A modular AI agent framework with secure CLI and toolkit architecture

Proto-Agent

An educational AI agent framework demonstrating capability-based security and modular toolkit architecture. Built for learning secure AI agent patterns with human oversight and permission controls.

Features

• Capability-based security with granular permission controls
• CLI tool with human-in-the-loop approval for dangerous operations
• Python framework for building custom agents with programmatic control
• Modular toolkits for file operations, system monitoring, and version control
• Educational focus - clear, readable code demonstrating AI agent security patterns

Quick Start

Installation

pip install proto-agent 
# or if you prefer the cli to be used from anywhere
uv tool install proto-agent  # Recommended
# or using pipx
pipx install proto-agent

Configuration

proto-agent --help # View CLI options, which include your config path for your OS 
# Example config path for Linux: ~/.config/proto-agent/ will have .env file and config.toml 

For Model configuration, please refer to the Litellm documentation for your exact name of the model you want to use.

CLI Usage

# Safe read-only analysis
proto-agent "Analyze this codebase structure" ./my_project --read-only

# Interactive execution with approval prompts
proto-agent "Run the test suite" ./my_project
# Prompts: "Allow execution of function 'run_python_file'? (y/N):"

Framework Usage

from proto_agent import Agent, AgentConfig
from proto_agent.tool_kits import FileOperationToolkit

# Autonomous mode - no human approval needed
agent = Agent(AgentConfig(
    api_key="your_api_key",
    working_directory="./my_project",
    tools=[FileOperationToolkit(
        enable_read=True,
        enable_write=False,    # Disable risky operations
        enable_execute=False
    ).tool]
))

response = agent.generate_content("Analyze this project's structure")
print(response.text)