GitHub Copilot Monitoring with OpenTelemetry and SigNoz

GitHub Copilot Chat now supports OpenTelemetry-based monitoring in VS Code 1.131 or later, enabling developers to send traces and metrics to SigNoz without additional instrumentation. The Copilot Chat extension includes a built-in OTLP exporter, and configuration requires enabling the feature in VS Code settings and pointing it to a SigNoz endpoint. This provides real-time visibility into agent turns, model routing, token usage, and tool calls, helping teams track performance and catch failures.

What is GitHub Copilot Observability? GitHub Copilot observability gives you real-time visibility into how the Copilot Chat agent works inside VS Code by collecting traces and metrics with OpenTelemetry https://opentelemetry.io/ . The Copilot Chat extension ships its own OTLP exporter, so there is no instrumentation library to install and no collector to run. Every agent turn becomes a trace whose spans follow the OpenTelemetry GenAI semantic conventions https://opentelemetry.io/docs/specs/semconv/gen-ai/ : an invoke agent root span with chat , execute tool , and embeddings spans beneath it, carrying gen ai.request.model , gen ai.usage.input tokens , gen ai.tool.name , and gen ai.conversation.id . With full GitHub Copilot monitoring in SigNoz, you can see which models the agent routes work to, track token spend and prompt-cache savings, measure time to first chunk as the developer actually experiences it, break down which tools the agent reaches for, and catch failing tool calls and rate limits before they slow the team down. Prerequisites - A SigNoz Cloud account https://signoz.io/teams/ with an active ingestion key or Self Hosted SigNoz instance https://signoz.io/docs/install/self-host/ - VS Code 1.131 or later, which bundles GitHub Copilot Chat 0.59.0 or later - A GitHub Copilot subscription of any tier, including the free tier Monitor GitHub Copilot with OpenTelemetry Copilot Chat reads its OpenTelemetry configuration from VS Code settings and applies your headers directly to its OTLP exporter, so you point it at SigNoz and reload. For more details, refer to the VS Code guide to monitoring agents with OpenTelemetry https://code.visualstudio.com/docs/agents/guides/monitoring-agents . Step 1: In VS Code, open your user settings JSON. Open VS Code, then open the Command Palette with Cmd+Shift+P Ctrl+Shift+P on Windows and Linux and run Preferences: Open User Settings JSON . All four steps below are performed in VS Code. Step 2: Add the OpenTelemetry settings to that file. settings.json { "github.copilot.chat.otel.enabled": true, "github.copilot.chat.otel.exporterType": "otlp-http", "github.copilot.chat.otel.protocol": "http/protobuf", "github.copilot.chat.otel.otlpEndpoint": "https://ingest.<region .signoz.cloud:443", "github.copilot.chat.otel.serviceName": "<service name ", "github.copilot.chat.otel.headers": { "signoz-ingestion-key": "<your-ingestion-key " }, "github.copilot.chat.otel.captureContent": false } Verify these values: <region : Your SigNoz Cloud region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint . <your-ingestion-key : Your SigNoz ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ . <service name : The name Copilot appears under in SigNoz, for example copilot-chat . Give each team or rollout its own value if you want to tell them apart. protocol defaults to an empty string, which means http/json . Setting http/protobuf explicitly is what selects the protobuf exporter, so do not leave it out. Step 3: Save the file, then reload the VS Code window. Save settings.json , then open the Command Palette again and run Developer: Reload Window . Every one of these settings is read once at startup, so changes never apply to an already-running window. Step 4: Send a chat message from Copilot Chat. Once VS Code has reloaded, open the Copilot Chat view and ask it anything. Spans start arriving in SigNoz within about 30 seconds, and metrics follow on their own export interval. Each agent turn emits a trace whose spans carry gen ai.operation.name invoke agent , chat , execute tool , embeddings along with model, token usage, and tool attributes. OpenTelemetry batches data before sending, so allow a few seconds after a chat turn for it to appear in SigNoz. View GitHub Copilot Traces in SigNoz Once configured, Copilot Chat automatically emits traces for every agent turn. Traces are available in SigNoz under the Traces tab: When you click on a trace in SigNoz, you'll see a detailed view of the trace, including all associated spans, along with their events and attributes. The invoke agent span wraps one agent turn, chat spans are the individual model calls, and execute tool spans capture each tool the agent ran. Attributes and metrics worth knowing about Span operations gen ai.operation.name | Span kind | Emitted when | |---|---|---| invoke agent | Internal | Wraps one agent turn. Root span. | chat | Client | One per model call | execute tool | Internal | One per tool invocation | embeddings | Client | Workspace indexing | execute hook | Internal | One per hook execution | Attributes | Attribute | Where it appears | |---|---| gen ai.request.model , gen ai.response.model | chat , embeddings , invoke agent | gen ai.usage.input tokens , gen ai.usage.output tokens | chat , invoke agent | gen ai.usage.cache read.input tokens | chat , invoke agent | gen ai.usage.reasoning.output tokens | chat , invoke agent reasoning models only | gen ai.response.time to first chunk | chat only | gen ai.response.finish reasons | chat only | gen ai.tool.name | execute tool only | gen ai.agent.name | chat , invoke agent | gen ai.conversation.id | everything except embeddings | Metrics Copilot emits the standard GenAI instruments gen ai.client.token.usage , gen ai.client.operation.duration , gen ai.client.operation.time to first chunk , gen ai.client.operation.time per output chunk plus Copilot-specific ones copilot chat.agent.invocation.duration , copilot chat.agent.turn.count , copilot chat.time to first token , copilot chat.tool.call.count , copilot chat.tool.call.duration , copilot chat.session.count , github.copilot.mcp.server.connection.count . GitHub Copilot Observability Dashboard You can also check out our custom GitHub Copilot dashboard https://signoz.io/docs/dashboards/dashboard-templates/github-copilot-dashboard/ which provides specialized visualizations for monitoring Copilot Chat. The dashboard includes pre-built charts for token usage and prompt-cache hit rate, per-model call volume and latency, tool activity split between MCP and built-in tools, and errors, along with import instructions to get started quickly. Troubleshooting GitHub Copilot Observability No traces in SigNoz - Confirm github.copilot.chat.otel.enabled is true in your user settings rather than workspace settings, then reload the window. Settings changes never apply to an already-running window. - Confirm you actually sent a chat message. Enabling the settings alone emits nothing. - Check that the region in the OTLP endpoint matches your SigNoz account. - OpenTelemetry batches data before sending, so wait 10-30 seconds after a chat turn. Confirm whether Copilot is emitting at all Set "github.copilot.chat.otel.exporterType": "console" , reload the window, and check the Output panel. If spans appear there, Copilot is instrumented correctly and the problem is the endpoint, the key, or the region. Use "file" with github.copilot.chat.otel.outfile to capture the same output as JSON lines. Spans appear but metrics do not Metrics export on an interval rather than per request, so allow a minute or two. Data goes nowhere and there is no error An ingestion key is region scoped. A key issued for one region sent to another region's endpoint fails quietly. Re-check the ingestion key in the signoz-ingestion-key header against your SigNoz Ingestion Settings, with no extra spaces or quotes, and confirm the endpoint region matches. OTEL EXPORTER OTLP HEADERS seems to be ignored Expected if you launched VS Code from the Dock or Spotlight, since GUI-launched apps on macOS do not inherit your shell environment. Use the github.copilot.chat.otel.headers setting instead, which is what this guide does. Setup OpenTelemetry Collector Optional What is the OpenTelemetry Collector? Think of the OTel Collector as a middleman between your app and SigNoz. Instead of your application sending data directly to SigNoz, it sends everything to the Collector first, which then forwards it along. Why use it? Cleaning up data - Filter out noisy traces you don't care about, or remove sensitive info before it leaves your servers. Keeping your app lightweight - Let the Collector handle batching, retries, and compression instead of your application code. Adding context automatically - The Collector can tag your data with useful info like which Kubernetes pod or cloud region it came from. Future flexibility - Want to send data to multiple backends later? The Collector makes that easy without changing your app. See Switch from direct export to Collector https://signoz.io/docs/opentelemetry-collection-agents/opentelemetry-collector/switch-to-collector/ for step-by-step instructions to convert your setup. For more details, see Why use the OpenTelemetry Collector? https://signoz.io/docs/opentelemetry-collection-agents/opentelemetry-collector/why-to-use-collector/ and the Collector configuration guide https://signoz.io/docs/opentelemetry-collection-agents/opentelemetry-collector/configuration/ . Related integrations Instrument the other AI coding agents and assistants your team runs, using the same OpenTelemetry pipeline: Monitor Claude Code with OpenTelemetry https://signoz.io/docs/claude-code-monitoring/ - track token usage, cost, session activity, and tool decisions Monitor OpenAI Codex with OpenTelemetry https://signoz.io/docs/codex-monitoring/ - trace Codex runs from the IDE extension and the CLI OpenCode observability with OpenTelemetry https://signoz.io/docs/opencode-observability/ - trace OpenCode sessions, tool calls, and per-message cost Monitor the Claude Agent SDK with OpenTelemetry https://signoz.io/docs/claude-agent-monitoring/ - trace agent runs, subagents, and tool execution Monitor GitHub Actions with OpenTelemetry https://signoz.io/docs/cicd/github/github-actions-traces/ - trace the CI pipelines your agents open pull requests against Browse all LLM observability integrations https://signoz.io/docs/llm-observability/ to instrument the rest of your stack.