StateGraph

A shared, typed state object is how multi-step agents pass context (messages, tool results, flags) without global variables or opaque message lists.

StateGraph is a shared whiteboard in a war room: every specialist writes their section, and the next person reads the whole board before acting.

Scenario

State holds `{messages, retrieved_docs, confidence, user_id}`. Each node updates one or two keys; the router reads `confidence` to decide escalation.

Solution

In Agent Frameworks, apply StateGraph to this scenario: State holds `{messages, retrieved_docs, confidence, user_id}`. 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 StateGraph (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 StateGraph happens in the code.

1from typing import Annotated  # import dependencies2from langgraph.graph.message import add_messages  # import dependencies3
4class AgentState(TypedDict):  # define a data structure or component5    messages: Annotated[list, add_messages]6    confidence: float7
8graph = StateGraph(AgentState)  # key line for StateGraph9graph.add_node("retrieve", retrieve_node)10graph.add_node("answer", answer_node)

• Treating StateGraph as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for langgraph stategraph

Quick recap — the most important points from this module.