Step-by-Step CrewAI Agent Build - Real Use Case! (Part 1)

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AI Summary

Summary of Video Transcript

• The video is a tutorial on building an educational portal using Crew AI.
• The portal aims to provide AI proficiency content, from basics to complex tutorials.
• The content will be text-based with images and step-by-step guides.
• The tutorial includes testing, making mistakes, and using AI models like GPT-4.01.
• The process starts with setting up a new Conda environment and installing Crew AI and Lang Chain.
• The video demonstrates creating a Crew AI app, setting up OpenAI models, and obtaining API keys.
• The tutorial covers Python environment management and the challenges associated with it.
• The video features the installation of various tools and APIs, including Crew AI, Lang Chain, and Perplexity.
• The tutorial shows how to integrate and test APIs within the Crew AI app.
• The video explains how to handle errors and debug issues with Python environments and API calls.
• The tutorial explores using web search tools like Serp API with Crew AI for up-to-date information.
• The video demonstrates how to refine agent definitions and tasks for better content creation.
• The tutorial concludes with a comparison of results using different AI models and the cost implications.

Detailed Instructions and URLs

• Create a new Conda environment:

  conda create -n edu python=3.12  
  conda activate edu  
  

• Install Crew AI and Lang Chain:

  pip install crew-ai  
  pip install langchain-python-sdk  
  

• Create a Crew AI app:

  crew-ai create crew --name edu  
  

• Install Crew AI tools:

  pip install crew-ai[tools]  
  

• Set environment variables for API keys (example for Serp API key):

  export SERP_API_KEY='your_api_key'  
  

• Import and use Serp API in the Crew AI app:

  from crew_ai.tools import SerpDevTool  
  # Instantiate the tool with the API key  
  search_tool = SerpDevTool(api_key=os.getenv('SERP_API_KEY'))  

• Run the Crew AI app:

  crew-ai run  
  

Tips

• Always use Python environment management (Conda, venv, etc.).
• When encountering errors, check the Python environment and installed packages.
• Use the AI assistant to debug and correct code snippets.
• Test API endpoints separately before integrating them into the main application.
• Refine agent definitions in Crew AI to improve the quality of generated content.
• Consider the cost versus quality tradeoff when choosing AI models.

URLs

• No complete exact URLs were provided in the transcript.