# AirChief Project: 10,000-Foot View for Project Management

## What It Is

AirChief is an **AI-powered coding assistant CLI tool** that runs in the terminal, similar to Claude Code but 100% open source and provider-agnostic. It's a sophisticated terminal-based interface for AI-assisted software development.

## Architecture & Technical Stack

### Core Technology

- Runtime: Bun (JavaScript runtime) + TypeScript
- Frontend: Go-based TUI (Terminal User Interface)
- Backend: Node.js server with Hono web framework
- Infrastructure: SST (Serverless Stack) deployed on Cloudflare
- Package Management: Bun workspaces (monorepo structure)

### Key Components

1. CLI Interface (`packages/airchief/src/cli/`) - Command handling and user interaction
2. Session Management (`packages/airchief/src/session/`) - Conversation state and message handling
3. Tool System (`packages/airchief/src/tool/`) - Extensible tools for file operations, code analysis, etc.
4. Provider System (`packages/airchief/src/provider/`) - Multi-provider AI model support (Anthropic, OpenAI, Google, local models)
5. Server Component (`packages/airchief/src/server/`) - HTTP API for TUI communication
6. Authentication (`packages/airchief/src/auth/`) - Provider authentication management

## Business Model & Distribution

### Open Source Strategy

- MIT licensed, fully open source
- Provider-agnostic (not locked to Anthropic like Claude Code)
- Community-driven development via GitHub

### Distribution Channels

- npm/bun package managers
- Homebrew (macOS)
- Arch Linux (AUR)
- Direct install script (`curl | bash`)
- GitHub releases with automated publishing

## Development & Operations

### Development Workflow

- Monorepo: Single repository with multiple packages
- CI/CD: GitHub Actions for automated testing, building, and publishing
- Testing: Minimal test coverage (only basic tool tests found)
- Code Style: Strict guidelines in AGENTS.md (avoid try/catch, prefer single functions, etc.)

### Deployment Pipeline

- Development: `dev` branch auto-deploys to staging
- Production: Tagged releases trigger full deployment
- Infrastructure: Cloudflare Workers via SST
- Artifacts: Multi-platform binaries, npm packages, AUR packages

## Key Dependencies & Risk Assessment

### Critical Dependencies

- AI SDK: Vercel's AI SDK (patched version) for model interactions
- Bun: Core runtime dependency
- Go: Required for TUI component
- SST: Infrastructure deployment
- Hono: Web framework

### Technical Risks

1. Limited Testing: Very minimal test coverage could lead to stability issues
2. Complex Architecture: Client/server split with Go TUI adds complexity
3. Dependency on External Models: Relies on third-party AI providers
4. Bun Dependency: Relatively new runtime, potential stability concerns

## Feature Set & Capabilities

### Core Features

- Multi-provider AI model support
- File system operations (read, write, edit, glob, grep)
- Session management with conversation history
- Tool system for extensible functionality
- Sharing capabilities for collaboration
- Auto-update mechanism
- LSP (Language Server Protocol) integration

### Advanced Features

- Context-aware conversation summarization
- MCP (Model Context Protocol) support
- Cost tracking for AI usage
- Session sharing and collaboration
- Provider authentication management

## Project Maturity & Status

### Strengths

- Well-structured codebase with clear separation of concerns
- Comprehensive tool system architecture
- Multi-provider support provides flexibility
- Active development with automated deployment

### Areas of Concern

- Testing: Extremely limited test coverage
- Documentation: Basic README, could use more comprehensive docs
- Windows Support: Acknowledged issues, WSL required
- Error Handling: Complex error handling across multiple layers

## Competitive Position

- vs Claude Code: Open source, provider-agnostic, terminal-focused
- vs GitHub Copilot: More conversational, terminal-native
- vs Cursor: Terminal-based vs GUI, different target audience

## Management Recommendations

1. Immediate Priority: Expand test coverage significantly
2. Documentation: Invest in comprehensive user and developer documentation
3. Windows Support: Address Windows compatibility issues
4. Error Handling: Simplify and standardize error handling patterns
5. Performance: Monitor and optimize the client/server architecture
6. Community: Build contributor guidelines and community engagement

The project shows strong technical architecture and clear vision, but needs investment in testing, documentation, and platform compatibility to reach production readiness.
