LangChain Integration

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import tracium

# Enable auto-instrumentation
tracium.trace()

# Import clients after enabling tracing
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Use LangChain normally - all operations are traced
llm = ChatOpenAI(model="gpt-4")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{input}")
])

chain = prompt | llm

# The entire chain execution is traced
response = chain.invoke({"input": "Hello!"})

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import tracium

tracium.trace()

# Import clients after enabling tracing
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

# Setup retriever
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_texts(documents, embeddings)
retriever = vectorstore.as_retriever()

# Setup chain
prompt = ChatPromptTemplate.from_template(
    "Answer based on context: {context}\n\nQuestion: {question}"
)
llm = ChatOpenAI(model="gpt-4")

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
)

# Full RAG pipeline is traced with retrieval and LLM spans
response = chain.invoke("What is machine learning?")

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import tracium

tracium.trace()

# Import clients after enabling tracing
from langchain_openai import ChatOpenAI
from langchain.agents import create_openai_tools_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.tools import tool

@tool
def search(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

@tool
def calculator(expression: str) -> str:
    """Evaluate a math expression."""
    return str(eval(expression))

llm = ChatOpenAI(model="gpt-4")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{input}"),
    MessagesPlaceholder("agent_scratchpad"),
])

agent = create_openai_tools_agent(llm, [search, calculator], prompt)
executor = AgentExecutor(agent=agent, tools=[search, calculator])

# Agent execution is traced with tool call spans
result = executor.invoke({"input": "What is 2+2?"})

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import tracium

tracium.trace()

# Import clients after enabling tracing
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Complex LCEL chain
chain = (
    ChatPromptTemplate.from_template("Translate to French: {text}")
    | ChatOpenAI(model="gpt-4")
    | StrOutputParser()
    | ChatPromptTemplate.from_template("Now translate to Spanish: {text}")
    | ChatOpenAI(model="gpt-4")
    | StrOutputParser()
)

# Each step in the chain is traced
result = chain.invoke({"text": "Hello, world!"})

import tracium

# Disable LangChain auto-instrumentation if needed
client = tracium.init(
    api_key="sk_live_...",
    auto_instrument_langchain=False,  # Disable
    auto_instrument_llm_clients=True,  # Keep LLM tracing
)