Annotate code for tracing

There are several ways to log traces to LangSmith.

Use @traceable / traceable

LangSmith makes it easy to log traces with minimal changes to your existing code with the @traceable decorator in Python and traceable function in TypeScript.

note

The LANGCHAIN_TRACING_V2 environment variable must be set to 'true' in order for traces to be logged to LangSmith, even when using @traceable or traceable. This allows you to toggle tracing on and off without changing your code.

Additionally, you will need to set the LANGCHAIN_API_KEY environment variable to your API key (see Setup for more information).

By default, the traces will be logged to a project named default. To log traces to a different project, see this section.

Python

The @traceable decorator is a simple way to log traces from the LangSmith Python SDK. Simply decorate any function with @traceable.

from langsmith import traceable
from openai import Client

openai = Client()

@traceable
def format_prompt(subject):
    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):
    return openai.chat.completions.create(
        messages=messages, model="gpt-3.5-turbo", temperature=0
    )

@traceable
def parse_output(response):
    return response.choices[0].message.content

@traceable
def run_pipeline():
    messages = format_prompt("colorful socks")
    response = invoke_llm(messages)
    return parse_output(response)

run_pipeline()

TypeScript

The traceable function is a simple way to log traces from the LangSmith TypeScript SDK. Simply wrap any function with traceable.

Note that when wrapping a sync function with traceable, (e.g. formatPrompt in the example below), you should use the await keyword when calling it to ensure the trace is logged correctly.

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

const openai = new OpenAI();

const formatPrompt = traceable(
  (subject: string) => {
    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 }: { messages: { role: string; content: string }[] }) => {
        return openai.chat.completions.create({
            model: "gpt-3.5-turbo",
            messages: messages,
            temperature: 0,
        });
    },
    { run_type: "llm", name: "invokeLLM" }
);

const parseOutput = traceable(
    (response: any) => {
        return response.choices[0].message.content;
    },
    { name: "parseOutput" }
);

const runPipeline = traceable(
    async () => {
        const messages = await formatPrompt("colorful socks");
        const response = await invokeLLM({ messages });
        return parseOutput(response);
    },
    { name: "runPipeline" }
);

await runPipeline();

Wrap the OpenAI client

The wrap_openai/wrapOpenAI methods in Python/TypeScript allow you to wrap your OpenAI client in order to automatically log traces -- no decorator or function wrapping required! The wrapper works seamlessly with the @traceable decorator or traceable function and you can use both in the same application.

Tool calls are automatically rendered

note

The LANGCHAIN_TRACING_V2 environment variable must be set to 'true' in order for traces to be logged to LangSmith, even when using wrap_openai or wrapOpenAI. This allows you to toggle tracing on and off without changing your code.

Additionally, you will need to set the LANGCHAIN_API_KEY environment variable to your API key (see Setup for more information).

By default, the traces will be logged to a project named default. To log traces to a different project, see this section.

Python

import openai
from langsmith import traceable
from langsmith.wrappers import wrap_openai

client = wrap_openai(openai.Client())

@traceable(run_type="tool", name="Retrieve Context")
def my_tool(question: str) -> str:
    return "During this morning's meeting, we solved all world conflict."

@traceable(name="Chat Pipeline")
def chat_pipeline(question: str):
    context = my_tool(question)
    messages = [
        { "role": "system", "content": "You are a helpful assistant. Please respond to the user's request only based on the given context." },
        { "role": "user", "content": f"Question: {question}\nContext: {context}"}
    ]
    chat_completion = client.chat.completions.create(
        model="gpt-3.5-turbo", messages=messages
    )
    return chat_completion.choices[0].message.content

chat_pipeline("Can you summarize this morning's meetings?")

TypeScript

import OpenAI from "openai";
import { traceable } from "langsmith/traceable";
import { wrapOpenAI } from "langsmith/wrappers";

const client = wrapOpenAI(new OpenAI());

const myTool = traceable(async (question: string) => {
    return "During this morning's meeting, we solved all world conflict.";
}, { name: "Retrieve Context", run_type: "tool" });

const chatPipeline = traceable(async (question: string) => {
    const context = await myTool(question);
    const messages = [
        {
            role: "system",
            content:
                "You are a helpful assistant. Please respond to the user's request only based on the given context.",
        },
        { role: "user", content: `Question: ${question} Context: ${context}` },
    ];
    const chatCompletion = await client.chat.completions.create({
        model: "gpt-3.5-turbo",
        messages: messages,
    });
    return chatCompletion.choices[0].message.content;
}, { name: "Chat Pipeline" });

await chatPipeline("Can you summarize this morning's meetings?");

Use the RunTree API

Another, more explicit way to log traces to LangSmith is via the RunTree API. This API allows you more control over your tracing - you can manually create runs and children runs to assemble your trace. You still need to set your LANGCHAIN_API_KEY, but LANGCHAIN_TRACING_V2 is not necessary for this method.

Python

import openai
from langsmith.run_trees import RunTree
# This can be a user input to your app
question = "Can you summarize this morning's meetings?"
# Create a top-level run
pipeline = RunTree(
    name="Chat Pipeline",
    run_type="chain",
    inputs={"question": question}
)
# This can be retrieved in a retrieval step
context = "During this morning's meeting, we solved all world conflict."
messages = [
    { "role": "system", "content": "You are a helpful assistant. Please respond to the user's request only based on the given context." },
    { "role": "user", "content": f"Question: {question}\nContext: {context}"}
]
# Create a child run
child_llm_run = pipeline.create_child(
    name="OpenAI Call",
    run_type="llm",
    inputs={"messages": messages},
)
# Generate a completion
client = openai.Client()
chat_completion = client.chat.completions.create(
    model="gpt-3.5-turbo", messages=messages
)
# End the runs and log them
child_llm_run.end(outputs=chat_completion)
child_llm_run.post()
pipeline.end(outputs={"answer": chat_completion.choices[0].message.content})
pipeline.post()

TypeScript

import OpenAI from "openai";
import { RunTree } from "langsmith";
// This can be a user input to your app
const question = "Can you summarize this morning's meetings?";
const pipeline = new RunTree({
    name: "Chat Pipeline",
    run_type: "chain",
    inputs: { question }
});
// This can be retrieved in a retrieval step
const context = "During this morning's meeting, we solved all world conflict.";
const messages = [
    { role: "system", content: "You are a helpful assistant. Please respond to the user's request only based on the given context." },
    { role: "user", content: `Question: ${question}
Context: ${context}` }
];
// Create a child run
const childRun = await pipeline.createChild({
    name: "OpenAI Call",
    run_type: "llm",
    inputs: { messages },
});
// Generate a completion
const client = new OpenAI();
const chatCompletion = await client.chat.completions.create({
    model: "gpt-3.5-turbo",
    messages: messages,
});
// End the runs and log them
childRun.end(chatCompletion);
await childRun.postRun();
pipeline.end({ outputs: { answer: chatCompletion.choices[0].message.content } });
await pipeline.postRun();