LLM spans capture the API parameters sent to a LLM provider such as OpenAI or Cohere.
All LLM spans MUST include:
openinference.span.kind: Set to "LLM"llm.system: The AI system/product (e.g., “openai”, “anthropic”)LLM spans typically include:
llm.model_name: The specific model used (e.g., “gpt-4-0613”)llm.invocation_parameters: JSON string of parameters sent to the modelinput.value: The raw input as a JSON stringinput.mime_type: Usually “application/json”output.value: The raw output as a JSON stringoutput.mime_type: Usually “application/json”llm.input_messages: Flattened list of input messagesllm.output_messages: Flattened list of output messagesllm.token_count.*: Token usage metricsAll LLM spans automatically inherit context attributes when they are set via the instrumentation context API. These attributes are propagated to every span in the trace without needing to be explicitly set on each span:
| Attribute | Description |
|---|---|
session.id |
Unique identifier for the session |
user.id |
Unique identifier for the user |
metadata |
JSON string of key-value metadata associated with the trace |
tag.tags |
List of string tags for categorizing the span |
llm.prompt_template.template |
The prompt template used to generate the LLM input |
llm.prompt_template.variables |
JSON of key-value pairs applied to the prompt template |
llm.prompt_template.version |
Version identifier for the prompt template |
See Configuration for details on how to set these context attributes.
Note that while the examples below show attributes in a nested JSON format for readability, in actual OpenTelemetry spans, these attributes are flattened using indexed dot notation:
llm.input_messages.0.message.role instead of llm.input_messages[0].message.rolellm.output_messages.0.message.tool_calls.0.tool_call.function.name for nested tool callsllm.tools.0.tool.json_schema for tool definitionsWhen a message with message.role set to "tool" represents the result of a function call, the message.name attribute MAY be set to identify which function produced the result. This complements message.tool_call_id, which links the result back to the original tool call request. For example:
{
"message.role": "tool",
"message.content": "2001",
"message.name": "multiply",
"message.tool_call_id": "call_62136355"
}
See Tool Calling for the complete tool calling flow.
A span for a tool call with OpenAI (shown in logical JSON format for clarity)
{
"name": "ChatCompletion",
"context": {
"trace_id": "409df945-e058-4829-b240-cfbdd2ff4488",
"span_id": "01fa9612-01b8-4358-85d6-e3e067305ec3"
},
"span_kind": "SPAN_KIND_INTERNAL",
"parent_id": "2fe8a793-2cf1-42d7-a1df-bd7d46e017ef",
"start_time": "2024-01-11T16:45:17.982858-07:00",
"end_time": "2024-01-11T16:45:18.517639-07:00",
"status_code": "OK",
"status_message": "",
"attributes": {
"openinference.span.kind": "LLM",
"llm.system": "openai",
"llm.input_messages": [
{
"message.role": "system",
"message.content": "You are a Shakespearean writing assistant who speaks in a Shakespearean style. You help people come up with creative ideas and content like stories, poems, and songs that use Shakespearean style of writing style, including words like \"thou\" and \"hath\u201d.\nHere are some example of Shakespeare's style:\n - Romeo, Romeo! Wherefore art thou Romeo?\n - Love looks not with the eyes, but with the mind; and therefore is winged Cupid painted blind.\n - Shall I compare thee to a summer's day? Thou art more lovely and more temperate.\n"
},
{ "message.role": "user", "message.content": "what is 23 times 87" }
],
"llm.model_name": "gpt-3.5-turbo-0613",
"llm.invocation_parameters": "{\"model\": \"gpt-3.5-turbo-0613\", \"temperature\": 0.1, \"max_tokens\": null}",
"output.value": "{\"tool_calls\": [{\"id\": \"call_Re47Qyh8AggDGEEzlhb4fu7h\", \"function\": {\"arguments\": \"{\\n \\\"a\\\": 23,\\n \\\"b\\\": 87\\n}\", \"name\": \"multiply\"}, \"type\": \"function\"}]}",
"output.mime_type": "application/json",
"llm.output_messages": [
{
"message.role": "assistant",
"message.tool_calls": [
{
"tool_call.function.name": "multiply",
"tool_call.function.arguments": "{\n \"a\": 23,\n \"b\": 87\n}"
}
]
}
],
"llm.token_count.prompt": 229,
"llm.token_count.completion": 21,
"llm.token_count.total": 250
},
"events": []
}
A synthesis call using a function call output
{
"name": "llm",
"context": {
"trace_id": "409df945-e058-4829-b240-cfbdd2ff4488",
"span_id": "f26d1f26-9671-435d-9716-14a87a3f228b"
},
"span_kind": "SPAN_KIND_INTERNAL",
"parent_id": "2fe8a793-2cf1-42d7-a1df-bd7d46e017ef",
"start_time": "2024-01-11T16:45:18.519427-07:00",
"end_time": "2024-01-11T16:45:19.159145-07:00",
"status_code": "OK",
"status_message": "",
"attributes": {
"openinference.span.kind": "LLM",
"llm.system": "openai",
"llm.input_messages": [
{
"message.role": "system",
"message.content": "You are a Shakespearean writing assistant who speaks in a Shakespearean style. You help people come up with creative ideas and content like stories, poems, and songs that use Shakespearean style of writing style, including words like \"thou\" and \"hath\u201d.\nHere are some example of Shakespeare's style:\n - Romeo, Romeo! Wherefore art thou Romeo?\n - Love looks not with the eyes, but with the mind; and therefore is winged Cupid painted blind.\n - Shall I compare thee to a summer's day? Thou art more lovely and more temperate.\n"
},
{
"message.role": "user",
"message.content": "what is 23 times 87"
},
{
"message.role": "assistant",
"message.content": null,
"message.tool_calls": [
{
"tool_call.function.name": "multiply",
"tool_call.function.arguments": "{\n \"a\": 23,\n \"b\": 87\n}"
}
]
},
{
"message.role": "tool",
"message.content": "2001",
"message.name": "multiply"
}
],
"llm.model_name": "gpt-3.5-turbo-0613",
"llm.invocation_parameters": "{\"model\": \"gpt-3.5-turbo-0613\", \"temperature\": 0.1, \"max_tokens\": null}",
"output.value": "The product of 23 times 87 is 2001.",
"output.mime_type": "text/plain",
"llm.output_messages": [
{
"message.role": "assistant",
"message.content": "The product of 23 times 87 is 2001."
}
],
"llm.token_count.prompt": 259,
"llm.token_count.completion": 14,
"llm.token_count.total": 273
},
"events": [],
"conversation": null
}
A span for a simple completion (shown in logical JSON format for clarity)
{
"name": "Completion",
"context": {
"trace_id": "12345678-1234-5678-1234-567812345678",
"span_id": "87654321-4321-8765-4321-876543218765"
},
"span_kind": "SPAN_KIND_INTERNAL",
"parent_id": null,
"start_time": "2025-09-29T03:42:49.000000Z",
"end_time": "2025-09-29T03:42:50.284841Z",
"status_code": "OK",
"status_message": "",
"attributes": {
"openinference.span.kind": "LLM",
"llm.system": "openai",
"llm.model_name": "babbage:2023-07-21-v2",
"llm.invocation_parameters": "{\"model\": \"babbage-002\", \"temperature\": 0.4, \"top_p\": 0.9, \"max_tokens\": 25}",
"input.value": "{\"model\": \"babbage-002\", \"prompt\": \"def fib(n):\\n if n <= 1:\\n return n\\n else:\\n return fib(n-1) + fib(n-2)\", \"temperature\": 0.4, \"top_p\": 0.9, \"max_tokens\": 25}",
"input.mime_type": "application/json",
"llm.prompts.0.prompt.text": "def fib(n):\n if n <= 1:\n return n\n else:\n return fib(n-1) + fib(n-2)",
"output.value": "{\"id\": \"cmpl-CKz4klHa1MMqAa4hQn3yzIMlLMZHd\", \"object\": \"text_completion\", \"created\": 1759117370, \"model\": \"babbage:2023-07-21-v2\", \"choices\": [{\"text\": \" + fib(n-3) + fib(n-4)\\n\\ndef fib(n):\\n if n <= 1:\\n return\", \"index\": 0, \"finish_reason\": \"length\"}], \"usage\": {\"prompt_tokens\": 31, \"completion_tokens\": 25, \"total_tokens\": 56}}",
"output.mime_type": "application/json",
"llm.choices.0.completion.text": " + fib(n-3) + fib(n-4)\n\ndef fib(n):\n if n <= 1:\n return",
"llm.token_count.prompt": 31,
"llm.token_count.completion": 25,
"llm.token_count.total": 56
},
"events": []
}