🤖🔧 LLM Agent Team 🚀

• AI-powered agents that can do several tasks using natural language commands.
• Built with phidata and groq.

I used Groq (llama-3.3-70b-versatile) which provides a Free API Key & 1200Tokens/second.

🚀 Features

• File Manager Agent: Enables you to do File I/O using natural language commands.
• Finance Agent: Provides Financial Knowledge / Comparisons between stocks/companies.
• Search Agent: Search agents are mostly implemented in other agents to provide information based on the web.
• Teach Agent: Summarizes YouTube videos.

📋 Prerequisites

1. Libraries:

   pip install phidata

🔐 Configuration

Environment Variables (dotenv)

export GROQ_API_KEY = "YOUR_API_KEY"
$env:GROQ_API_KEY= "YOUR_API_KEY"

Imports

from phi.agent import Agent
from phi.model.groq import Groq

from phi.tools.duckduckgo import DuckDuckGo
from phi.tools.file import FileTools
from phi.tools.googlesearch import GoogleSearch
from phi.tools.crawl4ai_tools import Crawl4aiTools
from phi.tools.youtube_tools import YouTubeTools
from phi.tools.exa import ExaTools

from dotenv import load_dotenv

from bs4 import BeautifulSoup
import requests

load_dotenv()

💡 Examples

File Manager Agent

fileManager_agent.print_response("Find me 10 great Restaurants in Izmir and save them in a related named file", stream=True)

Finance Agent

finance_agent.print_response("NVDA or META? Buying which stock would be a better idea based on the news?", stream=True)

Web-Content Agent

web_agent.print_response("Can You Briefly Explain What LangChain is used for?", stream=True)

Search Agent

search_agent.print_response("What are some AI Startups launched this month?", stream=True)

Teach Agent

study_partner.print_response("Summarize the latest Andrej Karpathy video on YouTube", stream=True)

Agents as a Tool

• You can implement these agents as a tool in your projects!

• Making an agent team with related agents as a tool can save you from deciding which agent to use.

• Check [https://github.com/openai/openai-agents-python] for more Agents as a tool implementation.

• You can avoid hallucinated generations by implementing the parallelization & tool routing examples.

• Phidata Documentation: https://phidata.com

NOTE: A freshman creates this repository. I believe some great opinions about this repo would lead me to learn & develop more.