LangChain / LangGraph

Getting Started

Step 1: Install dependencies

Run the following command to install the required libraries:

pip install langchain langchain-openai langgraph

Step 2: Configure Environment Variables

You will need: • An Orq.ai API Key (from your Orq.ai workspace). If you don’t have an account, sign up here. • An API key for the LLM provider you’ll be using (e.g., OpenAI).

Set the following environment variables:

import os

# Orq.ai OpenTelemetry exporter
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://api.orq.ai/v2/otel"
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = "Authorization=Bearer $ORQ_API_KEY"

# Enable LangSmith tracing in OTEL-only mode
os.environ["LANGSMITH_OTEL_ENABLED"] = "true"
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_OTEL_ONLY"] = "true"

# OpenAI API key
os.environ["OPENAI_API_KEY"] = "$OPENAI_API_KEY"

Once set, all LangChain and LangGraph traces will automatically be sent to your Orq.ai workspace.

Step 3: Sending traces to Orq

Here’s an example of running a simple LangGraph ReAct agent with a custom tool..

from langgraph.prebuilt import create_react_agent


def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"


agent = create_react_agent(
    model="openai:gpt-5-mini",
    tools=[get_weather],
    prompt="You are a helpful assistant.",
)

# Run the agent — this will generate traces in Orq.ai
agent.invoke(
    {"messages": [{"role": "user", "content": "What is the weather in San Francisco?"}]}
)

More Examples

Sending Traces with LangChain

The following snippet shows how to create and run a simple LangChain app that also sends traces to Orq.ai.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Define a prompt and chain
prompt = ChatPromptTemplate.from_template("Tell me a {action} about {topic}")
model = ChatOpenAI(temperature=0.7)
chain = prompt | model

# Invoke the chain
result = chain.invoke({"topic": "programming", "action": "joke"})
print(result.content)

✅
At this point, you should see traces from both LangGraph and LangChain examples appear in your Orq.ai workspace.