Documentation Index
Fetch the complete documentation index at: https://mintlify.com/Arize-ai/openinference/llms.txt
Use this file to discover all available pages before exploring further.
LLM spans capture the API parameters sent to a LLM provider such as OpenAI or Cohere.
Required Attributes
All LLM spans MUST include:
openinference.span.kind: Set to "LLM"llm.system: The AI system/product (e.g., “openai”, “anthropic”)
Common Attributes
LLM spans typically include:
| Attribute | Description |
|---|
llm.model_name | The specific model used (e.g., “gpt-4-0613”) |
llm.invocation_parameters | JSON string of parameters sent to the model |
input.value | The raw input as a JSON string |
input.mime_type | Usually “application/json” |
output.value | The raw output as a JSON string |
output.mime_type | Usually “application/json” |
llm.input_messages | Flattened list of input messages |
llm.output_messages | Flattened list of output messages |
llm.token_count.* | Token usage metrics |
Context Attributes
All 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. Attribute Flattening
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 definitions
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"
}
Examples
Chat Completions
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..."
},
{ "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": []
}
Synthesis Call Using Function Output
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..."
},
{
"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": []
}
Completions
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": []
}