LSP-AI provides a unified, open-source language server backend for AI-powered coding assistance, targeting software engineers who want to integrate LLM features into their existing LSP-compatible editors. It aims to democratize advanced AI coding tools by abstracting complex LLM integrations, enabling broader editor support and simplified plugin development.

How It Works

LSP-AI acts as a central hub for AI functionalities, abstracting LLM backend complexities and prompt engineering. It communicates with editors via the Language Server Protocol (LSP), allowing any LSP-supported editor to leverage its features. This approach centralizes development effort, enabling a single backend to power AI features across multiple editors, simplifying plugin creation and fostering community collaboration.

Quick Start & Requirements

• Installation: pip install lsp-ai
• Prerequisites: Python 3.8+, LSP-compatible editor. Backend LLM setup (e.g., Ollama, llama.cpp, OpenAI API keys) is required for functionality.
• Documentation: https://github.com/SilasMarvin/lsp-ai/wiki

Highlighted Details

• Supports in-editor chatting with LLMs and AI-powered code completions.
• Offers custom actions for code refactoring and other tasks using LLMs.
• Compatible with numerous editors including VS Code, NeoVim, Emacs, Helix, and Sublime.
• Flexible backend support includes llama.cpp, Ollama, OpenAI, Anthropic, Gemini, and Mistral AI APIs.

Maintenance & Community

• The project is actively used daily by many users, though the original author states it has reached a stage with desired features, with no new features currently in development.
• Community engagement via Discord: https://discord.gg/9p7h7f6x
• Roadmap includes semantic search context, Tree-sitter integration, and additional backend support.

Licensing & Compatibility

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

Limitations & Caveats

• Current development focus is on maintenance rather than new feature additions by the primary author, though community contributions are welcomed.
• Performance of AI features is dependent on the chosen LLM backend and hardware.