LinGoose is a modular Go framework designed for building AI/LLM applications, offering flexibility through interchangeable implementations of core features. It targets Go developers seeking a comprehensive solution for AI-driven applications, enabling them to construct projects from the ground up with customizable components.

How It Works

LinGoose provides an abstraction layer over LLM functionalities, allowing developers to select or create their preferred implementations for features like prompt management, model interaction, and data retrieval. This modular design promotes code reusability and adaptability, enabling developers to swap out components without extensive refactoring.

Quick Start & Requirements

• Install: go mod tidy
• Prerequisites: Go toolchain, OpenAI API key (as environment variable OPENAI_API_KEY).
• Run: go run .
• Docs: https://github.com/henomis/lingoose

Highlighted Details

• Modular design with import-on-demand modules.
• Supports pluggable implementations for LLM features.
• Offers a complete solution for building AI/LLM applications.
• Blog posts highlight integrations with Claude, Q&A pipelines, and RAG with Redis.

Maintenance & Community

The project is maintained by Simone Vellei. Community engagement is encouraged through GitHub issues and contributions.

Licensing & Compatibility

• License: MIT License
• Compatibility: Permissive MIT license allows for commercial use and integration into closed-source projects.

Limitations & Caveats

The provided example specifically uses OpenAI; integration with other LLM providers would require implementing or finding compatible modules. The framework is relatively new, and its ecosystem of interchangeable components may still be developing.