Trace using the LangSmith REST API

It is HIGHLY recommended to use our Python or TypeScript SDKs to send traces to LangSmith. We have designed these SDKs with several optimizations, including batching and backgrounding, to ensure that your application's performance is not impacted by sending traces to LangSmith. However, if you are unable to use our SDKs, you can use the LangSmith REST API to send traces. Performance may be impacted if you send traces synchronously in your application. This guide will show you how to trace a request using the LangSmith REST API. Please view our API documentation here for a full list of endpoints and request/response schemas.

note

When using the LangSmith REST API, you will need to provide your API key in the request headers as "x-api-key".

Additionally, you should IGNORE and not set the dotted_order and trace_id fields in the request body. These fields will be automatically generated by the system.

The following example shows how you might leverage our API directly in Python. The same principles apply to other languages.

import openai
import os
import requests
from datetime import datetime
from uuid import uuid4

def post_run(run_id, name, run_type, inputs, parent_id=None):
    """Function to post a new run to the API."""
    data = {
        "id": run_id.hex,
        "name": name,
        "run_type": run_type,
        "inputs": inputs,
        "start_time": datetime.utcnow().isoformat(),
    }
    if parent_id:
        data["parent_run_id"] = parent_id.hex
    requests.post(
        "https://api.smith.langchain.com/runs", # Update appropriately for self-hosted installations or the EU region
        json=data,
        headers=headers
    )

def patch_run(run_id, outputs):
    """Function to patch a run with outputs."""
    requests.patch(
        f"https://api.smith.langchain.com/runs/{run_id}",
        json={
            "outputs": outputs,
            "end_time": datetime.utcnow().isoformat(),
        },
        headers=headers,
    )

# Send your API Key in the request headers
headers = {"x-api-key": os.environ["LANGCHAIN_API_KEY"]}

# This can be a user input to your app
question = "Can you summarize this morning's meetings?"

# 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 parent run
parent_run_id = uuid4()
post_run(parent_run_id, "Chat Pipeline", "chain", {"question": question})

# Create child run
child_run_id = uuid4()
post_run(child_run_id, "OpenAI Call", "llm", {"messages": messages}, parent_run_id)

# Generate a completion
client = openai.Client()
chat_completion = client.chat.completions.create(model="gpt-3.5-turbo", messages=messages)

# End runs
patch_run(child_run_id, chat_completion.dict())
patch_run(parent_run_id, {"answer": chat_completion.choices[0].message.content})

See the doc on the Run (span) data format for more information.