LLM Spans

Attributes for language model calls: messages, token counts, model parameters, and tool definitions

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.role
• llm.output_messages.0.message.tool_calls.0.tool_call.function.name for nested tool calls
• llm.tools.0.tool.json_schema for tool definitions

Tool Role Messages

When 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.

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": []
}