Langtrace integrates directly with Langchain, offering detailed, real-time insights into performance metrics such as cost, token usage, accuracy, and latency.
Setup
- Install Langtrace’s SDK and initialize the SDK in your code.
Note: You’ll need API key from Langtrace. Sign up for Langtrace if you haven’t done so already.
# Install the SDK
pip install -U langtrace-python-sdk langchain langchain-chroma langchainhub
npm install @pinecone-database/pinecone-client
- Setup environment variables:
export LANGTRACE_API_KEY=YOUR_LANGTRACE_API_KEY
export OPENAI_API_KEY=YOUR_OPENAI_API_KEY
Usage
Generate a simple output with your deployment’s model:
import os
from langtrace_python_sdk import langtrace # Must precede any llm module imports
langtrace.init(api_key = os.environ['LANGTRACE_API_KEY'])
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
import bs4
from langchain import hub
from langchain_chroma import Chroma
from langchain_community.document_loaders import TextLoader
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
Create a vector store index and query it
# We will be loading a document from the assets folder to create embeddings and query it
loader = TextLoader("assets/soccer_rules.txt")
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
# Retrieve and generate using the relevant snippets of the blog.
retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")
Retrieve and generate using the relevant snippets of the document.
retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")
Format our document ,setup a rag chain and query
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
rag_chain.invoke("What is Offside??")
You can now view your traces on the Langtrace dashboard

Want to see more supported methods? Checkout the sample code in the Langtrace Langchain Python Example
Responses are generated using AI and may contain mistakes.