LangGraph Core: Beginner

This lesson focuses on LangGraph Core at the beginner level. Use it to move from definition to implementation-ready explanation.

Concept

LangGraph is an open-source Python library (21,700+ GitHub stars, v1.0 stable Oct 2025) for building stateful AI agent workflows as directed graphs. It models agent execution as nodes (computation steps) connected by edges (control flow) sharing a common State TypedDict. Unlike LangChain chains, LangGraph agents can loop, branch, remember, and recover from failures. Trusted in production by Klarna, Replit, Elastic, Uber, and LinkedIn.

Key Facts

• Install: pip install langgraph langgraph-prebuilt langchain-openai
• Requires Python 3.10+ - dropped 3.8/3.9 in v1.0
• MIT-licensed, 21,700+ GitHub stars
• v1.0 breaking change: set_entry_point() REMOVED - use add_edge(START, ‘node’)
• Inspired by Google Pregel and Apache Beam bulk-synchronous parallel model

Reference Implementation

from langgraph.graph import StateGraph, START, END
from typing import TypedDict

class MyState(TypedDict):
    message: str
    count: int

def greet(state: MyState):
    return {"message": "Hello!", "count": state["count"] + 1}

def farewell(state: MyState):
    return {"message": state["message"] + " Goodbye!"}

graph = StateGraph(MyState)
graph.add_node("greet", greet)
graph.add_node("farewell", farewell)
graph.add_edge(START, "greet")   # v1.0: no more set_entry_point()
graph.add_edge("greet", "farewell")
graph.add_edge("farewell", END)  # v1.0: no more set_finish_point()

app = graph.compile()
result = app.invoke({"message": "", "count": 0})
# {"message": "Hello! Goodbye!", "count": 1}

Interview Q&A

Q1. What is LangGraph and why was it created?

LangGraph is a low-level orchestration framework for building stateful, long-running AI agents as directed graphs. It was created because traditional LLM chains are linear and stateless - they cannot loop, branch conditionally, or resume after failure. LangGraph adds cycles, persistent state, and explicit control flow.

Q2. What are the three core components of every LangGraph app?

State (a TypedDict schema defining shared data), Nodes (Python functions that read and update state), and Edges (connections between nodes - deterministic or conditional). Everything else - checkpointers, interrupts, tools - builds on this foundation.

Q3. What changed between LangGraph v0.x and v1.0?

set_entry_point() and set_finish_point() were removed - replace with add_edge(START, ‘node’) and add_edge(‘node’, END). Python 3.8/3.9 support was dropped. add_conditional_edges() is completely unchanged. Most online tutorials still use deprecated v0.x patterns - a common interview trap.

Q4. What is the difference between state and memory?

State is the data passed through one graph run or thread. Memory usually means data persisted across runs, such as checkpoints for thread history or a Store for long-term user preferences.

Q5. Why are START and END useful?

START and END make entry and exit points explicit. That improves visualization, validation, and interview explanations because every graph has a clear beginning and a clear terminal path.

Practice Task

Explain when this LangGraph pattern is safer than a linear chain, then name one production failure it prevents.