Ciphernutz

Posted on Apr 18

How to Build Agentic AI Chatbot Using LangGraph

#agenticai #aichatbot #langgraph #webdev

What’s Agentic AI and Why Should You Care?

Most chatbots today follow a linear path — think "if the user says X, reply with Y." But real conversations? They loop, backtrack, branch off, and get messy. That’s where agentic AI shines.

Agentic AI is a framework where AI doesn't just react — it reasons, takes actions, and adapts over time. It acts like an agent that:

Analyzes user intent deeply
Calls APIs or tools autonomously
Decides what to do next (or when to stop)
Learns from its past steps in real time

LangGraph makes building this possible, without reinventing the wheel.

What is LangGraph?

LangGraph is an open-source library. It lets you create multi-step, stateful, decision-making agents as graphs. Think of it as a finite state machine powered by LLMs — but flexible, modular, and Pythonic.

With LangGraph, you define nodes (LLMs, tools, decision logic) and edges (transitions), making the chatbot capable of looping, retrying, and learning from the current conversation state.

🛠️ Let’s Build: Agentic AI Chatbot That Can Research + Summarize + Ask Clarifying Questions

Step 1: Install Your Stack

pip install langgraph langchain openai

Step 2: Define the Nodes
Node 1: Clarifier Agent
Checks if the user query is vague. If yes, it asks for clarification.

Node 2: Research Agent
Uses LLM to fetch data (e.g., from a search API).

Node 3: Summarizer
Condenses research into digestible chunks.

Node 4: Decision Node
Checks: Did we get what we needed? If not, loop back.

Step 3: Build the Graph

from langgraph.graph import StateGraph
from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(temperature=0)

# Step 1: Define the logic for each node
def clarify(state):
    if "?" not in state['user_input']:
        return {"clarified_input": f"What exactly do you mean by '{state['user_input']}'?"}
    return state

def research(state):
    query = state.get('clarified_input') or state['user_input']
    return {"research": f"Searching for: {query}... [mocked data]"}

def summarize(state):
    return {"summary": f"Here’s a short summary of the data on {state['research']}"}

def decide(state):
    if "mocked data" in state.get("research", ""):
        return "summarizer"
    return "research"

# Step 2: Build the state graph
builder = StateGraph()
builder.add_node("clarifier", clarify)
builder.add_node("research", research)
builder.add_node("summarizer", summarize)
builder.add_conditional_edges("research", decide)
builder.set_entry_point("clarifier")

graph = builder.compile()

Step 4: Run the Conversation Loop

state = {"user_input": "Tell me about the future of quantum computing"}
output = graph.invoke(state)
print(output)

Your bot will:

Check if the input is ambiguous
Ask follow-up questions if needed
Do research
Summarize results
Decide whether to continue or stop

Why This is Cool (and Powerful)

Unlike traditional bots, your agent can:

Handle ambiguity
Retry or ask for clarification
Call functions or APIs at any node
Loop intelligently instead of getting stuck

LangGraph lets you think in workflows, not just prompts.

🔮 What’s Next?

Add memory with LangChain's memory modules
Integrate tools like SerpAPI or Google Search
Add voice support using Whisper or ElevenLabs
Make it multi-agent (e.g., clarifier agent, researcher agent, summarizer agent)

📌 Final Thoughts

Agentic AI isn’t just hype — it’s a paradigm shift. With tools like LangGraph, we’re stepping into an era where AI chatbots can navigate tasks, hold nuanced conversations, and make smart decisions autonomously.

Whether you’re building customer support agents, personal assistants, or research tools — agentic design is the future, and LangGraph gives you the perfect canvas to paint on.

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