Gemini Terminal Agent

A powerful terminal-based agent using Google’s Gemini model with web search capabilities. This agent lets you interact with Gemini through your terminal while leveraging real-time web search for up-to-date information.

Features

• Conversational AI Interface - Talk with Google’s Gemini models directly from your terminal
• Web Search Integration - Get real-time information from the web
• Conversation History - Maintain context throughout your conversation
• Advanced Search Options - Filter by domains, exclude sites, and more
• Clean, Modular Architecture - Well-structured codebase that’s easy to extend

Installation

Prerequisites

• Python 3.9+
• Google API key for Gemini models
• Google Custom Search Engine (CSE) API key and ID

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/gemini-terminal-agent.git
   cd gemini-terminal-agent
   

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venvScriptsactivate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Create a .env file in the project root with your API keys:

   GOOGLE_GENAI_API_KEY=your_gemini_api_key_here
   SEARCH_ENGINE_API_KEY=your_google_api_key_here
   SEARCH_ENGINE_CSE_ID=your_cse_id_here
   DEFAULT_MODEL=gemini-2.5-flash-preview-04-17
   

Setting Up Google Search Engine

To use the web search functionality, you need to set up a Google Custom Search Engine:

1. Get a Google API Key:

   • Go to Google Cloud Console
   • Create a new project or select an existing one
   • Navigate to “APIs & Services” > “Library”
   • Search for “Custom Search API” and enable it
   • Go to “APIs & Services” > “Credentials”
   • Create an API key and copy it (this will be your SEARCH_ENGINE_API_KEY)

2. Create a Custom Search Engine:

   • Go to Programmable Search Engine
   • Click “Create a Programmable Search Engine”
   • Add sites to search (use *.com to search the entire web)
   • Give your search engine a name
   • In “Customize” > “Basics”, enable “Search the entire web”
   • Get your Search Engine ID from the “Setup” > “Basics” page (this will be your SEARCH_ENGINE_CSE_ID)

3. Get a Gemini API Key:

   • Go to Google AI Studio
   • Sign in with your Google account
   • Go to “API Keys” and create a new API key
   • Copy the API key (this will be your GOOGLE_GENAI_API_KEY)

Usage

Run the agent from the terminal:

python main.py

Commands

• Type your question or prompt to interact with the agent
• Type help to see available tools and commands
• Type clear to clear the conversation history
• Type exit, quit, or q to exit the program

Example Queries

>>> What is the capital of France?
Paris is the capital of France. It is located in the north-central part of the country on the Seine River.

>>> search for recent developments in quantum computing
Searching the web for recent developments in quantum computing...
[Agent response with up-to-date information]

>>> help
 Available Tools:
  - search: Search for information online based on a query
  - advanced_search: Perform an advanced search with domain filtering and time range options

 Terminal Commands:
  - help: Show this help message
  - clear: Clear conversation history
  - exit/quit/q: Exit the program

Project Structure

gemini-terminal-agent/
│
├── main.py               # Main entry point
├── search_server.py      # Search server entry point
├── .env                  # Environment variables (not versioned)
│
├── agent/                # Agent implementation
│   ├── __init__.py
│   ├── terminal_agent.py # Core agent implementation
│   └── config.py         # Agent configuration
│
├── search/               # Search functionality
│   ├── __init__.py
│   ├── server.py         # MCP search server
│   ├── engine.py         # Search engine implementation
│   └── content.py        # Web content extraction 
│
└── utils/                # Shared utilities
    ├── __init__.py
    ├── config.py         # Global configuration
    └── logging.py        # Logging setup

Advanced Configuration

You can customize the agent’s behavior by modifying settings in your .env file:

# Model settings
DEFAULT_MODEL=gemini-2.5-flash-preview-04-17
# Other models: gemini-1.5-pro, gemini-1.5-flash

# Search settings
MAX_CONCURRENT_REQUESTS=5
CONNECTION_TIMEOUT=10
CONTENT_TIMEOUT=15
MAX_CONTENT_LENGTH=5000
CACHE_TTL=3600

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• This project uses LangChain for the agent framework
• Web search functionality powered by Google Custom Search Engine
• Built with Google’s Gemini models