jupyter-core
Jupyter core package. A base package on which Jupyter projects rely.
Decision gist · record as of 2026-08-14
Yes, if you're building on the Jupyter ecosystem or developing Jupyter-based tools. It's a stable, actively maintained foundational dependency with no known vulnerabilities and permissive licensing. For end users or developers not working within the Jupyter framework, it's not directly useful—install it as a transitive dependency of other Jupyter packages rather than as a standalone tool.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction install with only two runtime dependencies (platformdirs and traitlets).
- Active maintenance with last commit on 2026-08-06 and a release on 2025-10-16.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause permissive license allows use in most projects without significant restrictions.
last release 2025-10-16 (302 days) · last repo commit 2026-08-06 · 215 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 100,600,082 downloads/mo, #344 on PyPI
Alternatives
Verify before relying
pip install jupyter-core
from jupyter_core.paths import jupyter_config_dir
config_path = jupyter_config_dir()- What specific application classes or configuration patterns are most commonly inherited by downstream Jupyter projects.
- Whether the package is intended for direct end-user use or primarily as a dependency for other Jupyter tools.
What it is and what it does
jupyter_core is a foundational package within the Jupyter ecosystem that provides shared base classes, configuration management, and path utilities used by other Jupyter projects. It doesn't provide user-facing features on its own but rather establishes common patterns and infrastructure that downstream projects like JupyterLab, Notebook, and other Jupyter tools build upon. The package depends on platformdirs for cross-platform path handling and traitlets for configuration management, making it a lightweight but essential piece of the Jupyter architecture.
If you're developing a Jupyter-based tool or extending the Jupyter ecosystem, you'll likely depend on jupyter_core indirectly through other Jupyter packages. Direct use is typically limited to framework developers and maintainers working on Jupyter projects themselves, rather than end users or application developers using Jupyter.
Use it for
- Developing a custom Jupyter application that needs to inherit base classes and configuration patterns from the Jupyter ecosystem.
- Accessing standard Jupyter configuration and data directories in a way that respects platform conventions.
- Building extensions or plugins that integrate with the Jupyter architecture and need shared infrastructure.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you're building on the Jupyter ecosystem or developing Jupyter-based tools.
It's a stable, actively maintained foundational dependency with no known vulnerabilities and permissive licensing. For end users or developers not working within the Jupyter framework, it's not directly useful—install it as a transitive dependency of other Jupyter packages rather than as a standalone tool.
Install
jupyter-core on PyPI
Before you install
Low friction install with only two runtime dependencies (platformdirs and traitlets). Active maintenance with last commit on 2026-08-06 and a release on 2025-10-16.
Requires Python 3.10 or later.
License in practice
BSD-3-Clause permissive license allows use in most projects without significant restrictions.
Quickstart
pip install jupyter-core
from jupyter_core.paths import jupyter_config_dir
config_path = jupyter_config_dir()Verify before relying
- What specific application classes or configuration patterns are most commonly inherited by downstream Jupyter projects.
- Whether the package is intended for direct end-user use or primarily as a dependency for other Jupyter tools.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesplatformdirstraitlets |
| Maintenance | Actively maintained 302 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 100,600,082 / month, #344 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Framework :: JupyterIntended Audience :: DevelopersIntended Audience :: Science/ResearchIntended Audience :: System AdministratorsProgramming Language :: PythonProgramming Language :: Python :: 3 |
Evidence: jupyter_core-5.9.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “jupyter base classes”
- jupyter-coreProvides base application classes and configuration infrastructure…
- jupyter-ai-persona-managerProvides the foundational infrastructure for managing and registering…
- metakernelMetaKernel is a base class for building custom Jupyter kernels in…
Give your agent the search over MCP, or paste the wish link into any chat.
More Application Frameworks packages
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Textual is a Python framework for building cross-platform user interfaces that run in the terminal or web browser using a modern, component-based API.
Install it if you're developing CLI tools, dashboards, or interactive terminal applications.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Build and connect to Model Context Protocol servers that expose tools, resources, and prompts to LLM applications over stdio, HTTP, or SSE transports.
Install it if you need to build or connect to servers.
Werkzeug is a WSGI utility library providing request/response objects, URL routing, an interactive debugger, HTTP utilities, and a development server for building web applications.
See also jupyter-client · jupyter-server · jupyter-events · jupyter-console · ipykernel · jupyter-contrib-core · notebook · jupyter-server-terminals · jupyter · traitlets