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When you trace a LangGraph graph execution, Laminar automatically captures the graph structure and workflow, allowing you to visualize the entire graph flow directly in the trace view.

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Prerequisites

You’ll need to install LangChain to work with LangGraph: 
pip install langchain langchain-openai langgraph

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Overview

LangGraph creates complex, stateful, multi-actor applications with Large Language Models. When these graphs are traced with Laminar, you can see complete graph structure and node relationships.

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Example: Multi-Step Research Workflow

Here’s how to set up a LangGraph workflow with Laminar tracing:

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1. Initialize Laminar

from lmnr import observe, Laminar

Laminar.initialize(project_api_key="your-project-api-key")

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2. Define Your Graph State

from typing import TypedDict, List
from langchain_core.messages import BaseMessage, HumanMessage
from langchain_openai import ChatOpenAI

class AgentState(TypedDict):
    messages: List[BaseMessage]
    research_results: str
    analysis: str
    final_recommendation: str

llm = ChatOpenAI(
    model="gpt-4o-mini",
    temperature=0.7,
    openai_api_key=OPENAI_API_KEY
)

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3. Create Traced Graph Nodes

@observe(name="research_step")
async def research_step(state: AgentState) -> AgentState:
    result = await llm.ainvoke([HumanMessage(content=research_prompt)])
    state["research_results"] = result.content
    return state

@observe(name="analysis_step")
async def analysis_step(state: AgentState) -> AgentState:
    result = await llm.ainvoke([HumanMessage(content=analysis_prompt)])
    state["analysis"] = result.content
    return state

@observe(name="recommendation_step")
async def recommendation_step(state: AgentState) -> AgentState:
    result = await llm.ainvoke([HumanMessage(content=recommendation_prompt)])
    state["final_recommendation"] = result.content
    return state

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4. Build the Graph Workflow

from langgraph.graph import StateGraph, END, START
from langgraph.checkpoint.memory import MemorySaver

@observe(name="langchain_graph_workflow")
async def create_and_run_graph():
    workflow = StateGraph(AgentState)

    workflow.add_node("research_node", research_step)
    workflow.add_node("analysis_node", analysis_step)
    workflow.add_node("recommendation_node", recommendation_step)

    workflow.add_edge(START, "research_node")
    workflow.add_edge("research_node", "analysis_node")
    workflow.add_edge("analysis_node", "recommendation_node")
    workflow.add_edge("recommendation_node", END)

    memory = MemorySaver()
    app = workflow.compile(checkpointer=memory)

    config = {"configurable": {"thread_id": "demo-thread"}}

    final_state = await app.aget_state(config)

    return final_state
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