Workflow
LangGraph for Beginners
LangGraph is used to build advanced AI workflows. It is especially useful when your AI system needs multiple steps, branching, retries, or state-based decision making.
What Is LangGraph?
LangGraph helps build workflows where each step is connected like a graph.
A node is one step. An edge is the connection from one step to another.
It is useful when one simple chain is not enough.
Start
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Understand Question
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┌─────┴─────┐
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Search Tool Database
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Reasoning
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Final AnswerWhy LangGraph Is Important
Some AI tasks need retries, loops, branching, or multi-step logic.
LangGraph makes these flows easier to manage than a simple chain.
Simple LangGraph Example
This example shows a very small workflow with two connected steps.
from langgraph.graph import StateGraph
graph = StateGraph()
def step1(state):
return "Step 1 processed"
def step2(state):
return "Step 2 processed"
graph.add_node("step1", step1)
graph.add_node("step2", step2)
graph.set_entry_point("step1")
graph.add_edge("step1", "step2")
workflow = graph.compile()Frequently Asked Questions
What is a node in LangGraph?
A node is one step or function inside the workflow.
When should I learn LangGraph?
After you understand Python, prompts, and basic LangChain concepts.