Access the current run (span) within a traced function

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.

Python

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-3.5-turbo", 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()

TypeScript

import { traceable, getCurrentRunTree } from "langsmith/traceable";
import OpenAI from "openai";

const openai = new OpenAI();

const formatPrompt = traceable(
  (subject: string) => {
    const run = getCurrentRunTree();
    console.log("formatPrompt Run ID", run.id)
    console.log("formatPrompt Trace ID", run.trace_id)
    console.log("formatPrompt Parent Run ID", run.parent_run.id)
    return [
      {
        role: "system" as const,
        content: "You are a helpful assistant.",
      },
      {
        role: "user" as const,
        content: `What's a good name for a store that sells ${subject}?`,
      },
    ];
  },
  { name: "formatPrompt" }
);

const invokeLLM = traceable(
    async (messages: { role: string; content: string }[]) => {
        const run = getCurrentRunTree();
        console.log("invokeLLM Run ID", run.id)
        console.log("invokeLLM Trace ID", run.trace_id)
        console.log("invokeLLM Parent Run ID", run.parent_run.id)
        return openai.chat.completions.create({
            model: "gpt-3.5-turbo",
            messages: messages,
            temperature: 0,
        });
    },
    { run_type: "llm", name: "invokeLLM" }
);

const parseOutput = traceable(
    (response: any) => {
        const run = getCurrentRunTree();
        console.log("parseOutput Run ID", run.id)
        console.log("parseOutput Trace ID", run.trace_id)
        console.log("parseOutput Parent Run ID", run.parent_run.id)
        return response.choices[0].message.content;
    },
    { name: "parseOutput" }
);

const runPipeline = traceable(
    async () => {
        const run = getCurrentRunTree();
        console.log("runPipline Run ID", run.id)
        console.log("runPipline Trace ID", run.trace_id)
        console.log("runPipline Parent Run ID", run.parent_run?.id)
        const messages = await formatPrompt("colorful socks");
        const response = await invokeLLM(messages);
        return parseOutput(response);
    },
    { name: "runPipeline" }
);

await runPipeline();