LangGraph Coding

LangGraph is a common interview and production choice for explicit agent control flow. Live coding rounds ask you to model loops, branching, and persistence without hiding logic in prompt magic.

Building a flowchart where each box is a function and the arrows are 'if tests fail, go back to fix' — code, not a whiteboard-only diagram.

Scenario

Implement a graph: user message → router (needs_tool?) → tool node or direct answer → checker (valid JSON?) → retry or END. Add checkpoint so conversation resumes after server restart.

Solution

In Interview & System Design, apply LangGraph Coding to this scenario: Implement a graph: user message → router (needs_tool?) → tool node or direct answer → checker (valid JSON?) → retry or END. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to LangGraph Coding (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where LangGraph Coding happens in the code.

1from typing import TypedDict, Annotated  # import dependencies2from langgraph.graph import StateGraph, END  # import dependencies3from operator import add  # import dependencies4
5class State(TypedDict):  # define a data structure or component6    messages: Annotated[list, add]7
8def agent(state: State): ...  # define a reusable function9def should_continue(state: State) -> str:  # define a reusable function10    return "tools" if needs_tool(state) else END  # return the result11
12graph = StateGraph(State)13graph.add_node("agent", agent)14graph.add_node("tools", run_tools)15graph.add_conditional_edges("agent", should_continue)16graph.add_edge("tools", "agent")17app = graph.compile(checkpointer=MemorySaver())

• Treating LangGraph Coding as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for langgraph coding

Quick recap — the most important points from this module.