🤖 Gemini Coding Agent

This project implements a Python-based interactive coding agent powered by Google's Gemini models (using the newer google-genai SDK).

Adapted from Thorsten Ball's "How to Build an Agent" https://ampcode.com/how-to-build-an-agent

The agent can:

• Understand and respond to natural language prompts.
• Interact with your local file system (read, list, edit files).
• Execute shell commands.
• Run commands within a secure Docker sandbox (no network access, resource limits).
• Maintain conversation history.
• Display the token count of the current conversation context in the input prompt.

✨ Features

• Interactive Chat: Engage in a conversational manner with the AI.
• File Operations: Ask the agent to read, list, or modify files within the project directory.
• Command Execution: Request the agent to run shell commands in the project's context.
• Sandboxed Execution: Safely run potentially risky commands in an isolated Docker container using the run_in_sandbox tool (requires Docker).
• Tool Integration: Leverages Gemini's function calling capabilities to use defined Python tools.
• Context Token Count: Displays the approximate token count for the next API call right in the prompt (e.g., You (123):).

🛠️ Setup

1. Clone the repository:

   git clone https://github.com/voxmenthe/coding-agent.git
   cd coding-agent

2. Set up a virtual environment and install dependencies:

   python -m venv <your-env-name>
   source <your-env-name>/bin/activate
   sh project_setup.sh

3. Set up API Key:

   • The agent expects your Google Gemini API key to be available.
   • Currently, it's hardcoded in src/main.py to read from all_creds['GEMINI_API_KEY']. You'll need to adapt this to your own credential management (e.g., environment variables, a .env file).
   • Example using environment variable: Modify src/main.py to use os.environ.get("GEMINI_API_KEY") and set the variable in main.py:

     api_key = os.environ.get("GEMINI_API_KEY")

4. (Optional) Docker for Sandbox:

   • If you want to use the run_in_sandbox tool, ensure you have Docker installed and running on your system.
   • You might need to pull the base image specified in src/tools.py (e.g., python:3.12-slim) if it's not already available locally:

     docker pull python:3.12-slim

▶️ Running the Agent

Navigate to the src directory and run the main script:

The agent will initialize and present you with a prompt like:

⚒️ Agent ready. Ask me anything. Type 'exit' to quit.

🔵 You (0): 

💬 Usage

• Simply type your requests or questions at the You (<token_count>): prompt.
• To exit the agent, type exit or quit.
• Ask it to perform tasks like:
  • "Read the file src/tools.py"
  • "List files in the root directory"
  • "Edit README.md and add a section about future plans"
  • "run the command 'ls -l'"
  • "run 'pip list' in the sandbox"
• The number in parentheses indicates the approximate token count of the conversation history that will be sent with your next message.

📝 Notes

• The agent operates relative to the project root directory defined in src/main.py.
• Ensure the API key handling is secure and not committed to version control.