jupyter-nbmodel-client
Decision gist · record as of 2026-08-14
Yes, if you need to automate or control Jupyter notebooks from Python code. The package is actively maintained, has low install friction, carries a permissive BSD license, and requires only standard dependencies. It is most useful for automation, testing, and integration scenarios where you control or have access to a JupyterLab server. Not suitable if you need to run notebooks in isolation without a live server.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running JupyterLab server with a known token and notebook path; async context manager requires an async-capable Python environment (e.g., IPython or Jupyter console).
- Low friction install with a pure-Python wheel.
- Active maintenance as of 2026-08-14 with recent releases.
License · maintenance · safety
permissive license (permissive) — BSD 3-Clause License permits commercial and private use with attribution and liability disclaimer; no restrictions on modification or redistribution.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 16 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 92,552 downloads/mo, #13,444 on PyPI
Alternatives
Verify before relying
from jupyter_nbmodel_client import NbModelClient, get_jupyter_notebook_websocket_url
ws_url = get_jupyter_notebook_websocket_url(
server_url="http://localhost:8888",
token="MY_TOKEN",
path="test.ipynb"
)
async with NbModelClient(ws_url) as nbmodel:
nbmodel.add_code_cell("print('hello world')")- Whether the package works with JupyterLab versions other than those shown in examples
- Performance characteristics when managing many concurrent cells or large notebooks
- Compatibility with Jupyter Server versions and authentication schemes beyond token-based auth
What it is and what it does
Jupyter NbModel Client is a Python library that connects to a live Jupyter notebook server via WebSocket and allows you to programmatically manipulate notebooks—adding cells, executing code, and retrieving outputs—without directly managing a kernel process yourself. It acts as a remote control for notebooks running on a JupyterLab server, useful for automation, testing, or building tools that need to interact with notebooks as live objects.
The library depends on jupyter-ydoc, nbformat, pycrdt, requests, and websockets to handle notebook synchronization, serialization, HTTP setup, and WebSocket communication. It supports both standalone JupyterLab instances and Datalayer collaborative notebook rooms. Execution requires an active JupyterLab server, a valid authentication token, and an async-capable Python environment.
Use it for
- Automate notebook cell creation and execution in a running JupyterLab instance for testing or batch processing.
- Build CI/CD pipelines that execute notebook cells and validate outputs without spawning separate kernel processes.
- Create tools or agents that programmatically generate and run Jupyter notebooks on a remote server.
- Integrate notebook execution into larger Python applications that need to interact with live Jupyter environments.
- Test notebook code and visualizations by adding cells, executing them, and capturing results programmatically.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to automate or control Jupyter notebooks from Python code.
The package is actively maintained, has low install friction, carries a permissive BSD license, and requires only standard dependencies. It is most useful for automation, testing, and integration scenarios where you control or have access to a JupyterLab server. Not suitable if you need to run notebooks in isolation without a live server.
Install
jupyter-nbmodel-client on PyPI
Before you install
Low friction install with a pure-Python wheel. Active maintenance as of 2026-08-14 with recent releases. Five runtime dependencies (jupyter-ydoc, nbformat, pycrdt, requests, websockets) are all established packages.
Requires a running JupyterLab server with a known token and notebook path; async context manager requires an async-capable Python environment (e.g., IPython or Jupyter console).
License in practice
BSD 3-Clause License permits commercial and private use with attribution and liability disclaimer; no restrictions on modification or redistribution.
Quickstart
from jupyter_nbmodel_client import NbModelClient, get_jupyter_notebook_websocket_url
ws_url = get_jupyter_notebook_websocket_url(
server_url="http://localhost:8888",
token="MY_TOKEN",
path="test.ipynb"
)
async with NbModelClient(ws_url) as nbmodel:
nbmodel.add_code_cell("print('hello world')")Verify before relying
- Whether the package works with JupyterLab versions other than those shown in examples
- Performance characteristics when managing many concurrent cells or large notebooks
- Compatibility with Jupyter Server versions and authentication schemes beyond token-based auth
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesjupyter-ydocnbformatpycrdtrequestswebsockets |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 92,552 / month, #13,444 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Framework :: JupyterLicense :: OSI Approved :: BSD LicenseProgramming Language :: PythonProgramming Language :: Python :: 3 |
Evidence: jupyter_nbmodel_client-1.5.1-py3-none-any.whl
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See also jupyter-kernel-client · bash_kernel · execnb · nbval · jupyterlite-pyodide-kernel · ipyflow-core · jupyter-mcp-server · nbmake · jupyter-ui-poll · jupyter-mcp-tools