Building an Iterative AI Workflow Agent with LangGraph and Gemini: A Complete Coding Guide

Overview of the AI Workflow Agent

This tutorial walks you through creating an intelligent multi-step AI agent using LangGraph and Gemini 1.5 Flash. The agent treats AI reasoning as a stateful workflow where queries pass through specific nodes responsible for routing, analysis, research, response generation, and validation. Each node functions as an independent module, enabling the agent to be analytically aware rather than merely reactive.

Required Libraries and Setup

Install the necessary Python packages:

!pip install langgraph langchain-google-genai python-dotenv

These packages provide tools for orchestrating AI workflows (langgraph), integrating with Gemini models (langchain-google-genai), and managing environment variables securely (python-dotenv).

Defining Shared State

We define an AgentState dataclass to keep track of the query, context, analysis, response, next action, and iteration count:

@dataclass
class AgentState:
    """State shared across all nodes in the graph"""
    query: str = ""
    context: str = ""
    analysis: str = ""
    response: str = ""
    next_action: str = ""
    iteration: int = 0
    max_iterations: int = 3

This state persists across nodes enabling iterative reasoning and decision-making.

Building the GraphAIAgent Class

The GraphAIAgent class encapsulates the workflow logic:

• Initializes Gemini models for response and analysis
• Constructs a LangGraph StateGraph with nodes for routing, analysis, research, responding, and validation
• Sets up edges to control flow and iteration

Key node functions include:

• _router_node: Categorizes and routes queries
• _analyzer_node: Decides whether to research more or respond directly
• _researcher_node: Performs additional research
• _responder_node: Generates the final response
• _validator_node: Validates response quality and controls iteration

Example Usage

The main() function demonstrates running the agent on various query types:

def main():
    agent = GraphAIAgent("Use Your API Key Here")
   
    test_queries = [
        "Explain quantum computing and its applications",
        "What are the best practices for machine learning model deployment?",
        "Create a story about a robot learning to paint"
    ]
   
    print(" Graph AI Agent with LangGraph and Gemini")
    print("=" * 50)
   
    for i, query in enumerate(test_queries, 1):
        print(f"\n Query {i}: {query}")
        print("-" * 30)
       
        try:
            response = agent.run(query)
            print(f" Response: {response}")
        except Exception as e:
            print(f" Error: {str(e)}")
       
        print("\n" + "="*50)
 
 
if __name__ == "__main__":
    main()

This example illustrates the agent's ability to handle technical, strategic, and creative tasks by iterating through the workflow nodes.

Summary

Combining LangGraph’s structured state management with Gemini’s conversational AI capabilities creates a modular, extensible AI agent. This agent can autonomously process complex queries via iterative cycles of inquiry, analysis, and validation, reflecting human-like reasoning patterns.