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RA.Aid

RA.Aid (ReAct Aid) is a powerful AI-driven command-line tool that integrates aider (https://aider.chat/) within a LangChain ReAct agent loop. This unique combination allows developers to leverage aider's code editing capabilities while benefiting from LangChain's agent-based task execution framework. The tool provides an intelligent assistant that can help with research, planning, and implementation of development tasks.

⚠️ IMPORTANT: USE AT YOUR OWN RISK ⚠️

• This tool can and will automatically execute shell commands on your system
• No warranty is provided, either express or implied
• Always review the actions the agent proposes before allowing them to proceed

Key Features

• Multi-Step Task Planning: The agent breaks down complex tasks into discrete, manageable steps and executes them sequentially. This systematic approach ensures thorough implementation and reduces errors.

• Automated Command Execution: The agent can run shell commands automatically to accomplish tasks. While this makes it powerful, it also means you should carefully review its actions.

• Three-Stage Architecture:

  1. Research: Analyzes codebases and gathers context
  2. Planning: Breaks down tasks into specific, actionable steps
  3. Implementation: Executes each planned step sequentially

What sets RA.Aid apart is its ability to handle complex programming tasks that extend beyond single-shot code edits. By combining research, strategic planning, and implementation into a cohesive workflow, RA.Aid can:

• Break down and execute multi-step programming tasks
• Research and analyze complex codebases to answer architectural questions
• Plan and implement significant code changes across multiple files
• Provide detailed explanations of existing code structure and functionality
• Execute sophisticated refactoring operations with proper planning

Table of Contents

• Features
• Installation
• Usage
• Architecture
• Dependencies
• Development Setup
• Contributing
• License
• Contact

Features

• Three-Stage Architecture: The workflow consists of three powerful stages:

  1. Research 🔍 - Gather and analyze information
  2. Planning 📋 - Develop execution strategy
  3. Implementation ⚡ - Execute the plan with AI assistance

  Each stage is powered by dedicated AI agents and specialized toolsets.

• Advanced AI Integration: Built on LangChain and leverages the latest LLMs for natural language understanding and generation.

• Comprehensive Toolset:

  • Shell command execution
  • Expert querying system
  • File operations and management
  • Memory management
  • Research and planning tools
  • Code analysis capabilities

• Interactive CLI Interface: Simple yet powerful command-line interface for seamless interaction

• Modular Design: Structured as a Python package with specialized modules for console output, processing, text utilities, and tools

• Git Integration: Built-in support for Git operations and repository management

Installation

RA.Aid can be installed directly using pip:

pip install ra-aid

Prerequisites

Before using RA.Aid, you'll need:

1. Python package aider installed and available in your PATH:

pip install aider-chat

2. API keys for the required AI services:

# Required: Set up your Anthropic API key
export ANTHROPIC_API_KEY=your_api_key_here

# Optional: Set up OpenAI API key if using OpenAI features
export OPENAI_API_KEY=your_api_key_here

You can get your API keys from:

• Anthropic API key: https://console.anthropic.com/
• OpenAI API key: https://platform.openai.com/api-keys

Usage

RA.Aid is designed to be simple yet powerful. Here's how to use it:

# Basic usage
ra-aid -m "Your task or query here"

# Research-only mode (no implementation)
ra-aid -m "Explain the authentication flow" --research-only

Command Line Options

• -m, --message: The task or query to be executed (required)
• --research-only: Only perform research without implementation

Example Tasks

1. Code Implementation:

   ra-aid -m "Add input validation to the user registration endpoint"
   

2. Code Research:

   ra-aid -m "Explain how the authentication middleware works" --research-only
   

3. Refactoring:

   ra-aid -m "Refactor the database connection code to use connection pooling"
   

Environment Variables

RA.Aid uses the following environment variables:

• ANTHROPIC_API_KEY (Required): Your Anthropic API key for accessing Claude
• OPENAI_API_KEY (Optional): Your OpenAI API key if using OpenAI features

You can set these permanently in your shell's configuration file (e.g., ~/.bashrc or ~/.zshrc):

export ANTHROPIC_API_KEY=your_api_key_here
export OPENAI_API_KEY=your_api_key_here

Architecture

RA.Aid implements a three-stage architecture for handling development and research tasks:

1. Research Stage:

   • Gathers information and context
   • Analyzes requirements
   • Identifies key components and dependencies

2. Planning Stage:

   • Develops detailed implementation plans
   • Breaks down tasks into manageable steps
   • Identifies potential challenges and solutions

3. Implementation Stage:

   • Executes planned tasks
   • Generates code or documentation
   • Performs necessary system operations

Core Components

• Console Module (console/): Handles console output formatting and user interaction
• Processing Module (proc/): Manages interactive processing and workflow control
• Text Module (text/): Provides text processing and manipulation utilities
• Tools Module (tools/): Contains various utility tools for file operations, search, and more

Dependencies

Core Dependencies

• langchain-anthropic: LangChain integration with Anthropic's Claude
• langgraph: Graph-based workflow management
• rich>=13.0.0: Terminal formatting and output
• GitPython==3.1.41: Git repository management
• fuzzywuzzy==0.18.0: Fuzzy string matching
• python-Levenshtein==0.23.0: Fast string matching
• pathspec>=0.11.0: Path specification utilities

Development Dependencies

• pytest>=7.0.0: Testing framework
• pytest-timeout>=2.2.0: Test timeout management

Development Setup

1. Clone the repository:

git clone https://github.com/ai-christianson/ra-aid.git
cd ra-aid

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install development dependencies:

pip install -r requirements-dev.txt

4. Run tests:

python -m pytest

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch:

git checkout -b feature/your-feature-name

3. Make your changes and commit:

git commit -m 'Add some feature'

4. Push to your fork:

git push origin feature/your-feature-name

5. Open a Pull Request

Guidelines

• Follow PEP 8 style guidelines
• Add tests for new features
• Update documentation as needed
• Keep commits focused and message clear
• Ensure all tests pass before submitting PR

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Copyright (c) 2024 AI Christianson

Contact

• Issues: Please report bugs and feature requests on our Issue Tracker
• Repository: https://github.com/ai-christianson/ra-aid
• Documentation: https://github.com/ai-christianson/ra-aid#readme