LangGraph for stateful agents in Agentic AI

Introduction

LangGraph helps agents remember and use past information to make better decisions. It keeps track of what happened before so the agent can act smarter over time.

When building a chatbot that needs to remember previous conversations.

When creating a virtual assistant that adapts based on user history.

When designing a game AI that learns from past moves.

When developing a recommendation system that considers user preferences over time.

When managing complex workflows where past steps affect future actions.

Syntax

Agentic AI

class LangGraph:
    def __init__(self):
        self.nodes = {}
        self.edges = {}

    def add_node(self, node_id, state):
        self.nodes[node_id] = state

    def add_edge(self, from_node, to_node, action):
        if from_node not in self.edges:
            self.edges[from_node] = []
        self.edges[from_node].append((to_node, action))

    def get_state(self, node_id):
        return self.nodes.get(node_id, None)

    def update_state(self, node_id, new_state):
        if node_id in self.nodes:
            self.nodes[node_id] = new_state

    def next_actions(self, node_id):
        return self.edges.get(node_id, [])

This class stores states as nodes and actions as edges connecting them.

It allows updating states and querying possible next actions from a state.

Examples

Adds a starting state and an action leading to a happy state.

Agentic AI

lang_graph = LangGraph()
lang_graph.add_node('start', {'mood': 'neutral'})
lang_graph.add_edge('start', 'happy_state', 'say_hello')

Shows that querying a state not added returns None.

Agentic AI

empty_graph = LangGraph()
print(empty_graph.get_state('unknown'))  # None

A graph with one node and no edges returns empty list for next actions.

Agentic AI

single_node_graph = LangGraph()
single_node_graph.add_node('only_state', {'count': 1})
print(single_node_graph.next_actions('only_state'))  # []

Updates the state data for an existing node.

Agentic AI

lang_graph = LangGraph()
lang_graph.add_node('start', {'mood': 'neutral'})
lang_graph.update_state('start', {'mood': 'happy'})
print(lang_graph.get_state('start'))  # {'mood': 'happy'}

Sample Model

This program creates a LangGraph, adds a starting state, connects it to two possible next states with actions, and updates the state to show how the agent's memory changes.

Agentic AI

class LangGraph:
    def __init__(self):
        self.nodes = {}
        self.edges = {}

    def add_node(self, node_id, state):
        self.nodes[node_id] = state

    def add_edge(self, from_node, to_node, action):
        if from_node not in self.edges:
            self.edges[from_node] = []
        self.edges[from_node].append((to_node, action))

    def get_state(self, node_id):
        return self.nodes.get(node_id, None)

    def update_state(self, node_id, new_state):
        if node_id in self.nodes:
            self.nodes[node_id] = new_state

    def next_actions(self, node_id):
        return self.edges.get(node_id, [])


# Create LangGraph instance
lang_graph = LangGraph()

# Add initial state node
lang_graph.add_node('start', {'mood': 'neutral', 'step': 0})

# Add edges representing actions leading to new states
lang_graph.add_edge('start', 'happy_state', 'say_hello')
lang_graph.add_edge('start', 'sad_state', 'ignore')

# Print initial state
print('Initial state:', lang_graph.get_state('start'))

# Show possible actions from 'start'
print('Possible actions from start:', lang_graph.next_actions('start'))

# Update state after action
lang_graph.update_state('start', {'mood': 'happy', 'step': 1})
print('Updated state:', lang_graph.get_state('start'))

Important Notes

Time complexity for adding nodes or edges is O(1).

Space complexity grows with number of states and actions stored.

Common mistake: forgetting to check if a node exists before updating its state.

Use LangGraph when you need to track how an agent's state changes over time with actions.

Summary

LangGraph stores states as nodes and actions as edges to keep agent memory.

It helps agents remember past states and decide next actions.

Updating and querying states is simple and efficient.

Practice

1. What is the main purpose of LangGraph in stateful agents?

easy

A. To store states as nodes and actions as edges for memory

B. To train deep learning models faster

C. To generate random actions without memory

D. To visualize data without storing states

Solution

1. Step 1: Understand LangGraph structure

   LangGraph uses nodes to represent states and edges to represent actions connecting those states.

2. Step 2: Identify the purpose of this structure

   This structure helps agents remember past states and decide next actions based on memory.

3. Final Answer:

   To store states as nodes and actions as edges for memory -> Option A

4. Quick Check:

   LangGraph = state nodes + action edges [OK]

Hint: Remember: LangGraph = states (nodes) + actions (edges) [OK]

Common Mistakes:

• Confusing LangGraph with model training
• Thinking LangGraph generates random actions
• Assuming LangGraph only visualizes data

2. Which of the following is the correct way to add a new state node in a LangGraph agent?

easy

A. langgraph.add_edge(state1, state2, action)

B. langgraph.remove_node(state)

C. langgraph.add_node(new_state)

D. langgraph.update_action(state, new_action)

Solution

1. Step 1: Identify method to add nodes

   Adding a new state means adding a node, so the method should be add_node.

2. Step 2: Check options for adding nodes

   Only langgraph.add_node(new_state) uses add_node(new_state), which correctly adds a state node.

3. Final Answer:

   langgraph.add_node(new_state) -> Option C

4. Quick Check:

   Add state = add_node() method [OK]

Hint: Add states with add_node(), not add_edge() [OK]

Common Mistakes:

• Using add_edge() to add states
• Confusing remove_node() with adding
• Trying to update actions to add states

3. Given the code snippet:

langgraph.add_node('S1')
langgraph.add_node('S2')
langgraph.add_edge('S1', 'S2', 'move')
print(langgraph.get_next_action('S1'))

What will be the output?

medium

A. 'S2'

B. 'move'

C. None

D. Error: method not found

Solution

1. Step 1: Understand the graph setup

   Two states 'S1' and 'S2' are added, then an edge from 'S1' to 'S2' with action 'move'.

2. Step 2: Check get_next_action('S1')

   This method returns the action on the edge from 'S1' to its next state, which is 'move'.

3. Final Answer:

   'move' -> Option B

4. Quick Check:

   Edge action from S1 = 'move' [OK]

Hint: Edges store actions; get_next_action returns that action [OK]

Common Mistakes:

• Confusing action with next state
• Expecting None if not familiar with method
• Assuming method does not exist

4. What is wrong with this code snippet for updating an action in LangGraph?

langgraph.add_node('S1')
langgraph.add_node('S2')
langgraph.add_edge('S1', 'S2', 'jump')
langgraph.update_edge('S1', 'S2', 'run')

medium

A. Edges cannot be updated once added

B. add_node should be called after update_edge

C. The action 'run' is invalid

D. update_edge method does not exist; should remove and add edge

Solution

1. Step 1: Check if update_edge method exists

   LangGraph typically does not have update_edge; edges are removed and re-added to update.

2. Step 2: Identify correct update approach

   To change an action, remove the old edge and add a new edge with the new action.

3. Final Answer:

   update_edge method does not exist; should remove and add edge -> Option D

4. Quick Check:

   No update_edge method in LangGraph [OK]

Hint: Update edges by remove + add, no update_edge method [OK]

Common Mistakes:

• Assuming update_edge exists
• Trying to update nodes instead of edges
• Thinking action strings are invalid

5. You want your LangGraph agent to remember a sequence of states and actions to avoid loops. Which approach best helps achieve this?

hard

A. Store visited states as nodes and add edges only for new actions

B. Clear the graph after each action to reset memory

C. Add duplicate nodes for repeated states to track loops

D. Use a separate list outside LangGraph to track visited states

Solution

1. Step 1: Understand loop avoidance in LangGraph

   Storing visited states as nodes and adding edges only for new actions helps the agent remember paths and avoid loops.

2. Step 2: Evaluate other options

   Clearing the graph loses memory, duplicates confuse state identity, and external lists separate memory from LangGraph.

3. Final Answer:

   Store visited states as nodes and add edges only for new actions -> Option A

4. Quick Check:

   Memory in LangGraph = nodes + edges tracking [OK]

Hint: Keep states as nodes and edges for memory, avoid duplicates [OK]

Common Mistakes:

• Resetting graph loses memory
• Duplicating nodes breaks state tracking
• Using external lists splits memory logic