Skip to main content

A Swarmauri tool designed to export Jupyter Notebooks to Markdown.

Project description

Swarmauri Logo

PyPI - Downloads Hits PyPI - Python Version PyPI - License PyPI - swarmauri_tool_jupyterexportmarkdown


Swarmauri Tool Jupyter Export Markdown

Converts a Jupyter NotebookNode to Markdown using nbconvert’s MarkdownExporter. Injectable CSS and JS snippets let you tweak the output for static publishing.

Features

  • Accepts a notebook JSON string and returns rendered Markdown.
  • Optional inline CSS/JS injection to customize the exported document.
  • Returns a dict with exported_markdown or error if conversion fails.

Prerequisites

  • Python 3.10 or newer.
  • nbconvert/nbformat installed (pulled in automatically).

Installation

# pip
pip install swarmauri_tool_jupyterexportmarkdown

# poetry
poetry add swarmauri_tool_jupyterexportmarkdown

# uv (pyproject-based projects)
uv add swarmauri_tool_jupyterexportmarkdown

Quickstart

import json
import nbformat
from swarmauri_tool_jupyterexportmarkdown import JupyterExportMarkdownTool

notebook = nbformat.read("notebooks/example.ipynb", as_version=4)
notebook_json = json.dumps(notebook)

exporter = JupyterExportMarkdownTool()
response = exporter(
    notebook_json=notebook_json,
    extra_css="blockquote { color: gray; }",
    extra_js="console.log('Markdown ready');",
)

if "exported_markdown" in response:
    Path("notebooks/example.md").write_text(response["exported_markdown"], encoding="utf-8")
else:
    print("Error:", response["error"])

Tips

  • Use Markdown export when preparing notebooks for static docs, blogs, or README content.
  • Apply lightweight CSS/JS to adjust styling when the Markdown is embedded in HTML environments.
  • Combine with notebook execution tools to build pipelines (execute → convert to Markdown → publish).

Want to help?

If you want to contribute to swarmauri-sdk, read up on our guidelines for contributing that will help you get started.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

File details

Details for the file swarmauri_tool_jupyterexportmarkdown-1.3.3.dev17.tar.gz.

File metadata

  • Download URL: swarmauri_tool_jupyterexportmarkdown-1.3.3.dev17.tar.gz
  • Upload date:
  • Size: 8.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.10.12 {"installer":{"name":"uv","version":"0.10.12","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for swarmauri_tool_jupyterexportmarkdown-1.3.3.dev17.tar.gz
Algorithm Hash digest
SHA256 f87dd98a90c5848e3057131d34510a054f1cd1fbc318dddeea02fa884f33843a
MD5 a75c64b1bd6d705b5ea63ee2907d1a4c
BLAKE2b-256 8601c00325288ac7558e75c8ec99bc3f76e295c9669a31e6941e14020fa3ecb7

See more details on using hashes here.

File details

Details for the file swarmauri_tool_jupyterexportmarkdown-1.3.3.dev17-py3-none-any.whl.

File metadata

  • Download URL: swarmauri_tool_jupyterexportmarkdown-1.3.3.dev17-py3-none-any.whl
  • Upload date:
  • Size: 9.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.10.12 {"installer":{"name":"uv","version":"0.10.12","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for swarmauri_tool_jupyterexportmarkdown-1.3.3.dev17-py3-none-any.whl
Algorithm Hash digest
SHA256 58b2d8cb1876aac924729f2208493551c6733eed1a9e8390bf9336d3e7c71f25
MD5 9758f103865bf1fd770f42f7b8e63bba
BLAKE2b-256 9f51ce0f78e8fa0d9cf0b7ac0b3dbf02d8e58e56ff913c7f679d6b68be66dcac

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page