Sponge is a Go development framework designed to accelerate backend service creation through a "low-code" approach, targeting developers who need to build stable, high-performance services rapidly. It automates significant portions of the development lifecycle, from code generation to deployment, by reverse-engineering SQL, Protobuf, or JSON definitions.

How It Works

Sponge employs a code generation engine that parses data definitions (SQL, Protobuf, JSON) to produce modular backend service code. This approach allows for the creation of RESTful APIs, gRPC services, and hybrid architectures with minimal manual coding, especially for CRUD operations. It integrates AI assistants (DeepSeek, ChatGPT, Gemini) to assist with business logic implementation, creating a closed-loop development process where Sponge handles infrastructure and AI handles business logic.

Quick Start & Requirements

• Install via go install github.com/go-dev-frame/sponge@latest.
• Run the UI with sponge run.
• Access the UI at http://localhost:24631.
• Supports Windows, macOS, and Linux.
• Detailed installation guide available.

Highlighted Details

• Supports various backend service types: RESTful API, Gin-based Web, gRPC, HTTP+gRPC hybrid, gRPC Gateway, and cloud-native microservices.
• Offers extensive built-in components for logging, ORM, caching, authentication, message queues, distributed transactions, service discovery, and more.
• Provides flexible code generation using built-in or custom templates, supporting multiple languages for generated code.
• Integrates AI assistants for business logic implementation, enhancing development efficiency.
• Generates projects following standard layouts, supporting monolithic and microservice architectures (monorepo/multi-repo).

Maintenance & Community

• Active development indicated by GitHub Actions and Go Report Card.
• Community links (Discord/Slack) are not explicitly provided in the README.
• Encourages stars on GitHub.

Licensing & Compatibility

• Licensed under MIT.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

The framework's reliance on code generation and AI integration may introduce a learning curve for customization beyond standard patterns. Specific performance benchmarks are provided for HTTP and gRPC services under load, but broader performance characteristics across all integrated components are not detailed.