AI Tools
Multi Agent AI Finance Assistant
This application provides market insights and financial analysis on your portfolio or specific stocks. Enter a query about specific stocks or market conditions to get an AI-generated analysis
Year :
2025
Industry :
Tech
Client :
Personal Project
Project Duration :
8 weeks
Problem :
Modern financial analysis demands rapid, multi-dimensional, and data-driven decision-making. However, financial data is highly fragmented and fast-moving:
Heterogeneous Sources: Crucial information is scattered across real-time stock exchanges, financial news portals, regulatory filings (RAG databases), and historical balance sheets.
Access & Cost Barriers: High-quality analytics tools (like Bloomberg Terminal) are prohibitively expensive for individual researchers, developers, or retail investors, while free alternatives offer disjointed data with no synthesis.
Manual Analysis Fatigue: Performing portfolio risk calculations, running machine learning-based earnings forecasts, scraping web articles, and compiling structured investment briefs requires manual data wrangling across multiple Python libraries or spreadsheets.
There is a distinct gap for a unified, open-source platform that automates the end-to-end ingestion, computation, forecasting, and narrative synthesis of financial assets.
Solution :
The Multi-Agent AI Finance Assistant is a modular, open-source platform built on FastAPI (backend) and Streamlit (frontend).
Specialized Agent Architecture:
API Agent: Fetches real-time market data for 460+ stocks, ETFs, and cryptocurrencies using yfinance.
Scraping Agent: Collects financial news and articles using BeautifulSoup and Newspaper3k.
Retriever Agent: Performs semantic document search using RAG, FAISS, and LangChain.
Analysis & Prediction Agent: Analyzes portfolio risk and predicts market trends using Scikit-Learn.
Language & Graphing Agent: Generates AI-powered market summaries and interactive charts.
Voice Agent: Supports voice input and output using SpeechRecognition and gTTS.
User Value: Compare assets, analyze trends, and generate financial insights instantly.
Challenge :
Developing and deploying a multi-source, multi-agent AI system presented several significant engineering hurdles:
API Rate Limits & Stability: Google Gemini's free tier imposes strict limits. To overcome this, we implemented an intelligent fallback and graceful degradation system that automatically shifts workloads to serves cached financial data if rate limits or news scraping fails.
Real-time Synchronization & Latency: Aggregating data across live scrapers, stock APIs, and LLM text generation can create high-latency requests.
Summary :
The Multi-Agent AI Finance Assistant is a state-of-the-art platform that democratizes equity research and portfolio analysis. By combining real-time market data APIs, machine learning forecasts, FAISS-based retrieval, and Google Gemini’s reasoning within a FastAPI/Streamlit microservices architecture, it orchestrates cooperative AI agents to deliver instant, actionable market insights. Supporting over 460+ assets, the project showcases the power of multi-agent LLM systems in solving real-world financial workflows.
More Projects
AI Tools
Multi Agent AI Finance Assistant
This application provides market insights and financial analysis on your portfolio or specific stocks. Enter a query about specific stocks or market conditions to get an AI-generated analysis
Year :
2025
Industry :
Tech
Client :
Personal Project
Project Duration :
8 weeks
Problem :
Modern financial analysis demands rapid, multi-dimensional, and data-driven decision-making. However, financial data is highly fragmented and fast-moving:
Heterogeneous Sources: Crucial information is scattered across real-time stock exchanges, financial news portals, regulatory filings (RAG databases), and historical balance sheets.
Access & Cost Barriers: High-quality analytics tools (like Bloomberg Terminal) are prohibitively expensive for individual researchers, developers, or retail investors, while free alternatives offer disjointed data with no synthesis.
Manual Analysis Fatigue: Performing portfolio risk calculations, running machine learning-based earnings forecasts, scraping web articles, and compiling structured investment briefs requires manual data wrangling across multiple Python libraries or spreadsheets.
There is a distinct gap for a unified, open-source platform that automates the end-to-end ingestion, computation, forecasting, and narrative synthesis of financial assets.
Solution :
The Multi-Agent AI Finance Assistant is a modular, open-source platform built on FastAPI (backend) and Streamlit (frontend).
Specialized Agent Architecture:
API Agent: Fetches real-time market data for 460+ stocks, ETFs, and cryptocurrencies using yfinance.
Scraping Agent: Collects financial news and articles using BeautifulSoup and Newspaper3k.
Retriever Agent: Performs semantic document search using RAG, FAISS, and LangChain.
Analysis & Prediction Agent: Analyzes portfolio risk and predicts market trends using Scikit-Learn.
Language & Graphing Agent: Generates AI-powered market summaries and interactive charts.
Voice Agent: Supports voice input and output using SpeechRecognition and gTTS.
User Value: Compare assets, analyze trends, and generate financial insights instantly.
Challenge :
Developing and deploying a multi-source, multi-agent AI system presented several significant engineering hurdles:
API Rate Limits & Stability: Google Gemini's free tier imposes strict limits. To overcome this, we implemented an intelligent fallback and graceful degradation system that automatically shifts workloads to serves cached financial data if rate limits or news scraping fails.
Real-time Synchronization & Latency: Aggregating data across live scrapers, stock APIs, and LLM text generation can create high-latency requests.
Summary :
The Multi-Agent AI Finance Assistant is a state-of-the-art platform that democratizes equity research and portfolio analysis. By combining real-time market data APIs, machine learning forecasts, FAISS-based retrieval, and Google Gemini’s reasoning within a FastAPI/Streamlit microservices architecture, it orchestrates cooperative AI agents to deliver instant, actionable market insights. Supporting over 460+ assets, the project showcases the power of multi-agent LLM systems in solving real-world financial workflows.
More Projects
AI Tools
Multi Agent AI Finance Assistant
This application provides market insights and financial analysis on your portfolio or specific stocks. Enter a query about specific stocks or market conditions to get an AI-generated analysis
Year :
2025
Industry :
Tech
Client :
Personal Project
Project Duration :
8 weeks
Problem :
Modern financial analysis demands rapid, multi-dimensional, and data-driven decision-making. However, financial data is highly fragmented and fast-moving:
Heterogeneous Sources: Crucial information is scattered across real-time stock exchanges, financial news portals, regulatory filings (RAG databases), and historical balance sheets.
Access & Cost Barriers: High-quality analytics tools (like Bloomberg Terminal) are prohibitively expensive for individual researchers, developers, or retail investors, while free alternatives offer disjointed data with no synthesis.
Manual Analysis Fatigue: Performing portfolio risk calculations, running machine learning-based earnings forecasts, scraping web articles, and compiling structured investment briefs requires manual data wrangling across multiple Python libraries or spreadsheets.
There is a distinct gap for a unified, open-source platform that automates the end-to-end ingestion, computation, forecasting, and narrative synthesis of financial assets.
Solution :
The Multi-Agent AI Finance Assistant is a modular, open-source platform built on FastAPI (backend) and Streamlit (frontend).
Specialized Agent Architecture:
API Agent: Fetches real-time market data for 460+ stocks, ETFs, and cryptocurrencies using yfinance.
Scraping Agent: Collects financial news and articles using BeautifulSoup and Newspaper3k.
Retriever Agent: Performs semantic document search using RAG, FAISS, and LangChain.
Analysis & Prediction Agent: Analyzes portfolio risk and predicts market trends using Scikit-Learn.
Language & Graphing Agent: Generates AI-powered market summaries and interactive charts.
Voice Agent: Supports voice input and output using SpeechRecognition and gTTS.
User Value: Compare assets, analyze trends, and generate financial insights instantly.
Challenge :
Developing and deploying a multi-source, multi-agent AI system presented several significant engineering hurdles:
API Rate Limits & Stability: Google Gemini's free tier imposes strict limits. To overcome this, we implemented an intelligent fallback and graceful degradation system that automatically shifts workloads to serves cached financial data if rate limits or news scraping fails.
Real-time Synchronization & Latency: Aggregating data across live scrapers, stock APIs, and LLM text generation can create high-latency requests.
Summary :
The Multi-Agent AI Finance Assistant is a state-of-the-art platform that democratizes equity research and portfolio analysis. By combining real-time market data APIs, machine learning forecasts, FAISS-based retrieval, and Google Gemini’s reasoning within a FastAPI/Streamlit microservices architecture, it orchestrates cooperative AI agents to deliver instant, actionable market insights. Supporting over 460+ assets, the project showcases the power of multi-agent LLM systems in solving real-world financial workflows.