Generative AI attributes
Key attributes include the following:
Span identification#
gen_ai.capability.name: The high-level capability name you defined inwithSpan.gen_ai.step.name: The specific step within the capability.gen_ai.operation.name: The operation type. For example:chat,execute_tool.
Model information#
gen_ai.provider.name: The model provider. For example:openai,anthropic.gen_ai.request.model: The model requested for the completion.gen_ai.response.model: The model that actually fulfilled the request.gen_ai.output.type: The output type. For example:text,json.
Token usage#
gen_ai.usage.input_tokens: The number of tokens in the prompt.gen_ai.usage.output_tokens: The number of tokens in the generated response.
Messages#
gen_ai.input.messages: The full, rendered prompt or message history sent to the model (as a JSON string).gen_ai.output.messages: The full response from the model (as a JSON string).gen_ai.response.finish_reasons: The reason the model stopped generating tokens. For example:stop,tool-calls.gen_ai.response.id: The unique identifier for the model response.
Tool attributes#
gen_ai.tool.name: The name of the executed tool.gen_ai.tool.call.arguments: The arguments passed to the tool (as a JSON string).gen_ai.tool.call.result: The result returned by the tool (as a JSON string).
Additional attributes#
For a more thorough list of attributes, see the OpenTelemetry Semantic Conventions for Generative AI.
What’s next?#
After capturing and analyzing production telemetry:
- Visualize traces in Console.
- Use GenAI APL functions to query and analyze your AI telemetry.