How Does CrewAI Enhance Tesla Model 3 Performance Assessment?

Many Tesla Model 3 enthusiasts and automotive analysts struggle to decipher the vehicle’s real-world performance and maintenance requirements. CrewAI, a cutting-edge framework for orchestrating role-playing, autonomous AI agents, acts as your dedicated technical analyst. By fostering collaborative intelligence, CrewAI empowers multiple agents to work together seamlessly to deliver detailed performance analysis and precise maintenance specifications. This data-driven approach not only deepens your understanding of the Tesla Model 3’s engineering but also helps you enjoy a smoother, more informed ownership experience.

Context & Overview

The Tesla Model 3 has redefined the electric vehicle market with its innovative design and performance capabilities. In this analysis, we focus on the vehicle’s core technical specifications, maintenance schedules, pricing models, and performance metrics to provide a comprehensive understanding of its operation and upkeep.

Specifications

• Trim Levels: Standard Range Plus, Long Range, Performance
• Base Price: $54,900
• Central Touchscreen: 15-inch
• Interior: Minimalist design

Pricing Analysis

• Lease Deals: $249 – $410 monthly payments
• Financing: 0% interest available with a 15% down payment
• Cash Back Rebates: Not available

Maintenance Information

Analyzing the maintenance aspects of the Tesla Model 3 is crucial for understanding its long-term viability. The report examines service intervals, common technical issues, estimated maintenance costs, and warranty details.

• Service Interval: Annually or every 12,000–15,000 miles
• Common Issues: Quality control challenges and occasional software glitches
• Estimated Maintenance Costs (over 10 years): Approximately $3,587
• Warranty: Comprehensive coverage with additional software update benefits

Performance Metrics

• Acceleration (0-60 mph):
  • Performance Variant: 2.8 seconds
  • Long Range Variant: 2.9 seconds
• Top Speed: Up to 163 mph (Performance)
• Handling: Designed for balanced performance with advanced driver assists
• Braking Distance: Comparable to high-performance electric vehicles

Technical Overview

This project leverages Python and CrewAI to automate data collection and analysis. Various agents perform tasks like web scraping and market analysis to generate the report. Here’s a brief code snippet illustrating how the maintenance data is gathered:

# Required imports
from crewai import Agent, Task, Crew
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
import os
import requests
from bs4 import BeautifulSoup
from crewai.tools import BaseTool

# Load environment variables
load_dotenv()
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")

# Initialize the language model
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

# Custom Tools using CrewAI's BaseTool
class WebScrapeTool(BaseTool):
    name: str = "Web Scraper"
    description: str = "Scrapes content from a given URL"

    def _run(self, url: str) -> str:
        try:
            response = requests.get(url, timeout=5)
            soup = BeautifulSoup(response.content, 'html.parser')
            return soup.get_text(separator='\n', strip=True)
        except Exception as e:
            return f"Error scraping URL: {str(e)}"

class FileWriterTool(BaseTool):
    name: str = "File Writer"
    description: str = "Writes content to a specified file"

    def _run(self, content: str, filename: str) -> str:
        try:
            with open(filename, 'w', encoding='utf-8') as f:
                f.write(content)
            return f"Successfully wrote content to {filename}"
        except Exception as e:
            return f"Error writing to file: {str(e)}"

class DuckDuckGoSearchTool(BaseTool):
    name: str = "DuckDuckGo Search"
    description: str = "Performs a web search using DuckDuckGo"

    def _run(self, query: str) -> str:
        search = DuckDuckGoSearchRun()
        return search.run(query)

# Initialize tools
duckduckgo_search_tool = DuckDuckGoSearchTool()
web_scrape_tool = WebScrapeTool()
file_writer_tool = FileWriterTool()

# Define Agents
research_agent = Agent(
    role="Automobile Research Specialist",
    goal="Gather detailed specifications and features of automobiles",
    backstory="You're an expert in automotive technology with years of experience researching vehicle specifications.",
    verbose=True,
    llm=llm,
    tools=[duckduckgo_search_tool, web_scrape_tool]
)

pricing_agent = Agent(
    role="Automobile Pricing Analyst",
    goal="Analyze pricing trends and provide cost estimates for automobiles",
    backstory="You're a skilled pricing analyst specializing in the automotive market.",
    verbose=True,
    llm=llm,
    tools=[duckduckgo_search_tool, web_scrape_tool]
)

maintenance_agent = Agent(
    role="Maintenance Analyst",
    goal="Provide maintenance schedules and cost estimates",
    backstory="You're an experienced mechanic with expertise in vehicle maintenance.",
    verbose=True,
    llm=llm,
    tools=[duckduckgo_search_tool, web_scrape_tool]
)

performance_agent = Agent(
    role="Performance Tester",
    goal="Analyze vehicle performance metrics and capabilities",
    backstory="You're a test driver with deep knowledge of vehicle performance characteristics.",
    verbose=True,
    llm=llm,
    tools=[duckduckgo_search_tool, web_scrape_tool]
)

report_agent = Agent(
    role="Automobile Report Writer",
    goal="Create comprehensive reports based on all collected data",
    backstory="You're an experienced technical writer specializing in automobile reports.",
    verbose=True,
    llm=llm,
    tools=[file_writer_tool]
)

# Define Tasks
def create_research_task(car_model):
    return Task(
        description=f"""Research detailed specifications for the {car_model}. 
        Include:
        - Engine specifications
        - Dimensions
        - Fuel economy
        - Safety features
        - Technology features
        Use both search and web scraping tools.""",
        expected_output="A detailed summary of the car's specifications",
        agent=research_agent
    )

def create_pricing_task(car_model):
    return Task(
        description=f"""Analyze pricing for the {car_model}. 
        Include:
        - Base MSRP
        - Trim levels and prices
        - Average market price
        - Incentives/discounts
        Use web scraping for additional data.""",
        expected_output="A comprehensive pricing analysis",
        agent=pricing_agent
    )

def create_maintenance_task(car_model):
    return Task(
        description=f"""Research maintenance information for the {car_model}.
        Include:
        - Recommended maintenance schedule
        - Common issues
        - Average maintenance costs
        - Warranty information""",
        expected_output="A detailed maintenance analysis",
        agent=maintenance_agent
    )

def create_performance_task(car_model):
    return Task(
        description=f"""Analyze performance metrics for the {car_model}.
        Include:
        - Acceleration (0-60 mph)
        - Top speed
        - Handling characteristics
        - Braking distance""",
        expected_output="A comprehensive performance analysis",
        agent=performance_agent
    )

def create_report_task(car_model):
    return Task(
        description=f"""Create a comprehensive report for the {car_model} using all data.
        Include:
        - Executive summary
        - Specifications
        - Pricing analysis
        - Maintenance information
        - Performance metrics
        Save the report as '{car_model}_report.md'""",
        expected_output="A complete report in markdown format saved to file",
        agent=report_agent
    )

# Main function
def run_automobile_agent(car_model="Toyota Camry"):
    # Create tasks
    research_task = create_research_task(car_model)
    pricing_task = create_pricing_task(car_model)
    maintenance_task = create_maintenance_task(car_model)
    performance_task = create_performance_task(car_model)
    report_task = create_report_task(car_model)

    # Create crew
    automobile_crew = Crew(
        agents=[research_agent, pricing_agent, maintenance_agent, 
                performance_agent, report_agent],
        tasks=[research_task, pricing_task, maintenance_task, 
               performance_task, report_task],
        verbose=True,  # Changed from verbose=2 to verbose=True
        process="sequential"
    )

    # Execute the crew
    result = automobile_crew.kickoff()
    return result

# Example usage
if __name__ == "__main__":
    try:
        result = run_automobile_agent("Tesla Model 3")
        print("\nFinal Result:")
        print(result)
        print("\nCheck the generated report file: 'Tesla Model 3_report.md'")
    except Exception as e:
        print(f"An error occurred: {str(e)}")
    

For the full code and additional technical details, visit our repository at GitHub Repository.