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GitHub - SilasMarvin/lsp-ai: LSP-AI is an open-source language server that serves as a backend for AI-powered functionality, designed to assist and empower software engineers, not replace them.

Jun 08, 2024 - github.com
LSP-AI is an open-source language server designed to provide AI-powered functionality to various editors. It aims to assist software engineers by integrating with their existing tools, rather than replacing them. The server is compatible with numerous editors, including VS Code, NeoVim, Emacs, Helix, and Sublime. LSP-AI centralizes AI features into a single backend, simplifies plugin development, enhances collaboration, and supports a broad range of editors. It also offers flexible LLM backend support and is committed to keeping up with the latest advancements in LLM-driven software development.

The project has a roadmap outlining future plans, including implementing semantic search-powered context building, supporting additional backends, and exploring agent-based systems. LSP-AI can serve as an alternative to Github Copilot and is recommended for fastest completions using a small local model or Groq. The platform aims to fill the gap in the open-source community for an AI-powered language server, providing a collaborative platform for developers to add new functionalities and reduce duplicated work.

Key takeaways:

  • LSP-AI is an open-source language server that integrates AI-powered functionality into various editors, serving as an alternative to Github Copilot.
  • The goal of LSP-AI is to assist software engineers by integrating with the tools they already use, not to replace them. It works with a wide range of editors that have LSP support.
  • LSP-AI abstracts complex implementation details from editor specific plugin authors, centralizing open-source development work into one shareable backend. This allows for unified AI features, simplified plugin development, enhanced collaboration, broad compatibility, flexible LLM backend support, and future readiness.
  • The roadmap for LSP-AI includes implementing semantic search-powered context building, supporting additional backends, and exploring agent-based systems.
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