OpenTelemetry reference

OpenTelemetry monitoring

The Copilot runtime can export traces and metrics via OpenTelemetry (OTel), giving you visibility into agent interactions, LLM calls, tool executions, and token usage. GenAI signal names and attributes follow the OTel GenAI Semantic Conventions. The runtime also emits Copilot-specific signals under the github.copilot.* namespace.

The configuration and telemetry tables in this article describe the shared Copilot runtime used by these clients and harnesses:

• Copilot CLI
• The GitHub Copilot app
• VS Code Copilot sessions using Agent Host

VS Code's Local agent harness has different environment variables, defaults, and telemetry signals. For example, it uses COPILOT_OTEL_CAPTURE_CONTENT for content capture, defaults service.name to copilot-chat, and emits events such as copilot_chat.session.start. The tables below do not describe that harness or extension-emitted telemetry. See Monitor agent usage with OpenTelemetry in the VS Code documentation.

OTel configuration can also be set in VS Code, or in an enterprise-wide managed-settings.json file. See Enable OTel monitoring in the VS Code documentation and Enterprise managed settings.

OTel environment variables

Variable	Default	Description
COPILOT_OTEL_ENABLED	false	Explicitly enable OTel. Not required if OTEL_EXPORTER_OTLP_ENDPOINT is set.
OTEL_EXPORTER_OTLP_ENDPOINT	—	OTLP endpoint URL. Setting this automatically enables OTel.
COPILOT_OTEL_EXPORTER_TYPE	otlp-http	Exporter type: otlp-http or file. Auto-selects file when COPILOT_OTEL_FILE_EXPORTER_PATH is set.
OTEL_EXPORTER_OTLP_PROTOCOL	http/json	OTLP HTTP wire protocol: http/json or http/protobuf. Only applies to the otlp-http exporter.
OTEL_EXPORTER_OTLP_TRACES_PROTOCOL	—	Override OTEL_EXPORTER_OTLP_PROTOCOL for traces only.
OTEL_EXPORTER_OTLP_METRICS_PROTOCOL	—	Override OTEL_EXPORTER_OTLP_PROTOCOL for metrics only.
OTEL_SERVICE_NAME	github-copilot	Service name in resource attributes.
OTEL_RESOURCE_ATTRIBUTES	—	Extra resource attributes as comma-separated key=value pairs. Use percent-encoding for special characters.
OTEL_INSTRUMENTATION_GENAI_CAPTURE_CONTENT	NO_CONTENT	Capture full prompt and response content. Accepts NO_CONTENT, SPAN_ONLY, SPAN_AND_EVENT, or EVENT_ONLY. See Content capture.
OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT	false	Legacy boolean fallback, used only when the standard content variable is empty or unset. See Content capture.
OTEL_LOG_LEVEL	—	OTel diagnostic log level: NONE, ERROR, WARN, INFO, DEBUG, VERBOSE, ALL.
COPILOT_OTEL_FILE_EXPORTER_PATH	—	Write all signals to this file as JSON-lines. Setting this automatically enables OTel.
COPILOT_OTEL_SOURCE_NAME	github.copilot	Instrumentation scope name for tracer and meter.
OTEL_EXPORTER_OTLP_HEADERS	—	Authentication headers for the OTLP exporter, as comma-separated key=value pairs.

Transport security

These variables apply only to the otlp-http exporter with an https:// endpoint, and only once OTel export is enabled. They do not enable OTel on their own, and they have no effect on the file exporter.

Variable	Description
OTEL_EXPORTER_OTLP_CERTIFICATE	Path to a PEM file with extra CA certificates to trust for the OTLP endpoint, such as a private or corporate CA. Merged with the operating system trust store, so public-CA endpoints still validate.
OTEL_EXPORTER_OTLP_CLIENT_CERTIFICATE	Path to the client certificate PEM for mutual TLS (mTLS).
OTEL_EXPORTER_OTLP_CLIENT_KEY	Path to the matching unencrypted private key PEM. Both certificate and key must be set together, and passphrase-protected keys are not supported.

Per-signal variants override the generic variables for a single signal: OTEL_EXPORTER_OTLP_TRACES_CERTIFICATE, OTEL_EXPORTER_OTLP_METRICS_CERTIFICATE, and the matching _CLIENT_CERTIFICATE and _CLIENT_KEY names.

Dynamic authentication headers

In Copilot CLI, set telemetry.headersProvider in your user settings or in an enterprise-managed telemetry policy to run a command that supplies fresh OTLP headers. Repository settings never apply this setting.

{
  "telemetry": {
    "headersProvider": {
      "path": "node",
      "args": ["bin/otel-headers.js"],
      "refreshIntervalMs": 1740000
    }
  }
}

Field	Description
path	Executable name, absolute path, or home-relative path. This is not a shell command, and workspace-relative paths are rejected.
args	Optional arguments, passed literally. Select an interpreter explicitly for scripts (for example, node).
refreshIntervalMs	Optional refresh interval. Default: 29 minutes.

Set headersProvider to false to disable dynamic headers.

• The command runs from your home directory with a 30-second deadline. Its stdout must be a JSON object of string header values. An empty object clears dynamic headers.
• The provider runs only for active HTTP OTLP exporters. Use HTTPS, because an HTTP endpoint produces a warning and OTLP redirects are rejected.
• Header precedence, from highest to lowest: managed helper output, managed static headers, user helper output, then OTEL_EXPORTER_OTLP_HEADERS. A managed helper overrides a user helper, and a managed false disables helpers.
• If a refresh fails, exports that need dynamic headers are blocked until the next refresh.

Traces

The runtime emits a hierarchical span tree for each agent interaction. Each tree contains an invoke_agent root span, with chat and execute_tool child spans. In plan mode, an intermediate plan span wraps the task-decomposition work and parents its own chat and execute_tool spans. chat spans are named chat {model}, falling back to chat when the model is unavailable. Subagent invocations are linked into the same trace by context propagation.

invoke_agent span attributes

Wraps the entire agent invocation: all LLM calls and tool executions for one user message.

• Both top-level sessions and subagent invocations (for example, explore, task) use span kind INTERNAL (in-process); provider-facing inference is represented by child CLIENT chat spans.
• Top-level sessions additionally carry server.address and server.port; subagent invocations do not.

Attribute	Description	Scope
gen_ai.operation.name	invoke_agent	Both
gen_ai.provider.name	Provider (for example, github, anthropic)	Both
gen_ai.agent.id	Stable agent-definition identifier when known; top-level default uses github.copilot.default	Both
gen_ai.agent.name	Agent name (when available)	Both
gen_ai.agent.description	Agent description (when available)	Both
gen_ai.agent.version	Agent definition version when known; otherwise runtime version	Both
gen_ai.conversation.id	Session identifier	Both
enduser.pseudo.id	Pseudonymous Copilot user identifier from analytics_tracking_id when available	Both
gen_ai.request.model	Requested model	Both
gen_ai.response.finish_reasons	["stop"] or ["error"]	Both
gen_ai.usage.input_tokens	Total input tokens (all turns)	Both
gen_ai.usage.output_tokens	Total output tokens (all turns)	Both
gen_ai.usage.cache_read.input_tokens	Cached input tokens read	Both
gen_ai.usage.cache_write.input_tokens	Cached input tokens written	Both
gen_ai.usage.reasoning.output_tokens	Reasoning output tokens	Both
github.copilot.turn_count	Number of LLM round-trips	Both
github.copilot.cost	Per-request model multiplier used for billing. Not a monetary value.	Both
github.copilot.nano_aiu	AI units consumed, in nano AI units (1 AIU = 1,000,000,000 nano AIU)	Both
server.address	Server hostname	Top-level only
server.port	Server port	Top-level only
error.type	Error class name (on error)	Both
gen_ai.input.messages	Full input messages as JSON (content capture only)	Both
gen_ai.output.messages	Full output messages as JSON (content capture only)	Both
gen_ai.system_instructions	System prompt content as JSON (content capture only)	Both
gen_ai.tool.definitions	Tool schemas as JSON (content capture only)	Both

chat span attributes

One span per LLM request. Span kind: CLIENT.

Attribute	Description
gen_ai.operation.name	chat
gen_ai.provider.name	Provider name
gen_ai.request.model	Requested model
gen_ai.request.stream	Whether streaming mode was used (streaming only)
gen_ai.conversation.id	Session identifier
gen_ai.response.finish_reasons	Stop reasons
gen_ai.response.id	Response ID
gen_ai.response.model	Resolved model
gen_ai.response.time_to_first_chunk	Time to first streaming chunk, in seconds (streaming only)
gen_ai.usage.cache_write.input_tokens	Cached tokens written
gen_ai.usage.cache_read.input_tokens	Cached tokens read
gen_ai.usage.input_tokens	Input tokens this turn
gen_ai.usage.output_tokens	Output tokens this turn
gen_ai.usage.reasoning.output_tokens	Reasoning output tokens this turn
github.copilot.request.type	Set to compaction on spans emitted for conversation compaction
github.copilot.cost	Per-request model multiplier used for billing. Not a monetary value.
github.copilot.nano_aiu	AI units consumed this turn, in nano AI units (1 AIU = 1,000,000,000 nano AIU)
github.copilot.server_duration	Server-side duration
github.copilot.initiator	Request initiator
github.copilot.turn_id	Turn identifier
github.copilot.interaction_id	Interaction identifier
server.address	Server hostname
server.port	Server port
error.type	Error class name (on error)
gen_ai.input.messages	Full prompt messages as JSON (content capture only)
gen_ai.output.messages	Full response messages as JSON (content capture only)
gen_ai.system_instructions	System prompt content as JSON (content capture only)

execute_tool span attributes

One span per tool call. Span kind: INTERNAL.

Attribute	Description
gen_ai.operation.name	execute_tool
gen_ai.provider.name	Provider name (when available)
gen_ai.tool.name	Tool name (for example, readFile)
gen_ai.tool.type	function
gen_ai.tool.call.id	Tool call identifier
gen_ai.tool.description	Tool description
error.type	Error class name (on error)
gen_ai.tool.call.arguments	Tool input arguments as JSON (content capture only)
gen_ai.tool.call.result	Tool output as JSON (content capture only)

Metrics

The following metrics are emitted.

GenAI convention metrics

Metric	Type	Unit	Description
gen_ai.client.operation.duration	Histogram	s	Duration of a GenAI operation other than inference
gen_ai.client.inference.duration	Histogram	s	Duration of a single LLM API call
gen_ai.client.inference.operation.input_tokens	Histogram	{token}	Input tokens per model call
gen_ai.client.inference.operation.output_tokens	Histogram	{token}	Output tokens per model call
gen_ai.client.inference.usage.input_tokens	Counter	{token}	Input tokens used, including cached tokens
gen_ai.client.inference.usage.output_tokens	Counter	{token}	Output tokens used, including reasoning tokens
gen_ai.client.inference.usage.cache_read.input_tokens	Counter	{token}	Input tokens served from a provider-managed cache
gen_ai.client.inference.usage.cache_write.input_tokens	Counter	{token}	Input tokens written to a provider-managed cache
gen_ai.client.inference.usage.reasoning.output_tokens	Counter	{token}	Output tokens used for reasoning
gen_ai.client.inference.time_to_first_chunk	Histogram	s	Time to receive the first chunk of a streaming response
gen_ai.client.inference.time_per_output_chunk	Histogram	s	Inter-chunk latency after the first chunk
gen_ai.invoke_agent.duration	Histogram	s	End-to-end duration of one agent invocation
gen_ai.invoke_agent.inference_calls	Histogram	{inference_call}	Number of model calls made during one agent invocation, counted at provider dispatch (failed and partial calls included; requests blocked before dispatch excluded). Dimension: gen_ai.agent.name.
gen_ai.invoke_agent.tool_calls	Histogram	{tool_call}	Number of client-side tool calls made during one agent invocation (failed and partial calls included; synthetic tool lifecycles and provider-executed server-side tools excluded). Dimension: gen_ai.agent.name.
gen_ai.execute_tool.duration	Histogram	s	Duration of a single tool execution
gen_ai.invoke_workflow.duration	Histogram	s	End-to-end duration of a GenAI workflow (for example, a fleet mode run orchestrating parallel subagents).

Token metrics aggregate foreground and compaction work. github.copilot.request.type is a span attribute only, so it is not available as a metric dimension for separating them.

Vendor-specific metrics

Metric	Type	Unit	Description
github.copilot.tool.call.count	Counter	{call}	Tool invocations by gen_ai.tool.name and success
github.copilot.tool.call.duration	Histogram	s	Tool execution latency by gen_ai.tool.name
github.copilot.agent.turn.count	Histogram	{turn}	LLM round-trips per agent invocation
github.copilot.mcp.server.connection.count	Counter	{attempt}	Completed MCP server connection attempts by transport and outcome
github.copilot.code.lines_added	Counter	{line}	Lines added by file-editing tools, recorded in real time
github.copilot.code.lines_removed	Counter	{line}	Lines removed by file-editing tools, recorded in real time
github.copilot.sandbox.operation.count	Counter	{decision}	Bounded sandbox governance decisions, deduplicated
github.copilot.sandbox.spawn.duration	Histogram	s	Sandboxed process spawn latency
github.copilot.sandbox.policy.path.count	Histogram	{path}	Effective filesystem policy paths by access class

Span events

Key lifecycle events recorded on the active chat or invoke_agent span. This is not an exhaustive list; the runtime also emits sandbox governance events and additional session events.

Event	Description	Key attributes
github.copilot.hook.start	A hook began executing	github.copilot.hook.type, github.copilot.hook.invocation_id
github.copilot.hook.end	A hook completed successfully	github.copilot.hook.type, github.copilot.hook.invocation_id
github.copilot.hook.error	A hook failed	github.copilot.hook.type, github.copilot.hook.invocation_id, github.copilot.hook.error_message
github.copilot.session.truncation	Conversation history was truncated	github.copilot.token_limit, github.copilot.pre_tokens, github.copilot.post_tokens, github.copilot.pre_messages, github.copilot.post_messages, github.copilot.tokens_removed, github.copilot.messages_removed, github.copilot.performed_by
github.copilot.session.compaction_start	History compaction began	None
github.copilot.session.compaction_complete	History compaction completed	github.copilot.success, github.copilot.pre_tokens, github.copilot.post_tokens, github.copilot.tokens_removed, github.copilot.messages_removed, github.copilot.message (content capture only)
github.copilot.skill.invoked	A skill was invoked	github.copilot.skill.name, github.copilot.skill.path, github.copilot.skill.plugin_name, github.copilot.skill.plugin_version
github.copilot.session.shutdown	Session is shutting down	github.copilot.shutdown_type, github.copilot.total_premium_requests, github.copilot.lines_added, github.copilot.lines_removed, github.copilot.files_modified_count
github.copilot.session.abort	User cancelled the current operation	github.copilot.abort_reason
gen_ai.client.operation.exception	Session error	github.copilot.error_type, github.copilot.error_status_code, github.copilot.error_provider_call_id

Resource attributes

All signals carry these resource attributes.

Attribute	Value
service.name	github-copilot (configurable via OTEL_SERVICE_NAME)
service.version	Runtime version

Content capture

By default, no prompt content, responses, or tool arguments are captured—only metadata like model names, token counts, and durations. To capture full content, set OTEL_INSTRUMENTATION_GENAI_CAPTURE_CONTENT to SPAN_ONLY or SPAN_AND_EVENT. The values NO_CONTENT (the default) and EVENT_ONLY leave span content capture off.

If OTEL_INSTRUMENTATION_GENAI_CAPTURE_CONTENT is empty or unset, the runtime falls back to the legacy boolean variable OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT. An unrecognized value for the standard variable disables span content capture and logs a warning.

When content capture is enabled, the following attributes are populated.

Attribute	Content
gen_ai.input.messages	Full prompt messages (JSON)
gen_ai.output.messages	Full response messages (JSON)
gen_ai.system_instructions	System prompt content (JSON)
gen_ai.tool.definitions	Tool schemas (JSON). Schema metadata keeps an independent opt-in: this attribute is populated only by the legacy OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT variable, not by OTEL_INSTRUMENTATION_GENAI_CAPTURE_CONTENT.
gen_ai.tool.call.arguments	Tool input arguments
gen_ai.tool.call.result	Tool output