Creating Tool Spans

Example Tool Instrumentation

The example below demonstrates how to manually trace a tool function along with a chat completion response. You'll see how to create spans for both the tool and LLM to capture their input, output, and key events.

Before diving into the code, ensure you have configured your tracer correctly:

import json

#from your_tracer import tracer

def run_tool(tool_function, tool_args):
    #first set context for current span 
    with tracer.start_as_current_span(
        name="Tool - some tool",
        attributes={
            # Set these attributes before calling the tool, in case an exception is raised by the tool
            **{
                "openinference.span.kind": "TOOL",
                "input.value": question,
                "message.tool_calls.0.tool_call.function.name": tool_function.__name__,
                "message.tool_calls.0.tool_call.function.arguments": json.dumps(
                    tool_args
                ),
            },
        },
    ) as tool_span:
        #run tool; output is formatted prompt for chat completion
        resulting_prompt = tool_function(input=tool_args)
        # optional - set the resulting prompt as the tool span output
        tool_span.set_attribute(
            "message.tool_calls.0.tool_call.function.output", resulting_prompt
        )

        # This LLM span nests under the tool span in the trace
        with tracer.start_as_current_span(
            name="Tool - llm response",
            # Set these attributes before calling the LLM
            attributes={
                "openinference.span.kind": "LLM",
                "input.value": resulting_prompt,
            },
        ) as llm_span:
            llm_response = openai_client.chat.completions.create(
                    model=model_version,
                    messages=[current_user_message],
                    temperature=TEMPERATURE,
                )
            llm_span.set_attribute("output.value", llm_response)