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Pre-release

This release is a pre-release and may not be stable for production use.

jupyter_ai_jupyternaut

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Package providing the default AI persona, Jupyternaut, in Jupyter AI.

This extension is composed of a Python package named jupyter_ai_jupyternaut for the server extension and a NPM package named @jupyter-ai/jupyternaut for the frontend extension.

QUICK START

Everything that follows after this section was from the extension template. We will need to revise the rest of this README.

Development install:

micromamba install uv jupyterlab nodejs=22
jlpm
jlpm dev:install

Requirements

  • JupyterLab >= 4.0.0

Install

To install the extension, execute:

pip install jupyter_ai_jupyternaut

Optional features

The core package provides Jupyternaut's chat and notebook tools, keeping conversation memory in memory. Persistent conversation memory is available as an optional extra:

Extra Enables Without it
persistence Conversation memory persisted to a local SQLite database so it survives server restarts (via langgraph-checkpoint-sqlite) Conversation memory is kept only for the lifetime of the server process
all All optional runtime features. Currently this is the same as persistence

For example:

# persistent conversation memory
pip install "jupyter_ai_jupyternaut[persistence]"
# every optional feature
pip install "jupyter_ai_jupyternaut[all]"

Uninstall

To remove the extension, execute:

pip uninstall jupyter_ai_jupyternaut

Troubleshoot

If you are seeing the frontend extension, but it is not working, check that the server extension is enabled:

jupyter server extension list

If the server extension is installed and enabled, but you are not seeing the frontend extension, check the frontend extension is installed:

jupyter labextension list

Contributing

Development install

Note: You will need NodeJS to build the extension package.

The jlpm command is JupyterLab's pinned version of yarn that is installed with JupyterLab. You may use yarn or npm in lieu of jlpm below.

# Clone the repo to your local environment
# Change directory to the jupyter_ai_jupyternaut directory
# Install package in development mode
pip install -e ".[test]"
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Server extension must be manually installed in develop mode
jupyter server extension enable jupyter_ai_jupyternaut
# Rebuild extension Typescript source after making changes
jlpm build

You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.

# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm watch
# Run JupyterLab in another terminal
jupyter lab

With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).

By default, the jlpm build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:

jupyter lab build --minimize=False

Development uninstall

# Server extension must be manually disabled in develop mode
jupyter server extension disable jupyter_ai_jupyternaut
pip uninstall jupyter_ai_jupyternaut

In development mode, you will also need to remove the symlink created by jupyter labextension develop command. To find its location, you can run jupyter labextension list to figure out where the labextensions folder is located. Then you can remove the symlink named @jupyter-ai/jupyternaut within that folder.

Testing the extension

Server tests

This extension is using Pytest for Python code testing.

Install test dependencies (needed only once):

pip install -e ".[test]"
# Each time you install the Python package, you need to restore the front-end extension link
jupyter labextension develop . --overwrite

To execute them, run:

pytest -vv -r ap --cov jupyter_ai_jupyternaut

Frontend tests

This extension is using Jest for JavaScript code testing.

To execute them, execute:

jlpm
jlpm test

Integration tests

This extension uses Playwright for the integration tests (aka user level tests). More precisely, the JupyterLab helper Galata is used to handle testing the extension in JupyterLab.

More information are provided within the ui-tests README.

Packaging the extension

See RELEASE

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