jupyter-lsp and jupyterlab-lsp are open source software, and all contributions conforming to good sense, good taste, and the Jupyter Code of Conduct are welcome, and will be reviewed by the contributors, time-permitting.

You can contribute to the project through:

  • creating language server specs

  • you can publish them yourself (it might be a single file)…

  • or advocate for adding your spec to the github repository and its various distributions

    • these are great first issues, as you might not need to know any python or javascript

  • proposing parts of the architecture that can be extended

  • improving documentation

  • tackling Big Issues from the future roadmap

  • improving testing

  • reviewing pull requests

Set up the environment

Development requires:

  • nodejs 10+

  • python 3.5+

  • jupyterlab 2

It is recommended to use a virtual environment (e.g. virtualenv or conda env) for development.

conda env update -n jupyterlab-lsp   # create a conda env
source activate jupyterlab-lsp       # activate it
# or...
pip install -r requirements/dev.txt  # in a virtualenv, probably
                                     # ... and install nodejs, somehow

The Easy Way

Once your environment is created and activated, on Linux/OSX you can run:

bash binder/postBuild

This performs all of the basic setup steps, and is used for the binder demo.

The Hard Way

Install jupyter-lsp from source in your virtual environment:

python -m pip install -e .

Enable the server extension:

jupyter serverextension enable --sys-prefix --py jupyter_lsp

Install npm dependencies, build TypeScript packages, and link to JupyterLab for development:

jlpm build
jlpm lab:link

Frontend Development

To rebuild the schemas, packages, and the JupyterLab app:

jlpm build
jupyter lab build

To watch the files and build continuously:

jlpm watch   # leave this running...
jupyter lab --watch  # another terminal

Note: the backend schema is not included in ``watch``, and is only refreshed by ``build``

To check and fix code style:

jlpm lint

To run test the suite (after running jlpm build or watch):

jlpm test

To run tests matching specific phrase, forward -t argument over yarn and lerna to the test runners with two --:

jlpm test -- -- -t match_phrase

Server Development

Testing jupyter-lsp

python scripts/


To build the documentation:

python scripts/

To watch documentation sources and build continuously:

python scripts/ --watch

To check internal links in the docs after building:

python scripts/ --check --local-only

To check internal and external links in the docs after building:

python scripts/ --check

Note: you may get spurious failures due to rate limiting, especially in CI, but it's good to test locally

Browser-based Acceptance Tests

The browser tests will launch JupyterLab on a random port and exercise the Language Server features with Robot Framework and SeleniumLibrary. It is recommended to peruse the Robot Framework User’s Guide (and the existing .robot files in atest) before working on tests in anger.

First, ensure you’ve prepared JupyterLab for jupyterlab-lsp frontend and server development.

Prepare the environment:

conda env update -n jupyterlab-lsp --file requirements/atest.yml
# or
pip install -r requirements/atest.txt  # ... and install geckodriver, somehow
apt-get install firefox-geckodriver    # ... e.g. on debian/ubuntu

Run the tests:

python scripts/

The Robot Framework reports and screenshots will be in atest/output, with <operating system>_<python version>_<attempt>.<log|report>.html and subsequent screenshots being the most interesting artifact, e.g.


Customizing the Acceptance Test Run

By default, all of the tests will be run, once.

The underlying robot command supports a vast number of options and many support wildcards (* and ?) and boolean operators (NOT, OR). For more, start with simple patterns.

Run a suite

python scripts/ --suite "05_Features.Completion"

Run a single test

python scripts/ --test "Works With Kernel Running"

Run test with a tag

Tags are preferrable to file names and test name matching in many settings, as they are aggregated nicely between runs.

python scripts/ --include feature:completion

… or only Python completion

python scripts/ --include feature:completionANDlanguage:python

Just Keep Testing with ATEST_RETRIES

Run tests, and rerun only failed tests up to two times:

ATEST_RETRIES=2 python scripts/ --include feature:completion

After running a bunch of tests, it may be helpful to combine them back together into a single log.html and report.html with rebot. Like, also passes through extra arguments

python scripts/


  • If you see the following error message:

python   Parent suite setup failed:   TypeError: expected str, bytes or os.PathLike object, not NoneType

it may indicate that you have no firefox, or geckodriver installed (or discoverable in the search path).

  • If a test suite for a specific language fails it may indicate that you have no appropriate server language installed (see LANGUAGESERVERS)

  • If you are seeing errors like Element is blocked by .jp-Dialog, caused by the JupyterLab Build suggested dialog, (likely if you have been using jlpm watch) ensure you have a “clean” lab (with production assets) with:

bash   jupyter lab clean   jlpm build   jlpm lab:link   jupyter lab build --dev-build=False --minimize=True

and re-run the tests.

  • To display logs on the screenshots, write logs with virtual_editor.console.log method, and change create_console('browser') to create_console('floating') in VirtualEditor constructor (please feel free to add a config option for this).


Minimal code style is enforced with pytest-flake8 during unit testing. If installed, pytest-black and pytest-isort can help find potential problems, and lead to cleaner commits, but are not enforced during CI tests (but are checked during lint).

You can clean up your code, and check for using the project’s style guide with:

python scripts/


It is convenient to collect common patterns for connecting to installed language servers as pip-installable packages that Just Work with jupyter-lsp.

If an advanced user installs, locates, and configures, their own language server it will always win vs an auto-configured one.

Writing a spec

See the built-in specs for implementations and some helpers.

A spec is a python function that accepts a single argument, the LanguageServerManager, and returns a dictionary of the form:

  "python-language-server": {            # the name of the implementation
      "version": 1,                      # the version of the spec schema
      "argv": ["python", "-m", "pyls"],  # a list of command line arguments
      "languages": ["python"]            # a list of languages it supports

The absolute minimum listing requires argv (a list of shell tokens to launch the server) and languages (which languages to respond to), but many number of other options to enrich the user experience are available in the schema and are exercised by the current entry_points-based specs.

The spec should only be advertised if the command could actually be run:

  • its runtime (e.g. julia, nodejs, python, r, ruby) is installed

  • the language server itself is installed (e.g. python-language-server)

Common Concerns

  • some language servers need to have their connection mode specified

  • the stdio interface is the only one supported by jupyter_lsp

    • PRs welcome to support other modes!

  • because of its VSCode heritage, many language servers use nodejs

  • LanguageServerManager.nodejs will provide the location of our best guess at where a user’s nodejs might be found

  • some language servers are hard to start purely from the command line

  • use a helper script to encapsulate some complexity.

    • See the r spec for an example

Example: making a pip-installable cool-language-server spec

Consider the following (absolutely minimal) directory structure:


You should consider adding a LICENSE, some documentation, etc.

Define your spec:

from shutil import which

def cool(app):
    cool_language_server = shutil.which("cool-language-server")

    if not cool_language_server:
        return {}

    return {
        "cool-language-server": {
            "version": 1,
            "argv": [cool_language_server],
            "languages": ["cool"]

Tell pip how to package your spec:

import setuptools
        "jupyter_lsp_spec_v1": [

Test it!

python -m pip install -e .

Build it!

python sdist bdist_wheel