Creating a Multi-Based Agent Agent's Framework at Aigent's Complex Task Automation
In this lesson, we guide you in the development of Agent Graph Agent, enabled by Google Gemini API. Our goal is to build wise agents, magth-Step killed jobs in a well-defined part of the connected areas. Each node represents a specific work, from making a logical process, making decisions, and production results. We use Python, NetworkX of Graph Modeling, and Matplotlib to see. At the end, we use and use two full examples, research assistant and a Solver Problem, to show how the frame can handle the complex operations.
!pip install -q google-generativeai networkx matplotlib
import google.generativeai as genai
import networkx as nx
import matplotlib.pyplot as plt
from typing import Dict, List, Any, Callable
import json
import asyncio
from dataclasses import dataclass
from enum import Enum
API_KEY = "use your API key here"
genai.configure(api_key=API_KEY)
We start by installing the required libraries, Google-Generativeai, Networkx, and matployli, supporting the agent frame based on graph-based graph-based graph-based. After importing essential modules, we prepare the Gemini API using our API key to enable powerful content skills to our agent system.
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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] = NoneIt describes nodetype that is allowed to distinguish different types of agent areas: Input, process, decision, and output. After that, using the data data agentnode, we plan each place for ID, type, promptly, optional, optional, allowing a variable and variable agent list.
def create_research_agent():
agent = GraphAgent()
# Input node
agent.add_node(AgentNode(
id="topic_input",
type=NodeType.INPUT,
prompt="Research topic input"
))
agent.add_node(AgentNode(
id="research_plan",
type=NodeType.PROCESS,
prompt="Create a comprehensive research plan for the topic. Include 3-5 key research questions and methodology.",
dependencies=["topic_input"]
))
agent.add_node(AgentNode(
id="literature_review",
type=NodeType.PROCESS,
prompt="Conduct a thorough literature review. Identify key papers, theories, and current gaps in knowledge.",
dependencies=["research_plan"]
))
agent.add_node(AgentNode(
id="analysis",
type=NodeType.PROCESS,
prompt="Analyze the research findings. Identify patterns, contradictions, and novel insights.",
dependencies=["literature_review"]
))
agent.add_node(AgentNode(
id="quality_check",
type=NodeType.DECISION,
prompt="Evaluate research quality. Is the analysis comprehensive? Are there missing perspectives? Return 'APPROVED' or 'NEEDS_REVISION' with reasons.",
dependencies=["analysis"]
))
agent.add_node(AgentNode(
id="final_report",
type=NodeType.OUTPUT,
prompt="Generate a comprehensive research report with executive summary, key findings, and recommendations.",
dependencies=["quality_check"]
))
return agentWe create an additional survey agent in chronological order at the graph. It begins with the title, describing the flow of the process that includes planning, book reviews, and analysis. The Agent made a career-based quality decision and finally released the full research report, photographed full-time offers of the organized work.
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def create_problem_solver():
agent = GraphAgent()
agent.add_node(AgentNode(
id="problem_input",
type=NodeType.INPUT,
prompt="Problem statement"
))
agent.add_node(AgentNode(
id="problem_analysis",
type=NodeType.PROCESS,
prompt="Break down the problem into components. Identify constraints and requirements.",
dependencies=["problem_input"]
))
agent.add_node(AgentNode(
id="solution_generation",
type=NodeType.PROCESS,
prompt="Generate 3 different solution approaches. For each, explain the methodology and expected outcomes.",
dependencies=["problem_analysis"]
))
agent.add_node(AgentNode(
id="solution_evaluation",
type=NodeType.DECISION,
prompt="Evaluate each solution for feasibility, cost, and effectiveness. Rank them and select the best approach.",
dependencies=["solution_generation"]
))
agent.add_node(AgentNode(
id="implementation_plan",
type=NodeType.OUTPUT,
prompt="Create a detailed implementation plan with timeline, resources, and success metrics.",
dependencies=["solution_evaluation"]
))
return agentWe are building a problem-solving agent by explaining logical chronological order, from receipt of the problem statement. Agent has analyzed the problem, producing many ways to solve solutions, evaluate it based on performance and efficiency, and generate a systematic process, which allow the default problem adjustment.
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def run_research_demo():
"""Run the research agent demo"""
print("🚀 Advanced Graph Agent Framework Demo")
print("=" * 50)
research_agent = create_research_agent()
print("n📊 Research Agent Graph Structure:")
research_agent.visualize()
print("n🔍 Executing Research Task...")
research_agent.results["topic_input"] = "Artificial Intelligence in Healthcare"
execution_order = list(nx.topological_sort(research_agent.graph))
for node_id in execution_order:
if node_id == "topic_input":
continue
context = {}
node = research_agent.nodes[node_id]
if node.dependencies:
for dep in node.dependencies:
context[dep] = research_agent.results.get(dep, "")
prompt = node.prompt
if context:
context_str = "n".join([f"{k}: {v}" for k, v in context.items()])
prompt = f"Context:n{context_str}nnTask: {prompt}"
try:
response = research_agent.model.generate_content(prompt)
result = response.text.strip()
research_agent.results[node_id] = result
print(f"✓ {node_id}: {result[:100]}...")
except Exception as e:
research_agent.results[node_id] = f"Error: {str(e)}"
print(f"✗ {node_id}: Error - {str(e)}")
print("n📋 Research Results:")
for node_id, result in research_agent.results.items():
print(f"n{node_id.upper()}:")
print("-" * 30)
print(result)
return research_agent.results
def run_problem_solver_demo():
"""Run the problem solver demo"""
print("n" + "=" * 50)
problem_solver = create_problem_solver()
print("n🛠️ Problem Solver Graph Structure:")
problem_solver.visualize()
print("n⚙️ Executing Problem Solving...")
problem_solver.results["problem_input"] = "How to reduce carbon emissions in urban transportation"
execution_order = list(nx.topological_sort(problem_solver.graph))
for node_id in execution_order:
if node_id == "problem_input":
continue
context = {}
node = problem_solver.nodes[node_id]
if node.dependencies:
for dep in node.dependencies:
context[dep] = problem_solver.results.get(dep, "")
prompt = node.prompt
if context:
context_str = "n".join([f"{k}: {v}" for k, v in context.items()])
prompt = f"Context:n{context_str}nnTask: {prompt}"
try:
response = problem_solver.model.generate_content(prompt)
result = response.text.strip()
problem_solver.results[node_id] = result
print(f"✓ {node_id}: {result[:100]}...")
except Exception as e:
problem_solver.results[node_id] = f"Error: {str(e)}"
print(f"✗ {node_id}: Error - {str(e)}")
print("n📋 Problem Solving Results:")
for node_id, result in problem_solver.results.items():
print(f"n{node_id.upper()}:")
print("-" * 30)
print(result)
return problem_solver.results
print("🎯 Running Research Agent Demo:")
research_results = run_research_demo()
print("n🎯 Running Problem Solver Demo:")
problem_results = run_problem_solver_demo()
print("n✅ All demos completed successfully!")It is a tutorial through two powerful demo agents, their research and one of the problems. In each case, we can imagine a graph structure, start input, and release the agent with Node-Node using Order Order. Gemini produces content responses to all steps, we see how each agent develops in order, analyzing, decision-making, and output, eventually shows the full potential for our Graphic.
In conclusion, we were successfully developed and built up wise agents and solve jobs by step, using the arts driven by a graph. We see how node decides how to rely on the context, finds the strength of Gemino's content, and it passes results on the following nodes. The modular design enhances flexibility and also allows us to visualize accurate flow clearly.
Look Codes. All credit for this study goes to research for this project. Sign up now In our Ai Newsletter
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