A Step-by-Step Coding Guide to Building an Iterative AI Workflow Agent Using LangGraph and Gemini
In this tutorial, we explore how to create a sophisticated query-handling agent using LangGraph and Gemini 1.5 Flash. This project centers around structuring AI reasoning as a stateful workflow, where an incoming query navigates through a series of purposeful nodes: routing, analysis, research, response generation, and validation. Each node plays a specific role, transforming the agent from a reactive system into an analytically aware entity. By utilizing LangGraph’s StateGraph, we design a looping system that reassesses and enhances its output until the response meets the validation criteria or reaches a predefined iteration limit.
Prerequisites
Before diving into the coding aspects, ensure you have the essential Python packages installed. You can do this by running:
!pip install langgraph langchain-google-genai python-dotenvThis command installs three critical packages:
- langgraph: Facilitates the graph-based orchestration of AI agents.
- langchain-google-genai: Integrates with Google’s Gemini models for enhanced AI capabilities.
- python-dotenv: Securely loads environment variables from .env files, safeguarding sensitive data.
Setting Up the Environment
Next, we need to set up our environment by importing necessary libraries and configuring our API key:
import os
from typing import Dict, Any
from dataclasses import dataclass
from langgraph.graph import Graph, StateGraph, END
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.schema import HumanMessage, SystemMessage
import json
os.environ["GOOGLE_API_KEY"] = "Use Your API Key Here"This step ensures that we can interact with the Gemini models securely by handling the API key correctly.
Defining the Agent State
The agent’s state is foundational for our workflow. We define it using a dataclass:
@dataclass
class AgentState:
query: str = ""
context: str = ""
analysis: str = ""
response: str = ""
next_action: str = ""
iteration: int = 0
max_iterations: int = 3The AgentState dataclass allows us to retain the necessary information across different nodes in the workflow, including user queries and the results of our analyses. This persistence is crucial for iterative reasoning, enabling the agent to refine its output over time.
Building the Graph AI Agent
Now, we construct the agent:
class GraphAIAgent:
def __init__(self, api_key: str = None):
if api_key:
os.environ["GOOGLE_API_KEY"] = api_key
self.llm = ChatGoogleGenerativeAI(
model="gemini-1.5-flash",
temperature=0.7,
convert_system_message_to_human=True
)
self.analyzer = ChatGoogleGenerativeAI(
model="gemini-1.5-flash",
temperature=0.3,
convert_system_message_to_human=True
)
self.graph = self._build_graph()
def _build_graph(self) -> StateGraph:
workflow = StateGraph(AgentState)
workflow.add_node("router", self._router_node)
workflow.add_node("analyzer", self._analyzer_node)
workflow.add_node("researcher", self._researcher_node)
workflow.add_node("responder", self._responder_node)
workflow.add_node("validator", self._validator_node)
workflow.set_entry_point("router")
workflow.add_edge("router", "analyzer")
workflow.add_conditional_edges(
"analyzer",
self._decide_next_step,
{
"research": "researcher",
"respond": "responder"
}
)
workflow.add_edge("researcher", "responder")
workflow.add_edge("responder", "validator")
workflow.add_conditional_edges(
"validator",
self._should_continue,
{
"continue": "analyzer",
"end": END
}
)
return workflow.compile()This class initializes our AI agent with the Gemini API key and builds a structured workflow that consists of nodes responsible for distinct tasks, from routing to validation.
Node Implementation
Each node within our workflow fulfills a specific function:
- router_node: Directs and categorizes the incoming query based on context.
- analyzer_node: Assesses the query to determine if research or direct response is needed.
- researcher_node: Gathers additional information or conducts research as required.
- responder_node: Crafts the final response using the analyzed context.
- validator_node: Ensures the response’s quality and completeness before finalizing it.
Running the Agent
Let’s put our agent to the test:
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"\nQuery {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 function runs the agent with a set of diverse test queries that range from technical explanations to creative storytelling. It demonstrates how the LangGraph-driven agent leverages Gemini’s capabilities to tackle various inputs effectively.
Conclusion
Combining LangGraph’s structured workflows with the advanced conversational intelligence of Gemini, we have crafted an agent that closely mirrors human reasoning cycles. This tutorial outlines a modular and extensible approach to developing AI agents capable of autonomously handling tasks—from complex query responses to generating creative narratives. By following these steps, you can create your own intelligent systems that evolve and learn over time, pushing the boundaries of what AI can achieve.
