AI-Powered Financial & Web Intelligence Agents  

Project Overview

The goal of this project is to build a multi-agent AI system that autonomously retrieves, processes, and presents structured data from various sources. It is designed to cater to financial analysts, business professionals, researchers, and general users who require real-time information on stocks, market trends, and general knowledge without manually searching through multiple sources.

The project consists of three specialized AI agents, each with distinct capabilities:

1. A Web Intelligence Agent that fetches the latest information from the web with cited sources.

2. A Stock & Financial Data Agent that extracts, analyzes, and compares stock fundamentals.

3. A Content Generation Agent capable of structuring responses in various domains, such as finance, research, and even creative writing.

By integrating state-of-the-art AI models (Groq Llama-3.3-70B Versatile & OpenAI GPT-4o) with external data tools like YFinanceTools and DuckDuckGo, this system provides a comprehensive solution for data-driven decision-making.

Key Features & Functionalities

1. Web Intelligence Agent (agent_team_1.py)​

• Fetches real-time web-based information using DuckDuckGo as a search tool.

• Uses OpenAI’s GPT-4o to generate well-structured responses with proper citations.

• Ensures responses always include sources for credibility.

2. Stock & Financial Data Agent (First_Finance_Agent.py & agent_team_1.py)​​

• Retrieves stock prices, analyst recommendations, and fundamental financial data via YFinanceTools.

• Provides tabulated summaries for better data visualization.

• Designed to compare multiple stocks (e.g., TSLA vs. NVDA) based on financial fundamentals.

3. Simple Grok-Based Content Generator (Simple_Grok_Agent.py)​

• Uses Groq’s Llama-3.3-70B model to generate textual content.

• Example use case: Generating structured recipes such as “Butter Chicken.”

• Can be extended for creative writing, blog content, or instructional guides.

4. AI Agent Collaboration (agent_team_1.py)​

• Agents function in teams, with the Web Agent and Stock Agent working together.

• Responses follow structured formats with sources and tabular data for clarity.

• Enables multi-faceted query handling, such as retrieving stock insights while providing relevant industry news.

Technologies Used

• Python: Core programming language.

• Phi Framework: AI agent development.

• Groq Llama-3.3-70B Versatile & OpenAI GPT-4o: Advanced language models for NLP tasks.

• YFinanceTools: Real-time stock and financial data retrieval.

• DuckDuckGo API: Web search automation.

• dotenv: Environment variable management for secure API key handling.

Potential Applications

• Financial Analysis: Automating stock market research and recommendations.

• Market Intelligence: Fetching and summarizing web-based news and insights.

• Personalized AI Assistants: Generating responses for user queries in various domains.

• Content Generation: Automating report writing, recipes, or instructional content.

Here is the github link, where the python code is available for public if anybody need the coding reference.