Observability with OpenTelemetry

The Copilot SDK can emit OpenTelemetry traces from the underlying Copilot CLI. This page shows how to enable tracing in the Go and Python tutorials and inspect the spans in Grafana using a minimal, two-service Docker Compose stack.

Reference: OpenTelemetry instrumentation for Copilot SDK.

How it works

Copilot CLI / VS Code Copilot Chat ──OTLP/HTTP :4318──▶ otel-collector ──OTLP/gRPC :4317──▶ grafana-lgtm ──▶ Grafana UI :3000

Telemetry is opt-in via environment variables, so the tutorials behave exactly as before unless you configure an endpoint (VS Code Copilot Chat is wired separately via .vscode/settings.json — see Visualizing VS Code Copilot Chat metrics):

Variable	Description
`OTEL_EXPORTER_OTLP_ENDPOINT`	OTLP HTTP endpoint (e.g. `http://localhost:4318`). When unset, telemetry is disabled.
`OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT`	Optional `true`/`false` to capture prompt/response content in spans.
`OTEL_BSP_SCHEDULE_DELAY`	Span batch flush interval in ms. Keep low (e.g. `500`) — see Troubleshooting.

The shared helpers that build the TelemetryConfig:

Python — src/python/scripts/tutorials/_telemetry.py (make_client()).
Go — src/go/cmd/tutorial/telemetry.go (newClientOptions()).

1. Start the observability stack

All Docker assets live under docker/.

# from the repository root
docker compose -f docker/compose.yaml up -d

This launches two services:

Service	Image	Host ports
`otel-collector`	`otel/opentelemetry-collector-contrib`	`4317` (gRPC), `4318` (HTTP)
`grafana-lgtm`	`grafana/otel-lgtm` (Loki + Grafana + Tempo + Prometheus)	`3000` (Grafana UI)

2. Point the tutorials at the collector

export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318
# Flush spans quickly (see "Troubleshooting" below)
export OTEL_BSP_SCHEDULE_DELAY=500
# optional:
export OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true

Python

cd src/python
uv run python scripts/tutorials/01_chat_bot.py --prompt "Hello, Copilot!"

Go

cd src/go
make build
./dist/template-github-copilot-go tutorial chat-bot --prompt "Hello, Copilot!"

3. Explore the traces

Open Grafana at http://localhost:3000 (login admin / admin), then go to Explore → Tempo and search for recent traces.

You can also confirm spans are flowing straight from the collector logs:

docker compose -f docker/compose.yaml logs -f otel-collector

4. Tear down

docker compose -f docker/compose.yaml down

Visualizing VS Code Copilot Chat metrics

The same collector can also receive OpenTelemetry traces, metrics, and logs emitted directly by GitHub Copilot Chat in VS Code — no extra services or dependencies, the existing two-container stack is enough.

This repository ships .vscode/settings.json pre-wired to the local collector:

{
  "github.copilot.chat.otel.enabled": true,
  "github.copilot.chat.otel.exporterType": "otlp-http",
  "github.copilot.chat.otel.otlpEndpoint": "http://localhost:4318",
  "github.copilot.chat.otel.captureContent": false
}

Steps:

Start the stack: docker compose -f docker/compose.yaml up -d.
Open this folder in VS Code (the workspace settings above are applied automatically). Reload the window if Copilot was already running.
Use Copilot Chat / an agent as usual — VS Code exports OTLP to the collector.
In Grafana (http://localhost:3000, admin/admin) open Explore:
Tempo data source → agent traces (invoke_agent, chat, execute_tool).
Prometheus data source → metrics such as github_copilot_agent_turn_count and github_copilot_mcp_server_connection_count_total.

Notes:

Signal names follow the OTel GenAI Semantic Conventions under the gen_ai.* and github.copilot.* namespaces.
When the collector is not running, VS Code's export fails silently (connection refused) and Copilot keeps working normally.
captureContent is false by default. Enable it only in trusted environments — it records full prompts, responses, and tool arguments.
For the equivalent Azure pipeline (OTel Collector → Application Insights → Azure Managed Grafana), see Monitor AI coding agents with Grafana.

Troubleshooting: no spans arrive

When the SDK launches the CLI over stdio (the tutorial default), it kills the CLI with SIGKILL (client.Stop() → process.Kill()) the moment a single-shot prompt finishes. The CLI batches spans and flushes on an interval whose default is 5 seconds, so a short prompt is terminated before the first flush and no spans are ever sent.

Set the standard OpenTelemetry batch env var so the CLI flushes before it is killed:

export OTEL_BSP_SCHEDULE_DELAY=500   # milliseconds

Alternatively, run the CLI in server mode and connect the tutorial with --cli-url; the long-lived process flushes on its normal interval. A direct copilot -p "..." run always works because it exits gracefully and flushes on shutdown.

Advanced: distributed trace context

The TelemetryConfig above is all you need to collect CLI spans. If your own application creates its own OpenTelemetry spans and you want them linked into the same distributed trace as the CLI, see the Trace context propagation section of the official guide. For Python this also requires the opentelemetry-api package (pip install copilot-sdk[telemetry]); Go already depends on go.opentelemetry.io/otel.