Semantic conventions for generative AI metrics

Status: Development

[!Warning]

Existing GenAI instrumentations that are using v1.36.0 of this document (or prior):

• SHOULD NOT change the version of the GenAI conventions that they emit by default. Conventions include, but are not limited to, attributes, metric, span and event names, span kind and unit of measure.
• SHOULD introduce an environment variable OTEL_SEMCONV_STABILITY_OPT_IN as a comma-separated list of category-specific values. The list of values includes:
  • gen_ai_latest_experimental - emit the latest experimental version of GenAI conventions (supported by the instrumentation) and do not emit the old one (v1.36.0 or prior).
  • The default behavior is to continue emitting whatever version of the GenAI conventions the instrumentation was emitting (1.36.0 or prior).

This transition plan will be updated to include stable version before the GenAI conventions are marked as stable.

Generative AI client metrics

The conventions described in this section are specific to Generative AI client applications.

Disclaimer: These are initial Generative AI client metric instruments and attributes but more may be added in the future.

The following metric instruments describe Generative AI operations. An operation may be a request to an LLM, a function call, or some other distinct action within a larger Generative AI workflow.

Individual systems may include additional system-specific attributes. It is recommended to check system-specific documentation, if available.

Metric: gen_ai.client.token.usage

This metric is recommended when an operation involves the usage of tokens and the count is readily available.

For example, if GenAI system returns usage information in the streaming response, it SHOULD be used. Or if GenAI system returns each token independently, instrumentation SHOULD count number of output tokens and record the result.

If instrumentation cannot efficiently obtain number of input and/or output tokens, it MAY allow users to enable offline token counting. Otherwise it MUST NOT report usage metric.

When systems report both used tokens and billable tokens, instrumentation MUST report billable tokens.

This metric SHOULD be specified with ExplicitBucketBoundaries of [1, 4, 16, 64, 256, 1024, 4096, 16384, 65536, 262144, 1048576, 4194304, 16777216, 67108864].

Name	Instrument Type	Unit (UCUM)	Description	Stability	Entity Associations
gen_ai.client.token.usage	Histogram	{token}	Number of input and output tokens used.

Attribute	Type	Description	Examples	Requirement Level	Stability
gen_ai.operation.name	string	The name of the operation being performed. [1]	chat; generate_content; text_completion	Required
gen_ai.provider.name	string	The Generative AI provider as identified by the client or server instrumentation. [2]	openai; gcp.gen_ai; gcp.vertex_ai	Required
gen_ai.token.type	string	The type of token being counted.	input; output	Required
gen_ai.request.model	string	The name of the GenAI model a request is being made to.	gpt-4	Conditionally Required If available.
server.port	int	GenAI server port. [3]	80; 8080; 443	Conditionally Required If server.address is set.
gen_ai.response.model	string	The name of the model that generated the response.	gpt-4-0613	Recommended
server.address	string	GenAI server address. [4]	example.com; 10.1.2.80; /tmp/my.sock	Recommended

[1] gen_ai.operation.name: If one of the predefined values applies, but specific system uses a different name it’s RECOMMENDED to document it in the semantic conventions for specific GenAI system and use system-specific name in the instrumentation. If a different name is not documented, instrumentation libraries SHOULD use applicable predefined value.

[2] gen_ai.provider.name: The attribute SHOULD be set based on the instrumentation’s best knowledge and may differ from the actual model provider.

Multiple providers, including Azure OpenAI, Gemini, and AI hosting platforms are accessible using the OpenAI REST API and corresponding client libraries, but may proxy or host models from different providers.

The gen_ai.request.model, gen_ai.response.model, and server.address attributes may help identify the actual system in use.

The gen_ai.provider.name attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the gen_ai.provider.name set to aws.bedrock and include applicable aws.bedrock.* attributes and are not expected to include openai.* attributes.

[3] server.port: When observed from the client side, and when communicating through an intermediary, server.port SHOULD represent the server port behind any intermediaries, for example proxies, if it’s available.

[4] server.address: When observed from the client side, and when communicating through an intermediary, server.address SHOULD represent the server address behind any intermediaries, for example proxies, if it’s available.

gen_ai.operation.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
chat	Chat completion operation such as OpenAI Chat API
create_agent	Create GenAI agent
embeddings	Embeddings operation such as OpenAI Create embeddings API
execute_tool	Execute a tool
generate_content	Multimodal content generation operation such as Gemini Generate Content
invoke_agent	Invoke GenAI agent
text_completion	Text completions operation such as OpenAI Completions API (Legacy)

gen_ai.provider.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
anthropic	Anthropic
aws.bedrock	AWS Bedrock
azure.ai.inference	Azure AI Inference
azure.ai.openai	Azure OpenAI
cohere	Cohere
deepseek	DeepSeek
gcp.gemini	Gemini [5]
gcp.gen_ai	Any Google generative AI endpoint [6]
gcp.vertex_ai	Vertex AI [7]
groq	Groq
ibm.watsonx.ai	IBM Watsonx AI
mistral_ai	Mistral AI
openai	OpenAI
perplexity	Perplexity
x_ai	xAI

[5]: Used when accessing the ‘generativelanguage.googleapis.com’ endpoint. Also known as the AI Studio API.

[6]: May be used when specific backend is unknown.

[7]: Used when accessing the ‘aiplatform.googleapis.com’ endpoint.

gen_ai.token.type has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
input	Input tokens (prompt, input, etc.)
output	Output tokens (completion, response, etc.)

Metric: gen_ai.client.operation.duration

This metric is required.

This metric SHOULD be specified with ExplicitBucketBoundaries of [0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56, 5.12, 10.24, 20.48, 40.96, 81.92].

Name	Instrument Type	Unit (UCUM)	Description	Stability	Entity Associations
gen_ai.client.operation.duration	Histogram	s	GenAI operation duration.

Attribute	Type	Description	Examples	Requirement Level	Stability
gen_ai.operation.name	string	The name of the operation being performed. [1]	chat; generate_content; text_completion	Required
gen_ai.provider.name	string	The Generative AI provider as identified by the client or server instrumentation. [2]	openai; gcp.gen_ai; gcp.vertex_ai	Required
error.type	string	Describes a class of error the operation ended with. [3]	timeout; java.net.UnknownHostException; server_certificate_invalid; 500	Conditionally Required if the operation ended in an error
gen_ai.request.model	string	The name of the GenAI model a request is being made to.	gpt-4	Conditionally Required If available.
server.port	int	GenAI server port. [4]	80; 8080; 443	Conditionally Required If server.address is set.
gen_ai.response.model	string	The name of the model that generated the response.	gpt-4-0613	Recommended
server.address	string	GenAI server address. [5]	example.com; 10.1.2.80; /tmp/my.sock	Recommended

[1] gen_ai.operation.name: If one of the predefined values applies, but specific system uses a different name it’s RECOMMENDED to document it in the semantic conventions for specific GenAI system and use system-specific name in the instrumentation. If a different name is not documented, instrumentation libraries SHOULD use applicable predefined value.

[2] gen_ai.provider.name: The attribute SHOULD be set based on the instrumentation’s best knowledge and may differ from the actual model provider.

Multiple providers, including Azure OpenAI, Gemini, and AI hosting platforms are accessible using the OpenAI REST API and corresponding client libraries, but may proxy or host models from different providers.

The gen_ai.request.model, gen_ai.response.model, and server.address attributes may help identify the actual system in use.

The gen_ai.provider.name attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the gen_ai.provider.name set to aws.bedrock and include applicable aws.bedrock.* attributes and are not expected to include openai.* attributes.

[3] error.type: The error.type SHOULD match the error code returned by the Generative AI provider or the client library, the canonical name of exception that occurred, or another low-cardinality error identifier. Instrumentations SHOULD document the list of errors they report.

[4] server.port: When observed from the client side, and when communicating through an intermediary, server.port SHOULD represent the server port behind any intermediaries, for example proxies, if it’s available.

[5] server.address: When observed from the client side, and when communicating through an intermediary, server.address SHOULD represent the server address behind any intermediaries, for example proxies, if it’s available.

error.type has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
_OTHER	A fallback error value to be used when the instrumentation doesn’t define a custom value.

gen_ai.operation.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
chat	Chat completion operation such as OpenAI Chat API
create_agent	Create GenAI agent
embeddings	Embeddings operation such as OpenAI Create embeddings API
execute_tool	Execute a tool
generate_content	Multimodal content generation operation such as Gemini Generate Content
invoke_agent	Invoke GenAI agent
text_completion	Text completions operation such as OpenAI Completions API (Legacy)

gen_ai.provider.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
anthropic	Anthropic
aws.bedrock	AWS Bedrock
azure.ai.inference	Azure AI Inference
azure.ai.openai	Azure OpenAI
cohere	Cohere
deepseek	DeepSeek
gcp.gemini	Gemini [6]
gcp.gen_ai	Any Google generative AI endpoint [7]
gcp.vertex_ai	Vertex AI [8]
groq	Groq
ibm.watsonx.ai	IBM Watsonx AI
mistral_ai	Mistral AI
openai	OpenAI
perplexity	Perplexity
x_ai	xAI

[6]: Used when accessing the ‘generativelanguage.googleapis.com’ endpoint. Also known as the AI Studio API.

[7]: May be used when specific backend is unknown.

[8]: Used when accessing the ‘aiplatform.googleapis.com’ endpoint.

Generative AI model server metrics

The following metric instruments describe Generative AI model servers’ operational metrics. It includes both functional and performance metrics.

Metric: gen_ai.server.request.duration

This metric is recommended to report the model server latency in terms of time spent per request.

This metric SHOULD be specified with ExplicitBucketBoundaries of [0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56, 5.12, 10.24, 20.48, 40.96, 81.92].

Name	Instrument Type	Unit (UCUM)	Description	Stability	Entity Associations
gen_ai.server.request.duration	Histogram	s	Generative AI server request duration such as time-to-last byte or last output token.

Attribute	Type	Description	Examples	Requirement Level	Stability
gen_ai.operation.name	string	The name of the operation being performed. [1]	chat; generate_content; text_completion	Required
gen_ai.provider.name	string	The Generative AI provider as identified by the client or server instrumentation. [2]	openai; gcp.gen_ai; gcp.vertex_ai	Required
error.type	string	Describes a class of error the operation ended with. [3]	timeout; java.net.UnknownHostException; server_certificate_invalid; 500	Conditionally Required if the operation ended in an error
gen_ai.request.model	string	The name of the GenAI model a request is being made to.	gpt-4	Conditionally Required If available.
server.port	int	GenAI server port. [4]	80; 8080; 443	Conditionally Required If server.address is set.
gen_ai.response.model	string	The name of the model that generated the response.	gpt-4-0613	Recommended
server.address	string	GenAI server address. [5]	example.com; 10.1.2.80; /tmp/my.sock	Recommended

[1] gen_ai.operation.name: If one of the predefined values applies, but specific system uses a different name it’s RECOMMENDED to document it in the semantic conventions for specific GenAI system and use system-specific name in the instrumentation. If a different name is not documented, instrumentation libraries SHOULD use applicable predefined value.

[2] gen_ai.provider.name: The attribute SHOULD be set based on the instrumentation’s best knowledge and may differ from the actual model provider.

Multiple providers, including Azure OpenAI, Gemini, and AI hosting platforms are accessible using the OpenAI REST API and corresponding client libraries, but may proxy or host models from different providers.

The gen_ai.request.model, gen_ai.response.model, and server.address attributes may help identify the actual system in use.

The gen_ai.provider.name attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the gen_ai.provider.name set to aws.bedrock and include applicable aws.bedrock.* attributes and are not expected to include openai.* attributes.

[3] error.type: The error.type SHOULD match the error code returned by the Generative AI service, the canonical name of exception that occurred, or another low-cardinality error identifier. Instrumentations SHOULD document the list of errors they report.

[4] server.port: When observed from the client side, and when communicating through an intermediary, server.port SHOULD represent the server port behind any intermediaries, for example proxies, if it’s available.

[5] server.address: When observed from the client side, and when communicating through an intermediary, server.address SHOULD represent the server address behind any intermediaries, for example proxies, if it’s available.

error.type has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
_OTHER	A fallback error value to be used when the instrumentation doesn’t define a custom value.

gen_ai.operation.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
chat	Chat completion operation such as OpenAI Chat API
create_agent	Create GenAI agent
embeddings	Embeddings operation such as OpenAI Create embeddings API
execute_tool	Execute a tool
generate_content	Multimodal content generation operation such as Gemini Generate Content
invoke_agent	Invoke GenAI agent
text_completion	Text completions operation such as OpenAI Completions API (Legacy)

gen_ai.provider.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
anthropic	Anthropic
aws.bedrock	AWS Bedrock
azure.ai.inference	Azure AI Inference
azure.ai.openai	Azure OpenAI
cohere	Cohere
deepseek	DeepSeek
gcp.gemini	Gemini [6]
gcp.gen_ai	Any Google generative AI endpoint [7]
gcp.vertex_ai	Vertex AI [8]
groq	Groq
ibm.watsonx.ai	IBM Watsonx AI
mistral_ai	Mistral AI
openai	OpenAI
perplexity	Perplexity
x_ai	xAI

[6]: Used when accessing the ‘generativelanguage.googleapis.com’ endpoint. Also known as the AI Studio API.

[7]: May be used when specific backend is unknown.

[8]: Used when accessing the ‘aiplatform.googleapis.com’ endpoint.

Metric: gen_ai.server.time_per_output_token

This metric is recommended to report the model server latency in terms of time per token generated after the first token for any model servers which support serving LLMs. It is measured by subtracting the time taken to generate the first output token from the request duration and dividing the rest of the duration by the number of output tokens generated after the first token. This is important in measuring the performance of the decode phase of LLM inference.

This metric SHOULD be specified with ExplicitBucketBoundaries of [0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75, 1.0, 2.5].

Name	Instrument Type	Unit (UCUM)	Description	Stability	Entity Associations
gen_ai.server.time_per_output_token	Histogram	s	Time per output token generated after the first token for successful responses.

Attribute	Type	Description	Examples	Requirement Level	Stability
gen_ai.operation.name	string	The name of the operation being performed. [1]	chat; generate_content; text_completion	Required
gen_ai.provider.name	string	The Generative AI provider as identified by the client or server instrumentation. [2]	openai; gcp.gen_ai; gcp.vertex_ai	Required
gen_ai.request.model	string	The name of the GenAI model a request is being made to.	gpt-4	Conditionally Required If available.
server.port	int	GenAI server port. [3]	80; 8080; 443	Conditionally Required If server.address is set.
gen_ai.response.model	string	The name of the model that generated the response.	gpt-4-0613	Recommended
server.address	string	GenAI server address. [4]	example.com; 10.1.2.80; /tmp/my.sock	Recommended

[1] gen_ai.operation.name: If one of the predefined values applies, but specific system uses a different name it’s RECOMMENDED to document it in the semantic conventions for specific GenAI system and use system-specific name in the instrumentation. If a different name is not documented, instrumentation libraries SHOULD use applicable predefined value.

[2] gen_ai.provider.name: The attribute SHOULD be set based on the instrumentation’s best knowledge and may differ from the actual model provider.

Multiple providers, including Azure OpenAI, Gemini, and AI hosting platforms are accessible using the OpenAI REST API and corresponding client libraries, but may proxy or host models from different providers.

The gen_ai.request.model, gen_ai.response.model, and server.address attributes may help identify the actual system in use.

The gen_ai.provider.name attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the gen_ai.provider.name set to aws.bedrock and include applicable aws.bedrock.* attributes and are not expected to include openai.* attributes.

[3] server.port: When observed from the client side, and when communicating through an intermediary, server.port SHOULD represent the server port behind any intermediaries, for example proxies, if it’s available.

[4] server.address: When observed from the client side, and when communicating through an intermediary, server.address SHOULD represent the server address behind any intermediaries, for example proxies, if it’s available.

gen_ai.operation.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
chat	Chat completion operation such as OpenAI Chat API
create_agent	Create GenAI agent
embeddings	Embeddings operation such as OpenAI Create embeddings API
execute_tool	Execute a tool
generate_content	Multimodal content generation operation such as Gemini Generate Content
invoke_agent	Invoke GenAI agent
text_completion	Text completions operation such as OpenAI Completions API (Legacy)

gen_ai.provider.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
anthropic	Anthropic
aws.bedrock	AWS Bedrock
azure.ai.inference	Azure AI Inference
azure.ai.openai	Azure OpenAI
cohere	Cohere
deepseek	DeepSeek
gcp.gemini	Gemini [5]
gcp.gen_ai	Any Google generative AI endpoint [6]
gcp.vertex_ai	Vertex AI [7]
groq	Groq
ibm.watsonx.ai	IBM Watsonx AI
mistral_ai	Mistral AI
openai	OpenAI
perplexity	Perplexity
x_ai	xAI

[5]: Used when accessing the ‘generativelanguage.googleapis.com’ endpoint. Also known as the AI Studio API.

[6]: May be used when specific backend is unknown.

[7]: Used when accessing the ‘aiplatform.googleapis.com’ endpoint.

Metric: gen_ai.server.time_to_first_token

This metric is recommended to report the model server latency in terms of time spent to generate the first token of the response for any model servers which support serving LLMs. It helps measure the time spent in the queue and the prefill phase. It is important especially for streaming requests. It is calculated at a request level and is reported as a histogram using the buckets mentioned below.

This metric SHOULD be specified with ExplicitBucketBoundaries of [0.001, 0.005, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.25, 0.5, 0.75, 1.0, 2.5, 5.0, 7.5, 10.0].

Name	Instrument Type	Unit (UCUM)	Description	Stability	Entity Associations
gen_ai.server.time_to_first_token	Histogram	s	Time to generate first token for successful responses.

Attribute	Type	Description	Examples	Requirement Level	Stability
gen_ai.operation.name	string	The name of the operation being performed. [1]	chat; generate_content; text_completion	Required
gen_ai.provider.name	string	The Generative AI provider as identified by the client or server instrumentation. [2]	openai; gcp.gen_ai; gcp.vertex_ai	Required
gen_ai.request.model	string	The name of the GenAI model a request is being made to.	gpt-4	Conditionally Required If available.
server.port	int	GenAI server port. [3]	80; 8080; 443	Conditionally Required If server.address is set.
gen_ai.response.model	string	The name of the model that generated the response.	gpt-4-0613	Recommended
server.address	string	GenAI server address. [4]	example.com; 10.1.2.80; /tmp/my.sock	Recommended

[1] gen_ai.operation.name: If one of the predefined values applies, but specific system uses a different name it’s RECOMMENDED to document it in the semantic conventions for specific GenAI system and use system-specific name in the instrumentation. If a different name is not documented, instrumentation libraries SHOULD use applicable predefined value.

[2] gen_ai.provider.name: The attribute SHOULD be set based on the instrumentation’s best knowledge and may differ from the actual model provider.

Multiple providers, including Azure OpenAI, Gemini, and AI hosting platforms are accessible using the OpenAI REST API and corresponding client libraries, but may proxy or host models from different providers.

The gen_ai.request.model, gen_ai.response.model, and server.address attributes may help identify the actual system in use.

The gen_ai.provider.name attribute acts as a discriminator that identifies the GenAI telemetry format flavor specific to that provider within GenAI semantic conventions. It SHOULD be set consistently with provider-specific attributes and signals. For example, GenAI spans, metrics, and events related to AWS Bedrock should have the gen_ai.provider.name set to aws.bedrock and include applicable aws.bedrock.* attributes and are not expected to include openai.* attributes.

[3] server.port: When observed from the client side, and when communicating through an intermediary, server.port SHOULD represent the server port behind any intermediaries, for example proxies, if it’s available.

[4] server.address: When observed from the client side, and when communicating through an intermediary, server.address SHOULD represent the server address behind any intermediaries, for example proxies, if it’s available.

gen_ai.operation.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
chat	Chat completion operation such as OpenAI Chat API
create_agent	Create GenAI agent
embeddings	Embeddings operation such as OpenAI Create embeddings API
execute_tool	Execute a tool
generate_content	Multimodal content generation operation such as Gemini Generate Content
invoke_agent	Invoke GenAI agent
text_completion	Text completions operation such as OpenAI Completions API (Legacy)

gen_ai.provider.name has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description	Stability
anthropic	Anthropic
aws.bedrock	AWS Bedrock
azure.ai.inference	Azure AI Inference
azure.ai.openai	Azure OpenAI
cohere	Cohere
deepseek	DeepSeek
gcp.gemini	Gemini [5]
gcp.gen_ai	Any Google generative AI endpoint [6]
gcp.vertex_ai	Vertex AI [7]
groq	Groq
ibm.watsonx.ai	IBM Watsonx AI
mistral_ai	Mistral AI
openai	OpenAI
perplexity	Perplexity
x_ai	xAI

[5]: Used when accessing the ‘generativelanguage.googleapis.com’ endpoint. Also known as the AI Studio API.

[6]: May be used when specific backend is unknown.

[7]: Used when accessing the ‘aiplatform.googleapis.com’ endpoint.