A frontend-independent language server implementing the Language Server Protocol for Elixir, with debugger support via VS Code's debugger protocol.
ElixirLS is a language server that implements the Language Server Protocol (LSP) for the Elixir programming language. It provides IDEs, editors, and tools with features like autocomplete, diagnostics, and go-to-definition for Elixir Mix projects, along with debugger support via VS Code's debugger protocol.
Elixir developers using editors or IDEs that support the Language Server Protocol, such as VS Code, Neovim, or Emacs, who want enhanced code intelligence and debugging capabilities.
It offers a standardized, frontend-independent way to bring rich IDE features to Elixir development, eliminating the need for editor-specific plugins and ensuring consistent tooling across different environments.
A frontend-independent IDE "smartness" server for Elixir. Implements the JSON-based "Language Server Protocol" standard and provides debugger support via VS Code's debugger protocol.
Implements the Language Server Protocol, enabling consistent autocomplete, go-to-definition, and diagnostics across any LSP-compatible editor, as stated in the README.
Supports debugging through VS Code's debugger protocol, allowing for seamless debugging of Elixir applications in supported IDEs like VS Code.
Works with any editor that supports LSP, providing flexibility and eliminating the need for editor-specific plugins, as highlighted in the project's philosophy.
Runs as a background server to continuously analyze Elixir Mix projects, delivering immediate feedback and intelligence to the developer.
The project has moved to a community-maintained repository, which could lead to slower updates or instability during the transition period, as noted in the README.
Specifically targets Elixir Mix projects, so it may not function optimally with alternative build tools or non-standard project structures.
As a background server, ElixirLS can consume significant memory and CPU, potentially affecting performance on systems with limited resources or in large codebases.
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