Develop software autonomously.

RA.Aid (pronounced "raid") helps you develop software autonomously. It was made by putting aider (https://aider.chat/) in 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 multi-step development tasks.

The result is near-fully-autonomous software development.

Enjoying RA.Aid? Show your support by giving us a star ⭐ on GitHub!

Here's a demo of RA.Aid adding a feature to itself:

Features
Installation
Usage
Architecture
Dependencies
Development Setup
Contributing
License
Contact

👋 Pull requests are very welcome! Have ideas for how to impove RA.Aid? Don't be shy - your help makes a real difference!

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⚠️ IMPORTANT: USE AT YOUR OWN RISK ⚠️

This tool can and will automatically execute shell commands and make code changes
The --cowboy-mode flag can be enabled to skip shell command approval prompts
No warranty is provided, either express or implied
Always use in version-controlled repositories
Review proposed changes in your git diff before committing

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.
Ability to Leverage Expert Reasoning Models: The agent can use advanced reasoning models such as OpenAI's o1 just when needed, e.g. to solve complex debugging problems or in planning for complex feature implementation.
Web Research Capabilities: Leverages Tavily API for intelligent web searches to enhance research and gather real-world context for development tasks
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

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.
Human-in-the-Loop Interaction: Optional mode that enables the agent to ask you questions during task execution, ensuring higher accuracy and better handling of complex tasks that may require your input or clarification
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:

Python package aider installed and available in your PATH:

pip install aider-chat

API keys for the required AI services:

# Set up API keys based on your preferred provider:

# For Anthropic Claude models (recommended)
export ANTHROPIC_API_KEY=your_api_key_here

# For OpenAI models
export OPENAI_API_KEY=your_api_key_here

# For OpenRouter provider (optional)
export OPENROUTER_API_KEY=your_api_key_here

# For OpenAI-compatible providers (optional)
export OPENAI_API_BASE=your_api_base_url

# For Gemini provider (optional)
export GEMINI_API_KEY=your_api_key_here

# For web research capabilities
export TAVILY_API_KEY=your_api_key_here

Note: The programmer tool (aider) will automatically select its model based on your available API keys:

If ANTHROPIC_API_KEY is set, it will use Claude models
If only OPENAI_API_KEY is set, it will use OpenAI models
You can set multiple API keys to enable different features

You can get your API keys from:

Anthropic API key: https://console.anthropic.com/
OpenAI API key: https://platform.openai.com/api-keys
OpenRouter API key: https://openrouter.ai/keys
Gemini API key: https://aistudio.google.com/app/apikey

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

# Enable verbose logging for detailed execution information
ra-aid -m "Add new feature" --verbose

Command Line Options

-m, --message: The task or query to be executed (required except in chat mode)
--research-only: Only perform research without implementation
--provider: The LLM provider to use (choices: anthropic, openai, openrouter, openai-compatible, gemini)
--model: The model name to use (required for non-Anthropic providers)
--research-provider: Provider to use specifically for research tasks (falls back to --provider if not specified)
--research-model: Model to use specifically for research tasks (falls back to --model if not specified)
--planner-provider: Provider to use specifically for planning tasks (falls back to --provider if not specified)
--planner-model: Model to use specifically for planning tasks (falls back to --model if not specified)
--cowboy-mode: Skip interactive approval for shell commands
--expert-provider: The LLM provider to use for expert knowledge queries (choices: anthropic, openai, openrouter, openai-compatible, gemini)
--expert-model: The model name to use for expert knowledge queries (required for non-OpenAI providers)
--hil, -H: Enable human-in-the-loop mode for interactive assistance during task execution
--chat: Enable chat mode with direct human interaction (implies --hil)
--verbose: Enable verbose logging output
--temperature: LLM temperature (0.0-2.0) to control randomness in responses
--disable-limit-tokens: Disable token limiting for Anthropic Claude react agents
--recursion-limit: Maximum recursion depth for agent operations (default: 100)
--test-cmd: Custom command to run tests. If set user will be asked if they want to run the test command
--auto-test: Automatically run tests after each code change
--max-test-cmd-retries: Maximum number of test command retry attempts (default: 3)
--version: Show program version number and exit
--webui: Launch the web interface (alpha feature)
--webui-host: Host to listen on for web interface (default: 0.0.0.0) (alpha feature)
--webui-port: Port to listen on for web interface (default: 8080) (alpha feature)

Example Tasks

Code Analysis:

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

Complex Changes:

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

Automated Updates:

ra-aid -m "Update deprecated API calls across the entire codebase" --cowboy-mode

Code Research:

ra-aid -m "Analyze the current error handling patterns" --research-only

Code Research:

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

Refactoring:

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

Human-in-the-Loop Mode

Enable interactive mode to allow the agent to ask you questions during task execution:

ra-aid -m "Implement a new feature" --hil
# or
ra-aid -m "Implement a new feature" -H

This mode is particularly useful for:

Complex tasks requiring human judgment
Clarifying ambiguous requirements
Making architectural decisions
Validating critical changes
Providing domain-specific knowledge

Web Research

The agent features autonomous web research capabilities powered by the Tavily API, seamlessly integrating real-world information into its problem-solving workflow. Web research is conducted automatically when the agent determines additional context would be valuable - no explicit configuration required.

For example, when researching modern authentication practices or investigating new API requirements, the agent will autonomously:

Search for current best practices and security recommendations
Find relevant documentation and technical specifications
Gather real-world implementation examples
Stay updated on latest industry standards

While web research happens automatically as needed, you can also explicitly request research-focused tasks:

# Focused research task with web search capabilities
ra-aid -m "Research current best practices for API rate limiting" --research-only

Make sure to set your TAVILY_API_KEY environment variable to enable this feature.

Chat Mode

Enable with --chat to transform ra-aid into an interactive assistant that guides you through research and implementation tasks. Have a natural conversation about what you want to build, explore options together, and dispatch work - all while maintaining context of your discussion. Perfect for when you want to think through problems collaboratively rather than just executing commands.

Web Interface

RA.Aid includes a modern web interface that provides:

Beautiful dark-themed chat interface
Real-time streaming of command output
Request history with quick resubmission
Responsive design that works on all devices

To launch the web interface:

# Start with default settings (0.0.0.0:8080)
ra-aid --webui

# Specify custom host and port
ra-aid --webui --webui-host 127.0.0.1 --webui-port 3000

Command line options for web interface:

--webui: Launch the web interface
--webui-host: Host to listen on (default: 0.0.0.0)
--webui-port: Port to listen on (default: 8080)

After starting the server, open your web browser to the displayed URL (e.g., http://localhost:8080). The interface provides:

Left sidebar showing request history
Main chat area with real-time output
Input box for typing requests
Automatic reconnection handling
Error reporting and status messages

All ra-aid commands sent through the web interface automatically use cowboy mode for seamless execution.

Command Interruption and Feedback

You can interrupt the agent at any time by pressing Ctrl-C. This pauses the agent, allowing you to provide feedback, adjust your instructions, or steer the execution in a new direction. Press Ctrl-C again if you want to completely exit the program.

Shell Command Automation with Cowboy Mode 🏇

The --cowboy-mode flag enables automated shell command execution without confirmation prompts. This is useful for:

CI/CD pipelines
Automated testing environments
Batch processing operations
Scripted workflows

ra-aid -m "Update all deprecated API calls" --cowboy-mode

⚠️ Important Safety Notes:

Cowboy mode skips confirmation prompts for shell commands
Always use in version-controlled repositories
Ensure you have a clean working tree before running
Review changes in git diff before committing

Model Configuration

RA.Aid supports multiple AI providers and models. The default model is Anthropic's Claude 3 Sonnet (claude-3-5-sonnet-20241022).

The programmer tool (aider) automatically selects its model based on your available API keys. It will use Claude models if ANTHROPIC_API_KEY is set, or fall back to OpenAI models if only OPENAI_API_KEY is available.

Note: The expert tool can be configured to use different providers (OpenAI, Anthropic, OpenRouter, Gemini) using the --expert-provider flag along with the corresponding EXPERT_*API_KEY environment variables. Each provider requires its own API key set through the appropriate environment variable.

Environment Variables

RA.Aid supports multiple providers through environment variables:

ANTHROPIC_API_KEY: Required for the default Anthropic provider
OPENAI_API_KEY: Required for OpenAI provider
OPENROUTER_API_KEY: Required for OpenRouter provider
DEEPSEEK_API_KEY: Required for DeepSeek provider
OPENAI_API_BASE: Required for OpenAI-compatible providers along with OPENAI_API_KEY
GEMINI_API_KEY: Required for Gemini provider

Expert Tool Environment Variables:

EXPERT_OPENAI_API_KEY: API key for expert tool using OpenAI provider
EXPERT_ANTHROPIC_API_KEY: API key for expert tool using Anthropic provider
EXPERT_OPENROUTER_API_KEY: API key for expert tool using OpenRouter provider
EXPERT_OPENAI_API_BASE: Base URL for expert tool using OpenAI-compatible provider
EXPERT_GEMINI_API_KEY: API key for expert tool using Gemini provider
EXPERT_DEEPSEEK_API_KEY: API key for expert tool using DeepSeek provider

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

# Default provider (Anthropic)
export ANTHROPIC_API_KEY=your_api_key_here

# For OpenAI features and expert tool
export OPENAI_API_KEY=your_api_key_here

# For OpenRouter provider
export OPENROUTER_API_KEY=your_api_key_here

# For OpenAI-compatible providers
export OPENAI_API_BASE=your_api_base_url

# For Gemini provider
export GEMINI_API_KEY=your_api_key_here

Custom Model Examples

Using Anthropic (Default)

# Uses default model (claude-3-5-sonnet-20241022)
ra-aid -m "Your task"

# Or explicitly specify:
ra-aid -m "Your task" --provider anthropic --model claude-3-5-sonnet-20241022

Using OpenAI

ra-aid -m "Your task" --provider openai --model gpt-4o

Using OpenRouter

ra-aid -m "Your task" --provider openrouter --model mistralai/mistral-large-2411

Using DeepSeek

# Direct DeepSeek provider (requires DEEPSEEK_API_KEY)
ra-aid -m "Your task" --provider deepseek --model deepseek-reasoner

# DeepSeek via OpenRouter
ra-aid -m "Your task" --provider openrouter --model deepseek/deepseek-r1

Configuring Expert Provider

The expert tool is used by the agent for complex logic and debugging tasks. It can be configured to use different providers (OpenAI, Anthropic, OpenRouter, Gemini, openai-compatible) using the --expert-provider flag along with the corresponding EXPERT_*API_KEY environment variables.

# Use Anthropic for expert tool
export EXPERT_ANTHROPIC_API_KEY=your_anthropic_api_key
ra-aid -m "Your task" --expert-provider anthropic --expert-model claude-3-5-sonnet-20241022

# Use OpenRouter for expert tool
export OPENROUTER_API_KEY=your_openrouter_api_key
ra-aid -m "Your task" --expert-provider openrouter --expert-model mistralai/mistral-large-2411

# Use DeepSeek for expert tool
export DEEPSEEK_API_KEY=your_deepseek_api_key
ra-aid -m "Your task" --expert-provider deepseek --expert-model deepseek-reasoner

# Use default OpenAI for expert tool
export EXPERT_OPENAI_API_KEY=your_openai_api_key
ra-aid -m "Your task" --expert-provider openai --expert-model o1

# Use Gemini for expert tool
export EXPERT_GEMINI_API_KEY=your_gemini_api_key
ra-aid -m "Your task" --expert-provider gemini --expert-model gemini-2.0-flash-thinking-exp-1219

Aider specific Environment Variables you can add:

AIDER_FLAGS: Optional comma-separated list of flags to pass to the underlying aider tool (e.g., "yes-always,dark-mode")

# Optional: Configure aider behavior
export AIDER_FLAGS="yes-always,dark-mode,no-auto-commits"

Note: For AIDER_FLAGS, you can specify flags with or without the leading --. Multiple flags should be comma-separated, and spaces around flags are automatically handled. For example, both "yes-always,dark-mode" and "--yes-always, --dark-mode" are valid.

Important Notes:

Performance varies between models. The default Claude 3 Sonnet model currently provides the best and most reliable results.
Model configuration is done via command line arguments: --provider and --model
The --model argument is required for all providers except Anthropic (which defaults to claude-3-5-sonnet-20241022)

Architecture

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

Research Stage:
- Gathers information and context
- Analyzes requirements
- Identifies key components and dependencies
Planning Stage:
- Develops detailed implementation plans
- Breaks down tasks into manageable steps
- Identifies potential challenges and solutions
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
tavily-python: Tavily API client for web research
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

Clone the repository:

git clone https://github.com/ai-christianson/RA.Aid.git
cd RA.Aid

Create and activate a virtual environment:

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

Install development dependencies:

pip install -r requirements-dev.txt

Run tests:

python -m pytest

Contributing

Contributions are welcome! Please follow these steps:

Fork the repository
Create a feature branch:

git checkout -b feature/your-feature-name

Make your changes and commit:

git commit -m 'Add some feature'

Push to your fork:

git push origin feature/your-feature-name

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

License

AxDSan/RA.Aid

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