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A multi-agent stock analysis tool using CrewAI, LLM/OpenAI and vector embeddings to combine technical indicators, news sentiment, and social media signals into orchestrated insights.

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πŸ€– Multi-Agent Stock Analyzer

πŸš€ A weekend experiment using CrewAI, Gemini LLM, and vector embeddings to orchestrate agent-based analysis of a stock ticker β€” combining technical indicators, news sentiment, and social media signals. The pipeline is modular and can be adapted to other research and monitoring use cases across industries.

What It Does

  • Retrieves and processes stock-related data
  • Analyzes technical indicators
  • Fetches news and social media content
  • Applies sentiment analysis using LLM
  • Summarizes insights via agent-based orchestration

Tools Used

  • CrewAI for multi-agent task orchestration
  • Gemini (LLM) for summarization and sentiment classification
  • Vector embeddings for semantic search and clustering

πŸ–₯️ Usage

GUI Mode (Recommended)

python main.py

The GUI will open in your browser at http://localhost:8501

CLI Mode

python main.py AAPL
python main.py TSLA
python main.py --help

πŸ“ Project Structure

stock-analyzer/
β”œβ”€β”€ main.py                      # Main entry point
β”œβ”€β”€ src/
β”‚   β”œβ”€β”€ gui.py                   # Streamlit GUI
β”‚   β”œβ”€β”€ agents/                  # Multi-agent system
β”‚   β”œβ”€β”€ data/                    # Data utilities
β”‚   β”œβ”€β”€ utils/                   # Shared utilities
β”‚   └── workflows/               # Workflow orchestration
β”œβ”€β”€ tests/                       # Test files
β”œβ”€β”€ chroma_db/                   # Vector database storage
β”œβ”€β”€ requirements.txt             # Python dependencies
└── README.md                   # This file

🀝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests if applicable
  5. Submit a pull request

Powered by Google Gemini AI, Vector Embeddings, and real-time market data πŸš€

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A multi-agent stock analysis tool using CrewAI, LLM/OpenAI and vector embeddings to combine technical indicators, news sentiment, and social media signals into orchestrated insights.

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