This repository showcases a series of projects utilizing multi-agent systems developed with the crewAI framework. Each project highlights unique applications of AI agents to automate processes across various domains, including customer support, sales outreach, event planning, financial analysis, and job application tailoring.
- Multi-Agent Customer Support Automation
- Customer Outreach Campaign Tool
- Automated Event Planning
- Multi-Agent Collaboration for Financial Analysis
- Multi-Agent System for Tailoring Job Applications
- Installation
- Usage
- Acknowledgments
This project demonstrates a multi-agent system designed to automate customer support interactions using the crewAI framework. The goal is to create a cooperative environment where agents can handle customer inquiries effectively while ensuring high-quality support.
- Role Playing: Each agent has a defined role and backstory, allowing them to simulate realistic interactions with customers.
- Focus: Agents are programmed to concentrate on their roles, which enhances the quality of the support provided.
- Cooperation: Agents can delegate tasks to each other, fostering collaboration to resolve customer inquiries more efficiently.
- Tools Integration: The system utilizes various tools for enhanced functionality, such as scraping documentation, accessing CRM data, and checking bug reports.
- Guardrails: The system maintains quality assurance by ensuring agents operate within their defined scope.
- Memory: Agents can remember past interactions, allowing for more personalized support in future interactions.
The Customer Outreach Campaign Tool leverages multi-agent systems to optimize the process of identifying high-value leads and crafting personalized outreach strategies. Built using the CrewAI framework, this project demonstrates the power of AI agents in enhancing sales and marketing efforts.
-
Versatile Agents: Two distinct agents collaborate to streamline the outreach process:
- Sales Representative Agent: Identifies potential leads by analyzing data and trends.
- Lead Sales Representative Agent: Nurtures leads with tailored communication.
-
Custom Tools: Utilizes both pre-built and custom tools for agent tasks, including:
- Sentiment Analysis Tool: Ensures positive communication.
- SerperDevTool: Searches for relevant information to enrich lead profiles.
-
Task Management: Guides agents in conducting lead profiling and creating personalized outreach campaigns.
Ensure you have the following libraries installed:
pip install crewai==0.28.8 crewai_tools==0.1.6 langchain_community==0.0.29To kick off the outreach campaign, provide input parameters such as lead name, industry, key decision-maker, position, and recent milestone:
inputs = {
"lead_name": "DeepLearningAI",
"industry": "Online Learning Platform",
"key_decision_maker": "Andrew Ng",
"position": "CEO",
"milestone": "product launch"
}
result = crew.kickoff(inputs=inputs)This project demonstrates an automated event planning system using the crewAI framework. It leverages multiple agents to handle various aspects of event organization, ensuring a seamless planning experience.
- Venue Coordination: Identifies and books suitable venues.
- Logistics Management: Manages all logistical aspects, including catering and equipment.
- Marketing and Communications: Promotes the event and engages with potential participants.
- Set up API keys and initialize the agents.
- Provide event details to the crew and kick off the planning process.
# Initialize the crew and run the planning system
event_management_crew = Crew(
agents=[venue_coordinator, logistics_manager, marketing_communications_agent],
tasks=[venue_task, logistics_task, marketing_task],
verbose=True
)
event_details = {
'event_topic': "Tech Innovation Conference",
'event_description': "A gathering of tech innovators to explore future technologies.",
'event_city': "San Francisco",
'tentative_date': "2024-09-15",
'expected_participants': 500,
'budget': 20000,
'venue_type': "Conference Hall"
}
result = event_management_crew.kickoff(inputs=event_details)This project leverages multi-agent systems to perform real-time financial analysis, focusing on stock market trading.
- Data Analyst Agent: Monitors and analyzes market data.
- Trading Strategy Developer Agent: Refines trading strategies based on data insights.
- Trade Advisor Agent: Suggests optimal trade execution strategies.
- Risk Advisor Agent: Evaluates risks associated with trading activities.
financial_trading_inputs = {
'stock_selection': 'AAPL',
'initial_capital': '100000',
'risk_tolerance': 'Medium',
'trading_strategy_preference': 'Day Trading',
'news_impact_consideration': True
}
result = financial_trading_crew.kickoff(inputs=financial_trading_inputs)This project demonstrates a multi-agent system to help job applicants tailor their resumes and prepare for interviews.
- Research Agent: Analyzes job postings for key qualifications.
- Profiler Agent: Compiles a comprehensive profile of the applicant.
- Resume Strategist Agent: Tailors resumes to highlight relevant skills.
- Interview Preparer Agent: Develops interview questions and talking points.
The system generates:
tailored_resume.md: A tailored resume.interview_materials.md: Interview questions and talking points.
To run the projects locally, install the required libraries:
pip install crewai==0.28.8 crewai_tools==0.1.6 langchain_community==0.0.29Refer to the specific sections for each project to understand how to set up and run the corresponding systems.
- Thanks to the developers of crewAI and the contributors to the tools and libraries used in this project.