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()