Span attribute inventory

Complete inventory of gen_ai.* and related span attributes consumed by Coralogix AI Center. Use this reference when instrumenting manually or when verifying that spans from your instrumentation library carry the attributes AI Center expects.

How OTEL GenAI semantic conventions work

Every LLM call generates a span representing the request-response lifecycle. The span carries attributes (key-value metadata) that describe what happened:
Concept Example
Provider gen_ai.provider.name = "openai"
Model gen_ai.request.model = "gpt-4o"
Input gen_ai.input.messages = [{"role": "user", "parts": [{"type": "text", "content": "Hello"}]}]
Output gen_ai.output.messages = [{"role": "assistant", "parts": [{"type": "text", "content": "Hi!"}]}]
Token usage gen_ai.usage.input_tokens = 12, gen_ai.usage.output_tokens = 8
Operation gen_ai.operation.name = "chat"

Span attributes

Attribute	Type	Description
`gen_ai.provider.name`	string	LLM provider name (for example, `openai`, `anthropic`)
`gen_ai.request.model`	string	Model name requested (for example, `gpt-4o`)
`gen_ai.response.model`	string	Model name in response (may differ from request)
`gen_ai.request.temperature`	number	Temperature parameter
`gen_ai.usage.input_tokens`	number	Input token count
`gen_ai.usage.output_tokens`	number	Output token count
`gen_ai.input.messages`	JSON array	All input messages as JSON parts array
`gen_ai.output.messages`	JSON array	All output messages as JSON parts array
`gen_ai.response.finish_reasons`	JSON array	Array of finish reasons
`gen_ai.request.tools`	JSON array	Tool definitions in request
`gen_ai.tool.definitions`	JSON array	Tool definitions (alternative key)
`gen_ai.tool.name`	string	Tool name (on tool-call child spans)
`gen_ai.operation.name`	string	Operation type (`"chat"`, `"text_completion"`, or `"embeddings"`)
`gen_ai.system_instructions`	string	System instructions sent with the request

User identity resolution

AI Center resolves user identity from multiple tag keys (checked in order via firstNonNull()):
Attribute Source
gen_ai.request.user OTel semconv
enduser.id OTel semconv
user.id OTel semconv
traceloop.association.properties.user_id OpenLLMetry
langsmith.metadata.user_id LangSmith

Next steps

Look up which open-source library to use for your provider in Provider compatibility.

Need help? Contact Support.

What's new? Find out here.

LLM? Read llms.txt.

Previous Framework compatibility

Next Code examples

Concept	Example
Provider	`gen_ai.provider.name = "openai"`
Model	`gen_ai.request.model = "gpt-4o"`
Input	`gen_ai.input.messages = [{"role": "user", "parts": [{"type": "text", "content": "Hello"}]}]`
Output	`gen_ai.output.messages = [{"role": "assistant", "parts": [{"type": "text", "content": "Hi!"}]}]`
Token usage	`gen_ai.usage.input_tokens = 12`, `gen_ai.usage.output_tokens = 8`
Operation	`gen_ai.operation.name = "chat"`

Attribute	Source
`gen_ai.request.user`	OTel semconv
`enduser.id`	OTel semconv
`user.id`	OTel semconv
`traceloop.association.properties.user_id`	OpenLLMetry
`langsmith.metadata.user_id`	LangSmith