LangGraph Interview Questions

This document provides a curated list of LangGraph interview questions commonly asked in technical interviews for AI Engineer, Agentic AI Developer, and Senior Machine Learning Engineer roles. It covers fundamental concepts of stateful agents, graph-based orchestration, cyclic workflows, and multi-agent systems.

This is updated frequently but right now this is the most exhaustive list of type of questions being asked.

Code Examples

1. Basic StateGraph Definition

from typing import TypedDict, Annotated, Sequence
import operator
from langchain_core.messages import BaseMessage
from langgraph.graph import StateGraph, END

class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], operator.add]

def agent(state):
    # Agent logic here
    return {"messages": ["Agent response"]}

workflow = StateGraph(AgentState)
workflow.add_node("agent", agent)
workflow.set_entry_point("agent")
workflow.add_edge("agent", END)

app = workflow.compile()

2. Multi-Agent Coordinator (Supervisor)

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser

members = ["researcher", "coder"]
system_prompt = (
    "You are a supervisor tasked with managing a conversation between the"
    " following workers: {members}. Given the following user request,"
    " respond with the worker to act next. Each worker will perform a"
    " task and respond with their results and status. When finished,"
    " respond with FINISH."
)
options = ["FINISH"] + members
function_def = {
    "name": "route",
    "description": "Select the next role.",
    "parameters": {
        "title": "routeSchema",
        "type": "object",
        "properties": {
            "next": {
                "title": "Next",
                "anyOf": [
                    {"enum": options},
                ],
            }
        },
        "required": ["next"],
    },
}
prompt = ChatPromptTemplate.from_messages([
    ("system", system_prompt),
    ("user", "{messages}"),
    ("system", "Given the conversation above, who should act next? or should we FINISH?"),
])

supervisor_chain = (
    prompt
    | ChatOpenAI(model="gpt-4-turbo").bind_functions(functions=[function_def], function_call="route")
    | JsonOutputFunctionsParser()
)

3. Human-in-the-loop with Checkpointer

from langgraph.checkpoint.memory import MemorySaver

memory = MemorySaver()
graph = workflow.compile(checkpointer=memory, interrupt_before=["human_review"])

# Run until interruption
thread = {"configurable": {"thread_id": "1"}}
for event in graph.stream(inputs, thread):
    pass

# Review and continue
full_state = graph.get_state(thread)
# ... human reviews state ...
graph.stream(None, thread) # Resume execution

Questions asked in Google interview

• Design a multi-agent system for software development (Coder, Reviewer, Tester)
• How would you debug an infinite loop in a cyclic graph?
• Implement a human-in-the-loop workflow for content approval
• How to optimize state management for very long conversations?
• Explain the supervisor pattern trade-offs vs hierarchical teams
• How would you implement "Self-Refining" agents?
• Write code to implement a custom persisted checkpointer
• How to handle race conditions in parallel branches?
• Explain strategies for detailed observability in agent networks
• How to implement cost controls for autonomous agents?

Questions asked in Amazon interview

• Design a customer support agent system with escalation paths
• How would you implement a "Plan-and-Execute" architecture?
• Explain how to handle tool failures gracefully in a graph
• How to implement efficient memory management for agents?
• Explain the difference between compiled vs dynamic graphs
• How to implement reliable event-driven triggers?
• Write code for a custom state reducer function
• How to implement secure sandboxed code execution?
• Explain strategies for A/B testing agent workflows
• How to implement automated regression tests for graphs?

Questions asked in Meta interview

• Design a social simulation using Generative Agents
• How would you implement "Reflection" to improve agent quality?
• Explain how to manage shared state in a complex graph
• How to implement dynamic routing based on content classification?
• Explain the benefits of functional state management
• How to implement privacy-preserving collaborative agents?
• Write code to implement semantic routing logic
• How to implement effective human-feedback loops?
• Explain strategies for preventing agent hallucination loops
• How to scale graph execution to millions of users?

Questions asked in OpenAI interview

• Design an autonomous research assistant using LangGraph
• How would you implement "Language Agent Tree Search" (LATS)?
• Explain how to control the "recursion limit" effectively
• How to implement Time Travel debugging?
• Explain the "Teacher-Student" training pattern for agents
• How to implement secure tool use verification?
• Write code to implement a subgraph pattern
• How to implement context-aware token usage optimization?
• Explain strategies for evaluating multi-agent systems
• How to implement "Shadow Mode" deployment safely?

Questions asked in Microsoft interview

• Design an enterprise document processing workflow
• How would you integrate legacy SQL databases with LangGraph?
• Explain how to implement role-based access control (RBAC) in graphs
• How to implement reliable state durability and recovery?
• Explain the integration of LangGraph with copilots
• How to implement compliance logging for all agent decisions?
• Write code to implement parallel map-reduce processing
• How to implement secure API key handling in shared graphs?
• Explain strategies for versioning agent behaviors
• How to implement cross-geography distributed execution?

Additional Resources

• Official LangGraph Documentation
• LangChain Academy: Intro to LangGraph
• LangGraph Tutorials
• Multi-Agent Systems with LangGraph
• LangSmith Evaluation