Language servers#

By default jupyter-lsp does not come with any language servers preinstalled. However, we will try to use them if they are installed and we know about them (i.e. someone contributed a full specification).

You can disable auto-detection by configuring autodetect

You can add another language server for languages that are not listed on this page:

using a minimal JSON or Python configuration file (good for experimenting or configuring a niche server), or
contributing a full specification (to enable better integration and help other users of the same language)

The existing language servers are listed on the official list and on the community-curated list.

For the language servers in the tables below, use one of the suggested package managers to install them: these implementations are tested to work with jupyter-lsp.

Servers tested with Notebooks#

While most servers should work with notebooks and standalone files, some require additional adjustments. The following language servers are tested against jupyterlab-lsp and any issues when using them in notebooks will be prioritised.

If you choose to install multiple language servers for the same language, the one with the highest priority (which can be set in the Advanced Settings Editor) will be used.

Languages	Implementation	Installation
python	jedi-language-server	pip: `pip install -U jedi-language-server` conda: `conda install -c conda-forge jedi-language-server`
julia	julia-language-server	julia: `using Pkg; Pkg.add("LanguageServer")`
python	pylsp	pip: `pip install 'python-lsp-server[all]'` conda: `conda install -c conda-forge python-lsp-server`
r	r-languageserver	cran: `install.packages("languageserver")` conda: `conda install -c conda-forge r-languageserver`

If you plan to add a custom language server for the use with notebooks, please note that a complete set of information should be provided by the kernel, as described in making custom servers work with notebooks.

Servers tested with file editor#

Servers requiring Node.js#

These servers have mostly been tested with file editors.

Languages	Implementation	Installation
bash sh	bash-language-server	npm: `npm install --save-dev bash-language-server` yarn: `yarn add --dev bash-language-server` jlpm: `jlpm add --dev bash-language-server`
dockerfile	dockerfile-language-server-nodejs	npm: `npm install --save-dev dockerfile-language-server-nodejs` yarn: `yarn add --dev dockerfile-language-server-nodejs` jlpm: `jlpm add --dev dockerfile-language-server-nodejs`
python	pyright	npm: `npm install --save-dev pyright` yarn: `yarn add --dev pyright` jlpm: `jlpm add --dev pyright`
sql	sql-language-server	npm: `npm install --save-dev sql-language-server` yarn: `yarn add --dev sql-language-server` jlpm: `jlpm add --dev sql-language-server`
javascript jsx typescript typescript-jsx typescriptreact javascriptreact	typescript-language-server	npm: `npm install --save-dev typescript-language-server` yarn: `yarn add --dev typescript-language-server` jlpm: `jlpm add --dev typescript-language-server`
markdown ipythongfm gfm	unified-language-server	npm: `npm install --save-dev unified-language-server` yarn: `yarn add --dev unified-language-server` jlpm: `jlpm add --dev unified-language-server`
css less scss	vscode-css-languageserver-bin	npm: `npm install --save-dev vscode-css-languageserver-bin` yarn: `yarn add --dev vscode-css-languageserver-bin` jlpm: `jlpm add --dev vscode-css-languageserver-bin`
html	vscode-html-languageserver-bin	npm: `npm install --save-dev vscode-html-languageserver-bin` yarn: `yarn add --dev vscode-html-languageserver-bin` jlpm: `jlpm add --dev vscode-html-languageserver-bin`
json	vscode-json-languageserver-bin	npm: `npm install --save-dev vscode-json-languageserver-bin` yarn: `yarn add --dev vscode-json-languageserver-bin` jlpm: `jlpm add --dev vscode-json-languageserver-bin`
yaml	yaml-language-server	npm: `npm install --save-dev yaml-language-server` yarn: `yarn add --dev yaml-language-server` jlpm: `jlpm add --dev yaml-language-server`

NodeJS (preferably even-numbered an Active or Maintenance Long Term Support release) is a prerequisite for installation of any of the above language servers; you can get it with:

conda install --channel conda-forge nodejs
# or one of the following, as an administrator
choco install nodejs            # Windows with Chocolatey
sudo apt-get install nodejs     # Debian/Ubuntu
sudo brew install nodejs        # MacOS with Homebrew
sudo dnf install nodejs         # Fedora
sudo yum install nodejs         # RHEL/CentOS

Example: getting all the Node.js-based language servers#

A number of language servers are built on the reference implementation, powered by Node.js. The most reliable place to install these is in a node_modules in the directory where you launch jupyter lab.

For example, to install all the servers which are tested as part of jupyterlab-lsp:

jlpm add --dev  \
    bash-language-server \
    dockerfile-language-server-nodejs \
    pyright \
    sql-language-server \
    typescript-language-server \
    unified-language-server \
    vscode-css-languageserver-bin \
    vscode-html-languageserver-bin \
    vscode-json-languageserver-bin \
    yaml-language-server

This will create (or add to):

package.json (check this in!)
yarn.lock (check this in!)
node_modules/ (add to your VCS ignore file)

If you wish to install these someplace else, you may need to specify where you install them with extra_node_roots.

Standalone servers#

These servers have been mostly tested with file editor.

Languages	Implementation	Installation
tex latex	texlab	conda: `conda install -c conda-forge texlab chktex`

Example: Getting a \(\LaTeX\) stack#

conda install --channel conda-forge tectonic texlab chktex

This will install:

tectonic, a cross-platform \(\LaTeX\) processing tool
- note, it will download a large number of packages when first executed
texlab, a Language Server for .tex files that supports completion and reference navigation
chktex, a .tex style checker

Community-supported servers#

Servers and extensions listed below are not included in the testing suite of jupyterlab-lsp; the support may be provided by community members or a third-party:

Languages	Implementation	Comment
Spark SQL	CybercentreCanada/jupyterlab-sql-editor
GraphQL, SPARQL, Turtle	jupyrdf/graph-lsp
JSON	jupyter-lsp/json-lsp	(1)
YAML	jupyter-lsp/yaml-lsp	(1)
Scala	scalameta/metals	(2)

(1) convenience wrappers for installation with pip, require Node.js.
(2) metals is not currently auto-detected, but can be configured as demonstrated in the configuration example.

Troubleshooting#

r-languageserver

Rscript not found. Please ensure that RScript executable is in the PATH; this should happen automatically when using Linux, Mac OS or Conda, but will require manual configuration when using the default R installer on Windows. For more details please consult documentation: https://cran.r-project.org/bin/windows/base/rw-FAQ.html#Rcmd-is-not-found-in-my-PATH_0021 If Rscript is already in the PATH, you can check whether the language server package is properly installed with: Rscript -e "cat(system.file(package='languageserver'))" which should return the path to the installed package.

texlab

texlab not found. Please ensure that texlab executable is in the PATH; this should happen automatically when installing texlab from Conda, but may require manual configuration of PATH environment variable if you compiled texlab from source. You can ensure check if texlab is in the PATH, by running: which texlab which should return the path to the executable (if found).

Language servers

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