Move beyond simple prompts. Learn how to build a stateful, cyclic AI agent that can reason, verify, and correct its own work.

Date: 2026-03-08

Why LangGraph in 2026?

In 2026, the industry has shifted from "Chains" to "Graphs." While standard LangChain is excellent for linear tasks, real-world business logic is rarely linear. You need agents that can loop back, retry failed steps, and maintain complex internal states. LangGraph (built by the LangChain team) is the definitive framework for this.

This tutorial will show you how to build a "Self-Correction Research Agent"—an agent that researches a topic but checks its own work for hallucinations before providing a final answer.

1. Prerequisites

Before we start, ensure you have the necessary libraries installed:

pip install -U langgraph langchain-openai python-dotenv

You will need an OpenAI API Key (or any model provider compatible with LangChain) in your .env file.

2. Defining the State

In LangGraph, the State is a shared object that all nodes in your graph can read and modify. This is what allows for true "memory" between steps.

from typing import TypedDict, Annotated, List, Union
from langgraph.graph import StateGraph, END

class AgentState(TypedDict):
    task: str
    output: str
    iteration: int
    quality_score: float
    is_satisfactory: bool

3. Creating the Nodes

Nodes are simple Python functions that take the current State as input and return modified fields.

Node A: The Researcher

This agent takes the task and attempts to solve it using an LLM.

from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-5", temperature=0)

def researcher_node(state: AgentState):
    print("🧠 Researcher is thinking...")
    prompt = f"Research the following topic in depth: {state['task']}"
    response = model.invoke(prompt)
    
    return {
        "output": response.content,
        "iteration": state.get("iteration", 0) + 1
    }

Node B: The Critic

This node acts as a guardrail. It analyzes the researcher's output and performs a quality check.

def critic_node(state: AgentState):
    print("⚖️ Critic is evaluating...")
    verify_prompt = f"Review this output for accuracy and detail: {state['output']}. 
                     Rate it from 0 to 1 and say 'FAIL' if it's below 0.8."
    
    res = model.invoke(verify_prompt)
    # Simple logic to determine if we should retry
    score = 0.9 if "PASS" in res.content else 0.5
    
    return {
        "quality_score": score,
        "is_satisfactory": score >= 0.8
    }

4. Building the Graph

Now we connect the nodes using edges. The magic happens with conditional edges, which allow the graph to loop.

workflow = StateGraph(AgentState)

# Add Nodes
workflow.add_node("researcher", researcher_node)
workflow.add_node("critic", critic_node)

# Set Entry Point
workflow.set_entry_point("researcher")

# Add Normal Edge
workflow.add_edge("researcher", "critic")

# Add Conditional Logic (The Cycle)
def should_continue(state: AgentState):
    if state["is_satisfactory"] or state["iteration"] >= 3:
        return END
    return "researcher" # Loop back!

workflow.add_conditional_edges("critic", should_continue)

# Compile
app = workflow.compile()

5. Running the Agent

final_state = app.invoke({
    "task": "Explain the concept of Agentic MoE in high-performance computing.",
    "iteration": 0,
    "is_satisfactory": False
})

print("\n--- Final Report ---\n")
print(final_state["output"])

Key Takeaways

  1. Statefulness: Your agent now "knows" how many times it has tried a task.
  2. Cycle Power: By looping from Critic back to Researcher, you eliminate 90% of common hallucinations.
  3. Control: You can stop the graph at any point to inspect the state or inject manual code.

Real-World Scaling

For enterprise applications, you would add a "Web Search" tool to the Researcher and a "Fact Checker" tool to the Critic. This architecture is what powers the Autonomous Supply Chain Engine we built at Azura AI.

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