Semantic conventions for AWS Bedrock operations

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 (1.34.0 or prior).

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

AWS Bedrock Spans

The Semantic Conventions for AWS Bedrock extend and override the semantic conventions for Gen AI Spans.

gen_ai.provider.name MUST be set to "aws.bedrock".

These attributes track input data and metadata for a request to an AWS Bedrock model. The attributes include general Generative AI attributes and ones specific the AWS Bedrock.

Status:

Describes an AWS Bedrock operation span.

Span kind SHOULD be CLIENT.

Span status SHOULD follow the Recording Errors document.

Attribute	Type	Description	Examples	Requirement Level	Stability
aws.bedrock.guardrail.id	string	The unique identifier of the AWS Bedrock Guardrail. A guardrail helps safeguard and prevent unwanted behavior from model responses or user messages.	sgi5gkybzqak	Required
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.conversation.id	string	The unique identifier for a conversation (session, thread), used to store and correlate messages within this conversation. [4]	conv_5j66UpCpwteGg4YSxUnt7lPY	Conditionally Required when available
gen_ai.output.type	string	Represents the content type requested by the client. [5]	text; json; image	Conditionally Required [6]
gen_ai.request.choice.count	int	The target number of candidate completions to return.	3	Conditionally Required if available, in the request, and !=1
gen_ai.request.model	string	The name of the GenAI model a request is being made to. [7]	gpt-4	Conditionally Required If available.
gen_ai.request.seed	int	Requests with same seed value more likely to return same result.	100	Conditionally Required if applicable and if the request includes a seed
server.port	int	GenAI server port. [8]	80; 8080; 443	Conditionally Required If server.address is set.
aws.bedrock.knowledge_base.id	string	The unique identifier of the AWS Bedrock Knowledge base. A knowledge base is a bank of information that can be queried by models to generate more relevant responses and augment prompts.	XFWUPB9PAW	Recommended
gen_ai.request.frequency_penalty	double	The frequency penalty setting for the GenAI request.	0.1	Recommended
gen_ai.request.max_tokens	int	The maximum number of tokens the model generates for a request.	100	Recommended
gen_ai.request.presence_penalty	double	The presence penalty setting for the GenAI request.	0.1	Recommended
gen_ai.request.stop_sequences	string[]	List of sequences that the model will use to stop generating further tokens.	["forest", "lived"]	Recommended
gen_ai.request.temperature	double	The temperature setting for the GenAI request.	0.0	Recommended
gen_ai.request.top_k	double	The top_k sampling setting for the GenAI request.	1.0	Recommended
gen_ai.request.top_p	double	The top_p sampling setting for the GenAI request.	1.0	Recommended
gen_ai.response.finish_reasons	string[]	Array of reasons the model stopped generating tokens, corresponding to each generation received.	["stop"]; ["stop", "length"]	Recommended
gen_ai.response.id	string	The unique identifier for the completion.	chatcmpl-123	Recommended
gen_ai.response.model	string	The name of the model that generated the response. [9]	gpt-4-0613	Recommended
gen_ai.usage.input_tokens	int	The number of tokens used in the GenAI input (prompt).	100	Recommended
gen_ai.usage.output_tokens	int	The number of tokens used in the GenAI response (completion).	180	Recommended
server.address	string	GenAI server address. [10]	example.com; 10.1.2.80; /tmp/my.sock	Recommended
gen_ai.input.messages	any	The chat history provided to the model as an input. [11]	[   {     “role”: “user”,     “parts”: [       {         “type”: “text”,         “content”: “Weather in Paris?"       }     ]   },   {     “role”: “assistant”,     “parts”: [       {         “type”: “tool_call”,         “id”: “call_VSPygqKTWdrhaFErNvMV18Yl”,         “name”: “get_weather”,         “arguments”: {           “location”: “Paris”         }       }     ]   },   {     “role”: “tool”,     “parts”: [       {         “type”: “tool_call_response”,         “id”: " call_VSPygqKTWdrhaFErNvMV18Yl”,         “result”: “rainy, 57°F”       }     ]   } ]	Opt-In
gen_ai.output.messages	any	Messages returned by the model where each message represents a specific model response (choice, candidate). [12]	[   {     “role”: “assistant”,     “parts”: [       {         “type”: “text”,         “content”: “The weather in Paris is currently rainy with a temperature of 57°F."       }     ],     “finish_reason”: “stop”   } ]	Opt-In
gen_ai.system_instructions	any	The system message or instructions provided to the GenAI model separately from the chat history. [13]	[   {     “type”: “text”,     “content”: “You are an Agent that greet users, always use greetings tool to respond”   } ]; [   {     “type”: “text”,     “content”: “You are a language translator."   },   {     “type”: “text”,     “content”: “Your mission is to translate text in English to French."   } ]	Opt-In

[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] gen_ai.conversation.id: Instrumentations SHOULD populate conversation id when they have it readily available for a given operation, for example:

• when client framework being instrumented manages conversation history (see LlamaIndex chat store)

• when instrumenting GenAI client libraries that maintain conversation on the backend side (see AWS Bedrock agent sessions, OpenAI Assistant threads)

Application developers that manage conversation history MAY add conversation id to GenAI and other spans or logs using custom span or log record processors or hooks provided by instrumentation libraries.

[5] gen_ai.output.type: This attribute SHOULD be used when the client requests output of a specific type. The model may return zero or more outputs of this type. This attribute specifies the output modality and not the actual output format. For example, if an image is requested, the actual output could be a URL pointing to an image file. Additional output format details may be recorded in the future in the gen_ai.output.{type}.* attributes.

[6] gen_ai.output.type: when applicable and if the request includes an output format.

[7] gen_ai.request.model: The name of the GenAI model a request is being made to. If the model is supplied by a vendor, then the value must be the exact name of the model requested. If the model is a fine-tuned custom model, the value should have a more specific name than the base model that’s been fine-tuned.

[8] 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.

[9] gen_ai.response.model: If available. The name of the GenAI model that provided the response. If the model is supplied by a vendor, then the value must be the exact name of the model actually used. If the model is a fine-tuned custom model, the value should have a more specific name than the base model that’s been fine-tuned.

[10] 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.

[11] gen_ai.input.messages: Instrumentations MUST follow Input messages JSON schema. When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise.

Messages MUST be provided in the order they were sent to the model. Instrumentations MAY provide a way for users to filter or truncate input messages.

[!Warning] This attribute is likely to contain sensitive information including user/PII data.

See Recording content on attributes section for more details.

[12] gen_ai.output.messages: Instrumentations MUST follow Output messages JSON schema

Each message represents a single output choice/candidate generated by the model. Each message corresponds to exactly one generation (choice/candidate) and vice versa - one choice cannot be split across multiple messages or one message cannot contain parts from multiple choices.

When the attribute is recorded on events, it MUST be recorded in structured form. When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise.

Instrumentations MAY provide a way for users to filter or truncate output messages.

[!Warning] This attribute is likely to contain sensitive information including user/PII data.

See Recording content on attributes section for more details.

[13] gen_ai.system_instructions: This attribute SHOULD be used when the corresponding provider or API allows to provide system instructions or messages separately from the chat history.

Instructions that are part of the chat history SHOULD be recorded in gen_ai.input.messages attribute instead.

Instrumentations MUST follow System instructions JSON schema.

When recorded on spans, it MAY be recorded as a JSON string if structured format is not supported and SHOULD be recorded in structured form otherwise.

Instrumentations MAY provide a way for users to filter or truncate system instructions.

[!Warning] This attribute may contain sensitive information.

See Recording content on attributes section for more details.

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.output.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
image	Image
json	JSON object with known or unknown schema
speech	Speech
text	Plain text

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 [14]
gcp.gen_ai	Any Google generative AI endpoint [15]
gcp.vertex_ai	Vertex AI [16]
groq	Groq
ibm.watsonx.ai	IBM Watsonx AI
mistral_ai	Mistral AI
openai	OpenAI
perplexity	Perplexity
x_ai	xAI

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

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

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