Creating a Multi-Node Graph AI Agent Framework to Automate Complex Tasks

Overview of the Graph-Based AI Agent Framework

This tutorial walks through building an advanced Graph Agent framework powered by the Google Gemini API. The framework enables the creation of intelligent agents that automate complex, multi-step tasks using a graph structure composed of interconnected nodes. Each node serves a distinct function such as input handling, processing, decision-making, or output generation. Python is used alongside NetworkX for graph modeling and matplotlib for visualization.

Setting Up the Environment

We start by installing necessary libraries: google-generativeai for interacting with Gemini API, networkx for graph structures, and matplotlib for graph visualization. The Gemini API is configured with a provided API key to enable content generation capabilities within the agent system.

Defining Node Types and Agent Nodes

A NodeType enumeration categorizes agent nodes into four types: INPUT, PROCESS, DECISION, and OUTPUT. The AgentNode dataclass structures each node with an identifier, type, prompt, an optional function, and dependencies. This design supports building a modular and flexible graph of agent nodes.

Building the Research Agent

The research agent is constructed by adding nodes representing the research workflow steps: starting from topic input, creating a research plan, conducting literature review, analyzing findings, evaluating quality, and finally generating a detailed research report.

class NodeType(Enum):
    INPUT = "input"
    PROCESS = "process"
    DECISION = "decision"
    OUTPUT = "output"
 
@dataclass
class AgentNode:
    id: str
    type: NodeType
    prompt: str
    function: Callable = None
    dependencies: List[str] = None

Building the Problem Solver Agent

The problem solver agent is designed to automate problem-solving workflows. It takes a problem statement, breaks it down, generates multiple solutions, evaluates them, and produces a detailed implementation plan.

Executing the Agents

Two demo functions, run_research_demo() and run_problem_solver_demo(), demonstrate the framework's capabilities. Each function visualizes the graph structure, initializes input data, and runs the agent nodes in topological order. At each node, Gemini API generates content based on contextual prompts, and results are stored and passed along the graph.

Results and Insights

The demos showcase how complex workflows can be decomposed into a graph of nodes, each performing a specialized task. This modular architecture allows for flexible, interpretable, and scalable AI agents capable of handling sophisticated reasoning and decision-making tasks.

All code snippets are preserved as provided in the tutorial, demonstrating how to define nodes, build agents, and execute workflows step-by-step using Python, NetworkX, and Gemini API.