In some cases you will want to access the current run (span) within a traced function. This can be useful for extracting UUIDs, tags, or other information from the current run. You can access the current run by calling the get_current_run_tree/getCurrentRunTree function in the Python or TypeScript SDK, respectively. For a full list of available properties on the RunTree object, see this reference. 
from langsmith import traceable
from langsmith.run_helpers import get_current_run_tree
from openai import Client

openai = Client()

@traceable
def format_prompt(subject):
    run = get_current_run_tree()
    print(f"format_prompt Run Id: {run.id}")
    print(f"format_prompt Trace Id: {run.trace_id}")
    print(f"format_prompt Parent Run Id: {run.parent_run.id}")
    return [
        {
            "role": "system",
            "content": "You are a helpful assistant.",
        },
        {
            "role": "user",
            "content": f"What's a good name for a store that sells {subject}?"
        }
    ]

@traceable(run_type="llm")
def invoke_llm(messages):
    run = get_current_run_tree()
    print(f"invoke_llm Run Id: {run.id}")
    print(f"invoke_llm Trace Id: {run.trace_id}")
    print(f"invoke_llm Parent Run Id: {run.parent_run.id}")
    return openai.chat.completions.create(
        messages=messages, model="gpt-4o-mini", temperature=0
    )

@traceable
def parse_output(response):
    run = get_current_run_tree()
    print(f"parse_output Run Id: {run.id}")
    print(f"parse_output Trace Id: {run.trace_id}")
    print(f"parse_output Parent Run Id: {run.parent_run.id}")
    return response.choices[0].message.content

@traceable
def run_pipeline():
    run = get_current_run_tree()
    print(f"run_pipeline Run Id: {run.id}")
    print(f"run_pipeline Trace Id: {run.trace_id}")
    messages = format_prompt("colorful socks")
    response = invoke_llm(messages)
    return parse_output(response)

run_pipeline()