blitzcoder 1.0.15

AI-powered development assistant for code generation, refactoring, and project management

BlitzCoder

⚡ AI-Powered Development Assistant - A comprehensive CLI tool for code generation, refactoring, and project management.

Features

• 🤖 AI-Powered Code Generation - Generate code using Google's Gemini model
• 🔧 Code Refactoring - Automatically refactor and improve existing code
• 📁 Project Scaffolding - Create complete project structures with architecture plans
• 🧠 Memory System - Remember previous conversations and context
• 🛠️ Development Tools - File inspection, execution, and management tools
• 🔍 Code Analysis - Explain and analyze code functionality

Installation

Option 1: Install from Source (Recommended)

# Clone the repository
git clone https://github.com/Raghu6798/Blitz_Coder.git
cd BlitzCoder/blitz_cli

# Install in development mode
python install.py

Option 2: Manual Installation

cd blitz_cli
pip install -e .

Option 3: Direct Script Execution

# Windows
python scripts/blitzcoder.bat

# Linux/Mac
python scripts/blitzcoder

Quick Start

1. Set up your API Keys

You'll need a Google API key for the Gemini model:

# Set environment variable
export GOOGLE_API_KEY="your-api-key-here"

# Or on Windows
set GOOGLE_API_KEY=your-api-key-here

2. Start Interactive Chat

blitzcoder chat

3. Search Memories

blitzcoder search-memories --query "your search term"

Usage Examples

Interactive Chat Mode

blitzcoder chat

This starts an interactive session where you can:

• Ask questions about code
• Request code generation
• Get help with refactoring
• Search through previous conversations

Search Previous Conversations

blitzcoder search-memories --query "React component"

Use with API Key Parameter

blitzcoder chat --google-api-key "your-api-key"

Available Commands

Command	Description
chat	Start interactive AI chat session
search-memories	Search through conversation history

Development

Project Structure

.
├── src/
│   ├── blitzcoder/
│   │   └── cli/
│   │       └── cli_coder.py      # Click-based CLI entrypoint
│   └── main/
│       └── graphapi.py           # Core LangGraph agent logic and state graph
├── tools/
│   ├── scaffold_and_generate_files.py # High-level tool for project creation
│   ├── run_shell_command_in_sandbox.py # Secure command execution tool
│   └── ... (many other modular tools)
├── config/
│   ├── settings.py
│   └── templates/                # Contains templates for dozens of frameworks
│       ├── Backend/
│       ├── Frontend/
│       └── ...
├── models/
│   ├── llm_model.py              # LLM (Gemini) configuration
│   └── embedding_model.py        # Embedding model configuration
├── observability/
│   └── tracing.py                # Tracing and observability setup
├── tests/
│   ├── unit/
│   └── integration/
├── pyproject.toml                # Project metadata and dependencies (for PyPI)
├── install.py                    # Main installation script
└── Dockerfile         

Running Tests

# Install development dependencies
pip install -e ".[dev]"

### Quick Start
1. Set up your API Keys
BlitzCoder requires two API keys to function. It will prompt you for them on the first run, or you can set them as environment variables.
Google Gemini API Key: For the core AI reasoning.

export GOOGLE_API_KEY=your-gemini-api-key

E2B Sandbox API Key: For secure command execution. Get a free key at e2b.dev.

export E2B_API_KEY=your-e2b-api-key

# Run tests
pytest

Code Formatting

# Format code
black src/
isort src/

# Type checking
mypy src/

Configuration

The package uses environment variables for configuration:

• GOOGLE_API_KEY - Required for Gemini model access
• GROQ_API_KEY - Optional for additional models
• NOVITA_API_KEY - Optional for embeddings

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

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

Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitHub Issues
• 📖 Documentation: GitHub README

Acknowledgments

• Built with LangGraph
• Powered by Google Gemini
• Enhanced with Rich for beautiful CLI output