https://github.com/llamacto/llama-gin-kit
llama-gin-kit is a modern Go scaffold for Vibe Coding, built with Cursor and Windsurf conventions. It supports automated testing, LLM APIs, and agent-based architecture out of the box.base go gin
https://github.com/llamacto/llama-gin-kit
aicoding cursor gin golang windsurf
Last synced: 4 months ago
JSON representation
llama-gin-kit is a modern Go scaffold for Vibe Coding, built with Cursor and Windsurf conventions. It supports automated testing, LLM APIs, and agent-based architecture out of the box.base go gin
- Host: GitHub
- URL: https://github.com/llamacto/llama-gin-kit
- Owner: llamacto
- Created: 2025-05-23T20:21:21.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-06-01T16:53:55.000Z (4 months ago)
- Last Synced: 2025-06-02T02:13:56.266Z (4 months ago)
- Topics: aicoding, cursor, gin, golang, windsurf
- Language: Go
- Homepage: https://www.llamacto.com/llama-gin-kit
- Size: 19.4 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Llama Gin Kit
Llama Gin Kit is a modern Go scaffold for AI-powered development, built with Cursor and Windsurf conventions. It supports automated testing, LLM APIs, and agent-based architecture out of the box.
## Features
- 📦 Modular architecture designed for AI coding
- 🤖 Built-in LLM API integrations (OpenAI, Claude, etc.)
- 🔐 JWT authentication with AI-enhanced security
- 📝 Auto-generated Swagger API documentation
- 🚦 Intelligent rate limiting
- 📨 Asynchronous task queue for AI workflows
- 🔄 WebSocket support for real-time AI interactions
- 📊 GORM database operations with AI query optimization
- 💾 Redis cache optimized for LLM responses
- 📧 Smart email service with AI templates
- 🔍 Unified error handling with AI diagnostics
- 📝 Structured logging (Zap) for AI debugging
- ⚙️ Configuration management optimized for AI services
- 🎯 Agent-based architecture support
- 🧠 Ready-to-use AI prompt templates
- 🔄 Streaming response support for LLM interactions
## Quick Start
### Requirements
- Go 1.21+
- PostgreSQL 12+
- Redis 6.0+
- OpenAI API Key (or other LLM provider)
### Installation
```bash
git clone https://github.com/llamacto/llama-gin-kit.git
cd llama-gin-kit
go mod download
```
### Configuration
1. Copy the environment variable template:
```bash
cp .env.example .env
```
2. Configure your LLM API keys and other services in `.env`:
```bash
# LLM Configuration
OPENAI_API_KEY=your_openai_api_key
# Add other LLM providers as needed
# Database
DB_USERNAME=your_db_username
DB_PASSWORD=your_db_password
DB_NAME=llama_gin_kit
# JWT for AI-enhanced auth
JWT_SECRET=your_jwt_secret
# Other services...
```
3. Copy the config file template (if available):
```bash
cp config/config.example.yaml config/config.yaml
```
### Run
```bash
# Run database migration
make migrate
# Start the AI-powered service
make run
```
## Project Structure
```
llama-gin-kit/
├── cmd/ # Entry files (server, migrate, tools, etc.)
├── app/ # Business modules (user, ai-agents, etc.)
│ ├── user/ # User management
│ └── agents/ # AI agent implementations
├── config/ # Configuration management
├── middleware/ # Gin middleware (including AI middleware)
├── pkg/ # Utility packages
│ ├── ai/ # AI service integrations
│ ├── llm/ # LLM client implementations
│ └── agents/ # Agent framework
├── routes/ # Route management
├── storage/ # Static/persistent resources
├── docs/ # API documentation
└── templates/ # AI prompt templates
```
## AI Features
### LLM Integration
The kit comes with built-in support for multiple LLM providers:
- **OpenAI GPT models** (GPT-4, GPT-3.5-turbo)
- **Streaming responses** for real-time AI interactions
- **Prompt template management** for consistent AI outputs
- **Token usage tracking** and cost optimization
### Agent-Based Architecture
Build sophisticated AI agents with:
- **Multi-step reasoning** workflows
- **Tool integration** for external API calls
- **Memory management** for context retention
- **Parallel processing** for complex tasks
### AI-Enhanced APIs
- **Intelligent text processing** endpoints
- **Automated content generation** services
- **Real-time language translation** with context awareness
- **Smart data analysis** and insights generation
## Development Guide
### Add a New AI Module
1. Create a new module directory under `app/`
2. Implement model, repository, service, and handler with AI integration
3. Add LLM-specific functionality in `pkg/ai/`
4. Register routes in `routes/` with appropriate middleware
### Add Custom LLM Provider
1. Implement the LLM interface in `pkg/llm/`
2. Add configuration in `config/`
3. Register the provider in your service initialization
### Run Tests
```bash
make test
```
### Generate API Documentation
```bash
make swagger
```
## Environment Variables
All sensitive information, secrets, and API keys are configured via the `.env` file. Do not commit real secrets to the repository; only commit `.env.example`.
Critical environment variables for AI features:
```bash
# LLM APIs
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
# Database
DB_USERNAME=
DB_PASSWORD=
DB_NAME=llama_gin_kit
# Security
JWT_SECRET=
# Redis for caching LLM responses
REDIS_HOST=localhost
REDIS_PASSWORD=
```
## Deployment
### Docker
```bash
# Build the AI-powered image
docker build -t llama-gin-kit .
# Run the container with AI services
docker run -p 8080:8080 -e OPENAI_API_KEY=your_key llama-gin-kit
```
### Production Considerations
- Use environment variables for all LLM API keys
- Configure proper rate limiting for AI endpoints
- Set up monitoring for LLM usage and costs
- Implement proper error handling for AI service failures
## AI Coding Optimizations
This scaffold is specifically optimized for AI-assisted development:
- **Cursor IDE integration** with proper .cursorrules
- **Windsurf conventions** for seamless AI coding
- **Automated test generation** templates
- **AI-friendly code structure** for better LLM understanding
- **Built-in prompt engineering** utilities
## Contributing
Pull requests and issues are welcome! This project is designed to evolve with the AI coding ecosystem.
## License
MIT License
---
Built with ❤️ for the AI coding community. Optimized for Cursor, Windsurf, and modern LLM workflows.