voria Documentation

Welcome to voria's comprehensive documentation. This folder contains everything you need to understand, use, and contribute to voria.

Documentation Map

Getting Started (Start Here!)

• ▶INSTALL.md - Installation and setup instructions
• ▶QUICKSTART.md - Get voria running in 5 minutes
• ▶USER_GUIDE.md - Complete usage guide and commands
• ▶EXAMPLES.md - Real-world usage examples

Understanding voria

• ▶ARCHITECTURE.md - System design and component overview
• ▶IPC_PROTOCOL.md - NDJSON protocol specification
• ▶DESIGN_DECISIONS.md - Why we made certain choices
• ▶MODULES.md - Detailed module documentation

Development & Extension

• ▶DEVELOPMENT.md - Development environment setup
• ▶CONTRIBUTING.md - Contributor guidelines
• ▶PLUGINS.md - Writing custom plugins
• ▶LLM_INTEGRATION.md - Adding new LLM providers

Operations & Reference

• ▶PERFORMANCE.md - Performance tuning and optimization
• ▶SECURITY.md - Security best practices
• ▶TROUBLESHOOTING.md - Common issues and solutions
• ▶ROADMAP.md - Feature roadmap and future plans

Quick Navigation

I want to...

• ▶Get started → QUICKSTART.md
• ▶Learn to use voria → USER_GUIDE.md
• ▶See examples → EXAMPLES.md
• ▶Understand how it works → ARCHITECTURE.md
• ▶Contribute code → CONTRIBUTING.md
• ▶Set up development → DEVELOPMENT.md
• ▶Write a plugin → PLUGINS.md
• ▶Add an LLM provider → LLM_INTEGRATION.md
• ▶Fix a problem → TROUBLESHOOTING.md
• ▶Optimize performance → PERFORMANCE.md
• ▶Check security → SECURITY.md

Reading Order

For Users:

1. ▶QUICKSTART.md
2. ▶USER_GUIDE.md
3. ▶EXAMPLES.md
4. ▶ARCHITECTURE.md (optional, for understanding)

For Developers:

1. ▶DEVELOPMENT.md
2. ▶ARCHITECTURE.md
3. ▶IPC_PROTOCOL.md
4. ▶MODULES.md
5. ▶CONTRIBUTING.md

For Contributors:

1. ▶CONTRIBUTING.md
2. ▶DEVELOPMENT.md
3. ▶MODULES.md
4. ▶Specific module docs (e.g., PLUGINS.md, LLM_INTEGRATION.md)

voria Overview

voria is an AI-powered CLI tool that helps open source contributors fix issues automatically:

bash
# Run one command
voria issue 42

# voria will:
# 1. Fetch issue from GitHub
# 2. Analyze the codebase
# 3. Plan a fix (using AI)
# 4. Generate code patches
# 5. Apply and test changes
# 6. Create a pull request

Architecture at a Glance

terminal
User Interface (Rust CLI)
    ↓
NDJSON Protocol (stdin/stdout)
    ↓
Python Engine (LLM + GitHub + Testing)
    ↓
LLM Providers (OpenAI, Gemini, Claude, Modal)

What's Included

• ▶Rust CLI - High-performance command-line interface
• ▶Python Engine - AI intelligence and orchestration
• ▶NDJSON Protocol - Cross-platform IPC
• ▶LLM Support - 4 AI providers (OpenAI, Gemini, Claude, Modal)
• ▶GitHub Integration - Issue and PR automation
• ▶Test Orchestration - Automatic testing and validation

Key Features

• ▶AI-powered issue analysis
• ▶Automatic code patch generation
• ▶Integrated test execution
• ▶Iterative refinement (up to 5 itrations)
• ▶Multi-provider LLM support
• ▶Automatic backup and rollback
• ▶Token usage tracking
• ▶Production-ready error handling

External Resources

• ▶GitHub Repository
• ▶Issue Tracker
• ▶Email Contact

Questions?

• ▶Check TROUBLESHOOTING.md for common issues
• ▶Read EXAMPLES.md for usage patterns
• ▶Contact: srizd449@gmail.com
• ▶Open an issue on GitHub

Last Updated: April 12, 2026 Documentation Version: 0.0.3

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