Agentic AI is a cutting-edge framework for developing and deploying multiple specialized AI agents. These agents are designed to perform tasks such as web search, financial analysis, and more. The project utilizes advanced AI models and tools to provide robust, efficient, and insightful solutions.
-
Web Search Agent
- Uses DuckDuckGo for retrieving web information.
- Always includes sources in the response.
- Leverages the Groq model for enhanced tool usage.
-
Finance AI Agent
- Retrieves financial data such as stock prices, analyst recommendations, and company news using YFinance tools.
- Presents data in tabular format for better readability.
- Powered by the Groq model for precise financial insights.
-
Multi-AI Agent
- Combines the capabilities of Web Search Agent and Finance AI Agent.
- Unified response format adhering to instructions like including sources and using tables.
-
Playground App
- Interactive UI for testing and deploying agents.
- Built using the
phi
library's Playground module.
Agentic AI/
├── agents/ # AI agents implementation
│ ├── web_search_agent.py # Web Search Agent code
│ ├── finance_agent.py # Finance AI Agent code
│
├── app.py # Playground app for interacting with agents
├── requirements.txt # Python dependencies
├── README.md # Project documentation
├── .env # Environment variables
└── .gitignore # Files to ignore in Git
-
Clone the Repository
git clone https://github.com/yourusername/AgenticAI.git cd AgenticAI
-
Install Dependencies Ensure Python 3.8+ is installed.
pip install -r requirements.txt
-
Environment Variables Create a
.env
file in the root directory with the following keys:OPENAI_API_KEY=your_openai_api_key PHI_API_KEY=your_phi_api_key
-
Run the Playground App
python app.py
The app will be available at
http://localhost:8000
.
response = web_search_agent.print_response("Find the latest news about AI technologies.", stream=True)
print(response)
response = finance_agent.print_response("Provide analyst recommendations and company news for Tesla.")
print(response)
response = multi_ai_agent.print_response("Summarize analyst recommendation and share the latest news for NVDA.", stream=True)
print(response)
- Fork the repository.
- Create a new branch:
git checkout -b feature-name
- Commit your changes:
git commit -m 'Add some feature'
- Push to the branch:
git push origin feature-name
- Open a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.
Special thanks to the developers of the phi
library, OpenAI, and YFinance for their incredible tools and support.