries:

export OTEL_RESOURCE_ATTRIBUTES="team.id=platform,department=engineering"

Content capture

By default, no prompt content, responses, or tool arguments are captured. Only metadata like model names, token counts, and durations are included.

To capture full content, enable the github.copilot.chat.otel.captureContent Open in VS Code Open in VS Code Insiders setting or set COPILOT_OTEL_CAPTURE_CONTENT=true. This populates span attributes with full prompt messages, response messages, system prompts, tool schemas, tool arguments, and tool results.

Enable OTel monitoring

OTel activates when any of the following conditions is true:

• github.copilot.chat.otel.enabled Open in VS Code Open in VS Code Insiders is true
• COPILOT_OTEL_ENABLED=true
• OTEL_EXPORTER_OTLP_ENDPOINT is set

VS Code settings

Open Settings (⌘, (Windows, Linux Ctrl+,)) and search for copilot otel:

Environment variables

Environment variables always take precedence over VS Code settings.

Use with observability backends

Copilot Chat's OTel output works with any backend that supports the OTLP protocol. Point the github.copilot.chat.otel.otlpEndpoint Open in VS Code Open in VS Code Insiders setting or OTEL_EXPORTER_OTLP_ENDPOINT environment variable at the backend's OTLP ingestion URL, and configure the exporter type to match the backend's protocol (otlp-http or otlp-grpc).

Aspire Dashboard

The Aspire Dashboard is the simplest option for local development. It is a single Docker container with a built-in OTLP endpoint and trace viewer, and requires no cloud account.

docker run --rm -d \
  -p 18888:18888 \
  -p 4317:18889 \
  --name aspire-dashboard \
  mcr.microsoft.com/dotnet/aspire-dashboard:latest

{
  "github.copilot.chat.otel.enabled": true,
  "github.copilot.chat.otel.exporterType": "otlp-grpc",
  "github.copilot.chat.otel.otlpEndpoint": "http://localhost:4317"
}

Open http://localhost:18888 and go to Traces to view your agent interaction spans.

Jaeger

Jaeger is an open-source distributed tracing platform that accepts OTLP directly.

docker run -d --name jaeger -p 16686:16686 -p 4318:4318 jaegertracing/jaeger:latest

{
  "github.copilot.chat.otel.enabled": true,
  "github.copilot.chat.otel.otlpEndpoint": "http://localhost:4318"
}

Open http://localhost:16686, select service copilot-chat, and select Find Traces.

Azure Application Insights

Use an OTel Collector with the Azure Monitor exporter to forward Copilot Chat telemetry to Application Insights. Point the VS Code github.copilot.chat.otel.otlpEndpoint Open in VS Code Open in VS Code Insiders setting at the collector's OTLP endpoint, and configure the collector to export to your Application Insights connection string.

Langfuse

Langfuse is an open-source LLM observability platform with native OTLP ingestion and support for OTel GenAI Semantic Conventions.

{
  "github.copilot.chat.otel.enabled": true,
  "github.copilot.chat.otel.otlpEndpoint": "http://localhost:3000/api/public/otel",
  "github.copilot.chat.otel.captureContent": true
}

Set the auth header with the OTEL_EXPORTER_OTLP_HEADERS environment variable. See the Langfuse OTel docs for details.

Other backends

Any OTLP-compatible backend works, including Grafana Tempo, Honeycomb, and Datadog. Refer to each backend's documentation for OTLP ingestion setup.

Security and privacy

OTel monitoring is off by default and emits no data until you explicitly enable it. You control what is collected and where it goes.

Related content

• Copilot settings reference
• Troubleshoot AI in VS Code
• OTel GenAI Semantic Conventions
• Aspire Dashboard standalone docs