LangGraph

Simple agent loops break down for complex workflows: branching logic, parallel execution, human approval gates, persistent state, and multi-agent coordination. LangGraph provides graph-based orchestration with cycles, conditional routing, checkpointing, and first-class state management — the production backbone for serious agent systems.

LangGraph is like a flowchart engine for agents — instead of a single while-loop, you draw the decision tree: 'if search succeeds, go to summarize; if it fails, go to fallback; always checkpoint before sending email.'

Scenario

Customer support agent: classify ticket → route to billing/technical subgraph → execute tools → human review for refunds > $500 → send response. LangGraph manages state across all nodes with checkpoint recovery if the server restarts mid-ticket.

Solution

In Agent Frameworks, apply LangGraph to this scenario: Customer support agent: classify ticket → route to billing/technical subgraph → execute tools → human review for refunds > $500 → send response. 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 (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 happens in the code.

1from langgraph.graph import StateGraph, START, END  # import dependencies2from langgraph.checkpoint.memory import MemorySaver  # import dependencies3from typing import TypedDict, Annotated  # import dependencies4from langgraph.graph.message import add_messages  # import dependencies5
6class AgentState(TypedDict):  # define a data structure or component7    messages: Annotated[list, add_messages]8    plan: list[str]9    iteration: int10
11def planner(state: AgentState) -> dict:  # define a reusable function12    # generate plan steps13    return {"plan": ["search", "summarize", "respond"], "iteration": 0}  # return the result14
15def executor(state: AgentState) -> dict:  # define a reusable function16    step = state["plan"][state["iteration"]]17    # execute current step18    return {"iteration": state["iteration"] + 1}  # return the result19
20def should_continue(state: AgentState) -> str:  # define a reusable function21    if state["iteration"] >= len(state["plan"]):22        return "done"  # return the result23    return "continue"  # return the result24
25graph = StateGraph(AgentState)  # define workflow with shared state object26graph.add_node("planner", planner)  # register a processing step27graph.add_node("executor", executor)  # register a processing step28graph.add_conditional_edges("executor", should_continue, {"continue": "executor", "done": END})29graph.add_edge(START, "planner")  # connect steps in the workflow30graph.add_edge("planner", "executor")  # connect steps in the workflow31
32app = graph.compile(checkpointer=MemorySaver())  # build the runnable graph33result = app.invoke({"messages": [("user", "Research AI trends")]}, config={"configurable": {"thread_id": "1"}})

• Using LangGraph for simple single-loop agents — over-engineering
• Untyped state — bugs from missing fields
• No checkpointing in production — lose state on crash
• Monolithic graph instead of subgraphs for complex systems
• Not using thread_id — can't resume sessions

Quick recap — the most important points from this module.