mohamedhassan218 / hello-crewai

Building multi-agent systems with different use cases

Hands-on crewAI

About

crewAI is a collaborative working system designed to enable various artificial intelligence agents to work together as a team to efficiently accomplish complex tasks.

Suffice it to say that the documentation of crewAI is actually written by a crew 🤯!

In this repo I've added all the practical examples explained in the insightful-short course introduced from DeepLearning.ai, Multi AI Agent Systems with crewAI, and due to the code explained in the course depends on OpenAI token and I don't have a pro account, I've modified some line to use Google Gemeni API instead as it's free.

I've finished this course successfully and get my badge from crewAI, you can check it: crewAI badge.

Content

• L2: contains a notebook that implements a nice multi-agent system to generate articles about a specific topic. It has three agents: planner, writer, and editor and tasks for each agent.
• L3: contains a multi-agent system features a Senior Support Representative Agent that delivers friendly and thorough customer support, and a Support Quality Assurance Specialist Agent that ensures the quality and accuracy of the support provided, both aiming to enhance customer satisfaction and support standards.
• L4: contains a multi-agent system includes a Sales Representative Agent that identifies high-value leads and a Lead Sales Representative Agent that nurtures these leads through personalized communication, both working towards enhancing the sales process.
• L5: contains a multi-agent system includes a Venue Coordinator Agent for venue selection, a Logistics Manager Agent for event logistics, and a Marketing and Communications Agent for event promotion and participant communication, all utilizing search and scrape tools and operating in verbose mode for detailed feedback.
• L6: contains a multi-agent system for financial trading, featuring four specialized agents: Data Analyst, Trading Strategy Developer, Trade Advisor, and Risk Management, each equipped with tools for data gathering and analysis, operating verbosely, and capable of task delegation.
• L7: contains a multi-agent system that looks at the job postings, understand it's requirements and then cross that over with your current resume and your skills and make sure that we tailor your resume to apply for this job.

Usage

Steps to use this code on your machine:

1. Clone the repo:

   git clone [email protected]:mohamedhassan218/hello-crewAI.git

2. Create a virtual environment:

   python -m venv .venv

3. Activate your virtual environment:

   • On Windows:

     .venv\Scripts\activate

   • On Unix or MacOS:

     source .venv/bin/activate

4. Install the dependencies:

   pip install -r requirements.txt

5. Create a .env file in the project root and add the following environment variables:

   GOOGLE_API_KEY=""
   SERPER_API_KEY=""
   

   • Get your free Google API key from here
   • Get your free Serper API key from here

6. And now ensure to connect the notebook to your venv and try the code yourself.

Acknowledgement

This platform has been instrumental in advancing my knowledge and practical abilities in AI and machine learning. I would like to express my deepest gratitude to DeepLearning.ai for providing an exceptional learning experience through their course on Multi AI Agent Systems. The insightful lessons and hands-on coding exercises have significantly enhanced my understanding and skills in this area. A special thanks to the team at DeepLearning.ai for their dedication to making high-quality AI education accessible to everyone.