Graph It Till You Make It

We will be using LangGraph. It’s a framework for building stateful, multi-step AI workflows with clear control over execution paths.

1 . Introduction: Why Do We Need Langchain

• Ever tried building a GenAI app and ended up with “if-this-else-that” chaos? Your AI codebase looks more like tangled earphones than a product roadmap.

• That’s where LangGraph comes in. It takes all that messy logic, puts it on a flowchart, and says: “Relax, I’ll handle it.”

• In this article, we’ll break down the problems LangGraph solves, what it is, and why it’s becoming a game-changer in the generative AI ecosystem.

2 . Problem Space: What’s broken?

LLMs are powerful, but…

• They don’t always get it right → you need retries.

• They can go in circles → you need loops.

• They may need different paths → you need conditional routing.

• You want to track what happened when → you need visibility.

3 . This is where LangGraph steps in:

• LangGraph = Flow Orchestrator for LLM Apps.

  It lets you design your AI workflow as a graph:

  • Nodes = steps (LLM call, DB query, validation, etc.)

  • Edges = transitions (what happens next).

  • Supports loops, branching, retries, and memory.

Think of it as React.js for your AI app’s logic—modular, predictable, and easy to debug.

4 . THE CODE PART

1. Define Your State

LangGraph uses typed state definitions to keep track of everything across steps.

from typing import TypedDict

class State(TypedDict):
    user_query: str
    retries: int
    accuracy: float

2. Build Your Graph

from langgraph.graph import StateGraph, END, START

graph_builder = StateGraph(State)

graph_builder.add_edge(START, "classify_query")
graph_builder.add_edge("classify_query", "handle_general")
graph_builder.add_edge("classify_query", "handle_code")
graph_builder.add_edge("handle_code", "validator")
graph_builder.add_edge("validator", "accuracy_check")
graph_builder.add_edge("accuracy_check", END)

graph = graph_builder.compile()

3. Add logic functions

def validator(state: State) -> State:
    # Check correctness of code output
    if state["accuracy"] < 90:
        state["retries"] += 1
    return state

4. Run it

initial_state = {"user_query": "write me Python code", "retries": 0, "accuracy": 85}
response = graph.invoke(initial_state)
print(response)

5 . Conclusion

LangGraph is the traffic controller for your AI agents. It ensures they don’t crash into each other, don’t get lost, and reach the right destination—every single time.

As GenAI apps move from toys to production, predictability > magic. LangGraph brings that predictability.

6 . Call to Action

💡 Next time you build with LangChain, don’t just stack functions. Sketch your workflow as a graph, let LangGraph do the heavy lifting, and enjoy cleaner, smarter, and more reliable AI apps.

👉 Check out the LangGraph docs.

How we tried a simple LangGraph

👉 Explore the LangGraph docs and start experimenting today.

7 . Related Resources

• LangGraph Documentation

• LangChain Official GitHub

• Building AI Agents with LangGraph