Agentic AI systems represent a new frontier in artificial intelligence, where agents exhibit the ability to autonomously perceive, reason, and act within complex environments. At the heart of agentic behavior are three core capabilities: memory (the ability to recall and use past information), iteration (repeatedly performing tasks or actions), and conditional logic (making decisions based on environmental or internal state). This article provides a hands-on guide to building such systems using LangChain and LangGraph, two powerful frameworks designed to facilitate the development of sophisticated, agentic AI workflows.

To summarize, Agentic AI systems requires following mechanisms:

• Memory: Store and retrieve information about previous steps, user interactions, or environment states.
• Iteration: Repeat tasks, loop through data, or retry actions until a goal is met.
• Conditional Logic: Branch workflows based on results, errors, or user choices.

Overview of LangChain and LangGraph

LangChain

LangChain is an open-source Python framework for developing AI applications powered by language models. It simplifies the integration of LLMs (like GPT-4), tools, and external data sources, enabling the creation of complex chains and agents that can interact, reason, and perform tasks autonomously.

LangGraph

LangGraph builds on LangChain’s foundation, providing graph-based workflow management. It enables developers to define workflows as directed graphs, where each node represents a computation or decision, and edges represent transitions. This structure is ideal for modeling agentic behaviors such as memory recall, iterative loops, and conditional branching making LangGraph a natural choice for agentic AI systems.

Setting Up the Environment

Before building agentic AI systems, install LangChain and LangGraph. Ensure you have Python 3.8+ and pip installed.

Install LangChain:

pip install langchain

Install LangGraph:

pip install langgraph

(Optional) Install an LLM provider, e.g., OpenAI:

pip install openai

Building Memory Support

Memory in agentic AI can be implemented using LangChain’s ConversationBufferMemory or custom memory classes. This allows the agent to access previous exchanges and maintain context.

Step-by-Step Code Example: Adding Memory

from langchain.memory import ConversationBufferMemory

from langchain.agents import initialize_agent, AgentType

from langchain.llms import OpenAI

# Initialize language model

llm = OpenAI(openai_api_key="YOUR_OPENAI_API_KEY")

# Create a memory buffer for conversation history

memory = ConversationBufferMemory()

# Initialize agent with memory

agent = initialize_agent(

llm=llm,

agent_type=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,

memory=memory

)

# Use agent

response = agent.run("Hello, what did I say last time?")

print(response)

This setup ensures the agent can reference previous interactions, enabling coherent and context-aware responses.

Implementing Iteration

Iteration is crucial for tasks requiring multiple attempts or processing steps. LangGraph allows you to design iterative loops within your workflow graph.

Step-by-Step Code Example: Enabling Iteration

import langgraph

# Define nodes (functions)

def process_step(state):

print(f"Processing step with state: {state}")

# Modify state or perform task

state['counter'] += 1

return state

def check_continue(state):

# Return True to continue, False to stop

return state['counter']  = 3)

# Run graph

initial_state = {'counter': 0}

result = graph.run(initial_state)

This graph will process steps iteratively until the counter reaches 3, illustrating how to implement loops in agentic workflows.

Adding Conditional Logic

Conditional logic enables agents to branch their behavior based on state, results, or external inputs. In LangGraph, conditions on edges allow for dynamic workflow transitions.

Step-by-Step Code Example: Conditional Branching

def branch_decision(state):

print(f"Branch decision with value: {state['value']}")

return state

def action_a(state):

print("Action A executed")

return state

def action_b(state):

print("Action B executed")

return state

graph = langgraph.Graph()

graph.add_node('branch', branch_decision)

graph.add_node('A', action_a)

graph.add_node('B', action_b)

# Conditional edges

graph.add_edge('branch', 'A', condition=lambda state: state['value'] > 0)

graph.add_edge('branch', 'B', condition=lambda state: state['value'] <= 0)

initial_state = {'value': 5}

result = graph.run(initial_state)

Depending on the value in state, the workflow branches to either action A or B, demonstrating conditional logic.

Complete Example: Automated Wi-Fi Troubleshooter Agent

Here’s how to combine all elements into a single agentic workflow using LangChain and LangGraph:

Let’s consider a real-world use case: an automated support agent that helps users troubleshoot Wi-Fi connectivity issues. The agent uses memory to track past user responses, iterates through possible solutions, and applies conditional logic to decide which troubleshooting steps to suggest next.

Step 1: Define Actions

In this step,we have four functions that represent steps in troubleshooting Wi-Fi issues. Each function takes a state dictionary, updates it with user input, and returns it.

def ask_issue(state):

print("What problem are you experiencing with your Wi-Fi?")

state['issue'] = input()

return state

def check_router(state):

print("Is your router powered on? (yes/no)")

state['router_on'] = input()

return state

def restart_router(state):

print("Please restart your router. Did this fix the issue? (yes/no)")

state['restarted'] = input()

return state

def escalate(state):

print("We'll escalate your issue to a human technician.")

return state

Step 2: Create the Graph

This step sets up the steps as nodes in a directed graph.

• A WorkflowGraph object is created.
• Each node is added to the graph with a name and the corresponding function.

graph = WorkflowGraph()

graph.add_node('ask_issue', ask_issue)

graph.add_node('check_router', check_router)

graph.add_node('restart_router', restart_router)

graph.add_node('escalate', escalate)

Step 3: Add Edges for Iteration and Conditional Logic

This step creates conditional branching based on user input.

graph.add_edge('ask_issue', 'check_router')

graph.add_edge('check_router', 'restart_router', condition=lambda state: state['router_on'].lower() == 'yes')

graph.add_edge('check_router', 'escalate', condition=lambda state: state['router_on'].lower() != 'yes')

graph.add_edge('restart_router', 'escalate', condition=lambda state: state['restarted'].lower() != 'yes')

Step 4: Run the Workflow

In this step,we are setting up the workflow

• Starts with an empty state dictionary.
• Executes the workflow starting from the first node (ask_issue), following edges and conditions.
• Collects user input at each step and updates state.

initial_state = {}

result = graph.run(initial_state)

This example guides the user through troubleshooting steps, remembers their answers, iterates through solutions, and escalates if the problem persists. The agentic workflow demonstrates memory (tracking state), iteration (moving through steps), and conditional logic (branching based on user responses).

Conclusion

Agentic AI systems are rapidly transforming the landscape of intelligent automation. By leveraging frameworks like LangChain and LangGraph, developers can build agents that remember, iterate, and make decisions autonomously. This article outlined the theory and practice behind these capabilities, providing step-by-step code to get started. Experiment, iterate, and unlock new possibilities for agentic AI!