This guide demonstrates how to use TrueFoundry OtelCollector along with the Traceloop SDK to instrument LangGraph agent code. In this example, the LangGraph agent is a research agent which researches the latest trends to conduct detailed market research. For example, it can generate “A comprehensive report on AI and machine learning.”

1

Create Tracing Project, API Key and copy tracing code

Follow the instructions in Getting Started to create a tracing project, generate API key and copy the tracing code.

2

Install Dependencies

First, you need to install the following

pip install langgraph==0.3.22 traceloop-sdk langchain_openai dotenv
3

Add Tracing code to LangGraph application

For LangGraph agents, we need to add the Traceloop.init() call to the application. The Traceloop SDK will automatically trace all agent activities.

LangGraph Code
from dotenv import load_dotenv
from langchain_core.tools import tool
from langgraph.prebuilt import ToolNode
from langchain_openai import ChatOpenAI
from langgraph.graph import MessagesState, END, StateGraph
from langchain_core.messages import HumanMessage, SystemMessage
import random
import os
from typing import Literal

# importing traceloop sdk
from traceloop.sdk import Traceloop

load_dotenv()

# Add the traceloop init code to your application
TFY_API_KEY = os.environ.get("TFY_API_KEY")
Traceloop.init(
    api_endpoint="<enter_your_api_endpoint>",
    headers = {
        "Authorization": f"Bearer {TFY_API_KEY}",
        "TFY-Tracing-Project": "<enter_your_tracing_project_fqn>",
    },
)

# Define a Tool the Agent Can Call
@tool("get_random_topic", parse_docstring=True)
def get_random_topic() -> str:
    """Get a random topic from a list of AI-related subjects for research."""
    topics = [
        "AI", "Machine Learning", "Data Science", "Deep Learning",
        "Computer Vision", "Natural Language Processing", "Robotics",
        "Blockchain", "Quantum Computing", "Gen AI", "LLMs", "RAG",
        "LLM Agents", "LLM Tool Calling", "LLM Planning", "LLM Self-Reflection"
    ]
    return random.choice(topics)

# Set Up the Model and Register Tools
tools = [get_random_topic]
tool_node = ToolNode(tools)
model = ChatOpenAI(model="gpt-4o", temperature=0).bind_tools(tools)

# Define Agent Behavior (Core Model Invocation Logic)
def call_model(state: MessagesState):
    messages = state["messages"]
    response = model.invoke(messages)
    return {"messages": [response]}

# Define Conditional Logic for Tool Use
def should_continue(state: MessagesState) -> Literal["tools", END]:
    messages = state["messages"]
    last_message = messages[-1]
    if last_message.tool_calls:
        return "tools"
    return END

# Build the LangGraph Workflow
workflow = StateGraph(MessagesState)
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
workflow.set_entry_point("agent")
workflow.add_conditional_edges("agent", should_continue)
workflow.add_edge("tools", "agent")

app = workflow.compile()

# Craft the Initial Prompt for the Agent
system_message = SystemMessage(content="Expert in market analysis with keen attention to detail ")
human_message  = HumanMessage(content="Please use the `get_random_topic` tool to pick a trending topic in AI, "
    "Then provide a brief summary of recent developments in that area")
initial_prompt = [system_message, human_message]

# Run the Agent Workflow and Print the Result
final_state = app.invoke({"messages": initial_prompt})
print("\n✅ Final Output:\n", final_state["messages"][-1].content)
4

Run your application and view logged trace