Python / Python Modern Generative AI and Agents Interview Questions

What is LangGraph and how does it differ from LangChain's AgentExecutor for building agents?

LangGraph is a framework for building stateful, multi-step agents as directed graphs where each node is a function (LLM call, tool call, or logic) and edges define the flow of control. Unlike LangChain's AgentExecutor (a simple Thought-Action-Observation loop), LangGraph gives you explicit control over state transitions, conditional routing, cycles, parallelism, and human-in-the-loop checkpoints.

LangGraph excels at complex agent workflows: routers that choose different paths based on intent, agents that call multiple tools in parallel, agents that require human approval before taking irreversible actions, and systems where the same state graph runs across multiple user sessions (persistence via checkpointers).

# pip install langgraph
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from typing import TypedDict, Annotated
import operator

# Define agent state
class AgentState(TypedDict):
    messages: Annotated[list, operator.add]  # appends each step

@tool
def search_web(query: str) -> str:
    '''Search the web for current information.'''
    return f'Search results for: {query}'

tools = [search_web]
model = ChatOpenAI(model='gpt-4o').bind_tools(tools)

def call_model(state: AgentState):
    response = model.invoke(state['messages'])
    return {'messages': [response]}

def should_continue(state: AgentState):
    '''Route to tools or end based on whether LLM called a tool.'''
    last = state['messages'][-1]
    return 'tools' if last.tool_calls else END

# Build the graph
graph = StateGraph(AgentState)
graph.add_node('agent', call_model)
graph.add_node('tools', ToolNode(tools))

graph.set_entry_point('agent')
graph.add_conditional_edges('agent', should_continue)
graph.add_edge('tools', 'agent')  # after tools, return to agent

app = graph.compile()

result = app.invoke({'messages': [{'role': 'user', 'content': 'What happened in AI news today?'}]})
print(result['messages'][-1].content)

Take quiz

What key capability does LangGraph provide that LangChain's AgentExecutor does not?LangGraph supports more LLM providers than AgentExecutor

✗ Try again.

LangGraph exposes the agent loop as an explicit state machine graph — enabling conditional branching, cycles, parallel node execution, human-in-the-loop interrupts, and persistent state across sessions; AgentExecutor abstracts this as a fixed Thought-Action-Observation loop with limited customisation

✓ Correct! Well done.

LangGraph automatically evaluates agent responses for quality

✗ Try again.

LangGraph requires no tools — it handles all actions with pure LLM calls

✗ Try again.

What does the conditional edge in LangGraph's should_continue function decide?Whether to retry the current node if it produced an error

✗ Try again.

Whether to route execution to the 'tools' node (if the LLM called a tool) or to END (if the LLM produced a final answer without calling a tool) — implementing the agent's decision of whether to act further or respond to the user

✓ Correct! Well done.

The order in which parallel tool nodes execute

✗ Try again.

Whether the agent should ask the user for clarification

✗ Try again.

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Python / Python Modern Generative AI and Agents Interview Questions

What is LangGraph and how does it differ from LangChain's AgentExecutor for building agents?

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