Getting Started with LangGraph: A Guide to Building AI Workflow Graphs

LangGraph is a powerful tool built on top of LangChain that allows developers to create structured AI workflows using graph-based execution. It enables handling complex AI interactions in a modular and scalable way.

What is LangGraph?

LangGraph extends LangChain by allowing developers to structure their AI pipelines as directed graphs. This means different components, like LLMs, memory modules, and tools, can be connected in a logical flow, allowing for iterative and branching conversations.

Installation

To get started with LangGraph, install it using pip:

pip install langgraph langchain openai

Creating a Simple AI Workflow

Let’s build a basic application where a user inputs a query, and LangGraph routes the request to different processing nodes based on intent.

1. Import Dependencies

import langgraph

from langchain.chat_models import ChatOpenAI

from langchain.schema import HumanMessage

# Initialize OpenAI Model

llm = ChatOpenAI(model_name="gpt-4")

2. Define Graph Nodes

Each node represents a processing step in the workflow.

def process_input(data):

    return {"message": HumanMessage(content=data["input"]) }

def generate_response(data):

    response = llm([data["message"]])

    return {"output": response.content} 

3. Build the Graph

graph = langgraph.Graph()

graph.add_node("input", process_input)

graph.add_node("llm", generate_response)

graph.set_entry_point("input")

graph.add_edge("input", "llm")

graph.set_termination_nodes(["llm"])

executor = graph.compile()

4. Run the Application

query = {"input": "What is the capital of France?"}

result = executor.invoke(query)

print("AI Response:", result["output"])

5. Sample Output

AI Response: The capital of France is Paris.

LangGraph provides a structured way to build AI-powered workflows efficiently. By using a directed graph model, it enhances modularity, debugging, and scalability, making it ideal for advanced AI applications.

Try extending this workflow by adding memory or integrating APIs for richer interactions!  You can follow LangGraph tutorial here at - https://langchain-ai.github.io/langgraph/tutorials/introduction/